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Statistical Modeling With R : A Dual Frequentist and Bayesian Approach for Li...

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Item specifics

Condition
Like New: A book that has been read, but looks new. The book cover has no visible wear, and the dust ...
Book Title
Statistical Modeling With R : A Dual Frequentist and Bayesian App
ISBN
9780192859020

About this product

Product Identifiers

Publisher
Oxford University Press, Incorporated
ISBN-10
0192859021
ISBN-13
9780192859020
eBay Product ID (ePID)
26057249516

Product Key Features

Number of Pages
480 Pages
Language
English
Publication Name
Statistical Modeling with R : a Dual Frequentist and Bayesian Approach for Life Scientists
Subject
Life Sciences / Taxonomy, Mechanics / Statics, Probability & Statistics / General, Life Sciences / Biology
Publication Year
2023
Type
Textbook
Author
Pablo Inchausti
Subject Area
Mathematics, Science
Format
Trade Paperback

Dimensions

Item Height
0.9 in
Item Weight
36.9 Oz
Item Length
9.7 in
Item Width
7.4 in

Additional Product Features

Intended Audience
Scholarly & Professional
Reviews
"A book that should attract curious minds of various backgrounds, knowledge, and expertise in statistics, as well as work nicely to support an enthusiastic teacher of statistical modeling. I thus recommend this book most enthusiastically." -- Christian P. Robert, Journal of the American Statistical Association, A book that should attract curious minds of various backgrounds, knowledge, and expertise in statistics, as well as work nicely to support an enthusiastic teacher of statistical modeling. I thus recommend this book most enthusiastically.
Dewey Edition
23
Dewey Decimal
570.15195
Table Of Content
Part 1: The Conceptual Basis For Fitting Statistical Models1. General introduction2. Statistical modeling: a short historical background3. Estimating parameters: the main purpose of statistical inferencePart II: Applying The Generalized Linear Model to Varied Data Types4. The General Linear Model I: numerical explanatory variables5. The General Linear Model II: categorical explanatory variables6. The General Linear Model III: interactions between explanatory variables7. Model selection: one, two, and more models fitted to the data8. The Generalized Linear Model9. When the response variable is binary10. When the response variables are counts, often with many zeros11. Further issues involved in the modeling of counts12. Models for positive real-valued response variables: proportions and othersPart III: Incorporating Experimental and Survey Design Using Mixed Models13. Accounting for structure in mixed/hierachical structures14. Experimental design in the life sciences - the basics15. Mixed-hierachical models and experimental design dataAfterwordR packages used in the bookAppendix 1: Using R and RStudio: the basics (only available online at www.oup.com/companion/InchaustiSMWR)Appendix 2: Exploring and describing the evidence in graphics (only available online at www.oup.com/companion/InchaustiSMWR)
Synopsis
To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. Statistical Modeling with R is aimed at senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world scenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology., An accessible textbook that explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists.
LC Classification Number
QH323.5

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