42062 - Linear Models

Course Unit Page

Academic Year 2018/2019

Learning outcomes

By the end of the course the student should know the basic theory of normal linear models and should be able to apply them to the analysis of real phenomena.

In particular the student should be able:

- to define a statistical model

- to formulate the normal linear model, estimate its parameters and test their significance

- to use variable selection procedures

- to check the validity of the assumptions the model relies upon

- to perform an anlysis of variance and interpret the results

Course contents

Multiple Linear Regression: Definition, Estimation and Hypothesis Testing. Selection of Predictor Variables in the Multiple Linear Regression Model. Normal Linear Models: Analysis of Variance.

Readings/Bibliography

Lecture notes downloadable from AMS campus

Teaching methods

Lectures and labs using the software R

Assessment methods

Computer and oral examination

Office hours

See the website of Angela Montanari