Business Statistics

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 Professor of Statistics at Babson College, where she is also Chair of the Division of Mathematics and Science. 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 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

 

2. Data

2.1 What Are Data?

2.2 Variable Types

2.3 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. Randomness and Probability

5.1 Random Phenomena and Probability

5.2 The Non-existent 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     

 

6. Displaying and Describing Quantitative Data

6.1 Displaying Distributions                              

6.2 Shape

6.3 Center

6.4 Spread of the Distribution   

6.5 Shape, Center, and Spread–A Summary     

6.6 Five-Number Summary and Boxplots

6.7 Comparing Groups

6.8 Identifying Outliers

6.9 Standardizing

6.10 Time Series Plots

*6.11 Transforming Skewed Data

 

PART II: UNDERSTANDING DATA AND DISTRIBUTIONS

 

7. Scatterplots, Association, and Correlation

7.1 Looking at Scatterplots

7.2 Assigning Roles to Variables in Scatterplots

7.3 Understanding Correlation

*7.4 Straightening Scatterplots

7.5 Lurking Variables and Causation      

 

8. Linear Regression

8.1 The Linear Model    

8.2 Correlation and the Line

8.3 Regression to the Mean

8.4 Checking the Model

8.5 Learning More from the Residuals                            

8.6 Variation in the Model and R2

8.7 Reality Check: Is the Regression Reasonable?

 

9. Sampling Distributions and the Normal Model

9.1 Modeling the Distribution of Sample Proportions     

9.2 Simulation  

9.3 The Normal Distribution

9.4 Practice with Normal Distribution Calculations

9.5 The Sampling Distribution for Proportions

9.6 Assumptions and Conditions

9.7 The Central Limit Theorem–The Fundamental Theorem of Statistics

9.8 The Sampling Distribution of the Mean

9.9 Sample Size–Diminishing Returns

9.10 How Sampling Distribution Models Work

 

10. Confidence Intervals for Proportions

10.1 A Confidence Interval

10.2 Margin of Error: Certainty vs. Precision      

10.3 Critical Values

10.4 Assumptions and Conditions

*10.5 A Confidence Interval for Small Samples

10.6 Choosing Sample Size


11. Testing Hypotheses about Proportions

11.1 Hypotheses

11.2 A Trial as a Hypothesis Test

11.3 P-values

11.4 The Reasoning of Hypothesis Testing                    

11.5 Alternative Hypotheses

11.6 Alpha Levels and Significance

11.7 Critical Values

11.8 Confidence Intervals and Hypothesis Tests

11.9 Two Types of Errors

*11.10 Power

 

12. Confidence Intervals and Hypothesis Tests for Means

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 One-Sample t-Test

12.6 Sample Size         

12.7 Degrees of Freedom–Why (n-1)?

 

13. Comparing Two Means

13.1 Testing Differences Between 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 Tukey’s Quick Test

 

14. Paired Samples and Blocks

14.1 Paired Data

14.2 Assumptions and Conditions

14.3 The Paired t-Test

14.4 How the Paired t-Test Works

 

15. Inference for Counts: Chi-Square Tests

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

 

PART III: EXPLORING RELATIONSHIPS AMONG VARIABLES

 

16. Inference for Regression

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

 

17. Understanding Residuals

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 Linearity

17.7 Transforming (Re-expressing) Data

17.8 The Ladder of Powers

 

18. Multiple Regression

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 Multiple Regression F-statistic

*18.6 The Logistic Regression Model

 

19. Building Multiple Regression Models

19.1 Indicator (or Dummy) Variables

19.2 Adjusting for Different Slopes — Interaction Terms

19.3 Multiple Regression Diagnostics

19.4 Building Regression Models

19.5 Colinearity

19.6 Quadratic Terms

 

20. Time Series Analysis

20.1 What is a Time-Series?

20.2 Components of a Time Series

20.3 Smoothing Methods

20.4 Simple Moving Average Methods

20.5 Weighted Moving Averages

20.6 Exponential Smoothing Methods

20.7 Summarizing Forecast Error

20.8 Autoregressive Models

20.9 Random Walks

20.10 Multiple Regression-based Models

20.11 Additive and Multiplicative Models

20.12 Cyclical and Irregular Components

20.13 Forecasting with Regression-based Models

20.14 Choosing a Time Series Forecasting Method

20.15 Interpreting Time Series Models: The Whole Food Data Revisited

 

PART IV: BUILDING MODELS FOR DECISION MAKING

 

21. Probability Models

21.1 Expected Value of a Random Variable

21.2 Standard Deviation of a Random Variable

21.3 Properties of Expected Values and Variances

21.4 Discrete Probability Methods

21.5 Continuous Random Variables

 

22. Decision Making and Risk

22.1 Actions, Stats of Nature, and Outcomes

22.2 Payoff Tables and Decision Trees

22.3 Minimizing Loss and Maximizing Gain

22.4 The Expected Value of an Action

22.5 Expected Value with Perfect Information

22.6 Decisions Made with Sample Information

22.7 Estimating Variation

22.8 Sensitivity

22.9 Simulation

22.10 Probability Trees

* 22.11 Reversing the Conditioning: Babyes's Rule

22.12 More Complex Decisions

 

23. Design and Analysis of Experiments and Observational Studies

23.1 Observational Studies

23.2 Randomized, Comparative Experiments

23.3 The Four Principles of Experimental Design

23.4 Experimental Designs

23.5 Blinding and Placebos

23.6 Confounding and Lurking Variables

23.7 Analyzing a Design in One Factor - The Analysis of Variance

23.8 Assumptions and Conditions for ANOVA

*23.9 Multiple Comparisons

23.10 Analysis of Multi Factor Designs

 

24. Introduction to Data Mining

24.1 Direct Marketing

24.2 Data

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

 

*Indicates an optional topic