This course deals with statistical models for the analysis of categorical data. It is designed for undergraduate students taking an introductory course in categorical data analysis, which has a low technical level and does not require familiarity with advanced mathematics such as calculus or matrix algebra. Topics to be covered include introduction to categorical data, inference for contingency tables, generalized linear models, with emphasis on logistic regression and logit models, and so on.

This course adopts an experimental teaching way which combines teacher’s teaching with student’s practice, striving to form a two-way interaction between teacher and student. Teacher’s teaching is the main teaching way, aided by the operation instruction of software (R language). Combining theory with practice, students can apply their learnings to practical problems. In each lecture, there will be a session designed for experiments using R by students themselves to practice the example, understand the corresponding theory and learn to use the R to obtain the result. Students are required to submit a report at the end of the semester.

Through this course, students can acquire the basic statistic theory related with categorical data. Topics to be covered include introduction to categorical data, inference for contingency tables, generalized linear models, with emphasis on logistic regression and logit models, and a little bit on models for matched pairs. Teaching is the main approach for theoretical part, and experiments is designed for real data analysis. There will be a lot of examples in the classes and students will also be taught how to use software R for example, to deal and analyze the categorical data.

Through this course, students can acquire the basic statistic theory related with categorical data. Topics to be covered include introduction to categorical data, inference for contingency tables, generalized linear models, with emphasis on logistic regression and logit models, and a little bit on models for matched pairs.

This course require basic statistic knowledge, such as mathematical statistics and regression analysis, and does not require familiarity with advanced mathematics such as calculus or matrix algebra

Textbook:
An Introduction to Categorical Data Analysis. Second Edition. Alan Agresti (2007). John Wiley & Sons.

Reference:

[1] Analysis of Categorical Data. Agresti, A., New York: Wiley, 2002.

[2] Generalized Linear Models. 2^nd Ed. McCullagh, P. and Nelder, J., London: CRC Publishers, 1989.

[3] 属性数据分析引论（第二版）. 张淑梅, 王睿,  曾莉, 译, 高等教育出版社. 

[4] 实用多元统计方法与SAS系统. 高惠璇, 北京大学出版社.