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 introductory business statistics courses. 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.
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PART I: EXPLORING AND COLLECTING DATA 1. Statistics and Variation 2. Data2.1 What Are Data? 2.2 Variable Types 2.3 Where, How, and When 3. Surveys and Sampling3.1 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 Data4.1 The Three Rules of Data Analysis 4.2 Frequency Tables 4.3 Charts 4.4 Contingency Tables 5. Randomness and Probability5.1 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 Data6.1 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 Regression8.1 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 Model9.1 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 Proportions10.1 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 Proportions11.1 Hypotheses 11.2 A Trial as a Hypothesis Test11.3 P-values 11.4 The Reasoning of Hypothesis Testing 11.5 Alternative Hypotheses11.6 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 Means12.1 The Sampling Distribution for the Mean 12.2 A Confidence Interval for Means 12.3 Assumptions and Conditions 12.4 Cautions About Interpreting Confidence Intervals12.5 One-Sample t-Test 12.6 Sample Size 12.7 Degrees of Freedom-Why (n-1)? 13. Comparing Two Means13.1 Testing Differences Between Two Means13.2 The Two-Sample t-test 13.3 Assumptions and Conditions *13.4 A Confidence Interval for the Difference Between Two Means13.5 The Pooled t-test *13.6 Tukey's Quick Test 14. Paired Samples and Blocks14.1 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 Tests15.1 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 Regression16.1 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 Residuals17.1 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 Regression18.1 The Multiple Regression Model18.2 Interpreting Multiple Regression Coefficients18.3 Assumptions and Conditions for the Multiple Regression Model18.4 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 Models19.1 Indicator (or Dummy) Variables19.2 Adjusting for Different Slopes - Interaction Terms 19.3 Multiple Regression Diagnostics 19.4 Building Regression Models19.5 Colinearity19.6 Quadratic Terms 20. Time Series Analysis20.1 What is a Time-Series?20.2 Components of a Time Series20.3 Smoothing Methods20.4 Simple Moving Average Methods20.5 Weighted Moving Averages20.6 Exponential Smoothing Methods20.7 Summarizing Forecast Error20.8 Autoregressive Models20.9 Random Walks20.10 Multiple Regression-based Models20.11 Additive and Multiplicative Models20.12 Cyclical and Irregular Components20.13 Forecasting with Regression-based Models20.14 Choosing a Time Series Forecasting Method20.15 Interpreting Time Series Models: The Whole Food Data Revisited PART IV: BUILDING MODELS FOR DECISION MAKING 21. Probability Models21.1 Expected Value of a Random Variable21.2 Standard Deviation of a Random Variable21.3 Properties of Expected Values and Variances21.4 Discrete Probability Methods21.5 Continuous Random Variables 22. Decision Making and Risk22.1 Actions, Stats of Nature, and Outcomes22.2 Payoff Tables and Decision Trees22.3 Minimizing Loss and Maximizing Gain22.4 The Expected Value of an Action 22.5 Expected Value with Perfect Information22.6 Decisions Made with Sample Information22.7 Estimating Variation22.8 Sensitivity22.9 Simulation22.10 Probability Trees* 22.11 Reversing the Conditioning: Babyes's Rule22.12 More Complex Decisions 23. Design and Analysis of Experiments and Observational Studies23.1 Observational Studies23.2 Randomized, Comparative Experiments23.3 The Four Principles of Experimental Design23.4 Experimental Designs23.5 Blinding and Placebos23.6 Confounding and Lurking Variables23.7 Analyzing a Design in One Factor - The Analysis of Variance23.8 Assumptions and Conditions for ANOVA*23.9 Multiple Comparisons23.10 Analysis of Multi Factor Designs 24. Introduction to Data Mining24.1 Direct Marketing24.2 Data24.3 The Goals of Data Mining24.4 Data Mining Myths24.5 Successful Data Mining24.6 Data Mining Problems24.7 Data Mining Algorithms24.8 The Data Mining Process24.9 Summary *Indicates an optional topic
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Produktdetaljer

ISBN
9780321426598
Publisert
2009-02-28
Utgiver
Vendor
Pearson
Høyde
276 mm
Bredde
216 mm
Aldersnivå
05, U
Språk
Product language
Engelsk
Format
Product format
Kombinasjonsprodukt
Antall sider
872

Biographical note

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).