Analysis of Regression and Variance

10 credits

Syllabus, Bachelor's level, 1MS004

A revised version of the syllabus is available.
Code
1MS004
Education cycle
First cycle
Main field(s) of study and in-depth level
Mathematics G2F
Grading system
Pass with distinction (5), Pass with credit (4), Pass (3), Fail (U)
Finalised by
The Faculty Board of Science and Technology, 15 April 2015
Responsible department
Department of Mathematics
What is a course syllabus?

Entry requirements

60 credit points Mathematics including Inference Theory I. Stochastics is recommended.

Learning outcomes

In order to pass the course (grade 3) the student should be able to

  • perform simple and multiple linear regression;
  • give an account for and apply the general linear model;
  • perform validation of a regression model;
  • perform one-way and multi-way analysis of variance;
  • give an account for common experimental designs: factorial and block designs, Latin squares;
  • use statistical software for analysis of regression and variance.

Content

Regression: simple and multiple linear, nonlinear, transformation of variables, residual analysis,. Analysis of variance: one-sided, multivariate, multiple comparisons, variance component models. Design of experiments: randomisation, blocks, factors. Use of statistical software.

Instruction

Lectures, problem solving sessions and computer-assisted laboratory work.

Assessment

Written examination in the end of the course combined with assignments given during the course.