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• Get the Most Out of MyLab Statistics
• Title Page
• Copyright
• Dedication
• Meet the Authors
• Table of Contents
• Preface
• MyLab Statistics for Stats: Data & Models, 5e
• Resources for Success
• Acknowledgments
• Index of Applications
• Part I: Exploring and Understanding Data
• 1. Stats Starts Here
• 1.1 What Is Statistics?
• 1.2 Data
• 1.3 Variables
• 1.4 Models
• 2. Displaying and Describing Data
• 2.1 Summarizing and Displaying a Categorical Variable
• 2.2 Displaying a Quantitative Variable
• 2.3 Shape
• 2.4 Center
• 2.5 Spread
• 3. Relationships Between Categorical Variables—Contingency Tables
• 3.1 Contingency Tables
• 3.2 Conditional Distributions
• 3.3 Displaying Contingency Tables
• 3.4 Three Categorical Variables
• 4. Understanding and Comparing Distributions
• 4.1 Displays for Comparing Groups
• 4.2 Outliers
• 4.3 Timeplots: Order, Please!
• 4.4 Re-Expressing Data: A First Look
• 5. The Standard Deviation as a Ruler and the Normal Model
• 5.1 Using the Standard Deviation to Standardize Values
• 5.2 Shifting and Scaling
• 5.3 Normal Models
• 5.4 Working with Normal Percentiles
• 5.5 Normal Probability Plots
• Review of Part I: Exploring and Understanding Data
• Part II: Exploring Relationships Between Variables
• 6. Scatterplots, Association, and Correlation
• 6.1 Scatterplots
• 6.2 Correlation
• 6.3 Warning: Correlation ≠ Causation
• *6.4 Straightening Scatterplots
• 7. Linear Regression
• 7.1 Least Squares: The Line of “Best Fit”
• 7.2 The Linear Model
• 7.3 Finding the Least Squares Line
• 7.4 Regression to the Mean
• 7.5 Examining the Residuals
• 7.6 R2—The Variation Accounted for by the Model
• 7.7 Regression Assumptions and Conditions
• 8. Regression Wisdom
• 8.1 Examining Residuals
• 8.2 Extrapolation: Reaching Beyond the Data
• 8.3 Outliers, Leverage, and Influence
• 8.4 Lurking Variables and Causation
• 8.5 Working with Summary Values
• *8.6 Straightening Scatterplots—The Three Goals
• *8.7 Finding a Good Re-Expression
• 9. Multiple Regression
• 9.1 What Is Multiple Regression?
• 9.2 Interpreting Multiple Regression Coefficients
• 9.3 The Multiple Regression Model—Assumptions and Conditions
• 9.4 Partial Regression Plots
• *9.5 Indicator Variables
• Review of Part II: Exploring Relationships Between Variables
• Part III: Gathering Data
• 10. Sample Surveys
• 10.1 The Three Big Ideas of Sampling
• 10.2 Populations and Parameters
• 10.3 Simple Random Samples
• 10.4 Other Sampling Designs
• 10.5 From the Population to the Sample: You Can’t Always Get What You Want
• 10.6 The Valid Survey
• 10.7 Common Sampling Mistakes, or How to Sample Badly
• 11. Experiments and Observational Studies
• 11.1 Observational Studies
• 11.2 Randomized, Comparative Experiments
• 11.3 The Four Principles of Experimental Design
• 11.4 Control Groups
• 11.5 Blocking
• 11.6 Confounding
• Review of Part III: Gathering Data
• Part IV: Randomness and Probability
• 12. From Randomness to Probability
• 12.1 Random Phenomena
• 12.2 Modeling Probability
• 12.3 Formal Probability
• 13. Probability Rules!
