Simple Linear Regression

This chapter deals with the very simple situation where the mean of a variable, the response variable, usually denoted Y , is linearly depending on another variable, the regressor, here denoted x 1 . The least squared method is used to get the parameter estimators and estimates of their precisions. This leads to design confidence and prediction intervals, significance tests, anova table. Residuals, diagnostics to identify influent observations and outliers are presented. Methods to detect departures from the model's assumptions and ways of dealing with these departures are addressed. Along the chapter a data set is used to illustrate the methods with the sofware R.