| Introduction.
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.
| 
