LEADER 06618nam 22006974a 450 001 9910143422703321 005 20200520144314.0 010 $a9786610551743 010 $a9781280551741 010 $a1280551747 010 $a9780470055465 010 $a0470055464 010 $a9780470055458 010 $a0470055456 035 $a(CKB)1000000000354960 035 $a(EBL)269142 035 $a(SSID)ssj0000234926 035 $a(PQKBManifestationID)11203035 035 $a(PQKBTitleCode)TC0000234926 035 $a(PQKBWorkID)10242768 035 $a(PQKB)11225093 035 $a(MiAaPQ)EBC269142 035 $a(CaSebORM)9780471746966 035 $a(OCoLC)85820790 035 $a(Perlego)2774380 035 $a(EXLCZ)991000000000354960 100 $a20060403d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRegression analysis by example 205 $a4th ed. /$bSamprit Chatterjee, Ali S. Hadi. 210 $aHoboken, N.J. $cWiley-Interscience$dc2006 215 $a1 online resource (403 pages) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 08$a9780471746966 311 08$a0471746967 320 $aIncludes bibliographical references (p. 363-370) and index. 327 $aPreface; 1 Introduction; 1.1 What Is Regression Analysis?; 1.2 Publicly Available Data Sets; 1.3 Selected Applications of Regression Analysis; 1.3.1 Agricultural Sciences; 1.3.2 Industrial and Labor Relations; 1.3.3 History; 1.3.4 Government; 1.3.5 Environmental Sciences; 1.4 Steps in Regression Analysis; 1.4.1 Statement of the Problem; 1.4.2 Selection of Potentially Relevant Variables; 1.4.3 Data Collection; 1.4.4 Model Specification; 1.4.5 Method of Fitting; 1.4.6 Model Fitting; 1.4.7 Model Criticism and Selection 327 $a1.4.8 Objectives of Regression Analysis1.5 Scope and Organization of the Book; Exercises; 2 Simple Linear Regression; 2.1 Introduction; 2.2 Covariance and Correlation Coefficient; 2.3 Example: Computer Repair Data; 2.4 The Simple Linear Regression Model; 2.5 Parameter Estimation; 2.6 Tests of Hypotheses; 2.7 Confidence Intervals; 2.8 Predictions; 2.9 Measuring the Quality of Fit; 2.10 Regression Line Through the Origin; 2.11 Trivial Regression Models; 2.12 Bibliographic Notes; Exercises; 3 Multiple Linear Regression; 3.1 Introduction; 3.2 Description of the Data and Model 327 $a3.3 Example: Supervisor Performance Data3.4 Parameter Estimation; 3.5 Interpretations of Regression Coefficients; 3.6 Properties of the Least Squares Estimators; 3.7 Multiple Correlation Coefficient; 3.8 Inference for Individual Regression Coefficients; 3.9 Tests of Hypotheses in a Linear Model; 3.9.1 Testing All Regression Coefficients Equal to Zero; 3.9.2 Testing a Subset of Regression Coefficients Equal to Zero; 3.9.3 Testing the Equality of Regression Coefficients; 3.9.4 Estimating and Testing of Regression Parameters Under Constraints; 3.10 Predictions; 3.11 Summary; Exercises 327 $aAppendix: Multiple Regression in Matrix Notation4 Regression Diagnostics: Detection of Model Violations; 4.1 Introduction; 4.2 The Standard Regression Assumptions; 4.3 Various Types of Residuals; 4.4 Graphical Methods; 4.5 Graphs Before Fitting a Model; 4.5.1 One-Dimensional Graphs; 4.5.2 Two-Dimensional Graphs; 4.5.3 Rotating Plots; 4.5.4 Dynamic Graphs; 4.6 Graphs After Fitting a Model; 4.7 Checking Linearity and Normality Assumptions; 4.8 Leverage, Influence, and Outliers; 4.8.1 Outliers in the Response Variable; 4.8.2 Outliers in the Predictors; 4.8.3 Masking and Swamping Problems 327 $a4.9 Measures of Influence4.9.1 Cook's Distance; 4.9.2 Welsch and Kuh Measure; 4.9.3 Hadi's Influence Measure; 4.10 The Potential-Residual Plot; 4.11 What to Do with the Outliers?; 4.12 Role of Variables in a Regression Equation; 4.12.1 Added-Variable Plot; 4.12.2 Residual Plus Component Plot; 4.13 Effects of an Additional Predictor; 4.14 Robust Regression; Exercises; 5. Qualitative Variables as Predictors; 5.1 Introduction; 5.2 Salary Survey Data; 5.3 Interaction Variables; 5.4 Systems of Regression Equations; 5.4.1 Models with Different Slopes and Different Intercepts 327 $a5.4.2 Models with Same Slope and Different Intercepts 330 $aThe essentials of regression analysis through practical applications Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. This new edition features the following enhancements: Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis A new chapter entitled Further Topics discusses advanced areas of regression analysis Reorganized, expanded, and upgraded exercises appear at the end of each chapter A fully integrated Web page provides data sets Numerous graphical displays highlight the significance of visual appeal Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. 410 0$aWiley series in probability and statistics. 606 $aRegression analysis 615 0$aRegression analysis. 676 $a519.5/36 700 $aChatterjee$b Samprit$f1938-$014454 701 $aHadi$b Ali S$021014 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143422703321 996 $aRegression Analysis by Example$94102577 997 $aUNINA