Statistical learning with regression models
About
This course is very relevant for anyone working with data. Regression methods are the most important statistical tools to study the dependency between a target variable of interest (response) and several other variables (covariates).
By building a robust and interpretable regression model, you will be able to perform predictions and quantify the corresponding uncertainties.
The course integrates methodology with hands-on data analysis using the R software.
Main topics:
- linear regression model building: how to select important covariates
- diagnostics: how to identify outliers and influential observations
- inference: hypothesis testing and confidence intervals
- robust modelling using cross-validation
- generalized linear models: expanding beyond Gaussian errors
Prerequisites and selection
Entry requirements
MSG100/MSG110 and MSG200 or other courses covering the material of these courses.
Facilities
Mathematical Sciences is a joint department of Chalmers/University of Gothenburg. Your education takes place in the spacious and bright premises of Mathematical Sciences at the Chalmers campus Johanneberg, where there are lecture halls, computer rooms and group rooms. Here you can also find student lunch room and reading room, as well as student counsellors and student office.
