Course syllabus adopted 2021-02-12 by Head of Programme (or corresponding).
Overview
- Swedish nameGrundkurs i matematisk statistik
- CodeTMS146
- Credits7.5 Credits
- OwnerTKBIO
- Education cycleFirst-cycle
- Main field of studyBioengineering, Mathematics
- DepartmentMATHEMATICAL SCIENCES
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language Swedish
- Application code 48114
- Maximum participants145
- 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 |
|---|---|---|---|---|---|---|---|
| 0115 Laboratory 1.5 c Grading: UG | 1.5 c | ||||||
| 0215 Examination 6 c Grading: TH | 6 c |
|
In programmes
Examiner
Stefan Lemurell- Associate Professor, Algebra and Geometry, 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
Linear algebra, analysis in one and in several variables.Aim
The course aims to introduce probability theory and statistical inference theory and to give practical experience in probability-based modeling, probability calculations and statistical inference. These are necessary components for handling, comparing and drawing conclusions from real experimental data.Learning outcomes (after completion of the course the student should be able to)
- understand and apply basic statistical concepts
- analyze simple stochastic problems
- understand and apply basic statistical inference theory based on descriptive statistics, concepts and methods of parameter estimation, confidence and prediction intervals, statistical hypothesis testing, statistical regression analysis, non-parametric methods and model validation
- identify suitable statistical models and adapt and use statistical methods in basic technological applications
Content
The course aims to introduce probability theory and model based statistical inference, and give practical skills in probability based modelling, probability calculations and statistical inference methods. Equal time will be spent on lectures and exercises. The Mathematical Statistics part of the course contains: Probability theory: combinatorics, notions of independence and conditioning, Bayes theory, discrete and continuous random variables in one and several dimensions, special distributions, law of large numbers and central limit theorem. Basic statistical inference: Descriptive statistics, notions and methods of parameter estimation, confidence and prediction intervals, statistical hypothesis testing, statistical regression analysis, nonparametric methods and model validation.Organisation
The course is given by Mathematical Statistics at the Department of Mathematical Sciences. The focus is on both understanding and application, with roughly equal amounts of lectures and practical assignments. The practical parts are to a large extent computer based, and are obligatory.Literature
Devore J L, Probability and Statistics for Engineering and Science, 9:th edition, (ISBN 9781305251809) and handouts.Examination including compulsory elements
Computer exercises, final written exam.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.