Statistical Modeling With R: A Dual Frequentist and Bayesian Approach for Life Scientists Pablo Inchausti

Statistical Modeling With R: A Dual Frequentist and Bayesian Approach for Life Scientists Pablo Inchausti – Ebook Instant Download/Delivery ISBN(s): 9780192859020,0192859021, 9780192675033, 0192675036

Product details:

• ISBN 10: 0192675036
• ISBN 13: 9780192675033
• Author: Pablo Inchausti

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.

Table contents:

1 General Introduction

2 Statistical Modeling

3 Estimating Parameters

4 The General Linear Model I

5 The General Linear Model II

6 The General Linear Model III

6 The General Linear Model III

8 The Generalized Linear Model

9 When the Response Variable is Binary

10 When the Response Variable is a Count, Often with Many Zeros

11 Further Issues Involved in the Modeling of Counts

12 Models for Positive, Real-Valued Response Variables

13 Accounting for Structure in Mixed/Hierarchical Models

14 Experimental Design in the Life Sciences

15 Mixed Hierarchical Models and Experimental Design Data

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