Business Statistics

As a researcher of statistical problems in business and a professor at a business school, Norean Radke Sharpe (Ph.D. University of Virginia) 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 Senior Associate Dean and Director of Undergraduate Programs. Prior to joining Georgetown, she taught business statistics and operations research courses to both undergraduates and MBA students for fourteen years at Babson College. Before moving into business education, she taught statistics for several years at Bowdoin College and conducted research at Yale University. Norean is coauthor of the recent text, A Casebook for Business Statistics: Laboratories for Decision Making, and has authored more than 30 articles--primarily in the areas of statistics education and women in science. Norean currently serves as Associate Editor for the journal Cases in Business, Industry, and Government Statistics. Her research focuses on business forecasting and statistics education. She is also co-founder of DOME Foundation, Inc., a nonprofit foundation that works to increase Diversity and Outreach in Mathematics and Engineering for the greater Boston area. She has been active in increasing the participation of women and underrepresented students in science and mathematics for several years and has two children of her own.   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, although he returned to Princeton for the academic year 2006–2007 as the William R. Kenan Jr. Visiting Professor of Distinguished Teaching. 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 Fortune 500 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) (http://lib.stat.cmu.edu/DASL/), 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.   Richard De Veaux and Paul Velleman have authored successful books in the introductory college and AP High School market with Dave Bock, including Intro Stats, Fourth Edition (Pearson, 2014), Stats: Modeling the World, Third Edition (Pearson, 2010), and Stats: Data and Models, Third Edition (Pearson, 2012).  

Preface

Index of Applications

 

PART I. EXPLORING AND UNDERSTANDING DATA

 

1. Stats Starts Here!

1.1 What Is Statistics?

1.2 Data

1.3 Variables

 

2. Displaying and Describing Categorical Data

2.1 Summarizing and Displaying a Single Categorical Variable

2.2 Exploring the Relationship Between Two Categorical Variables

 

3. Displaying and Summarizing Quantitative Data

3.1 Displaying Quantitative Variables

3.2 Shape

3.3 Center

3.4 Spread

3.5 Boxplots and 5-Number Summaries

3.6 The Center of Symmetric Distributions: The Mean

3.7 The Spread of Symmetric Distributions: The Standard Deviation

3.8 Summary—What to Tell About a Quantitative Variable

 

4. Understanding and Comparing Distributions

4.1 Comparing Groups with Histograms

4.2 Comparing Groups with Boxplots

4.3 Outliers

4.4 Timeplots: Order, Please!

4.5 Re-expressing Data: A First Look

 

5. The Standard Deviation as a Ruler and the Normal Model

5.1 Standardizing with z-Scores

5.2 Shifting and Scaling

5.3 Normal Models

5.4 Finding Normal Percentiles

5.5 Normal Probability Plots

 

Review of Part I. Exploring and Understanding Data

 

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 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

 

Review of Part II. Exploring Relationships Between Variables

 

PART III. GATHERING DATA

 

9. Understanding Randomness

9.1 What is Randomness?

9.2  Simulating By Hand

 

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 Treatments

11.5 Blocking

11.6 Confounding

 

Review of Part III  Gathering Data

 

PART IV. RANDOMNESS AND PROBABILITY

 

12. From Randomness to Probability

12.1 Random Phenomena

12.2 Modeling Probability

12.3 Formal Probability

 

13. Probability Rules!

13.1 The General Addition Rule

13.2 Conditional Probability and the General Multiplication Rule

13.3 Independence

13.4 Picturing Probability: Tables, Venn Diagrams and Trees

13.5 Reversing the Conditioning and Bayes' Rule

 

14. Random Variables and Probability Models

14.1 Expected Value: Center

14.2 Standard Deviation

14.3 Combining Random Variables

14.4 The Binomial Model

14.5 Modeling the Binomial with a Normal Model

*14.6 The Poisson Model

14.7 Continuous Random Variables

 

Review of Part IV Randomness and Probability

 

PART V. FROM THE DATA AT HAND TO THE WORLD AT LARGE

 

15. Sampling Distribution Models

15.1 Sampling Distribution of a Proportion

15.2 When Does the Normal Model Work? Assumptions and Conditions

15.3 The Sampling Distribution of Other Statistics

15.4 The Central Limit Theorem: The Fundamental Theorem of Statistics

15.5 Sampling Distributions: A Summary

 

16. Confidence Intervals for Proportions

16.1 A Confidence Interval

16.2 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?

16.3 Margin of Error: Certainty vs. Precision

16.4 Assumptions and Conditions

 

17. Testing Hypotheses About Proportions

17.1 Hypotheses

17.2 P-Values

17.3 The Reasoning of Hypothesis Testing

17.4 Alternative Alternatives

17.5 P-Values and Decisions: What to Tell About a Hypothesis Test

 

18. Inferences About Means

18.1: Getting Started: The Central Limit Theorem (Again)

18.2: Gosset's t

18.3 Interpreting Confidence Intervals

18.4 A Hypothesis Test for the Mean

18.5 Choosing the Sample Size

 

19. More About Tests and Intervals

19.1 Choosing Hypotheses

19.2 How to Think About P Values

19.3 Alpha Levels

19.4  Practical vs. Statistical Significance

19.5 Critical Values Again

19.6 Errors

19.7 Power

 

Review of Part V From the Data at Hand to the World at Large

 

PART VI. LEARNING ABOUT THE WORLD

 

20. Comparing Groups

20.1 The Variance of a Difference

20.2 The Standard Deviation of the Difference Between Two Proportions

20.3 Assumptions and Conditions for Comparing Proportions

20.4 The Sampling Distribution of the Difference between Two Proportions

20.5 Comparing Two Means

20.6 The Two-Sample t-Test: Testing for the Difference Between Two Means

20.7 The Two Sample z-Test: Testing for the Difference between Proportions

20.8 The Pooled t-Test: Everyone into the Pool?

20.9 Pooling

 

21. Paired Samples and Blocks

21.1 Paired Data

21.2 Assumptions and Conditions

21.3 Confidence Intervals for Matched Pairs

21.4 Blocking

 

22. Comparing Counts

22.1 Goodness-of-Fit Tests

22.2 Chi-Square Test of Homogeneity

22.3 Examining the Residuals

22.4 Chi-Square Test of Independence

 

23. Inferences for Regression

23.1 The Population and the Sample

23.2 Assumptions and Conditions

23.3 Intuition About Regression Inference

23.4 Regression Inference

23.5 Standard Errors for Predicted Values

23.6 Confidence Intervals for

Predicted Values

23.7* Logistic Regression

 

Review of Part VI. Learning About the World

 

PART VII. INFERENCE WHEN VARIABLES ARE RELATED

 

24. Analysis of Variance

24.1 Testing Whether the Means of Several Groups Are Equal

24.2 The ANOVA Table

24.3 Plot the Data . . .

24.4 Comparing Means

 

25. Multiple Regression

25.1 Two Predictors

25.2 What Multiple Regression Coefficients Mean

25.3 The Multiple Regression Model

25.4 Multiple Regression Inference

25.5 Comparing Multiple Regression Models

 

Appendices

A. Answers

B. Photo Acknowledgments

C. Index

D. Tables and Selected Formulas

 

*Indicates an optional chapter