statistics - Cengage Learning Asia

Transcription

SECTIONAL CATALOG 2013 - 2014
STATISTICS
CENGAGE LEARNING ASIA | www.cengageasia.com
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Contents
Statistics
Introductory Statistics, General.......................................... 2
Introductory Statistics—Behavioral and Social Sciences ......26
Intermediate/Advanced Statistics .................................... 36
Introductory Statistics—Engineering and Sciences......50
Software / Handbooks / Courseware / Casebooks .....105
Combined Author/Title Index .......................................... 109
Prices
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STATISTICS
INTRODUCTORY STATISTICS,
GENERAL
ELEMENTARY STATISTICS
Looking at the Big Picture, International Edition
Nancy Pfenning, University of Pittsburgh
Using a successfully class-tested approach that gives
coherence to a broad range of introductory topics, this
innovative text provides students with a real-world,
big picture view of statistics as well as problemsolving strategies that can be applied to the statistical
questions, real data, and examples that they will
encounter. Author Nancy Pfenning organizes content
around four basic processes of statistics: producing
data, displaying and summarizing data, understanding
probability, and using probability to perform statistical
inference. Within this framework, the book progresses
systematically through five basic problem situations
involving values of variables (quantitative, categorical,
or a blend). As a result, students learn to identify which
situation applies and how to choose the correct display,
summary, or inference tool or technique. As students
gain proficiency in specific statistical techniques, the
author also points out connections among topics and
techniques. More than 1,000 real-life examples and
categorized exercises support the approach, engaging
students in practicing and developing a variety of skills.
FEATURES
• Big picture problem-solving approach—An innovative
approach organizes content around four processes of
statistics and the basic situations that apply to any
statistical problem. Students learn specific techniques
while gaining a broad perspective on statistics
along with strategies for choosing the appropriate
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technique for any research situation.
• Flexible content and technology integration—The
presentation of each topic is self-contained enough
to allow for more advanced topics such as ANOVA
or chi-square to be skipped if desired with no loss in
course coherence. A “Using Software” section near
the end of each chapter makes it easy for instructors
to emphasize the use of software in the course, or
choose not to do so.
• Examples using real data—Hundreds of well-chosen
examples, based on current data from a wide variety
of statistical applications, clarify the presentation
of ideas. Each example follows a consistent format:
Background, Question, Response, and Practice
Exercise. Data sets feature values that allow easy
entering by hand into a computer or calculator; they
are also available at the Book Companion Website.
• Plentiful categorized exercises, computational and
conceptual—A set of problems after each major
section assures that students practice basic concepts
as they are introduced. A more comprehensive set at
the end of each chapter (“Warming Up,” “Exploring
the Big Picture,” “Using Software,” “Discovering
Research,” and “Reporting on Research”) encourages
students to integrate individual topics. Like the
examples, exercises cover a variety of topics and use
real data.
• Emphasis on connections among topics—Strategically
placed marginal notes show how each new topic
fits into the larger framework of statistics, relating
it to material covered earlier as well as to content
that follows. These connections can be digested by
students at their own pace, without disrupting the
flow of main ideas.
CONTENTS
1. Introduction: Variables and Processes in Statistics.
Types of Variables: Categorical or Quantitative.
Students Talk Stats: Identifying Types of Variables.
Handling. Data for Two Types of Variables. Roles of
Variables: Explanatory or Response. Statistics as a
Four-Stage Process. PART I: DATA PRODUCTION. 2.
Sampling: Which Individuals Are Studied. Sources of
Bias in Sampling: When Selected Individuals Are Not
Representative. Probability Sampling Plans: Relying on
Randomness. Role of Sample Size: Bigger Is Better if the
Sample Is Representative. From Sample to Population:
To What Extent Can We Generalize? Students Talk
Stats: Seeking a Representative Sample. 3. Design: How
Individuals Are Studied. Various Designs for Studying
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International Edition
Variables. Sample Surveys: When Individuals Report
Their Own Values. Observational Studies: When Nature
Takes Its Course. Experiments: When Researchers Take
Control. Students Talk Stats: Does TV Cause ADHD?
Considering Study Design. PART II: DISPLAYING AND
SUMMARIZING DATA. 4. Displaying and Summarizing
Data for a Single Variable. Single Categorical Variable.
Students Talk Stats: Biased Sample, Biased Assessment.
Single Quantitative Variables and the Shape of a
Distribution. Center and Spread: What’s Typical for
Quantitative Values, and How They Vary. Normal
Distributions: The Shape of Things to Come. 5. Displaying
and Summarizing Relationships. Relationship Between
One Categorical and One Quantitative Variable.
Students Talk Stats: Displaying and Summarizing
Paired Data. Relationship Between Two Categorical
Variables. Relationships Between Two Quantitative
Variables. Students Talk Stats: How Outliers and
Influential Observations Affect a Relationship. Students
Talk Stats: Confounding in a Relationship Between
Two Quantitative Variables. PART III: PROBABILITY.
6. Finding Probabilities. The Meaning of “Probability”
and Basic Rules. More General Probability Rules and
Conditional Probability. Students Talk Stats: Probability
as a Weighted Average of Conditional Probabilities.
7. Random Variables. Discrete Random Variables.
Binomial Random Variables. Students Talk Stats:
Calculating and Interpreting the Mean and Standard
Deviation of Count or Proportion. Continuous Random
Variables and the Normal Distribution. Students Talk
Stats: Means, Standard Deviations, and Below-Average
Heights. 8. Sampling Distributions. The Behavior of
Sample Proportion in Repeated Random Samples. The
Behavior of Sample Mean in Repeated Random Samples.
Students Talk Stats: When Normal Approximations
Are Appropriate. PART IV: STATISTICAL INFERENCE.
9. Inference for a Single Categorical Variable. Point
Estimate and Confidence Interval: A Best Guess and a
Range of Plausible Values for Population Proportion.
Students Talk Stats: Interpreting a Confidence Interval.
Test: Is a Proposed Population Proportion Plausible?
Students Talk Stats: Interpreting a P-value. Students Talk
Stats: What Type of Error Was Made? Students Talk Stats:
The Correct Interpretation of a Small P-value. Students
Talk Stats: The Correct Interpretation When a P-value
Is Not Small. 10. Inference for a Single Quantitative
Variable. Inference for a Mean when Population
Standard Deviation Is Known or Sample Size Is Large.
Students Talk Stats: Confidence Interval for a Mean.
Students Talk Stats: Interpreting a Confidence Interval
for the Mean Correctly. Inference for a Mean When
the Population Standard Deviation Is Unknown and
the Sample Size Is Small. Students Talk Stats: Practical
Application of a t Test. A Closer Look at Inference
for Means. 11. Inference for Relationships Between
Categorical and Quantitative Variables. Inference for a
Paired Design with t. Inference for a Two-Sample Design
with t. Students Talk Stats: Ordinary Vs. Pooled TwoSample t. Inference for a Several-sample Design with
F: Analysis of Variance. Students Talk Stats: Reviewing
Relationships between Categorical and Quantitative
Variables. 12. Inference for Relationships Between Two
Categorical Variables. Comparing Proportions with a
z Test. Comparing Counts with a Chi-Square Test. 13.
Inference for Relationships Between Two Quantitative
Variables. Inference for Regression: Focus on the Slope
of the Regression Line. Students Talk Stats: No Evidence
of a Relationship. Interval Estimates for an Individual or
Mean Response. 14. How Statistics Problems Fit into the
Big Picture. The Big Picture in Problem-Solving. Students
Talk Stats: Choosing the Appropriate Statistical Tools.
15. Non-Parametric Methods (Online). The Sign Test as
an Alternative to the Paired t Test. The Rank-Sum Test
as an Alternative to the Two-Sample t Test. Summary
of Non-Parametrics. 16. Two-Way ANOVA (Online). 17.
Multiple Regression (Online). PART V: SOLUTIONS TO
SELECTED EXERCISES.
© 2011, 608pp, Paperback, 9780495831457, Arts and Sciences
ELEMENTARY STATISTICS, ENHANCED
REVIEW EDITION (W/CD-ROM &
CENGAGENOW, INFOTRAC® 2-SEM.,
ILRN® HOMEWORK, PERSONAL TUTOR,
INTERNET COMPANION FOR STATISTICS
2-SEM. PAC), 10E
Robert R. Johnson, Monroe Community College; Patricia J. Kuby
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3
In their own classrooms, through their popular texts,
and in the conferences they lead, Bob Johnson and
Pat Kuby have inspired hundreds of thousands of
students and their instructors to see the utility and
practicality of statistics. Robert Johnson and Patricia
Kuby’s ELEMENTARY STATISTICS, Tenth Edition has
been consistently praised by users and reviewers for its
clear exposition and relevant examples, exercises, and
applications. Technology enhancements include the
addition of Video Skillbuilders and StatisticsNow™ (part
of the CengageNOW suite of technology products), our
personalized online learning companion. This increased
focus on technology to help students succeed, along
with the wealth of instructor supplements and
flexibility of technology coverage (with MINITAB, Excel,
and TI-83 output and instructions throughout) clearly
differentiate this text from its competitors as the most
accessible text for students to learn from and the most
straightforward text for instructors to teach from.
NEW TO THIS EDITION
• The new introductory concepts, review lessons, and
solutions located in the back of the text written by
author, Patricia Kuby, provide your students with the
help they need to grasp various algebraic and basic
statistical concepts.
• New Learning Outcomes have been added to the endof-chapter material. These outcomes relate to specific
section material and exercises and allow students to
diagnose their mastery of a topic.
• StatisticsNow™ (part of the CengageNOW suite of
technology products). Featured within chapters,
StatisticsNow is a robust, personalized online learning
companion that helps students gauge their own
unique study needs and makes the most of their study
time by building focused Personalized Learning Plans
that reinforce key concepts. Pre-Tests give students
an initial assessment of their knowledge. Personalized
Learning Plans, based on the students’ answers to the
pre-test questions, outline key elements for review.
Post-Tests assess students’ mastery of core concepts
for each chapter. Access to StatisticsNow is available
at no additional charge with purchase of a new text.
• Interactive Video Skillbuilders contain hours of
helpful, interactive video instruction. These videos
walk your students through key examples from the
text, step by stepgiving them a foundation in the
skills that they need to know. Video icons located in
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the margin guide students to view the video on the
Skillbuilder CD-ROM.
• New technology output has been added throughout
the text, reflecting the latest changes to MINITAB,
Excel, and TI-83/84 graphing calculator output.
• 30% of the exercises are new and many others have
been significantly updated. Additional critical thinking
exercises have been infused throughout the exercise
sets. In addition, Classic Exercises can still be found on
the CD-ROM accompanying the text, providing more
opportunity to assign practice exercises.
CONTENTS
Part I: DESCRIPTIVE STATISTICS. 1. Statistics. Americans,
Here’’s Looking At You. What is Statistics? Measurability
and Variability. Data Collection. Comparison of
Probability and Statistics. Statistics and Technology. 2.
Descriptive Analysis and Presentation of Single-Variable
Data. You and the Internet. Graphical Presentation of
Data. Graphs, Pareto Diagrams, and Stem-And-Leaf
Displays. Frequency Distributions and Histograms.
Numerical Descriptive Statistics. Measures of Central
Tendency. Measures of Dispersion. Measures of Position.
Interpreting and Understanding Standard Deviation.
The Art of Statistical Deception. Mean and Standard
Deviation of Frequency Distribution (Optional). 3.
Descriptive Analysis and Presentation of Bivariate
Data. The Kid is All Grown Up. Bivariate Data. Linear
Correlation. Linear Regression. Part II: PROBABILITY.
4. Probability. Sweet Statistics. Probability of Events.
Conditional Probability of Events. Rules of Probability.
Mutually Exclusive Events. Independent Events. Mutually
Exclusive, Independent Events—A Relationship? 5.
Probability Distributions (Discrete Variables). Caffeine
Drinking. Random Variables. Probability Distribution
of a Discrete Random Variable. Mean and Variance
of a Discrete Probability Distribution. The Binomial
Probability Distribution. Mean and Standard Deviation
of the Binomial Distribution. 6. Normal Probability
Distributions. Intelligence Scores. Normal Probability
Distributions. The Standard Normal Distribution.
Applications of Normal Distributions. Notation. Normal
Approximation of the Binomial. 7. Sample Variability.
275 Million Americans. Sampling Distributions. The
Sampling Distribution of Sample Means. Application
of the Sampling Distribution of Sample Means. Part III:
INFERENTIAL STATISTICS. 8. Introduction to Statistical
Inferences. Were They Shorter Back Then? The Nature of
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Estimation. Estimation of a Mean (ó known). The Nature
of Hypothesis Testing. Hypothesis Test of Mean ì (ó
Known): A Probability Value Approach. Hypothesis Test
of Mean ì (ó Known): A Classical Approach. 9. Inferences
Involving One Population. Get Enough Daily Exercise?
Inferences About Mean ì (ó Unknown). Inferences
About the Binomial Probability of Success. Inferences
About Variance and Standard Deviation. 10. Inferences
Involving Two Populations. Students, Credit Cards and
Debt. Independent and Dependent Samples. Inferences
Concerning the Mean Difference Using Two Dependent
Samples. Inferences Concerning the Difference Between
Means Using Two Independent Samples. Inferences
Concerning the Difference Between Proportions Using
Two Independent Samples. Inferences Concerning
the Ratio of Variances Using Two Independent
Samples. Part IV: MORE INFERENTIAL STATISTICS. 11.
Applications of Chi-Square. Cooling a Great Hot Taste.
Chi-Square Statistic. Inferences Concerning Multinomial
Experiments. Inferences Concerning Contingency
Tables. 12. Analysis of Variance. Time Spent Commuting
to Work. Introduction to the Analysis of Variance
Technique. The Logic Behind ANOVA. Applications
of Single-Factor ANOVA. 13. Linear Correlation and
Regression. Beautiful Golden Wheat! Linear Correlation
Analysis. Inferences About the Linear Correlation
Coefficient. Linear Regression Analysis. Inferences
Concerning the Slope of the Regression Line. Confidence
Interval Estimates For Regression. Understanding the
Relationship Between Correlation and Regression.
14. Elements of Nonparametric Statistics. Teenagers’’
Attitudes. Nonparametric Statistics. Comparing
Statistical Tests. The Sign Test. The Mann-Whitney U
Test. The Runs Test. Rank Correlation. Appendix A: Basic
Principles of Counting. Appendix B: Tables. Answers to
Odd-Numbered Exercises. Answers to Chapter Practice
Tests. Index for Applications. Index for Computer and
Calculator Instructions. Index. Credits. Formula Card.
© 2008, 832pp, Hardback, 9780495383864, Arts and Sciences
ELEMENTARY STATISTICS,
INTERNATIONAL EDITION, 11E
and in the conferences they lead, Robert Johnson and
Patricia Kuby have inspired hundreds of thousands of
practicality of statistics. Now in its Eleventh Edition,
ELEMENTARY STATISTICS, International Edition has
applications. A focus on technology to help students
succeed—including MINITAB®, Excel®, and TI-83/84
output and instructions throughout—is enhanced by a
wealth of supplements that save instructors time and
give students interactive guidance and support. All
this and more have established this text’s reputation
for being remarkably accessible for students to learn
from—and simple and straightforward for instructors
to teach from.
NEW TO THIS EDITION
• New chapter-opening vignettes—More than 50%
of the book’s chapter-opening vignettes, each of
which focuses on an everyday aspect of life, are
new. Illustrated with statistical information, each
chapter opener provides a relevant, familiar context
for students’ initial step into the concepts covered in
the chapter.
• New applied examples—Nearly 20% of the text’s
applied examples are new or updated to help engage
student interest. Enhanced step-by-step solutions
present key statistical concepts in a clear progression.
• Over 20% new and updated exercises—21% of the
exercises are new or updated to reflect current
events and other timely topics that involve the use of
statistics.The text’s more than 1700 exercises provide
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•
•
a wealth of practice problems, with each exercise set
including a range of exercise types that progress from
basic recall to multi-step to items requiring critical
thinking. As always, most exercises can be calculated
either by hand or by using technology.
Chapter 6, “Normal Probability Distributions,” has
been completely rewritten to present the Standard
Normal Distribution utilizing the cumulative area
approach, incorporating a more intuitive idea with
respect to the total area under a curve and following
more closely the format used with calculators and
statistical software. To support this change, a new,
corresponding two-page Table 3, “Cumulative Areas of
the Standard Normal Distribution,” is included among
the tables at the back of the text.
Statistics CourseMate—This new online resource
provides a rich array of interactive and supplementary
material to accompany the text, including an eBook
version of the text. For more information, see the full
description under Supplements.
Solution Builder for ELEMENTARY STATISTICS,
11e, International Edition—This new instructor
resource provides fully worked-out solutions to all
text exercises in customizable online format. For
more information, see the full description under
Supplements.
Aplia™ online homework for ELEMENTARY
STATISTICS, 11e, International Edition—Aplia is a
premium online homework system dedicated to
improving learning by increasing student effort
and engagement. For more information, see the full
FEATURES
• Emphasis on interpretation of statistical information
and real applications—Starting in Chapter 1, when
students learn key terms and procedures; in Chapter
4, “Probability,” where analysis rather than formula
is highlighted; and continuing throughout the text,
the authors emphasize the role of interpretation in
statistical analysis. Examples and exercises feature
real applications of statistics, and chapter-opening
vignettes enhance the relevance of the material
for students. Critical thinking exercises throughout
chapters further support the book’s practical, proven
approach.
• Skillbuilder Applet exercises and interactive video
skillbuilders—These exercises give students increased
6
opportunities for hands-on learning and shift their
attention from simply memorizing formulas and
concepts to actively internalizing the behavior of
concepts and interpreting data. Created to help
students build the foundational skills they need,
the online videos provide step-by-step instruction,
walking students through key examples from the text.
• Flexible organization and integrated, up-to-date
technology instructions—The p-value and classical
approaches to hypothesis testing are initially
introduced separately and thereafter presented sideby-side, to both emphasize their comparability and
allow a wide range of teaching approaches. Similarly,
descriptive regression and correlation are covered
early (Chapter 3), and MINITAB®, Excel®, and the
TI-83/84 graphing calculator instructions are found
throughout the text instead of being relegated to
end-of-chapter material or appendices.
CONTENTS
Part I: DESCRIPTIVE STATISTICS. 1. Statistics. 2.
Data. 3. Descriptive Analysis and Presentation of
Bivariate Data. Part II: PROBABILITY. 4. Probability. 5.
Probability Distributions (Discrete Variables). 6. Normal
Probability Distributions. 7. Sample Variability. Part III:
Inferences. 9. Inferences Involving One Population. 10.
Inferences Involving Two Populations. Part IV: MORE
INFERENTIAL STATISTICS. 11. Applications of ChiSquare. 12. Analysis of Variance. 13. Linear Correlation
and Regression. 14. Elements of Nonparametric
Statistics. Appendix A: Basic Principles of Counting.
Appendix B: Tables. Introductory Concepts. Review
Lessons. Answers to Odd-Numbered Exercises. Answers
to Chapter Practice Tests. Index for Applications. Index
for Computer and Calculator Instructions. Index.
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ELEMENTARY STATISTICS: LOOKING AT
THE BIG PICTURE, HYBRID
Reflecting Cengage Learning’s commitment to
offering flexible teaching solutions and value for
students and instructors, this new hybrid edition
features the instructional presentation found in the
printed text while delivering end-of-section exercises
online in Aplia™. The result—a briefer printed text
that engages students online! Using a successfully
class-tested approach that gives coherence to a broad
range of introductory topics, this innovative text
provides students with a real-world, big picture view
of statistics as well as problem-solving strategies
that can be applied to the statistical questions, real
data, and examples that they will encounter. Author
Nancy Pfenning organizes content around four basic
processes of statistics: producing data, displaying and
summarizing data, understanding probability, and using
probability to perform statistical inference. Within
this framework, the book progresses systematically
through five basic problem situations involving values
of variables (quantitative, categorical, or a blend). As
a result, students learn to identify which situation
applies and how to choose the correct display,
NEW TO THIS EDITION
• NEW! Hybrid edition of the text provide a lessexpensive, shorter option for students. A selection
of end-of-section exercises are omitted from the text
and appear online only. Every new hybrid edition is
packaged with Aplia™.
• Big picture problem-solving approach--An innovative
• Flexible content and technology integration--The
• Examples using real data--Hundreds of well-chosen
conceptual--A set of problems after each major
real data.
• Emphasis on connections among topics--Strategically
flow of main ideas.
CONTENTS
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SELECTED EXERCISES.
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and applications for which an applet is available on
the website.
• Technology Today features at the end of each chapter
offer Microsoft® Excel 2010 and MINITAB outputs
and instructions, giving students hands-on experience
performing statistical analysis with these widely used
programs.
FEATURES
INTRODUCTION TO PROBABILITY AND
STATISTICS, INTERNATIONAL EDITION,
14E
William Mendenhall, University of Florida, Emeritus; Robert J. Beaver,
University of California, Riverside; Barbara M. Beaver, University of
California, Riverside
Used by hundreds of thousands of students since its
first edition, INTRODUCTION TO PROBABILITY AND
STATISTICS, 14E, International Edition continues to
blend the best of its proven, error-free coverage with
new innovations. Written for the higher end of the
traditional introductory statistics market, the book
takes advantage of modern technology—including
computational software and interactive visual
tools—to facilitate statistical reasoning as well as
the interpretation of statistical results. In addition
to showing how to apply statistical procedures, the
authors explain how to describe real sets of data
meaningfully, what the statistical tests mean in terms of
their practical applications, how to evaluate the validity
of the assumptions behind statistical tests, and what
to do when statistical assumptions have been violated.
The new edition retains the statistical integrity,
examples, exercises, and exposition that have made this
text a market leader—and builds upon this tradition of
excellence with new technology integration.
NEW TO THIS EDITION
• The book’s website, Statistics CourseMate, includes
a Personal Trainer feature that offers further
explanation of some of the more difficult concepts
through detailed definitions, step-by-step hints on
problem solving, self-correcting exercises, and data
sets for text exercises. Applets demonstrate key
concepts so they are easily visualized, accompanied
by exercises that provide practice in the required
statistical methods and immediate feedback with
answers. An icon in the text margin identifies concepts
• Exercises: The book includes more than 1,300
exercises, many of which are new or updated for this
edition. New topics for the book’s chapter-ending
case study exercises include “How to Save Money
for Groceries,” “School Accountability Study: How
Is Your School Doing?” and “Are You Going to Curve
the Grades?”
• Market leader: The integrity of the statistics and the
quality of the examples and exercises keep this text in
the bestseller category. Comprehensive in coverage,
it delivers a more rigorous offering with traditional
coverage of probability. Instructors and students alike
appreciate its error-free material and exercises, and
its clear exposition.
• Real data: The first to incorporate case studies and
real data, this text continues to set the standard.
Many examples and exercises use authentic data sets,
helping students see the connections between their
studies and their lives.
• Quick reference: At the end of each chapter, Key
Concepts and Formulas sections provide quick
reference for students, helping them ensure they are
well prepared for assignments and tests.
CONTENTS
Introduction: What Is Statistics? 1. Describing Data
with Graphs. 2. Describing Data with Numerical
Measures. 3. Describing Bivariate Data. 4. Probability
and Probability Distributions. 5. Several Useful Discrete
Distributions. 6. The Normal Probability Distribution.
7. Sampling Distributions. 8. Large-Sample Estimation.
9. Large-Sample Tests of Hypotheses. 10. Inference
from Small Samples. 11. The Analysis of Variance.
12. Linear Regression and Correlation. 13. Multiple
Regression Analysis. 14. Analysis of Categorical Data. 15.
Nonparametric Statistics. Appendix I: Tables. Answers
to Selected Exercises.
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INTRODUCTION TO STATISTICS AND
DATA ANALYSIS, 4E
Roxy Peck, California Polytechnic State University, San Luis Obispo;
Chris Olsen, George Washington High School, Cedar Rapids, Iowa; Jay
L. Devore, California Polytechnic State University, San Luis Obispo
Roxy Peck, Chris Olsen, and Jay Devore’s new edition
uses real data and attention-grabbing examples to
introduce students to the study of statistics and
data analysis. Traditional in structure yet modern
in approach, this text guides students through
an intuition-based learning process that stresses
interpretation and communication of statistical
information. Simple notation--including the frequent
substitution of words for symbols--helps students grasp
concepts and cement their comprehension. Handson activities and interactive applets allow students
to practice statistics firsthand. INTRODUCTION TO
STATISTICS AND DATA ANALYSIS, 4th Edition, includes
updated coverage of the graphing calculator as well as
expanded coverage of probability.
NEW TO THIS EDITION
• New Cumulative Review Exercises appear after
selected chapter sets, allowing you to test students’
comprehension of topics spanning multiple chapters.
• More than 50 new examples and more than 270 new
exercises, which use data from current newspapers
and journals, help students understand statistical
concepts in a realistic context. In addition, more of
the exercises specifically ask students to write (for
example, by requiring them to explain their reasoning,
interpret results, and comment on important features
of an analysis).
• Examples and exercises using data sets that can be
accessed online from the text website (Statistics
CourseMate) are designated by an icon, as are
examples that are further illustrated in technology
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manuals for MINITAB®, SPSS®, etc.--which are also
available at the website.
Exercises have been added to the “Interpreting and
Communicating the Results of Statistical Analyses”
sections, giving students the chance to practice
these important skills. All of these sections now have
assignable end-of-section questions associated with
them.
Approximately 90 exercises have video solutions,
presented by Brian Kotz of Montgomery College,
which can be viewed online or downloaded for viewing
later. An icon designates these exercises in the text.
A greater number of end-of-chapter activities have
been added to the book, ideal for use as a chapter
capstone or integrated at appropriate places as the
chapter material is covered in class.
The “Graphing Calculator Explorations” have been
significantly revised and updated.
FEATURES
• The text is enhanced by a variety of online teaching and
learning resources. These include the book’s website,
Statistics CourseMate; Aplia™, an online interactive
learning solution that improves comprehension of key
statistical concepts; and Cengage Learning’s Enhanced
WebAssign®, which allows you to assign problems
from the text online and ensure that students receive
multimedia tutorial support as they complete their
assignments.
• Most chapters contain “Graphing Calculator
Explorations.” Found at the end of the chapter, these
explorations promote statistical thinking and allow
students to actively experience technology.
• Optional material on logistic regression in Chapter
5, “Summarizing Bivariate Data,” assures complete
coverage of this important topic.
CONTENTS
1. THE ROLE OF STATISTICS AND THE DATA ANALYSIS
PROCESS. Why Study Statistics. The Nature and Role
of Variability. Statistics and the Data Analysis Process.
Types of Data and Some Simple Graphical Displays.
2. COLLECTING DATA SENSIBLY. Statistical Studies:
Observation and Experimentation. Sampling. Simple
Comparative Experiments. More on Experimental
Design. More on Observational Studies: Designing
Surveys (Optional). Interpreting and Communicating
the Results of Statistical Analyses. 3. GRAPHICAL
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METHODS FOR DESCRIBING DATA. Displaying
Categorical Data: Comparative Bar Charts and Pie
Charts. Displaying Numerical Data: Stem-and-Leaf
Displays. Displaying Numerical Data: Frequency
Distributions and Histograms. Displaying Bivariate
Numerical Data. Interpreting and Communicating
the Results of Statistical Analyses. 4. NUMERICAL
METHODS FOR DESCRIBING DATA. Describing the
Center of a Data Set. Describing Variability in a Data
Set. Summarizing a Data Set: Boxplots. Interpreting
Center and Variability: Chebyshev’s Rule, the Empirical
Rule, and z Scores. Interpreting and Communicating
the Results of Statistical Analyses. 5. SUMMARIZING
BIVARIATE DATA. Correlation. Linear Regression:
Fitting a Line to Bivariate Data. Assessing the Fit of
a Line. Nonlinear Relationships and Transformations.
Logistic Regression (Optional). Interpreting and
Communicating the Results of Statistical Analyses.
6. PROBABILITY. Chance Experiments and Events.
Definition of Probability. Basic Properties of Probability.
Conditional Probability. Independence. Some General
Probability Rules. Estimating Probabilities Empirically
Using Simulation. 7. RANDOM VARIABLES AND
PROBABILITY DISTRIBUTIONS. Random Variables.
Probability Distributions for Discrete Random Variables.
Probability Distributions for Continuous Random
Variables. Mean and Standard Deviation of a Random
Variable. Binomial and Geometric Distributions. Normal
Distributions. Checking for Normality and Normalizing
Transformations. Using the Normal Distribution to
Approximate a Discrete Distribution. 8. SAMPLING
VARIABILITY AND SAMPLING DISTRIBUTION.
Statistics and Sampling Variability. The Sampling
Distribution of a Sample Mean. The Sampling
Distribution of a Sample Proportion. 9. ESTIMATION
USING A SINGLE SAMPLE. Point Estimation. LargeSample Confidence Interval for a Population Proportion.
Confidence Interval for a Population Mean. Interpreting
and Communicating the Results of Statistical Analyses.
10. HYPOTHESIS TESTING USING A SINGLE SAMPLE.
Hypotheses and Test Procedures. Errors in Hypotheses
Testing. Large-Sample Hypothesis Tests for a Population
Proportion. Hypotheses Tests for a Population Mean.
Power and Probability of Type II Error. Interpreting and
Communicating the Results of Statistical Analyses. 11.
COMPARING TWO POPULATIONS OR TREATMENTS.
Inferences Concerning the Difference Between Two
Population or Treatment Means Using Independent
Samples. Inferences Concerning the Difference
Between Two Population or Treatment Means Using
Paired Samples. Large Sample Inferences Concerning
a Difference Between Two Population or Treatment
Proportions. Interpreting and Communicating the
Results of Statistical Analyses. 12. THE ANALYSIS
OF CATEGORICAL DATA AND GOODNESS-OF-FIT
TESTS. Chi-Square Tests for Univariate Data. Tests
for Homogeneity and Independence in a Two-way
Table. Interpreting and Communicating the Results of
Statistical Analyses. 13. SIMPLE LINEAR REGRESSION
AND CORRELATION: INFERENTIAL METHODS. Simple
Linear Regression Model. Inferences About the Slope
of the Population Regression Line. Checking Model
Adequacy. Inferences Based on the Estimated Regression
Line (Optional). Inferences About the Population
Correlation Coefficient (Optional). Interpreting and
14. MULTIPLE REGRESSION ANALYSIS. Multiple
Regression Models. Fitting a Model and Assessing
Its Utility. Inferences Based on an Estimated Model
(online). Other Issues in Multiple Regression (online).
Interpreting and Communicating the Results of
Statistical Analyses (online). Activity 14.1: Exploring
the Relationship Between Number of Predictors and
Sample Size. 15. ANALYSIS OF VARIANCE. SingleFactor ANOVA and the F Test. Multiple Comparisons.
The F Test for a Randomized Block Experiment
(online). Two-Factor ANOVA (online). Interpreting
and Communicating the Results of Statistical Analyses
(online). 16. NONPARAMETRIC (DISTRIBUTION-FREE
STATISTICAL METHODS (ONLINE). Distribution-Free
Procedures for Inferences About a Difference Between
Two Population or Treatment Means Using Independent
Samples (Optional). Distribution-Free Procedures for
Inferences About a Difference Between Two Population
or Treatment Means Using Paired Samples. DistributionFree ANOVA.
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frequently substituting words for symbols. Hands-on
activities and interactive applets allow students to
practice statistics firsthand. This Enhanced Edition
includes new Teaching Tips for each chapter in the
book, specific references to other available instructor
resources, and suggestions for effectively teaching an
Advanced Placement Introduction to Statistics course.
Also, Enhanced WebAssign now complements a robust
supplement package.
NEW TO THIS EDITION
DATA ANALYSIS, AP EDITION, 4E
DATA ANALYSIS, ENHANCED REVIEW
EDITION (WITH CENGAGENOW PRINTED
ACCESS CARD), 3E
Roxy Peck, Chris Olsen and Jay Devore’s new edition
data analysis. The Third Edition includes coverage
of the graphing calculator and includes expanded
coverage of probability. Traditional in structure yet
modern in approach, this text guides students through
information. It helps students grasp concepts and
cement their comprehension by using simple notation-
12
• Chapter Opening Teaching Tips have been added to
beginning of each printed chapter in the Annotated
Instructor’s Edition. Broken down section-by-section,
these tips suggest additional assignments and
activities for a section, as well as the best uses of
the supplemental resources for the text (such as the
Activity Workbook, Instructor’s Binder and Testbank)
that could be included in a lesson plan.
• Margin Teaching Tips have been integrated throughout
the Annotated Instructor’s Edition. These tips are
strategically placed at points where an instructor
can integrate an activity, a clarifying example, a quiz
to test students on concept formation, and thoughtprovoking questions to ask your students.
• Easy to assign. Easy to use. Easy to manage. Enhanced
WebAssign allows you to assign, collect, grade, and
record homework assignments via the web and
includes links to text-specific content, video examples,
and problem-specific tutorials. Enchanced WebAssign
is more than a homework system; it is a complete
learning system for your students.
FEATURES
• Annotated Instructors Edition – designed specifically
to aid you in teaching the course.
• “Communicating and Interpreting the Results
of Statistical Analysis” sections emphasize the
importance of being able to interpret statistical output
and communicate its meaning to non-statisticians.
• Nearly 100 new examples and hundreds of new or
updated exercises with current data from journals,
newspapers, and other published sources have been
added to this edition, helping students understand
statistical concepts in a realistic, relevant context.
There are now over 1,100 exercises in the third edition.
• Eight new activities bring the total to 33. Found at
the ends of chapters, these hands-on activities allow
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students to see statistics unfold before their eyes as
they conduct their own experiments.
• New optional material on logistic regression in
Chapter 5, “Summarizing Bivariate Data,” assures
complete coverage of this important topic.
• This text continues to feature broad coverage of
sampling, survey design and experimental design
coverage of transformations and nonlinear regression;
and an emphasis on graphical display as a necessary
component of data analysis.
CONTENTS
PROCESS. Three Reasons to Study Statistics. The
Nature and Role of Variability. Statistics and the
Data Analysis Process. Types of Data and Some
Simple Graphical Displays. Activity 1.1: Head Sizes:
Understanding Variability. Activity 1.2: Estimating Sizes.
Activity 1.3: A Meaningful Paragraph. 2. COLLECTING
DATA SENSIBLY. Statistical Studies: Observation and
Experimentation. Sampling. Simple Comparative
Experiments. More on Experimental Design. More on
Observational Studies: Designing Surveys (optional).
Communicating and Interpreting the Results of
Statistical Analyses. Activity 2.1: Designing a Sampling
Plan. Activity 2.2: An Experiment to Test for the
Stroop Effect. Activity 2.3: McDonalds and the Next
100 Billion Burgers. Activity 2.4: Video Games and
Pain Management. Graphing Calculator Explorations.
3. GRAPHICAL METHODS FOR DESCRIBING DATA.
Displaying Categorical Data: Comparative Bar Charts
and Pie Charts. Displaying Numerical Data: Stem-andLeaf Displays. Displaying Numerical Data: Frequency
Numerical Data. Communicating and Interpreting
the Results of Statistical Analyses. Activity 3.1:
Locating States. Activity 3.2: Bean Counters! Graphing
Calculator Explorations. 4. NUMERICAL METHODS
FOR DESCRIBING DATA. Describing the Center
of a Data Set. Describing Variability in a Data Set.
