{"product_id":"applied-linear-regression-isbn-9781118386088","title":"Applied Linear Regression","description":"\u003cp\u003e\u003cb\u003ePraise for the \u003c\/b\u003e\u003cb\u003e\u003ci\u003eThird Edition\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\"...this is an excellent book which could easily be used as a course text...\"\u003cbr\u003e —International Statistical Institute\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eFourth Edition\u003c\/i\u003e of \u003ci\u003eApplied Linear Regression\u003c\/i\u003e provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the \u003ci\u003eFourth Edition\u003c\/i\u003e uses interesting, real-world exercises and examples.\u003c\/p\u003e \u003cp\u003eStressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition,\u003ci\u003eApplied Linear Regression, Fourth Edition\u003c\/i\u003e features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eGraphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis\u003c\/li\u003e \u003cli\u003eIn-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics\u003c\/li\u003e \u003cli\u003eNewly added material on topics including testing, ANOVA, and variance assumptions\u003c\/li\u003e \u003cli\u003eUpdated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Linear Regression, Fourth Edition\u003c\/i\u003e is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e1 Scatterplots 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Scatterplots 2\u003c\/p\u003e \u003cp\u003e1.2 Mean Functions 9\u003c\/p\u003e \u003cp\u003e1.3 Variance Functions 12\u003c\/p\u003e \u003cp\u003e1.4 Summary Graph 12\u003c\/p\u003e \u003cp\u003e1.5 Tools for Looking at Scatterplots 13\u003c\/p\u003e \u003cp\u003e1.6 Scatterplot Matrices 15\u003c\/p\u003e \u003cp\u003e1.7 Problems 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Simple Linear Regression 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Ordinary Least Squares Estimation 22\u003c\/p\u003e \u003cp\u003e2.2 Least Squares Criterion 24\u003c\/p\u003e \u003cp\u003e2.3 Estimating the Variance 𝜎2 26\u003c\/p\u003e \u003cp\u003e2.4 Properties of Least Squares Estimates 27\u003c\/p\u003e \u003cp\u003e2.5 Estimated Variances 28\u003c\/p\u003e \u003cp\u003e2.6 Confidence Intervals and 𝑡-Tests 29\u003c\/p\u003e \u003cp\u003e2.7 The Coefficient of Determination, 𝑅2 33\u003c\/p\u003e \u003cp\u003e2.8 The Residuals 35\u003c\/p\u003e \u003cp\u003e2.9 Problems 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Multiple Regression 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Adding a Regressor to a Simple Linear Regression Model 49\u003c\/p\u003e \u003cp\u003e3.2 The Multiple Linear Regression Model 53\u003c\/p\u003e \u003cp\u003e3.3 Predictors and Regressors 53\u003c\/p\u003e \u003cp\u003e3.4 Ordinary Least Squares 57\u003c\/p\u003e \u003cp\u003e3.5 Predictions, Fitted Values and Linear Combinations 65\u003c\/p\u003e \u003cp\u003e3.6 Problems 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Interpretation of Main Effects 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Understanding Parameter Estimates 71\u003c\/p\u003e \u003cp\u003e4.2 Dropping Regressors 81\u003c\/p\u003e \u003cp\u003e4.3 Experimentation Versus Observation 84\u003c\/p\u003e \u003cp\u003e4.4 Sampling from a Normal Population 86\u003c\/p\u003e \u003cp\u003e4.5 More on 𝑅2 88\u003c\/p\u003e \u003cp\u003e4.6 Problems 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Complex Regressors 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Factors 95\u003c\/p\u003e \u003cp\u003e5.2 Many Factors 105\u003c\/p\u003e \u003cp\u003e5.3 Polynomial Regression 106\u003c\/p\u003e \u003cp\u003e5.4 Splines 109\u003c\/p\u003e \u003cp\u003e5.5 Principal Components 112\u003c\/p\u003e \u003cp\u003e5.6 Missing Data 115\u003c\/p\u003e \u003cp\u003e5.7 Problems 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Testing and Analysis of Variance 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 𝐹-tests 130\u003c\/p\u003e \u003cp\u003e6.2 The Analysis of Variance 134\u003c\/p\u003e \u003cp\u003e6.3 Comparisons of Means 138\u003c\/p\u003e \u003cp\u003e6.4 Power and Non-null Distributions 138\u003c\/p\u003e \u003cp\u003e6.5 Wald Tests 140\u003c\/p\u003e \u003cp\u003e6.6 Interpreting Tests 142\u003c\/p\u003e \u003cp\u003e6.7 Problems 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Variances 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Weighted Least Squares 151\u003c\/p\u003e \u003cp\u003e7.2 Misspecified Variances 157\u003c\/p\u003e \u003cp\u003e7.3 General Correlation Structures 162\u003c\/p\u003e \u003cp\u003e7.4 Mixed Models 163\u003c\/p\u003e \u003cp\u003e7.5 Variance Stabilizing Transformations 165\u003c\/p\u003e \u003cp\u003e7.6 The Delta Method 166\u003c\/p\u003e \u003cp\u003e7.7 The Bootstrap 168\u003c\/p\u003e \u003cp\u003e7.8 Problems 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Transformations 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Transformation Basics 179\u003c\/p\u003e \u003cp\u003e8.2 A General Approach