Intro Stats

Richard D. De Veaux is an internationally known educator and consultant. He has taught at the Wharton School and the Princeton University School of Engineering, where he won a “Lifetime Award for Dedication and Excellence in Teaching.” He is the C. Carlisle and M. Tippit Professor of Statistics at Williams College, where he has taught since 1994. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality. He is a fellow of the American Statistical Association (ASA) and an elected member of the International Statistical Institute (ISI). In 2008, he was named Statistician of the Year by the Boston Chapter of the ASA. Dick is also well known in industry, where for more than 30 years he has consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. Because he consulted with Mickey Hart on his book Planet Drum, he has also sometimes been called the “Official Statistician for the Grateful Dead.” His real-world experiences and anecdotes illustrate many of this book’s chapters. Dick holds degrees from Princeton University in Civil Engineering (B.S.E.) and Mathematics (A.B.) and from Stanford University in Dance Education (M.A.) and Statistics (Ph.D.), where he studied dance with Inga Weiss and Statistics with Persi Diaconis. His research focuses on the analysis of large data sets and data mining in science and industry. In his spare time, he is an avid cyclist and swimmer. He also is the founder of the “Diminished Faculty,” an a cappella Doo-Wop quartet at Williams College, and sings bass in the college concert choir and with the Choeur Vittoria of Paris. Dick is the father of four children. Paul F. Velleman has an international reputation for innovative Statistics education. He is the author and designer of the multimedia Statistics program ActivStats, for which he was awarded the EDUCOM Medal for innovative uses of computers in teaching statistics, and the ICTCM Award for Innovation in Using Technology in College Mathematics. He also developed the award-winning statistics program Data Desk, and the Internet site Data and Story Library (DASL) (ASL.datadesk.com), which provides data sets for teaching Statistics. Paul’s understanding of using and teaching with technology informs much of this book’s approach. Paul has taught Statistics at Cornell University since 1975, where he was awarded the MacIntyre Award for Exemplary Teaching. He holds an A.B. from Dartmouth College in Mathematics and Social Science, and M.S. and Ph.D. degrees in Statistics from Princeton University, where he studied with John Tukey. His research often deals with statistical graphics and data analysis methods. Paul co-authored (with David Hoaglin) ABCs of Exploratory Data Analysis. Paul is a Fellow of the American Statistical Association and of the American Association for the Advancement of Science. Paul is the father of two boys. David E. Bock taught mathematics at Ithaca High School for 35 years. He has taught Statistics at Ithaca High School, Tompkins-Cortland Community College, Ithaca College, and Cornell University. Dave has won numerous teaching awards, including the MAA’s Edyth May Sliffe Award for Distinguished High School Mathematics Teaching (twice), Cornell University’s Outstanding Educator Award (three times), and has been a finalist for New York State Teacher of the Year. Dave holds degrees from the Universi

PART 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






Part I Review




PART 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 R 2–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






Part II Review




PART 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 Experiment Design
11.4 Control Groups
11.5 Blocking
11.6 Confounding






Part III Review




PART IV  INFERENCE FOR ONE PARAMETER

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: 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 your 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






Part IV Review




PART  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 Assumptions and Conditions
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






Part V Review




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