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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 2020-02-06 by Head of Programme (or corresponding)
Owner: MPENM
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES


Course round 1


Teaching language: English
Application code: 20139
Open for exchange students: Yes
Maximum participants: 100

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0108 Examination 7,5c Grading: TH   7,5c   12 Jan 2021 pm J   08 Apr 2021 pm J   19 Aug 2021 pm J  

In programs

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

Examiner:

Umberto Picchini

  Go to Course Homepage


Course round 2

 
Teaching language: English
Application code: 99222
Open for exchange students: No
Maximum participants: 10
Only students with the course round in the programme plan

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0108 Examination 7,5c Grading: TH   7,5c    

Examiner:

Umberto Picchini


  Go to Course Homepage


Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

MVE155 Statistical inference or a similar course

Aim

Understand the common mathematical structure of linear regression models and generalised linear models; construct and use these models for data analysis using statistical inference and suitable software; interpret the results and criticise the model limitations.

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

  • explain the common mathematical structure of linear regression models and generalized linear models 
  • construct and use these models for data analysis using statistical inference and suitable software 
  • interpret the results and criticize the model limitations 
  • identify data analysis situations for which linear models apply naturally and to estimate and interpret parameters
  • predict future observations and test hypotheses using suitable software such as R 
  • construct regression models that are suitable for the current data but can also generalize to future observations 
  • explain the model limitations, identify situations where the hypothesized model is not suitable for the given data, and possibly transform the data to increase the model predictive ability

Content

  • simple linear and multivariate linear models and underlying assumptions
  • the bias/variance trade-of
  • properties of least squares estimators
  • identification of outliers and the use of residuals and other diagnostics to verify if model assumptions are met;
  • the use of categorical covariates in regression.
  • testing parameters using the t-test;
  • goodness of fit indices (Rand adjusted R2).
  • confidence and prediction intervals.
  • the multicollinearity problem, its identification and remedial measures.
  • Model selection via greedy algorithms (stepwise procedures) and the AIC.
  • Model selection via the partial F test;
  • Prediction error and cross validation.
  • Interaction between covariates.
  • an introduction to generalised linear models, the exponential family, and asymptotic properties of the maximum likelihood estimators.
  • testing procedures for generalised linear models.

Organisation

Lectures; weekly (or almost weekly) mini-projects and presentations

Literature

Updated on a yearly basis - please check course homepage

Examination including compulsory elements

Summary report of the weekly mini-projects; a final project report; a written exam. Attendance to the weekly presentations of mini-analyses is mandatory. See the course page for how to compensate for missed attendance.


Page manager Published: Thu 04 Feb 2021.