Statistics

Professors Norean Sharpe (Babson College), Dick De Veaux (Williams College), and Paul Velleman (Cornell University) have teamed up to provide an innovative new textbook for the undergraduate introductory business statistics course. These authors have taught at the finest business schools and draw on their consulting experience at leading companies to show students how statistical thinking is vital to modern decision making.

 

Managers make better business decisions when they understand statistics, and Business Statistics gives students the statistical tools and understanding to take them from the classroom to the boardroom. Hundreds of examples are based on current events and timely business topics. Short, accessible chapters allow for flexible coverage of important topics, while the conversational writing style maintains student interest and improves understanding.

 

Business Statistics includes Guided Examples that feature the authors’ signature Plan/Do/Report problem-solving method. Each worked example shows students how to clearly define the business decision to be made and plan which method to use, do the calculations and make the graphical displays, and finally report their findings, often in the form of a business memo. Every chapter reminds students of What Can Go Wrong and teaches them how to avoid making common statistical mistakes.

Volume II contains chapters 16—24 of the main text.

• Chapter Openers present a statistical issue in a managerial setting from a well-known company. These scenarios use real data in the context of business disciplines such as marketing, finance, or economics.
• Plan/Do/Report Guided Examples provide a model to help students approach and solve any business statistics problem. Reports are frequently presented in the form of a business memo, helping students become familiar with framing and communicating results in a business setting.
• A focus on checking assumptions and conditions emphasizes the need to verify assumptions when using statistical procedures. This focus is reiterated throughout the examples and exercises.
• Emphasis on graphing and exploring data. The consistent emphasis on the importance of displaying data is evident from the first chapters on understanding data right through to the complex model-building chapters at the end.
• The flexible topic coverage features short, modular chapters to accommodate any course objective or syllabus.
• Real data is used throughout the book in exercises, examples, and applications. Hundreds of motivating examples are based on current events and well-known companies. Students learn from the authors’ consulting experience and see how statistical thinking is vital to modern business decision making. This book follows the GAISE Guidelines, using real data and emphasizing real-world interpretations of analyses as often as possible.
• Math Boxes show the mathematical underpinnings—proofs, derivations, and justifications—of statistical methods and concepts. These boxes are set apart from the main narrative to avoid interrupting the explanation of the topic at hand.
• What Can Go Wrong? sections near the end of each chapter prepare students with the tools to detect common statistical errors and offer practice in debunking misuses of statistics.
• By Hand Boxes guide students on how to compute and arrive at solutions by hand, without the aid of technology. These optional discussions distill and explain formulas to help students through the calculation of a worked example.
• Just Checking questions within sections ask students to stop and think about what they’ve just read. Designed to check student understanding, these questions involve little calculation. Answers are provided at the end of the chapter so students can easily check their work.
• Ethics in Action vignettes in every chapter illustrate the judgment needed in statistical analysis. Students learn to identify ethically challenging issues and to propose ethically and statistically sound solutions. Questions are included for study and reflection.
• What Have We Learned? sections at the end of each chapter provide a summary and overview of important new concepts discussed, define new terms, and list the skills that students should have acquired from reading the chapter.
• Technology Help chapter sections often include annotated examples and offer guidance on using the most common statistics packages (Excel^®, MINITAB^®, JMP^®, Data Desk, and SPSS^®) to practice concepts in the chapters and get started with the technology of their choice.
• Mini Case Study Projects at the end of each chapter use real data and ask students to investigate a question or make a business decision. Students are asked to define the objective, plan the process, complete the analysis, and report their conclusion. Data for these projects are available on the included CD-ROM and the companion website, and are formatted for multiple software programs.
• Exercises within a set progress in difficulty and complexity. Generally, they start with a straightforward application of the chapter ideas. Next, they tackle larger problems but are broken into several parts to guide students through the logic of a complete analysis. Finally, students are asked to synthesize and incorporate their own ideas. Some of more challenging exercises would be ideal for group projects. Large data sets are provided on the accompanying CD-ROM and the companion website.

Volume II contains chapters 16—24 of the main text.

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 Guided Example: Housing Prices

18.5 Testing the Multiple Regression Model

*18.6 Relationship between F and R2

*18.7 The Logistic Regression Model

 

19. Building Multiple Regression Models

19.1 Indicator Variables

19.2 Adjusting for Different Slopes – Interaction Terms

19.3 Multiple Regression Diagnostics

19.4 Building Regression Models

19.5 Guided Example: Roller Coaster Speeds

19.6 Colinearity

 

20. Time Series Analysis

20.1 What is a Time-Series?

20.2 Components of a Time Series

20.3 Forecasting

20.4 Smoothing Models

20.5 Measuring Forecast Error

20.6 Seasonal Regression Models

 

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 Continuous Random Variables

21.5 Probability Models

 

22. Decision Making and Risk

22.1 Alternative Decisions

22.2 Measuring Risk

22.3 Decision Trees

22.4 Reversing the Conditioning

*22.5 Bayes’s Rule

 

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 Types of 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 Goals of Data Mining

24.4 Data Mining Myths

24.5 Challenges of Data Mining

24.6 Data Mining Algorithms

24.7 Building a Predictive Model

24.8 The Data Mining Process

 

*Indicates an optional topic

Intro to Business Statistics - One or Two Term (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).

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Pearson Higher Education offers special pricing when you choose to package your text with other student resources. If you're interested in creating a cost-saving package for your students contact your Pearson Higher Education representative.