MARC Record from marc_columbia

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001 11590534
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008 120918t20122012njua ob 001 0 eng d
010 $a 2012003859
035 $a(OCoLC)ocn810183303
035 $a(NNC)11590534
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020 $a9781119180173$q(electronic bk.)
020 $a1119180171$q(electronic bk.)
020 $a9781118627365
020 $a1118627369
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035 $a(OCoLC)810183303$z(OCoLC)826866884$z(OCoLC)961696343$z(OCoLC)962349985$z(OCoLC)962730003$z(OCoLC)1040675887$z(OCoLC)1055365258$z(OCoLC)1066399083$z(OCoLC)1081241383$z(OCoLC)1096415954$z(OCoLC)1190692977$z(OCoLC)1228603779$z(OCoLC)1302080940
037 $aCL0500000190$bSafari Books Online
050 4 $aQA278.2$b.M65 2012 ebook
072 7 $aMAT$x003000$2bisacsh
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084 $a31.73$2bcl
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049 $aZCUA
100 1 $aMontgomery, Douglas C.,$eauthor.
245 10 $aIntroduction to linear regression analysis /$cDouglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining.
250 $aFifth edition.
264 1 $aHoboken, NJ :$bWiley,$c[2012]
264 4 $c©2012
300 $a1 online resource (xvi, 645 pages).
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aWiley series in probability and statistics
520 $a"This book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research"--$cProvided by publisher.
504 $aIncludes bibliographical references (pages 628-641) and index.
588 0 $aPrint version record.
505 0 $a1. INTRODUCTION -- 1.1 Regression and Model Building -- 1.2 Data Collection -- 1.3 Uses of Regression -- 1.4 Role of the Computer -- 2. SIMPLE LINEAR REGRESSION -- 2.1 Simple Linear Regression Model -- 2.2 Least-Squares Estimation of the Parameters -- 2.3 Hypothesis Testing on the Slope and Intercept -- 2.4 Interval Estimation in Simple Linear Regression -- 2.5 Prediction of New Observations -- 2.6 Coefficient of Determination -- 2.7 A Service Industry Application of Regression -- 2.8 Using SAS and R for Simple Linear Regression -- 2.9 Some Considerations in the Use of Regression -- 2.10 Regression Through the Origin -- 2.11 Estimation by Maximum Likelihood -- 2.12 Case Where the Regressor x is Random -- 3. MULTIPLE LINEAR REGRESSION -- 3.1 Multiple Regression Models -- 3.2 Estimation of the Model Parameters -- 3.3 Hypothesis Testing in Multiple Linear Regression -- 3.4 Confidence Intervals in Multiple Regression -- 3.5 Prediction of New Observations -- 3.6 A Multiple Regression Model for the Patient Satisfaction Data -- 3.7 Using SAS and R for Basic Multiple Linear Regression -- 3.8 Hidden Extrapolation in Multiple Regression -- 3.9 Standardized Regression Coeffi cients -- 3.10 Multicollinearity -- 3.11 Why Do Regression Coeffi cients Have the Wrong Sign?
505 0 $a4. MODEL ADEQUACY CHECKING -- 4.1 Introduction -- 4.2 Residual Analysis -- 4.3 PRESS Statistic -- 4.4 Detection and Treatment of Outliers -- 4.5 Lack of Fit of the Regression Model -- 5. TRANSFORMATIONS AND WEIGHTING TO CORRECT MODEL INADEQUACIES -- 5.1 Introduction -- 5.2 Variance-Stabilizing Transformations -- 5.3 Transformations to Linearize the Model -- 5.4 Analytical Methods for Selecting a Transformation -- 5.5 Generalized and Weighted Least Squares -- 5.6 Regression Models with Random Effect -- 6. DIAGNOSTICS FOR LEVERAGE AND INFLUENCE -- 6.1 Importance of Detecting Infl uential Observations -- 6.2 Leverage -- 6.3 Measures of Infl uence: Cook's D -- 6.4 Measures of Infl uence: DFFITS and DFBETAS -- 6.5 A Measure of Model Performance -- 6.6 Detecting Groups of Infl uential Observations -- 6.7 Treatment of Infl uential Observations -- 7. POLYNOMIAL REGRESSION MODELS -- 7.1 Introduction -- 7.2 Polynomial Models in One Variable -- 7.3 Nonparametric Regression -- 7.4 Polynomial Models in Two or More Variables -- 7.5 Orthogonal Polynomials.
