Course syllabus adopted 2024-02-12 by Head of Programme (or corresponding).
Overview
- Swedish nameMatematisk statistik
- CodeMVE635
- Credits7.5 Credits
- OwnerTKDES
- 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 56123
- 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 |
|---|---|---|---|---|---|---|---|
| 0120 Examination, part A 6 c Grading: TH | 6 c |
| |||||
| 0220 Written and oral assignments, part B 1.5 c Grading: UG | 1.5 c |
In programmes
Examiner
Patrik Albin- Associate Professor, 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 knowledge of calculus and linear algebra.
Aim
This course covers the basics of probability theory and statistics with special regard to those elements relevant to technology and ergonomics.
The course also introduces elements of statistical experimental design.
Learning outcomes (after completion of the course the student should be able to)
- Calculate probabilities using arguments from set theory and combinatorics
- Apply stochastic variables and known distributions to calculate probabilities in more complicated situations and calculate quantities like expectations, standard deviations, variances and covariances.
- Apply the central limit theorem
- Calculate point estimates and apply the method of moments and the maximum likelihood method in order to get estimates of parameters in probability distributions
- Calculate confidence intervals, statistics and p-values for (among other things) proportions, expectations and be able to apply these in hypotheses testing.
- Make regression analysis in simple cases
- Determine areas of application and restrictions of the statistical measurments named above
Content
Probability theory:- Basic concepts (for example: sample space, probability, inclusion, intersection, union...)
- Dependent and independent events. Conditional probabilities.
- Combinatorics
- Stochastic variables: discrete and continuous, one-dimensional and multi-dimensional. Expectation and variance.
- Known distributions: uniform (discrete and continuous), binomial, geometric, exponential, normal, t-distribution
- The central limit theorem and normal approximation
- Point estimates
- The maximum likelihood method and the method of moments
- Confidence intervals for expectations, variance and proportions
- Hypothesis testing
- Linear regression
- Simple experimental design
Statistical computations with computer
Organisation
Lectures, exercise sessions, and project.
Literature
Will be announced later
Examination including compulsory elements
Written exam, assignments, and project
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 about disability study support.