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Syllabus for

Academic year
MVE155 - Statistical inference
Statistisk slutledning
 
Syllabus adopted 2019-02-26 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


Teaching language: English
Application code: 20145
Open for exchange students: Yes

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0107 Examination 7,5c Grading: TH   7,5c    

In programs

MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory)
TKITE SOFTWARE ENGINEERING, Year 3 (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.


Published: Mon 28 Nov 2016.