PART I: EXPLORING AND COLLECTING DATA 1. Statistics and Variation 2. Data 2.1 What Are Data? 2.2 Variable Types 2.3 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. Randomness and Probability 5.1 Random Phenomena and Probability 5.2 The Non-existent Law of Averages 5.3 Different Types of Probability 5.4 Probability Rules 5.

5 Joint Probability and Contingency Tables 5.6 Conditional Probability 5.7 Constructing Contingency Tables 6. Displaying and Describing Quantitative Data 6.1 Displaying Distributions 6.2 Shape 6.3 Center 6.4 Spread of the Distribution 6.

5 Shape, Center, and Spread A Summary 6.6 Five-Number Summary and Boxplots 6.7 Comparing Groups 6.8 Identifying Outliers 6.9 Standardizing 6.10 Time Series Plots *6.11 Transforming Skewed Data PART II: UNDERSTANDING DATA AND DISTRIBUTIONS 7. Scatterplots, Association, and Correlation 7.

1 Looking at Scatterplots 7.2 Assigning Roles to Variables in Scatterplots 7.3 Understanding Correlation *7.4 Straightening Scatterplots 7.5 Lurking Variables and Causation 8. Linear Regression 8.1 The Linear Model 8.2 Correlation and the Line 8.

3 Regression to the Mean 8.4 Checking the Model 8.5 Learning More from the Residuals 8.6 Variation in the Model and R2 8.7 Reality Check: Is the Regression Reasonable? 9. Sampling Distributions and the Normal Model 9.1 Modeling the Distribution of Sample Proportions 9.2 Simulation 9.

3 The Normal Distribution 9.4 Practice with Normal Distribution Calculations 9.5 The Sampling Distribution for Proportions 9.6 Assumptions and Conditions 9.7 The Central Limit Theorem The Fundamental Theorem of Statistics 9.8 The Sampling Distribution of the Mean 9.9 Sample Size Diminishing Returns 9.10 How Sampling Distribution Models Work 10.

Confidence Intervals for Proportions 10.1 A Confidence Interval 10.2 Margin of Error: Certainty vs. Precision 10.3 Critical Values 10.4 Assumptions and Conditions *10.5 A Confidence Interval for Small Samples 10.6 Choosing Sample Size 11.

Testing Hypotheses about Proportions 11.1 Hypotheses 11.2 A Trial as a Hypothesis Test 11.3 P-values 11.4 The Reasoning of Hypothesis Testing 11.5 Alternative Hypotheses 11.6 Alpha Levels and Significance 11.7 Critical Values 11.

8 Confidence Intervals and Hypothesis Tests 11.9 Two Types of Errors *11.10 Power 12. Confidence Intervals and Hypothesis Tests for Means 12.1 The Sampling Distribution for the Mean 12.2 A Confidence Interval for Means 12.3 Assumptions and Conditions 12.4 Cautions About Interpreting Confidence Intervals 12.

5 One-Sample t -Test 12.6 Sample Size 12.7 Degrees of Freedom Why ( n -1)? 13. Comparing Two Means 13.1 Testing Differences Between Two Means 13.2 The Two-Sample t-test 13.3 Assumptions and Conditions *13.4 A Confidence Interval for the Difference Between Two Means 13.

5 The Pooled t-test *13.6 Tukey''s Quick Test 14. Paired Samples and Blocks 14.1 Paired Data 14.2 Assumptions and Conditions 14.3 The Paired t-Test 14.4 How the Paired t-Test Works 15. Inference for Counts: Chi-Square Tests 15.

1 Goodness of Fit Tests 15.2 Interpreting Chi-square Values 15.3 Examining the Residuals 15.4 The Chi-Square Test of Homogeneity 15.5 Comparing Two Proportions 15.6 Chi-Square Test of Independence PART III: EXPLORING RELATIONSHIPS AMONG VARIABLES 16. Inference for Regression 16.1 The Population and the Sample 16.

2 Assumptions and Conditions 16.3 The Standard Error of the Slope 16.4 A Test for the Regression Slope 16.5 A Hypothesis Test for Correlation 16.6 Standard Errors for Predicted Values 16.7 Using Confidence and Prediction Intervals 17. Understanding Residuals 17.1 Examining Residuals for Groups 17.

2 Extrapolation and Prediction 17.3 Unusual and Extraordinary Observations 17.4 Working with Summary Values 17.5 Autocorrelation 17.6 Linearity 17.7 Transforming (Re-expressing) Data 17.8 The Ladder of Powers 18. Multiple Regression 18.

1 The Multiple Regression Model 18.2 Interpreting Multiple Regression Coefficients 18.3 Assumptions and Conditions for the Multiple Regression Model 18.4 Guided Example: Housing Prices 18.5 Testing the Multiple Regression Model *18.6 Relationship between F and R2 *18.7 The Logistic Regression Model 19. Building Multiple Regression Models 19.

1 Indicator Variables 19.2 Adjusting for Different Slopes - Interaction Terms 19.3 Multiple Regression Diagnostics 19.4 Building Regression Models 19.5 Guided Example: Roller Coaster Speeds 19.6 Colinearity 20. Time Series Analysis 20.1 What is a Time-Series? 20.

2 Components of a Time Series 20.3 Forecasting 20.4 Smoothing Models 20.5 Measuring Forecast Error 20.6 Seasonal Regression Models PART IV: BUILDING MODELS FOR DECISION MAKING 21. Probability Models 21.1 Expected Value of a Random Variable 21.2 Standard Deviation of a Random Variable 21.

3 Properties of Expected Values and Variances 21.4 Continuous Random Variables 21.5 Probability Models 22. Decision Making and Risk 22.1 Alternative Decisions 22.2 Measuring Risk 22.3 Decision Trees 22.4 Reversing the Conditioning *22.

5 Bayes''s Rule 23. Design and Analysis of Experiments and Observational Studies 23.1 Observational Studies 23.2 Randomized, Comparative Experiments 23.3 The Four Principles of Experimental Design 23.4 Types of Designs 23.5 Blinding and Placebos 23.6 Confounding and Lurking Variables 23.

7 Analyzing a Design in One Factor - The Analysis of Variance 23.8 Assumptions and Conditions for ANOVA 23.9 Multiple Comparisons 23.10 Analysis of Multi Factor Designs 24. Introduction to Data Mining 24.1 Direct Marketing 24.2 Data 24.3 Goals of Data Mining 24.

4 Data Mining Myths 24.5 Challenges of Data Mining 24.6 Data Mining Algorithms 24.7 Building a Predictive Model 24.8 The Data Mining Process *Indicates an optional topic.