Course syllabus

Linjär och logistisk regression
Linear and Logistic Regression

FMSN30, 7,5 credits, A (Second Cycle)

Valid for: 2012/13
Decided by: Education Board 1
Date of Decision: 2012-03-27

General Information

Elective for: D4, F4, L4-fe, M4, Pi4
Language of instruction: The course will be given in English on demand

Aim

Regression analysis deals with modelling how one characteristic (height, weight, price, concentration, etc) varies with one or several other characteristics (sex, living area, expenditures, temperature, etc). Linear regression is introduced in the basic course in mathematical statistics but here we expand with, e.g., "how do I check that the model fits the data", "what should I do i it doesn't fit", "how uncertain is it", and "how do I use it to draw conclusions about reality".

When perfoming a survey where people can awnser yes/no or little/just fine/much, or car/bicycle/bus or some other categorical alternative, you cannot use linear regression. Then you need logistic regression instead. This is the topic in the second half of the course.

If you have a data material suitable for analysis using linear or logistic regression, you may analyse it as part of the project.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must

Judgement and approach
For a passing grade the student must

Contents

Least squares and maximum-likelihood-method; odds ratios; Multiple and linear regression; Matrix formulation; Methods for model validation, residuals, outliers, influential observations, multi co-linearity, change of variables; Choice of regressors, F-test, likelihood-ratio-test; Confidence intervals and prediction. Introduction to: Correlated errors, Poisson regression as well as multinomial and ordinal logistic regression.

Examination details

Grading scale: TH
Assessment: The examination is written and oral in the form of project reports, written and oral opposition, and individual oral examination.

Parts
Code: 0112. Name: Examination.
Credits: 5,5. Grading scale: TH. Assessment: Oral examination.
Code: 0212. Name: Project.
Credits: 2. Grading scale: UG. Assessment: Written project report with oral presentation and opposition of another report.

Admission

Admission requirements:

The number of participants is limited to: No
The course might be cancelled: If the number of applicants is less than 16.
The course overlaps following course/s: MASM22

Reading list

Contact and other information

Course coordinator: Anna Lindgren, anna@maths.lth.se
Course homepage: http://www.maths.lth.se/matstat/kurser/masm22/
Further information: The course is also given at the faculty of science with the code MASM22.