Norean Sharpe (Ph.D. University of Virginia), as a researcher of statistical problems in business and a professor at a business school, understands the challenges and specific needs of the business student. She is currently teaching at the McDonough School of Business at Georgetown University, where she is also Associate Dean and Director of Undergraduate Programs. Prior to joining Georgetown, she taught business statistics and operations research courses to both undergraduates and MBAs for fourteen years at Babson CollegeShe is the recipient of the 2008 Women Who Make a Difference Award for female faculty at Babson. Prior to joining Babson, she taught statistics and applied mathematics courses for several years at Bowdoin College. Norean is coauthor of the recent text, A Casebook for Business Statistics: Laboratories for Decision Making, and has authored over 30 articles-primarily in the areas of statistics education and women in science. Norean currently serves as Associate Editor for CAUSE (Consortium for the Advancement of Undergraduate Statistics Education) and Associate Editor for the journal Cases in Business, Industry, and Government Statistics. Her research focuses on business forecasting and statistics education.

Richard D. De Veaux (Ph.D. Stanford University) is an internationally known educator, consultant, and lecturer. Dick has taught Statistics at a business school (The Wharton School of the University of Pennsylvania), an engineering school (Princeton University) and a liberal arts college (Williams College). He is an internationally known lecturer in data mining and is a consultant for many Fortune 500 companies in a wide variety of industries. While at Princeton, he won a Lifetime Award for Dedication and Excellence in Teaching. Since 1994, he has been a Professor of Statistics at Williams College. Dick holds degrees from Princeton University in Civil Engineering and Mathematics, and from Stanford University in Dance Education and Statistics, where he studied with Persi Diaconis. His research focuses on the analysis of large data sets and data mining in science and industry. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality and is a Fellow of the American Statistical Association. Dick is well known in industry, having consulted for such companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. He was named the “Statistician of the Year” for 2008 by the Boston Chapter of the American Statistical Association for his contributions to teaching, research, and consulting. In his spare time he is an avid cyclist and swimmer. He also is the founder and bass for the Doo-wop group, “Diminished Faculty,” and is a frequent soloist with various local choirs and orchestras. Dick is the father of four children.

Paul F. Velleman (Ph.D. Princeton University) has an international reputation for innovative statistics education. He designed the Data Desk® software package and is also the author and designer of the award-winning ActivStats® statistics package, for which he received the EDUCOM Medal for innovative uses of computers in teaching statistics and the ICTCM Award for Innovation in Using Technology in College Mathematics. He is the founder and CEO of Data Description, Inc. (www.datadesk.com), which supports both of these programs. He also developed the Internet site, Data and Story Library (DASL) (dasl.datadesk.com), which provides data sets for teaching Statistics. Paul co-authored (with David Hoaglin) the book ABCs of Exploratory Data Analysis. Paul has taught Statistics at Cornell University on the faculty of the School of Industrial and Labor Relations since 1975. His research often focuses on statistical graphics and data analysis methods. Paul is a Fellow of the American Statistical Association and of the American Association for the Advancement of Science. Paul’s experience as a professor, entrepreneur and business leader brings a unique perspective to the book.

Dick De Veaux and Paul Velleman have authored successful books in the introductory college and AP High School market with Dave Bock, including Intro Stats, Third Edition (Pearson, 2009), Stats: Modeling the World, Third Edition (Pearson, 2010), and Stats: Data and Models, Second Edition (Pearson, 2008).