Summarizing a Data Set: Boxplots. Interpreting Center
and Variability: Chebyshev’’s Rule, the Empirical Rule,
and z Scores. Communicating and Interpreting the
Results of Statistical Analyses. Activity 4.1: Collecting
and Summarizing Numerical Data. Activity 4.2: Airline
Passenger Weights. Activity 4.3: Boxplot Shapes.
Graphing Calculator Explorations. 5. SUMMARIZING
fitting a Line to Bivariate Data. Assessing the Fit of a
Line. Nonlinear Relationships and Transformations.
Logistic Regression (Optional). Communicating
and Interpreting the Results of Statistical Analyses.
Activity 5.1: Exploring Correlation and Regression
Technology Activity (Applets). Activity 5.2: Age
and Flexibility. Graphing calculator Explorations. 6.
PROBABILITY. Chance Experiments and Events.
and Using Simulation. Activity 6.1: Kisses. Activity 6.2:
A Crisis for European Sports Fans? Activity 6.3: The “Hot
Hand” in Basketball. Graphing Calculator Explorations.
7. RANDOM VARIABLES AND PROBABILIT Y
DISTRIBUTIONS. Random Variables. Probability
Distributions for Discrete Random Variables. Probability
Distributions for continuous Random Variables.
Mean and Standard Deviation of a Random Variable.
The Binomial and Geometric Distributions. Normal
Approximate a Discrete Distribution. Activity 7.1: Rotten
Eggs. Graphing Calculator Explorations. 8. SAMPLING
VARIABILITY AND SAMPLING DISTRIBUTIONS.
distribution of a Sample Proportion. Activity 8.1: Do
Students Who Take the SAT Multiple Times Have an
Advantage in College Admissions? Graphing Calculator
Explorations. 9. ESTIMATION USING A SINGLE
SAMPLE. Point Estimates. Large-Sample Confidence
Interval for a Population Proportion. Confidence
Interval for a Population Mean. Communicating
Activity 9.1: Getting a Feel for Confidence Level.
Activity 9.2: An Alternative Confidence Interval
for a Population Proportion. Graphing Calculator
Explorations. 10. HYPOTHESIS TESTING USING A
SINGLE SAMPLE. Hypotheses and Test Procedures.
Errors in Hypothesis Testing. Large-Sample Hypothesis
Tests for a Population Proportion. Hypothesis Tests for
a Population Mean. Power and the Probability of Type
II Error. Communicating and Interpreting the Results of
Statistical Analyses. Activity 10.1: Comparing the t and
z Distributions. Graphing Calculator Explorations. 11.
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Population or Treatment Means using Independent
Between Two Population or Treatment Means using
Paired Samples. Large-Sample Inferences Concerning
Proportions. Communicating and Interpreting the
Results of Statistical Analyses. Activity 11.1: Helium-Filled
Footballs? Activity 11.2: Thinking About Data Collection.
Graphing Calculator Explorations. 12. THE ANALYSIS
TESTS. Chi-Square Tests for Univariate Categorical
Data. Tests for Homogeneity and Independence in
a Two-Way Table. Communicating and Interpreting
the Results of Statistical Analyses. Activity 12.1: Pick
a Number, Any Number. Activity 12.2: Color and
Perceived Taste. Graphing Calculator Explorations. 13.
SIMPLE LINEAR REGRESSION AND CORRELATION:
INFERENTIAL METHODS. The Simple Linear Regression
Model. Inferences About the Slope of the Population
Regression Line. Checking Model Adequacy. Inferences
Based on the Estimated Regression Line (Optional).
Inferences About the Population Correlation Coefficient
(Optional). Communicating and Interpreting the
Results of Statistical Analyses. Activity 13.1: Are Tall
Women from “Big” Families? Graphing Calculator
Explorations. 14. MULTIPLE REGRESSION ANALYSIS.
Multiple Regression Models. Fitting a Model and
Assessing Its Utility. Inferences Based on an Estimated
Model (Online). Variable Selection and Other Issues
in Multiple Regression (Online). Communicating
and Interpreting the Results of Statistical Analyses
(Online). Activity 14.1: Exploring the Relationship
Between Number of Predictors and Sample Size. 15.
ANALYSIS OF VARIANCE. Single-Factor ANOVA and
the F Test. Multiple Comparisons. The F Test for a
Randomized Block Experiment (Online). Two-Factor
ANOVA (Online). Communicating and Interpreting
the Results of Statistical Analysis (Online). Activity
15.1: Exploring Single-Factor ANOVA. Appendix:
ANOVA computations (Online). Graphing Calculator
Exploration. 16. NONPARAMETRIC STATISTICAL
METHODS (ONLINE). Distribution-Free Procedures for
Inferences about a Difference Between Two Population
or Treatment Means Using Independent Samples
(Online). Distribution-Free Procedures for Inferences
Concerning a Difference. Between Two Population
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Means Using Paired Samples (Online). Distribution-Free
ANOVA (Online). Appendix: Statistical Tables. Answers
to Selected odd-Numbered Exercises. Index.
MIND ON STATISTICS, 4E
Jessica M. Utts, University of California, Irvine; Robert F. Heckard,
Pennsylvania State University
MIND ON STATISTICS emphasizes the conceptual
development of statistical ideas and the importance
of looking for--and finding--meaning in data. Authors
Jessica Utts and Robert Heckard actively engage
students’ natural curiosity, motivating them with
intriguing questions and explaining statistical topics
in the context of a wide range of interesting, useful
examples and case studies. Throughout, the authors
balance the promotion of statistical literacy with
coverage of the statistical methodology taught
in general introductory statistics courses. Their
approach is based on the premises that new material
is much easier to learn and remember if it is related
to something interesting or previously known, and if
students actively ask questions and find the answers for
themselves. More than any other text available, MIND
ON STATISTICS motivates and encourages students
to develop their statistical intuition by focusing on
analyzing data and interpreting results, rather than on
mathematical formulation.
NEW TO THIS EDITION
• To create a more natural flow of topic coverage,
Chapters 5 and 6 have been moved to precede
Chapters 3 and 4 so that the material on describing
data comes before the two chapters on collecting
data.
• The language has been tightened and simplified in all
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chapters whenever possible. In particular, Chapters 9,
10, and 12 have been substantially honed.
New topics in this edition include multiple comparisons
in Chapters 1 and 13, power curves in Chapters 12 and
13, and McNemar’s test in Chapter 15.
The exercise sets have been significantly reorganized
and rewritten. For example, in response to reviewers’
suggestions, many exercises are now set up in oddeven pairs, featuring odd-numbered problems--which
are answered in the back of the book--followed by
similar even-numbered problems. In addition, a
number of new problems have been added, including
a significant number of new drill exercises.
The data in the problem sets, examples, and case
studies have been updated with the latest possible
information. In addition, the book includes all-new
examples and case studies as well as new and updated
data sets.
A greater number of In Summary boxes are placed
consistently throughout each chapter, reinforcing key
ideas as students read. A listing of the In Summary
boxes now appears at the end of the chapter, making
these features even more helpful as study aids.
Aplia™ is available with the new edition. This online
interactive learning solution--which improves
student comprehension and outcomes with detailed,
immediate explanations--also saves you time by
automatically grading assignments.
FEATURES
• Chapters 9–13, containing the core material on
sampling distributions and statistical inference,
are organized in a modular, flexible format. There
are six modules for each of the topics of sampling
distributions, confidence intervals, and hypothesis
testing. The first module provides an introduction
and each of the remaining five modules deals with a
specific parameter (one mean, one proportion, etc.).
This modular format emphasizes the similarity among
the inference procedures for the five parameters
discussed, and allows instructors to cover this material
in any order they choose.
• Technology Tips appear throughout the text for
MINITAB®, SPSS®, Excel®, JMP, and TI-83/84
calculators. Technical manuals for these technologies,
as well as for JMP and R, are available on the book’s
website, Statistics CourseMate.
• Original journal articles for selected case studies and
examples (identified in the text by an icon) can be
found on the book’s website, Statistics CourseMate.
Reading these original articles enables students to
learn more about how the research was conducted,
what methods were used, and the conclusions drawn.
CONTENTS
1. STATISTICS SUCCESS STORIES AND CAUTIONARY
TALES. What Is Statistics? Eight Statistical Stories with
Morals. The Common Elements in the Eight Stories.
2. TURNING DATA INTO INFORMATION. Raw Data.
Types of Variables. Summarizing One or Two Categorical
Variables. Exploring Features of Quantitative Data
with Pictures. Numerical Summaries of Quantitative
Variables. How to Handle Outliers. Bell-Shaped
Distributions and Standard Deviations. Skillbuilder
Applet: The Empirical Rule in Action. 3. RELATIONSHIPS
BETWEEN QUANTITATIVE VARIABLES. Looking for
Patterns with Scatterplots. Describing Linear Patterns
with a Regression Line. Measuring Strength and
Direction with Correlation. Regression and Correlation
Difficulties and Disasters. Correlation Does Not Prove
Causation. Skillbuilder Applet: Exploring Correlation. 4.
RELATIONSHIPS BETWEEN CATEGORICAL VARIABLES.
Displaying Relationships Between Categorical Variables.
Risk, Relative Risk, and Misleading Statistics About
Risk. The Effect of a Third Variable and Simpson’s
Paradox. Assessing the Statistical Significance of a 2
x 2 Table. 5. SAMPLING: SURVEYS AND HOW TO ASK
QUESTIONS. Collecting and Using Sample Data Wisely.
Margin of Error, Confidence Intervals, and Sample Size.
Choosing a Simple Random Sample. Other Sampling
Methods. Difficulties and Disasters in Sampling. How
to Ask Survey Questions. Skillbuilder Applet: Random
Sampling in Action. 6. GATHERING USEFUL DATA FOR
EXAMINING RELATIONSHIPS. Speaking the Language
of Research Studies. Designing a Good Experiment.
Designing a Good Observational Study. Difficulties
and Disasters in Experiments and Observational
Studies. 7. PROBABILITY. Random Circumstances.
Interpretations of Probability. Probability Definitions
and Relationships. Basic Rules for Finding Probabilities.
Finding Complicated Probabilities. Using Simulation
to Estimate Probabilities. Flawed Intuitive Judgments
About Probability. 8. RANDOM VARIABLES. What
Is a Random Variable? Discrete Random Variables.
Expectations for Random Variables. Binomial Random
Variables. Continuous Random Variables. Normal
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Random Variables. Approximating Binomial Distribution
Probabilities. Sums, Differences, and Combinations of
Random Variables. 9. UNDERSTANDING SAMPLING
DISTRIBUTIONS: STATISTICS AS RANDOM VARIABLES.
Parameters, Statistics, and Statistical Inference. From
Curiosity to Questions About Parameters. SD Module
0: An Overview of Sampling Distributions. SD Module
1: Sampling Distribution for One Sample Proportion.
SD Module 2: Sampling Distribution for the Difference
in Two Sample Proportions. SD Module 3: Sampling
Distribution for One Sample Mean. SD Module 4:
Sampling Distribution for the Sample Mean of Paired
Differences. SD Module 5: Sampling Distribution
for the Difference in Two Sample Means. Preparing
for Statistical Inference: Standardized Statistics.
Generalizations Beyond the Big Five. Skillbuilder Applet:
Finding the Pattern in Sample Means. 10. ESTIMATING
PROPORTIONS WITH CONFIDENCE. CI Module 0:
An Overview of Confidence Intervals. CI Module 1:
Confidence Interval for a Population Proportion. CI
Module 2: Confidence Intervals for the Difference in
Two Population Proportions. Using Confidence Intervals
to Guide Decisions. 11. ESTIMATING MEANS WITH
CONFIDENCE. Introduction to Confidence Intervals
for Means. CI Module 3: Confidence Interval for One
Population Mean. CI Module 4: Confidence Interval for
the Population Mean of Paired Differences. CI Module 5:
Confidence Interval for the Difference in Two Population
Means (Independent Samples). Understanding Any
Confidence Interval. Skillbuilder Applet: The Confidence
Level in Action. 12. TESTING HYPOTHESES ABOUT
PROPORTIONS. HT Module 0: An Overview of
Hypothesis Testing. HT Module 1: Testing Hypotheses
About a Population Proportion. HT Module 2: Testing
Hypotheses About the Difference in Two Population
Proportions. Sample Size, Statistical Significance,
and Practical Importance. 13. TESTING HYPOTHESES
ABOUT MEANS. Introduction to Hypothesis Tests for
Means. HT Module 3: Testing Hypotheses about One
Population Mean. HT Module 4: Testing Hypotheses
about the Population Mean of Paired Differences. HT
Module 5: Testing Hypotheses about the Difference in
Two Population Means (Independent Samples). The
Relationship Between Significance Tests and Confidence
Intervals. Choosing an Appropriate Inference Procedure.
Effect Size. Evaluating Significance in Research Reports.
14. INFERENCE ABOUT SIMPLE REGRESSION. Sample
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and Population Regression Models. Estimating the
Standard Deviation for Regression. Inference About
the Slope of a Linear Regression. Predicting y and
Estimating Mean y at a Specific x. Checking Conditions
for Using Regression Models for Inference. 15. MORE
ABOUT INFERENCE FOR CATEGORICAL VARIABLES.
The Chi-Square Test for Two-Way Tables. Analyzing 2
x 2 Tables. Testing Hypotheses About One Categorical
Variable: Goodness-of-Fit. 16. ANALYSIS OF VARIANCE.
Comparing Means with an ANOVA F-Test. Details of
One-Way Analysis of Variance. Other Methods for
Comparing Populations. Two-Way Analysis of Variance.
17. TURNING INFORMATION INTO WISDOM. Beyond
the Data. Transforming Uncertainty Into Wisdom.
Making Personal Decisions. Control of Societal Risks.
Understanding Our World. Getting to Know You. Words
to the Wise.
STAT 2 (WITH REVIEW CARDS AND
STATISTICS COURSEMATE PRINTED
ACCESS CARD), 2E
Created through a “student-tested, faculty-approved”
review process, STAT 2 is an engaging and accessible
solution to accommodate the diverse lifestyles of
today’s learners at a value-based price. This concise
STAT 2 includes all of the key concepts that you require
and a full suite of learning aids--including chapter-bychapter self-quizzes and downloadable flash cards--and
a wealth of online teaching and learning resources.
NEW TO THIS EDITION
• Many new real-world examples that students can
relate to have been added, such as Teens using
Cell Phones in Class, Busy Behind the Wheel, What
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Employers Look for in Seasonal Employees, and
Polishing a Microchip.
New problems have been added at the end of each
text chapter, as well as online.
Updated content throughout includes the use of
ogives in Chapter 2 and a new method of finding area
in Chapter 9.
New Key Terms have been added to many of the
chapters. Designed to be easy to spot, these key
terms come in handy when students are reviewing
the material or preparing for quizzes.
Some of the sections have been reorganized,
improving clarity and concept sequencing.
Chapter 4 includes new equations of properties of
probability numbers, and an expanded Notes section
in the discussion of probability numbers.
Chapter 6 includes a new section on common z
values.Specifically, the chapter has been completely
rewritten to present the Standard Normal Distribution
utilizing the cumulative area approach, incorporating
a more intuitive idea with respect to the total area
under a curve and following more closely the format
used with calculators and statistical software.
Chapter 7 includes a new discussion about the census.
Interested in a simple way to complement your
text and course content with study and practice
materials? Cengage Learning’s CourseMate brings
course concepts to life with interactive learning,
study, and exam preparation tools that support the
printed textbook. Watch student comprehension soar
as your class works with the printed textbook and the
textbook-specific website.
Available with this text, Aplia™ is an online interactive
learning solution that improves comprehension
and outcomes by increasing student effort and
engagement. Founded by a professor to enhance his
own courses, Aplia provides automatically graded
assignments with detailed, immediate explanations
on every question, and innovative teaching materials.
Our easy-to-use system has been used by more than
1,000,000 students at over 1800 institutions.
CONTENTS
Statistics.
STATISTICS, 7E
The Exploration & Analysis of Data
Roxy Peck, California Polytechnic State University, San Luis Obispo; Jay
Roxy Peck and Jay Devore’s STATISTICS: THE
EXPLORATION AND ANALYSIS OF DATA, 7th Edition
concepts and cement their comprehension. Hands-on
practice statistics firsthand.
NEW TO THIS EDITION
concepts in a realistic context. In addition, more
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of the exercises specifically ask students to write
(for example, by requiring students to explain
their reasoning, interpret results, and comment on
important features of an analysis).
Examples and exercises using data sets that can be
them.
More than 90 exercises have video solutions,
The text is enhanced by a variety of online teaching and
assignments.
FEATURES
• An optional section on logistic regression is included
in Chapter 5, “Summarizing Bivariate Data,” for those
who would like more complete coverage of data
analysis techniques for categorical data.
• Advanced topics that are often omitted in a onequarter or one-semester course, such as inference
and variable selection methods in multiple regression
and analysis of variance for randomized block and
two-factor designs, are available online at the book’s
• “Interpreting and Communicating the Results of
Statistical Analysis” sections, which emphasize the
importance of being able to interpret statistical
18
output and communicate its meaning to nonstatisticians, now have assignable end-of-section
questions associated with them.
CONTENTS
6. PROBABILITY. Interpreting Probabilities and Basic
Probability Rules. Probability as a Basis for Making
Decisions. Estimating Probabilities Empirically and
by Using Simulation. 7. RANDOM VARIABLES AND
PROBABILITY DISTRIBUTIONS. Describing the
Distribution of Values in a Population. Population
Models for Continuous Numerical Variables. Normal
Transformations. 8. SAMPLING VARIABILITY AND
SAMPLING DISTRIBUTION. Statistics and Sampling
Variability. The Sampling Distribution of a Sample Mean.
The Sampling Distribution of a Sample Proportion.
9. ESTIMATION USING A SINGLE SAMPLE. Point
Estimation. Large-Sample Confidence Interval for
a Population Proportion. Confidence Interval for a
Population Mean. Interpreting and Communicating
the Results of Statistical Analyses. 10. HYPOTHESIS
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TESTING USING A SINGLE SAMPLE. Hypotheses
and Test Procedures. Errors in Hypotheses Testing.
Large-Sample Hypothesis Tests for a Population
Regression Models. Fitting a Model and Assessing Its
Utility. Inferences Based on an Estimated Model (online).
Other Issues in Multiple Regression (online). Interpreting
(online). 15. ANALYSIS OF VARIANCE. Single-Factor
ANOVA and the F Test. Multiple Comparisons.
10E
Charles Henry Brase, Regis University; Corrinne Pellillo Brase,
Arapahoe Community College
STATISTICS, 10e, International Edition is a thorough,
yet accessible program designed to help students
overcome their apprehensions about statistics. The
authors provide clear guidance and informal advice
while showing students the links between statistics
and the world. To reinforce this approach, the book
integrates real-life data from a variety of sources,
including journals, periodicals, newspapers, and the
Internet. The Tenth Edition continues to address the
importance of developing students’ critical thinking
and statistical literacy skills through special features
and exercises throughout the text. The use of graphing
calculators, Excel®, MINITAB®, and SPSS® is covered
though not required. Extensive technology resources
include an algorithmic test bank and lecture slides,
along with interactive online resources and a marketleading DVD series designed to provide reinforcement
for students and support for instructors.
NEW TO THIS EDITION
• Updated technology-related examples include
instruction appropriate for the latest versions of
MINITAB®, SPSS®, and Excel®. Denoted by icons
in the text, “Tech Notes!” provide best practices and
accompanying screenshots to assist students who are
using these programs.
• Updated real-world applications throughout the text
include new examples from a variety of disciplines.
• A new “Foreshadowing” feature alerts students
to additional related material to be presented in
subsequent “Expand Your Knowledge” problems.
Uniform probability distribution and the exponential
probability distribution are introduced in the
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“Expand Your Knowledge” section of Chapter 6,
“Normal Distributions.” Polynomial regression
(curvilinear regression) is discussed in the “Expand
Your Knowledge” section of Chapter 9, “Correlation
and Regression.”
A new “Looking Ahead” feature gives students a
taste of forthcoming topics--showing them how the
concepts and skills they’re reading about will be useful
in helping them grasp material covered later.
New problems featuring basic computation and using
small data sets give students the chance to appreciate
the formulas and mathematical processes that their
calculators accomplish for them--and help them to
better understand what the end result means.
An updated Chapter 6, “Normal Distributions,” now
covers this topic as well as sampling distributions
to streamline the course and provide an immediate,
important application of the normal distribution. The
chapter also covers the normal approximation to the
binomial distribution.
Instructions for using the TI-Nspire calculator are
included in the TI technology guide (available online).
Updated and revised examples in Chapter 4,
“Probability,” incorporate sample spaces that are small
and easy for students to visualize.
Examples, guided exercises, and problem sets
throughout the text feature interpretation. They also
address the suitability of using a specified statistical
method or process for analysis.
FEATURES
assignments.
CONTENTS
Note: Each chapter concludes with a Summary,
Important Words and Symbols, Chapter Review
Problems, Data Highlights: Group Projects, Linking
Concepts: Writing Projects, and Using Technology.
Table of Prerequisite Material. 1. GETTING STARTED.
Focus Problem: Where Have All the Fireflies Gone?
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What Is Statistics. Random Samples. Introduction to
Experimental Design. 2. ORGANIZING DATA. Focus
Problem: Say It with Pictures. Frequency Distributions,
Histograms, and Related Topics. Bar Graphs, Circle
Graphs, and Time-Series Graphs. Stem-and-Leaf
Displays. 3. AVERAGES AND VARIATION. Focus
Problem: The Educational Advantage. Measures
of Central Tendency: Mode, Mediate, and Mean.
Measures of Variation. Percentiles and Box-andWhisker Plots. Cumulative Review Problems: Chapters
1–3. 4. ELEMENTARY PROBABILITY THEORY. Focus
Problem: How Often Do Lie Detectors Lie? What Is
Probability? Some Probability Rules—Compound Events.
Trees and Counting Techniques. 5. THE BINOMIAL
PROBABILITY DISTRIBUTION AND RELATED TOPICS.
Focus Problem: Personality Preference Types: Introvert
or Extrovert? Introduction to Random Variables and
Probability Distributions. Binomial Probabilities.
Additional Properties of the Binomial Distribution.
The Geometric and Poisson Probability Distributions.
6. NORMAL DISTRIBUTIONS. Focus Problem: Impulse
Buying. Graphs of Normal Probability Distributions.
Standard Units and Areas Under the Standard Normal
Distribution. Areas Under Any Normal Curve. Normal
Approximation to the Binomial Distribution. Sampling
Distributions. The Central Limit Theorem. Cumulative
Review Problems: Chapters 4-6. 7. ESTIMATION. Focus
Problem: The Trouble Is Unknown. Is Known. Estimating
When with Wood Ducks. Estimating µ When Estimating
p in the Binomial Distribution. Estimating µ 1-µ 2 and p1p2. 8. HYPOTHESIS TESTING. Focus Problem: Benford’s
Law: The Importance of Being Number 1. Introduction to
Statistical Tests. Testing the Mean. Testing a Proportion
p. Tests Involving Paired Differences (Dependent
Samples). Testing µ1-µ2 and p1-p2 (Independent
Samples). 9. CORRELATION AND REGRESSION.
Focus Problem: Changing Populations and Crime
Rate. Scatter Diagrams and Linear Correlation. Linear
Regression and the Coefficient of Determination.
Inferences for Correlation and Regression. Multiple
Regression. Cumulative Review Problems: Chapters
7-9. 10. CHI-SQUARE AND F DISTRIBUTIONS. Focus
Problem: Archaeology in Bandelier National Monument.
Part I: Inferences Using the Chi-Square Distribution.
Overview of the Chi-Square Distribution. Chi-Square:
Tests of Independence and of Homogeneity. ChiSquare: Goodness of Fit. Testing and Estimating a Single
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Variance or Standard Deviation. Part II: Inferences
Using the F Distribution. Testing Two Variances. OneWay ANOVA: Comparing Several Sample Means.
Introduction to Two-Way ANOVA. 11. NONPARAMETRIC
STATISTICS. Focus Problem: How Cold? Compared
to What? The Sign Test for Matched Pairs. The RankSum Test. Spearman Rank Correlation. Runs Test for
Randomness. Cumulative Review Problems: Chapters
10-12. APPENDIX I: ADDITIONAL TOPICS. Bayes’s
Theorem. The Hypergeometric Probability Distribution.
APPENDIX II: TABLES. Random Numbers. Binomial
Coefficients. Binomial Probability Distribution. Poisson
Probability Distribution. Areas of a Standard Normal
Distribution. Critical Values for Student’s t Distribution.
The X2 Distribution. Critical Values for F Distribution.
Critical Values for Spearman Rank Correlation. Critical
Values for Number of Runs R.
4E
Robert F. Heckard, Pennsylvania State University; Jessica M. Utts,
University of California, Irvine
STATISTICS, 4e, International Edition emphasizes the
conceptual development of statistical ideas and the
importance of looking for—and finding—meaning in
data. Authors Jessica Utts and Robert Heckard actively
engage students’ natural curiosity, motivating them
with intriguing questions and explaining statistical
topics in the context of a wide range of interesting,
useful examples and case studies. Throughout, the
authors balance the promotion of statistical literacy
with coverage of the statistical methodology taught
students actively ask questions and find the answers
for themselves. More than any other text available,
STATISTICS, 4e, International Edition motivates and
encourages students to develop their statistical
intuition by focusing on analyzing data and interpreting
results, rather than on mathematical formulation.
CourseSmart goes beyond traditional expectations–
providing instant, online access to the textbooks
and course materials you need and at a lower cost to
your students. To request an electronic sample of this
Cengage Learning title, go to: www.coursesmart.com/
instructors.
NEW TO THIS EDITION
data.
• New topics in this edition include multiple comparisons
• The exercise sets have been significantly reorganized
• The data in the problem sets, examples, and case
data sets.
• A greater number of In Summary boxes are placed
• Aplia™ is available with the new edition. This online
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21
FEATURES
CONTENTS
22
× 2 Table. 5. SAMPLING: SURVEYS AND HOW TO ASK
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× 2 Tables. Testing Hypotheses About One Categorical
to the Wise.
STUDENT SOLUTIONS MANUAL FOR
BRASE/BRASE’S UNDERSTANDING BASIC
STATISTICS, BRIEF, 5TH, 5E
Contains worked solutions to all odd-numbered
excercises in the text.
MENDENHALL/BEAVER/BEAVER’S
STATISTICS, 14TH, 14E
Go beyond the answers--see what it takes to get there
and improve your grade! This manual provides workedout, step-by-step solutions to the odd-numbered
problems in the text, giving you the information you
need to truly understand how these problems are
solved.
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23
PECK/OLSEN/DEVORE’S INTRODUCTION
TO STATISTICS AND DATA ANALYSIS,
3RD, 3E
UNDERSTANDABLE STATISTICS, 10E
UNDERSTANDABLE STATISTICS: CONCEPTS AND
METHODS, Tenth Edition, is a thorough, yet accessible
program designed to help students overcome their
apprehensions about statistics. The authors provide
clear guidance and informal advice while showing
students the links between statistics and the world.
To reinforce this approach, the book integrates reallife data from a variety of sources, including journals,
periodicals, newspapers, and the Internet. The Tenth
Edition continues to address the importance of
developing students’ critical thinking and statistical
literacy skills through special features and exercises
throughout the text. The use of graphing calculators,
Excel®, MINITAB®, and SPSS® is covered though
not required. Extensive technology resources include
an algorithmic test bank and lecture slides, along with
interactive online resources and a market-leading DVD
series designed to provide reinforcement for students
and support for instructors.
Check your work-and your understanding-with this
manual, which provides worked-out solutions to the
odd-numbered problems in the text
TECHNOLOGY GUIDE EXCEL® FOR
Technology Guide for Excel provides basic instruction
and examples to help students use this program
effectively. This guide can serve as a resource for
students using the calculator on assignments out of
class. The Fifth Edition Technology Guide is written
for Microsoft® Excel® 2007, but it includes notes for
users of Excel 2003. Users of both Excel 2003 and 2007
can use this guide effectively.
Concepts and Methods
NEW TO THIS EDITION
24
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•
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and Regression.”
assignments.
CONTENTS
Probability? Some Probability Rules--Compound Events.
Problem: The Trouble with Wood Ducks. Estimating
µ When sIs Known. Estimating µ When Is Unknown.
Estimating p in the Binomial Distribution. Estimating
µ 1-µ 2 and p1-p2. 8. HYPOTHESIS TESTING. Focus
Problem: Benford’s Law: The Importance of Being
Number 1. Introduction to Statistical Tests. Testing the
Mean µ. Testing a Proportion p. Tests Involving Paired
Differences (Dependent Samples). Testing µ1-µ2 and
p1-p2 (Independent Samples). 9. CORRELATION AND
REGRESSION. Focus Problem: Changing Populations
and Crime Rate. Scatter Diagrams and Linear Correlation.
Linear Regression and the Coefficient of Determination.
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25
INTRODUCTORY STATISTICS—
BEHAVIORAL AND SOCIAL SCIENCES
BASIC STATISTICS FOR THE BEHAVIORAL
SCIENCES, INTERNATIONAL EDITION, 6E
Gary W. Heiman, Buffalo State College
BASIC STATISTICS FOR THE BEHAVIORAL SCIENCES,
International Edition demystifies and fully explains
statistics without leaving out relevant topics or simply
presenting formulas, in a format that is non-threatening
and inviting to students. The author’s clear, patiently
crafted explanations, with an occasional touch of humor,
teach students not only how to compute an answer, but
also why they should perform the procedure or what
their answer reveals about the data. The book achieves
several objectives: it presents a conceptual-intuitive
approach, presents statistics within an understandable
research context, deals directly and positively with
student weaknesses in mathematics, and introduces
new terms and concepts in an integrated way. The result
is a text that students can learn from as well as enjoy.
UNDERSTANDABLE STATISTICS, AP*
EDITION, 10E
NEW TO THIS EDITION
26
• Every chapter has been honed and revised to further
improve the clarity for which this text is recognized.
• Many end-of-chapter problems have been revised for
clarity; new end-of-chapter Integration Questions
appear from Chapter 3 on.
• The guide to SPSS® in Appendix B has been revised
to be compatible with the new SPSS (PASW) Version
17 as well as with previous versions.
• Beginning in Chapter 3, “Frequency Distributions and
Percentiles,” end-of-chapter material includes a new
Using the SPSS Appendix section.
• Chapter 12, “The Two-Sample t-Test,” now includes
coverage of Effect Size using Cohen’s d.
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FEATURES
• Appendix B explains the basics of how to use SPSS
to analyze data. Tips on using SPSS are integrated
throughout the text, and are listed in an index to
guide students.
• A Quick Review sections contain a simple review of
previous concepts, additional examples, and study
questions.
• A section on Statistics in Published Research helps
students gain a perspective on how researchers use
statistics, and relates what students have learned in
the chapter to what they will see (and won’t see) when
reading published research. This section also includes
information about using the APA format for statistics.
• Additional pedagogical features include a Getting
Started section at the beginning of each chapter,
Statistical Notation sections, Remember callout
sections, computation formulas, Putting It All
Together sections, chapter summaries, and summaries
of formulas.
CONTENTS
1. Introduction to Statistics. 2. Statistics and the Research
Process. 3. Frequency Distributions and Percentiles. 4.
Measures of Central Tendency; The Mean, Median,
and Mode. 5. Measures of Variability: Range, Variance,
and Standard Deviation. 6. z-Scores and the Normal
Curve Model. 7. The Correlation Coefficient. 8. Linear
Regression. 9. Using Probability to Make Decisions
about Data. 10. Introduction to Hypothesis Testing.
11. Performing the One-Sample t-Test and Testing
Correlation Coefficients. 12. The Two-Sample t-Test.
13. The One-Way Analysis of Variance. 14. The TwoWay Analysis of Variance. 15. Chi Square and Other
Nonparametric Procedures. Appendices. A. Additional
Statistical Formulas. B. Using SPSS. C. Statistical Tables.
D. Answers to Odd-Numbered Questions.
CENGAGE ADVANTAGE BOOKS: BASIC
STATISTICS FOR THE BEHAVIORAL
SCIENCES, 6E
BASIC STATISTICS FOR THE BEHAVIORAL SCIENCES
demystifies and fully explains statistics without leaving
out relevant topics or simply presenting formulas, in a
format that is non-threatening and inviting to students.
The author’s clear, patiently crafted explanations, with
an occasional touch of humor, teach students not only
how to compute an answer, but also why they should
perform the procedure or what their answer reveals
about the data. The book achieves several objectives:
it presents a conceptual-intuitive approach, presents
statistics within an understandable research context,
deals directly and positively with student weaknesses in
mathematics, and introduces new terms and concepts
in an integrated way. The result is a text that students
can learn from as well as enjoy.
NEW TO THIS EDITION
FEATURES
www.cengageasia.com
27
guide students.
questions.
of formulas.
CONTENTS
CENGAGE ADVANTAGE BOOKS:
ESSENTIALS OF STATISTICS FOR
BEHAVIORAL SCIENCE, 7E
Frederick J Gravetter, State University of New York, Brockport; Larry B.
Wallnau, State University of New York, Brockport
This brief version of Gravetter and Wallnau’s proven
best seller offers the straightforward instruction,
accuracy, built-in learning aids, and wealth of realworld examples that professors AND students have
come to appreciate. The authors take time to explain
statistical procedures so that students can go beyond
memorizing formulas and begin gaining a conceptual
understanding of statistics. To ensure that even
students with a weak background in mathematics can
understand statistics, the authors skillfully integrate
applications that reinforce concepts. The authors take
care to show students how having an understanding
of statistical procedures will help them comprehend
published findings and will lead them to become savvy
consumers of information. Known for its exceptional
accuracy and examples, this text also has a complete
supplements package to support instructors with class
preparation and testing.
NEW TO THIS EDITION
• Throughout the book, research examples have been
updated, real-world examples have been added, and
the end-of-chapter problems have been extensively
revised.
• The book has been separated into five sections to
emphasize the similarities among groups of statistical
methods. Each section contains two to four chapters,
and begins with an introduction and concludes with
a review, including review exercises.
• Demonstration problems at the end of each chapter
have been simplified to focus on the content of the
chapter and minimize tedious computations.
FEATURES
• The authors have maintained the conversational style
28
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that explains statistical concepts and procedures
rather than simply presenting them.
Real-world, in-text examples from across the
social and behavioral sciences, make the book very
accessible for all students and demonstrate how
behavioral statistics apply to everyday life.
End-of-chapter Web Resources sections include
references to Wadsworth’s free Online Statistics
Workshops, online tutorials, and instructions for using
SPSS to perform the statistical calculations that are
introduced in the chapter (as appropriate).
The text reflects the most current APA guidelines.
A general introduction to SPSS is included in the
Instructor’s Manual and can be used as a student
handout for classes that use SPSS as part of the
statistics course.
CONTENTS
Part I: INTRODUCTION AND DESCRIPTIVE STATISTICS.
1. Introduction to Statistics. 2. Frequency Distributions.
3. Central Tendency. 4. Variability. Part I Review. Part
II: FOUNDATIONS OF INFERENTIAL STATISTICS. 5.
Z-Scores: Location of Scores and Standard Distributions.
6. Probability. 7. Probability and Samples: The
Distribution of Sample Means. 8. Introduction to
Hypothesis Testing. Part II Review. Part III: USING t
STATISTICS FOR INFERENCES ABOUT POPULATION
MEANS AND MEAN DIFFERENCES. 9. Introduction
to the t Statistic. 10. The t test for Two Independent
Samples. 11. The t test for Two Related Samples. 12.
