{"product_id":"introduction-to-linear-regression-analysis-hardback-9781119578727","title":"Introduction to Linear Regression Analysis (Hardback) 9781119578727","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eIntroduction to Linear Regression Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eDouglas C. Montgomery (Author), Elizabeth A. Peck (Author), G. Geoffrey Vining (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119578727, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 3 May 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e704 pages\u003cbr\u003e25.7 x 18.5 x 3.3 cm, 1.247 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eINTRODUCTION TO \u003cb\u003eLINEAR REGRESSION ANALYSIS\u003c\/b\u003e \u003cp\u003e\u003cb\u003eA comprehensive and current introduction to the fundamentals of regression analysis\u003c\/b\u003e  \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eIntroduction to Linear Regression Analysis, 6th Edition\u003c\/i\u003e is the most comprehensive, fulsome, and current examination of the foundations of linear regression analysis. Fully updated in this new sixth edition, the distinguished authors have included new material on generalized regression techniques and new examples to help the reader understand retain the concepts taught in the book. \u003c\/p\u003e\n\u003cp\u003eThe new edition focuses on four key areas of improvement over the fifth edition: \u003c\/p\u003e\n\u003cli\u003e\u003cbl\u003eNew exercises and data sets\u003c\/bl\u003e\u003c\/li\u003e \u003cli\u003e\u003cbl\u003eNew material on generalized regression techniques\u003c\/bl\u003e\u003c\/li\u003e \u003cli\u003e\u003cbl\u003eThe inclusion of JMP software in key areas\u003c\/bl\u003e\u003c\/li\u003e \u003cli\u003e\u003cbl\u003eCarefully condensing the text where possible\u003c\/bl\u003e\u003c\/li\u003e \u003cp\u003e\u003ci\u003eIntroduction to Linear Regression Analysis\u003c\/i\u003e skillfully blends theory and application in both the conventional and less common uses of regression analysis in today’s cutting-edge scientific research. The text equips readers to understand the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiii\u003cbr\u003eAbout the Companion Website xvi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Introduction 1\u003cbr\u003e\u003c\/b\u003e1.1 Regression and Model Building 1\u003cbr\u003e1.2 Data Collection 5\u003cbr\u003e1.3 Uses of Regression 9\u003cbr\u003e1.4 Role of the Computer 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Simple Linear Regression 12\u003cbr\u003e\u003c\/b\u003e2.1 Simple Linear Regression Model 12\u003cbr\u003e2.2 Least-Squares Estimation of the Parameters 13\u003cbr\u003e2.3 Hypothesis Testing on the Slope and Intercept 22\u003cbr\u003e2.4 Interval Estimation in Simple Linear Regression 29\u003cbr\u003e2.5 Prediction of New Observations 33\u003cbr\u003e2.6 Coefficient of Determination 35\u003cbr\u003e2.7 A Service Industry Application of Regression 37\u003cbr\u003e2.8 Does Pitching Win Baseball Games? 39\u003cbr\u003e2.9 Using SAS and R for Simple Linear Regression 41\u003cbr\u003e2.10 Some Considerations in the Use of Regression 44\u003cbr\u003e2.11 Regression Through the Origin 46\u003cbr\u003e2.12 Estimation by Maximum Likelihood 52\u003cbr\u003e2.13 Case Where the Regressor x Is Random 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Multiple Linear Regression 69\u003cbr\u003e\u003c\/b\u003e3.1 Multiple Regression Models 69\u003cbr\u003e3.2 Estimation of the Model Parameters 72\u003cbr\u003e3.3 Hypothesis Testing in Multiple Linear Regression 86\u003cbr\u003e3.4 Confidence Intervals in Multiple Regression 99\u003cbr\u003e3.5 Prediction of New Observations 106\u003cbr\u003e3.6 A Multiple Regression Model for the Patient Satisfaction Data 106\u003cbr\u003e3.7 Does Pitching and Defense Win Baseball Games? 