Ebook: Business analysis using regression: a casebook

Preface Statistics is seldom the most eagerly anticipated course of a business student. It typically has the reputation ofbeing aboring, complicated, and confusing mix of mathematical formulas and computers. Our goal in writing this casebook and the companion volume (Basic Business Statistics) was to change that impression by showing how statistics gives insights and answers interesting business questions. Rather than dwell on underlying formulas, we show how to use statistics to answer questions. Each case study begins with a business question and concludes with an answer. Formulas appear only as needed to address the questions, and we focus on the insights into the problem provided by the mathematics. The mathematics serves a purpose. The material is organized into 12 "classes" of related case studies that develop a single, key idea of statistics. The analysis of data using statistics is seldom very straightforward, and each analysis has many nuances. Part ofthe appeal ofstatistics is this richness, this blending of substantive theories and mathematics. For a newcomer, however, this blend is too rich and they are easily overwhelmed and unable to sort out the important ideas from nuances. Although later cases in these notes suggest this complexity, we do not begin that way. Each class has one main idea, something big like standard error. We begin a class by discussing an application chosen to motivate this key concept, and introduce the necessary terminology.

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This book provides a set of case studies that can supplement any text on regression analysis. Developed at the Wharton School of the University of Pennsylvania, it is used for the second semester of a beginning statistics course for MBA students. The casebook provides the latest in data analysis techniques, and includes a discussion of JMP and Minitab commands.

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Content:
Front Matter....Pages i-xvii
Fitting Equations to Data....Pages 1-38
Assumptions in Regression Modeling....Pages 39-83
Prediction and confidence intervals in regression....Pages 85-104
Multiple regression....Pages 105-132
Collinearity....Pages 133-155
Modeling Categorical Factors with Two Levels....Pages 157-176
Modeling Categorical Factors with Two or More Levels....Pages 177-198
Summary Regression Case....Pages 199-227
Comparing Many Mean Values....Pages 229-250
Analysis of Variance with two Factors....Pages 251-271
Modeling a Categorical Response....Pages 273-298
Modeling Time Series....Pages 299-332
Back Matter....Pages 333-348

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Content:
Front Matter....Pages i-xvii
Fitting Equations to Data....Pages 1-38
Assumptions in Regression Modeling....Pages 39-83
Prediction and confidence intervals in regression....Pages 85-104
Multiple regression....Pages 105-132
Collinearity....Pages 133-155
Modeling Categorical Factors with Two Levels....Pages 157-176
Modeling Categorical Factors with Two or More Levels....Pages 177-198
Summary Regression Case....Pages 199-227
Comparing Many Mean Values....Pages 229-250
Analysis of Variance with two Factors....Pages 251-271
Modeling a Categorical Response....Pages 273-298
Modeling Time Series....Pages 299-332
Back Matter....Pages 333-348
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