Chapter 5. Statistics and decision making

Box 5.1 Modeling continuous outcomes: the normal distribution

Box 5.2 Modeling integer (count) outcomes: the Poisson distribution

Box 5.3 Modeling “success/ failure” outcomes: the Binomial distribution

Box 5.4 . Summarizing and manipulating data using R

· R code and data file

Box 5.5. Building a linear model in R

· R code and data file

Box 5.6 Generalized linear models and multiple predictor models in R

· R code

· First data example, second data example

Box. 5.7. Evaluating and comparing multiple models

· R code

· Datafile

Box. 5.8. Linear hierarchical modeling and partitioning of variance components

R code

Data

Box 5.9. Bayesian example: binomial likelihood with a beta prior

Box 5.10. Modeling a random effect using WinBUGS: Binomial success with random variation

· R script

· OpenBUGS model

· Data

Box 5.11. Hierarchical model with random effects in WinBUGS

· R script

· OpenBUGS model

· Data

Box 5.12. Jackknife and bootstrap estimation of variance and bias

Box 5.13. Parametric bootstrap estimation of confidence intervals