This text helps business students develop competitive advantages for use in their future careers as decision makers. Students learn to build models using logic and experience, produce statistics using Excel 2007 with shortcuts, and translate results into implications for decision makers. The author emphasizes communicating results effectively in plain English and with compelling graphics in the form of memos and PowerPoints.

Statistics, from basics to sophisticated models, are illustrated with examples using real data such as students will encounter in their roles as managers. A number of examples focus on business in emerging global markets with particular emphasis on China and India. Results are linked to implications for decision making with sensitivity analyses to illustrate how alternate scenarios can be compared. Chapters include screenshots to make it easy to conduct analyses in Excel 2007 with time-saving shortcuts expected in the business world.

PivotTables and PivotCharts, used frequently in businesses, are introduced from the start. Monte Carlo simulation is introduced early, as a tool to illustrate the range of possible outcomes from decision makers' assumptions and underlying uncertainties. Model building with regression is presented as a process, adding levels of sophistication, with chapters on multicollinearity and remedies, forecasting and model validation, autocorrelation and remedies, indicator variables to represent segment differences, and seasonality, structural shifts or shocks in time series models. Special applications in market segmentation and portfolio analysis are offered, and an introduction to conjoint analysis is included. Nonlinear models are motivated with arguments of diminishing or increasing marginal response, and a chapter on logit regression models introduces models of market share or proportions.

Cynthia Fraser received her Ph.D. from The Wharton School, University of Pennsylvania, and is amember of the Marketing faculty at The McIntire School of Commerce, University of Virginia, where she teaches Quantitative Analysis I and II. Her research has appeared in a number of journals, including Decision Science, Management Science, Journal of Marketing, Journal of Consumer Research, Journal of International Business Studies, and Journal of Applied Social Psychology.

Exceptional managers know that they can create competitive advantages by basing decisions on performance response under alternative scenarios. To create these advantages, managers need to understand how to use statistics to provide information on performance response under alternative scenarios. Statistics are created to make better decisions. Statistics are essential and relevant. Statistics must be easily and quickly produced using widely available software, Excel. Then results must be translated into general business language and illustrated with compelling graphics to make them understandable and usable by decision makers. This book helps students master this process of using statistics to create competitive advantages as decision makers. Statistics are essential, relevant, easy to produce, easy to understand, valuable, and fun, when used to create competitive advantage. The Examples, Assignments, And Cases Used To Illustrate Statistics For Decision Making Come From Business Problems McIntire Corporate Sponsors and Partners, such as Rolls-Royce, Procter & Gamble, and Dell, and the industries that they do business in, provide many realistic examples. The book also features a number of examples of global business problems, including those from important emerging markets in China and India. It is exciting to see how statistics are used to improve decision making in real and important business decisions. This makes it easy to see how statistics can be used to create competitive advantages in similar applications in internships and careers. Learning Is Hands On With Excel and Shortcuts

Statistics for decision making and competitive advantage.- Describing your data.- Hypthesis tests, confidence intervals and simulation to infer population characteristics.- Quantifying the influence of performance drivers and forecasting: regression.- Marketing segmentation with descriptive statistics, inference, hypothesis tests and regression.- Finance application: portfolio analysis with a market index as a leading indicator in simple linear regression.- Association between two categorical variables: contingency analysis with chi-square.- Building multiple regression models.- Model building and forecasting with multicollinear time series.- Indicator variables.- Nonlinear multiple regression models.- Indicator interactions for structural differences or changes in response.- Logit regression for bounded responses.- Index.