Syllabus academic year 2011/2012
(Created 2011-09-01.)
LINEAR AND LOGISTIC REGRESSIONFMSN30
Credits: 7,5. Grading scale: TH. Cycle: A (Second Cycle). Main field: Technology. Language of instruction: The course will be given in English on demand. FMSN30 overlaps following cours/es: MASM22. Optional for: D4, F4, L4fe, M4, Pi4. Course coordinator: Anna Lindgren, anna@maths.lth.se, Mathematical Statistics. Prerequisites: FMS012/FMS032/FMS035/FMS086/FMS140 Mathematical statistics, basic course. The course might be cancelled if the number of applicants is less than 16. Assessment: The examination is written and oral in the form of project reports, written and oral opposition, and individual oral examination. Parts: 2. Further information: The course is also given at the faculty of science with the code MASM22. Home page: http://www.maths.lth.se/matstat/kurser/masm22/.

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.

Knowledge and understanding
For a passing grade the student must

Skills and abilities
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.

Literature
Rawlings, J.O., Pantula, S.G., Dickey, D.A.: Applied Regression Analysis - A Research Tool, 2ed, Springer 1998. ISBN: 0-387-98454-2 (finns som e-bok).
Christensen, R.: Log-Linear Models and Logistic Regression, 2ed, Springer 1997. ISBN: 0-387-98247-7 (finns som e-bok).

Parts

Code: 0112. Name: Examination.
Higher education credits: 5,5. Grading scale: TH. Assessment: Oral examination.

Code: 0212. Name: Project.
Higher education credits: 2. Grading scale: UG. Assessment: Written project report with oral presentation and opposition of another report.