The aim of this course is to study the Linear Regression models, we are going to study the output of a linear regression models, the assumptions and explain how these differents values are obtained. In a second part, we will focus on the study of time series and see some models to perform short prediction and show the limitations of these models.

Preliminaries

The document below provide an introduction on linear algebra and more precsisely on matrices and computations that can be made on these mathematical objects. Matrices are very important when it comes to data manipulations and will play an important role for the presentation of regression models.

• Matrices

Linear Regression

This first part deals with linear modeling, specifically linear regression in a Gaussian framework. It introduces the tools needed to understand and analyze the impact of different variables in our predictive model, as well as to test their significance.

The second part is dedicated to building a good predictive model. We will focus on assessing the quality and potential redundancy of the information while also identifying the most relevant variables for model construction.

• Course
• Notebook

• Attendance