Objectives

• To develop skills on analysis of experiments using R;
• To understand the basic principles of likelihood-based inference and how to apply it to tests and intervals regarding population proportions;
• To develop a critical approach to the analysis of contingency tables;
• To examine the basic ideas and methods of generalized linear models.

Syllabus

• Introduction: Distributions and Inference for Categorical Data
• Analyzing Contingency Tables
• Generalized Linear Models
• Logistic Regression Models

Bibliography

• Agresti, A., An introduction to categorical data analysis, 3rd Edition. John Wiley Sons, 2018.
• Agresti, A., Categorical data analysis, 3rd Edition. John Wiley Sons, 2013.
• Bilder, C. R., Loughin, T. M.. Analysis of categorical data with R. Chapman and Hall/CRC, 2014.
• Adler, J., R in a nutshell: A desktop quick reference, 1st Edition, O’Reilly Media, 2010;
• Teetor, P., R cookbook: Proven recipes for data analysis, statistics, and graphics, O’Reilly Media, 2011.