Business Statistics

by Sharpe, Norean R.; De Veaux, Richard D.; Velleman, Paul F.

• ISBN13: 9780321925831
• ISBN10: 0321925831
• Edition: 3rd
• Format: Hardcover
• Copyright: 2014-01-23
• Publisher: Pearson

Summary

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Business Statistics, Third Edition , by Sharpe, De Veaux, and Velleman , narrows the gap between theory and practice—relevant statistical methods empower business students to make effective, data-informed decisions. With their unique blend of teaching, consulting, and entrepreneurial experiences, this dynamic author team brings a modern edge to teaching statistics to business students. Focusing on statistics in the context of real business issues, with an emphasis on analysis and understanding over computation, the text helps students be analytical, prepares them to make better business decisions, and shows them how to effectively communicate results.

Author Biography

As a researcher of statistical problems in business and a professor of Statistics 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 undergraduate 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 she 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 (Wharton), an engineering school (Princeton), and a liberal arts college (Williams). While at Princeton, he won a Lifetime Award for Dedication and Excellence in Teaching. Since 1994, he has taught 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. He is currently the C. Carlisle and Margaret Tippit 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. He is an elected member of the International Statistics Institute (ISI) and a Fellow of the American Statistical Association (ASA). He currently serves on the Board of Directors of the ASA. 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, the Diminished Faculty, and is a frequent singer and soloist with various local choirs including the Choeur Vittoria of Paris, France. 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® multimedia software, 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; lib.stat.cmu.edu/DASL/), which provides data sets for teaching Statistics. Paul coauthored (with David Hoaglin) the book ABCs of Exploratory Data Analysis. Paul teaches Statistics at Cornell University in the Department of Statistical Sciences and in the School of Industrial and Labor Relations, for which he has been awarded the MacIntyre prize for Exemplary Teaching. 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 David Bock, including Intro Stats, Fourth Edition (Pearson, 2014), Stats: Modeling the World, Fourth Edition (Pearson, 2015), and Stats: Data and Models, Third Edition (Pearson, 2012).

 

Table of Contents

Preface

Index of Applications

 

1. Data and Decisions (E-Commerce)

1.1 Data and Decisions

1.2 Variable Types

1.3 Data Sources: Where, How, and When

            Ethics in Action

            Technology Help: Data on the Computer

            Brief Case: Credit Card Bank

 

2. Displaying and Describing Categorical Data (Keen, Inc.)

2.1 Summarizing a Categorical Variable

2.2 Displaying a Categorical Variable 

2.3 Exploring Two Categorical Variables: Contingency Tables 

2.4 Segmented Bar Charts and Mosaic Plots 

2.5 Simpson's Paradox

            Ethics in Action

            Technology Help: Displaying Categorical Data on the Computer

            Brief Case: Credit Card Bank

 

3. Displaying and Describing Quantitative Data (AIG)

3.1 Displaying Quantitative Variables 

3.2 Shape 

3.3 Center 

3.4 Spread of the Distribution

3.5 Shape, Center, and Spread–A Summary

3.6 Standardizing Variables 

3.7 Five-Number Summary and Boxplots 

3.8 Comparing Groups,

3.9 Identifying Outliers,

3.10 Time Series Plots

3.11 Transforming Skewed Data

            Ethics in Action

            Technology Help: Displaying and Summarizing Quantitative Variables

            Brief Cases: Detecting the Housing Bubble and Socio-Economic Data on States

 

4. Correlation and Linear Regression (Amazon.com)

4.1 Looking at Scatterplots

4.2 Assigning Roles to Variables in Scatterplots

4.3 Understanding Correlation

4.4 Lurking Variables and Causation

4.5 The Linear Model

4.6 Correlation and the Line

4.7 Regression to the Mean

4.8 Checking the Model

4.9 Variation in the Model and R²

4.10 Reality Check: Is the Regression Reasonable?

4.11 Nonlinear Relationships

            Ethics in Action

            Technology Help: Correlation and Regression

            Brief Cases: Fuel Efficiency, Cost of Living, and Mutual Funds

 

            Case Study I: Paralyzed Veterans of America

 

5. Randomness and Probability (Credit Reports and the Fair Isaacs Corporation)

5.1 Random Phenomena and Probability

5.2 The Nonexistent Law of Averages

5.3 Different Types of Probability

5.4 Probability Rules

5.5 Joint Probability and Contingency Tables

5.6 Conditional Probability

5.7 Constructing Contingency Tables

5.8 Probability Trees

5.9 Reversing the Conditioning: Bayes’ Rule

            Ethics in Action

            Technology Help: Generating Random Numbers

            Brief Case

 

