Statistics

Descriptive statistics. Probabilities for an event and stochastic variables. Independent and dependent stochastic variables. Conditional probability. Monte Carlo simulation. Univariate and multivariate probability distributions. The most important classes of univariate probability distributions. Point estimation, interval estimation and hypothesis testing for one and two sample data. Simple linear regression. Use of software and programming to visualise observed data, to do and use Monte Carlo simulation, and to perform other computations related to the topics of the course.

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.

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. In these analyses the student is capable of using computer programming when this is necessary.

3. General competence: The student has some knowledge in computer programming for making plots and for computing numerical quantities

Lectures and exercises. Parts of the teaching may be given in English.