Efnisyfirlit

• 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

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