Course syllabus adopted 2024-02-16 by Head of Programme (or corresponding).
Overview
- Swedish nameStatistiska modeller och metoder
- CodeMVE492
- Credits9 Credits
- OwnerTAFFS
- Education cycleFirst-cycle
- Main field of studyMathematics, Civil and Environmental Engineering
- DepartmentMATHEMATICAL SCIENCES
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language Swedish
- Application code 60124
- Maximum participants45
- 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 |
|---|---|---|---|---|---|---|---|
| 0124 Project 1.5 c Grading: UG | 1.5 c | 0 c | 0 c | 0 c | 0 c | 0 c | |
| 0224 Examination 7.5 c Grading: TH | 7.5 c | 0 c | 0 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.
Aim
The purpose of the course is to provide participants with basic knowledge in probability theory and statistical methods applicable to technology and social sciences.Learning outcomes (after completion of the course the student should be able to)
- Use descriptive statistical tools and statistical methods to describe and interpret data.
- Explain the impact of randomness in various situations, such as measurement outcomes.
- Utilize and explain mathematical concepts within mathematical statistics and be able to perform mathematical calculations based on presented concepts.
- Handle a dataset in Python using a predefined template that explains the process step by step.
Content
The course consists of three components: probability theory, an introduction to mathematical statistics, and a project. In probability theory, the following concepts are covered: probability, independent events, random variables, expected value and standard deviation, binomial distribution, and normal distribution. The mathematical statistics part includes the concepts of point estimation, interval estimation, linear regression, and principles of hypothesis testing. In the project, a large dataset on real estate prices is analyzed using a Python program, applying methods learned in the course.Organisation
The course is given in the format of lectures, practicals and a few sessions devoted to assistance with the project work.Literature
A compendium authored by Serik Sagitov and Lotta Eriksson. Available on Canvas.Examination including compulsory elements
The course is examined by a written exam, graded using the scale TH. To obtain a final grade the project work needs to have obtained a pass grade.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.