Course syllabus

Linjär och logistisk regression
Linear and Logistic Regression

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

Valid for: 2023/24
Faculty: Faculty of Engineering, LTH
Decided by: PLED I
Date of Decision: 2023-04-14

General Information

Elective for: BME4, D4, E4-ae, F4, F4-fm, F4-mai, I4, L4-fe, Pi4-fm, Pi4-biek, Pi4-bam, MMSR1, R4
Language of instruction: The course will be given in English


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


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 - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written and oral project presentation, peer assessment and oral exam.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Code: 0117. Name: Examination.
Credits: 3. Grading scale: TH. Assessment: Oral examination.
Code: 0217. Name: Project 1.
Credits: 1,5. Grading scale: UG. Assessment: Written project report and peer assessment Contents: Linear regression
Code: 0317. Name: Project 2.
Credits: 1,5. Grading scale: UG. Assessment: Written project report and peer assessment Contents: Logistic regression
Code: 0417. Name: Project 3.
Credits: 1. Grading scale: UG. Assessment: Oral project presentation Contents: Other regression models
Code: 0517. Name: Laboratory Work.
Credits: 0,5. Grading scale: UG. Assessment: Computer exercises


Admission requirements:

The number of participants is limited to: No
The course overlaps following course/s: MASM22, FMSN40

Reading list

Contact and other information

Director of studies: Johan Lindström,
Course administrator: Susann Nordqvist,
Course homepage:
Further information: The course is also given at the faculty of science with the code MASM22. Only one of the courses FMSN30 and FMSN40 may be included in a degree.