• 13.1 The General Addition Rule
• 13.2 Conditional Probability and the General Multiplication Rule
• 13.3 Independence
• 13.4 Picturing Probability: Tables, Venn Diagrams, and Trees
• 13.5 Reversing the Conditioning and Bayes’ Rule
• 14. Random Variables
• 14.1 Center: The Expected Value
• 14.2 Spread: The Standard Deviation
• 14.3 Shifting and Combining Random Variables
• 14.4 Continuous Random Variables
• 15. Probability Models
• 15.1 Bernoulli Trials
• 15.2 The Geometric Model
• 15.3 The Binomial Model
• 15.4 Approximating the Binomial with a Normal Model
• *15.5 The Continuity Correction
• 15.6 The Poisson Model
• 15.7 Other Continuous Random Variables: The Uniform and the Exponential
• Review of Part IV: Randomness and Probability
• Part V: Inference for One Parameter
• 16. Sampling Distribution Models and Confidence Intervals for Proportions
• 16.1 The Sampling Distribution Model for a Proportion
• 16.2 When Does the Normal Model Work? Assumptions and Conditions
• 16.3 A Confidence Interval for a Proportion
• 16.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?
• 16.5 Margin of Error: Certainty vs. Precision
• *16.6 Choosing the Sample Size
• 17. Confidence Intervals for Means
• 17.1 The Central Limit Theorem
• 17.2 A Confidence Interval for the Mean
• 17.3 Interpreting Confidence Intervals
• *17.4 Picking Our Interval up by Our Bootstraps
• 17.5 Thoughts About Confidence Intervals
• 18. Testing Hypotheses
• 18.1 Hypotheses
• 18.2 P-Values
• 18.3 The Reasoning of Hypothesis Testing
• 18.4 A Hypothesis Test for the Mean
• 18.5 Intervals and Tests
• 18.6 P-Values and Decisions: What to Tell About a Hypothesis Test
• 19. More About Tests and Intervals
• 19.1 Interpreting P-Values
• 19.2 Alpha Levels and Critical Values
• 19.3 Practical vs. Statistical Significance
• 19.4 Errors
• Review of Part V: Inference for One Parameter
• Part VI: Inference for Relationships
• 20. Comparing Groups
• 20.1 A Confidence Interval for the Difference Between Two Proportions
• 20.2 Assumptions and Conditions for Comparing Proportions
• 20.3 The Two-Sample z-Test: Testing the Difference Between Proportions
• 20.4 A Confidence Interval for the Difference Between Two Means
• 20.5 The Two-Sample t-Test: Testing for the Difference Between Two Means
• *20.6 Randomization Tests and Confidence Intervals for Two Means
• *20.7 Pooling
• *20.8 The Standard Deviation of a Difference
• 21. Paired Samples and Blocks
• 21.1 Paired Data
• 21.2 The Paired t-Test
• 21.3 Confidence Intervals for Matched Pairs
• 21.4 Blocking
• 22. Comparing Counts
• 22.1 Goodness-of-Fit Tests
• 22.2 Chi-Square Test of Homogeneity
• 22.3 Examining the Residuals
• 22.4 Chi-Square Test of Independence
• 23. Inferences for Regression
• 23.1 The Regression Model
• 23.2 Assumptions and Conditions
• 23.3 Regression Inference and Intuition
• 23.4 The Regression Table
• 23.5 Multiple Regression Inference
• 23.6 Confidence and Prediction Intervals
• *23.7 Logistic Regression
• *23.8 More About Regression
• Review of Part VI: Inference for Relationships
• Part VII: Inference When Variables Are Related
• 24. Multiple Regression Wisdom
• 24.1 Cleaning and Formatting Data
• 24.2 Diagnosing Regression Models: Looking at the Cases
• 24.3 Building Multiple Regression Models
• 25. Analysis of Variance
• 25.1 Testing Whether the Means of Several Groups Are Equal
• 25.2 The ANOVA Table
• 25.3 Assumptions and Conditions
• 25.4 Comparing Means
• 25.5 ANOVA on Observational Data
• 26. Multifactor Analysis of Variance
• 26.1 A Two-Factor ANOVA Model
• 26.2 Assumptions and Conditions
• 26.3 Interactions
• 27. Introduction to Statistical Learning and Data Science
• 27.1 Data Science and Big Data
• 27.2 The Data Mining Process
• 27.3 Data Mining Algorithms: A Sample
• 27.4 Models Built from Combining Other Models
• 27.5 Comparing Models
• 27.6 Summary
• Review of Part VII: Inference When Variables Are Related
• Cumulative Review Exercises
• Appendixes
• Appendix A. Answers
• Appendix B. Credits
• Appendix C. Index
• Appendix D. Tables and Selected Formulas