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|a Faraway, Julian James,
|e author
|0 http://viaf.org/viaf/73021388
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| 100 |
1 |
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|a Faraway, Julian James,
|e author
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| 245 |
1 |
0 |
|a Extending the linear model with R :
|b generalized linear, mixed effects and nonparametric regression models /
|c Julian J. Faraway
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| 250 |
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|a Second edition
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| 264 |
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1 |
|a Boca Raton, Florida :
|b CRC P.,
|c 2016
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| 300 |
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|a 1 online resource (xiii, 399 pages) :
|b illustrations
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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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| 490 |
1 |
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|a Texts in statistical science
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| 500 |
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|a A Chapman & Hall Book
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| 504 |
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|a Includes bibliographical references
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| 505 |
0 |
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|a Chapter 1: Introduction -- Chapter 2: Binary Response -- Chapter 3: Binomial and Proportion Responses -- Chapter 4: Variations on Logistic Regression -- Chapter 5: Count Regression -- Chapter 6: Contingency Tables -- Chapter 7: Multinomial Data -- Chapter 8: Generalized Linear Models -- Chapter 9: Other GLMs -- Chapter 10: Random Effects -- Chapter 11: Repeated Measures and Longitudinal Data -- Chapter 12: Bayesian Mixed Effect Models -- Chapter 13: Mixed Effect Models for Nonnormal Responses -- Chapter 14: Nonparametric Regression -- Chapter 15: Additive Models -- Chapter 16: Trees -- Chapter 17: Neural Networks -- Appendix A: Likelihood Theory -- Appendix B: About R
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| 520 |
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|a Start analyzing a wide range of problems. Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, second edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics. New to the second edition: Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models; New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs); Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods; New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA; Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available; Updated coverage of splines and confidence bands in the chapter on nonparametric regression; New material on random forests for regression and classification; Revamped R code throughout, particularly the many plots using the ggplot2 package; Revised and expanded exercises with solutions now included. Demonstrates the interplay of theory and practice. This textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses
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| 650 |
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|a Analysis of variance
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| 650 |
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0 |
|a Linear models
|x Statistics
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| 650 |
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0 |
|a R (Computer program language)
|x Mathematical models
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| 650 |
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0 |
|a Regression analysis
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| 830 |
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0 |
|a Texts in statistical science
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