108\u003cbr\u003e3.8 Using SAS and R for Basic Multiple Linear Regression 110\u003cbr\u003e3.9 Hidden Extrapolation in Multiple Regression 111\u003cbr\u003e3.10 Standardized Regression Coefficients 115\u003cbr\u003e3.11 Multicollinearity 121\u003cbr\u003e3.12 Why Do Regression Coefficients Have the Wrong Sign? 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Model Adequacy Checking 134\u003cbr\u003e\u003c\/b\u003e4.1 Introduction 134\u003cbr\u003e4.2 Residual Analysis 135\u003cbr\u003e4.3 PRESS Statistic 156\u003cbr\u003e4.4 Detection and Treatment of Outliers 157\u003cbr\u003e4.5 Lack of Fit of the Regression Model 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Transformations and Weighting To Correct Model Inadequacies 177\u003cbr\u003e\u003c\/b\u003e5.1 Introduction 177\u003cbr\u003e5.2 Variance-Stabilizing Transformations 178\u003cbr\u003e5.3 Transformations to Linearize the Model 182\u003cbr\u003e5.4 Analytical Methods for Selecting a Transformation 188\u003cbr\u003e5.5 Generalized and Weighted Least Squares 194\u003cbr\u003e5.6 Regression Models with Random Effects 200\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Diagnostics for Leverage and Influence 217\u003cbr\u003e\u003c\/b\u003e6.1 Importance of Detecting Influential Observations 217\u003cbr\u003e6.2 Leverage 218\u003cbr\u003e6.3 Measures of Influence: Cook's D 221\u003cbr\u003e6.4 Measures of Influence: \u003ci\u003eDFFITS\u003c\/i\u003e and \u003ci\u003eDFBETAS\u003c\/i\u003e 223\u003cbr\u003e6.5 A Measure of Model Performance 225\u003cbr\u003e6.6 Detecting Groups of Influential Observations 226\u003cbr\u003e6.7 Treatment of Influential Observations 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Polynomial Regression Models 230\u003cbr\u003e\u003c\/b\u003e7.1 Introduction 230\u003cbr\u003e7.2 Polynomial Models in One Variable 230\u003cbr\u003e7.3 Nonparametric Regression 243\u003cbr\u003e7.4 Polynomial Models in Two or More Variables 249\u003cbr\u003e7.5 Orthogonal Polynomials 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Indicator Variables 268\u003cbr\u003e\u003c\/b\u003e8.1 General Concept of Indicator Variables 268\u003cbr\u003e8.2 Comments on the Use of Indicator Variables 281\u003cbr\u003e8.3 Regression Approach to Analysis of Variance 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Multicollinearity 293\u003cbr\u003e\u003c\/b\u003e9.1 Introduction 293\u003cbr\u003e9.2 Sources of Multicollinearity 294\u003cbr\u003e9.3 Effects of Multicollinearity 296\u003cbr\u003e9.4 Multicollinearity Diagnostics 300\u003cbr\u003e9.5 Methods for Dealing with Multicollinearity 311\u003cbr\u003e9.6 Using SAS to Perform Ridge and Principal-Component Regression 336\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Variable Selection and Model Building 342\u003cbr\u003e\u003c\/b\u003e10.1 Introduction 342\u003cbr\u003e10.2 Computational Techniques for Variable Selection 353\u003cbr\u003e10.3 Strategy for Variable Selection and Model Building 367\u003cbr\u003e10.4 Case Study: Gorman and Toman Asphalt Data Using SAS 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Validation of Regression Models 388\u003cbr\u003e\u003c\/b\u003e11.1 Introduction 388\u003cbr\u003e11.2 Validation Techniques 389\u003cbr\u003e11.3 Data from Planned Experiments 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Introduction to Nonlinear Regression 405\u003cbr\u003e\u003c\/b\u003e12.1 Linear and Nonlinear Regression Models 405\u003cbr\u003e12.2 Origins of