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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
TMS061 - Mathematical statistics
 
Syllabus adopted 2008-02-24 by Head of Programme (or corresponding)
Owner: TKMAS
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: First-cycle
Department: 11 - MATHEMATICAL SCIENCES


Teaching language: Swedish

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0106 Examination 6,0 c Grading: TH   6,0 c   27 May 2009 am M,  14 Jan 2009 am V,  20 Aug 2009 am V
0206 Design exercise 1,5 c Grading: UG   1,5 c    

In programs

TKMAS MECHANICAL ENGINEERING, Year 3 (compulsory)
TKDES INDUSTRIAL DESIGN ENGINEERING, Year 3 (compulsory)

Examiner:

Forskarassistent  Anastassia Baxevani


Replaces

TMS060   Mathematical statistics

Course evaluation:

http://document.chalmers.se/doc/1080686592


  Go to Course Homepage

Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

Basic courses in mathematical analysis and linear algebra.

Aim

The course covers basic Probability theory and statistics with
emphases on concepts and methods of importance for technology.
The course also introduces elements of Experimental design.

Learning outcomes (after completion of the course the student should be able to)

- identify problems arising in technical studies for which the treatment requires use
of fundamental concepts and methods from Probability theory and Mathematical
statistics.
-describe and analyze such problems in terms of statistics and discrete mathematics.
- apply basic statistical methods such as parameter and interval estimation, testing
of statistical hypotheses, and linear regerssion, in problem solving.

Content

Probability theory:

Probability measure, dependent and independent events, basic combinatorics. Random variables,
pairs of random variables, expectation and variance. Some special probability distributions: the binomial distribution, the Poisson distribution, the exponential distribution. The low of large numbers. Application of the Central limit theorem.

Statistics:

Descriptive statistics. Sample mean and variance. General estimation methods, some properties of point estimators. Interval estimation of the expected value and the variance, the two-sample t-test. Regression and correlation: Curve fitting, confidence interval, and test for a parametric linear model, correlation coefficient.

Organisation

Lectures and exercises. Construction exercises. Written examination.

Literature

See the homepage of the course.

Examination

Passed construction exercises. Written examination.


Page manager Published: Thu 04 Feb 2021.