PART I EXPLORING AND COLLECTING DATA 1. Statistics and Variation 1.1 So, What Is Statistics? 1.2 How Will This Book Help? 2. Data 2.1 What Are Data? 2.2 Variable Types 2.3 Data Sources-Where, How, and When 3.
Surveys and Sampling 3.1 Three Ideas of Sampling 3.2 A Census-Does It Make Sense? 3.3 Populations and Parameters 3.4 Simple Random Sample (SRS) 3.5 Other Sample Designs 3.6 Defining the Population 3.7 The Valid Survey 4.
Displaying and Describing Categorical Data 4.1 The Three Rules of Data Analysis 4.2 Frequency Tables 4.3 Charts 4.4 Contingency Tables 5. Displaying and Describing Quantitative Data 5.1 Displaying Distributions 5.2 Shape 5.
3 Center 5.4 Spread of the Distribution 5.5 Shape, Center, and Spread-A Summary 5.6 Five-Number Summary and Boxplots 5.7 Comparing Groups 5.8 Identifying Outliers 5.9 Standardizing *5.10 Time Series Plots Transforming Skewed Data-On CD-ROM 6.
Correlation and Linear Regression 6.1 Looking at Scatterplots 6.2 Assigning Roles to Variables in Scatterplots 6.3 Understanding Correlation 6.4 Lurking Variables and Causation 6.5 The Linear Model 6.6 Correlation and the Line 6.7 Regression to the Mean 6.
8 Checking the Model 6.9 Variation in the Model and R2 6.10 Reality Check: Is the Regression Reasonable? Straightening Scatterplots-On CD-ROM PART II UNDERSTANDING DATA AND DISTRIBUTIONS 7. Randomness and Probability 7.1 Random Phenomena and Probability 7.2 The Nonexistent Law of Averages 7.3 Different Types of Probability 7.4 Probability Rules 7.
5 Joint Probability and Contingency Tables 7.6 Conditional Probability 7.7 Constructing Contingency Tables 7.8 Probability Trees *7.9 Reversing the Conditioning: Bayes''s Rule 8. Random Variables and Probability Models 8.1 Expected Value of a Random Variable 8.2 Standard Deviation of a Random Variable 8.
3 Properties of Expected Values and Variances 8.4 Discrete Probability Models 8.5 Continuous Random Variables 9. Sampling Distributions and Confidence Intervals for Proportions 9.1 Simulations 9.2 The Sampling Distribution for Proportions 9.3 Assumptions and Conditions 9.4 The Central Limit Theorem-The Fundamental Theorem of Statistics 9.
5 A Confidence Interval 9.6 Margin of Error: Certainty vs. Precision 9.7 Critical Values 9.8 Assumptions and Conditions 9.9 Choosing the Sample Size A Confidence Interval for Small Samples-On CD-ROM 10. Testing Hypotheses about Proportions 10.1 Hypotheses 10.
2 A Trial as a Hypothesis Test 10.3 P-Values 10.4 The Reasoning of Hypothesis Testing 10.5 Alternative Hypotheses 10.6 Alpha Levels and Significance 10.7 Critical Values 10.8 Confidence Intervals and Hypothesis Tests 10.9 Two Types of Errors *10.
10 Power 11. Confidence Intervals and Hypothesis Tests for Means 11.1 The Sampling Distribution for Means 11.2 How Sampling Distribution Models Work 11.3 Gossett and the t-Distribution 11.4 A Confidence Interval for Means 11.5 Assumptions and Conditions 11.6 Cautions About Interpreting Confidence Intervals 11.
7 One-Sample t-Test 11.8 Sample Size *11.9 Degrees of Freedom-Why n - 1 12. Comparing Two Groups 12.1 Comparing Two Means 12.2 The Two-Sample t-Test 12.3 Assumptions and Conditions 12.4 A Confidence Interval for the Difference Between Two Means *12.
5 The Pooled t-Test *12.6 Tukey''s Quick Test 12.7 Paired Data 12.8 The Paired t-Test 13. Inference for Counts: Chi-Square Tests 13.1 Goodness-of-Fit Tests 13.2 Interpreting Chi-Square Values 13.3 Examining the Residuals 13.
4 The Chi-Square Test of Homogeneity 13.5 Comparing Two Proportions 13.6 Chi-Square Test of Independence PART III BUILDING MODELS FOR DECISION MAKING 14. Inference for Regression 14.1 The Population and the Sample 14.2 Assumptions and Conditions 14.3 Regression Inference 14.4 Standard Errors for Predicted Values 14.
5 Using Confidence and Prediction Intervals 14.6 Extrapolation and Prediction 14.7 Unusual and Extraordinary Observations *14.8 Working with Summary Values *14.9 Linearity Re-expressing data-On CD-ROM The Ladder of Powers*-On CD-ROM 15. Multiple Regression 15.1 The Multiple Regression Model 15.2 Interpreting Multiple Regression Coefficients 15.
3 Assumptions and Conditions for the Multiple Regression Model 15.4 Testing the Multiple Regression Model 15.5 Adjusted R2 and the F-statistic The Logistic Regression Model-On CD-ROM Indicator Variables-On CD-ROM Adjusting for Different Slopes- Interaction Terms-On CD-ROM Collinearity-On CD-ROM 16. Introduction to Data Mining 16.1 Direct Marketing 16.2 The Data 16.3 The Goals of Data Mining 16.4 Data Mining Myths 16.
5 Successful Data Mining 16.6 Data Mining Problems 16.7 Data Mining Algorithms 16.8 The Data Mining Process 16.9 Summary *Indicates an optional topic.