Syllabus for |
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MVE155 - Statistical inference
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| Syllabus adopted 2014-02-13 by Head of Programme (or corresponding) |
| Owner: MPENM |
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7,5 Credits |
| Grading: TH - Five, Four, Three, Not passed |
| 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
Open for exchange students
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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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15 Mar 2016 pm H
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07 Jun 2016 pm M
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22 Aug 2016 am SB
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In programs
TKITE SOFTWARE ENGINEERING, Year 3 (compulsory elective)
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 1 (compulsory elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory)
Examiner:
Professor
Serik Sagitov
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
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
Written examination.
Bonus points for optional assignments work only for the first scheduled examination.