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[null,null,"Titolo",null,"Generalized linear models : with applications in engineering and the sciences / / Raymond H. Myers ... [et al.]"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, N.J., : Wiley, c2010"]
[null,null,"ISBN",null,"9786613446022"]
[{"$nulls":4},"9781283446020"]
[{"$nulls":4},"1283446022"]
[{"$nulls":4},"9780470556986"]
[{"$nulls":4},"0470556986"]
[{"$nulls":4},"9780470556979"]
[{"$nulls":4},"0470556978"]
[null,null,"Edizione",null,"[2nd ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (521 p.)"]
[null,null,"Collana",null,"Wiley series in probability and statistics"]
[null,null,"Disciplina",null,"519.5/35"]
[null,null,"Soggetti",null,"Linear models (Statistics)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Rev. ed. of: Generalized linear models / Raymond H. Myers, Douglas C. Montgomery, G. Geoffrey Vining. c2002."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Generalized 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 σ2; 2.2.3 Hypothesis Testing in Multiple Regression; 2.2.4 Confidence Intervals in Multiple Regression"]
[{"$nulls":4},"2.2.5 Prediction of New Response Observations2.2.6 Linear Regression Computer Output; 2.3 Parameter Estimation Using Maximum Likelihood; 2.3.1 Parameter Estimation Under the Normal-Theory Assumptions; 2.3.2 Properties of the Maximum Likelihood Estimators; 2.4 Model Adequacy Checking; 2.4.1 Residual Analysis; 2.4.2 Transformation of the Response Variable Using the Box-Cox Method; 2.4.3 Scaling Residuals; 2.4.4 Influence Diagnostics; 2.5 Using R to Perform Linear Regression Analysis; 2.6 Parameter Estimation by Weighted Least Squares; 2.6.1 The Constant Variance Assumption"]
[{"$nulls":4},"2.6.2 Generalized and Weighted Least Squares2.6.3 Generalized Least "]
[{"$nulls":4},"Squares and Maximum Likelihood; 2.7 Designs for Regression Models; Exercises; 3. Nonlinear Regression Models; 3.1 Linear and Nonlinear Regression Models; 3.1.1 Linear Regression Models; 3.1.2 Nonlinear Regression Models; 3.1.3 Origins of Nonlinear Models; 3.2 Transforming to a Linear Model; 3.3 Parameter Estimation in a Nonlinear System; 3.3.1 Nonlinear Least Squares; 3.3.2 The Geometry of Linear and Nonlinear Least Squares; 3.3.3 Maximum Likelihood Estimation; 3.3.4 Linearization and the Gauss-Newton Method"]
[{"$nulls":4},"3.3.5 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"]
[{"$nulls":4},"4.2.3 Interpellation of the Parameters in a Logistic Regression Model4.2.4 Statistical Inference on Model Parameters; 4.2.5 Lack-of-Fit Tests in Logistic Regression; 4.2.6 Diagnostic Checking in Logistic Regression; 4.2.7 Classification and the Receiver Operating Characteristic Curve; 4.2.8 A Biological Example of Logistic Regression; 4.2.9 Other Models for Binary Response Data; 4.2.10 More than Two Categorical Outcomes; 4.3 Poisson Regression; 4.4 Overdispersion in Logistic and Poisson Regression; Exercises; 5. The Generalized Linear Model; 5.1 The Exponential Family of Distributions"]
[{"$nulls":4},"5.2 Formal Structure for the Class of Generalized Linear Models"]
[null,null,null,"Sommario/riassunto","Praise 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"]