Logistic Regression

Description:

Outline

This course examines the fitting of models to predict a binary response variable from a mixture of binary and interval explanatory variables, using STATA software.

The approach is illustrated using examples from a social science perspective, including cases where logistic regression models are used as a means of analysing tabular data where one of the dimensions of the table is a two-category outcome variable.

You will also learn how to fit a logistic regression model, and how to interpret the results.

Objectives

At the end of the course participants should be able to:

• Understand the concepts of odds and odds ratios.
• Generate odds for given contingency tables. 
• Understand the basic theory behind binary logistic regression. 
• Run and interpret a logistic regression model. 
• Interpret Log Likelihoods to evaluate models.
• Choose between different models.

Prerequisites

Participants should have:

• a basic familiarity with statistical software;
• an understanding of basic data analysis techniques and concepts such as cross-tabulations, graphing, variance, significance testing and correlation;
• an understanding of linear regression would be helpful but not essential. 

The course is designed for users of survey data with some experience in data analysis who want to expand their understanding of more sophisticated techniques.

Cost:

£195/£140/£60

Website and registration:

https://www.cmi.manchester.ac.uk/study/short/introductory/logistic-regression/

Region:

North West

Keywords:

Logistic regression

Related publications and presentations:

Logistic regression

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