Estimation. Part III Review. Part IV: ANALYSIS OF
VARIANCE: TESTS FOR DIFFERENCES AMONG TWO
OR MORE POPULATION MEANS. 13. Introduction
to Analysis of Variance. 14. Repeated-Measures and
Two-Factor Analysis of Variance. Part IV Review. Part
V: CORRELATIONS AND NON-PARAMETRIC TESTS.
15. Correlation and Regression. 16. The Chi-Square
Statistic: Tests for Goodness of Fit and Independence.
Part V Review. Appendix A: Basic Mathematics Review.
Appendix B: Statistical Tables. Appendix C: Solutions
for Odd-Numbered Problems in the Text. Appendix D:
General Instructions for Using SPSS.
SCIENCES, 9E
This field-leading introduction to statistics for the
behavioral and social sciences text continues to offer
straightforward instruction, accuracy, built-in learning
aids, and real-world examples. The goals of STATISTICS
FOR THE BEHAVIORAL SCIENCES, 9th Edition are to
teach the methods of statistics and convey the basic
principles of objectivity and logic that are essential for
science- and valuable in everyday life. Authors Frederick
Gravetter and Larry Wallnau help students understand
statistical procedures through a conceptual context
that explains why the procedures were developed and
when they should be used. Students have numerous
opportunities to practice statistical techniques
through learning checks, examples, step-by-step
demonstrations, and problems. Aplia™, an assignable,
gradeable online homework solution, joins an alreadyrobust set of available book resources that includes
PowerLecture™, WebTutor™, an Instructor’s Manual/
TestBank, and more.
NEW TO THIS EDITION
• The authors continue to refine and improve each
edition, and this revision is no exception. Throughout
the book, research examples have been updated,
real world examples have been added, and the endof-chapter problems have been extensively revised.
• For the first time, the text is integrated with Aplia™, an
interactive online homework solution that improves
learning by increasing student effort and engagement.
Aplia’s statistics solution provides regularly scheduled
assignments, automatically graded homework with
detailed, immediate feedback on every question, and
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innovative teaching materials. The Aplia assignments
include text problems and supplemental questions
that match the language, style, and structure of the
text, allowing your students to apply what they learn
in the text directly to their homework.
Based on reviewers’ suggestions, this edition includes
more samples of SPSS® output--which students find
to be helpful--instead of only descriptions of the
output.
The previous edition’s Chapter 12, “Estimation,” has
been deleted. Content from this chapter that covered
confidence intervals has been added to Chapters 9,
10, and 11.
The previous edition’s Chapter 20, covering hypothesis
tests for ordinal data, has been substantially shortened
and now appears in an Appendix.
A new final chapter outlines the process of selecting
the correct statistical procedures to use with different
sets of data.
FEATURES
• The book is now separated into five sections to
• The book’s conceptual context helps make learning
statistics as simple as possible for students who may
be intimidated by the subject. Statistical formulas are
presented in both standard mathematical notation
and in everyday language, with explanations of how
and why formulas are used.
• This edition’s excellent problems, which walk students
step-by-step through procedures in a variety of ways,
reflect the straightforward writing for which Gravetter
and Wallnau are renowned. Each chapter ends with
25 to 30 problems.
• In-text learning aids give students many opportunities
to master the material with practice working through
problems. Numerous Learning Checks (with answers)
challenge students to test their comprehension as
they read. A Focus on Problem Solving section in
each chapter offers practical tips on deciding which
formulas to use and cautions for avoiding common
errors.
CONTENTS
30
1. Introduction to Statistics. 2. Frequency Distributions. 3.
Central Tendency. 4. Variability. Part II: FOUNDATIONS
OF INFERENTIAL STATISTICS. 5. z-Scores: Location of
Scores and Standardized Distributions. 6. Probability.
7. Probability and Samples: The Distribution of Sample
Means. 8. Introduction to Hypothesis Testing. Part
III: USING t STATISTICS FOR INFERENCES ABOUT
POPULATION MEANS AND MEAN DIFFERENCES. 9.
Introduction to the t Statistics. 10. The t Test for Two
Independent Samples. 11. The t Test for Two Related
Samples. Part IV: ANALYSIS OF VARIANCE: TESTS FOR
DIFFERENCES AMONG TWO OR MORE POPULATION
MEANS. 12. Introduction to Analysis of Variance. 13.
Repeated-Measures Analysis of Variance (ANOVA).
14. Two-Factor Analysis of Variance (Independent
Measures). Part V: CORRELATIONS AND NONPARAMETRIC TESTS. 15. Correlation. 16. Introduction
to Regression. 17. The Chi-Square Statistic: Tests for
Goodness of Fit and Independence. 18. The Binomial
Test. 19. How to Decide Which Statistics Technique Is
Appropriate for a Given Data Set.
© 2013, 792pp, Loose-Leaf, 9781111835767, Arts and Sciences
ESSENTIALS OF STATISTICS FOR THE
BEHAVIORAL SCIENCE, INTERNATIONAL
EDITION, 7E
www.cengageasia.com
NEW TO THIS EDITION
revised.
FEATURES
• Real-world, in-text examples from across the
• End-of-chapter Web Resources sections include
• The text reflects the most current APA guidelines.
• A general introduction to SPSS is included in the
statistics course.
CONTENTS
FUNDAMENTAL STATISTICS FOR
THE BEHAVIORAL SCIENCES,
David C. Howell, University of Vermont
David Howell’s practical approach focuses on the
context of statistics in behavioral research, with an
emphasis on looking at data before jumping into a
test. This provides students with an understanding of
the logic behind the statistics: why and how certain
methods are used rather than just doing techniques
by rote. Students move beyond number crunching to
discover the meaning of statistical results and how
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31
they relate to the research questions being asked.
FUNDAMENTAL STATISTICS FOR THE BEHAVIORAL
SCIENCES, International Edition contains an abundance
of real data and research studies as a base and moves
through an analysis of data.
NEW TO THIS EDITION
• Each chapter begins with a box suggesting terms that
students will need to use in this chapter that were
introduced in previous chapters. Sometimes you will
see the terms come back several times in successive
chapters. These tend to be the terms that students
have the hardest time keeping straight.
• The questions that previously began each chapter
have been turned into the first paragraph of the
chapter.
• Throughout the book, but especially in earlier
chapters, boxes are inserted that either pull together
important terms or make a summarizing statement.
These boxes highlight the stuff students need to
attend to as they continue reading. For example:
• Remember that the standard error of a statistic is the
standard deviation of the values that we obtain for
that statistic over repeated sampling.
• Especially in the early chapters, terms are redefined
when used a second, third, or fourth time. For
example, “you will see that I have plotted somethingor-other on the X (horizontal) axis.” Or, “whether it
be the mode (the most common value), the median
(the middle value), or the mean, ….” Or “In this the
population variance) is …” (Underlining used here only
to identify case definition) People often forget which
is the X axis, and they need to be constantly reminded
that the notation for the population variance is
• Biographies have been scattered throughout the text
both to highlight important people and give students
a break from the text.
• The chapter summaries have been rewritten to be
more complete and comprehensive.
• Many examples have been replaced with new ones
that are more interesting and timely.
• The book website as well as other helpful websites are
referenced throughout the text to steer students to
extra examples and helpful explanations.
FEATURES
• The authors’ clarity of writing, clear definition of
32
terms, and use of worked out examples helps the
student understand some of the more difficult
concepts in statistics.
CONTENTS
1. Introduction. The importance of Context. Basic
Terminology. Selection among Statistical Procedures.
Using Computers. Summary. Exercises. 2. Basic Concepts.
Scales of Measurement. Variables. Random Sampling.
Notation. Summary. Exercises. 3. Displaying Data.
Plotting Data. Stem-and-Leaf Displays. Histograms.
Reading Graphs. Alternative Methods of Plotting Data.
Describing Distributions. Using Computer Programs
to Display Data. Summary. Exercises. 4. Measures of
Central Tendency. The Mode. The Median. The Mean.
Relative Advantages of the Mode, the Median, and
the Mean. Obtaining Measures of Central Tendency
Using SPSS. A Simple Demonstration-Seeing Statistics.
Summary. Exercises. 5. Measures of Variability. Range.
Interquartile Range and Other Range Statistics.
The Average Deviation. The Variance. The Standard
Deviation. Computational Formulae for the Variance
and the Standard eviation. The Mean and the Variance
as Estimators. Boxplots: Graphical Representations of
Dispersion and Extreme Scores. A Return to Trimming.
Obtaining Measures of Dispersion Using SPSS. A Final
Worked Example. Seeing Statistics. Summary. Exercises.
6. The Normal Distribution. The Normal Distribution.
The Standard Normal Distribution. Setting Probable
Limits on an Observations. Measures Related to z.
Seeing Statistics. Summary. Exercises. 7. Basic Concepts
of Probability. Probability. Basic Terminology and Rules.
The Application of Probability to Controversial Issues.
Writing Up the Results. Discrete versus Continuous
Variables. Probability Distributions for Discrete
Variables. Probability Distributions for Continuous
Variables. Summary. Exercises. 8. Sampling Distributions
and Hypothesis Testing. Two Simple Examples Involving
Course Evaluations and Rude Motorists. Sampling
Distributions. Hypothesis Testing. The Null Hypothesis.
Test Statistics and Their Sampling Distributions. Using
the Normal Distribution to Test Hypotheses. Type I
and Type II Errors. One- and Two-Tailed Tests. Seeing
Statistics. A Final Worked Example. Back to Course
Evaluations and Rude Motorists. Summary. Exercises.
9. Correlation. Scatter Diagrams. The Relationship
Between Pace of Life and Heart Disease. The Covariance.
The Pearson Product-Moment Correlation Coefficient®.
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Correlations with Ranked Data. Factors that Affect
the Correlation. Beware Extreme Observations.
Correlation and Causation. If Something Looks Too
Good to Be True, Perhaps It Is. Testing the Significance
of a Correlation Coefficient. Intercorrelation Matrices.
Other Correlation Coefficients. Using SPSS to Obtain
Correlation Coefficients. Seeing Statistics. A Final
Worked Example. Summary . Exercises. 10. Regression.
The Relationship Between Stress and Health. The Basic
Data. The Regression Line. The Accuracy of Prediction.
The Influence of Extreme Values. Hypothesis Testing
in Regression. Computer Solutions using SPSS. Seeing
Statistics. Summary. Exercises. 11. Multiple Regression.
Overview. A Different Data Set. Residuals. The Visual
Representation of Multiple Regression. Hypothesis
Testing. Refining the Regression Equation. A Second
Example: Height and Weight. A Third Example:
Psychological Symptoms in Cancer Patients. Summary.
Exercises. 12. Hypothesis Testing Applied to Means:
One Sample. Sampling Distribution of the Mean.
Testing Hypotheses about Means When ƒã is Known.
Testing a Sample Mean When ƒã is Unknown (The OneSample t). Factors that Affect the Magnitude of t and
the Decision about H0. A Second Example: The Moon
Illusion. How Large is Our Effect?. Confidence Limits
on the Mean. Using SPSS to Run One-Sample t tests.
A Final Worked Example. Seeing Statistics. Summary.
Exercises. 13. Hypothesis Tests Applied to Means:
Two Related Samples. Related Samples. Student’’s t
Applied to Difference Scores. A Second Example: The
Moon Illusion Again. Advantages and Disadvantages
of Using Related Samples. How Large an Effect Have
We Found?. Confidence Limits on Changes. Using SPSS
for t Tests on Related Samples. Writing Up the Results.
Summary. Exercises. 14. Hypothesis Tests Applied
to Means: Two Independent Samples. Distribution
of Differences Between Means. Heterogeneity of
Variance. Nonnormality of Distributions. A Second
Example with Two Independent Samples. Effect Sizes
Again. Confidence Limits on ƒÝ1 ¡V ƒÝ2. Writing Up the
Results. Use of Computer Programs for Analysis of Two
Independent Sample Means. A Final Worked Example.
Seeing Statistics. Summary. Exercises. 15. Power. The
Basic Concept. Factors that Affect the Power of a Test.
Effect Size. Power Calculations for the One-Sample t
Test. Power Calculations for Differences Between Two
Independent Means. Power Calculations for the t Test
for Related Samples. Power Considerations in Terms of
Sample Size. You Don’’t Have to Do It by Hand. Seeing
Statistics. Summary. Exercises. 16. One-Way Analysis
of Variance. The General Approach. The Logic of the
Analysis of Variance. Calculations for the Analysis of
Variances. Unequal Sample Sizes. Multiple Comparison
Procedures. Violations of Assumptions. The Size of
the Effects. Writing Up the Results. The Use of SPSS
for a One-Way Analysis of Variance. A Final Worked
Example. Seeing Statistics. Summary. Exercises. 17.
Factorial Analysis of Variance Factorial Designs. The
Extension of the Eysenck Study. Interactions. Simple
Effects. Measures of Association and Effect Size.
Reporting the Results. Unequal Sample Sizes. A Second
Example: Maternal Adaptation Revisited. Using SPSS
for Factorial Analysis of Variance. Seeing Statistics.
Summary. Exercises. 18. Repeated-Measures Analysis
of Variance. An Example: Depression as a Response
to an Earthquake. Multiple Comparisons. Effect Size.
Assumptions involved in Repeated-Measures Designs.
Advantages and Disadvantages of Repeated-Measures
Designs. Using SPSS to Analyze Data in a RepeatedMeasures Design. Writing Up the Results. A Final
Worked Example. Summary. Exercises. 19. Chi-Square.
One Classification Variable: The Chi-Square Goodness
of Fit Test. Two Classification Variables: Analysis of
Contingency Tables. Possible Improvements on Standard
Chi-Square. Chi-Square for Larger Contingency Tables.
The Problem of Small Expected Frequencies. The Use of
Chi-Square as a Test of Proportions. Nonindependent
Observations. SPSS Analysis of Contingency Tables.
Measures of Effect Size. A Final Worked Example.
Writing Up the Results. Seeing Statistics. Summary.
Exercises. 20. Nonparametric and Distribution-Free
Statistical Tests. The Mann-Whitney Test. Wilcoxon’’s
Matched-Pairs Signed-Ranks Test. Kruskal-Wallis OneWay Analysis of Variance. Friedman’’s Rank Test for k
Correlated Samples. Measures of Effect Size. Writing
Up the Results. Summary. Exercises. 21. Choosing the
Appropriate Analysis. Exercises and Examples. Appendix
A Arithmetic Review. Appendix B Symbols and Notation.
Appendix C Basic Statistical Formulae. Appendix
D Dataset. Appendix E Statistical Tables. Glossary.
References. Answers to Selected Exercises. Index.
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•
•
SCIENCES, 9E
TestBank, and more.
NEW TO THIS EDITION
34
•
•
output.
10, and 11.
sets of data.
FEATURES
25 to 30 problems.
errors.
CONTENTS
www.cengageasia.com
STUDY GUIDE FOR GRAVETTER/
WALLNAU’S STATISTICS FOR THE
BEHAVIORAL SCIENCES, 8TH, 8E
Robert R. Pagano, University of Pittsburg
Based on over 30 years of successful teaching
experience in this course, Robert Pagano’s introductory
text takes an intuitive, concepts-based approach to
descriptive and inferential statistics. He uses the sign
test to introduce inferential statistics, empirically
derived sampling distributions, many visual aids,
and lots of interesting examples to promote student
understanding. One of the hallmarks of this text is the
positive feedback from students—even students who
are not mathematically inclined praise the text for its
clarity, detailed presentation, and use of humor to help
make concepts accessible and memorable. Thorough
explanations precede the introduction of every formula,
and the exercises that immediately follow include a
step-by-step model that lets students compare their
work against fully solved examples. This combination
makes the text perfect for students taking their first
statistics course in psychology or other social and
behavioral sciences.
NEW TO THIS EDITION
UNDERSTANDING STATISTICS IN THE
BEHAVIORAL SCIENCES, 10E
• UNDERSTANDING STATISTICS IN THE BEHAVIORAL
SCIENCES, 10E includes a major increase in coverage
of SPSS. A new Appendix E, “Introduction to SPSS,” is
included. The increase in SPSS coverage jumps from
two chapters in the previous edition to coverage in
Chapters 2–7 and 13–17.
• Each chapter has at least 2 additional end-of-chapter
problems requiring students to use SPSS. With the
inclusion of significantly more SPSS content, students
can learn how to use the tool without recourse
to additional material and are able to practice on
problems specifics to the textbook material.
• The end-of-chapter questions have been implemented
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35
into Aplia for instructors who prefer to use the text
end-of-chapter questions within Aplia.
• The Aplia product for UNDERSTANDING STATISTICS
IN THE BEHAVIORAL SCIENCES, 10E includes tutorials
like those on enhanced WebAssign in 9E, presenting
problems similar to the textbook illustrative and endof-chapter problems.
• The Newman-Keuls post-hoc test is replaced by
the Scheffe test and various symbols used in 9E are
replaced with more conventional choices.
14. Student’s t-Test for Correlated and Independent
Groups. 15. Introduction to the Analysis of Variance. 16.
Introduction to Two-Way Analysis of Variance. 17. ChiSquare and Other Nonparametric Tests. 18. Review of
Inferential Statistics. Appendix A: Review of Prerequisite
Mathematics. Appendix B: Equations. Appendix C:
Answers to End-of-Chapter Questions and Problems.
Appendix D: Tables. Appendix E: Introduction to SPSS®.
FEATURES
Intermediate/Advanced Statistics
• Unlike most texts, Pagano introduces inferential
statistics using the sign test. This intuitive and easily
understood inference test facilitates understanding
of the basic concepts underlying inferential statistics.
• Thorough explanations are integrated with equations
when discussing inference tests. Pagano uses
empirically derived sampling distributions to promote
understanding of each inference test.
• A summary chapter ties all of inference testing
together, and gives students practice in determining
the correct inference test to use across a wide variety
of experiments. A unique decision-tree diagram is
presented to help determine the correct test.
• Pagano provides a full chapter on power (Chapter
11). This complete chapter, used in conjunction with
the sign test, promotes better understanding of this
complex topic.
• Varied and interesting illustrative examples appear in
abundance throughout the text and are immediately
followed by at least two fully solved practice problems
giving students immediate practice in solving
problems with a step-by-step model against which
to compare his/her work.
CONTENTS
Part I: OVERVIEW. 1. Statistics and Scientific Method.
Part II: DESCRIPTIVE STATISTICS. 2. Basic Mathematical
and Measurement Concepts. 3. Frequency Distributions.
4. Measures of Central Tendency and Variability. 5. The
Normal Curve and Standard Scores. 6. Correlation. 7.
Linear Regression. Part III: INFERENTIAL STATISITCS.
8. Random Sampling and Probability. 9. Binomial
Distribution. 10. Introduction to Hypothesis Testing
Using the Sign Test. 11. Power. 12. Sampling Distributions,
Sampling Distribution of the Mean, the Normal
Deviate (z) Test. 13. Student’s t-Test for Single Samples.
36
INTERMEDIATE/ADVANCED
STATISTICS
AN INTRODUCTION TO STATISTICAL
METHODS AND DATA ANALYSIS,
R. Lyman Ott; Michael T. Longnecker
Ott and Longnecker’s AN INTRODUCTION TO
STATISTICAL METHODS AND DATA ANALYSIS, 6th
Edition, International Edition provides a broad overview
of statistical methods for advanced undergraduate and
graduate students from a variety of disciplines who
have little or no prior course work in statistics. The
authors teach students to solve problems encountered
in research projects, to make decisions based on data in
general settings both within and beyond the university
setting, and to become critical readers of statistical
analyses in research papers and in news reports.
The first eleven chapters present material typically
covered in an introductory statistics course, as well as
case studies and examples that are often encountered
in undergraduate capstone courses. The remaining
chapters cover regression modeling and design of
experiments.
www.cengageasia.com
NEW TO THIS EDITION
• Every chapter includes a case study to help students
appreciate the role statistics plays in the solution
of practical, real-world problems. Case studies are
selected from a variety of sources to ensure broad
reader appeal, and all feature the same four-step
process used in the text to facilitate connections to
the chapter material and reinforce learning.
• The text emphasizes step-by-step learning and
practical skill-building by providing frequent
opportunities for students to interpret results and
draw conclusions from studies that illustrate key
concepts, providing an immediate, effective way to
review and apply the material.
• Expanded and updated exercises at the end of
every chapter engage readers by exploring reallife scenarios drawn from a variety of disciplines,
including agriculture, business, economics, education,
engineering, medicine, law, political science,
psychology, environmental studies, and sociology.
• An expanded discussion of the proper methods for
designing studies and experiments provides students
with additional information to help them analyze and
evaluate published research results and to plan and
conduct their own research more effectively.
FEATURES
• A uniquely effective Four-Step Process to ProblemSolving and Understanding the Collected Data,
developed by the authors, helps students of all
backgrounds master the essentials of statistics by 1)
gathering data, 2) summarizing data, 3) analyzing data,
and 4) communicating the results of data analyses.
• Computer output from Minitab®, SAS, and SPSS
is provided in numerous examples and exercises to
familiarize students with the use of more sophisticated
graphical illustrations of statistical results.
• A special section titled “What is Statistics?” opens
the text by explaining why students should study
statistics and presenting an engaging discussion of
several major studies illustrating the use of statistics
to solve a variety of important real-life problems.
CONTENTS
Part 1: INTRODUCTION. 1. Statistics and the Scientific
Method. Introduction. Why Study Statistics? Some
Current Applications of Statistics. A Note to the
Student. Summary. Exercises. Part 2: COLLECTING
DATA. 2. Using Surveys and Scientific Studies to Collect
Data. Introduction and Abstract of Research Study.
Observational Studies. Sampling Designs for Surveys.
Experimental Studies. Designs for Experimental Studies.
Research Study: Exit Polls versus Election Results.
Summary. Exercises. Part 3: SUMMARIZING DATA. 3.
Data Description. Introduction and Abstract of Research
Study. Calculators, Computers, and Software Systems.
Describing Data on a Single Variable: Graphical
Methods. Describing Data on a Single Variable:
Measures of Central Tendency. Describing Data on a
Single Variable: Measures of Variability. The Boxplot.
Summarizing Data from More Than One Variable:
Graphs and Correlation. Research Study: Controlling for
Student Background in the Assessment of Teaching.
Summary and Key Formulas. Exercises. 4. Probability
And Probability Distributions. Introduction and Abstract
of Research Study. Finding the Probability of an Event.
Basic Event Relations and Probability Laws. Conditional
Probability and Independence. Bayes’ Formula.
Variables: Discrete and Continuous. Probability
Distributions for Discrete Random Variables. Two
Discrete Random Variables: The Binomial and the
Poisson. Probability Distributions for Continuous
Random Variables. A Continuous Probability
Distribution: The Normal Distribution. Random
Sampling. Sampling Distributions. Normal
Approximation to the Binomial. Evaluating Whether or
Not a Population Distribution Is Normal. Research
Study: Inferences about Performance Enhancing Drugs
among Athletes. Minitab Instructions. Summary and
Key Formulas. Exercises. Part 4: ANALYZING DATA,
I N T E R P R E T I N G T H E A N A LY S E S , A N D
COMMUNICATING RESULTS. 5. Inferences about
Population Central Values. Introduction and Abstract
of a Research Study. Estimation of μ. Choosing the
Sample Size for Estimating μ. A Statistical Test for μ.
Choosing the Sample Size for μ. The Level of Significance
of a Statistical Test. Inferences about μ for a Normal
Population, Unknown. Inferences about μ when
Population in Nonnormal and n is small: Bootstrap
Methods. Inferences about the Median. Research Study:
Percent Calories from Fat. Summary and Key Formulas.
Exercises. 6. Inferences Comparing Two Population
Central Values. Introduction and Abstract of a Research
Study. Inferences about μ1 - μ2: Independent Samples.
A Nonparametric Alternative: The Wilcoxon Rank Sum
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37
Test. Inferences about μ1 - μ2: Paired Data. A
Nonparametric Alternative: The Wilcoxon Signed-Rank
Test. Choosing Sample Sizes for Inferences about μ1 - μ2.
Research Study: Effects of Oil Spill on Plant Growth.
Summary. Exercises. 7. Inferences about Population
Variances. Introduction and Abstract of a Research
Study. Estimation and Tests for a Population Variance.
Estimation and Tests for Comparing Two Population
Variances. Tests for Comparing t > 2 Population
Variances. Research Study: Evaluation of Methods for
Detecting E. coli. Summary and Key Formulas. Exercises.
8. Inferences About More Than Two Population Central
Values. Introduction and Abstract of a Research Study.
A Statistical Test About More Than Two Population
Means: An Analysis of Variance. The Model for
Observations in a Completely Randomized Design.
Checking on the AOV Conditions. An Alternative
Analysis: Transformations of the Data. A Nonparametric
Alternative: The Kruskal-Wallis Test. Research Study:
Effect on Timing on the Treatment of Port-Wine Stains
with Lasers. Summary and Key Formulas. Exercises. 9.
Multiple Comparisons. Introduction and Abstract of
Research Study. Linear Contrasts. Which Error Rate Is
Controlled? Fisher’s Least Significant Difference. Tukey’s
W Procedure. Student-Neuman-Keuls Procedure.
Dunnett’s Procedure: Comparison of Treatments to a
Control. Scheffé’s S Method. A Nonparametric MultipleComparison Procedure. Research Study: Are
Interviewers’ Decisions Affected by Different Handicap
Types? Summary and Key Formulas. Exercises. 10.
Categorical Data. Introduction and Abstract of Research
Study. Inferences about a Population Proportion π.
Inferences about the Difference between Two Population
Proportions, π1 - π2. Inferences about Several
Proportions: Chi-Square Goodness-of-Fit Test. Tests for
Independence and Homogeneity. Measuring Strength
of Relaxation. Odds and Odd Ratios. Combining Sets of
2 X 2 Contingency Tables (optional). Research Study:
Does Gender Bias Exist in the Selection of Students for
Vocational Education? Summary and Key Formulas.
Exercises. Part 5: ANALYZING DATA: REGRESSION
METHODS AND MODEL BUILDING. 11. Linear
Regression and Correlation. Introduction and Abstract
of Research Study. Estimating Model Parameters.
Inferences about Regression Parameters. Predicting
New y Values Using Regression. Examining Lack of Fit
in Linear Regression. The Inverse Regression Problem
38
(Calibration). Correlation. Research Study: Two Methods
for Detecting E. coli. Summary and Key Formulas.
Exercises. 12. Multiple Regression and the General Linear
Model. Introduction and Abstract of Research Study.
The General Linear Model. Estimating Multiple
Regression Coefficients. Inferences in Multiple
Regression. Testing a Subset of Regression Coefficients.
Forecasting Using Multiple Regression. Comparing the
Slopes of Several Regression Lines. Logistic Regression.
Some Multiple Regression Theory (Optional). Research
Study: Designing an Electric Drill. Summary and Key
Formulas. Exercises. 13. Further Regression Topics.
Introduction and Abstract of Research Study. Selecting
the Variables (Step 1). Formulating the Model (Step 2).
Checking Model Assumptions (Step 3). Research Study:
Construction Costs for Nuclear Power Plants. Summary
and Key Formulas. Exercises. Part 6: DESIGN OF
EXPERIMENTS AND ANALYSIS OF VARIANCE. 14.
Analysis of Variance for Completely Randomized
Designs. Introduction and Abstract of Research Study.
Completely Randomized Design with Single Factor.
Factorial Treatment Structure. Factorial Treatment
Structures with an Unequal Number of Replications.
Estimation of Treatment Differences and Comparisons
of Treatment Means. Determining the Number of
Replications. Research Study: Development of a Low-Fat
Processed Meat. Summary and Key Formulas. Exercises.
15. Analysis of Variance for Blocked Designs. Introduction
and Abstract of Research Study. Randomized Complete
Block Design. Latin Square Design. Factorial Treatment
Structure in a Randomized Complete Block Design. A
Nonparametric Alternative—Friedman’s Test. Research
Study: Control of Leatherjackets. Summary and Key
Formulas. Exercises. 16. Analysis of Covariance.
Introduction and Abstract of Research Study. A
Completely Randomized Design with One Covariate.
The Extrapolation Problem. Multiple Covariates and
More Complicated Designs. Research Study: Evaluations
of Cool-Season Grasses for Putting Greens. Summary.
Exercises. 17. Analysis of Variance for Some Fixed-,
Random-, and Mixed-Effects Models. Introduction and
Abstract of Research Study. A One-Factor Experiment
with Random Treatment Effects. Extensions of RandomEffects Models. Mixed-Effects Models. Rules for
Obtaining Expecting Mean Squares. Nested Factors.
Research Study: Factors Affecting Pressure Drops Across
Expansion Joints . Summary. Exercises. 18. Split-Plot,
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Repeated Measures, and Crossover Designs. Introduction
and Abstract of Research Study. Split-Plot Designs.
Single-Factor Experiments with Repeated Measures on
One of the Factors. Two-Factor Experiments with
Repeated Measures on One of the Factors. Crossover
Design. Research Study: Effects of Oil Spill on Plant
Growth. Summary. Exercises. 19. Analysis of Variance
for Some Unbalanced Designs. Introduction and
Abstract of Research Study. A Randomized Block Design
with One or More Missing Observations. A Latin Square
Design with Missing Data. Balanced Incomplete Block
(BIB) Designs. Research Study: Evaluation of the
Consistency of Property Assessment. Summary and Key
Formulas. Exercises. PART 7: COMMUNICATING AND
DOCUMENTING THE RESULTS OF ANALYSES 20.
Communicating and Documenting the Results of a Study
or Experiment. Introduction. The Difficulty of Good
Communication. Communication Hurdles: Graphical
Distortions. Communication Hurdles: Biased Samples.
Communication Hurdles: Sample Size. The Statistical
Report. Documentation and Storage of Results.
Summary. Exercises.
exercises that can be solved with the computer. The
authors continue to emphasize model development,
the intuitive logic and assumptions that underlie
the techniques covered, the purposes, advantages,
and disadvantages of the techniques, and valid
interpretations of those techniques.
NEW TO THIS EDITION
• New chapters on Polytomous and Ordinal
Logistic Regression (Chapter 23) and Sample Size
Determination (Chapter 27).
• Completely revised chapters on maximum likelihood
(ML) estimation (Chapter 21), and analysis of
correlated data (Chapter 25, 26).
• Updated content, with new exercises added to several
chapters.
FEATURES
• Numerous examples and exercises illustrate
applications to real studies in a wide variety of
disciplines.
• Several advanced topics are presented in a unique,
nonmathematical manner.
• The quantity of computer results throughout the
text has been greatly expanded. Computer output
has been used to replace material that unnecessarily
emphasized numerical calculations.
• The complete set of data for most exercises is
provided, along with related computer results. This
allows the instructor to assign computer work based
on available packaged programs.
CONTENTS
APPLIED REGRESSION ANALYSIS
AND MULTIVARIABLE METHODS,
David G. Kleinbaum, Emory University; Lawrence L. Kupper, University
of North Carolina at Chapel Hill; Azhar Nizam, Emory University;
Keith E. Muller, University of North Carolina at Chapel Hill
This bestseller is known for its modern, practical
approach to regression-analysis methods that students
will find applicable to real-life problems. APPLIED
REGRESSION ANALYSIS AND MULTIVARIABLE
METHODS highlights the role of the computer in
contemporary statistics with numerous printouts and
1. CONCEPTS AND EXAMPLES OF RESEARCH.
Concepts. Examples. Concluding Remarks. References.
2. CLASSIFICATION OF VARIABLES AND THE CHOICE
OF ANALYSIS. Classification of Variables. Overlapping
of Classification Schemes. Choice of Analysis.
References. 3. BASIC STATISTICS: A REVIEW. Preview.
Descriptive Statistics. Random Variables and
Distributions. Sampling Distributions of t, ƒÓ2, and F.
Statistical Inference: Estimation. Statistical Inference:
Hypothesis Testing. Error Rate, Power, and Sample Size.
Problems. References. 4. INTRODUCTION TO
REGRESSION ANALYSIS. Preview. Association versus
Causality. Statistical versus Deterministic Models.
Concluding Remarks. References. 5. STRAIGHT-LINE
REGRESSION ANALYSIS. Preview. Regression with a
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39
Single Independent Variable. Mathematical Properties
of a Straight Line. Statistical Assumptions for a Straightline Model. Determining the Best-fitting Straight Line.
Measure of the Quality of the Straight-line Fit and
Estimate ƒã2. Inferences About the Slope and Intercept.
Interpretations of Tests for Slope and Intercept.
Inferences About the Regression Line ƒÝY|X = ƒÒ0 +
ƒÒ1X . Prediction of a New Value of Y at X0. Problems.
References. 6. THE CORRELATION COEFFICIENT AND
STRAIGHT-LINE REGRESSION ANALYSIS. Definition of
r. r as a Measure of Association. The Bivariate Normal
Distribution. r and the Strength of the Straight-line
Relationship. What r Does Not Measure. Tests of
Hypotheses and Confidence Intervals for the Correlation
Coefficient. Testing for the Equality of Two Correlations.
Problems. References. 7. THE ANALYSIS-OF-VARIANCE
TABLE. Preview. The ANOVA Table for Straight-line
Regression. Problems. 8. MULTIPLE REGRESSION
ANALYSIS: GENERAL CONSIDERATIONS. Preview.
Multiple Regression Models. Graphical Look at the
Problem. Assumptions of Multiple Regression.
Determining the Best Estimate of the Multiple
Regression Equation. The ANOVA Table for Multiple
Regression. Numerical Examples. Problems. References.
9. TESTING HYPOTHESES IN MULTIPLE REGRESSION.
Preview. Test for Significant Overall Regression. Partial
F Test. Multiple Partial F Test. Strategies for Using Partial
F Tests. Tests Involving the Intercept. Problems.
References. 10. CORRELATIONS: MULTIPLE, PARTIAL,
AND MULTIPLE PARTIAL. Preview. Correlation Matrix.
Multiple Correlation Coefficient. Relationship of RY|X1,
X2, ¡KXk to the Multivariate Normal Distribution. Partial
Correlation Coefficient. Alternative Representation of
the Regression Model. Multiple Partial Correlation.
Concluding Remarks. Problems. References. 11.
CONFOUNDING AND INTERACTION IN REGRESSION.
Preview. Overview. Interaction in Regression.
Confounding in Regression. Summary and Conclusions.
Problems. References. 12. DUMMY VARIABLES IN
REGRESSION. Preview. Definitions. Rule for Defining
Dummy Variables. Comparing Two Straight-line
Regression Equations: An Example. Questions for
Comparing Two Straight Lines. Methods of Comparing
Two Straight Lines. Method I: Using Separate Regression
Fits to Compare Two Straight Lines. Method II: Using a
Single Regression Equation to Compare Two Straight
Lines. Comparison of Methods I and II. Testing Strategies
40
and Interpretation: Comparing Two Straight Lines.
Other Dummy Variable Models. Comparing Four
Regression Equations. Comparing Several Regression
Equations Involving Two Nominal Variables. Problems.
References. 13. ANALYSIS OF COVARIANCE AND
OTHER METHODS FOR ADJUSTING CONTINUOUS
DATA. Preview. Adjustment Problem. Analysis of
Covariance. Assumption of Parallelism: A Potential
Drawback. Analysis of Covariance: Several Groups and
Several Covariates. Comments and Cautions. Summary
Problems. Reference. 14. REGRESSION DIAGNOSTICS.
