Handbook of Regression Analysis with Applications in R (Second Edition)

Abstract

The second edition of ‘Handbook of Regression Analysis with Applications in R’ motivated at providing a practical and comprehensive guide to practitioners of regression analysis using R programming language. The book has been divided into seven parts containing 16 chapters. The part I of the book consist of first 2 chapters discussing the basic concepts of linear regression model, model estimation, prediction and selection methodologies. Part II containing three chapters, 3–5, address the implications of violations of assumptions, analysis of leverage and outliers, robust regression techniques, estimation of linearisable non-linear models and models with auto-correlated disturbances. Part III of the book includes two chapters 6 and 7 on analysis of variance and analysis of covariance. Part IV covered in chapters 8–11 discuss various non-Gaussian regression models including Logistic and multinomial regression, count data model and survival models for time to event. The part V of the book consisting of chapters 12–14 demonstrates non-linear regression and non-linear least squares regression, longitudinal and nested data models, the sparsity in regression and regularisation methods of estimation. The last part VI covered in chapters 15 and 16 focusses on non-parametric and semi-parametric methods including smoothing and additive models and tree-based models such as CART.