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Package consists of 032192147X/ 9780321921475 - MyStatLab for Business Statistics -- Glue-In Access Card
0321929713/ 0321929713 / 9780321929716 - MyStatLab for Business Statistics Sticker 0321925831/ 9780321925831 - Business Statistics, 3/e 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 Notes
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 prTable 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 R 2 |
| 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 |
