Course syllabus adopted 2022-02-09 by Head of Programme (or corresponding).
Overview
- Swedish nameMatematisk statistik
- CodeTMA074
- Credits7.5 Credits
- OwnerTKKMT
- 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 53114
- Maximum participants65
- 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 Project 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
Analysis and linear algebra.Aim
The course will cover the basics of probability theory and statistics that are important for chemists.Learning outcomes (after completion of the course the student should be able to)
After passing the course the student should understand the basic conceptions in probability theory and statistical inference. The course should also give the students the ability to plan statistical experiments and perform simple statistical analyses.Content
Descriptive statistics, sample space, probability, conditioning, measures of central tendency and dispersion. Discrete and continuous distributions, and modelling using various standard distributions such as the binomial and normal distributions. Methods to compute with stochastic variables, some rules to compute expected values and variances. The central limit theorem, multivariate distributions, and covariance. Parameter estimation, confidence intervals, and tests in different standard situations. Introduction to linear regression and design of experiments.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.