Intro Stats, 6th Edition

Table of Contents

* Indicates optional section 

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 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 

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 

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 

IV: FROM THE DATA AT HAND TO THE WORLD AT LARGE 

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 

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 

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

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 

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 

V: INFERENCE FOR RELATIONSHIPS 

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 

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 

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 

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  

Parts I–V Cumulative Review Exercises

Appendixes: 

A. Answers 

B. Credits

C. Indexes

D. Tables and Selected Formulas