Preview. Simple Approaches to Diagnosing Problems in
Data. Residual Analysis: Detecting Outliers and
Violations of Model Assumptions. Strategies of Analysis.
Collinearity. Scaling Problems. Diagnostics Example. An
Important Caution. Problems. References. 15.
POLYNOMIAL REGRESSION. Preview. Polynomial
Models. Least-squares Procedure for Fitting a Parabola.
ANOVA Table for Second-order Polynomial Regression.
Inferences Associated with Second-order Polynomial
Regression. Example Requiring a Second-order Model.
Fitting and Testing Higher-order Model. Lack-of-fit Tests.
Orthogonal Polynomials. Strategies for Choosing a
Polynomial Model. Problems. 16. SELECTING THE BEST
REGRESSION EQUATION. Preview. Steps in Selecting
the Best Regression Equation. Step 1: Specifying the
Maximum Model. Step 2: Specifying a Criterion for
Selecting a Model. Step 3: Specifying a Strategy for
Selecting Variables. Step 4: Conducting the Analysis.
Step 5: Evaluating Reliability with Split Samples. Example
Analysis of Actual Data. Issues in Selecting the Most
Valid Model. Problems. References. 17. ONE-WAY
ANALYSIS OF VARIANCE. Preview. One-way ANOVA:
The Problem, Assumptions, and Data Configuration. for
One-way Fixed-effects ANOVA. Regression Model for
Fixed-effects One-way ANOVA Fixed-effects Model for
One-way ANOVA. Random-effects Model for One-way
ANOVA. -comparison Procedures for Fixed-effects Oneway ANOVA. a Multiple-comparison Technique.
Orthogonal Contrasts and Partitioning an ANOVA Sum
of Squares. Problems. References. 18. RANDOMIZED
BLOCKS: SPECIAL CASE OF TWO-WAY ANOVA.
Preview. Equivalent Analysis of a Matched-pairs
Experiment. Principle of Blocking. Analysis of a
Randomized-blocks Experiment. ANOVA Table for a
Randomized-blocks Experiment. Models for a
Randomized-blocks Experiment. Fixed-effects ANOVA
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Model for a Randomized-blocks Experiment. Problems.
References. 19. TWO-WAY ANOVA WITH EQUAL CELL
NUMBERS. Preview. Using a Table of Cell Means.
General Methodology. F Tests for Two-way ANOVA.
Regression Model for Fixed-effects Two-way ANOVA.
Interactions in Two-way ANOVA. Random- and Mixedeffects Two-way ANOVA Models. Problems. References.
20. TWO-WAY ANOVA WITH UNEQUAL CELL
NUMBERS. Preview. Problem with Unequal Cell
Numbers: Nonorthogonality. Regression Approach for
Unequal Cell Sample Sizes. Higher-way ANOVA.
Problems. References. 21. THE METHOD OF MAXIMUM
LIKELIHOOD. Preview. The Principle of Maximum
Likelihood. Statistical Inference Using Maximum
Likelihood. Summary. Problems. 22. LOGISTIC
REGRESSION ANALYSIS. Preview. The Logistic Model.
Estimating the Odds Ratio Using Logistic Regression. A
Numerical Example of Logistic Regression. Theoretical
Considerations. An Example of Conditional ML
Estimation Involving Pair-matched Data with Unmatched
Covariates. Summary. Problems. References. 23.
POLYTOMOUS AND ORDINAL LOGISTIC REGRESSION.
Preview. Why Not Use Binary Regression? An Example
of Polytomous Logistic Regression: One Predictor, Three
Outcome Categories. An Example: Extending the
Polytomous Logistic Model to Several Predictors.
Ordinal Logistic Regression: Overview. A “Simple”
Hypothetical Example: Three Ordinal Categories and
One Dichotomous Exposure Variable. Ordinal Logistic
Regression Example Using Real Data with Four Ordinal
Categories and Three Predictor Variables. Summary.
Problems. References. 24. POISSON REGRESSION
ANALYSIS. Preview. The Poisson Distribution. Example
of Poisson Regression. Poisson Regression: General
Considerations. Measures of Goodness of Fit.
Continuation of Skin Cancer Data Example. A Second
Illustration of Poisson Regression Analysis. Summary.
Problems. References. 25. ANALYSIS OF CORRELATED
DATA PART 1: THE GENERAL LINEAR MIXED MODEL.
Preview. Examples. General Linear Mixed Model
Approach. Example: Study of Effects of an Air Polluion
Episode on FEV1 Levels. Summary¡XAnalysis of
Correlated Data: Part 1. Problems. References. 26.
ANALYSIS OF CORRELATED DATA PART 2: RANDOM
EFFECTS AND OTHER ISSUES. Preview. Random Effects
Revisited. Results for Random Effects Models Applied
to Air Pollution Study Data. Second Example¡XAnalysis
of Posture Measurement Data. Recommendations about
Choice of Correlation Structure. Analysis of Data for
Discrete Outcomes. Problems. References. 27. SAMPLE
SIZE PLANNING FOR LINEAR AND LOGISTIC
REGRESSION AND ANALYSIS OF VARIANCE. Preview.
Review: Sample Size Calculations for Comparisons of
Means and Proportions. Sample Size Planning for Linear
Regression. Sample Size Planning for Logistic Regression.
Power and Sample Size Determination for Linear
Models: A General Approach. Sample Size Determination
for Matched Case-control Studies with a Dichotomous
Outcome. Practical Considerations and Cautions.
Problems. References. Appendix A. Appendix B.
Appendix C. Solutions to Exercises. Index.
ELEMENTARY SURVEY SAMPLING, 7E
Richard L. Scheaffer, University of Florida, Emeritus; William
Mendenhall, III, University of Florida, Emeritus; R. Lyman Ott; Kenneth
G. Gerow, University of Wyoming
ELEMENTARY SURVEY SAMPLING introduces students
to the design and analysis of sample surveys via a
practical, engaging approach. First, this introductory
text begins with brief chapters focused on the important
role that sample surveys play in the modern world.
Then, each successive chapter builds on this foundation.
These chapters start with the problem, describe the
methodology needed for solving the problem, and
provide the details of the estimation procedure using a
compact presentation of the necessary formulas. Each
chapter then works out the practical example in full
detail. Finally, at the end of each chapter, ELEMENTARY
SURVEY SAMPLING includes a wealth of exercises
that enable students to continue practicing and to
stretch their grasp of the content. The text includes
a complete package of interactive statistical tools for
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41
implementing all the calculations; text examples are
built in to the tools.
NEW TO THIS EDITION
• Integrates new software: By using software that
significantly decreases the need for long arithmetic,
the authors have allowed students to focus less on
arithmetic, and instead on the valid use of appropriate
statistical tools.
• New Icons: Excel Tools icons have been added to the
boxed equations and example solutions, so as to direct
students to use Excel when appropriate.
• More concise data: The data for examples and
exercises is now more concise and is also incorporated
with the electronic supplements.
• Updated Appendices: the Appendices for this edition
have been updated to be more concise and userfriendly.
FEATURES
• Bridges the gap between classroom and practice in
two significant ways: First, select sections on weights
in unequal probability sampling. Then, they expanded
the treatment of nonresponse in Chapter 11, including
the use of imputation as a technique for handling
some types of nonresponse.
• Emphasizes two cutting-edge sampling techniques:
the bootstrap, a modern technique for establishing
margins of error and confidence intervals in complex
designs, and an adaptive sampling technique for
improving estimates while the field work is in
progress.
• Incorporates compatible computations: ELEMENTARY
SURVEY SAMPLING’s computations are compatible
with modern statistical software, using much fewer
hand calculation formulas.
• Utilizes detailed simulations: Key statistical concepts
are demonstrated using step-by-step simulations.
• Emphasizes practical aspects: topics include sources
of errors in surveys, methods of data collection,
designing questionnaires, and guidelines for planning
surveys.
• Expands Student Understanding: The “Experiences
with Real Data” sections at the end of most chapters
enable students to work with both large and small
projects, some requiring computations to be handled
by a computer, giving them valuable experience.
42
CONTENTS
1. INTRODUCTION. 2. ELEMENTS OF THE SAMPLING
PROBLEM. Introduction. Technical Terms. How to Select
the Sample: The Design of the Sample Survey. Sources of
Errors in Surveys. Designing a Questionnaire. Planning
a Survey. Summary. 3. SOME BASIC CONCEPTS OF
STATISTICS. Introduction. Summarizing Information in
Populations and Samples: The Infinite Population Case.
Summarizing Information in Populations and Samples:
The Finite Population Case. Sampling Distributions.
Covariance and Correlation. Estimation. Summary. 4.
SIMPLE RANDOM SAMPLING. Introduction. How
to Draw a Simple Random Sample. Estimation of a
Population Mean and Total. Selecting the Sample Size
for Estimating Population Means and Totals. Estimation
of a Population Proportion. Comparing Estimates.
Summary. 5. STRATIFIED RANDOM SAMPLING.
Introduction. How to Draw a Stratified Random Sample.
Estimation of a Population Mean and Total. Selecting
the Sample Size for Estimating Population Means
and Totals. Allocation of the Sample. Estimation of
a Population Proportion. Selecting the Sample Size
and Allocating the Sample to Estimate Proportions.
Additional Comments on Stratified Sampling. An
Optimal Rule for Choosing Strata. Stratification
after Selection of the Sample. Double Sampling for
Stratification. Summary. 6. RATIO, REGRESSION, AND
DIFFERENCE ESTIMATION. Introduction. Surveys that
Require the Use of Ratio Estimators. Ratio Estimation
Using Simple Random Sampling. Selecting the Sample
Size. Ratio Estimation in Stratified Random Sampling.
Regression Estimation. Difference Estimation. Relative
Efficiency of Estimators. Summary. 7. SYSTEMATIC
SAMPLING. Introduction. How to Draw a Systematic
Sample. Estimation of a Population Mean and Total.
Estimation of a Population Proportion. Selecting the
Sample Size. Repeated Systematic Sampling. Further
Discussion of Variance Estimators. Summary. 8.
CLUSTER SAMPLING. Introduction. How to Draw
a Cluster Sample. Estimation of a Population Mean
and Total. Equal Cluster Sizes; Comparison to Simple
Random Sampling. Selecting the Sample Size for
Estimating Population Means and Totals. Estimation
of a Population Proportion. Selecting the Sample
Size for Estimating Proportions. Cluster Sampling
Combined with Stratification. Cluster Sampling with
Probabilities Proportional to Size. Summary. 9. TWO-
www.cengageasia.com
STAGE CLUSTER SAMPLING. Introduction. How to
Draw a Two-Stage Cluster Sample. Unbiased Estimation
of a Population Mean and Total. Ratio Estimation
of a Population Mean. Estimation of a Population
Proportion. Sampling Equal-Sized Clusters. Two-Stage
Cluster Sampling with Probabilities Proportional to
Size. Summary. 10. ESTIMATING THE POPULATION
SIZE. Introduction. Estimation of a Population Size
Using Direct Sampling. Estimation of a Population Size
Using Inverse Sampling. Choosing Sample Sizes for
Direct and Inverse Sampling. Estimating Population
Density and Size from Quadrat Samples. Estimating
Population Density and Size from Stocked Quadrats.
Adaptive Sampling. Summary. 11. SUPPLEMENTAL
TOPICS. Introduction. Interpenetrating Subsamples.
Estimation of Means and Totals over Subpopulations.
Random-Response Model. Use of Weights in Sample
Surveys. Adjusting for Nonresponse. Imputation.
Selecting the Number of Callbacks. The Bootstrap.
Summary. 12. SUMMARY. Summary of the Designs
and Methods. Comparisons among the Designs and
Methods. Appenidices. References and Bibliography
Tables. Derivation of Some Main Results. Macros for
MINITAB. Macros for SAS. Data Sets. Selected Answers.
Index.
practical uses in solving real-world problems. Numerous
applications using up-to-date real data in engineering
and the life, social, and physical sciences illustrate and
motivate the many ways probability affects our lives.
The text’s accessible presentation carefully progresses
from routine to more difficult problems to suit students
of different backgrounds, and carefully explains how
and where to apply methods. Students going on to
more advanced courses in probability and statistics will
gain a solid background in fundamental concepts and
theory, while students who must apply probability to
their courses engineering and the sciences will develop
a working knowledge of the subject and appreciation
of its practical power.
NEW TO THIS EDITION
• Greater variety of applications—Many applied
examples and problems, especially from the biological
sciences, have been added to the existing array from
the social and engineering sciences.
• Updated exercises using real data—Problems, often
based on surveys from the U.S. Census or some other
national survey, are updated with the most current
data available.
• Expanded discussions of key topics—Among other
changes, this edition includes more guidance on
methods and when to use specific methods, a fuller
discussion of sets as they relate to probability, and
explanation of discrete multivariate distributions and
transformations for discrete distributions.
• Applets for visualizing concepts—Referenced in the
text and available on the Book Companion Website,
applets provide an optional means for students’
active exploration by illustrating basic concepts while
allowing for easy computations.
FEATURES
ITS APPLICATIONS, INTERNATIONAL
EDITION, 3E
Richard L. Scheaffer, University of Florida, Emeritus; Linda Young,
University of Florida, Gainesville
This text focuses on the utility of probability in solving
real-world problems for students in a one-semester
calculus-based probability course. Theory is developed
to a practical degree and grounded in discussion of its
• Emphasis on practical aspects of probabilistic
interpretation of data—Real-life applications from a
variety of fields motivate the study of probability by
illustrating the many ways it affects students’ lives.
• Theory presented in context—Theory is developed to
a practical degree around models used in real-world
applications, while proofs of theorems and “tricky”
probability calculations are minimized.
• Variety of problems—Problem sets carefully progress
from routine to more difficult to suit students from
varying academic backgrounds.
www.cengageasia.com
43
CONTENTS
1. PROBABILITY IN THE WORLD AROUND US. Why
Study Probability? Deterministic and Probabilistic
Models. Modeling Reality. Deterministic Models.
Probabilistic Models. Applications in Probability. A
Brief Historical Note. A Look Ahead. 2. FOUNDATIONS
OF PROBABILITY. Understanding Randomness:
An Intuitive Notion of Probability. Randomness
with Known Structure. Randomness with Unknown
Structure. Sampling a Finite Universe. Sample Space
and Events. Definition of Probability. Counting Rules
Useful in Probability. More Counting Rules Useful in
Probability. Summary. 3. CONDITIONAL PROBABILITY
AND INDEPENDENCE. Conditional Probability.
Independence. Theorem of Total Probability and Bayes’
Rule. Odds, Odds Ratios, and Relative Risk. Summary.
4. DISCRETE PROBABILITY DISTRIBUTIONS. Random
Variables and Their Probability Distributions. Expected
Values of Random Variables. The Bernoulli Distribution.
The Binomial Distribution. Probability Function. Mean
and Variance. History and Applications. The Geometric
Distribution. Probability Function. Mean and Variance.
An Alternate Parameterization: Number of Trials
Versus Number of Failures. The Negative Binomial
Distribution. Probability Function. Mean and Variance.
An Alternate Parameterization: Number of Trials Versus
Number of Failures. History and Applications. The
Poisson Distribution. Probability Function. Mean and
Variance. History and Applications. The Hypergeometric
Distribution. The Probability Function. Mean and
Variance. History and Applications. The Momentgenerating Function. The Probability-generating
Function. Markov Chains. Summary. 5. CONTINUOUS
PROBABILITY DISTRIBUTIONS. Continuous Random
Variables and Their Probability Distributions. Expected
Values of Continuous Random Variables. The Uniform
Distribution. Probability Density Function. Mean and
Variance. History and Applications. The Exponential
Distribution. Probability Density Function. Mean and
Variance. Properties. History and Applications. The
Gamma Distribution. Probability Density Function. Mean
and Variance. History and Applications. The Normal
Distribution. The Normal Probability Density Function.
Mean and Variance. Calculating Normal Probabilities.
Applications to Real Data. Quantile-Quantile (Q-Q)
Plots. History. The Beta Distribution. Probability Density
Function. Mean and Variance. H istory and Applications.
The Weibull Distribution. Probability Density Function.
Mean and Variance. History and Applications to Real
Data. Reliability. Hazard Rate Function. Series and
Parallel Systems. Redundancy. Moment-generating
Functions for Continuous Random Variables.
44
Expectations of Discontinuous Functions and Mixed
Probability Distributions. Summary. 6. MULTIVARIATE
PROBABILITY DISTRIBUTIONS. Bivariate and Marginal
Probability Distributions. Conditional Probability
Distributions. Independent Random Variables. Expected
Values of Functions of Random Variables. Conditional
Expectations. The Multinomial Distribution. More on
the Moment-Generating Function. Compounding and
Its Applications. Summary. 7. FUNCTIONS OF RANDOM
VARIABLES. Introduction. Functions of Discrete
Random Variables. Method of Distribution Functions.
Method of Transformations in One Dimension. Method
of Conditioning. Method of Moment-Generating
Functions. Gamma Case. Normal Case. Normal and
Gamma Relationships. Method of Transformation—Two
Dimensions. Order Statistics. Probability-Generating
Functions: Applications to Rand om Sums of Random
Variables. Summary. 8. SOME APPROXIMATIONS TO
PROBABILITY DISTRIBUTIONS: LIMIT THEOREMS.
Introduction. Convergence in Probability. Convergence
in Distribution. The Central Limit Theorem. Combination
of Convergence in Probability and Convergence
in Distribution. Summary. 9. EXTENSIONS OF
PROBABILITY THEORY. The Poisson Process. Birth
and Death Processes: Biological Applications. Queues:
Engineering Applications. Arrival Times for the Poisson
Process. Infinite Server Queue. Renewal Theory:
Reliability Applications. Summary. Appendix Tables.
MATHEMATICAL STATISTICS WITH
APPLICATIONS, INTERNATIONAL
EDITION, 7E
Dennis Wackerly, University of Florida; William Mendenhall,
University of Florida, Emeritus; Richard L. Scheaffer, University of
Florida, Emeritus
In their bestselling MATHEMATICAL STATISTICS WITH
APPLICATIONS, premiere authors Dennis Wackerly,
www.cengageasia.com
William Mendenhall, and Richard L. Scheaffer present
a solid foundation in statistical theory while conveying
the relevance and importance of the theory in solving
practical problems in the real world. The authors’
use of practical applications and excellent exercises
helps students discover the nature of statistics and
understand its essential role in scientific research.
NEW TO THIS EDITION
• A new chapter introducing Bayesian Methods for
Inference (Chapter 16).
• A collection of new applets allows students to
further build their knowledge and understanding of
probability and inference with interactive examples of
key concepts. Many applets have associated exercises,
allowing instructors to easily integrate the applets
into students’ coursework.
• Some of the applets permit determination of
probabilities and quantiles associated with normal,
gamma, beta, t, F and Chi-square distributions.
• With other applets, students can assess the effect of
different parameter choices on the shape of gamma
and beta density functions.
• Hundreds of new and updated exercises throughout
the book complement the existing exercises.
FEATURES
• The authors efficiently use mathematics as a
necessary tool to promote a firm understanding of
statistical techniques.
• Stressing connectivity, the authors explain not only
how major topics play a role in statistical inference
but also how the topics are related to one another.
These integrating discussions appear most frequently
in chapter introductions and conclusions.
• This text takes a practical approach in both the
exercises throughout and the useful topics in
statistical methodology covered in the last five
chapters.
• Exercises are based on real data or actual experimental
scenarios which allow students to see the practical
uses of various statistical and probabilistic methods.
CONTENTS
1. What Is Statistics? Introduction. Characterizing a Set of
Measurements: Graphical Methods. Characterizing a Set
of Measurements: Numerical Methods. How Inferences
Are Made. Theory and Reality. Summary. 2. Probability.
Introduction. Probability and Inference. A Review of Set
Notation. A Probabilistic Model for an Experiment: The
Discrete Case. Calculating the Probability of an Event:
The Sample-Point Method. Tools for Counting Sample
Points. Conditional Probability and the Independence
of Events. Two Laws of Probability. Calculating the
Probability of an Event: The Event-Composition
Methods. The Law of Total Probability and Bayes’’’’s
Rule. Numerical Events and Random Variables. Random
Sampling. Summary. 3. Discrete Random Variables and
Their Probability Distributions. Basic Definition. The
Probability Distribution for Discrete Random Variable.
The Expected Value of Random Variable or a Function of
Random Variable. The Binomial Probability Distribution.
The Geometric Probability Distribution. The Negative
Binomial Probability Distribution (Optional). The
Hypergeometric Probability Distribution. Moments and
Moment-Generating Functions. Probability-Generating
Functions (Optional). Tchebysheff’’’’s Theorem.
Summary. 4. Continuous Random Variables and Their
Probability Distributions. Introduction. The Probability
Distribution for Continuous Random Variable. The
Expected Value for Continuous Random Variable. The
Uniform Probability Distribution. The Normal Probability
Distribution. The Gamma Probability Distribution. The
Beta Probability Distribution. Some General Comments.
Other Expected Values. Tchebysheff’’’’s Theorem.
Expectations of Discontinuous Functions and Mixed
Probability Distributions (Optional). Summary. 5.
Multivariate Probability Distributions. Introduction.
Bivariate and Multivariate Probability Distributions.
Independent Random Variables. The Expected Value
of a Function of Random Variables. Special Theorems.
The Covariance of Two Random Variables. The Expected
Value and Variance of Linear Functions of Random
Variables. The Multinomial Probability Distribution. The
Bivariate Normal Distribution (Optional). Conditional
Expectations. Summary. 6. Functions of Random
Variables. Introductions. Finding the Probability
Distribution of a Function of Random Variables. The
Method of Distribution Functions. The Methods
of Transformations. Multivariable Transformations
Using Jacobians. Order Statistics. Summary. 7.
Sampling Distributions and the Central Limit Theorem.
Introduction. Sampling Distributions Related to the
Normal Distribution. The Central Limit Theorem. A
Proof of the Central Limit Theorem (Optional). The
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45
Normal Approximation to the Binomial Distributions.
Summary. 8. Estimation. Introduction. The Bias and
Mean Square Error of Point Estimators. Some Common
Unbiased Point Estimators. Evaluating the Goodness
of Point Estimator. Confidence Intervals. Large-Sample
Confidence Intervals Selecting the Sample Size. SmallSample Confidence Intervals for u and u1-u2. Confidence
Intervals for o2. Summary. 9. Properties of Point
Estimators and Methods of Estimation. Introduction.
Relative Efficiency. Consistency. Sufficiency. The
Rao-Blackwell Theorem and Minimum-Variance
Unbiased Estimation. The Method of Moments. The
Method of Maximum Likelihood. Some Large-Sample
Properties of MLEs (Optional). Summary. 10. Hypothesis
Testing. Introduction. Elements of a Statistical Test.
Common Large-Sample Tests. Calculating Type II
Error Probabilities and Finding the Sample Size for
the Z Test. Relationships Between Hypothesis Testing
Procedures and Confidence Intervals. Another Way
to Report the Results of a Statistical Test: Attained
Significance Levels or p-Values. Some Comments
on the Theory of Hypothesis Testing. Small-Sample
Hypothesis Testing for u and u1-u2. Testing Hypotheses
Concerning Variances. Power of Test and the NeymanPearson Lemma. Likelihood Ration Test. Summary.
11. Linear Models and Estimation by Least Squares.
Introduction. Linear Statistical Models. The Method
of Least Squares. Properties of the Least Squares
Estimators for the Simple Linear Regression Model.
Inference Concerning the Parameters BI. Inferences
Concerning Linear Functions of the Model Parameters:
Simple Linear Regression. Predicting a Particular Value
of Y Using Simple Linear Regression. Correlation. Some
Practical Examples. Fitting the Linear Model by Using
Matrices. Properties of the Least Squares Estimators
for the Multiple Linear Regression Model. Inferences
Concerning Linear Functions of the Model Parameters:
Multiple Linear Regression. Prediction a Particular Value
of Y Using Multiple Regression. A Test for H0: Bg+1 +
Bg+2 = . = Bk = 0. Summary and Concluding Remarks. 12.
Considerations in Designing Experiments. The Elements
Affecting the Information in a Sample. Designing
Experiment to Increase Accuracy. The Matched Pairs
Experiment. Some Elementary Experimental Designs.
Summary. 13. The Analysis of Variance. Introduction. The
Analysis of Variance Procedure. Comparison of More
than Two Means: Analysis of Variance for a One-way
46
Layout. An Analysis of Variance Table for a One-Way
Layout. A Statistical Model of the One-Way Layout.
Proof of Additivity of the Sums of Squares and E (MST)
for a One-Way Layout (Optional). Estimation in the OneWay Layout. A Statistical Model for the Randomized
Block Design. The Analysis of Variance for a Randomized
Block Design. Estimation in the Randomized Block
Design. Selecting the Sample Size. Simultaneous
Confidence Intervals for More than One Parameter.
Analysis of Variance Using Linear Models. Summary.
14. Analysis of Categorical Data. A Description of the
Experiment. The Chi-Square Test. A Test of Hypothesis
Concerning Specified Cell Probabilities: A Goodnessof-Fit Test. Contingency Tables. r x c Tables with Fixed
Row or Column Totals. Other Applications. Summary
and Concluding Remarks. 15. Nonparametric Statistics.
Introduction. A General Two-Sampling Shift Model. A
Sign Test for a Matched Pairs Experiment. The Wilcoxon
Signed-Rank Test for a Matched Pairs Experiment.
The Use of Ranks for Comparing Two Population
Distributions: Independent Random Samples. The MannWhitney U Test: Independent Random Samples. The
Kruskal-Wallis Test for One-Way Layout. The Friedman
Test for Randomized Block Designs. The Runs Test: A
Test for Randomness. Rank Correlation Coefficient.
Some General Comments on Nonparametric Statistical
Test. 16. Introduction to Bayesian Methods for
Inference. Introduction. Bayesian Priors, Posteriors and
Estimators. Bayesian Credible Intervals. Bayesian Tests
of Hypotheses. Summary and Additional Comments.
Appendix 1. Matrices and Other Useful Mathematical
Results. Matrices and Matrix Algebra. Addition of
Matrices. Multiplication of a Matrix by a Real Number.
Matrix Multiplication. Identity Elements. The Inverse of
a Matrix. The Transpose of a Matrix. A Matrix Expression
for a System of Simultaneous Linear Equations.
Inverting a Matrix. Solving a System of Simultaneous
Linear Equations. Other Useful Mathematical Results.
Appendix 2. Common Probability Distributions, Means,
Variances, and Moment-Generating Functions. Discrete
Distributions. Continuous Distributions. Appendix
3. Tables. Binomial Probabilities. Table of e-x. Poisson
Probabilities. Normal Curve Areas. Percentage Points
of the t Distributions. Percentage Points of the F
Distributions. Distribution of Function U. Critical Values
of T in the Wilcoxon Matched-Pairs, Signed-Ranks Test.
Distribution of the Total Number of Runs R in Sample
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Size (n1,n2); P(R < a). Critical Values of Pearman’’s Rank
Correlation Coefficient. Random Numbers. Answer to
Exercises. Index.
SAMPLING, 2E
Design and Analysis, International Edition
Sharon L. Lohr, Arizona State University
Sharon L. Lohr’s SAMPLING: DESIGN AND ANALYSIS,
2e, International Edition provides a modern
introduction to the field of survey sampling intended
for a wide audience of statistics students. Practical
and authoritative, the book is listed as a standard
reference for training on real-world survey problems
by a number of prominent surveying organizations.
Lohr concentrates on the statistical aspects of taking
and analyzing a sample, incorporating a multitude of
applications from a variety of disciplines. The text gives
guidance on how to tell when a sample is valid or not,
and how to design and analyze many different forms
of sample surveys. Recent research on theoretical and
applied aspects of sampling is included, as well as
optional technology instructions for using statistical
software with survey data.
NEW TO THIS EDITION
• New Content—New chapters cover sampling rare
populations, estimating a population size, and survey
quality (which ties together much of the material in
the earlier chapters). In addition, there is expanded
treatment of computer-intensive methods such as
jackknife and bootstrap, and discussion of new modes
of data collection such as Internet surveys.
• SAS integration—SAS is incorporated in examples for
analyzing data from complex surveys, with SAS code
provided on the book’s website.
• Current research—Recent research in survey
methodology is incorporated, enhancing the book’s
real-world orientation. Examples include new
approaches to linearization variance estimation,
computer-intensive methods for variance estimation,
small area estimation, nonresponse models, and
plotting survey data.Current research—Recent
research in survey methodology is incorporated,
enhancing the book’s real-world orientation. Examples
include new approaches to linearization variance
estimation, computer-intensive methods for variance
estimation, small area estimation, nonresponse
models, and plotting survey data.
• New applied examples—As is the case with most
examples in the book, new ones are based on real
surveys conducted in social sciences, epidemiology,
and many other areas.
• Varied, categorized exercises to build skills—Exercises,
many of which are new, are categorized into four
groups: A, Introductory Exercises, many suitable for
hand calculations; B, Working with Survey Data, with
most requiring use of statistical software; C, Working
with Theory, ideal for more mathematically oriented
classes; and D, Projects and Activities, with activities
suitable either for classroom use or as assigned
student projects.
FEATURES
• Flexibility in content and level—The book can be used
for undergraduates who have had one introductory
statistics course or for a first-year graduate course.
• Use of real data—As much as possible, examples and
exercises come from social sciences, engineering,
agriculture, medicine, and other disciplines.
• Topic coverage not found in other texts at this level—
The book discusses analysis of complex surveys,
nonresponse, and other important topics. These
optional sections allow instructors the flexibility to
pick and choose the additional topics they want to
include with the core course content.
• Emphasis on the importance of graphing the data—
This helps students avoid flawed data analysis.
CONTENTS
1. INTRODUCTION. A Sample Controversy. Requirements
of a Good Sample. Selection Bias. Measurement Error.
Questionnaire Design. Sampling and Nonsampling
Errors. Exercises. 2. SIMPLE PROBABILITY SAMPLES.
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47
Types of Probability Samples. Framework for Probability
Sampling. Simple Random Sampling. Sampling
Weights. Confidence Intervals. Sample Size Estimation.
Systematic Sampling. Randomization Theory Results
for Simple Random Sampling. A Prediction Approach
for Simple Random Sampling. When Should a Simple
Random Sample Be Used? Chapter Summary. Exercises.
3. STRATIFIED SAMPLING. What Is Stratified Sampling?
Theory of Stratified Sampling. Sampling Weights in
Stratified Random Sampling. Allocating Observations
to Strata. Defining Strata. Model-Based Inference
for Stratified Sampling. Quota Sampling. Chapter
Summary. Exercises. 4. RATIO AND REGRESSION
ESTIMATION. Ratio Estimation in a Simple Random
Sample. Estimation in Domains. Regression Estimation
in Simple Random Sampling. Poststratification. Ratio
Estimation with Stratified Samples. Model-Based Theory
for Ratio and Regression Estimation. Chapter Summary.
Exercises. 5. CLUSTER SAMPLING WITH EQUAL
PROBABILITIES. Notation for Cluster Sampling. OneStage Cluster Sampling. Two-Stage Cluster Sampling.
Designing a Cluster Sample. Systematic Sampling.
Model-Based Inference in Cluster Sampling. Chapter
Summary. Exercises. 6. SAMPLING WITH UNEQUAL
PROBABILITIES. Sampling One Primary Sampling
Unit. One-Stage Sampling with Replacement. TwoStage Sampling with Replacement. Unequal Probability
Sampling Without Replacement. Examples of Unequal
Probability Samples. Randomization Theory Results
and Proofs. Models and Unequal Probability Sampling.
Chapter Summary. Exercises. 7. COMPLEX SURVEYS.
Assembling Design Components. Sampling Weights.
Estimating a Distribution Function. Plotting Data from
a Complex Survey. Univariate Plots. Design Effects.
The National Crime Victimization Survey. Sampling
and Experiment Design. Chapter Summary. Exercises.
8. NONRESPONSE. Effects of Ignoring Nonresponse.
Designing Surveys to Reduce Nonsampling Errors.
Callbacks and Two-Phase Sampling. Mechanisms for
Nonresponse. Weighting Methods for Nonresponse.
Imputation. Parametric Models for Nonresponse.
What Is an Acceptable Response Rate? Chapter
Summary. Exercises. 9. VARIANCE ESTIMATION IN
COMPLEX SURVEYS. Linearization (Taylor Series)
Methods. Random Group Methods. Resampling
and Replication Methods. Generalized Variance
Functions. Confidence Intervals. Chapter Summary.
Exercises. 10. CATEGORICAL DATA ANALYSIS IN
COMPLEX SURVEYS. Chi-Square Tests with Multinomial
Sampling. Effects of Survey Design on Chi-Square Tests.
48
Corrections to x2 Tests. Loglinear Models. Chapter
Summary. Exercises. 11. REGRESSION WITH COMPLEX
SURVEY DATA. Model-Based Regression in Simple
Random Samples. Regression in Complex Surveys.
Should Weights Be Used in Regression? Mixed Models
for Cluster Samples. Logistic Regression. Generalized
Regression Estimation for Population Totals. Chapter
Summary. Exercises. 12. TWO-PHASE SAMPLING.
Theory for Two-Phase Sampling. Two-Phase Sampling
with Stratification. Two-Phase Sampling with Ratio
Estimation. Subsampling Nonrespondents. Designing
a Two-Phase Sample. Chapter Summary. Exercises. 13.
ESTIMATING POPULATION SIZE. Capture-Recapture
Estimates. Contingency Tables for Capture-Recapture
Experiments. Assessing Undercoverage. Chapter
Summary. Exercises. 14. RARE POPULATIONS AND
SMALL AREA ESTIMATIONS. Sampling for Rare Events.
Small Area Estimation. Chapter Summary. Exercises. 15.
SURVEY QUALITY. Nonresponse Error. Measurement
Error. Sensitive Questions. Processing Error. Sampling
Error. Interaction of Error Sources. The Future of
Sampling. Chapter Summary. Exercises. APPENDICES:
PROBABILITY CONCEPTS USED IN SAMPLING.
Probability. Random Variables and Expected Value.
Conditional Probability. Conditional Expectation.
REFERENCES.
STUDENT SOLTUIONS MANUAL FOR
OTT/LONGNECKER’S AN INTRODUCTION
TO STATISTICAL METHODS AND DATA
ANALYSIS, 6TH, 6E
R. Lyman Ott; Michael T. Longnecker
Provides worked-out solutions to odd-numbered
exercises.
www.cengageasia.com
out the practical example in full detail. Finally, at the
end of each chapter, SURVEY SAMPLING, 7th Edition,
International Edition includes a wealth of exercises that
enable students to continue practicing and to stretch
their grasp of the content.
NEW TO THIS EDITION
WACKERLY/MENDENHALL/SCHEAFFER’S
MATHEMATICAL STATISTICS WITH
APPLICATIONS, 7TH, 7E
Dennis Wackerly, University of Florida; William Mendenhall,
University of Florida, Emeritus; Richard L. Scheaffer, University of
Florida, Emeritus
• Integrates new software: By using software that
significantly decreases the need for long arithmetic,
the authors have allowed students to focus less on
arithmetic, and instead on the valid use of appropriate
statistical tools.
• New Icons: Excel Tools icons have been added to the
boxed equations and example solutions, so as to direct
students to use Excel when appropriate.
