Content: pt. 1. Basic concepts. Introduction. The Everglades example ; Statistical issues --
R. What is R? ; Getting started with R ; The R commander --
Statistical assumptions. The normality assumption ; The independence assumption ; The constant variance assumption ; Exploratory data analysis ; From graphs to statistical thinking --
Statistical inference. Estimation of population mean and confidence interval ; Hypothesis testing ; A general procedure ; Nonparametric methods for hypothesis testing ; Significance level [alpha], power 1 --
[beta], and p-value ; One-way analysis of variance ; Examples --
pt. 2. Statistical modeling. Linear models. ANOVA as a linear model ; Simple and multiple linear regression models ; General considerations in building a predictive model ; Uncertainty in model predictions ; Two-way ANOVA --
Nonlinear models. Nonlinear regression ; Smoothing ; Smoothing and additive models --
Classification and regression tree. The Willamette River example ; Statistical methods ; Comments --
Generalized linear model. Logistic regression ; Model interpretation ; Diagnostics ; Seed predation by rodents : a second example of logistic regression ; Poisson regression model ; Generalized additive models --
pt. 3. Advanced statistical modeling. Simulation for model checking and statistical inference. Simulation ; Summarizing linear and nonlinear regression using simulation ; Simulation based on re-sampling --
Multilevel regression. Multilevel structure and exchangeability ; Multilevel ANOVA ; Multilevel linear regression ; Generalized multilevel models.