eBook For Statistical Modeling With R a dual frequentist and Bayesian approach for life scientists 1st Edition By Pablo Inchausti

Part 1: The Conceptual Basis For Fitting Statistical Models
1:General introduction
2:Statistical modeling: a short historical background
3:Estimating parameters: the main purpose of statistical inference
Part II: Applying The Generalized Linear Model to Varied Data Types
4:The General Linear Model I: numerical explanatory variables
5:The General Linear Model II: categorical explanatory variables
6:The General Linear Model III: interactions between explanatory variables
7:Model selection: one, two, and more models fitted to the data
8:The Generalized Linear Model
9:When the response variable is binary
10:When the response variables are counts, often with many zeros
11:Further issues involved in the modeling of counts
12:Models for positive real-valued response variables: proportions and others
Part III: Incorporating Experimental and Survey Design Using Mixed Models
13:Accounting for structure in mixed/hierachical structures
14:Experimental design in the life sciences – the basics
15:Mixed-hierachical models and experimental design data
Afterword
R packages used in the book
Appendix 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)