Applied Discrete Data Analysis - SFU Mathematics and Statistics

Transcription

1. Course Syllabus
2. Introduction to STAT-475/675
Applied Discrete Data Analysis
X. Joan Hu
Department of Statistics and Actuarial Science
Simon Fraser University
Spring 2015
X. Joan Hu: STAT-475/675
Department of Statistics and Actuarial Science Simon Fraser University
1. Course Syllabus
What to do today?
1. Course Syllabus
1. Course Syllabus
STAT-475/675: Applied Discrete Data Analysis
Instructor: X. Joan Hu (Email: [email protected])
Lecture:
Tue 10:30 - 11:20 AQ 3154; Thu 9:30 - 11:20 AQ 3003
Office Hour:
Tue/Thu 16:30 - 17:20, or by appointment; SSC K10555
Course Page:
http://people.stat.sfu.ca/∼joanh/stat475-675web/
Teaching Assistant: Yunlong (Ben) Nie ([email protected])
for tutorials; Terry Tang ([email protected]) for marking
Tutorials: (starting from the week of Jan 12 2015)
D101: 09:30 - 10:20, D102: 11:30 - 12:20, D103: 16:30 17:20, D104: 17:30 - 18:20 on Tu at AQ3148.1
1. Course Syllabus
Textbook: “Analysis of Categorical Data with R” by C. Bilder and
T. Loughin
Reference Books:
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“An Introduction to Categorical Data Analysis,” by A. Agresti
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“Introduction to the Statistical Analysis of Categorical Data,”
by E.B. Andersen
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“An Introduction To Generalised Linear Models,” by A.J.
Dobson
Computer Software: R and SAS are recommended; R will be
used in class (URL http://www.r-project.org/)
1. Course Syllabus
COURSE OUTLINE
1. Introduction and Preparation
(Appendices A and B)
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I
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1.1 General Introduction
1.2 Introduction to R (Appendix A)
1.3 Review: Likelihood methods (Appendix B)
2. Analysis with Binary Variables (Chp 1-2)
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2.1 Analysis with binary variables I (Chp 1)
2.2 Analysis with binary response II (Chp 2)
1. Course Syllabus
COURSE OUTLINE
3. Analysis with Multicategory Variables (Chp 3)
4. Analysis with Count Response (Chp 4)
5. Model Selection and Evaluation (Chp 5)
6. Further Topics (Chp 6)
1. Course Syllabus
COURSE EVALUATION – Grading Schemes:
I Homework - 10% (seven assignments: 2% per homework,
and the five highest marks are used in the final
evaluation):
I
Midterm - 40% (two midterms in class)
I
Final - 50% (30% for STAT-675)
Project - 20% (for STAT-675 only)
Remarks:
I Medical document is required to evidence the missing of a
homework due time, the midterm, or the final is due to
illness. When applied, the credit will then be recovered by
re-weighting the score at the final exam.
I
1. Course Syllabus
COURSE EVALUATION – Homework (10%: seven
assignments)
I Assigned in Weeks 1, 3, 5, 7, 9, 11 and 13: the
assignments will be posted at the course’s web page and
emailed to the class’s email list.
I Collected by 17:30 on Thursdays of Weeks 2, 4, 8, 10, 12
and 14, Monday of Week 7: using the drop-boxes labeled
with STAT-475/675, outside the statistics workshop
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I
Late homework is not accepted: if the delay is due to illness,
provide med note and hand in the homework for credit.
Marked HW will be returned at the tutorials of the
following week: key answers to the homework questions
will be posted in the course web page before the tutorials
1. Course Syllabus
COURSE EVALUATION – Midterm (40%: two
midterms; 20% per test)
I Midterm 1: at the 2nd class on Feb 5 (Thursday of Week
5); covering Chp 1-2.
I Midterm 2: at the 2nd class on Mar 19 (Thursday of
Week 11); covering Chp 1-4.
I
I
Open-book and calculators allowed
No makeups for midterms: if the missing is due to illness,
provide a med note to recover the credit by re-weighting
the final score accordingly.
1. Course Syllabus
COURSE EVALUATION – Final Exam (50%; 30% for
STAT-675)
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You need to pass the final exam to pass this course.
April 21, 2015 (Tuesday) 15:30 - 18:30; classroom TBA
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Final exam covers Chp 1-6.
Open book and calculators allowed
I
Official exam conflict?
I
1. Course Syllabus
COURSE EVALUATION – Final Project (20% for
STAT-675 only)
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Topic to be assigned in Week 11
Project to be collected by Week 14
1. Course Syllabus
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Why to study STAT-475/675?
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What to study in STAT-475/675?
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How to study STAT-475/675?
1. Course Syllabus
Recall: Data Analysis – Exploration and Inference
Statistical Thinking (e.g. “The Basic Practice of Statistics”, 6th
Edn, by Moore et al.)
Can we go beyond the data?
1. Course Syllabus
What will we study in the next class?
1. Introduction and Preparation
I
1.1 General Introduction
I
1.2 Introduction to R (Appendix A)
I
1.3 Review: Likelihood methods (Appendix B)

Applied Discrete Data Analysis - SFU Mathematics and Statistics

Transcription

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