Checking date: 30/05/2022

Course: 2022/2023

Categorial data analysis
(13733)
Study: Bachelor in Statistics and Business (203)

Coordinating teacher: CABRAS , STEFANO

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:

Requirements (Subjects that are assumed to be known)
Statistical Inference Techniques I Statistical Inference Techniques II Regression Methods
Objectives
1. Understanding the basic techniques for analyzing categorical data. 2. Knowing and managing statistical programs for the analysis of categorical data. 3. Using the methodology for the analysis of real data. 1. Capacity for analysis and synthesis. 2. Modeling and resolution of problems. 3. Oral and written communication.
Skills and learning outcomes
Description of contents: programme
1. Introduction. 1.1. Categorical Response Data. 1.2. General approach to different statistical techniques. 1.3. Examples. 2. Contingency Tables. Measures of relationship and association. Contrasts. 2.1. Association Measures for categorical data. 2.2. Statistical inference (parametric and nonparametric). 2.3. Examples. 3. Simple and multiple correspondence analysis. 3.1. Introduction: assumptions, estimation and interpretation. 3.2. Simple correspondence analysis. 3.3. Multiple correspondence analysis. 3.4. Examples. 4. Decision trees. 4.1. Introduction: assumptions, estimation and interpretation. 4.2. Algorithms: CHAID, CART and QUEST. 4.3. Examples. 5. Generalized Linear Models (GLM). Models for binary data (logistic regression) and multiple response. 5.1. Introduction to GLM and comparison with other models. 5.2. Limited dependent variable models: models for binary data. Binary logistic regression: assumptions, estimation and interpretation. 5.3. Models for multinomial data. Multiple Logistic Regression: assumptions, fitting and interpretation. 5.4. Examples.
Learning activities and methodology
Theory (4 ECTS). Theoretical classes with support material available on the Web. Practice (2 ECTS) problem-solving classes. Computing practices in computer lab.
Assessment System
• % end-of-term-examination 0
• % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
• Agresti, A. Categorical Data Analysis. New York: John Wiley & Sons. 2013 (third Edition)
• Agresti, A.. An introduction to Categorical data analysis. John Wiley & Sons,. 2007
• Andersen, E.B . Introduction to the Statistical Analysis of Categorical Data. Springer. 1997
• Collett D. . Analysis of Binary Data. Chapman & Hall.. 2003
• Cox D.R. & Snell E.J.. Analysis of Binary Data. Chapman & Hall. 1989
• Cox D.R. & Snell E.J.. Analysis of Binary Data. Chapman & Hall. 2018
• Kateri, M. Contingency Table: Analysis Methods and Implementation Using R. Birkhäuser. 2014
• Zelterman, D. Models for Discrete Data. Oxford University Press. 2006 (revised edition)
Electronic Resources *