Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Have knowledge about fundamental concepts in probability, including conditional probability and independence
• Have knowledge about discrete and continuous random variables and relevant properties of these
• Have knowledge about various examples of descriptive statistics and graphics, e.g. histograms, boxplots, scatterplots, lag plots and auto covariance plots
• Have knowledge about statistical inference, including estimation, confidence intervals and hypothesis testing
• Have knowledge about basic concepts related to stochastic processes such as stationarity, correlation function and spectral density
• Have elementary knowledge about wiener processes, white noise and linear stochastic differential equations
• Have comprehension of a concrete example of a model for a simple stochastic process

Skills

• Be able, given specific data, to specify a relevant statistical model and account for the assumptions and limitations of the chosen model
• Be able to use relevant software for carrying out the statistical analysis of given data and be able to interpret the results of the analysis
• Be able to use statistical models, like linear regression (simple and multiple) and analysis of variance

Competences

• Be able to judge the applicability of statistics within own area
• Be capable of performing a critical evaluation of the results of a statistical analysis
• Be capable of communicating the results of a statistical analysis to people with no or little background within statistics.

Type of instruction

Lectures in combination with practical exercises and self-study, e-learning or similar.

Extent and expected workload

Since it is a 5 ECTS course module, the work load is expected to be 150 hours for the student.

Exam

Exams