At the end of the course, the student will:
know the role of link functions and error distributions
be familiar with the most commonly used generalized linear models
know when to apply which model in practice
know the most commonly used methods for checking model appropriateness and model fit
be able to 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
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Education form |
Startdate |
Enddate |
Registration period |
Face-2-Face |
21-2-2022 |
25-2-2022 |
BMS_P3_A |
Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl
Registration:
Register via https://www.msc-epidemiology.nl/single-courses.html
Course coordinator:
Rebecca Stellato
Course description:
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.
Literature/study material used:
-
Mandatory for students in own Master’s programme:
MIght be for a specialization programme of Epidemiology & Epidemiology Postgraduate
Optional for students in other GSLS Master’s programme:
Yes
Prerequisite knowledge:
At least one course in basic statistical methods up to and including simple and multiple linear regression; familiarity with likelihood methods (Wald, score and likelihood ratio tests). Students will (preferably) have completed the courses Introduction to Statistics, Classical Methods in Data Analysis, Modern Methods in Data Analysis, and Inference and Models or their equivalents.
Familiarity with the statistical package R is required!
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