Course syllabus adopted 2024-02-14 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 74126
- Open for exchange studentsNo
- Only students with the course round in the programme overview.
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0121 Examination 7.5 c Grading: TH | 0 c | 0 c | 7.5 c | 0 c | 0 c | 0 c |
In programmes
Examiner
Serik Sagitov- Full Professor, Applied Mathematics and Statistics, Mathematical Sciences
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 examination. Optional assignments that may grant bonus points towards the examination may occur.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.