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A Course in Statistics with R by B. G. Manjunath, Suresh Ramaiah, Prabhanjan N. Tattar

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Chapter 12Linear Regression Models

Package(s): faraway, MASS, car

Dataset(s): Euphorbiaceae, anscombe, tc, usc, shelf_stocking, abrasion_index, Frog_survival, flight, viscos, prostate,

12.1 Introduction

Faraway (2002) is probably the first detailed account of the use of R for linear models. Interestingly, this book is allowed to be freely circulated and we may also print it and sell it at a cost covering the cost of print. This book makes an elegant read for the current R versions, although it was written when the R version was in the early 1.x versions. Faraway (2006) is an extended version which considers the generalized linear models, which we deal with in Chapter 17. Fox (2002) deals with regression problems in both R and S-plus. Sheather (2009) is also a very recent account of the use of R for analysis of linear models, and SAS users will also find it easier to use this book as it also gives parallel programs. Ritz and Streibig (2008) is dedicated to the applications of nonlinear regression models using R.

The covariates are also sometimes called explanatory variables, or regressors, or predictors. In general the covariate is an independent variable. The output c12-math-0001 is called a regressand.

A rather long route is adapted in this chapter. The reasons are two-fold. First, understanding of the statistical concepts using the simple linear regression model is of prime importance, ...

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