Date: 3 May 2023
Instructor: Tatjana Kecojevic
Please note that this course is open to University of Manchester staff and students only.
To book on to this course, please email firstname.lastname@example.org for further details.
This course examines the fitting of models to predict a binary response variable from a mixture of binary and interval explanatory variables, using the statistical software called R.
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.
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.
Some knowledge of the R programming language and basic understanding of regression would be helpful.