Business Analysis Using Regression: A Casebook

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Springer Science & Business Media, Jul 20, 2001 - Mathematics - 348 pages
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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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Contents

Class 1 Fitting Equations to Data
1
Efficiency of Cleaning Crews
7
Liquor Sales and Display Space
12
Managing Benefits Costs
23
Predicting Cellular Phone Use
29
Class 2 Assumptions in Regression Modeling
39
The Ideal Regression Model
47
Predicting Cellular Phone Use Revisited
53
Wage Discrimination
180
Timing Production Runs
189
Class 8 Summary Regression Case
199
Executive Compensation
202
Using Stepwise Regression for Prediction
220
Class 9 Comparing Many Mean Values
229
Selecting the Best Vendor
233
Headache Pain Relief
243

Efficiency of Cleaning Crews Revisited
57
Housing Prices and Crime Rates
62
Direct Mail Advertising and Sales
72
Housing Construction
78
Class 3 Prediction and confidence intervals in regression
85
Housing Construction Revisited
89
Liquor Sales and Display Space Revisited
99
Class 4 Multiple regression
105
Automobile Design
109
Class 5 Collinearity
133
Stock Prices and Market Indices
138
Improving Parcel Handling
148
Class 6 Modeling Categorical Factors with Two Levels
157
Employee Performance Study
161
Class 7 Modeling Categorical Factors with Two or More Levels
177
Analysis of Variance and Tests for Linearity
247
Class 10 Analysis of Variance with Two Factors
251
Package Design Experiment
255
Evaluating Employee Time Schedules
262
Class 11 Modeling a Categorical Response
273
The Challenger Disaster
277
Marketing Orange Juice
283
Class 12 Modeling Time Series
299
Predicting Cellular Phone Use Revisited
304
Trends in Computer Sales
316
Assignments
333
Use with Minitab
341
Index
345
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