u003cpu003eA Hands-On Way to Learning Data Analysisu003c/pu003e u003cpu003eu003c/pu003e u003cpu003ePart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.u003c/pu003e u003cpu003eNew to the Second Editionu003c/pu003e u003culu003e u003cpu003e u003cliu003eReorganized material on interpreting linear models, which distinguishes the main applications of prediction and explanation and introduces elementary notions of causality u003c/liu003e u003cliu003eAdditional topics, including QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery ratesu003c/liu003e u003cliu003eExtensive use of the ggplot2 graphics package in addition to base graphics u003c/liu003e u003cpu003eu003c/pu003eu003c/ulu003e u003cpu003eu003c/pu003e u003cpu003eLike its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.u003c/pu003e