{"product_id":"methods-and-applications-of-linear-models-isbn-9781118329504","title":"Methods and Applications of Linear Models","description":"\u003cb\u003ePraise for the \u003ci\u003eSecond Edition\u003c\/i\u003e\u003c\/b\u003e \u003cp\u003e\"An essential desktop reference book . . . it should definitely be on your bookshelf.\"\u003cbr\u003e—\u003cb\u003e\u003ci\u003eTechnometrics\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA thoroughly updated book, \u003ci\u003eMethods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition\u003c\/i\u003e features innovative approaches to understanding and working with models and theory of linear regression. The Third Edition provides readers with the necessary theoretical concepts, which are presented using intuitive ideas rather than complicated proofs, to describe the inference that is appropriate for the methods being discussed.\u003c\/p\u003e \u003cp\u003eThe book presents a unique discussion that combines coverage of mathematical theory of linear models with analysis of variance models, providing readers with a comprehensive understanding of both the theoretical and technical aspects of linear models. With a new focus on fixed effects models, \u003ci\u003eMethods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition\u003c\/i\u003e also features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eNewly added topics including least squares, the cell means model, and graphical inspection of data in the AVE method\u003c\/li\u003e \u003cli\u003eFrequent conceptual and numerical examples for clarifying the statistical analyses and demonstrating potential pitfalls\u003c\/li\u003e \u003cli\u003eGraphics and computations developed using JMP® software to accompany the concepts and techniques presented\u003c\/li\u003e \u003cli\u003eNumerous exercises presented to test readers and deepen their understanding of the material\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eAn ideal book for courses on linear models and linear regression at the undergraduate and graduate levels, the \u003ci\u003eThird Edition of Methods and Applications of Linear Models: Regression and the Analysis of Variance\u003c\/i\u003e is also a valuable reference for applied statisticians and researchers who utilize linear model methodology.\u003c\/p\u003e \u003cp\u003ePreface to the Third Edition xvii\u003c\/p\u003e \u003cp\u003ePreface to the Second Edition xix\u003c\/p\u003e \u003cp\u003ePreface to the First Edition xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Regression 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Linear Models 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Background Information 3\u003c\/p\u003e \u003cp\u003e1.2 Mathematical and Statistical Models 5\u003c\/p\u003e \u003cp\u003e1.3 Definition of the Linear Model 8\u003c\/p\u003e \u003cp\u003e1.4 Examples of Regression Models 13\u003c\/p\u003e \u003cp\u003e1.5 Concluding Comments 21\u003c\/p\u003e \u003cp\u003eExercises 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Regression on Functions of One Variable 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The Simple Linear Regression Model 23\u003c\/p\u003e \u003cp\u003e2.2 Parameter Estimation 25\u003c\/p\u003e \u003cp\u003e2.3 Properties of the Estimators and Test Statistics 34\u003c\/p\u003e \u003cp\u003e2.4 The Analysis of Simple Linear Regression Models 39\u003c\/p\u003e \u003cp\u003e2.5 Examining the Data and the Model 50\u003c\/p\u003e \u003cp\u003e2.6 Polynomial Regression Models 63\u003c\/p\u003e \u003cp\u003eExercises 72\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Transforming the Data 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 The Need for Transformations 81\u003c\/p\u003e \u003cp\u003e3.2 Weighted Least Squares 82\u003c\/p\u003e \u003cp\u003e3.3 Variance Stabilizing Transformations 85\u003c\/p\u003e \u003cp\u003e3.4 Transformations to Achieve a Linear Model 86\u003c\/p\u003e \u003cp\u003e3.5 Analysis of the Transformed Model 92\u003c\/p\u003e \u003cp\u003eExercises 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Regression on Functions of Several Variables 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 The Multiple Linear Regression Model 99\u003c\/p\u003e \u003cp\u003e4.2 Preliminary Data Analysis 100\u003c\/p\u003e \u003cp\u003e4.3 Analysis of the Multiple Linear Regression Model 103\u003c\/p\u003e \u003cp\u003e4.4 Partial Correlation and Added-Variable Plots 113\u003c\/p\u003e \u003cp\u003e4.5 Variable Selection 119\u003c\/p\u003e \u003cp\u003e4.6 Model Specification 130\u003c\/p\u003e \u003cp\u003eExercises 