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120130s2012 xx o 000 0 eng d |
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|a EBLCP
|b eng
|e pn
|c EBLCP
|d OCLCQ
|d N$T
|d YDXCP
|d UIU
|d OCLCQ
|d OCLCF
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|a 957636752
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|a 9780470556979
|q (electronic bk.)
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|a 0470556978
|q (electronic bk.)
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|a 9780470556986
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|a 0470556986
|q (electronic bk.)
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|a (OCoLC)774270995
|z (OCoLC)957636752
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|a QA276 .M94 2010
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| 072 |
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7 |
|a MAT
|x 029020
|2 bisacsh
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| 049 |
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|a HCDD
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| 100 |
1 |
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|a Myers, Raymond H.
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| 245 |
1 |
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|a Generalized linear models :
|b with applications in engineering and the sciences.
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| 250 |
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|a 2nd ed.
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| 260 |
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|a Hoboken :
|b John Wiley & Sons,
|c 2012.
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| 300 |
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|a 1 online resource (521 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 505 |
0 |
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|a 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 s2; 2.2.3 Hypothesis Testing in Multiple Regression; 2.2.4 Confidence Intervals in Multiple Regression.
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| 505 |
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|a 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.
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| 505 |
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|a 2.6.2 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.
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|a 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.
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|a 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.
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| 500 |
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|a 5.2 Formal Structure for the Class of Generalized Linear Models.
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| 520 |
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|a 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."--TechnometricsGeneralized 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 linear models (GLMs). Main.
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| 588 |
0 |
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|a Print version record.
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| 650 |
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0 |
|a Linear models (Statistics)
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| 650 |
|
7 |
|a MATHEMATICS
|x Probability & Statistics
|x Multivariate Analysis.
|2 bisacsh
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| 650 |
|
7 |
|a Linear models (Statistics)
|2 fast
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| 700 |
1 |
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|a Montgomery, Douglas C.
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| 700 |
1 |
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|a Vining, G. Geoffrey,
|d 1954-
|1 https://id.oclc.org/worldcat/entity/E39PBJwtjd3cGm7mWR9JD8VQv3
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| 700 |
1 |
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|a Robinson, Timothy J.
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| 758 |
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|i has work:
|a Generalized linear models (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCG6Hf6gr3GMhhTPKDBXtyq
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Myers, Raymond H.
|t Generalized Linear Models : with Applications in Engineering and the Sciences.
|d Hoboken : John Wiley & Sons, ©2012
|z 9780470454633
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=698819
|y Click for online access
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| 903 |
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|a EBC-AC
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| 994 |
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|a 92
|b HCD
|