Course syllabus for Statistics and probability

The course syllabus contains changes
See changes

Course syllabus adopted 2021-02-18 by Head of Programme (or corresponding).

Overview

  • Swedish nameStatistik och sannolikhetsteori
  • CodeMVE650
  • Credits7.5 Credits
  • OwnerTKGBS
  • Education cycleFirst-cycle
  • Main field of studyMathematics
  • DepartmentMATHEMATICAL SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language Swedish
  • Application code 74121
  • Open for exchange studentsNo
  • Only students with the course round in the programme overview.

Credit distribution

0121 Examination 7.5 c
Grading: TH		 7.5 c
  • 13 Mar 2023 pm J
  • 07 Jun 2023 am J
  • 21 Aug 2023 pm J

In programmes

Examiner

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Eligibility

General entry requirements for bachelor's level (first 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

The same as for the programme that owns the course.
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

Single variable calculus and multivariable calculus. It is recommended to also have taken Applied mathematical thinking and Linear algebra and experimental mathematics.

Aim

The course aims to introduce probability theory and statistical inference theory, to give insights into how these are used in science and society, and to equip the students to solve simple applied problems in these areas.

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

  • be able to perform calculations using basic probability theory
  • use a statistical mind-set
  • formulate and apply basic statistical models and methods, mainly using classical frequentist statistics but also some Bayesian statistics

Content

Sample space, probabilities, conditional probabilities. Modelling with various probability distributions, and the Poisson process. Basic manipulations of random variables, their expected value, and their variance.The Law of Large Numbers,The Central Limit Theorem, approximations in distribution, failure rate. Parameter estimation using, e.g.,maximum likelihood methods.Confidence intervals and significance tests.Stochastic vectors and basic linear regression.One-way ANOVA. Basic principles of Bayesian statistics.Examples taken from science and society. 

Organisation

Lectures end exercise sessions.

Literature

To be announced on the course home page. 

Examination including compulsory elements

Written exam, to which it will be possible to earn advance bonus points by completing an essay assignment.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.

The course syllabus contains changes

  • Changes to course rounds:
    • 2022-11-02: Examinator Examinator changed from Stefan Lemurell (sj) to Serik Sagitov (serik) by Viceprefekt/adm
      [Course round 1]