Statistical Data analysisLaajuus (6 cr)

Objective

The student understands the linear model and its most important extensions and is able to do these analyses using R-software.

Content

Simple linear regression and multiple linear regression using matrix notations, when residual errors are independent, and they have constant variance. Use of categorical and continuous predictors in the model and interpretation of model parameters. Use of transformations. Estimation using ordinary least squares (OLS) and the properties of the estimator. Test of hypothesis, model diagnostics and prediction. Model formulation if the assumption of constant error variance and independence is not met (so called general linear model). Generalized least squares (GLS), maximum likelihood (ML), and restricted maximum likelihood.