Introduction.
Part I: Tackling Data Analysis and Model-BuildingBasics.

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

Chapter 2: Finding the Right Analysis for the Job.

Chapter 3: Reviewing Confi dence Intervals and HypothesisTests.

Part II: Using Different Types of Regression to MakePredictions.

Chapter 4: Getting in Line with Simple Linear Regression.

Chapter 5: Multiple Regression with Two X Variables.

Chapter 6: How Can I Miss You If You Won't Leave?Regression Model Selection.

Chapter 7: Getting Ahead of the Learning Curve with NonlinearRegressio.

Chapter 8: Yes, No, Maybe So: Making Predictions by UsingLogistic Regression.

Part III: Analyzing Variance with ANOVA.

Chapter 9: Testing Lots of Means? Come On Over to ANOVA!

Chapter 10: Sorting Out the Means with Multiple Comparisons.

Chapter 11: Finding Your Way through Two-Way ANOVA.

Chapter 12: Regression and ANOVA: Surprise Relatives!

Part IV: Building Strong Connections with Chi-SquareTests.

Chapter 13: Forming Associations with Two-Way Tables.

Chapter 14: Being Independent Enough for the Chi-SquareTest.

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

Part V: Nonparametric Statistics: Rebels without aDistribution.

Chapter 16: Going Nonparametric.

Chapter 17: All Signs Point to the Sign Test and Signed RankTest.

Chapter 18: Pulling Rank with the Rank Sum Test.

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

Chapter 20: Pointing Out Correlations with Spearman s Rank.

Part VI: The Part of Tens.

Chapter 21: Ten Common Errors in Statistical Conclusions.

Chapter 22: Ten Ways to Get Ahead by Knowing Statistics.

Chapter 23: Ten Cool Jobs That Use Statistics.

Appendix: Reference Tables.

Index.