Chapter 3: Analysis of Variance

1Lecture slides

2Exercises

3Analysis of the Diet dataset - Part I

4Exercises on the Diet dataset

5Exercises on the COVID-19 dataset

6Analysis of the Diet dataset - Part II

7Exercises on the Pharmacy Attendance dataset

8Homework 2

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.