Course content

Descriptive statistics. Probability calculations, including probability laws, conditional probability and independence, random variables, discrete and continuous probability distributions,
expected value, variance, bivariate probability distributions, covariance, correlation and double expectation. The central limit theorem. Introduction to simple linear regression.

Learning outcome

1. Knowledge. The student knows basic concepts in probability, simple probability models
and statistical inference. In addition, the student knows a number of basic statistical methods
and the assumptions underlying these methods.
2. Skills. The student is able to use his or her knowledge in recognising and analysing simple
forms of data in the natural sciences. In addition, the student is able to communicate with
statisticians about analysis of data arising in more complex situations.

Learning methods and activities

Lectures and compulsory exercises. Written final examination is the basis for the grade
awarded in the course. Retake of examination may be given as an oral examination.

Compulsory assignments

• Exercises

Recommended previous knowledge

MA0001 Mathematical methods A and MA0002 Mathematical methods B, or equivalent.

Course materials

Will be announced at the start of the course.

Credit reductions