Business Statistics for Competitive Advantage with Excel 2016: Basics, Model Building, Simulation and Cases

The revised Fourth Edition of this popular textbook is redesigned with Excel 2016 to encourage business students to 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 2016 with shortcuts, and translate results into implications for decision makers.  The textbook features new examples and assignments on global markets, including cases featuring Chipotle and Costco.

Exceptional managers know that they can create competitive advantages by basing decisions on performance response under alternative scenarios, and managers need to understand how to use statistics to create such advantages.  Statistics, from basic 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.

The author emphasizes communicating results effectively in plain English and with screenshots and compelling graphics in the form of memos and PowerPoints.  Chapters include screenshots to make it easy to conduct analyses in Excel 2016.  PivotTables and PivotCharts, used frequently in business, are introduced from the start.  The Fourth Edition features Monte Carlo simulation in four 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 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.

• Cutting-edge coverage of Excel 2016 for use in business school classrooms and beyond
• New real-world examples and assignments on global markets incorporated throughout
• 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 Statistics, 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

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 business statistics.  Her research has appeared in a number of journals, including Decision Science, Management Science, Journal of Marketing, Journal of Consumer Research, Psychology and Marketing, Journal of International Business Studies, and Journal of Applied Social Psychology.

Table of Contents

Chapter 1 Statistics for Decision Making and Competitive Advantage
Chapter 2 Describing Your Data
Chapter 3 Hypothesis Tests, Confidence Intervals to Infer Population Characteristics and Differences
Chapter 4 Simulation to Infer Future Performance Levels Given Assumptions
Chapter 5 Simple Regression for Long Range Forecasts
Chapter 6 Consolidating Multiple Naïve Forecasts with Monte Carlo
Chapter 7 Presenting Statistical Analysis Results to Management
Chapter 8 Finance Application: Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regression
Chapter 9 Association Between Two Categorical Variables: Contingency Analysis with Chi Square
Chapter 10 Building Multiple Regression Models
Chapter 11 Indicator Variables
Chapter 12 Model Building and Forecasting with Multicollinear Time Series
Chapter 13 Nonlinear Multiple Regression Models
Chapter 14 Nonlinear Explanatory Multiple Regression Models