*Course objective*: Introducing the logistic linear regression
model for two or more class probabilities. The scope of methods covers
the maximum-likelihood estimation of the model, the tests for
significance of effects, the search for the best subset of explanatory
variables and the prediction of a class label using the model.

*Organization*: The course is divided into five sessions. Each
session is made of a lecture and practical classes. In those in-class
practical classes, students are given the opportunity to tackle
problem-solving exercises using R.

The following documents are provided for each session:

- a video lecture: R tutorial whose slides, data and R script are
provided;

- an Rmd file containing in-class exercises.

*Past exams*:

To start with Rmarkdown: brief
introduction.

## Session 1

*Objective*: A post on the blog of a French radio channel
reports that the risk of being infected by COVID19 is 33% lower in
people with blood type O. What does this assertion means?

The session introduces a statistical method to compare groups of
items regarding their exposition to a given risk.

## Session 2

*Objective*: How to use colorimetry to evaluate the maturity
of a fruit? The session introduces a mathematical framework and related
statistical methods to address this issue.

## Session 3

*Objective*: Are the components of a colorimetry profile all
equally important to predict the maturity of a fruit? The session
introduces the test for the significance of effects in the general setup
for modeling class probabilities introduced in Session 2.

## Session 4

*Objective*: How to choose the best subset of explanatory
variables to explain class probabilities in a large profile of possibly
correlated features?

## Session 5

*Objective*: Prediction of the class label of test items
exposes to errors. Those error rates need to be accurately evaluated, in
order to help designing optimal classification strategies.