Statistical methods

Learning outcomes: The student will understand basic concepts of statistics and data analysis, be able to: write programs that can be run on real data or self-programmed artificial data; calculate mean, standard deviations, and other statistical parameters; evaluate when normal distribution is a suitable approximation and when other probability distributions are needed; understand the meaning and difference between “sigma” and confidence levels; calculate estimates for the confidence levels of a set of measurements; compare statistical parameters from different datasets or to model data; quantify whether a small deviation observed in the data is significant; plan experiment based on statistical requirements for the data; perform a least squares fit to a dataset and critically evaluate when it is a sensible thing to do; evaluate the goodness of fit; understand the basic concepts of a Bayesian analysis method; make a real attempt for a Bayesian analysis solving.

Content: Introduction to statistical methods for data analysis and interpretation in astronomy and physics. Measurements and their handling, probability distributions, correlation coefficients, analysis of variances. Error estimation. Least squares methods, other optimization methods. Goodness of fit. Decision making. Introduction to Bayesian methods, Bayesian methods in practice. The course contains a number of computer exercises that require programming.

Teaching methods: lectures 26 h, exercises 12 h, project work.