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Foundations of Linear and Generalized Linear Models - eBook

Foundations of Linear and Generalized Linear Models – PDF

eBook details

  • Author: Alan Agresti
  • File Size: 5 MB
  • Format: PDF
  • Length: 480 pages
  • Publisher: Wiley
  • Publication Date: January 15, 2015
  • Language: English
  • ASIN: B013RKYR6C
  • ISBN-10: 1118730038
  • ISBN-13: 9781118730034

Original price was: $83.99.Current price is: $17.00.

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About The Author

Alan Agresti

Alan Agresti

Dr. Alan Agresti is Distinguished Professor in the Department of Statistics at the University of Florida. He has been teaching statistics there for over three decades, including the development of three courses in categorical data analysis and three courses in statistical methods for social science students. He is author of over 100 refereed article and four textbooks including "Statistics: The Art and Science of Learning From Data" and "Categorical Data Analysis."

Alan is a Fellow of the American Statistical Association and recipient of an Honorary Doctor of Science from De Montfort University in the UK. He was named "Statistician of the Year" by the Chicago chapter of the American Statistical Association and in Dr. Agresti was the first honoree of the Herman Callaert Leadership Award in Biostatistical Education and Dissemination awarded by the University of Limburgs, Belgium. He has held visiting positions at Boston University, London School of Economics, Harvard University, and Imperial College and has taught courses or short courses for companies and universities in about 20 countries worldwide. He has also received teaching awards from UF and an excellence in writing award from John Wiley and Sons.

A precious overview of crucial concepts and ends in statistical modeling Written by a extremely-seasoned writer, Foundations of Linear and Generalized Linear Models (PDF) is a transparent and complete information to the important thing ideas and outcomes of linear statistical fashions. The ebook presents a broad, in-depth overview of probably the most generally used statistical fashions by discussing the idea implicit within the fashions, R software program functions, and examples with crafted fashions to elucidate most important concepts and encourage sensible mannequin constructing. The ebook begins by illustrating the fundamentals of linear fashions, equivalent to how the mannequin-becoming initiatives the info onto a mannequin vector subspace and how orthogonal decompositions of the info yield details about the results of explanatory variables. Subsequently, the ebook covers the preferred generalized linear fashions, which embody binomial and multinomial logistic regression for categorical information, and Poisson and unfavourable binomial loglinear fashions for depend information. Emphasizing on the theoretical underpinnings of these fashions, Foundations of Linear and Generalized Linear Models additionally options:

  • Many examples that use R software program for all textual content information analyses
  • A supplementary web site with datasets for the examples and workouts
  • More than 400 workouts for readers to follow and prolong the idea, strategies, and information evaluation
  • An introduction to quasi-chance strategies that require weaker distributional assumptions, equivalent to generalized estimating equation strategies
  • An overview of linear combined fashions and generalized linear combined fashions with random results for clustered correlated information, Bayesian modeling, and extensions to deal with problematic circumstances equivalent to excessive dimensional issues

An indispensable textbook for higher-undergraduate and graduate-degree college students in statistics and biostatistics programs, Foundations of Linear and Generalized Linear Models can also be a wonderful reference for practising statisticians and biostatisticians, in addition to anybody who’s curious about studying about crucial statistical fashions for analyzing information. NOTE: The product contains the ebook, Foundations of Linear and Generalized Linear Models in PDF. No access codes are included.

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