## Business Analysis Using Regression: A CasebookPreface 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 |

345 | |

### Other editions - View all

Business Analysis Using Regression: A Casebook Robert A. Stine,Dean P. Foster,Richard P. Waterman Limited preview - 2012 |

Business Analysis Using Regression: A Casebook Dean P. Foster,Robert A. Stine,Richard P. Waterman No preview available - 1997 |

### Common terms and phrases

analysis of variance anova assumptions autocorrelation categorical factor categorical variable Citrus Hill Class collinearity comparison conﬁdence intervals correlation Crime Rate data set Display Feet equation Error t Ratio Estimate Std Error Estimates Term Estimate example ﬁgure ﬁnd ﬁrm ﬁrst ﬁt ﬁtted line ﬁtted model ﬁtting groups Horsepower House Price inﬂuence inﬂuential interaction Intercept interpretation least squares leverage plot Leverage Residuals Linear Fit Log 10 Sales Log 10 Tot logistic regression managers Mean of Response Mean Square Error Minitab multiple regression nonlinear Normal Quantile Plot Number of Crews Observations outlier output p-value Parameter Estimates Term prediction intervals predictors Prob>F Proﬁt Ratio Prob>|t regression analysis regression coefﬁcient regression line regression model residual plot Root Mean Square Salary scatterplot scatterplot matrix signiﬁcant signiﬁcantly simple regression slope Sq_Feet standard error stepwise regression Subscribers Sum of Squares t-test Term Estimate Std Tot Comp transformation variation Vendor Weight Weight(lb zero