Course syllabus

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

FMSN40, 9 credits, A (Second Cycle)

Valid for: 2014/15
Decided by: Education Board B
Date of Decision: 2014-04-08

General Information

Main field: Technology.
Elective Compulsory for: I3
Language of instruction: The course will be given in English on demand


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

Code: 0114. Name: Examination.
Credits: 5,5. Grading scale: TH. Assessment: Oral examination
Code: 0214. Name: Project.
Credits: 3,5. Grading scale: UG. Assessment: Collection of, or description of existing, own data material, written project report with oral presentation and opposition of another report.


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: Anna Lindgren,
Course homepage: