LEADER 05419nam 2200673 a 450 001 9910139730403321 005 20200520144314.0 010 $a9786613446022 010 $a9781283446020 010 $a1283446022 010 $a9780470556986 010 $a0470556986 010 $a9780470556979 010 $a0470556978 035 $a(CKB)2550000000079123 035 $a(EBL)698819 035 $a(OCoLC)774270995 035 $a(SSID)ssj0000590595 035 $a(PQKBManifestationID)11353900 035 $a(PQKBTitleCode)TC0000590595 035 $a(PQKBWorkID)10671961 035 $a(PQKB)10928549 035 $a(MiAaPQ)EBC698819 035 $a(Perlego)2768631 035 $a(EXLCZ)992550000000079123 100 $a20091208d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aGeneralized linear models $ewith applications in engineering and the sciences /$fRaymond H. Myers ... [et al.] 205 $a2nd ed. 210 $aHoboken, N.J. $cWiley$dc2010 215 $a1 online resource (521 p.) 225 1 $aWiley series in probability and statistics 300 $aRev. ed. of: Generalized linear models / Raymond H. Myers, Douglas C. Montgomery, G. Geoffrey Vining. c2002. 311 08$a9780470454633 311 08$a0470454636 320 $aIncludes bibliographical references and index. 327 $aGeneralized Linear Models: With Applications in Engineering and the Sciences; Contents; Preface; 1. Introduction to Generalized Linear Models; 1.1 Linear Models; 1.2 Nonlinear Models; 1.3 The Generalized Linear Model; 2. Linear Regression Models; 2.1 The Linear Regression Model and Its Application; 2.2 Multiple Regression Models; 2.2.1 Parameter Estimation with Ordinary Least Squares; 2.2.2 Properties of the Least Squares Estimator and Estimation of ?2; 2.2.3 Hypothesis Testing in Multiple Regression; 2.2.4 Confidence Intervals in Multiple Regression 327 $a2.2.5 Prediction of New Response Observations2.2.6 Linear Regression Computer Output; 2.3 Parameter Estimation Using Maximum Likelihood; 2.3.1 Parameter Estimation Under the Normal-Theory Assumptions; 2.3.2 Properties of the Maximum Likelihood Estimators; 2.4 Model Adequacy Checking; 2.4.1 Residual Analysis; 2.4.2 Transformation of the Response Variable Using the Box-Cox Method; 2.4.3 Scaling Residuals; 2.4.4 Influence Diagnostics; 2.5 Using R to Perform Linear Regression Analysis; 2.6 Parameter Estimation by Weighted Least Squares; 2.6.1 The Constant Variance Assumption 327 $a2.6.2 Generalized and Weighted Least Squares2.6.3 Generalized Least Squares and Maximum Likelihood; 2.7 Designs for Regression Models; Exercises; 3. Nonlinear Regression Models; 3.1 Linear and Nonlinear Regression Models; 3.1.1 Linear Regression Models; 3.1.2 Nonlinear Regression Models; 3.1.3 Origins of Nonlinear Models; 3.2 Transforming to a Linear Model; 3.3 Parameter Estimation in a Nonlinear System; 3.3.1 Nonlinear Least Squares; 3.3.2 The Geometry of Linear and Nonlinear Least Squares; 3.3.3 Maximum Likelihood Estimation; 3.3.4 Linearization and the Gauss-Newton Method 327 $a3.3.5 Using R to Perform Nonlinear Regression Analysis3.3.6 Other Parameter Estimation Methods; 3.3.7 Starting Values; 3.4 Statistical Inference in Nonlinear Regression; 3.5 Weighted Nonlinear Regression; 3.6 Examples of Nonlinear Regression Models; 3.7 Designs for Nonlinear Regression Models; Exercises; 4. Logistic and Poisson Regression Models; 4.1 Regression Models Where the Variance Is a Function of the Mean; 4.2 Logistic Regression Models; 4.2.1 Models with a Binary Response Variable; 4.2.2 Estimating the Parameters in a Logistic Regression Model 327 $a4.2.3 Interpellation of the Parameters in a Logistic Regression Model4.2.4 Statistical Inference on Model Parameters; 4.2.5 Lack-of-Fit Tests in Logistic Regression; 4.2.6 Diagnostic Checking in Logistic Regression; 4.2.7 Classification and the Receiver Operating Characteristic Curve; 4.2.8 A Biological Example of Logistic Regression; 4.2.9 Other Models for Binary Response Data; 4.2.10 More than Two Categorical Outcomes; 4.3 Poisson Regression; 4.4 Overdispersion in Logistic and Poisson Regression; Exercises; 5. The Generalized Linear Model; 5.1 The Exponential Family of Distributions 327 $a5.2 Formal Structure for the Class of Generalized Linear Models 330 $aPraise for the First Edition ""The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.""-Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized 410 0$aWiley series in probability and statistics. 606 $aLinear models (Statistics) 615 0$aLinear models (Statistics) 676 $a519.5/35 701 $aMyers$b Raymond H$0101835 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139730403321 996 $aGeneralized linear models$92210108 997 $aUNINA