Description
Book SynopsisAn 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.
Table of ContentsPart 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)
