Business Statistics for Competitive Advantage with Excel and JMP
Description
This book is the latest title of the popular Excel textbook; redesigned, while including interactive, user-friendly JMP to encourage business students to develop competitive advantages for use in their future careers. How can performance outcome drivers be identified? How can performance outcomes be forecast? Use of regression, conjoint analysis, Monte Carlo simulation provide answers and solutions for specific scenarios. Students learn to build models, produce statistics, and translate results into implications for decision makers.
The text features new and updated examples and assignments, and each chapter discusses a focal case from the business world which can be analyzed using the statistical strategies and software provided in the text. Paralleling recent interest in climate change and sustainability, new case studies concentrate on issues such as the impact of drought on business, automobile emissions, and sustainable package goods.
The book continues its coverage of inference, Monte Carlo simulation, contingency analysis, and linear and nonlinear regression. A new chapter is dedicated to conjoint analysis design and analysis, including complementary use of regression and JMP.
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Person
Cynthia Fraser has over 21 years experience, as 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.
Content
Chapter 1. Statistics for Decision Making and Competitive Advantage.- Chapter 2. Describing Your Data.- Chapter 3, Hypothesis Tests and Confidence Intervals to Infer Population Characteristics and Differences.- Chapter 4. Simulation to Infer Future Performance Levels Given Assumptions.- Chapter 5. Simple Regression.- Chapter 6. Finance Application: Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regression.- Chapter 7. Indicator Variables.- Chapter 8. Presenting Statistical Analysis Results to Management.- Chapter 9. Nonlinear Regression Models.- Chapter 10. Logit Regression for Bounded Dependent Variables.- Chapter 11. Building Multiple Regression Models.- Chapter 12. Model Building and Forecasting with Multicollinear Time Series.- Chapter 13. Association Between Two Categorical Variables: Contingency Analysis with Chi Square.- Chapter 14. Conjoint Analysis and Experimental Data.