Statistical Modeling With R

Statistical Modeling With R

a dual frequentist and Bayesian approach for life scientists

Inchausti, Pablo

Oxford University Press






15 a 20 dias


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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
R packages used in the book
Appendix 1: Using R and RStudio: the basics (only available online at
Appendix 2: Exploring and describing the evidence in graphics (only available online at
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