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Last updated: Fri Apr 25 2025, 15:49

Syllabus

Welcome to the course website for STA 395, Categorical Data Analysis. To begin, you can find the course syllabus linked below:

You can locate course content by scrolling, or by using the navigation bar in the upper left.

Course Materials

Solutions

Exam #1 Content

Week 1 (Discrete Distributions)

Date Lecture Lab Reading
Tue 1/21 Introduction - 1.1-1.2
Thur 1/23 Probability Distributions Distributions 1.1-1.2

Assignments and Deadlines:

  1. Download R
  2. Download R Studio
  3. Lab template

Week 2 (Inference)

Date Lecture Lab Reading
Tue 1/28 Likelihood and MLE Likelihood 1.3-1.4
Thur 1/30 Inference for Discrete Data - 1.3-1.4

Assignments and Deadlines:

Week 3 (Tables and Odds)

Date Lecture Lab Reading
Tue 2/4 Contingency Tables 2.1-2.2
Thur 2/6 Relative Risk and Odds Ratio Functions 2.2-2.3

Assignments and Deadlines:

Week 4 (Inference)

Date Lecture Lab Reading
Tue 2/11 Chi-square tests Functions 2.4
Thur 2/13 - Fisher Exact 2.6

Assignments and Deadlines:

Week 5 (Threeway Tables and Intro GLM)

Date Lecture Lab Reading
Tue 2/18 Threeway Tables 2.7
Thur 2/20 Components of a GLM Intro GLM 3.1-3.2

Week 6 (GLM)

Date Lecture Lab Reading
Tue 2/18 Statistical Inference for GLM Deviance 3.4-3.5
Thur 2/20 Exam day yay 😎

Week 7 (Poisson Regression)

Date Lecture Lab Reading
Tue 3/4 Poisson for Contingency Tables Loglinear Models 1 7.1
Thur 3/6 Poisson Models 7.1

Week 8 (Poisson and Negative Binomial)

Date Lecture Lab Reading
Tue 3/25 Poisson for Count 3.3
Thur 3/27 Poisson for Rate 3.3/7.6

Week 9 (Logistic Regression)

Date Lecture Lab Reading
Tue 4/1 Logistic Regression Model 4.1
Thur 4/3 Inference for Logistic 4.1-4.2

Week 10 (Logistic Regression)

Date Lecture Lab Reading
Tue 4/8 Review
Thur 4/10 Exam 2

Week 11 (Logistic Regression – Extending Predictors)

Date Lecture Lab Reading
Tue 4/15 Logistic with Categorical Predictors 4.3
Thur 4/17 Multiple Logistic Regression 4.4

Week 12 (Logistic Regression)

Date Lecture Lab Reading
Tue 4/22 ROC Curves 4.6
Thur 4/24 Logistic Regression Model Selection 5.1