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401-4634-07L 5 Credits BSC , DR , MSC D-USYS , D-MAVT , D-MTEC , D-MATH , D-BIOL , D-CHAB

Statistical Models for Count Data

Lecturers & Examiners: Dr. Johana Neslehova
VVZ CR n/a

Last Updated: 2026-02-05 15:19:32

Abstract

This is a basic course on categorical data analysis. The aim is to cover standard techniques for the analysis of categorical data, like presence or absence of a disease, size of a company or number of losses incurred within a period of time. We discuss theoretical properties of the models covered, statistical inference and model diagnostics, examples on real data and software illustrations in R.

Content

1. Introduction to statistical inference for categorical data 2. Contingency tables 3. Generalized linear models 4. Logistic regression 5. Logit and loglinear models 6. Methods for repeated measurement and random effects models

Resources

Literature

Alan Agresti (2002): Categorical Data Analysis, John Wiley & Sons, 2002, 2nd edition. J. K. Lindsey (1995): Modelling Frequency and Count Data, Oxford University Press, 1995. Daniel Zelterman (2006): Models for Discrete Data, Oxford University Press, 2006, revised edition.

General Information

Language
English
Levels
BSC , DR , MSC

Examination

Type
session examination
Mode
oral 20 minutes

Course Components

Type Title Time & Place Hours
lecture Statistical Models for Count Data
  • Fri 13:15-15:00 (HG D 5.2)
  • 01.06 Date 12:15-13:00 (HG D 5.2)
  • 08.06 Date 12:15-13:00 (HG D 5.2)
2 h weekly

Offered In