IntroductionSampling and MeasurementDescriptive StatisticsProbability DistributionsStatistical Inference: EstimationStatistical Inference: Significance TestsComparison of Two GroupsAnalyzing Association between Categorical VariablesLinear Regression and CorrelationIntroduction to Multivariate RelationshipsMultiple Regression and CorrelationModel Building with Multiple RegressionLogistic Regression: Modeling Categorical ResponsesAn Introduction to Advanced Methodology

New and updated features of this title Greater integration of statistical software: Software output shown now uses R and Stata instead of only SAS and SPSS. The appendix provides instructions about basic use.New examples and exercises ask students to use applets to help learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests. The text also now relies more on applets for finding tail probabilities from distributions such as the normal, t, and chi-squared. ANOVA coverage has been reorganized to put more emphasis on using regression models with dummy variables to handle categorical explanatory variables.Content updates: Chapter 5 has a new section that introduces maximum likelihood estimation and the bootstrap method.Chapter 13 on regression modeling now has a new section using case studies to illustrate how research studies commonly use regression with both types of explanatory variables. The chapter also has a new section introducing linear mixed models.Chapter 14 contains a new section on robust regression covering standard errors and nonparametric regression. Chapter 16 has 2 new sections: Multiple imputation methods to help deal with missing data and Multilevel Models.