Entry requirements

60 credits in statistics

Learning outcomes

After completing the course, a student is expected to:

* have obtained basic knowledge of the statistical theory behind multivariate statistical methods

* know and be able to apply general principles of inference about multivariate models

* be able to judge if the assumptions for a multivariate statistical method are fulfiled

* be able to estimate the parameters of a multivariate model using of statistical software

* be able to interpret the parameter estimates of a multivariate model

* be able to absorb the content of s scientific publications about multivariate statistic

* have ability to both in oral and written form present results of statistical analyses

* have basic knowledge of the statistical program packages SAS and LISREL.

Content

Different multivariate methods are handled, for example principal component analysis, factor analysis, cluster analysis, discriminant analysis, logistic regression, canonical correlation, structural equation models, analysis of variance , multivariate linear regression.

Instruction

Teaching is given in the form of lectures and computer exercises. The lectures can take place online or in class-room.

Assessment

The examination takes place partly through a written examination at the end of the course and through presentation orally and in writing of compulsory home assignments.

"If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator."