Course syllabus

Linjär och logistisk regression med datainsamling
Linear and Logistic Regression with Data Gathering

FMSN40, 9 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

Main field: Technology.
Elective Compulsory for: I3
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.

As part of the course you should construct a questionaire or experimental plan for a problem of your choice, collect the data and analyse it using an suitable regression model.

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. Questionaire construction and design of experiments.

Examination details

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

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: 2,5. Grading scale: UG. Assessment: Written project plan, data gathering and oral project presentation. Contents: The student's own regression problem
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: FMSN30, MASM22

Reading list

Contact and other information

Director of studies: Johan Lindström,
Course administrator: Susann Nordqvist,
Course homepage:
Further information: Only one of the courses FMSN30 and FMSN40 may be included in a degree.