Course Syllabus for

Linear and Logistic Regression
Linjär och logistisk regression

FMSN30F, 7.5 credits

Valid from: Autumn 2020
Decided by: Professor Thomas Johansson
Date of establishment: 2020-08-26

General Information

Division: Mathematical Statistics
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: FMSN30
Teaching language: English

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.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Judgement and Approach

For a passing grade the doctoral student must

Course 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.

Course Literature

Instruction Details

Types of instruction: Lectures, laboratory exercises, project

Examination Details

Examination formats: Oral exam, written report, seminars given by participants
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: Basic course in Mathematical Statistics

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page: www.maths.lth.se/matstat/kurser/fmsn30/


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