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, 14 December 2009
Responsible department
Department of Mathematics
What is a course syllabus?

Entry requirements

60 credit points Mathematics including Inference Theory

Learning outcomes

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

  • be able to perform simple and multiple linear regression;

  • be able to describe and use the general linear model;

  • be able to perform residual analysis and transformations of variables;

  • be capable to handle non-linear regression;

  • be able to use methods based on orthogonal polynomials;

  • be able to perform one-way and multi-way analysis of variance;

  • understand various designs of experiments: complete randomisation, blocks and factors, Latin squares, incomplete blocks;

  • be able to perform analysis of covariance;

  • be able to use statistical software for analysis of regression and variance;

  • – as a preparation for a future professional career as statistician – have obtained a knowledge of and feeling for statistical methods that are used in the area.

    Content

    Regression: simple and multiple linear, nonlinear, transformation of variables, residual analysis, orthogonal polynomials. 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 (7 credit points) at the end of the course. Assignments and laboratory work (3 credit points) during the course.