"This textbook focuses on the practice of regression and analysis of variance. Students will learn which methods are available and the various situations in which they can be applied."@en
"This textbook focuses on the practice of regression and analysis of variance. Students will learn which methods are available and the various situations in which they can be applied."
"Introduction Before You Start Initial Data Analysis When to Use Linear Modeling History Estimation Linear Model Matrix Representation Estimating b Least Squares Estimation Examples of Calculating ˆb Example QR Decomposition Gauss-Markov Theorem Goodness of Fit Identifiability Orthogonality Inference Hypothesis Tests to Compare Models Testing Examples Permutation Tests Sampling Confidence Intervals for b Bootstrap Confidence Intervals Prediction Confidence Intervals for Predictions Predicting Body Fat Autoregression What Can Go Wrong with Predictions? Explanation Simple Meaning Causality Design."@en
"Estimation; Inference; Diagnostico; Problems with the predictors; Problems with the error; Transformation; Variable selection; Shrinkage methods; Statistical strtegy and model uncertainty; Insurance redlining - Acomplete example; Missing data; Analysis of covariance; One-way analysis of variance; Factorial designs; R installation, functions and data; Quick introdution to R."@pt

Linear models with R