to Transformations 185\u003c\/p\u003e \u003cp\u003e8.3 Transforming the Response 190\u003c\/p\u003e \u003cp\u003e8.4 Transformations of Nonpositive Variables 192\u003c\/p\u003e \u003cp\u003e8.5 Additive Models 192\u003c\/p\u003e \u003cp\u003e8.6 Problems 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Regression Diagnostics 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The Residuals 199\u003c\/p\u003e \u003cp\u003e9.2 Testing for Curvature 206\u003c\/p\u003e \u003cp\u003e9.3 Nonconstant Variance 208\u003c\/p\u003e \u003cp\u003e9.4 Outliers 208\u003c\/p\u003e \u003cp\u003e9.5 Influence of Cases 212\u003c\/p\u003e \u003cp\u003e9.6 Normality Assumption 218\u003c\/p\u003e \u003cp\u003e9.7 Problems 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Variable Selection 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Variable Selection and Parameter Assessment 228\u003c\/p\u003e \u003cp\u003e10.2 Variable Selection for Discovery 230\u003c\/p\u003e \u003cp\u003e10.3 Model Selection for Prediction 238\u003c\/p\u003e \u003cp\u003e10.4 Problems 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Nonlinear Regression 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Estimation for Nonlinear Mean Functions 246\u003c\/p\u003e \u003cp\u003e11.2 Inference Assuming Large Samples 249\u003c\/p\u003e \u003cp\u003e11.3 Starting Values 249\u003c\/p\u003e \u003cp\u003e11.4 Bootstrap Inference 255\u003c\/p\u003e \u003cp\u003e11.5 Further Reading 257\u003c\/p\u003e \u003cp\u003e11.6 Problems 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Binomial and Poisson Regression 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Distributions for Counted Data 263\u003c\/p\u003e \u003cp\u003e12.2 Regression Models For Counts 265\u003c\/p\u003e \u003cp\u003e12.3 Poisson Regression 271\u003c\/p\u003e \u003cp\u003e12.4 Transferring What You Know about Linear Models 276\u003c\/p\u003e \u003cp\u003e12.5 Generalized Linear Models 278\u003c\/p\u003e \u003cp\u003e12.6 Problems 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Appendix 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Website 283\u003c\/p\u003e \u003cp\u003eA.2 Means, Variances, Covariances and Correlations 283\u003c\/p\u003e \u003cp\u003eA.3 Least Squares for Simple Regression 286\u003c\/p\u003e \u003cp\u003eA.4 Means and Variances of Least Squares Estimates 286\u003c\/p\u003e \u003cp\u003eA.5 Estimating E(𝑌 |𝑋) using a Smoother 288\u003c\/p\u003e \u003cp\u003eA.6 A Brief Introduction to Matrices and Vectors 290\u003c\/p\u003e \u003cp\u003eA.7 Random Vectors 295\u003c\/p\u003e \u003cp\u003eA.8 Least Squares Using Matrices 295\u003c\/p\u003e \u003cp\u003eA.9 The QR factorization 299\u003c\/p\u003e \u003cp\u003eA.10 Spectral Decomposition 300\u003c\/p\u003e \u003cp\u003eA.11 Maximum Likelihood Estimates 300\u003c\/p\u003e \u003cp\u003eA.12 The Box–Cox Method for Transformations 302\u003c\/p\u003e \u003cp\u003eA.13 Case Deletion in Linear Regression 305\u003c\/p\u003e \u003cp\u003eBibliography 321\u003c\/p\u003e \u003cp\u003eIndex 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSANFORD WEISBERG, PhD, \u003c\/b\u003eis Professor of Statistics and Director of the Statistical Consulting Service in the School of Statistics at the University of Minnesota. He is also a coauthor of \u003ci\u003eApplied Regression Including Computing and Graphics\u003c\/i\u003e and \u003ci\u003eAn Introduction to Regression Graphics,\u003c\/i\u003e both published by Wiley.\u003c\/p\u003e   \u003cp\u003e\u003cb\u003ePraise for the\u003c\/b\u003e \u003cb\u003e\u003ci\u003eThird Edition\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\"...this is an excellent book which could easily be used as a course text...\"\u003cbr\u003e —International Statistical Institute\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eFourth Edition\u003c\/i\u003e of \u003ci\u003eApplied Linear Regression\u003c\/i\u003e provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the \u003ci\u003eFourth Edition\u003c\/i\u003e uses interesting, real-world exercises and examples.\u003c\/p\u003e \u003cp\u003eStressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition, \u003ci\u003eApplied Linear Regression, Fourth Edition\u003c\/i\u003e features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eGraphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis\u003c\/li\u003e \u003cli\u003eIn-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics\u003c\/li\u003e \u003cli\u003eNewly added material on topics including testing, ANOVA, and variance assumptions\u003c\/li\u003e \u003cli\u003eUpdated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Linear Regression, Fourth Edition\u003c\/i\u003e is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988751466725,"sku":"NP9781118386088","price":120.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118386088.jpg?v=1761781448","url":"https:\/\/k12savings.com\/products\/applied-linear-regression-isbn-9781118386088","provider":"K12savings","version":"1.0","type":"link"}