{"product_id":"methods-and-applications-of-linear-models-9781118329504","title":"Methods and Applications of Linear Models","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePraise for the \u003ci\u003eSecond Edition\u003c\/i\u003e\u003c\/b\u003e \u003cp\u003eAn 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 \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePreface to the Third Edition xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface to the Second Edition xix\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface to the First Edition xxi\u003c\/b\u003e\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, 166\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 \u003ci\u003eβ\u003c\/i\u003e, 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 \u003ci\u003eAVE\u003c\/i\u003e Table, 524\u003c\/p\u003e \u003cp\u003e13.7 Additional Examples, 531\u003c\/p\u003e \u003cp\u003e13.8 The Computational Procedure for the \u003ci\u003eAVE\u003c\/i\u003e 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\u003e\u003cb\u003eAppendix A Mathematics 615\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.I Matrix Algebra, 615\u003c\/p\u003e \u003cp\u003eA.I.1 Notation, 615\u003c\/p\u003e \u003cp\u003eA.I.2 The Rank of a Matrix, 616\u003c\/p\u003e \u003cp\u003eA.I.3 The Trace of a Matrix, 617\u003c\/p\u003e \u003cp\u003eA.I.4 Eigenvalues and Eigenvectors, 617\u003c\/p\u003e \u003cp\u003eA.I.5 Quadratic Forms and Definite Matrices, 618\u003c\/p\u003e \u003cp\u003eA.I.6 Special Matrices, 619\u003c\/p\u003e \u003cp\u003eA.I.7 The Diagonalization of Matrices, 620\u003c\/p\u003e \u003cp\u003eA.I.8 Kronecker Products of Matrices, 620\u003c\/p\u003e \u003cp\u003eA.I.9 Factorization of Matrices, 621\u003c\/p\u003e \u003cp\u003eA.I.10 Matrix Inversion, 622\u003c\/p\u003e \u003cp\u003eA.I.11 The Solution of Linear Equations, 624\u003c\/p\u003e \u003cp\u003eA.I.12 Generalized Inverses, 627\u003c\/p\u003e \u003cp\u003eA.I.13 Cauchy–Schwartz Inequalities, 630\u003c\/p\u003e \u003cp\u003eA.II Optimization, 630\u003c\/p\u003e \u003cp\u003eA.II.1 The Differentiation of Matrices and Determinants, 630\u003c\/p\u003e \u003cp\u003eA.II.2 The Differentiation of a Function with Respect to a Vector, 631\u003c\/p\u003e \u003cp\u003eA.II.3 The Optimization of a Function, 632\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Statistics 634\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.I Distributions, 634\u003c\/p\u003e \u003cp\u003eB.I.1 The Normal Distribution, 634\u003c\/p\u003e \u003cp\u003eB.I.2 The \u003ci\u003eχ\u003c\/i\u003e2-Distribution, 637\u003c\/p\u003e \u003cp\u003eB.I.3 The \u003ci\u003et\u003c\/i\u003e-Distribution, 638\u003c\/p\u003e \u003cp\u003eB.I.4 The \u003ci\u003eF\u003c\/i\u003e-distribution, 639\u003c\/p\u003e \u003cp\u003eB.II The Distribution of Quadratic Forms, 639\u003c\/p\u003e \u003cp\u003eB.III Estimation, 642\u003c\/p\u003e \u003cp\u003eB.III.1 Maximum Likelihood Estimation, 642\u003c\/p\u003e \u003cp\u003eB.III.2 Constrained Maximum Likelihood Estimation, 642\u003c\/p\u003e \u003cp\u003eB.III.3 Complete, Sufficient Statistics, 643\u003c\/p\u003e \u003cp\u003eB.IV Tests of Hypotheses and Confidence Regions, 643\u003c\/p\u003e \u003cp\u003eB.IV.1 Tests of Hypotheses, 643\u003c\/p\u003e \u003cp\u003eB.IV.2 Confidence Intervals and Regions, 644\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Data Tables 645\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.I Downloading Data Files from FTP Server, 645\u003c\/p\u003e \u003cp\u003eC.II Listing of Data Set Files, 645\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D Statistical Tables 660\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences 669\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 677\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406851514711,"sku":"9781118329504","price":111.6,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118329504.jpg?v=1730497341","url":"https:\/\/bookcurl.com\/products\/methods-and-applications-of-linear-models-9781118329504","provider":"Book Curl","version":"1.0","type":"link"}