Chapter 5: Generalized Linear Models

1Lecture slides

2Analysis of the bronchitis dataset: Estimation

3Analysis of the bronchitis dataset: Prediction - Part I

4Analysis of the bronchitis dataset: Improving our model

5Analysis of the bronchitis dataset: How good is our model?

6Analysis of the bronchitis dataset: Prediction - Part II 😱

7Exercise: ICU admission of COVID-19 patients - Part I

8Exercise: ICU admission of COVID-19 patients - Part II

9Homework 4

About this course:

This course is intended to provide an introduction to the data analysis tools with R. These tools include statistical hypothesis testing, (generalized) linear models, machine learning and semi-parametric methods. Applications of these statistical methods are provided in the context of Pharmaceutical Sciences. At the end of this class, students are expected to understand the underlying theoretical foundations and the computational issues of different statistical methods, as well as to communicate the practical interpretation of the data analysis results.

The material presented in this website was mainly developed by Stéphane Guerrier, Lionel Voirol and Yuming Zhang. Moreover, we thank Francesco Gervasio, Yiannis Galdadas, Julien Boccard, Dominique-Laurent Couturier, Luca Insolia, Wenfei Chu and Jun Wu for their valuable contributions.