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## Syllabus for

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

#### 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.