Course syllabus adopted 2021-02-18 by Head of Programme (or corresponding).
Overview
- Swedish nameMatematisk statistik
- CodeMVE591
- Credits4 Credits
- OwnerTKMAS
- 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 English
- Application code 55136
- 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 4 c Grading: TH | 0 c | 0 c | 0 c | 4 c | 0 c | 0 c |
|
In programmes
Examiner
Sergey Zuev- Full 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 courses in mathematical analysis and linear algebra.Aim
The course covers basic Probability theory and statistics with emphases on concepts and methods of importance for technology. The course also introduces elements of Experimental design.Learning outcomes (after completion of the course the student should be able to)
- identify and describe problems arising in technical studies for which the treatment requires use of fundamental concepts and methods from Probability theory and Mathematical Statistics;
- summarise data by deriving summary statistics and use graphical methods for their representation;
- formulate probabillistic models for real-world situations and analyse them with the help of probability tools, e.g., conditional probabulities, random variables, distributions;
- understand the random sampling method to gather representative statistics from a population and know the properties of the corresponding estimators of the mean and of the variance;
- be able to check statistical hypotheses on the values of the mean and the variance for one and two samples;
- study linear dependence between two or multiple variates using the tools of regression analysis, analysis of variance and multiple regression.
Content
* Probability:- Probability measure, events, basic combinatorics.
- Random variables, expectation and variance.
- Main distributions: Binomial, Poisson, Exponential, Normal.
- Central Limit Theorem and Poisson Theorem and their applications.
- Descriptive statistics.
- Sample mean and variance.
- Estimates: point and interval, sampling.
- Hypotheses testing: one and two-sample tests.
- Regression and correlation, multiple regression.
Organisation
The teaching of the course is organised around the following components: lectures on the theory and computer lab work in the web-based Virtual Learning Environment (VLE) Particular emphasis is made on the own work with the VLE tasks which are assessed automatically in real time.Literature
The main source is the Study Guide which is available for browsing and download as a whole or by parts from within the VLE. Also recommended but not compulsory: Douglas C. Montgomery and George C. Runger. Applied Statistics and Probability for Engineers. 4th ed., Wiley, 2006.Examination including compulsory elements
The grade for the course is based on the results of the two tests performed during the course and the final examination. Students successfully completed the tests are exempt from the final examination. More details and exact dates are available from the course webpage. Students who have not passed the exam in May will be asked to take the re-sit examination in August to pass the course.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.