• More concise data: The data for examples and
exercises is now more concise and is also incorporated
with the electronic supplements.
• Updated Appendices: the Appendices for this edition
have been updated to be more concise and userfriendly.
FEATURES
SURVEY SAMPLING, INTERNATIONAL
EDITION, 7E
Richard L. Scheaffer, University of Florida, Emeritus; William
Mendenhall, III, University of Florida, Emeritus; R. Lyman Ott; Kenneth
G. Gerow, University of Wyoming
SURVEY SAMPLING, 7th Edition, International Edition
introduces students to the design and analysis of
sample surveys via a practical, engaging approach.
First, this introductory text begins with brief chapters
focused on the important role that sample surveys play
in the modern world. Then, each successive chapter
builds on this foundation. These chapters start with
the problem, describe the methodology needed for
solving the problem, and provide the details of the
estimation procedure using a compact presentation
of the necessary formulas. Each chapter then works
• Bridges the gap between classroom and practice in
two significant ways: First, select sections on weights
in unequal probability sampling. Then, they expanded
the treatment of nonresponse in Chapter 11, including
the use of imputation as a technique for handling
some types of nonresponse.
• Emphasizes two cutting-edge sampling techniques:
the bootstrap, a modern technique for establishing
margins of error and confidence intervals in complex
designs, and an adaptive sampling technique for
improving estimates while the field work is in
progress.
• Incorporates compatible computations: SURVEY
SAMPLING, 7th Edition, International Edition’s
computations are compatible with modern statistical
software, using much fewer hand calculation formulas.
• Utilizes detailed simulations: Key statistical concepts
are demonstrated using step-by-step simulations.
• Emphasizes practical aspects: topics include sources
of errors in surveys, methods of data collection,
designing questionnaires, and guidelines for planning
surveys.
• Expands Student Understanding: The “Experiences
with Real Data” sections at the end of most chapters
enable students to work with both large and small
www.cengageasia.com
49
projects, some requiring computations to be handled
by a computer, giving them valuable experience.
CONTENTS
1. INTRODUCTION. 2. ELEMENTS OF THE SAMPLING
PROBLEM. Introduction. Technical Terms. How to Select
the Sample: The Design of the Sample Survey. Sources of
Errors in Surveys. Designing a Questionnaire. Planning
a Survey. Summary. 3. SOME BASIC CONCEPTS OF
STATISTICS. Introduction. Summarizing Information in
Populations and Samples: The Infinite Population Case.
Summarizing Information in Populations and Samples:
The Finite Population Case. Sampling Distributions.
Covariance and Correlation. Estimation. Summary.
4. SIMPLE RANDOM SAMPLING. Introduction. How
to Draw a Simple Random Sample. Estimation of a
Population Mean and Total. Selecting the Sample Size
for Estimating Population Means and Totals. Estimation
of a Population Proportion. Comparing Estimates.
Summary. 5. STRATIFIED RANDOM SAMPLING.
Introduction. How to Draw a Stratified Random Sample.
Estimation of a Population Mean and Total. Selecting
the Sample Size for Estimating Population Means
and Totals. Allocation of the Sample. Estimation of
a Population Proportion. Selecting the Sample Size
and Allocating the Sample to Estimate Proportions.
Additional Comments on Stratified Sampling. An
Optimal Rule for Choosing Strata. Stratification
after Selection of the Sample. Double Sampling for
Stratification. Summary. 6. RATIO, REGRESSION, AND
DIFFERENCE ESTIMATION. Introduction. Surveys that
Require the Use of Ratio Estimators. Ratio Estimation
Using Simple Random Sampling. Selecting the Sample
Size. Ratio Estimation in Stratified Random Sampling.
Regression Estimation. Difference Estimation. Relative
Efficiency of Estimators. Summary. 7. SYSTEMATIC
SAMPLING. Introduction. How to Draw a Systematic
Sample. Estimation of a Population Mean and Total.
Estimation of a Population Proportion. Selecting
the Sample Size. Repeated Systematic Sampling.
Further Discussion of Variance Estimators. Summary.
8. CLUSTER SAMPLING. Introduction. How to Draw
a Cluster Sample. Estimation of a Population Mean
and Total. Equal Cluster Sizes; Comparison to Simple
Random Sampling. Selecting the Sample Size for
Estimating Population Means and Totals. Estimation of
a Population Proportion. Selecting the Sample Size for
Estimating Proportions. Cluster Sampling Combined
with Stratification. Cluster Sampling with Probabilities
Proportional to Size. Summary. 9. TWO-STAGE CLUSTER
SAMPLING. Introduction. How to Draw a Two-Stage
Cluster Sample. Unbiased Estimation of a Population
50
Mean and Total. Ratio Estimation of a Population
Mean. Estimation of a Population Proportion. Sampling
Equal-Sized Clusters. Two-Stage Cluster Sampling
with Probabilities Proportional to Size. Summary. 10.
ESTIMATING THE POPULATION SIZE. Introduction.
Estimation of a Population Size Using Direct Sampling.
Estimation of a Population Size Using Inverse Sampling.
Choosing Sample Sizes for Direct and Inverse Sampling.
Estimating Population Density and Size from Quadrat
Samples. Estimating Population Density and Size from
Stocked Quadrats. Adaptive Sampling. Summary. 11.
SUPPLEMENTAL TOPICS. Introduction. Interpenetrating
Subsamples. Estimation of Means and Totals over
Subpopulations. Random-Response Model. Use of
Weights in Sample Surveys. Adjusting for Nonresponse.
Imputation. Selecting the Number of Callbacks. The
Bootstrap. Summary. 12. SUMMARY. Summary of the
Designs and Methods. Comparisons among the Designs
and Methods. Appendices. References and Bibliography
Tables. Derivation of Some Main Results. Macros for
MINITAB. Macros for SAS. Data Sets. Selected Answers.
Index.
INTRODUCTORY STATISTICS
ENGINEERING AND SCIENCES
APPLIED STATISTICS FOR ENGINEERS
AND SCIENTISTS, INTERNATIONAL
EDITION, 3E
Jay L. Devore, California Polytechnic State University, San Luis Obispo;
Nicholas R. Farnum, California State University, Fullerton; Jimmy A.
Doi, California Polytechnic State University, San Luis Obispo
This concise book for engineering and sciences students
emphasizes modern statistical methodology and
data analysis. APPLIED STATISTICS FOR ENGINEERS
www.cengageasia.com
AND SCIENTISTS, 3E, International Edition is ideal
for courses that cover probability only to the extent
that it is needed for inference. The authors emphasize
application of methods to real problems, with real
examples throughout. The text is designed to meet
ABET standards and has been updated to reflect the
most current methodology and practice.
NEW TO THIS EDITION
• New exercises and examples, based on real data
and information from published sources, reinforce a
practical, realistic approach that helps students relate
to and understand statistical concepts better.
• Computer output has been updated to reflect the
latest technology.
FEATURES
• The integration of the “Six Sigma Terminology” in
Chapter 6 adds to the text’s modern approach.
• Describe the key features (e.g. a chapter in the TOC,
a feature to spotlight, a supplement to call out, and
technology to mention) instructors will want to be
aware of.
• Examples that use real data from industry reports and
articles introduce students to real-world situations
while they learn statistical concepts.
• The authors cover all the important topics concisely,
giving students a solid understanding of both
statistical methods and design with a problem-solving
focus.
• The authors emphasize modern statistical methods
including quality and design of experiments to give
students exposure to practical applications.
• An emphasis on graphical data analysis methods is
consistent with the authors’ computer-integrated
approach.
• Practical computer pedagogy is integrated throughout
the book so that learning of concepts can focus on real
applications, using output from the most widely used
statistical packages, such as MINITAB, JMP IN, SAS,
and S-Plus.
• Numerous relevant, current exercises and examples
appear throughout.
CONTENTS
1. DATA AND DISTRIBUTIONS. Populations, Samples
and Processes. Visual Displays for Univariate Data.
Describing Distributions. The Normal Distribution.
Other Continuous Distributions. Several Useful
Discrete Distributions. Supplementary Exercises.
Bibliography. 2. NUMERICAL SUMMARY MEASURES.
Measures of Center. Measures of Variability. More
Detailed Summary Quantities. Quantile Plots.
Supplementary Exercises. Bibliography. 3. BIVARIATE
AND MULTIVARIATE DATA AND DISTRIBUTIONS.
Scatter Plots. Correlation. Fitting a Line to Bivariate
Data. Nonlinear Relationships. Using More Than One
Predictor. Joint Distributions. Supplementary Exercises.
Bibliography. 4. OBTAINING DATA. Operational
Definitions. Data from Sampling. Data from Experiments.
Measurement Systems. Supplementary Exercises.
Bibliography. 5. PROBABILITY AND SAMPLING
DISTRIBUTIONS. Chance Experiments. Probability
Concepts. Conditional Probability and Independence.
Random Variables. Sampling Distributions. Describing
Sampling Distributions. Supplementary Exercises.
Bibliography. 6. QUALITY CONTROL. Terminology.
How Control Charts Work. Control Charts for Mean and
Variance. Process Capability Analysis. Control Charts
for Attribute Data. Reliability. Supplementary Exercises.
Bibliography. 7. ESTIMATION AND STATISTICAL
INTERVALS. Point Estimation. Large-Sample Confidence
Intervals for a Population Mean. More Large-Sample
Confidence Intervals. Small-Sample Intervals Based
on a Normal Population Distribution. Intervals for
µ1-µ2 Based on a Normal Population Distributions.
Other Topics in Estimation (Optional). Supplementary
Exercises. Bibliography. 8. TESTING STATISTICAL
HYPOTHESES. Hypotheses and Test Procedures. Tests
Concerning Hypotheses About Means. Tests Concerning
Hypotheses About a Categorical Population. Testing the
Form of a Distribution. Further Aspects of Hypothesis
Testing. Supplementary Exercises. Bibliography. 9. THE
ANALYSIS OF VARIANCE. Terminology and Concepts.
Single-Factor ANOVA. Interpreting ANOVA Results.
Randomized Block Experiments. Supplementary
Exercises. Bibliography. 10. EXPERIMENTAL DESIGN.
Terminology and Concepts. Two-Factor Designs.
Multifactor Designs. 2k Designs. Fractional Factorial
Designs. Supplementary Exercises. Bibliography.
11. INFERENTIAL METHODS IN REGRESSION AND
CORRELATION. Regression and Models Involving a
Single Independent Variable. Inferences About the
Slope Coefficient ß. Inferences Based on the Estimated
Regression Line. Multiple Regression Models. Inferences
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51
in Multiple Regression. Further Aspects of Regression
Analysis. Supplementary Exercises. Bibliography.
APPENDIX TABLES. ANSWERS TO ODD-NUMBERED
EXERCISES. INDEX.
guide students.
questions.
of formulas.
BASIC STATISTICS FOR THE BEHAVIORAL
SCIENCES, INTERNATIONAL EDITION, 6E
BASIC STATISTICS FOR THE BEHAVIORAL SCIENCES,
International Edition demystifies and fully explains
statistics without leaving out relevant topics or simply
presenting formulas, in a format that is non-threatening
and inviting to students. The author’s clear, patiently
crafted explanations, with an occasional touch of humor,
teach students not only how to compute an answer, but
also why they should perform the procedure or what
their answer reveals about the data. The book achieves
several objectives: it presents a conceptual-intuitive
approach, presents statistics within an understandable
research context, deals directly and positively with
student weaknesses in mathematics, and introduces
new terms and concepts in an integrated way. The result
is a text that students can learn from as well as enjoy.
NEW TO THIS EDITION
52
FEATURES
CONTENTS
www.cengageasia.com
CENGAGE ADVANTAGE BOOKS: BASIC
SCIENCES, 6E
BASIC STATISTICS FOR THE BEHAVIORAL SCIENCES
demystifies and fully explains statistics without leaving
out relevant topics or simply presenting formulas, in a
format that is non-threatening and inviting to students.
The author’s clear, patiently crafted explanations, with
an occasional touch of humor, teach students not only
how to compute an answer, but also why they should
perform the procedure or what their answer reveals
about the data. The book achieves several objectives:
it presents a conceptual-intuitive approach, presents
statistics within an understandable research context,
deals directly and positively with student weaknesses in
mathematics, and introduces new terms and concepts
in an integrated way. The result is a text that students
can learn from as well as enjoy.
NEW TO THIS EDITION
guide students.
questions.
of formulas.
CONTENTS
FEATURES
www.cengageasia.com
53
FEATURES
ESSENTIALS OF STATISTICS FOR
BEHAVIORAL SCIENCE, 7E
NEW TO THIS EDITION
revised.
54
statistics course.
CONTENTS
www.cengageasia.com
•
FUNDAMENTAL STATISTICS FOR THE
•
•
SCIENCES contains an abundance of real data and
research studies as a base and moves through an
analysis of data.
NEW TO THIS EDITION
chapter.
•
example, “you will see that I have plotted somethingor-other on the X (horizontal) axis.” Or, “whether it be
the mode (the most common value), the median (the
middle value), or the mean, ….” Or “In this case the
to identify definition) People often forget which is the
X axis, and they need to be constantly reminded that
the notation for the population variance is.
Biographies have been scattered throughout the text
The chapter summaries have been rewritten to be
Many examples have been replaced with new ones
The book website as well as other helpful websites are
FEATURES
CONTENTS
www.cengageasia.com
55
The Pearson Product-Moment Correlation Coefficient
(r). Correlations with Ranked Data. Factors that Affect
56
www.cengageasia.com
SCIENCES, 9E
TestBank, and more.
NEW TO THIS EDITION
• Based on reviewers’ suggestions, this edition includes
output.
• The previous edition’s Chapter 12, “Estimation,” has
10, and 11.
• The previous edition’s Chapter 20, covering hypothesis
• A new final chapter outlines the process of selecting
sets of data.
FEATURES
www.cengageasia.com
57
25 to 30 problems.
errors.
CONTENTS
© 2013, 792pp, Loose-Leaf, 9781111835767, Arts and Sciences
58
ELEMENTARY STATISTICS
Looking at the Big Picture, International Edition
Using a successfully class-tested approach that gives
coherence to a broad range of introductory topics, this
innovative text provides students with a real-world,
big picture view of statistics as well as problemsolving strategies that can be applied to the statistical
questions, real data, and examples that they will
encounter. Author Nancy Pfenning organizes content
around four basic processes of statistics: producing
data, displaying and summarizing data, understanding
probability, and using probability to perform statistical
inference. Within this framework, the book progresses
systematically through five basic problem situations
involving values of variables (quantitative, categorical,
or a blend). As a result, students learn to identify which
situation applies and how to choose the correct display,
FEATURES
• Big picture problem-solving approach—An innovative
• Flexible content and technology integration—The
www.cengageasia.com
• Examples using real data—Hundreds of well-chosen
conceptual—A set of problems after each major
real data.
• Emphasis on connections among topics—Strategically
flow of main ideas.
CONTENTS
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59
SELECTED EXERCISES.
ELEMENTARY STATISTICS, ENHANCED
REVIEW EDITION (W/CD-ROM &
CENGAGENOW, INFOTRAC® 2-SEM.,
ILRN® HOMEWORK, PERSONAL TUTOR,
INTERNET COMPANION FOR STATISTICS
2-SEM. PAC), 10E
60
and in the conferences they lead, Bob Johnson and
Pat Kuby have inspired hundreds of thousands of
practicality of statistics. Robert Johnson and Patricia
Kuby’s ELEMENTARY STATISTICS, Tenth Edition has
applications. Technology enhancements include the
addition of Video Skillbuilders and StatisticsNow™ (part
of the CengageNOW suite of technology products), our
personalized online learning companion. This increased
focus on technology to help students succeed, along
with the wealth of instructor supplements and
flexibility of technology coverage (with MINITAB, Excel,
and TI-83 output and instructions throughout) clearly
differentiate this text from its competitors as the most
accessible text for students to learn from and the most
straightforward text for instructors to teach from.
NEW TO THIS EDITION
• The new introductory concepts, review lessons, and
solutions located in the back of the text written by
author, Patricia Kuby, provide your students with the
help they need to grasp various algebraic and basic
statistical concepts.
• New Learning Outcomes have been added to the endof-chapter material. These outcomes relate to specific
section material and exercises and allow students to
diagnose their mastery of a topic.
• StatisticsNow™ (part of the CengageNOW suite of
technology products). Featured within chapters,
StatisticsNow is a robust, personalized online learning
companion that helps students gauge their own
unique study needs and makes the most of their study
time by building focused Personalized Learning Plans
that reinforce key concepts. Pre-Tests give students
an initial assessment of their knowledge. Personalized
Learning Plans, based on the students’ answers to the
pre-test questions, outline key elements for review.
Post-Tests assess students’ mastery of core concepts
for each chapter. Access to StatisticsNow is available
at no additional charge with purchase of a new text.
• Interactive Video Skillbuilders contain hours of
helpful, interactive video instruction. These videos
walk your students through key examples from the
text, step by stepgiving them a foundation in the
skills that they need to know. Video icons located in
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the margin guide students to view the video on the
Skillbuilder CD-ROM.
• New technology output has been added throughout
the text, reflecting the latest changes to MINITAB,
Excel, and TI-83/84 graphing calculator output.
• 30% of the exercises are new and many others have
been significantly updated. Additional critical thinking
exercises have been infused throughout the exercise
sets. In addition, Classic Exercises can still be found on
the CD-ROM accompanying the text, providing more
opportunity to assign practice exercises.
CONTENTS
Part I: DESCRIPTIVE STATISTICS. 1. Statistics. Americans,
Here’’s Looking At You. What is Statistics? Measurability
and Variability. Data Collection. Comparison of
Probability and Statistics. Statistics and Technology. 2.
Data. You and the Internet. Graphical Presentation of
Data. Graphs, Pareto Diagrams, and Stem-And-Leaf
Displays. Frequency Distributions and Histograms.
Numerical Descriptive Statistics. Measures of Central
Tendency. Measures of Dispersion. Measures of Position.
Interpreting and Understanding Standard Deviation.
The Art of Statistical Deception. Mean and Standard
Deviation of Frequency Distribution (Optional). 3.
Descriptive Analysis and Presentation of Bivariate
Data. The Kid is All Grown Up. Bivariate Data. Linear
Correlation. Linear Regression. Part II: PROBABILITY.
4. Probability. Sweet Statistics. Probability of Events.
Conditional Probability of Events. Rules of Probability.
Mutually Exclusive Events. Independent Events. Mutually
Exclusive, Independent Events—A Relationship? 5.
Probability Distributions (Discrete Variables). Caffeine
Drinking. Random Variables. Probability Distribution
of a Discrete Random Variable. Mean and Variance
of a Discrete Probability Distribution. The Binomial
Probability Distribution. Mean and Standard Deviation
of the Binomial Distribution. 6. Normal Probability
Distributions. Intelligence Scores. Normal Probability
Distributions. The Standard Normal Distribution.
Applications of Normal Distributions. Notation. Normal
Approximation of the Binomial. 7. Sample Variability.
275 Million Americans. Sampling Distributions. The
Sampling Distribution of Sample Means. Application
of the Sampling Distribution of Sample Means. Part III:
Inferences. Were They Shorter Back Then? The Nature of
Estimation. Estimation of a Mean (ó known). The Nature
of Hypothesis Testing. Hypothesis Test of Mean ì (ó
Known): A Probability Value Approach. Hypothesis Test
of Mean ì (ó Known): A Classical Approach. 9. Inferences
Involving One Population. Get Enough Daily Exercise?
Inferences About Mean ì (ó Unknown). Inferences
About the Binomial Probability of Success. Inferences
About Variance and Standard Deviation. 10. Inferences
Involving Two Populations. Students, Credit Cards and
Debt. Independent and Dependent Samples. Inferences
Concerning the Mean Difference Using Two Dependent
Samples. Inferences Concerning the Difference Between
Means Using Two Independent Samples. Inferences
Concerning the Difference Between Proportions Using
Two Independent Samples. Inferences Concerning
the Ratio of Variances Using Two Independent
Samples. Part IV: MORE INFERENTIAL STATISTICS. 11.
Applications of Chi-Square. Cooling a Great Hot Taste.
Chi-Square Statistic. Inferences Concerning Multinomial
Experiments. Inferences Concerning Contingency
Tables. 12. Analysis of Variance. Time Spent Commuting
to Work. Introduction to the Analysis of Variance
Technique. The Logic Behind ANOVA. Applications
of Single-Factor ANOVA. 13. Linear Correlation and
Regression. Beautiful Golden Wheat! Linear Correlation
Analysis. Inferences About the Linear Correlation
Coefficient. Linear Regression Analysis. Inferences
Concerning the Slope of the Regression Line. Confidence
Interval Estimates For Regression. Understanding the
Relationship Between Correlation and Regression.
14. Elements of Nonparametric Statistics. Teenagers’’
Attitudes. Nonparametric Statistics. Comparing
Statistical Tests. The Sign Test. The Mann-Whitney U
Test. The Runs Test. Rank Correlation. Appendix A: Basic
Principles of Counting. Appendix B: Tables. Answers to
Odd-Numbered Exercises. Answers to Chapter Practice
Tests. Index for Applications. Index for Computer and
Calculator Instructions. Index. Credits. Formula Card.
www.cengageasia.com
61
•
ELEMENTARY STATISTICS,
and in the conferences they lead, Robert Johnson and
Patricia Kuby have inspired hundreds of thousands of
practicality of statistics. Now in its Eleventh Edition,
ELEMENTARY STATISTICS, International Edition has
applications. A focus on technology to help students
succeed—including MINITAB®, Excel®, and TI-83/84
output and instructions throughout—is enhanced by a
wealth of supplements that save instructors time and
give students interactive guidance and support. All
this and more have established this text’s reputation
for being remarkably accessible for students to learn
from—and simple and straightforward for instructors
to teach from.
NEW TO THIS EDITION
• New chapter-opening vignettes—More than 50%
of the book’s chapter-opening vignettes, each of
which focuses on an everyday aspect of life, are
new. Illustrated with statistical information, each
chapter opener provides a relevant, familiar context
for students’ initial step into the concepts covered in
the chapter.
• New applied examples—Nearly 20% of the text’s
applied examples are new or updated to help engage
student interest. Enhanced step-by-step solutions
present key statistical concepts in a clear progression.
• Over 20% new and updated exercises—21% of the
exercises are new or updated to reflect current
events and other timely topics that involve the use of
statistics.The text’s more than 1700 exercises provide
62
•
•
•
a wealth of practice problems, with each exercise set
including a range of exercise types that progress from
basic recall to multi-step to items requiring critical
thinking. As always, most exercises can be calculated
either by hand or by using technology.
Chapter 6, “Normal Probability Distributions,” has
been completely rewritten to present the Standard
Normal Distribution utilizing the cumulative area
approach, incorporating a more intuitive idea with
respect to the total area under a curve and following
more closely the format used with calculators and
statistical software. To support this change, a new,
corresponding two-page Table 3, “Cumulative Areas of
the Standard Normal Distribution,” is included among
the tables at the back of the text.
Statistics CourseMate—This new online resource
provides a rich array of interactive and supplementary
material to accompany the text, including an eBook
version of the text. For more information, see the full
Solution Builder for ELEMENTARY STATISTICS,
11e, International Edition—This new instructor
resource provides fully worked-out solutions to all
text exercises in customizable online format. For
more information, see the full description under
Supplements.
Aplia™ online homework for ELEMENTARY
STATISTICS, 11e, International Edition—Aplia is a
premium online homework system dedicated to
improving learning by increasing student effort
and engagement. For more information, see the full
FEATURES
• Emphasis on interpretation of statistical information
and real applications—Starting in Chapter 1, when
students learn key terms and procedures; in Chapter
4, “Probability,” where analysis rather than formula
is highlighted; and continuing throughout the text,
the authors emphasize the role of interpretation in
statistical analysis. Examples and exercises feature
real applications of statistics, and chapter-opening
vignettes enhance the relevance of the material
for students. Critical thinking exercises throughout
chapters further support the book’s practical, proven
approach.
• Skillbuilder Applet exercises and interactive video
skillbuilders—These exercises give students increased
www.cengageasia.com
opportunities for hands-on learning and shift their
attention from simply memorizing formulas and
concepts to actively internalizing the behavior of
concepts and interpreting data. Created to help
students build the foundational skills they need,
the online videos provide step-by-step instruction,
walking students through key examples from the text.
• Flexible organization and integrated, up-to-date
technology instructions—The p-value and classical
approaches to hypothesis testing are initially
introduced separately and thereafter presented sideby-side, to both emphasize their comparability and
allow a wide range of teaching approaches. Similarly,
descriptive regression and correlation are covered
early (Chapter 3), and MINITAB®, Excel®, and the
TI-83/84 graphing calculator instructions are found
throughout the text instead of being relegated to
end-of-chapter material or appendices.
CONTENTS
Statistics. Appendix A: Basic Principles of Counting.
Appendix B: Tables. Introductory Concepts. Review
Lessons. Answers to Odd-Numbered Exercises. Answers
to Chapter Practice Tests. Index for Applications. Index
for Computer and Calculator Instructions. Index.
ELEMENTARY STATISTICS: LOOKING AT
THE BIG PICTURE, HYBRID
Reflecting Cengage Learning’s commitment to
offering flexible teaching solutions and value for
students and instructors, this new hybrid edition
features the instructional presentation found in the
printed text while delivering end-of-section exercises
online in Aplia™. The result—a briefer printed text
that engages students online! Using a successfully
class-tested approach that gives coherence to a broad
range of introductory topics, this innovative text
provides students with a real-world, big picture view
of statistics as well as problem-solving strategies
that can be applied to the statistical questions, real
data, and examples that they will encounter. Author
Nancy Pfenning organizes content around four basic
processes of statistics: producing data, displaying and
summarizing data, understanding probability, and using
probability to perform statistical inference. Within
this framework, the book progresses systematically
through five basic problem situations involving values
of variables (quantitative, categorical, or a blend). As
a result, students learn to identify which situation
applies and how to choose the correct display,
NEW TO THIS EDITION
• NEW! Hybrid edition of the text provide a lessexpensive, shorter option for students. A selection
of end-of-section exercises are omitted from the text
and appear online only. Every new hybrid edition is
www.cengageasia.com
63
packaged with Aplia™.
• Big picture problem-solving approach--An innovative
• Flexible content and technology integration--The
• Examples using real data--Hundreds of well-chosen
conceptual--A set of problems after each major
real data.
• Emphasis on connections among topics--Strategically
flow of main ideas.
CONTENTS
64
www.cengageasia.com
SELECTED EXERCISES.
ESSENTIALS OF STATISTICS FOR THE
BEHAVIORAL SCIENCE, INTERNATIONAL
EDITION, 7E
NEW TO THIS EDITION
revised.
www.cengageasia.com
65
FEATURES
statistics course.
CONTENTS
66
FUNDAMENTAL STATISTICS FOR
THE BEHAVIORAL SCIENCES,
SCIENCES, International Edition contains an abundance
of real data and research studies as a base and moves
through an analysis of data.
NEW TO THIS EDITION
chapter.
www.cengageasia.com
•
•
•
•
example, “you will see that I have plotted somethingor-other on the X (horizontal) axis.” Or, “whether it
be the mode (the most common value), the median
(the middle value), or the mean, ….” Or “In this the
to identify case definition) People often forget which
is the X axis, and they need to be constantly reminded
that the notation for the population variance is
Biographies have been scattered throughout the text
The chapter summaries have been rewritten to be
Many examples have been replaced with new ones
The book website as well as other helpful websites are
FEATURES
CONTENTS
The Pearson Product-Moment Correlation Coefficient®.
Correlations with Ranked Data. Factors that Affect
www.cengageasia.com
67
68
FUNDAMENTALS OF BIOSTATISTICS,
Bernard Rosner, Harvard University and Harvard Medical School
Bernard Rosner’s FUNDAMENTALS OF BIOSTATISTICS,
7e, International Edition is a practical introduction to
the methods, techniques, and computation of statistics
with human subjects. It prepares students for their
future courses and careers by introducing the statistical
methods most often used in medical literature. Rosner
minimizes the amount of mathematical formulation
(algebra-based) while still giving complete explanations
of all the important concepts. As in previous editions, a
major strength of this book is that every new concept is
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developed systematically through completely worked
out examples from current medical research problems.
NEW TO THIS EDITION
• New sections on Parametric Survival Analysis and
Parametric Regression Models for Survival Data.
• New sections on Attributable Risk, Extensions to
Logistic Regression, and Longitudinal Data Analysis.
• New section on Interval Estimation for Rank
Correlation Coefficients.
• New material on Regression Models for Clustered
Binary Data.
• More than 250 new and updated problems.
FEATURES
• Complete online data sets for SAS, SPSS, JMP,
MINITAB, Excel, and ASCII formats, covering all
problems in the text that make use of real data.
• Review problems for quick practice before the main
exercise sets.
• Large number of applications highlighted in the Index
of Applications on the endsheets (they are grouped
by medical specialty and summarize the examples
and exercises that address each application). Includes
cutting-edge research in fields that students will
actually be working in later in their academic and
professional careers. Many of the applications are
based on original research conducted by the author.
• Brief solutions for more than 300 problems are given
in text and approximately 600 additional problems
and complete solutions are given in the Study Guide
available on the Companion Website.
CONTENTS
1. General Overview. 2. Descriptive Statistics. 3.
Probability. 4. Discrete Probability Distributions. 5.
Continuous Probability Distributions. 6. Estimation. 7.
Hypothesis Testing: One-Sample Inference. 8. Hypothesis
Testing: Two-Sample Inference. 9. Nonparametric
Methods. 10. Hypothesis Testing: Categorical Data. 11.
Regression and Correlation Methods. 12. Multisample
Inference. 13. Design and Analysis Techniques for
Epidemiologic Studies. 14. Hypothesis Testing: PersonTime Data.
14E
Used by hundreds of thousands of students since its
first edition, INTRODUCTION TO PROBABILITY AND
STATISTICS, 14E, International Edition continues to
blend the best of its proven, error-free coverage with
new innovations. Written for the higher end of the
traditional introductory statistics market, the book
takes advantage of modern technology—including
computational software and interactive visual
tools—to facilitate statistical reasoning as well as
the interpretation of statistical results. In addition
to showing how to apply statistical procedures, the
authors explain how to describe real sets of data
meaningfully, what the statistical tests mean in terms of
their practical applications, how to evaluate the validity
of the assumptions behind statistical tests, and what
to do when statistical assumptions have been violated.
The new edition retains the statistical integrity,
examples, exercises, and exposition that have made this
text a market leader—and builds upon this tradition of
excellence with new technology integration.
NEW TO THIS EDITION
• The book’s website, Statistics CourseMate, includes
a Personal Trainer feature that offers further
explanation of some of the more difficult concepts
through detailed definitions, step-by-step hints on
problem solving, self-correcting exercises, and data
sets for text exercises. Applets demonstrate key
concepts so they are easily visualized, accompanied
by exercises that provide practice in the required
statistical methods and immediate feedback with
answers. An icon in the text margin identifies concepts
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69
and applications for which an applet is available on
the website.
• Technology Today features at the end of each chapter
offer Microsoft® Excel 2010 and MINITAB outputs
and instructions, giving students hands-on experience
performing statistical analysis with these widely used
programs.
FEATURES
• Exercises: The book includes more than 1,300
exercises, many of which are new or updated for this
edition. New topics for the book’s chapter-ending
case study exercises include “How to Save Money
for Groceries,” “School Accountability Study: How
Is Your School Doing?” and “Are You Going to Curve
the Grades?”
• Market leader: The integrity of the statistics and the
quality of the examples and exercises keep this text in
the bestseller category. Comprehensive in coverage,
it delivers a more rigorous offering with traditional
coverage of probability. Instructors and students alike
appreciate its error-free material and exercises, and
its clear exposition.
• Real data: The first to incorporate case studies and
real data, this text continues to set the standard.
Many examples and exercises use authentic data sets,
helping students see the connections between their
studies and their lives.
• Quick reference: At the end of each chapter, Key
Concepts and Formulas sections provide quick
reference for students, helping them ensure they are
well prepared for assignments and tests.
CONTENTS
Introduction: What Is Statistics? 1. Describing Data
with Graphs. 2. Describing Data with Numerical
Measures. 3. Describing Bivariate Data. 4. Probability
and Probability Distributions. 5. Several Useful Discrete
Distributions. 6. The Normal Probability Distribution.
7. Sampling Distributions. 8. Large-Sample Estimation.
9. Large-Sample Tests of Hypotheses. 10. Inference
from Small Samples. 11. The Analysis of Variance.
12. Linear Regression and Correlation. 13. Multiple
Regression Analysis. 14. Analysis of Categorical Data. 15.
Nonparametric Statistics. Appendix I: Tables. Answers
to Selected Exercises.
70
DATA ANALYSIS, 4E
Roxy Peck, Chris Olsen, and Jay Devore’s new edition
concepts and cement their comprehension. Handson activities and interactive applets allow students
to practice statistics firsthand. INTRODUCTION TO
STATISTICS AND DATA ANALYSIS, 4th Edition, includes
updated coverage of the graphing calculator as well as
expanded coverage of probability.
NEW TO THIS EDITION
concepts in a realistic context. In addition, more of
the exercises specifically ask students to write (for
example, by requiring them to explain their reasoning,
interpret results, and comment on important features
of an analysis).
• Examples and exercises using data sets that can be
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•
•
•
•
them.
Approximately 90 exercises have video solutions,
The “Graphing Calculator Explorations” have been
significantly revised and updated.
FEATURES
assignments.
• Most chapters contain “Graphing Calculator
Explorations.” Found at the end of the chapter, these
explorations promote statistical thinking and allow
students to actively experience technology.
• Optional material on logistic regression in Chapter
5, “Summarizing Bivariate Data,” assures complete
coverage of this important topic.
CONTENTS
6. PROBABILITY. Chance Experiments and Events.
Using Simulation. 7. RANDOM VARIABLES AND
PROBABILITY DISTRIBUTIONS. Random Variables.
Probability Distributions for Discrete Random Variables.
Probability Distributions for Continuous Random
Variables. Mean and Standard Deviation of a Random
Variable. Binomial and Geometric Distributions. Normal
Approximate a Discrete Distribution. 8. SAMPLING
VARIABILITY AND SAMPLING DISTRIBUTION.
Distribution of a Sample Proportion. 9. ESTIMATION
USING A SINGLE SAMPLE. Point Estimation. LargeSample Confidence Interval for a Population Proportion.
Confidence Interval for a Population Mean. Interpreting
and Communicating the Results of Statistical Analyses.
10. HYPOTHESIS TESTING USING A SINGLE SAMPLE.
Hypotheses and Test Procedures. Errors in Hypotheses
Testing. Large-Sample Hypothesis Tests for a Population
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71
Regression Models. Fitting a Model and Assessing
Its Utility. Inferences Based on an Estimated Model
(online). Other Issues in Multiple Regression (online).
Interpreting and Communicating the Results of
Statistical Analyses (online). Activity 14.1: Exploring
the Relationship Between Number of Predictors and
Sample Size. 15. ANALYSIS OF VARIANCE. SingleFactor ANOVA and the F Test. Multiple Comparisons.
