Statistics: The Art and Science of Learning from Data, 5th edition

• Preface

I: GATHERING AND EXPLORING DATA

1. Statistics: The Art and Science of Learning From Data
   • 1.1 Using Data to Answer Statistical Questions
   • 1.2 Sample Versus Population
   • 1.3 Organizing Data, Statistical Software, and the New Field of Data Science
   • Chapter Summary
   • Chapter Exercises
2. Exploring Data With Graphs and Numerical Summaries
   • 2.1 Different Types of Data
   • 2.2 Graphical Summaries of Data
   • 2.3 Measuring the Center of Quantitative Data
   • 2.4 Measuring the Variability of Quantitative Data
   • 2.5 Using Measures of Position to Describe Variability
   • 2.6 Linear Transformations and Standardizing
   • 2.7 Recognizing and Avoiding Misuses of Graphical Summaries
   • Chapter Summary
   • Chapter Exercises
3. Exploring Relationships Between Two Variables
   • 3.1 The Association Between Two Categorical Variables
   • 3.2 The Relationship Between Two Quantitative Variables
   • 3.3 Linear Regression: Predicting the Outcome of a Variable
   • 3.4 Cautions in Analyzing Associations
   • Chapter Summary
   • Chapter Exercises
4. Gathering Data
   • 4.1 Experimental and Observational Studies
   • 4.2 Good and Poor Ways to Sample
   • 4.3 Good and Poor Ways to Experiment
   • 4.4 Other Ways to Conduct Experimental and Nonexperimental Studies
   • Chapter Summary
   • Chapter Exercises

II: PROBABILITY, PROBABILITY DISTRIBUTIONS, AND SAMPLING DISTRIBUTIONS

5. Probability in Our Daily Lives
   • 5.1 How Probability Quantifies Randomness
   • 5.2 Finding Probabilities
   • 5.3 Conditional Probability
   • 5.4 Applying the Probability Rules
   • Chapter Summary
   • Chapter Exercises
6. Random Variables and Probability Distributions
   • 6.1 Summarizing Possible Outcomes and Their Probabilities
   • 6.2 Probabilities for Bell-Shaped Distributions
   • 6.3 Probabilities When Each Observation Has Two Possible Outcomes
   • Chapter Summary
   • Chapter Exercises
7. Sampling Distributions
   • 7.1 How Sample Proportions Vary Around the Population Proportion
   • 7.2 How Sample Means Vary Around the Population Mean
   • 7.3 Using the Bootstrap to Find Sampling Distributions
   • Chapter Summary
   • Chapter Exercises

III: INFERENTIAL STATISTICS

8. Statistical Inference: Confidence Intervals
   • 8.1 Point and Interval Estimates of Population Parameters
   • 8.2 Confidence Interval for a Population Proportion
   • 8.3 Confidence Interval for a Population Mean
   • 8.4 Bootstrap Confidence Intervals
   • Chapter Summary
   • Chapter Exercises
9. Statistical Inference: Significance Tests About Hypotheses
   • 9.1 Steps for Performing a Significance Test
   • 9.2 Significance Tests About Proportions
   • 9.3 Significance Tests About a Mean
   • 9.4 Decisions and Types of Errors in Significance Tests
   • 9.5 Limitations of Significance Tests
   • 9.6 The Likelihood of a Type II Error
   • Chapter Summary
   • Chapter Exercises
10. Comparing Two Groups
    • 10.1 Categorical Response: Comparing Two Proportions
    • 10.2 Quantitative Response: Comparing Two Means
    • 10.3 Comparing Two Groups with Bootstrap or Permutation Resampling
    • 10.4 Analyzing Dependent Samples
    • 10.5 Adjusting for the Effects of Other Variables
    • Chapter Summary
    • Chapter Exercises

IV: ANALYZING ASSOCIATION AND EXTENDED STATISTICAL METHODS

11. Analyzing the Association Between Categorical Variables
    • 11.1 Independence and Dependence (Association)
    • 11.2 Testing Categorical Variables for Independence
    • 11.3 Determining the Strength of the Association
    • 11.4 Using Residuals to Reveal the Pattern of Association
    • 11.5 Fisher’s Exact and Permutation Tests
    • Chapter Summary
    • Chapter Exercises
12. Analyzing the Association Between Quantitative Variables: Regression Analysis
    • 12.1 Modeling How Two Variables Are Related
    • 12.2 Inference About Model Parameters and the Association
    • 12.3 Describing the Strength of Association
    • 12.4 How the Data Vary Around the Regression Line
    • 12.5 Exponential Regression: A Model for Nonlinearity
    • Chapter Summary
    • Chapter Exercises
13. Multiple Regression
    • 13.1 Using Several Variables to Predict a Response
    • 13.2 Extending the Correlation and R2 for Multiple Regression
    • 13.3 Using Multiple Regression to Make Inferences
    • 13.4 Checking a Regression Model Using Residual Plots
    • 13.5 Regression and Categorical Predictors
    • 13.6 Modeling a Categorical Response
    • Chapter Summary
    • Chapter Exercises
14. Comparing Groups: Analysis of Variance Methods
    • 14.1 One-Way ANOVA: Comparing Several Means
    • 14.2 Estimating Differences in Groups for a Single Factor
    • 14.3 Two-Way ANOVA
    • Chapter Summary
    • Chapter Exercises
15. Nonparametric Statistics
    • 15.1 Compare Two Groups by Ranking
    • 15.2 Nonparametric Methods for Several Groups and for Matched Pairs
    • Chapter Summary
    • Chapter Exercises

Appendix

Answers

Index

Index of Applications

Credits