Please note that the official course website is on Canvas (log in with CNetID), NOT here. This webpage is for those who are interested in STAT 22600 to get an idea of what the course is like.
Click STAT 22600-2017 to view the older STAT 226 webpages for Winter 2017.
STAT 22000 or 23400 with a grade of at least B-; or 24500, or PBHS 32100, or AP Statistics, or equivalent, and two-quarters of calculus (MATH 13200, 15200, or 16200). Students are expected to have familiarity with
This course covers statistical methods for the analysis of qualitative and counted data. Topics include description and inference for binomial and multinomial data using proportions and odds ratios; multi-way contingency tables; generalized linear models for discrete data; logistic regression for binary responses; multi-category logit models for nominal and ordinal responses; loglinear models for count data; and inference for matched-pairs and correlated data. Applications and interpretations of statistical models are emphasized.
Primary Textbook: An Introduction to Categorical Data Analysis, 3rd Edition (2019), by Alan Agresti
Other recommended reference books (not required)
| Week/Date | Slides | Topic | Textbook |
|---|---|---|---|
| Week 1&2 – Jan. 4, 6, 9 | L01_02 | Binomial, Likelihood, MLE of Binomial Proportions; Wald, Score, Likelihood Ratio Tests of Binomial Proportions | Section 1.1-1.4 |
| L01_supp | L01 Supplement: Chi-Squared Distributions and Chi-Squared Tests | ||
| Week 2 – Jan. 9 | L03 | Small Sample Inference (Exact Binomial Tests and CIs) | Section 1.4.3 |
| Week 2 – Jan. 9, 11 | L04 | Diff. in Proportion, Relative Risk, Odds Ratio | Section 2.2-2.3 |
| Week 2 – Jan. 11, 13 | L05 | Type of Studies, Odds Ratios can be estimated Prospectively & Retrospectively | Section 2.1, 2.3 |
| Week 3 – Jan. 16, 18 | L06 | Pearson’s X^2 and Likelihood Ratio G^2 Test of Independence | Section 2.4 |
| Week 3 – Jan. 20 | L07 | Fisher’s Exact Tests | Section 2.6 |
| Week 3&4 – Jan. 20, 23 | L08 | Association in Three Way Tables | Section 2.7 |
| Week 4 – Jan. 23, 25 | L09 | Generalized Linear Models | Section 3.1-3.2 |
| Week 4 – Jan. 25-27 | L10_11 | Simple Logistic Regression Wald tests & CIs, Likelihood Ratio Tests & CI, Confidence Interval for \(\pi(x)\) | Section 4.1-4.2 |
| Week 5 – Jan. 30 | L12_13 | Logistic Regression w/ Categorical Predictors; Multiple Logistic Regression | Section 4.3-4.4 |
| Week 5 – Feb. 1 | L14 | Models w/ Ordinal Explanatory Variables; Models Allowing Interactions Btw Explanatory Variables | Section 4.3-4.4 |
| Week 5 – Feb. 3 | - | Midterm Exam, No Class | - |
| Week 6 – Feb. 6 | L15 | Bumpus Nature Selection Data (Models w/ Several Explanatory Variables) | Section 4.4 |
| Week 6 – Feb. 6, 8 | L16 | Logistic Regression for Retrospective Studies | Section 4.1.4 |
| Week 6 – Feb. 8, 10 | L17 | Conversion Between Tables, Long Format and Wide Format Data; Logistic Regression for Multiway Data | |
| Week 7 – Feb. 13 | L18 | Deviance, Goodness of Fit for Grouped and Ungrouped Data | Section 5.2-5.3 |
| Week 7 – Feb. 13 | L19 | Residuals, Sparse Data | Section 5.2-5.3 |
| Week 7 – Feb. 15, 17 | L20 | Baseline-Category Logit Models | Section 6.1 |
| Week 7&8 – Feb. 17, 20 | L21 | Cumulative Logit Models | Section 6.2 |
| Week 8 – Feb. 20, 22, 24 | L22_24 | Poisson Distributions Review; GLM Models for Poisson Response Data; Models for Rate Data; Overdispersion, Negative Binomial Regression | Section 3.3, 7.6 |
| Week 9 — Feb. 27 | L25 | McNemar’s Test and CI for Paired Data | Section 8.1 |
| Week 9 — Mar. 1 | L26 | Population Averaged and Subject-Specific Models | Section 8.2 |
| Week 9 — Mar. 3 | L27 | Comparing Proportions for Multi-Category Matched-Pairs Response | Section 8.3 |