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 College. She 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 more than 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) (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.
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, Third Edition (Pearson, 2012).
1. Statistics and Variation
1.1 So, What Is Statistics?
1.2 How Will This Book Help?
2. Data
Amazon.com
2.1 What Are Data?
2.2 Variable Types
2.3 Data Sources: Where, How, and When
Ethics in Action
Technology Help
Brief Cases: Credit Card Bank
3. Surveys and Sampling
Roper Polls
3.1 Three Ideas of Sampling
3.2 Populations and Parameters
3.3 Other Sample Designs
3.4 The Valid Survey
3.5 How to Sample Badly
Ethics in Action
Technology Help: Random Sampling
Brief Cases: Market Survey Research
The GfK Roper Reports Worldwide Survey
4. Displaying and Describing Categorical Data
Keen
4.1 Summarizing a Categorical Variable
4.2 Displaying a Categorical Variable
4.3 Exploring Two Categorical Variables: Contingency Tables
Ethics in Action
Technology Help: Displaying Categorical Data on the Computer
Brief Cases: KEEN
5. Displaying and Describing Quantitative Data
AIG
5.1 Displaying Quantitative Variables
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
*5.11 Transforming Skewed Data
Ethics in Action
Technology Help: Displaying and Summarizing
Quantitative Variables
Brief Cases Hotel Occupancy Rates 122
Value and Growth Stock Returns 122
6. Correlation and Linear Regression
Lowe's
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?
6.11 Non-linear Relationships
Ethics in Action
Technology Help: Correlation and Regression
Brief Cases: Fuel Efficiency
The U.S. Economy and Home Depot Stock Prices
Cost of Living
Mutual Funds
Case Study: Paralyzed Veterans of America
PART II. MODELING WITH PROBABLITY
7. Randomness and Probability
Credit Reports and the Fair Isaacs Corporation
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
Brief Case: Market Segmentation
8. Random Variables and Probability Models
Metropolitan Life Insurance Company
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 Distributions
Ethics in Action
Brief Case: Investment Options
9. The Normal Distribution
The NYSE
9.1 The Standard Deviation as a Ruler
9.2 The Normal Distribution
9.3 Normal Probability Plots
9.4 The Distribution of Sums of Normals
9.5 The Normal Approximation for the Binomial
9.6 Other Continuous Random Variables
Ethics In Action
Brief Cases: The CAPE10
Technology Help: Making Normal Probability Plots
10. Sampling Distributions
Marketing Credit Cards: The MBNA Story
10.1 The Distribution of Sample Proportions
10.2 Sampling Distribution for Proportions
10.3 The Central Limit Theorem
10.4 The Sampling Distribution of the Mean
10.5 How Sampling Distribution Models Work
Ethics in Action
Brief Cases Real Estate Simulation
Part 1: Proportions
Means
Case Study: Investigating the Central Limit Theorem
PART III. INFERENCE FOR DECISION MAKING
11. Confidence Intervals for Proportions
The Gallup Organization
11.1 A Confidence Interval
11.2 Margin of Error: Certainty vs. Precision
11.3 Assumptions and Conditions
11.4 Choosing the Sample Size
*11.5 A Confidence Interval for Small Samples
Ethics in Action
Technology Help: Confidence Intervals for Proportions
Brief Cases: Investment
Forecasting Demand
12. Confidence Intervals for Means
Guinness & Co.
12.1 The Sampling Distribution for the Mean
12.2 A Confidence Interval for Means
12.3 Assumptions and Conditions
12.4 Cautions About Interpreting Confidence Intervals
12.5 Sample Size
12.6 Degrees of Freedom - Why (n-1)?
Ethics in Action
Technology Help: Inference for Means
Brief Cases: Real Estate
Donor Profiles
13. Testing Hypotheses
Dow Jones Industrial Average
13.1 Hypotheses
13.2 A Trial as a Hypothesis Test
13.3 P-values
13.4 The Reasoning of Hypothesis Testing
13.5 Alternative Hypotheses
13.6 Testing Hypothesis about Means - the One
13.7 Alpha Levels and Significance
13.8 Critical Values
13.9 Confidence Intervals and Hypothesis Tests
13.10 Two Types of Errors
*13.11 Power
Ethics in Action
Technology Help
Brief Cases: Metal Production
Loyalty Program
14. Comparing Two Groups
Visa Global Organization
14.1 Comparing Two Means
14.2 The Two-Sample t-Test
14.3 Assumptions and Conditions
14.4 A Confidence Interval for the Difference Between Two Means
14.5 The Pooled t-Test
14.6 Tukey's Quick Test
14.7 Paired Data
14.8 The Paired t-Test
Ethics in Action
Technology Help: Two-Sample Methods
Brief Cases: Real Estate
Consumer Spending Patterns (Data Analysis)
15. Inference for Counts: Chi-Square Tests
SAC Capital
15.1 Goodness-of-Fit Tests
15.2 Interpreting Chi-Square Values
15.3 Examining the Residuals
15.4 The Chi-Square Test of Homogeneity
15.5 Comparing Two Proportions
15.6 Chi-Square Test of Independence
Ethics in Action
Technology Help: Chi-Square
Brief Cases: Health Insurance
Loyalty Program
Case Study
Part IV. MODELS FOR DECISION MAKING
16. Inference for Regression
Nambé Mills
16.1 The Population and the Sample
16.2 Assumptions and Conditions
16.3 The Standard Error of the Slope
16.4 A Test for the Regression Slope
16.5 A Hypothesis Test for Correlation
16.6 Standard Errors for Predicted Values
16.7 Using Confidence and Prediction Intervals
Ethics in Action
Technology Help: Regression Analysis
Brief Cases: Frozen Pizza
Global Warming?
