Intro Stats

Chapter 1: Stats Starts Here

• • 1.1 What Is Statistics?
• • 1.2 Data
• • 1.3 Variables
• • 1.4 Models

Chapter 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

Chapter 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

Chapter 4: Understanding and Comparing Distributions

• • 4.1 Displays for Comparing Groups
• • 4.2 Outliers
• • 4.3 Re-Expressing Data: A First Look

Chapter 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

Chapter 6: Scatterplots, Association, and Correlation

• • 6.1 Scatterplots
• • 6.2 Correlation
• • 6.3 Warning: Correlation ≠ Causation
• • 6.4 *Straightening Scatterplots

Chapter 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

Chapter 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

Chapter 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

Chapter 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

Chapter 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

Chapter 12: From Randomness to Probability

• • 12.1 Random Phenomena
• • 12.2 Modeling Probability
• • 12.3 Formal Probability
• • 12.4 Conditional Probability and the General Multiplication Rule
• • 12.5 Independence
• • 12.6 Picturing Probability: Tables, Venn Diagrams, and Trees
• • 12.7 Reversing the Conditioning and Bayes' Rule

Chapter 13: Sampling Distributions and Confidence Intervals for Proportions

• • 13.1 The Sampling Distribution for a Proportion
• • 13.2 When Does the Normal Model Work? Assumptions and Conditions
• • 13.3 A Confidence Interval for a Proportion
• • 13.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?
• • 13.5 Margin of Error: Certainty vs. Precision
• • 13.6 * Choosing the Sample Size

Chapter 14: Confidence Intervals for Means

• • 14.1 The Central Limit Theorem
• • 14.2 A Confidence interval for the Mean
• • 14.3 Interpreting confidence intervals
• • 14.4 * Picking our Interval Up by our Bootstraps
• • 14.5 Thoughts about Confidence Intervals

Chapter 15: Testing Hypotheses

• • 15.1 Hypotheses
• • 15.2 P-values
• • 15.3 The Reasoning of Hypothesis Testing
• • 15.4 A Hypothesis Test for the Mean
• • 15.5 Intervals and Tests
• • 15.6 P-Values and Decisions: What to Tell About a Hypothesis Test

Chapter 16: More About Tests and Intervals

• • 16.1 Interpreting P-values
• • 16.2 Alpha Levels and Critical Values
• • 16.3 Practical vs. Statistical Significance
• • 16.4 Errors
• • Review of Part IV: From the Data at Hand to the World at Large

Chapter 17: Comparing Groups

• • 17.1 A Confidence Interval for the Difference Between Two Proportions
• • 17.2 Assumptions and Conditions for Comparing Proportions
• • 17.3 The Two-Sample z-Test: Testing the Difference Between Proportions
• • 17.4 A Confidence Interval for the Difference Between Two Means
• • 17.5 The Two-Sample t-Test: Testing for the Difference Between Two Means
• • 17.6 * Randomization-Based Tests and Confidence Intervals for Two Means
• • 17.7 * Pooling
• • 17.8 * The Standard Deviation of a Difference

Chapter 18: Paired Samples and Blocks

• • 18.1 Paired Data
• • 18.2 The Paired t-Test
• • 18.3 Confidence Intervals for Matched Pairs
• • 18.4 Blocking

Chapter 19: Comparing Counts

• • 19.1 Goodness-of-Fit Tests
• • 19.2 Chi-Square Tests of Homogeneity
• • 19.3 Examining the Residuals
• • 19.4 Chi-Square Test of Independence

Chapter 20: Inferences for Regression

• • 20.1 The Regression Model
• • 20.2 Assumptions and Conditions
• • 20.3 Regression Inference and Intuition
• • 20.4 The Regression Table
• • 20.5 Multiple Regression Inference
• • 20.6 Confidence and Prediction Intervals
• • 20.7 * Logistic Regression
• • 20.8 * More About Regression
• • Review of Part V: Inference for Relationships