SDSC8013 - Statistical Methods for Categorical Data Analysis

Offering Academic Unit
School of Data Science
Credit Units
Course Duration
One Semester
Course Offering Term*:
Semester B 2021/22

* The offering term is subject to change without prior notice
Course Aims

In this course, students will learn how to use descriptive and test statistics, statistical models and statistical inferences to analyse categorical data. Topics include statistical inference using odds ratios and relative risks, multi-way contingency tables, tests for stratified analysis, odds ratio and relative risk, generalized linear models for discrete data, multi-category logit model for nominal and ordinal responses, inference for matched-pairs, and loglinear models. Students will learn where these methods may be applied, how to apply them, and how to properly interpret the results. The course is appealing to those interested in categorical data analysis, and examples are based on case studies. Students will learn statistical way of thinking in analyzing categorical data, and the methodology can be applied to different fields to solve similar problems.

Assessment (Indicative only, please check the detailed course information)

Continuous Assessment: 100%
Detailed Course Information


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School of Data Science