Produktbild: Generalized Linear Models With Examples in R
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Generalized Linear Models With Examples in R

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

11.11.2018

Abbildungen

XX, 115 illus., schwarz-weiss Illustrationen

Verlag

Springer Us

Seitenzahl

562

Maße (L/B/H)

24,1/16/3,7 cm

Gewicht

1033 g

Auflage

1st ed. 2018

Sprache

Englisch

ISBN

978-1-4419-0117-0

Beschreibung

Rezension

“This is a great book … . The book comprehensively covers almost everything you need to know or teach in this area. This book is an invaluable reference either as a classroom text or for the researcher’s bookshelf.” (Pablo Emilio Verde, ISCB News, iscb.info, Issue 69, July, 2020)

“I congratulate the authors for making an important contribution in this field. … the book represents an excellent and very comprehensible introduction into the world of generalized linear models and is recommended for all readers who are looking for a practical introduction to this topic using R.” (Dominic Edelmann, Biometrical Journal, Vol. 62, 2020)

“The book is targeted at students and notes it is appropriate for graduate students. It is also useful to the junior statistician needing to learn how to work a model they are unfamiliar with. The practicing and experienced statistician can use this as a quick reference for working a model they may have forgotten the specific of.” (James P. Howard II, zbMath 1416.62020, 2019)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

11.11.2018

Abbildungen

XX, 115 illus., schwarz-weiss Illustrationen

Verlag

Springer Us

Seitenzahl

562

Maße (L/B/H)

24,1/16/3,7 cm

Gewicht

1033 g

Auflage

1st ed. 2018

Sprache

Englisch

ISBN

978-1-4419-0117-0

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Generalized Linear Models With Examples in R
  • Statistical models.- Linear regression models.-  Linear regression models: diagnostics and model-building.- Beyond linear regression: the method of maximum likelihood.- Generalized linear models: structure.- Generalized linear models: estimation.- Generalized linear models: inference.- Generalized linear models: diagnostics.- Models for proportions: binomial GLMs.- Models for counts: Poisson and negative binomial GLMs.- Positive continuous data: gamma and inverse Gaussian GLMs.- Tweedie GLMs.- Extra problems.- Appendix A: Using R for data analysis.- Appendix B: The GLMsData package.- Index: Data sets.- Index: R commands.- Index: General Topics.