Overview

For one-semester courses in business statistics. This text offers a streamlined presentation of Business Statistics, Third Edition, by Sharpe, De Veaux, and Velleman. Better Decisions. Better Results. Business Statistics: A First Course, 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 think analytically, prepares them to make better business decisions, and shows them how to effectively communicate results. Also available with MyStatLab (TM) MyStatLab is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts.

Full Product Details

Author:   Norean Sharpe ,  Richard De Veaux ,  Paul Velleman
Publisher:   Pearson Education (US)
Imprint:   Pearson
Edition:   3rd edition
Dimensions:   Width: 1.00cm , Height: 1.00cm , Length: 1.00cm
Weight:   1.420kg
ISBN:  

9780134182445

ISBN 10:   0134182448
Pages:   640
Publication Date:   15 January 2016
Audience:   College/higher education ,  Tertiary & Higher Education
Replaced By:   9781292058696
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

<>Preface Index of Applications Part I: Exploring and Collecting Data 1. Data and Decisions (E-Commerce) 1.1 What Are Data? 1.2 Variable Types 1.3 Data Sources: Where, How, and When Ethics in Action Technology Help: Data 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 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 R2 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 Part II: Modeling with Probability 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: Global Markets 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 Other Continuous Random Variables Ethics in Action Technology Help: Probability Calculations and Plots Brief Case: Price/Earnings and Stock Value 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 The Sampling Distribution for Proportions 9.3 A Confidence Interval for a Proportion 9.4 Margin of Error: Certainty vs. Precision 9.5 Choosing the Sample Size Ethics in Action Technology Help: Confidence Intervals for Proportions Brief Cases: Has Gold Lost Its Luster? and Forecasting Demand Case Study II: Real Estate Simulation Part III: Inference for Decision Making 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 Alpha Levels and Significance 10.7 Critical Values 10.8 Confidence Intervals and Hypothesis Tests 10.9 Two Types of Errors 10.10 Power Ethics in Action Technology Help: Hypothesis Tests Brief Cases: Metal Production, Loyalty Program, and Confidence Intervals and Hypothesis Tests 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 Gosset and the t-Distribution 11.5 A Confidence Interval for Means 11.6 Assumptions and Conditions 11.7 Testing Hypotheses about Means-the One-Sample t-Test Ethics in Action Technology Help: Inference for Means Brief Cases: Real Estate and Donor Profiles 12. Comparing Two Means (Visa Global Organization) 12.1 Comparing Two Means 12.2 The Two-Sample t-Test 12.3 Assumptions and Conditions 12.4 A Confidence Interval for the Difference Between Two Means 12.5 The Pooled t-Test 12.6 Paired Data 12.7 Paired t-Methods Ethics in Action Technology Help: Comparing Two Groups Brief Cases: Real Estate and Consumer Spending Patterns (Data Analysis) 13. Inference for Counts: Chi-Square Tests (SAC Capital) 13.1 Goodness-of-Fit Tests 13.2 Interpreting Chi-Square Values 13.3 Examining the Residuals 13.4 The Chi-Square Test of Homogeneity 13.5 Comparing Two Proportions 13.6 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 Part IV Models for Decision Making 14. Inference for Regression (Nambe Mills) 14.1 A Hypothesis Test and Confidence Interval for the Slope 14.2 Assumptions and Conditions 14.3 Standard Errors for Predicted Values 14.4 Using Confidence and Prediction Intervals Ethics in Action Technology Help: Regression Analysis Brief Cases: Frozen Pizza and Global Warming? 15. Multiple Regression (Zillow.com) 15.1 The Multiple Regression Model 15.2 Interpreting Multiple Regression Coefficients 15.3 Assumptions and Conditions for the Multiple Regression Model 15.4 Testing the Multiple Regression Model 15.5 Adjusted R2 and the F-statistic *15.6 The Logistic Regression Model Ethics in Action Technology Help: Regression Analysis Brief Case: Golf Success Part V: Selected Topics in Decision Making 16. Introduction to Data Mining (Paralyzed Veterans of America) 16.1 The Big Data Revolution 16.2 Direct Marketing 16.3 The Goals of Data Mining 16.4 Data Mining Myths 16.5 Successful Data Mining 16.6 Data Mining Problems 16.7 Data Mining Algorithms 16.8 The Data Mining Process 16.9 Summary Ethics in Action Case Study V: Marketing Experiment Appendices A. Answers B. Tables and Selected Formulas C. Photo Acknowledgments Index

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Author Information

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

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