6. Random Variables and Probability Models (Metropolitan Life Insurance Company)

6.1 Expected Value of a Random Variable

6.2 Standard Deviation of a Random Variable

6.3 Properties of Expected Values and Variances

6.4 Bernoulli Trials

6.5 Discrete Probability Models

            Ethics in Action

            Technology Help: Random Variables and Probability Models

            Brief Case: Investment Options

 

7. The Normal and other Continuous Distributions (The NYSE)

7.1 The Standard Deviation as a Ruler

7.2 The Normal Distribution

7.3 Normal Probability Plots

7.4 The Distribution of Sums of Normals

7.5 The Normal Approximation for the Binomial

7.6 The Other Continuous Random Variables

            Ethics in Action

            Technology Help: Probability Calculations and Plots

             Brief Case

 

8. Surveys and Sampling (Roper Polls)

8.1 Three Ideas of Sampling

8.2 Populations and Parameters

8.3 Common Sampling Designs

8.4 The Valid Survey

8.5 How to Sample Badly

            Ethics in Action

            Technology Help: Random Sampling

            Brief Cases: Market Survey Research and The GfK Roper Reports Worldwide Survey

 

9. Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story)

9.1 The Distribution of Sample Proportions

9.2 A Confidence Interval

9.3 Margin of Error: Certainty vs. Precision

9.4 Choosing and Sample Size

            Ethics in Action

            Technology Help: Confidence Intervals for Proportions

            Brief Case: Real Estate Simulation

 

Case Study II

 

10. Testing Hypotheses about Proportions (Dow Jones Industrial Average)

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

            Ethics in Action

            Technology Help: Hypothesis Tests

            Brief Cases: Metal Production and Loyalty Program

 

11. Confidence Intervals and Hypothesis Tests for Means (Guinness & Co.)

11.1 The Central Limit Theorem

11.2 The Sampling Distribution of the Mean

11.3 How Sampling Distribution Models Work

11.4 Gossett and the t¿-Distribution

11.5 A Confidence Interval for Means

11.6 Assumptions and Conditions

11.7 Testing Hypothesis about Means–the One-Sample t-Test

            Ethics in Action

            Technology Help: Inference for Means

            Brief Cases: Real Estate and Donor Profiles

 

12. More About Tests and Intervals (Traveler’s Insurance)

12.1 How to Think About P-Values

12.2 Alpha Levels and Significance

12.3 Critical Values

12.4 Confidence Intervals and Hypothesis Tests

12.5 Two Types of Errors

12.6 Power

            Ethics in Action

            Technology Help: Hypothesis Tests

            Brief Case

 

13. Comparing Two Means (Visa Global Organization)

13.1 Comparing Two Means

13.2 The Two-Sample t-Test

13.3 Assumptions and Conditions

13.4 A Confidence Interval for the Difference Between Two Means

13.5 The Pooled t-Test

13.6 Paired Data

13.7 Paired Methods

Ethics in Action

            Technology Help: Two-Sample Methods

            Technology Help: Paired t

            Brief Cases: Real Estate and Consumer Spending Patterns (Data Analysis)

 

14. Inference for Counts: Chi-Square Tests (SAC Capital)

14.1 Goodness-of-Fit Tests

14.2 Interpreting Chi-Square Values

14.3 Examining the Residuals

14.4 The Chi-Square Test of Homogeneity

14.5 Comparing Two Proportions

14.6 Chi-Square Test of Independence

            Ethics in Action

            Technology Help: Chi-Square

            Brief Cases: Health Insurance and Loyalty Program

 

            Case Study III: Investment Strategy Segmentation

 

15. Inference for Regression (Nambé Mills)

15.1 A Hypothesis Test and Confidence Interval for the Slope

15.2 Assumptions and Conditions

15.3 Standard Errors for Predicted Values

15.4 Using Confidence and Prediction Intervals

            Ethics in Action

            Technology Help: Regression Analysis

            Brief Cases: Frozen Pizza and Global Warming?

