Statistical Methods

Aim

After completion of the course the student should be able to:

• explain and apply the common statistical distributions for building statistical models
• apply main principles within point estimation, interval estimation and hypothesis testing
• demonstrate and apply the main concepts of Bayesian analysis
• apply linear regression models, check their uncertainty and perform model comparison
• apply methods for sampling from large finite populations
• apply the basic imputation methods for model building and estimation
• present the underlying mathematical models for the above methods and perform theoretical calculations with these models
• critically discuss the fulfillment of the models' assumptions
• carry out tasks within given time frames

Content

The course covers:

• concept of probability
• random variable, common statistical univariate and multivariate distributions and their properties, central limit theorem
• point estimation – properties and methods
• interval estimation
• hypothesis testing
• simple and multiple linear regression; least squares estimation; residual and outlier analyses
• likelihood, prior and posterior distribution, and Bayes theorem
• imputation for model building