Checking date: 05/08/2021


Course: 2021/2022

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


Coordinating teacher: MARIN DIAZARAQUE, JUAN MIGUEL

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
SPECIFIC SKILLS 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. TRANSVERSAL COMPETENCES: 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 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
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)
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Bishop, Y. M., Fienberg, S. E., Holland, Paul W. . Discrete Multivariate Analysis: Theory and Practice. Springer (Originally published by MIT Press, 1975). 2007
  • Hosmer, D.W. and Lemeshow, S.. Applied Logistic regression. Willey. 2000
  • McCullagh, P. and Nelder, J.A.. Generalized Linear Models, Second Edition. London: Chapman & Hall. 1989
  • Stokes, M.E., Davis, C.S. and Koch, G.G.. Categorical Data Analysis Using The SAS System, Second Edition. NC: SAS Institute Inc.. 2000
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN


The course syllabus may change due academic events or other reasons.


More information: https://sites.google.com/site/nirianmartinswebsite/teaching/analisis-de-datos-categoricos