|Lectures||Mon 08-10 HG E 1.2 and/or via zoom|
|Exercises||Mon 10-12 (bi-weekly) HG E 1.2 and/or via zoom|
|Course catalogue data||Link to VVZ|
|Moodle course webpage||Link to Moodle|
Faraway (2005): Linear Models with R
This course offers a practically oriented introduction into regression modeling methods. The basic concepts and some mathematical background are included, with the emphasis lying in learning "good practice" that can be applied in every student's own projects and daily work life. A special focus will be laid in the use of the statistical software package R for regression analysis.Objective
The students acquire advanced practical skills in linear regression analysis and are also familiar with its extensions to generalized linear modeling.Content
The course starts with the basics of linear modeling, and then proceeds to parameter estimation, tests, confidence intervals, residual analysis, model choice, and prediction. More rarely touched but practically relevant topics that will be covered include variable transformations, multicollinearity problems and model interpretation, as well as general modeling strategies.
The last third of the course is dedicated to an introduction to generalized linear models: this includes the generalized additive model, logistic regression for binary response variables, binomial regression for grouped data and Poisson regression for count data.Notice
The exercises, but also the classes will be based on procedures from the freely available, open-source statistical software package R, for which an introduction will be held.
The course starts on Monday, September 26, 2022, 8:15am at ETHZ HG E1.2. Exercise classes also start on 26/09/2022 at 10:15 in room HG E1.2.
Course materialsAll course material will be provided on the Moodle course webpage, including a script and slides.
Exercises will be held roughly bi-weekly, starting on Monday, September 26, 2022. On these dates, the exercise classes will take place from 10:15 to 11:55 in HG E1.2. Exercise sheets (Series) and corresponding Solutions will be uploaded to the Moodle course webpage. For more details, please refer to the Moodle course webpage.
Help with R
During the first exercise class you will have the opportunity to ask questions regarding the software R. Further material can be found following the links below.