72
DATA ANALYSIS, AP EDITION, 4E
DATA ANALYSIS, ENHANCED REVIEW
EDITION (WITH CENGAGENOW PRINTED
ACCESS CARD), 3E
Roxy Peck, Chris Olsen and Jay Devore’s new edition
data analysis. The Third Edition includes coverage
of the graphing calculator and includes expanded
coverage of probability. Traditional in structure yet
modern in approach, this text guides students through
information. It helps students grasp concepts and
cement their comprehension by using simple notation-
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frequently substituting words for symbols. Hands-on
practice statistics firsthand. This Enhanced Edition
includes new Teaching Tips for each chapter in the
book, specific references to other available instructor
resources, and suggestions for effectively teaching an
Advanced Placement Introduction to Statistics course.
Also, Enhanced WebAssign now complements a robust
supplement package.
NEW TO THIS EDITION
• Chapter Opening Teaching Tips have been added to
beginning of each printed chapter in the Annotated
Instructor’s Edition. Broken down section-by-section,
these tips suggest additional assignments and
activities for a section, as well as the best uses of
the supplemental resources for the text (such as the
Activity Workbook, Instructor’s Binder and Testbank)
that could be included in a lesson plan.
• Margin Teaching Tips have been integrated throughout
the Annotated Instructor’s Edition. These tips are
strategically placed at points where an instructor
can integrate an activity, a clarifying example, a quiz
to test students on concept formation, and thoughtprovoking questions to ask your students.
• Easy to assign. Easy to use. Easy to manage. Enhanced
WebAssign allows you to assign, collect, grade, and
record homework assignments via the web and
includes links to text-specific content, video examples,
and problem-specific tutorials. Enchanced WebAssign
is more than a homework system; it is a complete
learning system for your students.
FEATURES
• Annotated Instructors Edition – designed specifically
to aid you in teaching the course.
• “Communicating and Interpreting the Results
of Statistical Analysis” sections emphasize the
importance of being able to interpret statistical output
and communicate its meaning to non-statisticians.
• Nearly 100 new examples and hundreds of new or
updated exercises with current data from journals,
newspapers, and other published sources have been
added to this edition, helping students understand
statistical concepts in a realistic, relevant context.
There are now over 1,100 exercises in the third edition.
• Eight new activities bring the total to 33. Found at
the ends of chapters, these hands-on activities allow
students to see statistics unfold before their eyes as
they conduct their own experiments.
• New optional material on logistic regression in
Chapter 5, “Summarizing Bivariate Data,” assures
complete coverage of this important topic.
• This text continues to feature broad coverage of
sampling, survey design and experimental design
coverage of transformations and nonlinear regression;
and an emphasis on graphical display as a necessary
component of data analysis.
CONTENTS
PROCESS. Three Reasons to Study Statistics. The
Nature and Role of Variability. Statistics and the
Data Analysis Process. Types of Data and Some
Simple Graphical Displays. Activity 1.1: Head Sizes:
Understanding Variability. Activity 1.2: Estimating Sizes.
Activity 1.3: A Meaningful Paragraph. 2. COLLECTING
DATA SENSIBLY. Statistical Studies: Observation and
Experimentation. Sampling. Simple Comparative
Experiments. More on Experimental Design. More on
Observational Studies: Designing Surveys (optional).
Communicating and Interpreting the Results of
Statistical Analyses. Activity 2.1: Designing a Sampling
Plan. Activity 2.2: An Experiment to Test for the
Stroop Effect. Activity 2.3: McDonalds and the Next
100 Billion Burgers. Activity 2.4: Video Games and
Pain Management. Graphing Calculator Explorations.
3. GRAPHICAL METHODS FOR DESCRIBING DATA.
Displaying Categorical Data: Comparative Bar Charts
and Pie Charts. Displaying Numerical Data: Stem-andLeaf Displays. Displaying Numerical Data: Frequency
Numerical Data. Communicating and Interpreting
the Results of Statistical Analyses. Activity 3.1:
Locating States. Activity 3.2: Bean Counters! Graphing
Calculator Explorations. 4. NUMERICAL METHODS
FOR DESCRIBING DATA. Describing the Center
of a Data Set. Describing Variability in a Data Set.
Summarizing a Data Set: Boxplots. Interpreting Center
and Variability: Chebyshev’’s Rule, the Empirical Rule,
and z Scores. Communicating and Interpreting the
Results of Statistical Analyses. Activity 4.1: Collecting
and Summarizing Numerical Data. Activity 4.2: Airline
Passenger Weights. Activity 4.3: Boxplot Shapes.
Graphing Calculator Explorations. 5. SUMMARIZING
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73
fitting a Line to Bivariate Data. Assessing the Fit of a
Line. Nonlinear Relationships and Transformations.
Logistic Regression (Optional). Communicating
Activity 5.1: Exploring Correlation and Regression
Technology Activity (Applets). Activity 5.2: Age
and Flexibility. Graphing calculator Explorations. 6.
PROBABILITY. Chance Experiments and Events.
and Using Simulation. Activity 6.1: Kisses. Activity 6.2:
A Crisis for European Sports Fans? Activity 6.3: The “Hot
Hand” in Basketball. Graphing Calculator Explorations.
7. RANDOM VARIABLES AND PROBABILIT Y
DISTRIBUTIONS. Random Variables. Probability
Distributions for continuous Random Variables.
The Binomial and Geometric Distributions. Normal
Approximate a Discrete Distribution. Activity 7.1: Rotten
Eggs. Graphing Calculator Explorations. 8. SAMPLING
VARIABILITY AND SAMPLING DISTRIBUTIONS.
distribution of a Sample Proportion. Activity 8.1: Do
Students Who Take the SAT Multiple Times Have an
Advantage in College Admissions? Graphing Calculator
Explorations. 9. ESTIMATION USING A SINGLE
SAMPLE. Point Estimates. Large-Sample Confidence
Interval for a Population Proportion. Confidence
Interval for a Population Mean. Communicating
Activity 9.1: Getting a Feel for Confidence Level.
Activity 9.2: An Alternative Confidence Interval
for a Population Proportion. Graphing Calculator
Explorations. 10. HYPOTHESIS TESTING USING A
SINGLE SAMPLE. Hypotheses and Test Procedures.
Errors in Hypothesis Testing. Large-Sample Hypothesis
Tests for a Population Proportion. Hypothesis Tests for
a Population Mean. Power and the Probability of Type
II Error. Communicating and Interpreting the Results of
Statistical Analyses. Activity 10.1: Comparing the t and
z Distributions. Graphing Calculator Explorations. 11.
74
Population or Treatment Means using Independent
Between Two Population or Treatment Means using
Paired Samples. Large-Sample Inferences Concerning
Proportions. Communicating and Interpreting the
Results of Statistical Analyses. Activity 11.1: Helium-Filled
Footballs? Activity 11.2: Thinking About Data Collection.
Graphing Calculator Explorations. 12. THE ANALYSIS
TESTS. Chi-Square Tests for Univariate Categorical
Data. Tests for Homogeneity and Independence in
a Two-Way Table. Communicating and Interpreting
the Results of Statistical Analyses. Activity 12.1: Pick
a Number, Any Number. Activity 12.2: Color and
Perceived Taste. Graphing Calculator Explorations. 13.
SIMPLE LINEAR REGRESSION AND CORRELATION:
INFERENTIAL METHODS. The Simple Linear Regression
Model. Inferences About the Slope of the Population
Regression Line. Checking Model Adequacy. Inferences
Based on the Estimated Regression Line (Optional).
Inferences About the Population Correlation Coefficient
(Optional). Communicating and Interpreting the
Results of Statistical Analyses. Activity 13.1: Are Tall
Women from “Big” Families? Graphing Calculator
Explorations. 14. MULTIPLE REGRESSION ANALYSIS.
Multiple Regression Models. Fitting a Model and
Assessing Its Utility. Inferences Based on an Estimated
Model (Online). Variable Selection and Other Issues
in Multiple Regression (Online). Communicating
and Interpreting the Results of Statistical Analyses
(Online). Activity 14.1: Exploring the Relationship
Between Number of Predictors and Sample Size. 15.
ANALYSIS OF VARIANCE. Single-Factor ANOVA and
the F Test. Multiple Comparisons. The F Test for a
Randomized Block Experiment (Online). Two-Factor
ANOVA (Online). Communicating and Interpreting
the Results of Statistical Analysis (Online). Activity
15.1: Exploring Single-Factor ANOVA. Appendix:
ANOVA computations (Online). Graphing Calculator
Exploration. 16. NONPARAMETRIC STATISTICAL
METHODS (ONLINE). Distribution-Free Procedures for
Inferences about a Difference Between Two Population
or Treatment Means Using Independent Samples
(Online). Distribution-Free Procedures for Inferences
Concerning a Difference. Between Two Population
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Means Using Paired Samples (Online). Distribution-Free
ANOVA (Online). Appendix: Statistical Tables. Answers
to Selected odd-Numbered Exercises. Index.
•
•
•
MIND ON STATISTICS, 4E
Jessica M. Utts, University of California, Irvine; Robert F. Heckard,
Pennsylvania State University
MIND ON STATISTICS emphasizes the conceptual
development of statistical ideas and the importance
of looking for--and finding--meaning in data. Authors
Jessica Utts and Robert Heckard actively engage
students’ natural curiosity, motivating them with
intriguing questions and explaining statistical topics
in the context of a wide range of interesting, useful
examples and case studies. Throughout, the authors
balance the promotion of statistical literacy with
coverage of the statistical methodology taught
students actively ask questions and find the answers for
themselves. More than any other text available, MIND
ON STATISTICS motivates and encourages students
to develop their statistical intuition by focusing on
analyzing data and interpreting results, rather than on
mathematical formulation.
NEW TO THIS EDITION
data.
•
•
New topics in this edition include multiple comparisons
The exercise sets have been significantly reorganized
The data in the problem sets, examples, and case
data sets.
A greater number of In Summary boxes are placed
Aplia™ is available with the new edition. This online
FEATURES
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75
CONTENTS
x 2 Table. 5. SAMPLING: SURVEYS AND HOW TO ASK
76
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x 2 Tables. Testing Hypotheses About One Categorical
to the Wise.
have the advantage of knowing how to use a real-world
business tool.
NEW TO THIS EDITION
• MINITAB HANDBOOK: UPDATED FOR RELEASE 16,
6E, International Edition reflects substantial changes
in the Minitab software and in statistics teaching that
have occurred since the fifth edition was published.
• Today’s students are most comfortable using a
menu-driven interface, so the sixth edition teaches
Minitab procedures using mostly the menus. Session
commands, however, are introduced early on and
referred to throughout the book so that students will
learn that they can accomplish some procedures more
efficiently with commands.
• The book is based on Minitab, Release 16, first
available in 2011. Release 16 is the most comprehensive
release to date and includes substantial new features.
• This edition makes significant use of Minitab’s latest
graphics capabilities and enhances several features,
such as plot editing, brushing and jittering, 3D graph
rotation, interaction plots, and more.
• A chapter on Creating Reports is included and the new
Minitab Assistant is introduced in several chapters.
• In Release 16 almost everything in Minitab can be
customized. You can create your own menus and
toolbars, add short cuts, and so on.
FEATURES
MINITAB® HANDBOOK,
Barbara F. Ryan; Brian L. Joiner; Jonathan D. Cryer, University of Iowa
Written by developers Barbara Ryan and Brian
Joiner, and professor of statistics Jon Cryer, this text
illustrates by example and step-by-step instruction
how to use MINITAB 16 to explore and analyze data.
The book introduces readers to MINITAB software
while understanding that they may also be learning
statistics at the same time. The authors illustrate how
statistical concepts apply to data analysis through
examples. More than 4,000 colleges, universities, and
high schools rely on MINITAB, making this software
the educational standard. Because MINITAB is the
leading package used in industry for quality and process
improvement, students who learn MINITAB in class will
• The sixth edition has been thoroughly updated to
reflect Release 16 of the MINITAB software.
• Those new to Windows and Minitab will find Chapter
1 helpful in introducing the menu interface and the
data management techniques available with projects.
• Chapter 2 introduces Session commands so that you
will be prepared to use these commands later in the
book when necessary.
• Chapter 3 introduces all Worksheet operations in
order to introduce students to the procedures they
will need later on to carry out such simple operations
as stacking columns or sorting data.
CONTENTS
1. INTRODUCTION TO MINITAB. Welcome to Minitab.
Working with Data. Working with Minitab Windows.
Working with Minitab Projects. The Project Manager.
2. SESSION COMMANDS. The Minitab Command
Language. Useful Session Commands. Working with
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77
the Contents of the Session Window. 3. WORKSHEET
OPERATIONS. Editing the Worksheet. Manipulating
Data. Importing and Exporting Data. 4. ONE-VARIABLE
GRAPHS AND SUMMARIES. Understanding Variable
Types. Exploring Graphs in Minitab. Creating Distribution
Plots 102. Histograms 111. Descriptive Statistics.
Creating a Boxplot. Summarizing and Displaying
Categorical Data. 5. TWO- AND THREE-VARIABLE
GRAPHS. Creating Scatterplots. Creating Plots with
Groups. Creating Plots with Several Variables. Graph
Editing and Annotation. Graphs with Three or More
Variables. Creating Time Series Plots. 6. STATISTICAL
DISTRIBUTIONS. The Normal Distribution. The Binomial
Distribution. Normal Approximation to the Binomial.
The Poisson Distribution. A Summary of the Theoretical
Distributions in Minitab 7. SIMULATION. Learning about
Randomness. Bernoulli Trials. Simulating Data from a
Normal Distribution. Simulating Data from the Binomial
Distribution. Sampling from Other Distributions.
Sampling Actual Populations. The Base for the Random
Number Generator. The Central Limit Theorem. 8. ONESAMPLE CONFIDENCE INTERVALS AND TESTS FOR
MEANS. How Sample Means Vary. Confidence Interval
for μ. Test of Hypothesis for μ. Confidence Intervals and
Tests for Proportions. Departures from Assumptions.
Hypothesis Tests with the Minitab Assistant 262 9.
COMPARING TWO MEANS: CONFIDENCE INTERVALS
AND TESTS. Paired and Independent Data. Difference
Between Two Means: Paired Data. Difference Between
Two Means. Two-Sample Procedures with the Minitab
Assistant . 10. ANALYSIS OF VARIANCE. Analysis of
Variance with One Factor. Analysis of Variance with
Two Factors. Randomized Block Designs. Residuals and
Fitted Values. 11. REGRESSION AND CORRELATION.
Correlation. Simple Regression: Fitting a Straight
Line. Making Inferences from Straight-Line Fits.
Multiple Regression. Fitting Polynomials. Interpreting
Residuals in Simple and Polynomial Regression. Using
Transformations. Plotting Regression Lines, Planes and
Surfaces. Regression with the Minitab Assistant. 12.
CHI-SQUARE TESTS AND CONTINGENCY. Chi-Square
Goodness-of-Fit Test. Contingency Tables. Making the
Table and Computing Chi-Square. Tables with Small
Expected Counts. 13. NONPARAMETRIC STATISTICS.
Sign Procedures. Wilcoxon Signed Rank Procedures.
Two-Sample Rank Procedures. Kruskal-Wallis Test.
14. CONTROL CHARTS. Control Charts and Process
78
Variation. Control Charts for Measurement Data.
Control Charts for Attribute Data. Control Charts
with the Minitab Assistant. 15. ADDITIONAL TOPICS
IN REGRESSION. Variable Selection in Regression.
Diagnostics in Regression. Additional Regression
Features. 16. ADDITIONAL TOPICS IN ANALYSIS OF
VARIANCE. Multiple Comparisons with One-Way.
Multifactor Balanced Designs. Unbalanced Designs.
Analysis of Covariance. Random Effects and Mixed
Models. 17. CREATING A STATISTICAL REPORT. Adding
Session Output to the ReportPad. Adding a Graph to
the ReportPad. Editing in ReportPad. Saving a Report.
Copying a Report to a Word Processor. 18. MINITAB
MACROS. Global Macros. Writing a Macro. Invoking
a Macro. Variable Column and Row Numbers. Control
Statements. Available Macros. Index.
PRELIMINARY EDITION OF STATISTICS
Learning from Data (with Printed Access Card for JMP)
Roxy Peck, California Polytechnic State University, San Luis Obispo
STATISTICS: LEARNING FROM DATA, by respected
and successful author Roxy Peck, resolves common
problems faced by both students and instructors
with an innovative approach to elementary statistics.
Instead of assuming that students will “pick it up
along the way,” Peck tackles the areas students
struggle with most--probability, hypothesis testing,
and selecting an appropriate method of analysis-unlike any text on the market. Probability coverage is
based on current research that shows how students
best learn the subject. Two unique chapters, one on
statistical inference and another on learning from
experiment data, address two common areas of student
confusion: choosing a particular inference method
and using inference methods with experimental
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data. Supported by learning objectives, real-data
examples and exercises, and technology notes, this
brand new text guides students in gaining conceptual
understanding, mechanical proficiency, and the ability
to put knowledge into practice.
FEATURES
• A New Approach to Probability: Research has
demonstrated how students develop an understanding
of probability and chance. Using natural frequencies
to reason about probability, especially conditional
probability, is much easier for students to understand.
The treatment of probability in this text is complete,
including conditional probability and Bayes’ Rule
type probability calculations. However, it’s done in a
way that eliminates the need for the symbolism and
formulas, which are a roadblock for many students.
• Chapter on Overview of Statistical Inference
(Chapter 7): This short chapter focuses on the things
students need to think about in order to select an
appropriate method of analysis. In most texts, these
considerations are “hidden” in the discussion that
occurs when a new method is introduced. Discussing
these considerations up front in the form of four key
questions that need to be answered before choosing
an inference method makes it easier for students to
make correct choices.
• An Organization That Reflects the Data Analysis
Process: Students are introduced early to the idea
that data analysis is a process that begins with
careful planning, followed by data collection, data
description using graphical and numerical summaries,
data analysis, and finally interpretation of results.
The ordering of topics in the textbook mirrors this
process: data collection, then data description, then
statistical inference.
• Inference for Proportions Before Inference for Means:
The book makes it possible to develop the concept
of a sampling distribution via simulation. Simulation
is simpler in the context of proportions, where it is
easy to construct a hypothetical population (versus
the more complicated context of means, which
requires assumptions about shape and spread.) In
addition, inferential procedures for proportions are
based on the normal distribution, allowing students
to focus on the new concepts of estimation and
hypothesis testing without having to grapple with
the introduction of the t distribution.
• Separate Treatment of Inference Based on Experiment
Data (Chapter 14): Many statistical studies involve
collecting data via experimentation. The same
inference procedures used to estimate or test
hypotheses about population parameters also are
used to estimate or test hypotheses about treatment
effects. However, the necessary assumptions are
slightly different (for example, random assignment
replaces the assumption of random selection), as
is the wording of conclusions. Treating both cases
together tends to confuse students; this text makes
the distinction clear.
• Chapter Learning Objectives--Keeping Students
Informed about Expectations: The learning objectives
explicitly state the expected student outcomes,
and are presented in three categories: Conceptual
Understanding, Mastery of Mechanics, and Putting
It into Practice.
• Preview--Motivation for Learning: Each chapter opens
with a Preview and Preview Example that provide
motivation for studying the concepts and methods
introduced in the chapter. They address why the
material is worth learning, provide the conceptual
foundation for the methods covered in the chapter,
and connect to what the student already knows. These
relevant and current examples provide a context in
which one or more questions are proposed for further
investigation. The context is revisited in the chapter
once students have the necessary understanding to
more fully address the questions posed.
• Real Data That Motivates and Engages: Examples
and exercises with overly simple settings don’t allow
students to practice interpreting results in real
situations. The exercises and examples are a particular
strength of this text. Most involve data extracted from
journal articles, newspapers, and other published
sources. They cover a wide range of disciplines and
subject areas of interest to today’s students, including,
among others, health and fitness, consumer research,
psychology and aging, environmental research, law
and criminal justice, and entertainment.
• Exercises Organized into Developmental Sets to
Structure the Out-of-Class Experience:
• End-of-section exercises are presented in two
developmental sets. The exercises in each set work
together to assess all of the learning objectives for
the section. Additional section exercises are included
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79
for those who want more practice.
CONTENTS
Section I: COLLECTING DATA. 1. Collecting Data in
Reasonable Ways. Statistical Studies: Observation
and Experimentation. Collecting Data: Planning an
Observational Study. Collecting Data: Planning an
Experiment. The Importance of Random Selection
and Random Assignment: What Types of Conclusions
are Reasonable? Section II: DESCRIBING DATA
DISTRIBUTIONS. 2. Graphical Methods for Describing
Data Distributions. Selecting an Appropriate Graphical
Display. Displaying Categorical Data: Bar Charts and
Comparative Bar Charts. Displaying Numerical Data:
Dotplots, Stem-and-Leaf Displays, and Histograms.
Displaying Bivariate Numerical Data: Scatterplots and
Time-Series Plots. Graphical Displays in the Media. 3.
Numerical Methods for Describing Data Distributions.
Selecting Appropriate Numerical Summaries. Describing
Center and Spread for Data Distributions that are
Approximately Symmetric. Describing Center and
Spread for Data Distributions that are Skewed or Have
Outliers. Summarizing a Data Set: Boxplots. Measures
of Relative Standing: z-scores and Percentiles. 4.
Describing Bivariate Numerical Data. Correlation. Linear
Regression: Fitting a Line to Bivariate Data. Assessing
the Fit of a Line. Describing Linear Relationships and
Making Predictions--Putting it all Together. Bonus
Material on Logistic Regression (Online). Section III: A
FOUNDATION FOR INFERENCE: REASONING ABOUT
PROBABILITY. 5. Probability. Interpreting Probabilities.
Computing Probabilities. Probabilities of More Complex
Events: Unions, Intersections and Complements.
Conditional Probability. Probability as a Basis for Making
Using Simulation (Optional) 6. Random Variables and
Probability Distributions. Random Variables. Probability
Distributions for Continuous Random Variables. The
The Normal Distribution. Checking for Normality. The
Binomial and Geometric Distributions (Optional). Using
the Normal Distribution to Approximate a Discrete
Distribution (Optional). Counting Rules, The Poisson
Distribution (Online). Section IV: LEARNING FROM
SAMPLE DATA. 7. An Overview of Statistical Inference-Learning from Data. Statistical Inference--What We Can
Learn From Data. Selecting an Appropriate Method-80
Four Key Questions. A Five-Step Process for Statistical
Inference. 8. Sampling Variability and Sampling
Distributions. Statistics and Sampling Variability. The
Sampling Distribution of a Sample Proportion. How
Sampling Distributions Support Learning From Data.
9. Estimating a Population Proportion. Selecting
an Estimator. Estimating a Population Proportion-Margin of Error. A Large-Sample Confidence Interval
for a Population Proportion. Choosing a Sample Size
to Achieve a Desired Margin of Error. 10. Asking and
Answering Questions about a Population Proportion.
Hypotheses and Possible Conclusions. Potential Errors
in Hypothesis Testing. The Logic of Hypothesis Testing-An Informal Example. A Procedure for Carrying Out a
Hypothesis Test. Large-Sample Hypothesis Tests for
a Population Proportion. 11. Asking and Answering
Questions about the Difference between Two Population
Proportions. Estimating the Difference between Two
Population Proportions. Testing Hypotheses about the
Difference between Two Population Proportions. 12.
Asking and Answering Questions about a Population
Mean. Sampling Distribution of the Sample Mean. A
Confidence Interval for a Population Mean. Testing
Hypotheses about a Population Mean. 13. Asking and
Answering Questions about the Difference between
Two Population Means. Testing Hypotheses about
the Difference between Two Population Means Using
Independent Samples. Testing Hypotheses about the
Difference between Two Population Means Using
Paired Samples. Estimating the Difference between
Two Population Means. Section V: ADDITIONAL
OPPORTUNITIES TO LEARN FROM DATA. 14. Learning
from Experiment Data. Variability and Random
Assignment. Testing Hypotheses about Differences
in Treatment Effects. Estimating a Difference in
Treatment Effects. 15. Learning from Categorical Data.
Chi-Square Tests for Univariate Categorical Data. Tests
for Homogeneity and Independence in a Two-Way
Table. 16. Understanding Relationships--Numerical Data
Part 2 (Online). The Simple Linear Regression Model.
Inferences Concerning the Slope of the Population
Regression Line. Checking Model Adequacy. 17. Asking
and Answering Questions about More Than Two
Means (Online). The Analysis of Variance--SingleFactor ANOVA and the F Test. Multiple Comparisons.
Appendix: ANOVA Computations.
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PROBABILITY AND STATISTICS FOR
ENGINEERING AND THE SCIENCES,
Jay L. Devore, California Polytechnic State University, San Luis Obispo
This market-leading text provides a comprehensive
introduction to probability and statistics for engineering
students in all specialties. Proven, accurate, and
lauded for its excellent examples, PROBABILITY AND
STATISTICS FOR ENGINEERING AND THE SCIENCES,
8e, International Edition evidences Jay Devore’s
reputation as an outstanding author and leader in the
academic community. Devore emphasizes concepts,
models, methodology, and applications as opposed to
rigorous mathematical development and derivations.
Aided by his lively and realistic examples, students go
beyond simply learning about statistics—they also learn
how to put statistical methods to use.
NEW TO THIS EDITION
• More than 40 new examples and 100 new problems
were carefully researched and written using the most
up-to-date real data.
• Chapter 1, “Overview and Descriptive Statistics,”
contains a new subsection on “The Scope of Modern
Statistics” that describes and exemplifies how
statistics is used in modern disciplines.
• A significantly revised and simplified Chapter 8,
“Tests of Hypotheses Based on a Single Sample,” also
has a new subsection entitled “More on Interpreting
P-values.”
• Wherever possible throughout the book, the language
has been tightened and simplified to improve clarity.
FEATURES
• Sample exams help students build confidence and
master concepts prior to taking class exams; the
glossary of symbols/acronyms, which includes text
page references, is another useful study aid.
• Exclusively from Cengage Learning, Enhanced
WebAssign® allows you to assign exercises from this
text and ensure that your students receive multimedia
tutorial support and immediate feedback as they
complete their automatically graded assignments.
• Several exercises refer to material covered in earlier
sections and chapters, allowing students to more
easily see the connections between concepts.
• Virtually every example and exercise has a real world
context. Real data in exercises and examples stimulate
students’ interest and enhance their comprehension
of concepts.
• Notable content includes a strong emphasis on
the role that variation plays in statistics, emphasis
on the nature of variation in the slope estimate in
simple linear regression, and inclusion of a detailed
description of pooled t procedures to provide a
balance between unpooled and pooled analyses.
• “Simulation Experiments” help students gain an
understanding of sampling distributions and the
insight they provide, especially when a derivation is
too complex to carry out.
CONTENTS
Contents 1. OVERVIEW AND DESCRIPTIVE STATISTICS.
Populations, Samples, and Processes. Pictorial and
Tabular Methods in Descriptive Statistics. Measures
of Location. Measures of Variability. Supplementary
Exercises . 2. PROBABILITY. Sample Spaces and Events.
Axioms, Interpretations, and Properties of Probability.
Counting Techniques. Conditional Probability.
Independence. Supplementary Exercises. 3. DISCRETE
RANDOM VARIABLES AND PROBABILITY. Random
Variables. Probability Distributions for Discrete Random
Variables. Expected Values. The Binomial Probability
Distribution. Hypergeometric and Negative Binomial
Distributions. The Poisson Probability Distribution.
Supplementary Exercises. 4. CONTINUOUS RANDOM
VARIABLES AND PROBABILITY DISTRIBUTIONS.
Probability Density Functions. Cumulative Distribution
Functions and Expected Values. The Normal
Distribution. The Exponential and Gamma Distributions.
Other Continuous Distributions. Probability Plots.
Supplementary Exercises. 5. JOINT PROBABILITY
DISTRIBUTIONS AND RANDOM SAMPLES. Jointly
Distributed Random Variables. Expected Values,
Covariance, and Correlation. Statistics and Their
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81
Distributions. The Distribution of the Sample Mean. The
Distribution of a Linear Combination. Supplementary
Exercises. 6. POINT ESTIMATION. Some General
Concepts of Point Estimation. Methods of Point
Estimation. Supplementary Exercises. 7. STATISTICAL
INTERVALS BASED ON A SINGLE SAMPLE. Basic
Properties of Confidence Intervals. Large-Sample
Confidence Intervals for a Population Mean and
Proportion. Intervals Based on a Normal Population
Distribution. Confidence Intervals for the Variance
and Standard Deviation of a Normal Population.
Supplementary Exercises. 8. TESTS OF HYPOTHESES
BASED ON A SINGLE SAMPLE. Hypotheses and
Test Procedures. Tests About a Population Mean.
Tests Concerning a Population Proportion. P-Values.
Some Comments on Selecting a Test. Supplementary
Exercises. 9. INFERENCES BASED ON TWO SAMPLES.
z Tests and Confidence Intervals for a Difference
Between Two Population Means. The Two-Sample
t Test and Confidence Interval. Analysis of Paired
Data. Inferences Concerning a Difference Between
Population Proportions. Inferences Concerning Two
Population Variances. Supplementary Exercises. 10.
THE ANALYSIS OF VARIANCE. Single-Factor ANOVA.
Multiple Comparisons in ANOVA. More on Single-Factor
ANOVA. Supplementary Exercises. 11. MULTIFACTOR
ANALYSIS OF VARIANCE. Two-Factor ANOVA with Kij =
1. Two-Factor ANOVA with Kij = 1. Three-Factor ANOVA.
2p Factorial Experiments. Supplementary Exercises. 12.
SIMPLE LINEAR REGRESSION AND CORRELATION.
The Simple Linear Regression Model. Estimating Model
Parameters. Inferences About the Slope Parameter
â1. Inferences Concerning µY-x* and the Prediction of
Future Y Values. Correlation. Supplementary Exercises.
13. NONLINEAR AND MULTIPLE REGRESSION.
Aptness of the Model and Model Checking. Regression
with Transformed Variables. Polynomial Regression.
Multiple Regression Analysis. Other Issues in Multiple
Regression. Supplementary Exercises. 14. GOODNESSOF-FIT TESTS AND CATEGORICAL DATA ANALYSIS.
Goodness-of-Fit Tests When Category Probabilities
Are Completely Specified. Goodness-of-Fit Tests for
Composite Hypotheses. Two-Way Contingency Tables.
Supplementary Exercises. 15. DISTRIBUTION-FREE
PROCEDURES. The Wilcoxon Signed-Rank Test. The
Wilcoxon Rank-Sum Test. Distribution-Free Confidence
Intervals. Distribution-Free ANOVA. Supplementary
82
Exercises. 16. QUALITY CONTROL METHODS. General
Comments on Control Charts. Control Charts for
Process Location. Control Charts for Process Variation.
Control Charts for Attributes. CUSUM Procedures.
Acceptance Sampling. Supplementary Exercises.
PROBABILITY AND STATISTICS
FOR ENGINEERS AND SCIENTISTS,
Anthony J. Hayter, Georgia Institute of Technology
PROBABILITY AND STATISTICS FOR ENGINEERS AND
SCIENTISTS, 4E, International Edition continues the
student-oriented approach that has made previous
editions successful. As a teacher and researcher at
a premier engineering school, author Tony Hayter
is in touch with engineers daily—and understands
their vocabulary. The result of this familiarity with the
professional community is a clear and readable writing
style that students understand and appreciate, as well
as high-interest, relevant examples and data sets that
keep students’ attention. A flexible approach to the
use of computer tools, including tips for using various
software packages, allows instructors to choose the
program that best suits their needs. At the same time,
substantial computer output (using MINITAB and other
programs) gives students the necessary practice in
interpreting output. Extensive use of examples and
data sets illustrates the importance of statistical data
collection and analysis for students in the fields of
aerospace, biochemical, civil, electrical, environmental,
industrial, mechanical, and textile engineering, as
well as for students in physics, chemistry, computing,
biology, management, and mathematics.
NEW TO THIS EDITION
• A new Guide of Statistical Methodologies can be used
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to match statistical inference methodologies to data
sets and research questions. It includes examples
that are typically used for each methodology. The
guide helps students address a common challenge—
determining which method or technique is best to use
with a particular type of problem.
• The Fourth Edition includes more than 200 new
and revised problems, with many new data sets
and interesting real world problems from every
engineering area. Students have raved about the true/
false questions in particular, commenting that they
provide a needed self-check opportunity.
• A new continuing case study, Internet Marketing,
begins before Chapter 1 and runs through Chapter 12.
This timely case study, on a topic relevant to students
in their daily lives, presents various problems and
shows how probability and statistics are used to
analyze and solve them. It also helps illustrate the
connections among topics within the first 12 chapters.
FEATURES
• Worked examples (77) and more than 150 data sets
represent the many different areas of engineering;
for instance, civil, mechanical, electrical, industrial,
aerospace, biomedical, textile, chemical, and
computing.
• Dozens of graphs, along with graphical tools, help
students learn concepts visually.
• To help students grasp concepts, each topic is
introduced with references to several real examples
from engineering and the sciences. After the topic
has been developed technically, a highlighted box
reinforces students’ learning by summarizing the
important points.
• Many examples illustrate proper application of new
methodologies, and are developed throughout the
chapters as increasingly sophisticated methodologies
are considered. This allows students to build on
their learning in a manageable way, and understand
connections among methodologies.
• Computer Note sections offer tips for using various
software packages to perform analysis of data sets,
which are referenced in the text and available for
download from the book’s website.
• The applied present ation emphasizes the
understanding of underlying concepts and the
application of statistical methodologies.
• Composition of the book allows flexibility in the order
in which the material is taught. The material has
been divided into four sections based on probability
(Chapters 1-5), basic statistics (Chapters 6-10),
advanced statistical methodologies (Chapters 11-14),
and additional topics (Chapters 15-17). The Preface
offers suggested paths that instructors may follow
based on topic preference, making the book ideal for
departments in which different methods of teaching
coexist.
CONTENTS
1. PROBABILITY THEORY. Probabilities. Events.
Combinations of Events. Conditional Probability.
Probabilities of Event Intersections. Posterior
Probabilities. Counting Techniques. 2. RANDOM
VARIABLES. Discrete Random Variables. Continuous
Random Variables. The Expectation of a Random
Variable. The Variance of a Random Variable. Jointly
Distributed Random Variables. Combinations
and Functions of Random Variables. 3. DISCRETE
PROBABILIT Y DISTRIBUTIONS. The Binomial
Distribution. The Geometric and Negative Binomial
Distributions. The Hypergeometric Distribution. The
Poisson Distribution. The Multinomial Distribution.
4. CONTINUOUS PROBABILITY DISTRIBUTIONS.
T h e Un i f o r m Di s t r i b u t i o n . T h e E x p o n e n t i a l
Distribution. The Gamma Distribution. The Weibull
Distribution. The Beta Distribution. 5. THE NORMAL
DISTRIBUTION. Probability Calculations Using the
Normal Distribution. Linear Combinations of Normal
Random Variables. Approximating Distributions with
the Normal Distribution. Distributions Related to the
Normal Distribution. 6. DESCRIPTIVE STATISTICS.
Experimentation. Data Presentation. Sample
Statistics. Examples. 7. STATISTICAL ESTIMATION
AND SAMPLING DISTRIBUTIONS. Point Estimates.
Properties of Point Estimates. Sampling Distributions.
Constructing Parameter Estimates. 8. INFERENCES
ON A POPULATION MEAN. Confidence Intervals.
Hypothesis Testing. Summary. 9. COMPARING TWO
POPULATION MEANS. Introduction. Analysis of Paired
Samples. Analysis of Independent Samples. Summary. 10.
DISCRETE DATA ANALYSIS. Inferences on a Population
Proportion. Comparing Two Population Proportions.
