Course syllabus

Matematisk statistik, allmän kurs
Mathematical Statistics, Basic Course

FMS140, 7,5 credits, G2 (First Cycle)

Valid for: 2012/13
Decided by: Education Board 1
Date of Decision: 2012-03-27

General Information

Main field: Technology.
Compulsory for: W3
Language of instruction: The course will be given in Swedish


The course is intended to give the student the basics in mathematical modelling of random variation and an understanding of the principles behind statistical analysis. It shall also give the students a toolbox containing the most commonly used models and methods, as well as the ability to use these in practical situations.

The course fills two purposes, providing a fundamental knowledge of mathematical statistics, as well as giving a foundation for further studies.

The fundamental knowledge is essential for those who, in their professional lives, will not necessarily be involved in statistical analyses on a daily basis, but who, on occasion, will be expected to perform basic statistical tests and present the results to their colleagues. They will also be expected to be able to read and assess the analyses of others.

The course shall also give a basis for further studies, particularly in design of experiments and risk evaluation.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must


The course contains fundamental concepts in probability theory, inference theory, regression analysis, and time series analysis.

In probability theory the concepts used are random variables and distributions for describing variation and random phenomena, often related to applications in environmental statistics. Different distributions, such as binomial, Poisson, normal, exponential, and log normal distributions, are studied and the concept of expectation and variance of a distribution is introduced. Special attention is paid to the normal distribution and its property as a limit distribution. Simulations from the distributions and studies of the models are performed in Matlab. This part constitutes approximately 2/7 of the course.

In inference theory we start with observed data and estimate parameters in simple probability models, and describe the uncertainty of the estimates. Emphasis is placed on the relationship between the model and the reality based problem, as well as the conclusions that can be drawn from observed data. In this analysis we use basic techniques, such as confidence intervals and hypothesis testing. This part constitutes approximately 2/7 of the course.

In regression analysis we study how the relationship between two or more variables can be described. Most often the relationship will be linear. Often in environmental applications one of the variables is a time variable which leads to trend analysis. We study different techniques for comparison and choice between different models for relationships. Environmental data if often dependent and therefore we introduce time series with concepts of trend, season, and noise. Techniques, such as auto-correlation function, are used to describe the dependence. A simple AR(1) model for dependent data is introduced. This part, resting heavily on the use of Matlab, constitutes approximately 3/7 of the course.

Examination details

Grading scale: TH
Assessment: Written exam, home assignments, written project report, and peer assessment of the project report. The course grade is based on the exam grade.

Code: 0108. Name: Examination.
Credits: 6. Grading scale: TH. Assessment: Written exam.
Code: 0208. Name: Project Work.
Credits: 1,5. Grading scale: UG. Assessment: Written report.


Admission requirements:

Required prior knowledge: Calculus in one and several variables and Linear algebra.
The number of participants is limited to: No
The course overlaps following course/s: FMS012, FMS032, FMS033, FMS035, FMS086

Reading list

Contact and other information

Director of studies: Studierektor Anna Lindgren,
Course homepage:
Further information: Cooperative learning in fixed smaller groups under tutelage of teacher, discussion and solving of exercises with constant access to the students' computers, individual work with home assignments, project work in groups of two with Matlab, peer assessment of reports, lectures, and seminars.