Chapter 13 covers one-way analysis of variance and includes the following specific topics, among others: between group variance, within group variance, the R ratio, ANOVA summary table, effect size, post hoc multiple comparison tests, the Bonferroni adjustment, and power analysis.

Chapter 15 covers correlation and simple regression as inferential techniques and includes the following specific topics, among others: bivariate normal distribution, statistical significance test of correlation, confidence intervals, statistical significance of the b weight, fit of the overall regression equation, R and R-squared, adjusted R-squared, regression diagnostics, residual plots, influential observations, discrepancy, leverage, influence, and power analysis.

Chapter 14 covers two-way analysis of variance and includes the following specific topics, among others: statistical interaction, balanced versus unbalanced factorial designs, F-ratio, effect size, fixed factors, random factors, post hoc multiple comparison tests, simple effects, and power analysis.

Chapter 11 covers inferences involving the mean when σ is not known, one- and two-sample designs, and includes the following specific topics, among others: t-distribution, degrees of freedom, t-test assumptions, one-sample t-test, two-sample t-test for independent groups, two-sample t-test for related groups, paired sample t-tests, effect size, the bootstrap, and power analysis.

Chapter 8 covers theoretical probability models and includes the following specific topics, among others: he binomial probability distribution and the normal probability distribution.

Chapter 20 covers accessing data from public-use sources and includes the following specific topics, among others: good research questions, desirable features of public-use data, and accessing publicly available datasets.

Chapter 16 covers an introduction to multiple regression and includes the following specific topics, among others: confidence intervals, statistical significance of the b weight, fit of the overall regression Eeuation, R and R-squared, adjusted R-squared, semipartial correlation, partial slope, confounding, and statistical control.

Chapter 17 covers two-way interactions in multiple regression and includes the following specific topics, among others: two-way interaction, first-order effects, main effects, interaction effects, model selection, AIC, BIC, and probing interactions.

Chapter 7 covers probability fundamentals and includes the following specific topics, among others: the discrete case, additive rules of probability, complement rule of probability, multiplicative rule of probability, conditional probability, Bayes’ theorem, and the law of large numbers.

Chapter 9 covers the role of sampling in inferential statistics and includes the following specific topics, among others: samples and populations, random samples, simple random sampling, sampling with and without replacement, sampling distributions, the sampling distribution of means, The central limit theorem, estimators and bias.

Chapter 12 covers an introduction to research design and includes the following specific topics, among others: descriptive, relational, and causal research studies, blocking, quasi-experimental designs, threats to internal validity, and threats to external validity.

Chapter 5 covers the relationship between two variables and includes the following specific topics, among others: scatterplots, Pearson product moment correlation coefficient, the Spearman rank correlation coefficient, the point biserial correlation coefficient, the phi coefficient, and visual displays of bivariate relationships.

Chapter 2 covers univariate distributions and includes the following specific topics, among others: frequency and percent distribution tables, bar charts, pie charts, stem-and-leaf displays, histograms, line graphs, shape of a distribution, cumulative percent. Distributions, Percentiles, Percentile Ranks, and Boxplots.

Chapter 1 provides an introdution to the study of statistics and covers the following specific topics among others: statistical software in data analysis, descriptive and inferential statistics, measurement of variables, and an introduction to the Stata software package.