Statistical Modeling With R: a dual frequentist and Bayesian approach for life s

Description: Statistical Modeling With R by Pablo Inchausti 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. FORMAT Paperback CONDITION Brand New Publisher Description 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 accessibletextbook 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 mostcommonly 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 worldscenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology. Author Biography Pablo Inchausti is Professor of Ecology at the Universidad de la RepÚblica, Centro Universitario Regional del Este, Uruguay. He is the co-editor of the influential and highly-cited book Biodiversity and Ecosystem Functioning: synthesis and perspectives (OUP, 2002) and has been successfully teaching statistics and mathematical modelling to students of the life and social sciences for over 15 years. Table of Contents 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 Appendix 2: Exploring and describing the evidence in graphics (only available online at Review 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 *The book is a novel contribution to the literature on statistical modelling, it has my highest endorsement, and I look forward to using it in future graduate courses on applied statistics. * Lars Rönnegård, Dalarna University * Long Description 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 accessibletextbook 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, andprogressive 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. Details ISBN0192859021 Author Pablo Inchausti Year 2022 ISBN-10 0192859021 ISBN-13 9780192859020 Format Paperback Imprint Oxford University Press Place of Publication Oxford Country of Publication United Kingdom Publisher Oxford University Press NZ Release Date 2022-11-02 Publication Date 2022-11-02 UK Release Date 2022-11-02 Subtitle a dual frequentist and Bayesian approach for life scientists Pages 480 Alternative 9780192859013 DEWEY 570.15195 Audience Professional & Vocational AU Release Date 2023-01-24 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:138940612;

Price: 120 AUD

Location: Melbourne

End Time: 2024-12-06T06:29:23.000Z

Shipping Cost: 0 AUD