Valid for: 2024/25
Faculty: Faculty of Engineering LTH
Decided by: PLED I
Date of Decision: 2024-04-16
Effective: 2024-05-08
Depth of study relative to the degree requirements: Second cycle, in-depth level of the course cannot be classified
Elective for: BME4, D4, E4-ae, F4, F4-fm, F4-mai, I4, L4-fe, MMSR1, Pi4-fm, Pi4-biek, Pi4-bam, 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.
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.
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.
Modules
Code: 0317. Name: Project 2.
Credits: 1.5. Grading scale: UG - (U, G).
Assessment: Written project report and peer assessment
The module includes: Logistic regression
Code: 0417. Name: Project 3.
Credits: 1.0. Grading scale: UG - (U, G).
Assessment: Oral project presentation
The module includes: Other regression models
Code: 0117. Name: Examination.
Credits: 3.0. Grading scale: TH - (U, 3, 4, 5).
Assessment: Oral examination.
Code: 0217. Name: Project 1.
Credits: 1.5. Grading scale: UG - (U, G).
Assessment: Written project report and peer assessment
The module includes: Linear regression
Code: 0517. Name: Laboratory Work.
Credits: 0.5. Grading scale: UG - (U, G).
Assessment: Computer exercises
Admission requirements:
Director of studies: Johan Lindström,
studierektor@matstat.lu.se
Course administrator: Susann Nordqvist,
expedition@matstat.lu.se
Teacher: Anna Lindgren,
anna.lindgren@matstat.lu.se
Course homepage: https://www.maths.lu.se/utbildning/civilingenjoersutbildning/matematisk-statistik-paa-civilingenjoersprogram/