Brief Introduction
Theory and ApplicationDescription
Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind logistic regression. The theory is explained in an intuitive way. The math is kept to a minimum. The course starts with an introduction to contingency tables, in which students learn how to calculate and interpret the odds and the odds ratios. From there, the course moves on to the topic of logistic regression, where students will learn when and how to use this regression technique. Topics such as model building, prediction, and assessment of model fit are covered. In addition, the course also covers diagnostics by covering the topics of residuals and influential observations.
In the second part of the course, students learn how to apply what they learned using Stata. In this part, students will walk through a large project in order to understand the type of questions that are raised throughout the process, and which commands to use in order to address these questions.
Requirements
- Requirements
- Have a basic understanding of linear regression
Knowledge
- Create contingency tables
- Calculate odds ratio
- Understand what is logistic regression
- Identify when logistic regression is used
- Understand the output produced by logistic regression
- Include categorical variables
- Test for linearity
- Predict probabilities
- Test model fit
- Apply logistic regression using Stata
- Visualise the best-fit model
Najib Mozahem
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