LEADER 05831oam 2200733 450 001 9910452282203321 005 20210111233129.0 010 $a1-118-59302-2 010 $a1-118-64019-5 010 $a1-118-59304-9 035 $a(CKB)2550000001096160 035 $a(EBL)1224710 035 $a(OCoLC)840927815 035 $a(SSID)ssj0000916973 035 $a(PQKBManifestationID)11956865 035 $a(PQKBTitleCode)TC0000916973 035 $a(PQKBWorkID)10890854 035 $a(PQKB)10916812 035 $a(DLC) 2013014663 035 $a(MiAaPQ)EBC1224710 035 $a(EXLCZ)992550000001096160 100 $a20130410d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMethods and applications of linear models $eregression and the analysis of variance /$fRonald R. Hocking, PenHock Statistical Consultants 205 $a3rd ed. 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons,$d2013. 215 $a1 online resource (717 p.) 225 1 $aWiley Series in Probability and Statistics 300 $aDescription based upon print version of record. 311 $a1-118-32950-3 311 $a1-299-71485-4 320 $aIncludes bibliographical references and index. 327 $aMethods and Applications of Linear Models; Contents; Preface to the Third Edition; Preface to the Second Edition; Preface to the First Edition; PART I REGRESSION; 1 Introduction to Linear Models; 1.1 Background Information; 1.2 Mathematical and Statistical Models; 1.3 Definition of the Linear Model; 1.4 Examples of Regression Models; 1.4.1 Single-Variable, Regression Model; 1.4.2 Regression Models with Several Inputs; 1.4.3 Discrete Response Variables; 1.4.4 Multivariate Linear Models; 1.5 Concluding Comments; Exercises; 2 Regression on Functions of One Variable 327 $a2.1 The Simple Linear Regression Model2.2 Parameter Estimation; 2.2.1 Least Squares Estimation; 2.2.2 Maximum Likelihood Estimation; 2.2.3 Coded Data: Centering and Scaling; 2.2.4 The Analysis of Variance Table; 2.3 Properties of the Estimators and Test Statistics; 2.3.1 Moments of Linear Functions of Random Variables; 2.3.2 Moments of Least Squares Estimators; 2.3.3 Distribution of the Least Squares Estimators; 2.3.4 The Distribution of Test Statistics; 2.4 The Analysis of Simple Linear Regression Models; 2.4.1 Two Numerical Examples; 2.4.2 A Test for Lack-of-Fit 327 $a2.4.3 Inference on the Parameters of the Model2.4.4 Prediction and Prediction Intervals; 2.5 Examining the Data and the Model; 2.5.1 Residuals; 2.5.2 Outliers, Extreme Points, and Influence; 2.5.3 Normality, Independence, and Variance Homogeneity; 2.6 Polynomial Regression Models; 2.6.1 The Quadratic Model; 2.6.2 Higher Ordered Polynomial Models; 2.6.3 Orthogonal Polynomials; 2.6.4 Regression through the Origin; Exercises; 3 Transforming the Data; 3.1 The Need for Transformations; 3.2 Weighted Least Squares; 3.3 Variance Stabilizing Transformations 327 $a3.4 Transformations to Achieve a Linear Model3.4.1 Transforming the Dependent Variable; 3.4.2 Transforming the Predictors; 3.5 Analysis of the Transformed Model; 3.5.1 Transformations with Forbes Data; Exercises; 4 Regression on Functions of Several Variables; 4.1 The Multiple Linear Regression Model; 4.2 Preliminary Data Analysis; 4.3 Analysis of the Multiple Linear Regression Model; 4.3.1 Fitting the Model in Centered Form; 4.3.2 Estimation and Analysis of the Original Data; 4.3.3 Model Assessment and Residual Analysis; 4.3.4 Prediction; 4.3.5 Transforming the Response 327 $a4.4 Partial Correlation and Added-Variable Plots4.4.1 Partial Correlation; 4.4.2 Added-Variable Plots; 4.4.3 Simple Versus Partial Correlation; 4.5 Variable Selection; 4.5.1 The Case of Orthogonal Predictors; 4.5.2 Criteria for Deletion of Variables; 4.5.3 Nonorthogonal Predictors; 4.5.4 Computational Considerations; 4.5.5 Selection Strategies; 4.6 Model Specification; 4.6.1 Application to Subset Selection; 4.6.2 Improved Mean Squared Error; 4.6.3 Development of the Cp Statistic; Exercises; 5 Collinearity in Multiple Linear Regression; 5.1 The Collinearity Problem; 5.1.1 Introduction 327 $a5.1.2 A Simple Example 330 $a"The new edition of this "essential desktop reference book. [that] should definitely be on your bookshelf" (Technometrics) features a newly reorganized approach to linear regression that promotes the understanding of theory and models concurrently, featuring newly-developed topics in the field and the use of software applications. It includes numerous exercises; graphics and computations developed using JMP software; a new chapter on recent developments with the distribution of linear and quadratic forms; and new topical coverage of least squares, the cell means model, and more"--$cProvided by publisher. 330 $a"The objective of this book is to present a discussion and a formal definition of a general class of linear models"--$cProvided by publisher. 410 0$aWiley Series in Probability and Statistics 606 $aRegression analysis 606 $aAnalysis of variance 606 $aLinear models (Statistics) 606 $aMATHEMATICS / Probability & Statistics / General$2bisacsh 608 $aElectronic books. 615 0$aRegression analysis. 615 0$aAnalysis of variance. 615 0$aLinear models (Statistics) 615 7$aMATHEMATICS / Probability & Statistics / General. 676 $a519.5/36 686 $aMAT029000$2bisacsh 700 $aHocking$b R. R$g(Ronald R.),$f1932-$0857382 801 0$bDLC 801 1$bDLC 801 2$bDLC 906 $aBOOK 912 $a9910452282203321 996 $aMethods and applications of linear models$91914320 997 $aUNINA