17. Understanding Residuals
Kellogg's
17.1 Examining Residuals for Groups
17.2 Extrapolation and Prediction
17.3 Unusual and Extraordinary Observations
17.4 Working with Summary Values
17.5 Autocorrelation
17.6 Transforming (Re-expressing) Data
17.7 The Ladder of Powers
Ethics in Action
Technology Help
Brief Cases: Gross Domestic Product
Energy Sources
18. Multiple Regression
Zillow.com
18.1 The Multiple Regression Model
18.2 Interpreting Multiple Regression Coefficients
18.3 Assumptions and Conditions for the Multiple Regression Model
18.4 Testing the Multiple Regression Model
18.5 Adjusted R2, and the F-statistic
*18.6 The Logistic Regression Model
Ethics in Action
Technology Help: Regression Analysis
Brief Case: Golf Success
19. Building Multiple Regression Models
Bolliger and Mabillard
19.1 Indicator (or Dummy) Variables
19.2 Adjusting for Different Slopes-Interaction
19.3 Multiple Regression Diagnostics
19.4 Building Regression Models
19.5 Collinearity
19.6 Quadratic
Ethics in Action
Technology Help: Regression Analysis on the Computer
Brief Cases: Paralyzed Veterans of America
20. Time Series Analysis
Whole Foods Market^{®}
20.1 What is a Time-Series?
20.2 Components of a Time Series
20.3 Smoothing Methods
20.4 Summarizing Forecast Error
20.5 Autoregressive Models
20.6 Multiple Regression-based Models
20.7 Choosing a Time Series Forecasting Method
20.8 Interpreting Time Series Models: The Whole Foods Data Revisited
Ethics in Action
Technology Help
Brief Cases: Intel Corporation
Tiffany & Co.
Case Study: title to come
PART V. SELECTED TOPICS IN DECISION MAKING
21. Design and Analysis of Experiments and Observational Studies
Capital One
21.1 Observational Studies
21.2 Randomized, Comparative Experiments
21.3 The Four Principles of Experimental Design
21.4 Experimental Designs
21.5 Issues in Experimental Design
21.6 Analyzing a Completely Randomized Design in One Factor-The One-Way Analysis of Variance
21.7 Assumptions and Conditions for ANOVA
*21.8 Multiple Comparisons
21.9 ANOVA on Observational Data
21.10 Analysis of Multi Factor Designs
Ethics in Action
Technology Help
Brief Cases: A Multifactor Experiment
22. Quality Control
Sony
22.1 A Short History of Quality Control
22.2 Control Charts for Individual Observations (Run Charts)
22.3 Control Charts for Measurements: X and R Charts
22.4 Actions for Out of Control Processes
22.5 Control Charts for Attributes: p Charts and c Charts
22.6 Philosophies of Quality Control
Ethics in Action
Technology Help: Quality Control Charts
Brief Cases
23. Nonparametric Methods
i4cp
23.1 Ranks
23.2 The Wilcoxon Rank-Sum/Mann-Whitney Statistic
23.3 Kruskal-Wallace Test
23.4 Paired Data: The Wilcoxon Signed-Rank Test
*23.5 Friedman Test for a Randomized Block Design
23.6 Kendall's Tau: Measuring Monotonicity
23.7 Spearman's Rho
23.8 When Should You Use Nonparametric Methods?
Ethics in Action
Brief Cases: Real Estate Reconsidered
24. Decision Making and Risk
Data Description, Inc.
24.1 Actions, States of Nature, and Outcomes
24.2 Payoff Tables and Decision Trees
24.3 Minimizing Loss and Maximizing Gain
24.4 The Expected Value of an Action
24.5 Expected Value with Perfect Information
24.6 Decisions Made with Sample Information
24.7 Estimating Variation
24.8 Sensitivity
24.9 Simulation
24.10 Probability Trees
*24.11 Reversing the Conditioning: Bayes's Rule
24.12 More Complex Decisions
Ethics in Action
Brief Cases: Texaco-Pennzoil
Insurance Services, Revisited
25. Introduction to Data Mining
Paralyzed Veterans of America
25.1 Direct Marketing
25.2 The Data
25.3 The Goals of Data Mining
25.4 Data Mining Myths
25.5 Successful Data Mining
25.6 Data Mining Problems
25.7 Data Mining Algorithms
25.8 The Data Mining Process
25.9 Summary
Ethics in Action
Case Study
*Indicates an optional topic
Appendices
A. Answers
B. XLStat
C. Photo Acknowledgments
D. Tables and Selected Formulas
E. Index