Nonlinear Models 407\u003cbr\u003e12.3 Nonlinear Least Squares 411\u003cbr\u003e12.4 Transformation to a Linear Model 413\u003cbr\u003e12.5 Parameter Estimation in a Nonlinear System 416\u003cbr\u003e12.6 Statistical Inference in Nonlinear Regression 425\u003cbr\u003e12.7 Examples of Nonlinear Regression Models 427\u003cbr\u003e12.8 Using SAS and R 428\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Generalized Linear Models 440\u003cbr\u003e\u003c\/b\u003e13.1 Introduction 440\u003cbr\u003e13.2 Logistic Regression Models 441\u003cbr\u003e13.3 Poisson Regression 463\u003cbr\u003e13.4 The Generalized Linear Model 469\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Regression Analysis of Time Series Data 495\u003cbr\u003e\u003c\/b\u003e14.1 Introduction to Regression Models for Time Series Data 495\u003cbr\u003e14.2 Detecting Autocorrelation: The Durbin–Watson Test 496\u003cbr\u003e14.3 Estimating the Parameters in Time Series Regression Models 501\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Other Topics in the Use of Regression Analysis 521\u003cbr\u003e\u003c\/b\u003e15.1 Robust Regression 521\u003cbr\u003e15.2 Effect of Measurement Errors in the Regressors 532\u003cbr\u003e15.3 Inverse Estimation—The Calibration Problem 534\u003cbr\u003e15.4 Bootstrapping in Regression 538\u003cbr\u003e15.5 Classification and Regression Trees (CART) 545\u003cbr\u003e15.6 Neural Networks 547\u003cbr\u003e15.7 Designed Experiments for Regression 549\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A. Statistical Tables 561\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B. Data Sets for Exercises 573\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C. Supplemental Technical Material 602\u003cbr\u003e\u003c\/b\u003eC.1 Background on Basic Test Statistics 602\u003cbr\u003eC.2 Background from the Theory of Linear Models 605\u003cbr\u003eC.3 Important Results on SS R and SS Res 609\u003cbr\u003eC.4 Gauss-Markov Theorem, Var(ε) = σ 2 I 615\u003cbr\u003eC.5 Computational Aspects of Multiple Regression 617\u003cbr\u003eC.6 Result on the Inverse of a Matrix 618\u003cbr\u003eC.7 Development of the PRESS Statistic 619\u003cbr\u003eC.8 Development of S(i) 2 621\u003cbr\u003eC.9 Outlier Test Based on R-Student 622\u003cbr\u003eC.10 Independence of Residuals and Fitted Values 624\u003cbr\u003eC.11 Gauss–Markov Theorem, Var(ε) = V 625\u003cbr\u003eC.12 Bias in MSRes When the Model Is Underspecified 627\u003cbr\u003eC.13 Computation of Influence Diagnostics 628\u003cbr\u003eC.14 Generalized Linear Models 629\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D. Introduction to SAS 641\u003cbr\u003e\u003c\/b\u003eD.1 Basic Data Entry 642\u003cbr\u003eD.2 Creating Permanent SAS Data Sets 646\u003cbr\u003eD.3 Importing Data from an EXCEL File 647\u003cbr\u003eD.4 Output Command 648\u003cbr\u003eD.5 Log File 648\u003cbr\u003eD.6 Adding Variables to an Existing SAS Data Set 650\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix E. Introduction to R to Perform Linear Regression Analysis 651\u003cbr\u003e\u003c\/b\u003eE.1 Basic Background on R 651\u003cbr\u003eE.2 Basic Data Entry 652\u003cbr\u003eE.3 Brief Comments on Other Functionality in R 654\u003cbr\u003eE.4 R Commander 655\u003c\/p\u003e \u003cp\u003eReferences 656\u003cbr\u003eIndex 670\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52509191110936,"sku":"9781119578727","price":92.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119578727.jpg?v=1786496891","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/introduction-to-linear-regression-analysis-hardback-9781119578727","provider":"Freshly Printed Books","version":"1.0","type":"link"}