MATH 511 — Analysis of Categorical Data

Probability distributions for categorical data, Analysis of 2X2 contingency tables, Multiway contingency tables, The Logistic regression, Logistic regression for categorical predictors, Logit models for nominal and ordinal responses, Log-linear models and modelling ordinal associations in contingency tables, Unsupervised learning techniques for categorical data, Non Linear Principal component analysis, Applications of unsupervised learning techniques using R, Item Response Theory, Rasch model. Some topics may be included or excluded as the time permits.

• Credits: 4
• Faculty: Faculty of Science
• Department: Mathematics and Statistics
• Prerequisites: (MATH 208 or equivalent) and (MATH 324 or MATH 357) and (MATH 423 or MATH 533 or equivalent).