505 0 $a8. INDICATOR VARIABLES -- 8.1 General Concept of Indicator Variables -- 8.2 Comments on the Use of Indicator Variables -- 8.3 Regression Approach to Analysis of Variance -- 9. MULTICOLLINEARITY -- 9.1 Introduction -- 9.2 Sources of Multicollinearity -- 9.3 Effects of Multicollinearity -- 9.4 Multicollinearity Diagnostics -- 9.5 Methods for Dealing with Multicollinearity -- 9.6 Using SAS to Perform Ridge and Principal-Component Regression -- 10. VARIABLE SELECTION AND MODEL BUILDING -- 10.1 Introduction -- 10.2 Computational Techniques for Variable Selection -- 10.3 Strategy for Variable Selection and Model Building -- 10.4 Case Study: Gorman and Toman Asphalt Data Using SAS -- 11. VALIDATION OF REGRESSION MODELS -- 11.1 Introduction 372 11.2 Validation Techniques -- 11.3 Data from Planned Experiments -- 12. INTRODUCTION TO NONLINEAR REGRESSION -- 12.1 Linear and Nonlinear Regression Models -- 12.2 Origins of Nonlinear Models -- 12.3 Nonlinear Least Squares -- 12.4 Transformation to a Linear Model -- 12.5 Parameter Estimation in a Nonlinear System -- 12.6 Statistical Inference in Nonlinear Regression -- 12.7 Examples of Nonlinear Regression Models -- 12.8 Using SAS and R.
505 0 $a13. GENERALIZED LINEAR MODELS -- 13.1 Introduction -- 13.2 Logistic Regression Models -- 13.3 Poisson Regression -- 13.4 The Generalized Linear Model -- 14. REGRESSION ANALYSIS OF TIME SERIES DATA -- 14.1 Introduction to Regression Models for Time Series Data -- 14.2 Detecting Autocorrelation: The Durbin-Watson Test -- 14.3 Estimating the Parameters in Time Series Regression Models -- 15. OTHER TOPICS IN THE USE OF REGRESSION ANALYSIS -- 15.1 Robust Regression -- 15.2 Effect of Measurement Errors in the Regressors -- 15.3 Inverse Estimation -- The Calibration Problem -- 15.4 Bootstrapping in Regression -- 15.5 Classifi cation and Regression Trees (CART) -- 15.6 Neural Networks -- 15.7 Designed Experiments for Regression -- APPENDIX A. STATISTICAL TABLES -- APPENDIX B. DATA SETS FOR EXERCISES -- APPENDIX C. SUPPLEMENTAL TECHNICAL MATERIAL -- C.1 Background on Basic Test Statistics -- C.2 Background from the Theory of Linear Models -- C.3 Important Results on SSR and SSRes -- C.4 Gauss-Markov Theorem, Var(epsilon) = sigma2I.
505 0 $aC.5 Computational Aspects of Multiple Regression -- C.6 Result on the Inverse of a Matrix -- C.7 Development of the PRESS Statistic -- C.8 Development of S2 (i) -- C.9 Outlier Test Based on R-Student -- C.10 Independence of Residuals and Fitted Values -- C.11 Gauss -- Markov Theorem, Var(epsilon) = V -- C.12 Bias in MSRes When the Model Is Underspecified -- C.13 Computation of Infl uence Diagnostics -- C.14 Generalized Linear Models -- APPENDIX D. INTRODUCTION TO SAS -- D.1 Basic Data Entry -- D.2 Creating Permanent SAS Data Sets -- D.3 Importing Data from an EXCEL File -- D.4 Output Command -- D.5 Log File -- D.6 Adding Variables to an Existing SAS Data Set -- APPENDIX E. INTRODUCTION TO R TO PERFORM LINEAR REGRESSION ANALYSIS -- E.1 Basic Background on R -- E.2 Basic Data Entry -- E.3 Brief Comments on Other Functionality in R -- E.4 R Commander.
650 0 $aRegression analysis.
650 2 $aRegression Analysis
650 6 $aAnalyse de régression.
650 7 $aMATHEMATICS$xProbability & Statistics$xGeneral.$2bisacsh
650 7 $aMATHEMATICS$xApplied.$2bisacsh
650 7 $aRegression analysis.$2fast$0(OCoLC)fst01432090
650 7 $aLineare Regression.$2idszbz
655 0 $aElectronic book.
655 4 $aElectronic books.
700 1 $aPeck, Elizabeth A.,$d1953-$eauthor.
700 1 $aVining, G. Geoffrey,$d1954-$eauthor.
776 08 $iPrint version:Montgomery, Douglas C.$tIntroduction to linear regression analysis.$b5th ed.$dHoboken, NJ : Wiley, 2012$z9780470542811$w(DLC) 2012003859$w(OCoLC)775329531
830 0 $aWiley series in probability and statistics.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio11590534$zAll EBSCO eBooks
852 8 $blweb$hEBOOKS