Learning outcomes

In-depth understanding of regression and multivariate methods and and their applications in data analyses.

Goals

Acquire a tool box for advanced data analyses.

Content

This course is divided into two parts: 1) Regression (Prof. Lima Mendez) and 2) Multivariate analysis (Prof. Depiereux).

The first part will focus on linear models with (1) multiple explanatory variables, (2) continuous and discrete explanatory variables. The second part will focus on multivariate analyses, in particular principal component analyses.

Table of contents

1. Linear regression: ANOVA; ANCOVA

2. Multivariate analyses: Matrix calculation; Multiple and nonlinear regression; Own values; Principal component analysis.

Exercices

Computer lab sessions using R.

Assessment method

The first part will be evaluated at the end of the semester, during the last session of practical work, the students will receive exercises similar to the exercises carried out during practical work.

Second part (Depiereux): continuous assessment (there is no second session).

Sources, references and any support material

Le matériel est accessible sur webcampus.

Language of instruction

Français