The course gives an introduction to basic probability theory, including an introduction to common discrete and continuous probability models. Further the course gives an introduction to descriptive statistics and statistical analyses, in particular estimation and confidence intervals, hypothesis testing and regression analysis. An integrated part of the course is an introduction to R for programming, data analysis and simulation.
Course description for study year 2021-2022. Please note that changes may occur.
Semester tution start
Number of semesters
Language of instruction
Faculty of Science and Technology, Department of Mathematics and Physics
After having completed the course one should:
Be able to use basic methods for analysis and presentation of data.
Be able to do basic probability calculations.
Know what a random variable, probability distribution, expectation and variance is.
Be able to calculate expectation, variance and probabilities for random variables and simple functions of random variables.
Be able to use basic probability distributions like binomial, poission, hypergeometric, exponential and normal.
Be able to use the central limit theorem.
Be able to find estimators and calculate confidence intervals for some important parameters in probability distributions.
Have a basic understanding of hypothesis testing and be able to perform hypothesis testing for one and several samples.
Know the theory for, and be able to use correlation, regression analysis and simple analysis of variance.
Know the assumptions for the various methods and be able to judge whether the assumptions are fulfilled.
Be able to use chi square tests
Be able to use some R for basic data analysis and simulation.
The course gives an introduction to descriptive statistics and basic probability theory for discrete and continuous probability models. Introductory theory for estimation and for statistical hypothesis testing in the most common situations is presented. Emphasis is made on both theoretical understanding and applications. Use of software (R) for data-analysis and modelling is an integrated part of the course.
Topics covered: Introduction to basic probability theory, included conditional probability, expectation, variance and the most common probability distributions like binomial, hypergeometric, poisson, exponential and normal. An introduction to point estimation, confidence intervals and hypothesis testing in situations with one and two samples. An introduction to correlation, linear regression, analysis of variance and chi squre tests.
Required prerequisite knowledge
BØK135 Mathematical analysis for economists, MAT100 Mathematical Methods 1
Form of assessment
A - F
No printed or written materials are allowed. Approved basic calculator allowed.
Six compulsory assignments
Compulsory exercises have to be approved in order to take an examination.
Jan Terje Kvaløy
Jan Terje Kvaløy
Head of Department:
Bjørn Henrik Auestad
Method of work
Four to six hours lof ectures, two hours of problem solving and four to eight hours of self study per week. Mandatory work demands (such as hand in assignments, lab- assignments, projects, etc) must be approved by subject teacher three weeks ahead of examination date.