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(R) software package and is also the author and designer of the award-winning ActivStats(R) 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