The generalized linear model (GLM) is a flexible generalization of ordinary least squares regression. The GLM allows the linear model to be related to the response variable via a link function together with an error function. Starting with the familiar linear regression and ANOVA, the course will expand the linear model to include link functions such as the logit with binomial and the log with Poisson error distributions, thereby enabling students to model outcome variables that are not continuous. Attention will be paid to likelihood estimation methods and the checking of model assumptions.
Generalized Linear Models
- Suitable for:
- Students will preferably have completed the courses Classical Methods in Data Analysis, Modern Methods in Data Analysis, and Inference and Models or their equivalents.
- Start date(s):
- 24 February 2020
- Time investment:
- Five full working days
- University Medical Center Utrecht
- Faculty of Medicine
- Fee: This fee is exempt from VAT
- € 830
- Extra information:
The assessment consists of a written exam and a case analysis. Teaching methods include lectures, computer practicals and self study.
Prerequisite for participation is sufficient capacity in terms of teachers and locations.
At the end of the course, you are able to:
- Describe the role of link functions and error distributions
- Be familiar with the most commonly used generalized linear models
- Recall when to use which model in practice
- Know the most commonly used methods for checking model appropriateness and model fit
- Perform GLM analyses using the appropriate software (R and SPSS)be able to interpret the output and report the results of GLM analyses in terms of the context of the research question