Course syllabus for Linear statistical models

The course syllabus contains changes
See changes

Course syllabus adopted 2020-02-06 by Head of Programme (or corresponding).

Overview

  • Swedish nameLinjära statistiska modeller
  • CodeMVE190
  • Credits7.5 Credits
  • OwnerMPENM
  • Education cycleSecond-cycle
  • Main field of studyMathematics
  • DepartmentMATHEMATICAL SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 20139
  • Maximum participants100
  • Open for exchange studentsYes

Credit distribution

0108 Examination 7.5 c
Grading: TH		 7.5 c
  • 12 Jan 2021 pm J
  • 08 Apr 2021 pm J
  • 19 Aug 2021 pm J

In programmes

Examiner

Go to coursepage (Opens in new tab)

Course round 2

  • Teaching language English
  • Application code 99222
  • Maximum participants10
  • Open for exchange studentsNo
  • Only students with the course round in the programme overview.

Credit distribution

0108 Examination 7.5 c
Grading: TH		 7.5 c

    Examiner

    Go to coursepage (Opens in new tab)

    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 (R2 and 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.

    The course syllabus contains changes

    • Changes to examination:
      • 2021-04-14: Exam date Exam date changed by Elisabeth Eriksson
        [32892, 53872, 3], New exam for academic_year 2020/2021, ordinal 3 (not discontinued course)
      • 2021-01-27: Examination datetime Examination datetime changed from 2021-04-08 Afternoon to 2021-04-08 Afternoon by E Eriksson
        [2021-04-08 7,5 hec, 0108]
      • 2021-01-27: Exam date Exam date changed by E Eriksson
        [32892, 53872, 2], New exam for academic_year 2020/2021, ordinal 2 (not discontinued course)
      • 2020-09-30: Grade raising No longer grade raising by GRULG