Course syllabus adopted 2021-02-26 by Head of Programme (or corresponding).
Overview
- Swedish nameSannolikhet och statistik
- CodeMVE302
- Credits7.5 Credits
- OwnerTKTEM
- 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 59126
- Maximum participants60
- 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 |
|---|---|---|---|---|---|---|---|
| 0318 Examination 6 c Grading: TH | 0 c | 0 c | 0 c | 6 c | 0 c | 0 c |
|
| 0418 Project 1.5 c Grading: UG | 0 c | 0 c | 0 c | 1.5 c | 0 c | 0 c |
In programmes
Examiner
- Erik Broman
- Senior Lecturer, Analysis and Probability Theory, 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
Basic one-variable and multi-variable calculus. Linear algebra is recommended.Aim
Basic skills in probability theory and statistics and the ability to solve simple practical problems and safety analyses. In more detail, the purpose is to treat the basics of probability theory and statistics and to introduce modern computer intensive methods to analyze data. This will include the introduction to Bayesian statistics as a way to work with different kinds of uncertainties.Learning outcomes (after completion of the course the student should be able to)
After the course, the student should be well acquainted with basic probability theory and a good practice of the statistical mind-set, statistical modeling and basic statistical methods. This also includes a knowledge of Bayesian statistics. One should also be able to use Matlab to simulate distributions of random variables and conduct statistical inference. A very detailed reading display will be posted on the course homepage.The course project will also give group-wise deepened knowledge in some area and training in oral presentation.
Content
Sample space, probabilities, conditional probabilities. Different probability distributions and its common applications. Means to operate on random variables, expected value, variance. The Central Limit Theorem, Law of Large numbers, failure rate. Inference, maximum likelihood methods, confidence intervals, and some significance tests. One-way ANOVA. Bayesian statistics, The Poisson process. Bootstrap. The course also contains a project, where different project groups will learn different extra material.Organisation
Lectures, exercises and a project with project supervision.Literature
P. Olofsson and M.Andersson, Probability, Statistics and Stochastic Processes, 2nd edition, Wiley 2011.Examination including compulsory elements
Written exam. The project via a written report and an oral presentation for the class. Attendance at the oral presentations is compulsory.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.