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Collinearity in Multiple Linear Regression 142\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 The Collinearity Problem 142\u003c\/p\u003e \u003cp\u003e5.2 An Example with Collinearity 150\u003c\/p\u003e \u003cp\u003e5.3 Collinearity Diagnostics 156\u003c\/p\u003e \u003cp\u003e5.4 Remedial Solutions: Biased Estimators 1665.4.3 Ridge Regression 174\u003c\/p\u003e \u003cp\u003eExercises 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Influential Observations in Multiple Linear Regression 182\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 The Influential Data Problem 182\u003c\/p\u003e \u003cp\u003e6.2 The Hat Matrix 183\u003c\/p\u003e \u003cp\u003e6.3 The Effects of Deleting Observations 188\u003c\/p\u003e \u003cp\u003e6.4 Numerical Measures of Influence 192\u003c\/p\u003e \u003cp\u003e6.5 The Dilemma Data 197\u003c\/p\u003e \u003cp\u003e6.6 Plots for Identifying Unusual Cases 201\u003c\/p\u003e \u003cp\u003e6.7 Robust\/Resistant Methods in Regression Analysis 209\u003c\/p\u003e \u003cp\u003eExercises 213\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Polynomial Models and Qualitative Predictors 216\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Polynomial Models 216\u003c\/p\u003e \u003cp\u003e7.2 The Analysis of Response Surfaces 220\u003c\/p\u003e \u003cp\u003e7.3 Models with Qualitative Predictors 225\u003c\/p\u003e \u003cp\u003eExercises 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Additional Topics 254\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Nonlinear Regression Models 254\u003c\/p\u003e \u003cp\u003e8.2 Nonparametric Model-Fitting Methods 260\u003c\/p\u003e \u003cp\u003e8.3 Generalized Linear Models 265\u003c\/p\u003e \u003cp\u003e8.4 Random Input Variables 274\u003c\/p\u003e \u003cp\u003e8.5 Errors in the Inputs 276\u003c\/p\u003e \u003cp\u003e8.6 Calibration 277\u003c\/p\u003e \u003cp\u003eExercises 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II the Analysis of Variance 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Classification Models I: Introduction 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Background Information 285\u003c\/p\u003e \u003cp\u003e9.2 The One-Way Classification Model 286\u003c\/p\u003e \u003cp\u003e9.3 The Two-Way Classification Model: Balanced Data 304\u003c\/p\u003e \u003cp\u003e9.4 The Two-Way Classification Model: Unbalanced Data 322\u003c\/p\u003e \u003cp\u003e9.5 The Two-Way Classification Model: No Interaction 334\u003c\/p\u003e \u003cp\u003e9.6 Concluding Comments 347\u003c\/p\u003e \u003cp\u003eExercises 347\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 The Mathematical Theory of Linear Models 359\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 The Distribution of Linear and Quadratic Forms 359\u003c\/p\u003e \u003cp\u003e10.2 Estimation and Inference for Linear Models 368\u003c\/p\u003e \u003cp\u003e10.3 Tests of Linear Hypotheses on β 380\u003c\/p\u003e \u003cp\u003e10.4 Confidence Regions and Intervals 392\u003c\/p\u003e \u003cp\u003eExercises 395\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Classification Models II: Multiple Crossed and Nested Factors 405\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 The Three-Factor Cross-Classified Model 406\u003c\/p\u003e \u003cp\u003e11.2 A General Structure for Balanced Factorial Models 412\u003c\/p\u003e \u003cp\u003e11.3 The Twofold Nested Model 417\u003c\/p\u003e \u003cp\u003e11.4 A General Structure for Balanced, Nested Models 426\u003c\/p\u003e \u003cp\u003e11.5 A Three-Factor, Nested-Factorial Model 429\u003c\/p\u003e \u003cp\u003e11.6 A General Structure for Balanced, Nested-Factorial Models 434\u003c\/p\u003e \u003cp\u003eExercises 438\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Mixed Models I: The AOV Method with Balanced Data 443\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 443\u003c\/p\u003e \u003cp\u003e12.2 Examples of the Analysis of Mixed Models 444\u003c\/p\u003e \u003cp\u003e12.3 The General Analysis for Balanced, Mixed Models 464\u003c\/p\u003e \u003cp\u003e12.4 Additional Examples 479\u003c\/p\u003e \u003cp\u003e12.5 Alternative Developments of Mixed Models 487\u003c\/p\u003e \u003cp\u003eExercises 493\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Mixed Models II: The AVE Method with Balanced Data 499\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 499\u003c\/p\u003e \u003cp\u003e13.2 The Two-Way Cross-Classification Model 500\u003c\/p\u003e \u003cp\u003e13.3 The Three-Factor, Cross-Classification Model 511\u003c\/p\u003e \u003cp\u003e13.4 Nested Models 515\u003c\/p\u003e \u003cp\u003e13.5 Nested-Factorial Models 518\u003c\/p\u003e \u003cp\u003e13.6 A General Description of the AVE Table 524\u003c\/p\u003e \u003cp\u003e13.7 Additional Examples 531\u003c\/p\u003e \u003cp\u003e13.8 The Computational Procedure for the AVE Method 537\u003c\/p\u003e \u003cp\u003eExercises 537\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Mixed Models III: Unbalanced Data 543\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 543\u003c\/p\u003e \u003cp\u003e14.2 Parameter Estimation: Likelihood Methods 545\u003c\/p\u003e \u003cp\u003e14.3 ml and REml Estimates with Balanced Data 554\u003c\/p\u003e \u003cp\u003e14.4 The EM Algorithm for REML Estimation 558\u003c\/p\u003e \u003cp\u003e14.5 Diagnostic Analysis with the EM Algorithm 572\u003c\/p\u003e \u003cp\u003e14.6 Models with Covariates 581\u003c\/p\u003e \u003cp\u003e14.7 Summary 585\u003c\/p\u003e \u003cp\u003eExercises 585\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Simultaneous Inference: Tests and Confidence Intervals 591\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Simultaneous Tests 591\u003c\/p\u003e \u003cp\u003e15.2 Simultaneous Confidence Intervals 610\u003c\/p\u003e \u003cp\u003eExercises 612\u003c\/p\u003e \u003cp\u003eAppendix A Mathematics 615\u003c\/p\u003e \u003cp\u003eAppendix B Statistics 634\u003c\/p\u003e \u003cp\u003eAppendix C Data Tables 645\u003c\/p\u003e \u003cp\u003eAppendix D Statistical Tables 660\u003c\/p\u003e \u003cp\u003eReferences 669\u003c\/p\u003e \u003cp\u003eIndex 677\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eRONALD R. HOCKING, PhD,\u003c\/b\u003e is Professor Emeritus in the Department of Statistics and Founder of the Ronald R. Hocking Lecture Series at Texas A\u0026amp;M University. A Fellow of the American Statistical Association, Dr. Hocking is the recipient of numerous honors in the statistical community including the Shewell Award, the Youden Award, the Wilcoxon Award, the Snedecor Award, and the Owen Award.\u003c\/p\u003e  \u003cp\u003ePraise for the \u003ci\u003eSecond Edition\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"An essential desktop reference book . . . it should definitely be on your bookshelf.\"\u003cbr\u003e \u003ci\u003e—Technometrics\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eA thoroughly updated book, \u003ci\u003eMethods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition\u003c\/i\u003e features innovative approaches to understanding and working with models and theory of linear regression. The \u003ci\u003eThird Edition\u003c\/i\u003e provides readers with the necessary theoretical concepts, which are presented using intuitive ideas rather than complicated proofs, to describe the inference that is appropriate for the methods being discussed.\u003c\/p\u003e \u003cp\u003eThe book presents a unique discussion that combines coverage of mathematical theory of linear models with analysis of variance models, providing readers with a comprehensive understanding of both the theoretical and technical aspects of linear models. With a new focus on fixed effects models, \u003ci\u003eMethods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition\u003c\/i\u003e also features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eNewly added topics including least squares, the cell means model, and graphical inspection of data in the AVE method\u003c\/li\u003e \u003cli\u003eFrequent conceptual and numerical examples for clarifying the statistical analyses and demonstrating potential pitfalls\u003c\/li\u003e \u003cli\u003eGraphics and computations developed using JMP® software to accompany the concepts and techniques presented\u003c\/li\u003e \u003cli\u003eNumerous exercises presented to test readers and deepen their understanding of the material\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eAn ideal book for courses on linear models and linear regression at the undergraduate and graduate levels, the \u003ci\u003eThird Edition\u003c\/i\u003e of \u003ci\u003eMethods and Applications of Linear Models: Regression and the Analysis of Variance\u003c\/i\u003e is also a valuable reference for applied statisticians and researchers who utilize linear model methodology.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989615034597,"sku":"NP9781118329504","price":128.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118329504.jpg?v=1761784819","url":"https:\/\/k12savings.com\/es\/products\/methods-and-applications-of-linear-models-isbn-9781118329504","provider":"K12savings","version":"1.0","type":"link"}