فهرست مطالب

• Front Matter
• Title Page
• Copyright
• Contents
• About the Authors
• Acknowledgments
• About the Companion Website
• Introduction
• Chapter 1
• 1.1 Big Data: Predicting Pregnancy
• 1.2 Phantom Protection from Vitamin E
• 1.3 Statistician, Heal Thyself
• 1.4 Identifying Terrorists in Airports
• 1.5 Looking Ahead
• 1.6 Big Data and Statisticians
• Chapter 2
• 2.1 Statistical Science
• 2.2 Big Data
• 2.3 Data Science
• 2.4 Example: Hospital Errors
• 2.5 Experiment
• 2.6 Designing an Experiment
• 2.7 The Data
• 2.8 Variables and Their Flavors
• 2.9 Python: Data Structures and Operations
• 2.10 Are We Sure We Made a Difference?
• 2.11 Is Chance Responsible? The Foundation of Hypothesis Testing
• 2.12 Probability
• 2.13 Significance or Alpha Level
• 2.14 Other Kinds of Studies
• 2.15 When to Use Hypothesis Tests
• 2.16 Experiments Falling Short of the Gold Standard
• 2.17 Summary
• 2.18 Python: Iterations and Conditional Execution
• 2.19 Python: Numpy, scipy, and pandas—The Workhorses of Data Science
• Exercises
• Chapter 3
• 3.1 Exploratory Data Analysis
• 3.2 What to Measure—Central Location
• 3.3 What to Measure—Variability
• 3.4 What to Measure—Distance (Nearness)
• 3.5 Test Statistic
• 3.6 Examining and Displaying the Data
• 3.7 Python: Exploratory Data Analysis/Data Visualization
• Exercises
• Chapter 4
• 4.1 Avoid Being Fooled by Chance
• 4.2 The Null Hypothesis
• 4.3 Repeating the Experiment
• 4.4 Statistical Significance
• 4.5 Power
• 4.6 The Normal Distribution
• 4.7 Summary
• 4.8 Python: Random Numbers
• Exercises
• Chapter 5
• 5.1 What Is Probability
• 5.2 Simple Probability
• 5.3 Probability Distributions
• 5.4 From Binomial to Normal Distribution
• 5.5 Appendix: Binomial Formula and Normal Approximation
• 5.6 Python: Probability
• Exercises
• Chapter 6
• 6.1 Two‐way Tables
• 6.2 Conditional Probability
• 6.3 Bayesian Estimates
• 6.4 Independence
• 6.5 Multiplication Rule
• 6.6 Simpson's Paradox
• 6.7 Python: Counting and Contingency Tables
• Exercises
• Chapter 7
• 7.1 Literary Digest—Sampling Trumps “All Data”
• 7.2 Simple Random Samples
• 7.3 Margin of Error: Sampling Distribution for a Proportion
• 7.4 Sampling Distribution for a Mean
• 7.5 The Bootstrap
• 7.6 Rationale for the Bootstrap
• 7.7 Standard Error
• 7.8 Other Sampling Methods
• 7.9 Absolute vs. Relative Sample Size
• 7.10 Python: Random Sampling Strategies
• Exercises
• Chapter 8
• 8.1 Count Data—R × C Tables
• 8.2 The Role of Experiments (Many Are Costly)
• 8.3 Chi‐Square Test
• 8.4 Single Sample—Goodness‐of‐Fit
• 8.5 Numeric Data: ANOVA
• 8.6 Components of Variance
• 8.7 Factorial Design
• 8.8 The Problem of Multiple Inference
• 8.9 Continuous Testing
• 8.10 Bandit Algorithms
• 8.11 Appendix: ANOVA, the Factor Diagram, and the F‐Statistic
• 8.12 More than One Factor or Variable—From ANOVA to Statistical Models
• 8.13 Python: Contingency Tables and Chi‐square Test
• 8.14 Python: ANOVA
• Exercises
• Chapter 9
• 9.1 Example: Delta Wire
• 9.2 Example: Cotton Dust and Lung Disease
• 9.3 The Vector Product Sum Test
• 9.4 Correlation Coefficient
• 9.5 Correlation is not Causation
• 9.6 Other Forms of Association
• 9.7 Python: Correlation
• Exercises
• Chapter 10
• 10.1 Finding the Regression Line by Eye
• 10.2 Finding the Regression Line by Minimizing Residuals
• 10.3 Linear Relationships
• 10.4 Prediction vs. Explanation
• 10.5 Python: Linear Regression
• Exercises
• Chapter 11
• 11.1 Terminology
• 11.2 Example—Housing Prices
• 11.3 Interaction
• 11.4 Regression Assumptions
• 11.5 Assessing Explanatory Regression Models
• 11.6 Assessing Regression for Prediction
• 11.7 Python: Multiple Linear Regression
• Exercises
• Chapter 12
• 12.1 K‐Nearest‐Neighbors
• 12.2 Python: Classification
• Exercises
• Index