PART I EXPLORING AND COLLECTING DATA

1. Statistics and Variation

1.1 So, What Is Statistics?

1.2 How Will This Book Help?

2. Data

2.1 What Are Data?

2.2 Variable Types

2.3 Data Sources–Where, How, and When

3. Surveys and Sampling

3.1 Three Ideas of Sampling

3.2 A Census–Does It Make Sense?

3.3 Populations and Parameters

3.4 Simple Random Sample (SRS)

3.5 Other Sample Designs

3.6 Defining the Population

3.7 The Valid Survey

4. Displaying and Describing Categorical Data

4.1 The Three Rules of Data Analysis

4.2 Frequency Tables

4.3 Charts

4.4 Contingency Tables

5. Displaying and Describing Quantitative Data

5.1 Displaying Distributions

5.2 Shape

5.3 Center

5.4 Spread of the Distribution

5.5 Shape, Center, and Spread–A Summary

5.6 Five-Number Summary and Boxplots

5.7 Comparing Groups

5.8 Identifying Outliers

5.9 Standardizing

*5.10 Time Series Plots

Transforming Skewed Data–On CD-ROM

6. Correlation and Linear Regression

6.1 Looking at Scatterplots

6.2 Assigning Roles to Variables in Scatterplots

6.3 Understanding Correlation

6.4 Lurking Variables and Causation

6.5 The Linear Model

6.6 Correlation and the Line

6.7 Regression to the Mean

6.8 Checking the Model

6.9 Variation in the Model and R2

6.10 Reality Check: Is the Regression Reasonable?

Straightening Scatterplots–On CD-ROM

PART II UNDERSTANDING DATA AND DISTRIBUTIONS

7. Randomness and Probability

7.1 Random Phenomena and Probability

7.2 The Nonexistent Law of Averages

7.3 Different Types of Probability

7.4 Probability Rules

7.5 Joint Probability and Contingency Tables

7.6 Conditional Probability

7.7 Constructing Contingency Tables

7.8 Probability Trees

*7.9 Reversing the Conditioning: Bayes’s Rule

8. Random Variables and Probability Models

8.1 Expected Value of a Random Variable

8.2 Standard Deviation of a Random Variable

8.3 Properties of Expected Values and Variances

8.4 Discrete Probability Models

8.5 Continuous Random Variables

9. Sampling Distributions and Confidence Intervals for Proportions

9.1 Simulations

9.2 The Sampling Distribution for Proportions

9.3 Assumptions and Conditions

9.4 The Central Limit Theorem–The Fundamental Theorem of Statistics

9.5 A Confidence Interval

9.6 Margin of Error: Certainty vs. Precision

9.7 Critical Values

9.8 Assumptions and Conditions

9.9 Choosing the Sample Size

A Confidence Interval for Small Samples–On CD-ROM

10. Testing Hypotheses about Proportions

10.1 Hypotheses

10.2 A Trial as a Hypothesis Test

10.3 P-Values

10.4 The Reasoning of Hypothesis Testing

10.5 Alternative Hypotheses

10.6 Alpha Levels and Significance

10.7 Critical Values

10.8 Confidence Intervals and Hypothesis Tests

10.9 Two Types of Errors

*10.10 Power

11. Confidence Intervals and Hypothesis Tests for Means

11.1 The Sampling Distribution for Means

11.2 How Sampling Distribution Models Work

11.3 Gossett and the t-Distribution

11.4 A Confidence Interval for Means

11.5 Assumptions and Conditions

11.6 Cautions About Interpreting Confidence Intervals

11.7 One-Sample t-Test

11.8 Sample Size

*11.9 Degrees of Freedom–Why n - 1

12. Comparing Two Groups

12.1 Comparing Two Means

12.2 The Two-Sample t-Test

12.3 Assumptions and Conditions

12.4 A Confidence Interval for the Difference Between Two Means

*12.5 The Pooled t-Test

*12.6 Tukey’s Quick Test

12.7 Paired Data

12.8 The Paired t-Test

13. Inference for Counts: Chi-Square Tests

13.1 Goodness-of-Fit Tests

13.2 Interpreting Chi-Square Values

13.3 Examining the Residuals

13.4 The Chi-Square Test of Homogeneity

13.5 Comparing Two Proportions

13.6 Chi-Square Test of Independence

PART III  BUILDING MODELS FOR DECISION MAKING

14. Inference for Regression

14.1 The Population and the Sample

14.2 Assumptions and Conditions

14.3 Regression Inference

14.4 Standard Errors for Predicted Values

14.5 Using Confidence and Prediction Intervals

14.6 Extrapolation and Prediction

14.7 Unusual and Extraordinary Observations

*14.8 Working with Summary Values

*14.9 Linearity

Re-expressing data–On CD-ROM

The Ladder of Powers*–On CD-ROM

15. Multiple Regression

15.1 The Multiple Regression Model

15.2 Interpreting Multiple Regression Coefficients

15.3 Assumptions and Conditions for the Multiple Regression Model

15.4 Testing the Multiple Regression Model

15.5 Adjusted R2 and the F-statistic

The Logistic Regression Model–On CD-ROM

Indicator Variables–On CD-ROM

Adjusting for Different Slopes– Interaction Terms–On CD-ROM

Collinearity–On CD-ROM

16. Introduction to Data Mining

16.1 Direct Marketing

16.2 The Data

16.3 The Goals of Data Mining

16.4 Data Mining Myths

16.5 Successful Data Mining

16.6 Data Mining Problems

16.7 Data Mining Algorithms

16.8 The Data Mining Process

16.9 Summary

*Indicates an optional topic

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