Course content

Descriptive statistics. Probabilities for an event and stochastic variables. Independent and dependent stochastic variables. Conditional probability. Univariate and multivariate probability distributions. The most important classes of univariate probability distributions. Extreme variables. Point estimation, interval estimation and hypothesis testing for one and two sample data. Simple linear regression.

Learning outcome

1. Knowledge. The student knows the mathematical basis for probability and statistical inference, can recognise simple statistical standard situations in technology and natural sciences, and know how these situations can best be analysed. Furthermore the student understands the concepts of statistics and probability sufficient to be able to communicate with a trained statistician in more complicated situations. 2. Skills. The student can calculate probabilities for events in simple situations. Further, the student can apply his or her knowledge of probability and statistics to recognise and analyse data from simple statistical situations in technology and natural sciences, also using descriptive statistics. Furthermore, the student is able to communicate with a trained statistician in more complicated situations.

Learning methods and activities

Lectures, recommended problems and compulsory exercises.

Compulsory assignments

• Exercises

Recommended previous knowledge

TMA4100 Calculus 1 or equivalent.

Course materials

Will be announced at the start of the semester.

Credit reductions