 

16. Understanding Residuals (Kellogg’s)

16.1 Examining Residuals for Groups

16.2 Extrapolation and Prediction

16.3 Unusual and Extraordinary Observations

16.4 Working with Summary Values

16.5 Autocorrelation

16.6 Transforming (Re-expressing) Data

16.7 The Ladder of Powers

            Ethics in Action

            Technology Help: Examining Residuals

            Brief Cases: Gross Domestic Product and Energy Sources

 

17. Multiple Regression (Zillow.com)

17.1 The Multiple Regression Model

17.2 Interpreting Multiple Regression Coefficients

17.3 Assumptions and Conditions for the Multiple Regression Model

17.4 Testing the Multiple Regression Model

17.5 Adjusted R ²  and the F-statistic

17.6 The Logistic Regression Model

            Ethics in Action

            Technology Help: Regression Analysis

            Brief Case: Golf Success

 

18. Building Multiple Regression Models (Bolliger and Mabillard)

18.1 Indicator (or Dummy) Variables

18.2 Adjusting for Different Slopes–Interaction Terms

18.3 Multiple Regression Diagnostics

18.4 Building Regression Models

18.5 Collinearity

18.6 Quadratic Terms

            Ethics in Action

            Technology Help: Building Multiple Regression Models

            Brief Case

 

19. Time Series Analysis (Whole Food Market)

19.1 What Is a Time Series?

19.2 Components of a Time Series

19.3 Smoothing Methods

19.4 Summarizing Forecast Error

19.5 Autoregressive Models

19.6 Multiples Regression-based Models

19.7 Choosing a Time Series Forecasting Method

19.8 Interpreting Time Series Models: The Whole Foods Data Revisited

            Ethics in Action

            Technology Help

            Brief Cases: Intel Corporation and Tiffany & Co.

 

            Case Study IV: Health Care Costs

 

20. Design and Analysis of Experiments and Observational Studies (Capital One)

20.1 Observational Studies

20.2 Randomized Comparative Experiments

20.3 The Four Principles of Experimental Design

20.4 Experimental Designs

20.5 Issues in Experimental Design

20.6 Analyzing a Design in One Factor–The One-Way Analysis of Variance

20.7 Assumptions and Conditions for ANOVA

20.8 Multiple Comparisons

20.9 ANOVA on Observational Data

20.10 Analysis of Multifactor Designs

            Ethics in Action

            Technology Help: Analysis of Variance

            Brief Case: Multifactor Experiment Design

 

 

21. Quality Control (Sony)

21.1 A Short History of Quality Control

21.2 Control Charts for Individual Observations (Run Charts)

21.3 Control Charts for Measurements: (x-bar) and R Charts

21.4 Actions for Out-of-Control Processes

21.5 Control Charts for Attributes: p Charts and c Charts

21.6 Philosophies of Quality Control

            Ethics in Action

            Technology Help: Quality Control Charts

            Brief Case: Laptop Touchpad Quality

 

22. Nonparametric Methods (i4cp)

22.1 Ranks

22.2 The Wilcoxon Rank-Sum/Mann-Whitney Statistic

22.3 Kruskal-Wallace Test

22.4 Paired Data: The Wilcoxon Signed-Rank Test

22.5 Friedman Test for a Randomized Block Design

22.6 Kendall’s Tau: Measuring Monotonicity

22.7 Spearman’s Rho

22.8 When Should You Use Nonparametric Methods?

            Ethics in Action

            Technology Help

            Brief Case: Real Estate Reconsidered

 

23. Decision Making and Risk (Data Description, Inc.)

23.1 Actions, States of Nature, and Outcomes

23.2 Payoff Tables and Decisions Trees

23.3 Minimizing Loss and Maximizing Gain

23.4 The Expected Value of an Action

23.5 Expected Value with Perfect Information

23.6 Decisions Made with Sample Information

23.7 Estimating Variation

23.8 Sensitivity

23.9 Simulation

23.10 More Complex Decisions

            Ethics in Action

            Technology Help

            Brief Cases: Texaco-Pennzoil and Insurance Services, Revisited

 

24. Introduction to Data Mining (Paralyzed Veterans of America)

24.1 The Big Data Revolution

24.2 Direct Marketing

24.3 The Goals of Data Mining

24.4 Data Mining Myths

24.5 Successful Data Mining

24.6 Data Mining Problems

24.7 Data Mining Algorithms

24.8 The Data Mining Process

24.9 Summary

            Ethics in Action

            Case Study V Marketing Experiment

 

Appendices

 

A. Answers

B. Photo Acknowledgments

C. Tables and Selected Formulas

 

Index