Statistics: The Art and Science of Learning from Data

Cover
Title Page
Copyright
Dedication
Contents
An Introduction to the Web Apps
Preface
About the Authors
Part One: Gathering and Exploring Data
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Part Two: Probability, Probability Distributions, and Sampling Distributions
Chapter 5. Probability in Our DailyLives
5.1 How Probability Quantifies Randomness
5.2 Finding Probabilities
5.3 Conditional Probability
5.4 Applying the Probability Rules
Chapter Summary
Chapter Exercises
Chapter 6. Random Variables and Probability Distributions
6.1 Summarizing Possible Outcomes and Their Probabilities
6.2 Probabilities for Bell-Shaped Distributions: The Normal Distribution
6.3 Probabilities When Each Observation Has Two Possible Outcomes: The Binomial Distribution
Chapter Summary
Chapter Exercises
Chapter 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
Part Three: Inferential Statistics
Chapter 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
Chapter 9. Statistical Inference: Significance Tests About Hypotheses
9.1 Steps for Performing a Significance Test
9.2 Significance Test About a Proportion
9.3 Significance Test 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 and the Power of a Test
Chapter Summary
Chapter Exercises
Chapter 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
Part Four: Extended Statistical Methods and Models for Analyzing Categorical and Quantitative Variables
Chapter 11. Categorical Data Analysis
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
Chapter 12. Regression Analysis
12.1 The Linear Regression Model
12.2 Inference About Model Parameters and the Relationship
12.3 Describing the Strength of the Relationship
12.4 How the Data Vary Around the Regression Line
12.5 Exponential Regression: A Model for Nonlinearity
Chapter Summary
Chapter Exercises
Chapter 13. Multiple Regression
13.1 Using Several Variables to Predict a Response
13.2 Extending the Correlation Coefficient and R2 for Multiple Regression
13.3 Inferences Using Multiple Regression
13.4 Checking a Regression Model Using Residual Plots
13.5 Regression and Categorical Predictors
13.6 Modeling a Categorical Response: Logistic Regression
Chapter Summary
Chapter Exercises
Chapter 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: Exploring Two Factors and Their Interaction
Chapter Summary
Chapter Exercises
Chapter 15. Nonparametric Statistics
15.1 Compare Two Groups by Ranking
15.2 Nonparametric Methods for Several Groups and for Dependent Samples
Chapter Summary
Chapter Exercises
Appendix
Answers
Index
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Index of Applications
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Credits