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, if necessary with the use of mathematical software. Furthermore, the student is able to communicate with a trained statistician in more complicated situations.

Learning methods and activities

Lectures and compulsory exercises partly using computers (Matlab). 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

TMA4100 Calculus 1, TMA4105 Calculus 2 and TDT4105/TDT4110 Information Technology, Introduction, or equivalent.

Course materials

Will be announced at the start of the semester.

Credit reductions