STA 4504: Categorical Data Analysis
Instructor. Aaron J. Molstad (amolstad@ufl.edu)
Office hours. 1:00 - 2:00PM (Friday, 202 FLO), 10:35 - 11:45AM (Wednesday, [Zoom])
Teaching assistant. Ziqian Yang (zi.yang@ufl.edu)
TA Office hours. 2:00 - 4:00PM (Thursday, 234 FLO), 4:00 - 5:00PM (Friday, [Zoom])
Syllabus. [pdf]
Lecture. 3:00pm - 3:50pm on Monday, Wednesday, and Friday in LEI 0207 and [Zoom]
Note that you must be logged into your UFL eLearning account to access course materials.
| Lecture | Topics | |||
| 1 (1/5) | Review of key concepts [slides][lecture] | |||
| 2 (1/7) | Maximum likelihood [slides][lecture] | |||
| 3 (1/10) | Inference on a proportion [slides][lecture] | |||
| 4 (1/12) | Contingency tables [slides][lecture] |
Homework 1 (Due Friday, January 21st at 6:00pm) [pdf][solutions][solutions Rmarkdown]
| 5 (1/14) | Relative risk and odds ratio [slides][lecture] | |||
| 6 (1/19) | Testing for independence in contingency tables [slides][lecture] | |||
| 7 (1/21) | Three-way contingency tables [slides][lecture] |
Homework 2 (Due Friday, January 28th at 6:00pm) [pdf][solutions][solutions Rmarkdown]
| 8 (1/24) | Introduction to GLMs [slides]lecture] | |||
| 9 (1/26) | GLMs for binary and count data [slides][lecture][challenger code][wafers code] | |||
| 10 (1/28) | Inference for GLMs [slides][lecture][malformation code] |
Homework 3 (Due Friday, February 4th at 6:00pm) [pdf][submission][plot code][solution][solutions Rmarkdown]
| 11 (1/31) | Deviance and residuals for GLMs [slides][lecture] | |||
| 12 (2/2) | Overdispersion, coeffficients [slides][crabs R][crabs data][lecture] | |||
| 13 (2/4) | Why models? [slides][lecture] | |||
| 14 (2/7) | Logistic regression with multiple predictors [slides][lecture] | |||
| 15 (2/9) | Performance metrics [slides][lecture] | |||
| 16 (2/11) | Cross-validation, ROC curves [slides][Office hours code][lecture] | |||
| 17 (2/14) | Model selection [slides][lecture][Grouped data code] | |||
| 00 (2/16) | R Code review for Exam 1 [pdf][R markdown][lecture] |
Homework 4 (Due Tuesday, February 15th at 6:00pm) [pdf][solution][Multiple predictors code][ROC Example Code]
Exam 1 (Due Tuesday, February 22nd at 11:59PM)[pdf][solutions][solutions rmd]
| 18 (2/23) | Checking ungrouped data, diagnostics [pdf][lecture] | |||
| 19 (2/25) | Sparsity, baseline-category logit [pdf][lecture] | |||
| 20 (2/28) | Baseline-category logit models [pdf][lecture] | |||
| 21 (3/2) | Cumulative logit models [pdf][lecture] |
Homework 5 (Due Friday, March 18th at 6:00pm) [pdf][solutions rmd][solutions pdf][code pdf][code rmd]
| 22 (3/14) | Cumulative logit models [pdf][lecture] | |||
| 00 (3/16) | Review of Exam 1 [lecture] | |||
| 23 (3/18) | Cumulative logit models and correlated data [slides][lecture][code pdf][code rmd] |
Homework 6 (Due Friday, March 25th at 6:00pm) [pdf][solutions pdf][solutions rmd]
| 24 (3/21) | McNemar’s test [slides][lecture] | |||
| 25 (3/23) | Marginal and conditional models [slides][lecture] | |||
| 26 (3/25) | Rater agreement [slides][lecture] | |||
| 27 (3/28) | Correlated data and GEEs [slides][lecture] | |||
| 28 (3/30) | GEE examples [slides][code rmd][code pdf][lecture] |
Homework 7 (Due Friday, April 8th at 6:00pm) [pdf][solutions rmd][solutions pdf]
| 29 (4/4) | Random effects models [slides][lecture] | |||
| 30 (4/8) | Log-linear models intro [slides][lecture] | |||
| 31 (4/11) | Log-linear models cont. [slides][lecture] | |||
| 32 (4/13) | R + Log-linear models cont. [slides][lecture 1][lecture 2}][code rmd][code pdf] | |||
| 33 (4/15) | Independence graphs [slides][lecture] | |||
| 00 (4/17) | R code for log-linear models [rmd][pdf][lecture] |
| Homework 8 (Due Monday, April 18th at 6:00pm) [pdf][solutions rmd][solutions pdf] |
Exam 2 (Due Tuesday, April 26th at 11:59PM)[pdf]