Syllabus for |
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MVE155 - Statistical inference
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| Statistisk slutledning |
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| Syllabus adopted 2019-02-26 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 |
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Major subject: Mathematics
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Department: 11 - MATHEMATICAL SCIENCES
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Teaching language: English
Application code: 20145
Open for exchange students: Yes
| Module |
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0107 |
Examination |
7,5 c |
Grading: TH |
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7,5 c
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16 Mar 2021 pm J
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09 Jun 2021 am J, |
17 Aug 2021 pm J
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In programs
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory)
TKITE SOFTWARE ENGINEERING, Year 3 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 1 (compulsory elective)
Examiner:
Serik Sagitov
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
A first course in probability and statistics worth of 7.5 credits.
Aim
To give the students insight in tecniques for treating multiple sample data, sampling designs, and appropriate statistical tests for this kind of data
Learning outcomes (after completion of the course the student should be able to)
- summarize multiple sample data in a meaningful and informative way,
- recognize several basic types of statistical problems corresponding to various sampling designs,
- estimate relevant parameters and perform appropriate statistical tests for multiple sample data sets.
Content
This is a second course in mathematical statistics introducing the following key topics of statistical inference:
- sampling designs and summarizing data
- maximum likelihood estimation of parameters, bootstrap
- parametric and non-parametric inference
- the analysis of variance, linear least squares, categorical data
- elements of Bayesian inference.
Organisation
Lectures, exercises, and optional computer assignments.
Literature
Mathematical statistics and data analysis by John A. Rice.
Lecture notes downloadable from the internet.
Examination including compulsory elements
Written examination.