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Mathematics - Probability Theory and Stochastic Processes | Business Statistics for Competitive Advantage with Excel 2007 - Basics, Model Building and Cases

Business Statistics for Competitive Advantage with Excel 2007

Basics, Model Building and Cases

Fraser, Cynthia

2009, XVIII, 410p.

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  • To get the supplementary files: https://blackboard.comm.virginia.edu UserLogin Username: BSCAuser Password: BSCAuser Courses in which you are enrolled: BSCA Course Documents
  • Basic statistics coverage is streamlined and modeling constitutes more than half of the content
  • Results of statistical analysis are translated into business English Applications are taken from real business problems
  • Analyses are shown in Excel and Data Desk's DDXL add-in

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 a member 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.

Content Level » Graduate

Keywords » Descriptive statistics - Excel - Monte Carlo Simulation - Multiple Regression - Time series - linear optimization - linear regression - modeling - simulation

Related subjects » Business, Economics & Finance - Econometrics / Statistics - Marketing - Probability Theory and Stochastic Processes - Quantitative Finance

Table of contents 

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.

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