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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
MVE190 - Linear statistical models  
Linjära statistiska modeller
 
Syllabus adopted 2019-02-22 by Head of Programme (or corresponding)
Owner: MPENM
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES


Teaching language: English
Application code: 20125
Open for exchange students: Yes
Maximum participants: 100

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0108 Examination 7,5 c Grading: TH   7,5 c   14 Jan 2020 pm SB   07 Apr 2020 pm DIST   18 Aug 2020 pm J  

In programs

MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (elective)

Examiner:

Umberto Picchini

  Go to Course Homepage


Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

MVE155 Statistical Inference or a similar course

Aim

The course covers the following topics:
- multidimensional normal distributions
- general linear models in linear algebra terms
- statistical analysis of general linear models using algebraic tools like projections,
generalized matrix inverses and quadratic forms
- the duality of hypothesis tests and confidence sets
- Sheffe's and Tukey's methods for multiple tests and confidence intervals
- noncentral t- and F-distributions and their use for test power computations
- tolerance and prediction intervals
- graphical methods for model validation
- introduction to generalizations towards general heteroscedastic and covariance structures
- generalized linear models with link function for some exponential families
- estimation algorithms for various link functions.
- hypothesis testing and confidence intervals for linear combinations of parameters in
the generalized linear models: the binomial, Poisson and multinomial distribution cases
- generalized residuals and their use.

Learning outcomes (after completion of the course the student should be able to)

Build a general linear model for practical applications and perform the statistical analysis, mostly multiple linear regression by some statistical software.

Describe and analyze linear models using matrix algebra.

Content

Simple and multiple linear regression models. Model validation and model selection. Regression trees. Logistic regression.

Organisation

Lectures, mathematical exercises, computer labs,
practical work in groups with real data

Literature

Updated on a yearly basis - please check course homepage

Examination including compulsory elements

Written examination, assignments


Page manager Published: Thu 04 Feb 2021.