Goodness-of-Fit Tests for One-Way Contingency Tables.
Testing for Independence in Two-Way Contingency
Tables. 11. THE ANALYSIS OF VARIANCE. One Factor
Analysis of Variance. Randomized Block Designs. 12.
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83
SIMPLE LINEAR REGRESSION AND CORRELATION.
The Simple Linear Regression Model. Fitting the
Regression Line. Inferences on the Slope Parameter
ß1. Inferences on the Regression Line. Prediction
Intervals for Future Response Values. The Analysis
of Variance Tables. Residual Analysis. Variable
Transformation. Correlation Analysis. 13. MULTIPLE
LINEAR REGRESSION AND NONLINEAR REGRESSION.
Introduction to Multiple Linear Regression. Examples of
Multiple Linear Regression. Matrix Algebra Formulation
of Multiple Linear Regression. Evaluating Model
Adequacy. Nonlinear Regression. 14. MULTIFACTOR
EXPERIMENTAL DESIGN AND ANALYSIS. Experiments
with Two Factors. Experiments with Three or More
Factors. 15. NONPARAMETRIC STATISTICAL ANALYSIS.
The Analysis of a Single Population. Comparing Two
Populations. Comparing Three or More Populations.
16. QUALITY CONTROL METHODS. Introduction.
Statistical Process Control. Variable Control Charts.
Attribute Control Charts. Acceptance Sampling. 17.
RELIABILITY ANALYSIS AND LIFE TESTING. System
Reliability. Modeling Failure Rates. Life Testing. Tables.
Answers to Odd-Numbered Problems. Index.
PROBABILITY AND STATISTICS FOR
ENGINEERS, INTERNATIONAL EDITION,
5E
Richard L. Scheaffer, University of Florida, Emeritus; Madhuri Mulekar,
University of South Alabama; James T. McClave
PROBABILITY AND STATISTICS FOR ENGINEERS,
5e, International Edition provides a one-semester,
calculus-based introduction to engineering statistics
that focuses on making intelligent sense of real
engineering data and interpreting results. Traditional
topics are presented thorough an accessible modern
84
framework that emphasizes the statistical thinking,
data collection and analysis, decision-making, and
process improvement skills that engineers need on a
daily basis to solve real problems. The text continues
to be driven by its hallmark array of engineering
applications—thoroughly expanded and modernized
for the 5th edition—which tackle timely, interesting,
and illuminating scenarios that show students the rich
context behind the concepts. Within the presentation
of topics and applications the authors continually
develop students’ intuition for collecting their own real
data, analyzing it with the latest graphical tools, and
interpreting the results with a goal of improving quality
control and problem-solving process. Students will not
only gain solid understanding of concepts and their
real-life practicality, but will learn to become active
statistical practitioners for their own future careers.
NEW TO THIS EDITION
• The text’s hallmark applications are thoroughly
expanded and modernized with many new problems
covering additional engineering situations and more
timely, modern issues. New and refreshed problems
alike are backed by the most up-to-date available
real datasets.
• Data collection and analysis is given added emphasis,
with more examples and activities given to further
develop students’ intuition. A new chapter on
obtaining data introduces the common ways of
obtaining data in studies, discusses biases introduced
by inaccurately applied methods, and shows how
technology is used for data collection.
• Use of hand calculations has been further scaled back
in situations where computations are now more easily
accomplished using a computer. Software calculation
and analysis is given added emphasis throughout,
with updated graphs generated in statistical software
to better align with the use of technology and many
more visuals overall.
• Chapters have been thoroughly reorganized and
streamlined to better align with modern courses,
improve the overall readability and flow of topics,
and provide better consistency and intuitiveness with
how concepts introduced later in the book build on
concepts introduced earlier.
• New hands-on simulation activities have been added
providing deeper exploration of key concepts.
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FEATURES
• The text has a user-friendly and less formal empirical
approach, consistently stressing quality improvement
and real data collection and analysis to develop
students’ intuition for actively applying concepts to
their careers.
• Examples and Exercises strongly highlight real-data
engineering applications that capture the full depth
and breadth of societal issues that engineers and
scientists are called upon to solve.
• Graphical techniques such as stemplots, boxplots,
and scatterplots are emphasized to build data analysis
skills for identifying patterns in data and assessing the
underlying assumptions.
• Use of statistical software packages is encouraged for
easy handling of data when making inferences and
building models. Downloadable datasets are available
for all real data used in the book in native file formats
for the most popular software packages.
• All essential traditional engineering statistics topics
are covered, and presented concisely and to-thepoint to effectively fit within a one-semester course.
Instructors have flexibility to omit optional sections or
place added emphasis on some topics to suit different
course types and audiences.
CONTENTS
1. DATA COLLECTION AND EXPLORING UNIVARIATE
DISTRIBUTIONS Introduction. A model for problem
solving and its application. Types of data and frequency
distribution tables. Tools for describing data: Graphical
methods. Graphing Categorical Data. Graphing
Numerical Data. Visualizing distributions. Tool for
Describing Data: Numerical measures. Measures of
Center. Measures of Position. Measures of variation
(or spread). Reading Computer Printouts. The effect
of shifting and scaling of measurements on summary
measures. Summary Measures and Decisions. The
Empirical Rule. Standardized Values and z-scores.
Boxplots. Detecting Outliers. Summary. Supplemental
Exercises. 2. EXPLORING BIVARIATE DISTRIBUTIONS
AND ESTIMATING RELATIONS Introduction. Twoway table for categorical data. Time series analysis.
Scatterplots: Graphical analysis of association
between measurements. Correlation: Estimating the
strength of linear relation. Regression: Modeling
linear relationships. The Coefficient of Determination.
Residual Analysis: Assessing the adequacy of the
model. Transformations. Reading Computer Printout.
Summary. Supplemental Exercises. 3. OBTAINING
DATA. Introduction. Overview of methods of data
collection. Planning and Conducting Surveys. Planning
and Conducting Experiments. Completely Randomized
Design. Randomized Block Design. Planning and
Conducting an Observational Study. Summary.
Supplemental Exercises. 4. PROBABILITY. Introduction.
Sample space and relationships among events.
Definition of probability. Counting rules useful in
probability. Conditional probability and independence.
Rules of probability. Odds, odds ratios, and risk ratio.
Summary. Supplemental Exercises. 5. DISCRETE
PROBABILITY DISTRIBUTIONS. Introduction. Random
variables and their probability distributions Expected
values of random variables. The Bernoulli distribution.
The Binomial distribution. The Geometric and Negative
Binomial distributions. The Geometric distribution.
The Negative Binomial distribution. The Poisson
distribution. The hypergeometric distribution. The
Moment-Generating Function. Simulating probability
distributions. Summary. Supplementary Exercises.
6. CONTINUOUS PROBABILITY DISTRIBUTIONS.
Introduction. Continuous random variables and their
probability distributions. Expected values of continuous
random variables. The Uniform distribution. The
exponential distribution. The Gamma distribution. The
Normal distribution. The Lognormal Distribution. The
Beta distribution. The Weibull distribution. Reliability.
The Moment-generating Functions for Continuous
Random Variables. Simulating probability distributions.
Summary. Supplementary Exercises. 7. MULTIVARIATE
PROBABILITY DISTRIBUTIONS. Introduction. Bivariate
and Marginal Probability Distributions. Conditional
Probability Distributions. Independent Random
Variables. Expected Values of Functions of Random
Variables. The Multinomial Distribution. More on
the Moment-Generating Function. Conditional
Expectations. Compounding and Its Applications.
Summary. Supplementary Exercises. 8. STATISTICS,
SAMPLING DISTRIBUTIONS, AND CONTROL CHARTS.
Introduction. The sampling distributions. The sampling
distribution of X (General Distribution). The sampling
distribution of X (Normal Distribution). The sampling
distribution of sample proportion Y/n (Large sample).
The sampling distribution of S² (Normal Distribution).
Sampling Distributions: The multiple-sample case.
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85
The sampling distribution of (X1 – X2). The sampling
distribution of XD. The sampling distribution of
(^p1 – ^p2). The sampling distribution of S²1/S²2.
Control Charts. The X-Chart: Known μ. The X and
R-Charts: Unknown μ. The X and S-Charts: Unknown
μ. The p-Chart. The c-chart. The u-chart. Process
Capability. Summary. Supplementary Exercises. 9.
ESTIMATION. Introduction. Point estimators and their
properties. Confidence Intervals: The Single-Sample
Case. Confidence Interval for μ: General Distribution.
Confidence Interval for Mean: Normal Distribution.
Confidence Interval for Proportion: Large sample case.
Confidence interval. Confidence Intervals: The Multiple
Samples Case. Confidence Interval for Linear Functions
of Means: General Distributions. Confidence Interval
for Linear Functions of Means: Normal Distributions.
Large Samples Confidence Intervals for Linear Functions
of Proportions. Normal distribution case. Prediction
Intervals. Tolerance Intervals. The Method of Maximum
Likelihood. Bayes Estimators. Summary. Supplementary
Exercises. 10. HYPOTHESIS TESTING. Introduction.
Terminology of Hypothesis Testing. Hypothesis
Testing: The Single-Sample Case. Testing for Mean:
General Distributions Case. Testing a Mean: Normal
distribution Case. Testing for Proportion: Large
Sample Case. Testing for Variance: Normal Distribution
Case. Hypothesis Testing: The Multiple-Sample Case.
Testing the Difference between Two means: General
Distributions Case. Testing the Difference between
Two means: Normal Distributions case. Testing the
difference between the means for paired samples.
Testing the ratio of variances: Normal distributions
case. Tests on Frequency data. Testing parameters
of the multinomial distribution. Testing equality
among Binomial parameters. Test of Independence.
Goodness of Fit Tests. Test. Kolmogorov-Smirnov
test. Using Computer Programs to Fit Distributions.
Acceptance Sampling. Acceptance Sampling by
Attributes. Acceptance Sampling by Variables.
Summary. Supplementary Exercises. 11. ESTIMATION
AND INFERENCE FOR REGRESSION PARAMETERS.
Introduction. Regression models with one predictor
variable. The probability distribution of random error
component. Making inferences about slope. Estimating
slope using a confidence interval. Testing a hypothesis
about slope. Connection between inference for slope
and correlation coefficient. Using the simple linear
86
model for estimation and prediction. Multiple regression
analysis. Fitting the model: The least-squares approach.
Estimation of error variance. Inferences in multiple
regression. A test of model adequacy. Estimating
and testing hypothesis about individual parameters.
Using the multiple regression model for estimation
and prediction. Model building: A test for portion of
a model. Other regression models. Response surface
method. Modeling a time trend. Logistic regression.
Checking conditions and some pitfalls. Checking
conditions. Some pitfalls. Reading printouts. Summary.
Supplemental Exercises. 12. ANALYSIS OF VARIANCE.
Introduction. Review of Designed Experiments. Analysis
of Variance (ANOVA) Technique. Analysis of Variance
for Completely Randomized Design. Relationship
of ANOVA for CRD with a t test and Regression.
Equivalence between a t test and an F test for CRD with
2 treatments. ANOVA for CRD and Regression Analysis.
Estimation for Completely randomized design. Analysis
of Variance for the Randomized Block Design. ANOVA
for RBD. Relation between a Paired t test and an F test
for RBD. ANOVA for RBD and Regression Analysis.
Bonferroni Method for Estimation for RBD. Factorial
Experiments. Analysis of variance for the Factorial
Experiment. Fitting Higher Order Models. Summary.
Supplemental Exercises. APPENDIX. REFERENCES.
PROBABILITY FOR ENGINEERING,
MATHEMATICS, AND SCIENCE,
INTERNATIONAL EDITION
Chris P. Tsokos, University of South Florida
This text blends theory and applications, reinforcing
concepts with practical real-world examples that
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illustrate the importance of probability to undergraduate
students who will use it in their subsequent courses and
careers. The author emphasizes the study of probability
distributions that characterize random variables,
because this knowledge is essential in performing
parametric statistical analysis. Explanations include the
“why” as well as the “how” of probability distributions
for random variables to help engage students and
further promote their understanding. In addition, the
text includes a self-contained chapter on finite Markov
chains, which introduces the basic aspects of Markov
chains and illustrates their usefulness with several real
examples.
FEATURES
• This text offers a clear and illustrative presentation of
the fundamental aspects of probability, focusing on
both its meaning and its usefulness.
• Throughout the text, topics are developed using a
step-by-step approach and clarified by graphical
illustrations.
• New concepts are illustrated and interpreted both
graphically and analytically, addressing students’
different learning styles and promoting their
understanding by showing them that a problem can
be solved in multiple ways.
• Boxes highlight important concepts, results, and the
probability distributions, assisting students in study
and review.
• The author places special emphasis on the
development of the classical discrete and continuous
probability distributions, their properties, and their
interdisciplinary relevance to real life problems in
our global society.
• The numerous exercises include items that test
theoretical understanding as well as applications
drawn from several areas in engineering, mathematics,
and the sciences.
• A self-contained chapter on discrete finite Markov
chains includes several applications that demonstrate
their relevance as well as step-by-step development
of the theory, clearly illustrated by state and tree
diagrams.
• An extensive review of necessary mathematics that
may not be covered in a basic calculus course—such
as set theory, computational methods, binomial
and multinomial theorems, matrices, Jacobians,
and gamma and beta functions—is presented in an
appendix for the convenience of the student.
CONTENTS
1. PROBABILITY. Definition of Probability. Axiomatic
Definition of Probability. Conditional Probability.
Marginal Probabilities. Bayes’ Theorem. Independent
Events. Combinatorial Probability. 2. DISCRETE
PROBABILITY DISTRIBUTIONS. Discrete Probability
Density Function. Cumulative Distribution Function. The
Point Binomial Distribution. The Binomial Probability
Distribution. The Poisson Probability Distribution.
The Hypergeometric Probability Distribution. The
Geometric Probability Distribution. The Negative
Binomial Probability Distribution. 3. PROBABILITY
DISTRIBUTIONS OF CONTINUOUS RANDOM
VARIABLES. Continuous Random Variable and
Probability Density Function. Cumulative Distribution
Function of a Continuous Random Variable. The
Continuous Probability Distributions. 4. FUNCTIONS
OF A RANDOM VARIABLE. Introduction. Distribution
of a Continuous Function of a Discrete Random
Variable. Distribution of a Continuous Function of a
Continuous Random Variable. Other Types of Derived
Distributions. 5. EXPECTED VALUES, MOMENTS AND
MOMENT GENERATING FUNCTIONS. Mathematical
Expectation. Properties of Expectation. Moments.
Moment Generating Function. 6. TWO RANDOM
VARIABLES. Joint Probability Density Function. Bivariate
Cumulative Distribution Function. Marginal Probability
Distributions. Conditional Probability Density and
Cumulative Distribution Functions. Independent
Random Variables. Function of Two Random Variables.
Expected Value and Moments. Conditional Expectation.
Bivariate Normal Distribution. 7. SEQUENCE OF
RANDOM VARIABLES. Multivariate Probability Density
Functions. Multivariate Cumulative Distribution
Functions. Marginal Probability Distributions.
Conditional Probability Density and Cumulative
Distribution Functions. Sequence of Independent
Random Variables. Functions of Random Variables.
Expected Value and Moments. Conditional Expectation.
8. LIMIT THEOREMS. Chebyshev’s Inequality. Bernoulli’s
Law of Large Numbers. Weak and Strong Laws of Large
Numbers. The Central Limit Theorem. The DeMoivreLaplace Theorem. Normal Approximation to the
Poisson Distribution. Normal Approximation to the
Gamma Distribution. 9. FINITE MARKOV CHAINS. Basic
Concepts. N-Step Transitions Problems. Evaluation of
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87
Pn. Classification of States. Appendix. Index.
STAT 2 (WITH REVIEW CARDS AND
STATISTICS COURSEMATE PRINTED
ACCESS CARD), 2E
Created through a “student-tested, faculty-approved”
review process, STAT 2 is an engaging and accessible
solution to accommodate the diverse lifestyles of
today’s learners at a value-based price. This concise
STAT 2 includes all of the key concepts that you require
and a full suite of learning aids--including chapter-bychapter self-quizzes and downloadable flash cards--and
a wealth of online teaching and learning resources.
NEW TO THIS EDITION
• Many new real-world examples that students can
relate to have been added, such as Teens using
Cell Phones in Class, Busy Behind the Wheel, What
Employers Look for in Seasonal Employees, and
Polishing a Microchip.
• New problems have been added at the end of each
text chapter, as well as online.
• Updated content throughout includes the use of
ogives in Chapter 2 and a new method of finding area
in Chapter 9.
• New Key Terms have been added to many of the
chapters. Designed to be easy to spot, these key
terms come in handy when students are reviewing
the material or preparing for quizzes.
• Some of the sections have been reorganized,
improving clarity and concept sequencing.
• Chapter 4 includes new equations of properties of
probability numbers, and an expanded Notes section
in the discussion of probability numbers.
88
• Chapter 6 includes a new section on common z
values.Specifically, the chapter has been completely
rewritten to present the Standard Normal Distribution
utilizing the cumulative area approach, incorporating
a more intuitive idea with respect to the total area
under a curve and following more closely the format
used with calculators and statistical software.
• Chapter 7 includes a new discussion about the census.
• Interested in a simple way to complement your
text and course content with study and practice
materials? Cengage Learning’s CourseMate brings
course concepts to life with interactive learning,
study, and exam preparation tools that support the
printed textbook. Watch student comprehension soar
as your class works with the printed textbook and the
textbook-specific website.
• Available with this text, Aplia™ is an online interactive
learning solution that improves comprehension
and outcomes by increasing student effort and
engagement. Founded by a professor to enhance his
own courses, Aplia provides automatically graded
assignments with detailed, immediate explanations
on every question, and innovative teaching materials.
Our easy-to-use system has been used by more than
1,000,000 students at over 1800 institutions.
CONTENTS
Statistics.
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STATISTICAL METHODS FOR ENGINEERS,
G. Geoffrey Vining, Virginia Polytechnic Institute and State University;
Scott Kowalski, Minitab, Inc.
STATISTICAL METHODS FOR ENGINEERS, 3e,
International Edition offers a balanced, streamlined
one-semester introduction to Engineering Statistics
that emphasizes the statistical tools most needed by
practicing engineers. Using real engineering problems
with real data based on actual journals and consulting
experience in the field, students see how statistics fits
within the methods of engineering problem solving.
The text teaches students how to think like an engineer
at analyzing real data and planning a project the same
way they will in their careers. Case studies simulate
problems students will encounter professionally and
tackle on long-term job projects. The presentation
makes extensive use of graphical analysis, and use of
statistical software is encouraged for problem-solving
to illustrate how engineers rely on computers for
data analysis. The authors relate their own extensive
professional experience as engineers in short margin
notes called Voice of Experience that lend valuable
context to how students will apply concepts in the
field and why they’re important to learn. And a rich
companion website provides hours of multimedia
lecture presentation narrated by the authors to show
the material related live by different voices, simulating
how students will listen and learn from multiple
colleagues in their jobs. A flexible organization allows
instructors to emphasize the topics they need and
cater the presentation to different engineering majors
in their courses.
NEW TO THIS EDITION
• Innovative new multimedia lecture presentations
on the companion website present a full semester’s
course with audio lectures delivered by the authors
and backed by slides that summarize key concepts.
These presentations provide a different source for
in-class lectures and self-study, and give valuable
additional help to students who need more guidance.
• The chapter on Control Charts and Statistical
Process Control contains expanded coverage of
process capability and a new formal section on
process capability indices in industry. New coverage
of measurement systems analysis, introduction to
the concept of reliability for life time data, and BoxBehnken design have been added.
• Exercises throughout the text are refreshed to include
the latest data, with new exercises added where
appropriate to further emphasize real data.
• Additional Voice of Experience margin notes are
included to enhance the utility of this popular feature.
FEATURES
• Most examples and exercises in the text use real
engineering data taken from actual engineering
journals and consulting experience, emphasizing
good data analysis in specific engineering settings.
Whenever possible, the text shows the full engineering
context of these problems.
• Voice of Experience margin notes provide snippets
from the authors’ own experience as engineers about
the proper application of statistics within engineering,
lending students important career-related context to
the material.
• The text relies on the computer to do calculations,
emphasizing the computer-based data analysis
students will do in their careers. The book is
independent of any specific software package and can
be used with Microsoft® Excel, MINITAB®, JMP®,
STATA®, and others.
• Well over 500 exercises at the end of each section
provide extensive practice that test students’ general
understanding of concepts and procedures, with focus
on analyzing real engineering data. Separate sets of
computer exercises require statistical software for
computer practice on applying concepts, performing
calculations, interpreting results, and doing deeper
analysis of real data.
• An instructive engineering case study concludes each
chapter, designed to illustrate the complex statistical
decisions that need to be made to maintain the
quality, consistency, and effectiveness of the process.
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89
The chapters also present ideas for small student
projects complementing homework assignments,
designed to simulate the way real engineers are asked
to plan, execute, and interpret experiments.
CONTENTS
1. ENGINEERING METHOD AND DATA COLLECTION.
Need for Statistical Methods in Engineering. Engineering
Method and Statistical Thinking. Statistical Thinking and
Structured Problem Solving. Models. Obtaining Data.
Sampling. Basic Principles of Experimental Design.
Examples of Engineering Experiments. Purpose of
Engineering Statistics. Case Study: Manufacture of
Writing Instruments. Ideas for Projects References. 2.
DATA DISPLAYS. Importance of Data Displays. Stemand-Leaf Displays. Boxplots. Using Computer Software.
Using Boxplots to Analyze Designed Experiments. Case
Study. Need for Probability and Distributions. Ideas
for Projects References. 3. MODELING RANDOM
BEHAVIOR. Probability. Random Variables and
Distributions. Discrete Random Variables. Continuous
Random Variables. The Normal Distribution. Random
Behavior of Means. Random Behavior of Means When
the Variance Is Unknown. Normal Approximation to
the Binomial. The Weibull Distribution for Reliability
Applications. Case Study References. 4. ESTIMATION
AND TESTING. Estimation. Hypothesis Testing.
Inference for a Single Mean. Inference for a Single
Proportion. Inference for Two Independent Samples. The
Paired t-Test. Inference for Two Proportions. Inference
for Variances. Transformations and Nonparametric
Analyses. Case Study. Ideas for Projects References.
5. CONTROL CHARTS AND STATISTICAL PROCESS
CONTROL. Overview. Specification Limits. X- and
R-Charts. X- and s²-Charts. X-Chart. np-Chart. c-Chart.
Average Run Lengths. Standard Control Charts with
Runs Rules. CUSUM and EWMA Charts. Basic Process
Capability Indices. The SPC Approach to Gage R Studies.
Case Study. Ideas for Projects References. 6. LINEAR
REGRESSION ANALYSIS. Relationships Among Data.
Simple Linear Regression. Multiple Linear Regression.
Residual Analysis. Collinearity Diagnostics. Case Study.
Ideas for Projects References. 7. INTRODUCTION
TO 2k FACTORIAL-BASED EXPERIMENTS. The 2²
Factorial Design. The 2k Factorial Design. Fractions of
the 2k Factorial Design. Case Study. Ideas for Projects
References. 8. INTRODUCTION TO RESPONSE
SURFACE METHODOLOGY. Sequential Philosophy
90
of Experimentation. Central Composite Designs. BoxBehnken Designs. Multiple Responses. Experimental
Designs for Quality Improvement. Case Study. Ideas
for Projects References. 9. CODA. The Themes of
This Course. Integrating the Themes. Statistics and
Engineering. Appendix. Tables.
STATISTICS, 7E
The Exploration & Analysis of Data
Roxy Peck, California Polytechnic State University, San Luis Obispo; Jay
Roxy Peck and Jay Devore’s STATISTICS: THE
EXPLORATION AND ANALYSIS OF DATA, 7th Edition
concepts and cement their comprehension. Hands-on
practice statistics firsthand.
NEW TO THIS EDITION
concepts in a realistic context. In addition, more
of the exercises specifically ask students to write
(for example, by requiring students to explain
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•
•
•
•
•
their reasoning, interpret results, and comment on
important features of an analysis).
Examples and exercises using data sets that can be
them.
More than 90 exercises have video solutions,
assignments.
FEATURES
• An optional section on logistic regression is included
in Chapter 5, “Summarizing Bivariate Data,” for those
who would like more complete coverage of data
analysis techniques for categorical data.
• Advanced topics that are often omitted in a onequarter or one-semester course, such as inference
and variable selection methods in multiple regression
and analysis of variance for randomized block and
two-factor designs, are available online at the book’s
• “Interpreting and Communicating the Results of
Statistical Analysis” sections, which emphasize the
importance of being able to interpret statistical
output and communicate its meaning to nonstatisticians, now have assignable end-of-section
questions associated with them.
CONTENTS
6. PROBABILITY. Interpreting Probabilities and Basic
Probability Rules. Probability as a Basis for Making
by Using Simulation. 7. RANDOM VARIABLES AND
PROBABILITY DISTRIBUTIONS. Describing the
Distribution of Values in a Population. Population
Models for Continuous Numerical Variables. Normal
Transformations. 8. SAMPLING VARIABILITY AND
SAMPLING DISTRIBUTION. Statistics and Sampling
Variability. The Sampling Distribution of a Sample Mean.
The Sampling Distribution of a Sample Proportion.
9. ESTIMATION USING A SINGLE SAMPLE. Point
Estimation. Large-Sample Confidence Interval for
a Population Proportion. Confidence Interval for a
Population Mean. Interpreting and Communicating
the Results of Statistical Analyses. 10. HYPOTHESIS
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TESTING USING A SINGLE SAMPLE. Hypotheses
and Test Procedures. Errors in Hypotheses Testing.
Large-Sample Hypothesis Tests for a Population
Regression Models. Fitting a Model and Assessing Its
Utility. Inferences Based on an Estimated Model (online).
Other Issues in Multiple Regression (online). Interpreting
(online). 15. ANALYSIS OF VARIANCE. Single-Factor
ANOVA and the F Test. Multiple Comparisons.
92
SCIENCES, 9E
TestBank, and more.
NEW TO THIS EDITION
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output.
10, and 11.
sets of data.
FEATURES
25 to 30 problems.
errors.
CONTENTS
4E
Robert F. Heckard, Pennsylvania State University; Jessica M. Utts,
University of California, Irvine
STATISTICS, 4e, International Edition emphasizes the
conceptual development of statistical ideas and the
importance of looking for—and finding—meaning in
data. Authors Jessica Utts and Robert Heckard actively
engage students’ natural curiosity, motivating them
with intriguing questions and explaining statistical
topics in the context of a wide range of interesting,
useful examples and case studies. Throughout, the
authors balance the promotion of statistical literacy
with coverage of the statistical methodology taught
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students actively ask questions and find the answers
for themselves. More than any other text available,
STATISTICS, 4e, International Edition motivates and
encourages students to develop their statistical
intuition by focusing on analyzing data and interpreting
results, rather than on mathematical formulation.
CourseSmart goes beyond traditional expectations–
providing instant, online access to the textbooks
and course materials you need and at a lower cost to
your students. To request an electronic sample of this
Cengage Learning title, go to: www.coursesmart.com/
instructors.
NEW TO THIS EDITION
data.
• New topics in this edition include multiple comparisons
• The exercise sets have been significantly reorganized
• The data in the problem sets, examples, and case
data sets.
• A greater number of In Summary boxes are placed
• Aplia™ is available with the new edition. This online
94
FEATURES
CONTENTS
www.cengageasia.com
× 2 Table. 5. SAMPLING: SURVEYS AND HOW TO ASK
× 2 Tables. Testing Hypotheses About One Categorical
to the Wise.
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10E
STATISTICS, 10e, International Edition is a thorough,
yet accessible program designed to help students
overcome their apprehensions about statistics. The
authors provide clear guidance and informal advice
while showing students the links between statistics
and the world. To reinforce this approach, the book
integrates real-life data from a variety of sources,
including journals, periodicals, newspapers, and the
Internet. The Tenth Edition continues to address the
importance of developing students’ critical thinking
and statistical literacy skills through special features
and exercises throughout the text. The use of graphing
calculators, Excel®, MINITAB®, and SPSS® is covered
though not required. Extensive technology resources
include an algorithmic test bank and lecture slides,
along with interactive online resources and a marketleading DVD series designed to provide reinforcement
for students and support for instructors.
NEW TO THIS EDITION
96
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and Regression.”
FEATURES
assignments.
CONTENTS
www.cengageasia.com
Probability? Some Probability Rules—Compound Events.
Review Problems: Chapters 4-6. 7. ESTIMATION.
Focus Problem: The Trouble Is Unknown.Is Known.
Estimating µ When with Wood Ducks. Estimating
µ When Estimating p in the Binomial Distribution.
Estimating µ 1-µ 2 and p1-p2. 8. HYPOTHESIS TESTING.
Focus Problem: Benford’s Law: The Importance of Being
Contains worked solutions to all odd-numbered
excercises in the text.
www.cengageasia.com
97
DEVORE/FARNUM/DOI’S APPLIED
STATISTICS FOR ENGINEERS AND
SCIENTISTS, 3RD, 3E
Jay L. Devore, California Polytechnic State University, San Luis Obispo;
Nicholas R. Farnum, California State University, Fullerton; Jimmy A.
Doi, California Polytechnic State University, San Luis Obispo
PECK/OLSEN/DEVORE’S INTRODUCTION
TO STATISTICS AND DATA ANALYSIS,
3RD, 3E
Contains fully worked-out solutions to all of the oddnumbered exercises in the text, giving students a way
to check their answers and ensure that they took the
correct steps to arrive at an answer.
Check your work-and your understanding-with this
manual, which provides worked-out solutions to the
odd-numbered problems in the text
MENDENHALL/BEAVER/BEAVER’S
STATISTICS, 14TH, 14E
PECK’S STATISTICS
and improve your grade! This manual provides workedout, step-by-step solutions to selected problems in the
text. This gives you the information you need to truly
understand how these problems are solved.
and improve your grade! This manual provides workedout, step-by-step solutions to the odd-numbered
problems in the text, giving you the information you
need to truly understand how these problems are
solved.
Roxy Peck, California Polytechnic State University, San Luis Obispo
98
www.cengageasia.com
STUDY GUIDE FOR GRAVETTER/
WALLNAU’S STATISTICS FOR THE
BEHAVIORAL SCIENCES, 8TH, 8E
UNDERSTANDABLE STATISTICS, 10E
Concepts and Methods
TECHNOLOGY GUIDE EXCEL® FOR
UNDERSTANDABLE STATISTICS: CONCEPTS AND
METHODS, Tenth Edition, is a thorough, yet accessible
program designed to help students overcome their
apprehensions about statistics. The authors provide
clear guidance and informal advice while showing
students the links between statistics and the world.
To reinforce this approach, the book integrates reallife data from a variety of sources, including journals,
periodicals, newspapers, and the Internet. The Tenth
Edition continues to address the importance of
developing students’ critical thinking and statistical
literacy skills through special features and exercises
throughout the text. The use of graphing calculators,
Excel®, MINITAB®, and SPSS® is covered though
not required. Extensive technology resources include
an algorithmic test bank and lecture slides, along with
interactive online resources and a market-leading DVD
series designed to provide reinforcement for students
and support for instructors.
NEW TO THIS EDITION
Technology Guide for Excel provides basic instruction
and examples to help students use this program
effectively. This guide can serve as a resource for
students using the calculator on assignments out of
class. The Fifth Edition Technology Guide is written
for Microsoft® Excel® 2007, but it includes notes for
users of Excel 2003. Users of both Excel 2003 and 2007
can use this guide effectively.
www.cengageasia.com
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and Regression.”
assignments.
CONTENTS
100
Probability? Some Probability Rules--Compound Events.
Problem: The Trouble with Wood Ducks. Estimating
µ When Is Known. Estimating µ When Is Unknown.
Estimating p in the Binomial Distribution. Estimating
µ 1-µ 2 and p1-p2. 8. HYPOTHESIS TESTING. Focus
Problem: Benford’s Law: The Importance of Being
to What? The Sign Test for Matched Pairs. The Rank-
www.cengageasia.com
Sum Test. Spearman Rank Correlation. Runs Test for
flexible teaching solutions and value for students
and instructors, these new hybrid versions feature
the instructional presentation found in the printed
text while delivering end-of section exercises online
in Aplia™. The result—a briefer printed text that
engages students online! Help students overcome
their apprehension about statistics Brase and Brase’s
UNDERSTANDING BASIC STATISTICS, Sixth Edition. A
condensed and more streamlined version of the same
authors’ bestselling UNDERSTANDABLE STATISTICS,
Tenth Edition, this book offers instructors an effective
way to teach the essentials of statistics, including
early coverage of regression, within a more limited
time frame. Thorough yet abbreviated, the text
helps students realize the real-world significance of
statistics, and offers an accessible exposition and a
new, complete technology package. The Sixth Edition
addresses the growing importance of developing
students’ critical thinking and statistical literacy skills
with the introduction of new features and exercises
throughout the text. The use of the graphing calculator,
Microsoft® Excel®, MINITAB®, and SPSS® is covered
but not required.
NEW TO THIS EDITION
UNDERSTANDABLE STATISTICS, AP*
EDITION, 10E
UNDERSTANDING BASIC STATISTICS,
HYBRID (WITH APLIA PRINTED ACCESS
CARD), 6E
Reflecting Cengage Learning’s commitment to offering
• NEW! Hybrid versions of the text provide a lessexpensive, shorter option for students. A selection
of end-of-section exercises are omitted from the
text and appear online only. The hybrid edition also
is packaged with Aplia™.
• Available with access to Aplia™ online homework
system. Aplia helps students improve comprehension—
and their grade—by integrating a variety of mediums
and tools such as video, tutorials, practice tests, and an
interactive eBook. Created by a professor to enhance
his own courses, Aplia™ provides automatically
graded assignments with detailed, immediate
feedback on every question, and innovative teaching
materials. More than 1,000,000 students at over 1,800
institutions have used Aplia™.
• NEW Complement your text and course content with
study and practice materials. Cengage Learning’s
Statistics CourseMate brings course concepts to life
with interactive learning, study, and exam preparation
tools that support the printed textbook. Watch
student comprehension soar as your class works
with the printed textbook and the textbook-specific
website. Statistics CourseMate goes beyond the book
to deliver what you need!
www.cengageasia.com
101
• NEW More than 200 new and revised problems
that feature basic computations, interpretation,
and reinforce statistical literacy. Basic computation
problems focus student attention on relevant formulas,
requirements, and computational procedures. After
practicing these skills, students are more confident
as they approach real-world applications.
• NEW The Looking Forward feature shows students
where the presented material will be used later and
motivates them to pay extra attention to key topics.
• NEW Interpretation problems ask students to explain
the meaning of statistical results in the context of a
particular application.
• NEW Expand Your Knowledge problems present the
geometric mean and the harmonic mean (Section 3.1),
moving averages (Section 3.2), correlation of averages
(Section 4.2), odds for and odds against (Section 5.1),
uniform probability distribution (Section 7.1), plus
four method for confidence intervals (Section 8.3),
probability of two confidence intervals (Chapter 8
review), and serial correlation (Section 11.4).
• NEW CONTENT: Section 2.1 now includes a discussion
of cumulative frequency and ogives.Problem sets in
Sections 3.1 and 3.2 provide problems discussing the
effects of data transformation (adding a constant to
each data value or multiplying each data value by a
constant) on the mean, median, mode, and standard
deviation.Examples in Probability (Chapter 5) have
been revised to incorporate sample spaces that are
small and easy for students to visualize.
• REVISED Tech Notes throughout the text give students
helpful hints on using TI-84 Plus and TI-Nspire (with
84 Plus keypad) and TI-83 calculators, Microsoft Excel
2010, and MINITAB to solve a problem. They include
display screens to help students visualize and better
understand the solution.
• REVISED Using Technology instructions are available
at the end of each chapter in the Using Technology
section. Problems are presented with real-world data
from a variety of disciplines that can be solved by
using TI-84 Plus, TI-Nspire (with 84 Plus keypad) and
TI-83 Plus calculators, Microsoft Excel 2010, MINITAB,
and SPSS.
Displays. 3. AVERAGES AND VARIATION. Measures
of Central Tendency: Mode, Median, and Mean.
Measures of Variation. Percentiles and Box-and-Whisker
Plots. 4. CORRELATION AND REGRESSION. Scatter
Diagrams and Linear Correlation. Linear Regression
and the Coefficient of Determination. 5. ELEMENTARY
PROBABILITY THEORY. What is Probability? Some
Probability Rules—Compound Events. Trees and
Counting Techniques. 6. THE BINOMIAL PROBABILITY
DISTRIBUTION AND RELATED TOPICS. Introduction
to Random Variables and Probability Distributions.
Binomial Probabilities. Additional Properties of the
Binomial Distribution. 7. NORMAL CURVES AND
SAMPLING DISTRIBUTIONS. Graphs and Normal
Probability Distributions. Standard Units and Areas
Under the Standard Normal Distribution. Areas Under
Any Normal Curve. Sampling Distributions. The Central
Limit Theorem. Normal Approximation to the Binomial
Distribution and to p^ Distribution. 8. ESTIMATION.
Estimating µ When Is Known. Estimating µ When is
Unknown. Estimating p in a Binomial Distribution. 9.
HYPOTHESIS TESTING. Introduction to Statistical
Tests. Testing the Mean µ. Testing a Proportion p. 10.
INFERENCES ABOUT DIFFERENCES. Tests Involving
Paired Differences (Dependent Samples). Inferences
About the Difference of Two Means µ1 – µ2. Inferences
About the Difference of Two Proportions p1 – p2.
11. ADDITIONAL TOPICS USING INFERENCE. ChiSquare: Tests of Independence and of Homogeneity.
Chi-Square: Goodness of Fit. Testing and Estimating a
Single Variance or Standard Deviation. Inferences for
Correlation and Regression.
CONTENTS
1. GETTING STARTED. What is Statistics? Random
Samples. Introduction to Experimental Design.
2. ORGANIZING DATA. Frequency Distributions,
102
UNDERSTANDING BASIC STATISTICS,
www.cengageasia.com
Help students overcome their apprehension about
statistics Brase and Brase’s UNDERSTANDING BASIC
STATISTICS, 6E, International Edition. A condensed
and more streamlined version of the same authors’
bestselling STATISTICS, 10E, International Edition this
book offers instructors an effective way to teach the
essentials of statistics, including early coverage of
regression, within a more limited time frame. Thorough
yet abbreviated, the text helps students realize the
real-world significance of statistics, and offers an
accessible exposition and a new, complete technology
package. The UNDERSTANDING BASIC STATISTICS, 6E,
International Edition addresses the growing importance
of developing students’ critical thinking and statistical
literacy skills with the introduction of new features and
exercises throughout the text. The use of the graphing
calculator, Microsoft® Excel®, MINITAB®, and SPSS
is covered but not required.
•
•
•
•
NEW TO THIS EDITION
• NEW Complement your text and course content with
study and practice materials. Cengage Learning’s
Statistics CourseMate brings course concepts to life
with interactive learning, study, and exam preparation
tools that support the printed textbook. Watch
student comprehension soar as your class works
with the printed textbook and the textbook-specific
website. Statistics CourseMate goes beyond the book
to deliver what you need!
• NEW More than 200 new and revised problems
that feature basic computations, interpretation,
and reinforce statistical literacy. Basic computation
problems focus student attention on relevant formulas,
requirements, and computational procedures. After
practicing these skills, students are more confident
as they approach real-world applications.
• NEW The Looking Forward feature shows students
where the presented material will be used later and
motivates them to pay extra attention to key topics.
• NEW Interpretation problems ask students to explain
the meaning of statistical results in the context of a
particular application.
• NEW Expand Your Knowledge problems present the
geometric mean and the harmonic mean (Section 3.1),
moving averages (Section 3.2), correlation of averages
(Section 4.2), odds for and odds against (Section 5.1),
uniform probability distribution (Section 7.1), plus
four method for confidence intervals (Section 8.3),
•
probability of two confidence intervals (Chapter 8
review), and serial correlation (Section 11.4).
Section 2.1 now includes a discussion of cumulative
frequency and ogives.
Problem sets in Sections 3.1 and 3.2 provide problems
discussing the effects of data transformation (adding
a constant to each data value or multiplying each data
value by a constant) on the mean, median, mode, and
standard deviation.
Examples in Probability (Chapter 5) have been revised
to incorporate sample spaces that are small and easy
for students to visualize.
REVISED Tech Notes throughout the text give students
helpful hints on using TI-84 Plus and TI-Nspire (with
84 Plus keypad) and TI-83 calculators, Microsoft Excel
2010, and MINITAB to solve a problem. They include
display screens to help students visualize and better
understand the solution.
REVISED Using Technology instructions are available
at the end of each chapter in the Using Technology
section. Problems are presented with real-world data
from a variety of disciplines that can be solved by
using TI-84 Plus, TI-Nspire (with 84 Plus keypad) and
TI-83 Plus calculators, Microsoft Excel 2010, MINITAB,
and SPSS.
CONTENTS
1. GETTING STARTED. What is Statistics? Random
Samples. Introduction to Experimental Design.
2. ORGANIZING DATA. Frequency Distributions,
Displays. 3. AVERAGES AND VARIATION. Measures
of Central Tendency: Mode, Median, and Mean.
Measures of Variation. Percentiles and Box-and-Whisker
Plots. 4. CORRELATION AND REGRESSION. Scatter
Diagrams and Linear Correlation. Linear Regression
and the Coefficient of Determination. 5. ELEMENTARY
PROBABILITY THEORY. What is Probability? Some
Probability Rules—Compound Events. Trees and
Counting Techniques. 6. THE BINOMIAL PROBABILITY
DISTRIBUTION AND RELATED TOPICS. Introduction
to Random Variables and Probability Distributions.
Binomial Probabilities. Additional Properties of the
Binomial Distribution. 7. NORMAL CURVES AND
SAMPLING DISTRIBUTIONS. Graphs and Normal
Probability Distributions. Standard Units and Areas
Under the Standard Normal Distribution. Areas Under
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103
Any Normal Curve. Sampling Distributions. The Central
Limit Theorem. Normal Approximation to the Binomial
Distribution and to p^ Distribution. 8. ESTIMATION.
Estimating µ When Is Known. Estimating µ When is
Unknown. Estimating p in a Binomial Distribution. 9.
HYPOTHESIS TESTING. Introduction to Statistical
Tests. Testing the Mean µ. Testing a Proportion p. 10.
INFERENCES ABOUT DIFFERENCES. Tests Involving
Paired Differences (Dependent Samples). Inferences
About the Difference of Two Means µ1 – µ2. Inferences
About the Difference of Two Proportions p1 – p2.
11. ADDITIONAL TOPICS USING INFERENCE. ChiSquare: Tests of Independence and of Homogeneity.
Chi-Square: Goodness of Fit. Testing and Estimating a
Single Variance or Standard Deviation. Inferences for
Correlation and Regression.
work against fully solved examples. This combination
makes the text perfect for students taking their first
statistics course in psychology or other social and
behavioral sciences.
NEW TO THIS EDITION
• UNDERSTANDING STATISTICS IN THE BEHAVIORAL
SCIENCES, 10E includes a major increase in coverage
of SPSS. A new Appendix E, “Introduction to SPSS,” is
included. The increase in SPSS coverage jumps from
two chapters in the previous edition to coverage in
Chapters 2–7 and 13–17.
• Each chapter has at least 2 additional end-of-chapter
problems requiring students to use SPSS. With the
inclusion of significantly more SPSS content, students
can learn how to use the tool without recourse
to additional material and are able to practice on
problems specifics to the textbook material.
• The end-of-chapter questions have been implemented
into Aplia for instructors who prefer to use the text
end-of-chapter questions within Aplia.
• The Aplia product for UNDERSTANDING STATISTICS
IN THE BEHAVIORAL SCIENCES, 10E includes tutorials
like those on enhanced WebAssign in 9E, presenting
problems similar to the textbook illustrative and endof-chapter problems.
• The Newman-Keuls post-hoc test is replaced by
the Scheffe test and various symbols used in 9E are
replaced with more conventional choices.
FEATURES
UNDERSTANDING STATISTICS IN THE
Robert R. Pagano, University of Pittsburg
Based on over 30 years of successful teaching
experience in this course, Robert Pagano’s introductory
text takes an intuitive, concepts-based approach to
descriptive and inferential statistics. He uses the sign
test to introduce inferential statistics, empirically
derived sampling distributions, many visual aids,
and lots of interesting examples to promote student
understanding. One of the hallmarks of this text is the
positive feedback from students—even students who
are not mathematically inclined praise the text for its
clarity, detailed presentation, and use of humor to help
make concepts accessible and memorable. Thorough
explanations precede the introduction of every formula,
and the exercises that immediately follow include a
step-by-step model that lets students compare their
104
• Unlike most texts, Pagano introduces inferential
statistics using the sign test. This intuitive and easily
understood inference test facilitates understanding
of the basic concepts underlying inferential statistics.
• Thorough explanations are integrated with equations
when discussing inference tests. Pagano uses
empirically derived sampling distributions to promote
understanding of each inference test.
• A summary chapter ties all of inference testing
together, and gives students practice in determining
the correct inference test to use across a wide variety
of experiments. A unique decision-tree diagram is
presented to help determine the correct test.
• Pagano provides a full chapter on power (Chapter
11). This complete chapter, used in conjunction with
the sign test, promotes better understanding of this
complex topic.
• Varied and interesting illustrative examples appear in
www.cengageasia.com
abundance throughout the text and are immediately
followed by at least two fully solved practice problems
giving students immediate practice in solving
problems with a step-by-step model against which
to compare his/her work.
CONTENTS
Part I: OVERVIEW. 1. Statistics and Scientific Method.
Part II: DESCRIPTIVE STATISTICS. 2. Basic Mathematical
and Measurement Concepts. 3. Frequency Distributions.
4. Measures of Central Tendency and Variability. 5. The
Normal Curve and Standard Scores. 6. Correlation. 7.
Linear Regression. Part III: INFERENTIAL STATISITCS.
8. Random Sampling and Probability. 9. Binomial
Distribution. 10. Introduction to Hypothesis Testing
Using the Sign Test. 11. Power. 12. Sampling Distributions,
Sampling Distribution of the Mean, the Normal
Deviate (z) Test. 13. Student’s t-Test for Single Samples.
14. Student’s t-Test for Correlated and Independent
Groups. 15. Introduction to the Analysis of Variance. 16.
Introduction to Two-Way Analysis of Variance. 17. ChiSquare and Other Nonparametric Tests. 18. Review of
Inferential Statistics. Appendix A: Review of Prerequisite
Mathematics. Appendix B: Equations. Appendix C:
Answers to End-of-Chapter Questions and Problems.
Appendix D: Tables. Appendix E: Introduction to SPSS®.
Software / Handbooks / Courseware / Casebooks
SOFTWARE / HANDBOOKS /
COURSEWARE / CASEBOOKS
This popular best-selling book shows students and
professionals how to do data analysis with Microsoft®
Excel. DATA ANALYSIS WITH MICROSOFT EXCEL,
3e, International Edition teaches the fundamental
concepts of statistics and how to use Microsoft Excel
to solve the kind of data-intensive problems that arise
in business and elsewhere. Even students with no
previous experience using spreadsheets will find that
this text’s step-by-step approach, extensive tutorials,
and real-world examples make it easy to learn how to
use Excel for analyzing data. A downloadable StatPlus
add-in for Microsoft Excel, data sets for exercises, and
interactive concept tutorials are available on the Book
Companion Website.
NEW TO THIS EDITION
• This new version is completely updated for Microsoft
Office 2007 and Windows Vista, and remains
compatible with Office 2000 and 2003.
• The text contains the most updated version of the
StatPlus add-in for Microsoft Excel, which expands
some of Excel’s statistical capabilities; for instance,
allowing users create random sets of data and
manipulate data columns.
FEATURES
• Introductory sections walk first-time users of Excel
through the software’s basic functions.
• Step-by-step instructions walk students through data
analysis in a simple and systematic way.
• Summary boxes allow students to quickly apply
techniques to new data sets.
• Interactive and instructional Excel templates provide
visual guidance that allows users to develop an
intuitive understanding of difficult concepts, such as
hypothesis testing and the Central Limit Theorem.
• Real-world examples show students how the skills
they are learning apply to a wide range of situations,
from business scenarios to physiological studies on
NASA astronauts.
CONTENTS
DATA ANALYSIS WITH MICROSOFT®
EXCEL™, 3E
Updated for Office 2007, International Edition
Kenneth N. Berk, Illinois State University; Patrick M. Carey, University
of Wisconsin
1. Getting Started with Excel. Special Files for This
Book. Excel and Spreadsheets. Excel Workbooks and
Worksheets. Worksheet Cells. Printing from Excel.
Saving Your Work. Excel Add-Ins. Features of Statplus.
Exiting Excel. 2. Working with Data. Data entry. Data
Formats. Formulas and Functions. Cell References.
www.cengageasia.com
105
Range Names. Sorting Data. Querying Data. Importing
Data from Text Files. Importing Data from Databases.
Exercises. 3. Working with Charts. Working with Excel
Charts. Introducing Scatterplots. Creating Charts with
the Chart Wizard. Editing a Chart. Identifying Data
Points. Creating Bubble Plots. Breaking a Scatterplot
into Categories. Plotting Several Variables. Exercises.
PART II: FUNDAMENTALS OF STATISTICS. 4. Describing
Your Data. Variables and Descriptive Statistics. Looking
at Distributions with Frequency Tables. Working
with Histograms. Working with Stem and Leaf Plots.
Distribution Statistics. Working with Boxplots. Exercises.
5. Probability Distributions. Probability. Probability
Distributions. Random Variables and Random Samples.
The Normal Distribution. Parameters and Estimators.
The Sampling Distribution. The Central Limit Theorem.
Exercises. 6. Statistical Inference. Confidence Intervals.
Hypothesis Testing. The t-Distribution. Applying the
t-Test to Paired Data. Applying a Nonparametric Test to
Paired Data. The Two-Sample t-Test. Applying the t-Test
to Two-Sample Data. Applying a Nonparametric Test
to Two-Sample Data. Final Thoughts about Statistical
Inference. Exercises. 7. Tables. Pivot Tables. Two-Way
Tables. Computing Expected Counts. The Pearson ChiSquare Statistic. Other Table Statistics. Validity of the
Chi-Square Test with Small Frequencies. Tables with
Ordinal Variables. Exercises. PART III: STATISTICAL
METHODS. 8. Regression and Correlation. Simple
Linear Regression. Regression Functions in Excel.
Performing a Regression on Analysis. Checking the
Regression Model. Correlation. Creating a Correlation
Matrix. Creating a Scatterplot Matrix. Exercises.
9. Multiple Regression. Regression Models with
Multiple Parameters. Regression Example: Predicting
Grades. Testing Regression Assumptions. Plotting
Residuals s. Predicted Values. Regression Example: Sex
Discrimination. Exercises. 10. Analysis of Variance. OneWay Analysis of Variance. Analysis of Variance Example:
Comparing Hotel Prices. Comparing Means. Using the
Bonferroni Correction Factor. When to Use Bonferroni.
One-Way Analysis of Variance and Regression. Two-Way
Analysis of Variance. A Two-Factor Example. Two-Way
Analysis Example: Comparing Soft Drinks. Exercises. 11.
Time Series. Time Series Concepts. Time Series Example:
The Dow in the 1980s. The Autocorrelation Function.
Moving Averages. Simple Exponential Smoothing.
Two-Parameter Exponential Smoothing. Seasonality.
106
Seasonal Example: Beer Production. Three-Parameter
Exponential. Smoothing. Optimizing the Exponential.
Smoothing Constant (Optional). Exercises. 12. Quality
Control. Statistical Quality Control. Control Charts. The
Chi [Overbar] Chart. The Range Chart. The C-Chart. The
P-Chart. Control Charts for Individual Observations. The
Pareto Chart. Exercises. Appendix. Excel Reference.
Bibliography. Index.
LEARNING SAS IN THE COMPUTER LAB,
3E
Rebecca J. Elliott; Christopher H. Morrell, Loyola University Maryland
LEARNING SAS IN THE COMPUTER LAB, 3RD EDITION,
gets students up and running quickly with SAS.
Explaining concepts and commands in clear steps, the
authors provide SAS basics so students can successfully
apply computing techniques to all types of statistics
problems and acquire an appreciation of data analysis.
This brief, affordable manual also ensures that students
learn enough about programming with SAS to be able to
find their way around the many SAS reference manuals
that they will use in their professional activities.
NEW TO THIS EDITION
• Current – The new edition features code and graphics
that are up-to-date with a more recent version of
SAS while maintaining the book’s clear, accessible
coverage of SAS basics.
• New data sets and problems—Two data sets have been
added; most modules have additional problems to
provide students with ample opportunities to practice
programming.
• New modules—A new module covers matrix
computations in SAS using IML; another new module
introduces macro variables and macro programs.
www.cengageasia.com
• Microsoft® Excel instructions—Instructions are now
included on how to import/export data from and to
Microsoft Excel.
FEATURES
• Modular—Instructors can easily emphasize different
points in statistics as appropriate for their courses.
The 25 modules can be used in almost any order and
the book works well with any introductory statistics
text; students do not need to know calculus.
• Realistic—Each module contains several problems
that ask students to write a SAS program to analyze a
set of data. Nearly all of the data sets (many collected
by students for a statistics class) are real, enabling
students to understand the connection of the material
to the real world. Data sets are described in the
Appendix; files can be found on the Book Companion
Website.
• Clear explanations with visuals—Each of 25 modules
contains explanations of the appropriate SAS code
and tips on what to look for on the output SAS
generates. Examples of input and output let students
see what data looks like in SAS. In addition, a tear-out
road map (flowchart) at the back of the book explains
which SAS procedures to use for different kinds of
statistical analysis.
• Flexible—The material can be used in a lab session or
as a self-study aid for learning SAS.
CONTENTS
1. THE BASICS. Data Set Structure. Data Steps and
PROC Steps. The Data Step. PROC PRINT. The Input
Statement. Comparison of Character Formats. SAS
Program, Log, and List Files. Problems. 2. MORE SAS
BASICS. Subsetting If. If. . . Then . . . Else Statements.
PROC FORMAT. Subsetting Where. Using Data from
Outside Files. Missover Option on Infile Statement.
PROC IMPORT for Microsoft® Excel Files. PROC SORT.
Set Statement. Problems. 3. DATA MANAGEMENT. How
SAS “Thinks.” Lag Function. Drop and Keep Statements.
Combining Data Sets. Comment Lines. Limiting the
Number of Observations Read from External Files.
Setting the Size of the Output. The Put Statement.
PROC EXPORT for Microsoft Excel Files. Problems.
4. SAS FUNCTIONS. Numeric Functions. Character
Functions. Probability Functions. Descriptive Statistics
Functions. Problems. 5. DESCRIPTIVE STATISTICS I.
Label Statements. Title Statements. PROC UNIVARIATE.
PROC BOXPLOT. Problems. 6. PROC GCHART. PROC
GCHART. Problems. 7. DESCRIPTIVE STATISTICS II.
PROC MEANS. PROC FREQ. Problems. 8. GENERATING
RANDOM OBSERVATIONS. SAS Distributional
Functions. Do Loops. Problems. 9. X-Y PLOTS. PROC
GPLOT. Problems. 10. ONE-SAMPLE TESTS FOR µ, P.
One-Sample t-Tests. One-Sample Test for P. Problems.
11. TWO-SAMPLE T-TESTS. PROC TTEST. T-Tests:
Two-Sample, Pooled, and Paired. Problems. 12. ONEWAY ANOVA. Analysis of Variance—PROC GLM and
PROC ANOVA. Problems. 13. TWO-WAY ANOVA AND
MORE. Two-Way ANOVA. General ANOVA Analysis.
Problems. 14. MODEL CHECKING IN ANOVA. Plots
for Model Checking. More on PROC GLM. Problems.
15. CORRELATIONS. PROC CORR. Problems. 16.
SIMPLE LINEAR REGRESSION PROC REG. Problems. 17.
MODEL CHECKING IN REGRESSION. Plots for Model
Checking. More on PROC REG. Problems. 18. MULTIPLE
LINEAR REGRESSION. Multiple Regression. Problems.
19. MULTIPLE REGRESSION: AIDS FOR SELECTING
AND ASSESSING MODELS. Statistics Used in Multiple
Regression Model Checking. PROC REG Selection =
Option. Problems. 20. TESTS FOR CATEGORICAL DATA.
Chi-Square Tests. Problems. 21. NONPARAMETRIC
TESTS. One-Sample Tests. Tests for Two or More
Samples. Problems. 22. ANALYSIS OF COVARIANCE.
Using PROC GLM for ANCOVA. Problems. 23. LOGISTIC
REGRESSION PROC LOGISTIC. Problems. 24. MATRIX
COMPUTATIONS. PROC IML. Problems. 25. MACRO
VARIABLES AND PROGRAMS. Macro Variables. Macro
Programs. Problems. DESCRIPTION OF DATA SETS.
INDEX.
STATISTICS WITH STATA, 8E
Version 12, International Edition
Lawrence C. Hamilton, University of New Hampshire
For students and practicing researchers alike,
STATISTICS WITH STATA Version 12, International
Edition opens the door to the full use of the popular
Stata program—a fast, flexible, and easy-to-use
environment for data management and statistics
www.cengageasia.com
107
analysis. Integrating Stata’s impressive graphics, this
comprehensive book presents hundreds of examples
showing how to apply Stata to accomplish a wide
variety of tasks. Like Stata itself, STATISTICS WITH
STATA Version 12, International Edition will make it
easier for readers to move fluidly through the world of
modern data analysis.
NEW TO THIS EDITION
• The text has been updated throughout to reflect
recent enhancements to Stata commands, output, and
features—providing readers with a reliable, up-to-date
guide to the current software package that is ideal for
use in the classroom, lab, or office.
FEATURES
• Chapters on Survey Data Analysis and Multilevel and
Mixed-Effects Modeling provide detailed guidelines
to help users appreciate and apply the sophisticated
capabilities that the latest version of Stata offers in
these areas.
• Sections in many chapters provide additional
information or emphasis reflecting the needs of
today’s real-world users and the growing capabilities
of Stata, including material on extended missing value
codes, the Graph Editor and Creative Graphing, and
a demonstration of ARMAX time series regression
models.
• The final chapter contains two sections to help
advanced Stata users create even more precise and
powerful programs: an example program that draws
multiple graphs for reporting survey results, and a first
look at matrix programming with Mata.
• Methods for drawing time plots, fitting nonlinear
models, estimating standard errors, and obtaining
predictions or diagnostic statistics complement
additional options provided for long-established
commands, giving users even more options to make
the most of their work with Stata.
• With full integration of Stata’s graphics, this
comprehensive book provides a thorough conceptual
and practical overview of the software in an especially
easy-to-follow, visually appealing way, making it easier
to plan and perform analysis with Stata.
• Bridging the gap between statistics textbooks and
software reference manuals, this immensely practical
and reader-friendly text offers a solid introduction to
important statistical concepts and how to work with
108
them in Stata, providing an indispensable learning
and reference tool for students and professional
researchers alike.
• Readers of this one-of-a-kind text benefit from the
unique insights and expertise Lawrence Hamilton
provides, based on his more than 25 years of using,
writing about, and teaching statistics courses at all
levels with Stata.
• The first six chapters roughly mirror an introductory
statistics course, while the second half of the text
explores more complex material (including the basics
of programming with Stata) appropriate for advanced
courses or original research, making it easy to use the
text effectively in a variety of classes and contexts.
• For readers beginning to explore more advanced
material, basic information and illustrations provide
an introduction to topics such as how to obtain and
interpret diagnostic statistics and graphs and how
to perform robust, quantile, nonlinear, logit, ordered
logit, multinomial logit, or Poisson regression.
CONTENTS
Preface. 1. Stata and Stata Resources. 2. Data
Management. 3. Graphs. 4. Summary Statistics and
Tables. 5. ANOVA and other Comparison Methods. 6.
Linear Regression Analysis. 7. Regression Diagnostics.
8. Fitting Curves. 9. Robust Regression. 10. Logistic
Regression. 11. Survival and Event-Count Models. 12.
Principal Components, Factor, and Cluster Analysis.
13. Time Series Analysis. 14. Survey Data Analysis. 15.
Multilevel and Mixed-Effects Modeling. 16. Introduction
to Programming. References. Index.
www.cengageasia.com
Combined Author/Title Index
A
An Introduction to Statistical Methods and Data Analysis,
International Edition, 6e, p.36
Applied Regression Analysis and Multivariable Methods,
Applied Statistics for Engineers and Scientists, International
Edition, 3e, p.50
B
Basic Statistics for the Behavioral Sciences, International
Edition, 6e, p.26/p.52
Berk/Carey, Data Analysis with Microsoft® Excel™, 3e, p.105
Brase/Brase, Statistics, International Edition, 10e, p.19/p.96
Brase/Brase, Student Solutions Manual for Brase/Brase’s
Understanding Basic Statistics, Brief, 5th, 5e, p.23/p.97
Brase/Brase, Technology Guide Excel® for Brase/Brase’s
Understanding Basic Statistics, Brief, 5th, 5e, p.24/p.99
Brase/Brase, Understandable Statistics, 10e, p.24/p.99
Brase/Brase, Understandable Statistics, AP* Edition, 10e,
p.26/p.101
Brase/Brase, Understanding Basic Statistics, Hybrid (with
Aplia Printed Access Card), 6e, p.101
Brase/Brase, Understanding Basic Statistics, International
Edition, 6e, p.102
C
Cengage Advantage Books: Basic Statistics for the Behavioral
Sciences, 6e, p.26/p.51/p.52
Cengage Advantage Books: Essentials of Statistics for
Behavioral Science, 7e, p.28/p.54
Cengage Advantage Books: Fundamental Statistics for the
Behavioral Sciences, 7e, p.55
Cengage Advantage Books: Statistics for the Behavioral
Sciences, 9e, p.29/p.57
D
Data Analysis with Microsoft® Excel™, 3e, p.105
Devore, Probability and Statistics for Engineering and the
Sciences, International Edition, 8e, p.81
Devore/Farnum/Doi, Applied Statistics for Engineers and
Scientists, International Edition, 3e, p.50
Devore/Farnum/Doi, Student Solutions Manual for Devore/
Farnum/Doi’s Applied Statistics for Engineers and Scientists,
3rd, 3e, p.97
E
Elementary Statistics, p.2/p.58
Elementary Statistics, Enhanced Review Edition (w/CD-ROM &
CengageNOW, InfoTrac® 2-Sem., iLrn® Homework, Personal
Tutor, Internet Companion for Statistics 2-Sem. PAC), 10e,
p.3/p.60
Elementary Statistics, International Edition, 11e, p.5/p.62
Elementary Statistics: Looking at the Big Picture, Hybrid,
p.7/p.63
Elementary Survey Sampling, 7e, p.41
Elliott/Morrell, Learning SAS in the Computer Lab, 3e, p.106
Essentials of Statistics for the Behavioral Science, International
Edition, 7e, p.30/p.65
F
Fundamental Statistics for the Behavioral Sciences,
International Edition, 7e, p.31/p.66
Fundamentals of Biostatistics, International Edition, 7e, p.68
G
Gravetter/Wallnau, Cengage Advantage Books: Essentials of
Statistics for Behavioral Science, 7e, p.28/p.54
Gravetter/Wallnau, Cengage Advantage Books: Statistics for
the Behavioral Sciences, 9e, p.29/p.57
Gravetter/Wallnau, Essentials of Statistics for the Behavioral
Science, International Edition, 7e, p.30/p.65
Gravetter/Wallnau, Statistics for the Behavioral Sciences, 9e,
p.34/p.92
Gravetter/Wallnau, Study Guide for Gravetter/Wallnau’s
Statistics for the Behavioral Sciences, 8th, 8e, p.35/p.98
H
Hamilton, Statistics with STATA, 8e, p.107
Hayter, Probability and Statistics for Engineers and Scientists,
Heckard/Utts, Statistics, International Edition, 4e, p.21/p.93
Heiman, Basic Statistics for the Behavioral Sciences,
Heiman, Cengage Advantage Books: Basic Statistics for the
Behavioral Sciences, 6e, p.27/p.53
Howell, Cengage Advantage Books: Fundamental Statistics for
the Behavioral Sciences, 7e, p.55
Howell, Fundamental Statistics for the Behavioral Sciences,
I
Introduction to Probability and Its Applications, International
Edition, 3e, p.43
Introduction to Probability and Statistics, International Edition,
14e, p.9/p.69
Introduction to Statistics and Data Analysis, 4e, p.10/p.70
Introduction to Statistics and Data Analysis, AP Edition, 4e,
p.12/p.72
Introduction to Statistics and Data Analysis, Enhanced Review
Edition (with CengageNOW Printed Access Card), 3e,
p.12/p.72
www.cengageasia.com
109
J
Johnson/Kuby, Elementary Statistics, Enhanced Review Edition
(w/CD-ROM & CengageNOW, InfoTrac® 2-Sem., iLrn®
Homework, Personal Tutor, Internet Companion for Statistics
2-Sem. PAC), 10e, p.3/p.60
Johnson/Kuby, Elementary Statistics, International Edition, 11e,
p.5/p.62
Johnson/Kuby, STAT 2 (with Review Cards and Statistics
CourseMate Printed Access Card), 2e, p.16/p.88
K
Kleinbaum/Kupper/Nizam/Muller, Applied Regression
Analysis and Multivariable Methods, International Edition,
4e, p.39
R
L
Rosner, Fundamentals of Biostatistics, International Edition,
7e, p.68
Ryan/Joiner/Cryer, MINITAB® Handbook, International
Edition, 6e, p.77
Learning SAS in the Computer Lab, 3e, p.106
Lohr, Sampling, 2e, p.47
M
MINITAB® Handbook, International Edition, 6e, p.77
Mathematical Statistics with Applications, International
Edition, 7e, p.44
Mendenhall/Beaver/Beaver, Introduction to Probability and
Statistics, International Edition, 14e, p.9/p.69
Mendenhall/Beaver/Beaver, Student Solutions Manual for
Mendenhall/Beaver/Beaver’s Introduction to Probability and
Statistics, 14th, 14e, p.23/p.98
Mind on Statistics, 4e, p.14/p.75
O
Ott/Longnecker, An Introduction to Statistical Methods and
Data Analysis, International Edition, 6e, p.36
Ott/Longnecker, Student Soltuions Manual for Ott/
Longnecker’s An Introduction to Statistical Methods and
Data Analysis, 6th, 6e, p.48
P
Pagano, Understanding Statistics in the Behavioral Sciences,
10e, p.35/p.104
Peck, Preliminary Edition of Statistics, p.78
Peck, Student Solutions Manual for Peck’s Statistics, p.98
Peck/Devore, Statistics, 7e, p.17/p.90
Peck/Olsen/Devore, Introduction to Statistics and Data
Analysis, 4e, p.10/p.70
Analysis, AP Edition, 4e, p.12/p.72
Analysis, Enhanced Review Edition (with CengageNOW
Printed Access Card), 3e, p.12/p.72
110
Peck/Olsen/Devore, Student Solutions Manual for Peck/
Olsen/Devore’s Introduction to Statistics and Data Analysis,
3rd, 3e, p.24/p.98
Pfenning, Elementary Statistics, p.2/p.58
Pfenning, Elementary Statistics: Looking at the Big Picture,
Hybrid, p.7/p.63
Preliminary Edition of Statistics, p.78
Probability and Statistics for Engineering and the Sciences,
Probability and Statistics for Engineers and Scientists,
Probability and Statistics for Engineers, International Edition,
5e, p.84
Probability for Engineering, Mathematics, and Science,
International Edition, p.86
S
STAT 2 (with Review Cards and Statistics CourseMate Printed
Access Card), 2e, p.16/p.88
Sampling, 2e, p.47
Scheaffer/Mendenhall, III/Ott/Gerow, Elementary Survey
Sampling, 7e, p.41
Scheaffer/Mendenhall, III/Ott/Gerow, Survey Sampling,
Scheaffer/Mulekar/McClave, Probability and Statistics for
Engineers, International Edition, 5e, p.84
Scheaffer/Young, Introduction to Probability and Its
Applications, International Edition, 3e, p.43
Statistical Methods for Engineers, International Edition, 3e, p.89
Statistics for the Behavioral Sciences, 9e, p.34/p.92
Statistics with STATA, 8e, p.107
Statistics, 7e, p.17/p.90
Student Soltuions Manual for Ott/Longnecker’s An
Introduction to Statistical Methods and Data Analysis, 6th,
6e, p.48
Student Solutions Manual for Brase/Brase’s Understanding
Basic Statistics, Brief, 5th, 5e, p.23/p.97
Student Solutions Manual for Devore/Farnum/Doi’s Applied
Statistics for Engineers and Scientists, 3rd, 3e, p.97
Student Solutions Manual for Mendenhall/Beaver/Beaver’s
Introduction to Probability and Statistics, 14th, 14e, p.23/p.98
Student Solutions Manual for Peck’s Statistics, p.98
Student Solutions Manual for Peck/Olsen/Devore’s
Introduction to Statistics and Data Analysis, 3rd, 3e,
p.24/p.98
Student Solutions Manual for Wackerly/Mendenhall/Scheaffer’s
Mathematical Statistics with Applications, 7th, 7e, p.48
www.cengageasia.com
Study Guide for Gravetter/Wallnau’s Statistics for the
Behavioral Sciences, 8th, 8e, p.35/p.98
Survey Sampling, International Edition, 7e, p.49
T
Technology Guide Excel® for Brase/Brase’s Understanding
Basic Statistics, Brief, 5th, 5e, p.24/p.99
Tsokos, Probability for Engineering, Mathematics, and Science,
International Edition, p.86
U
Understandable Statistics, 10e, p.24/p.99
Understandable Statistics, AP* Edition, 10e, p.26/p.101
Understanding Basic Statistics, Hybrid (with Aplia Printed
Access Card), 6e, p.101
Understanding Basic Statistics, International Edition, 6e, p.102
Understanding Statistics in the Behavioral Sciences, 10e,
p.35/p.104
Utts/Heckard, Mind on Statistics, 4e, p.14/p.75
V
Vining/Kowalski, Statistical Methods for Engineers,
W
Wackerly/Mendenhall/Scheaffer, Mathematical Statistics with
Applications, International Edition, 7e, p.44
Wackerly/Mendenhall/Scheaffer, Student Solutions Manual
for Wackerly/Mendenhall/Scheaffer’s Mathematical Statistics
with Applications, 7th, 7e, p.48
www.cengageasia.com
111
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Applied Regression Analysis and Multivariable Methods,
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