ABSTRACT
Chapter 2 reviews essential concepts from statistics and probability, including conditional probability and expectation, conditional and marginal independence, the laws of total probability and total expectation, joint probability, the multiplication rule, mean independence and conditional mean independence, uncorrelation and conditional uncorrelation, regression models, saturated or nonparametric as well as parametric regression models, statistical interaction, borrowing information, linear models, nonlinear parametric models, loglinear models, logistic models, covariates, estimation, superpopulation, sampling variability, estimator, unbiased, simple random sample, linear operator, unbiased estimating equations, large sample theory, rule of thumb, estimand, estimate, sampling distribution, bootstrap, confidence interval, statistical efficiency, and bias-variance tradeoff. Examples and R code are also provided.
