Business Statistics for Competitive Advantage with Excel 2013

This book covers the full range of Excel properties and utilities for business stats, including time saving shortcuts, communicated very clearly with concise tables and screen shots. Coverage includes statistical analysis, model building, and simulation.

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. This updated edition of the popular 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 2013 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 emerging markets in Latin America, 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 2013 with time-saving shortcuts expected in the business world.PivotTables and PivotCharts, used frequently in businesses, are introduced from the start. The Third Edition features Monte Carlo simulation in three chapters, 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 inmarket 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.

Focuses on statistical analysis, model building, simulation, sensitivity analysis and translation of results to improve business decisions Covers the full gamut of Excel properties and utilities for Business Stats, including time saving shortcuts communicated very clearly with concise tables and screen shots Statistical Analyses are translated into concise Business English Applications that are taken from actual Business problems Includes supplementary material: sn.pub/extras Request lecturer material: sn.pub/lecturer-material

Autorentext

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.

Inhalt
Statistics for Decision Making and Competitive Advantage.- Describing Your Data Chapter.- Hypothesis Tests, Confidence Intervals, and Simulation to Infer Population Characteristics and Differences.- Quantifying the Influence of Performance Drivers and Forecasting: Regression.- Market Simulation and 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 Segment Differences or Changes in Response.- Logit Regression for Bounded Responses.- Index.