Part I: Data Analysis and Model-Building Basics.

Chapter 1: Beyond Number Crunching: The Art and Science of Data Analysis.

Chapter 2: Sorting through Statistical Techniques.

Chapter 3: Building Confidence and Testing Models.

Part II: Making Predictions by Using Regression.

Chapter 4: Getting in Line with Simple Linear Regression.

Chapter 5: When Two Variables Are Better than One: Multiple Regression.

Chapter 6: One Step Forward and Two Steps Back: Regression Model Selection.

Chapter 7: When Data Throws You a Curve: Using Nonlinear Regression.

Chapter 8: Yes, No, Maybe So: Making Predictions By Using Logistic Regression.

Part III: Comparing Many Means with ANOVA.

Chapter 9: Going One-Way with Analysis of Variance.

Chapter 10: Pairing Things Down with Multiple Comparisons.

Chapter 11: Getting a Little Interaction with Two-Way ANOVA.

Chapter 12: Rock My World: Relating Regression to ANOVA.

Part IV: Building Strong Connections with Chi-Square Tests.

Chapter 13: Forming Associations with Two-Way Tables.

Chapter 14: Being Independent Enough for the Chi-Square Test.

Chapter 15: Using Chi-Square Tests for Goodness-of-Fit (Your Data, Not Your Jeans).

Part V: Rebels without a Distribution.

Chapter 16: Going Nonparametric.

Chapter 17: The Sign Test and Signed Rank Test.

Chapter 18: Pulling Rank with the Rank Sum Test.

Chapter 19: Do the Kruskal-Wallis and Rank the Sums with Wilcox.

Chapter 20: Pointing Out Correlations with Spearman’s Rank.

Part VI: The Part of Tens.

Chapter 21: Ten Errors in Statistical Conclusions.

Chapter 22: Ten Practice Problems.

Appendix: Tables for Your Reference.

Index.