Syllabus for |
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MVE190 - Linear statistical models
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Linjära statistiska modeller |
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Syllabus adopted 2020-02-06 by Head of Programme (or corresponding) |
Owner: MPENM |
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7,5 Credits
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Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail |
Education cycle: Second-cycle |
Major subject: Mathematics
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Department: 11 - MATHEMATICAL SCIENCES
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Course round 1
Teaching language: English
Application code: 20139
Open for exchange students: Yes
Maximum participants: 100
Module |
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Credit distribution |
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Examination dates |
Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
0108 |
Examination |
7,5 c |
Grading: TH |
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7,5 c
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12 Jan 2021 pm J
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08 Apr 2021 pm J
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19 Aug 2021 pm J
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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 |
7,5 c |
Grading: TH |
|
|
7,5 c
|
|
|
|
|
|
|
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 (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.