Course description

The course covers probability theory, such as random variables, probability distribution, expected values, independent samples, the central limit theorem, the law of large numbers, and consistency. Estimation of the population mean, hypothesis testing, and confidence intervals are also discussed. The course then discusses the linear regression model and how it can be applied. The regression model is also discussed using matrix algebra. The course further discus hypothesis tests and confidence intervals for both simple and multiple regressions. Different types of typical problems for the linear model, such as non-linear functions, omitted and irrelevant variables, simultaneity, measurement errors and heteroscedasticity are analysed


Lecturer and Examiner:
Peter Skogman-Thoursie


Group Teachers:

Group 1 and 2: Monir Bounadi

Group 3 and 4: Mattias Hallberg

Course administrator:
Cecilia von Mentzingen

Course information