Business Statistics for Competitive Advantage with Excel 2013: Basics, Model Building, Simulation and Cases
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 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.
What people are saying - Write a review
Chapter 3 Hypothesis Tests Confidence Intervals to Infer Population
Chapter 4 Simulation to Infer Future Performance Levels Given Assumptions
Chapter 6 Naïve Forecasting with Regression
Chapter 7 Marketing Segmentation with Descriptive Statistics Inference Hypothesis Tests and Regression
Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regression
Chapter 10 Building Multiple Regression Models
Chapter 11 Model Building and Forecasting with Multicollinear Time Series
Chapter 12 Indicator Variables
Chapter 13 Nonlinear Multiple Regression Models
Chapter 14 Sensitivity Analysis with Nonlinear Multiple Regression Models
Contingency Analysis with Chi Square