Business Analysis Using Regression

by Foster, Dean P.; Stine, Robert A.; Waterman, Richard P.

• ISBN13: 9780387983561
• ISBN10: 0387983562
• Format: Paperback
• Copyright: 1998-02-01
• Publisher: Springer Verlag

Summary

Providing an introduction to modern data analysis techniques, these casebooks can be used as the primary or secondary text for elementary business statistics courses. Statistics has the reputation of being a boring, complicated, and confusing mix of mathematical formulas accompanied by computers used to do something. This casebook nad its companion volume Business Analysis Usuing Regression change that impression by showing how statistics gives insights and ansswers interesting business questions.The material is organized into classes of related case studies that develop a single key idea of statistics. The authors begin by discussing an application that motivates the key idea. Students are then shown how to analyze a data set. The emphasis of the analysis is to answer important business questions with statistics rather than talk about statistics.Basic Business Statistics introduces ideas often not emphasized in elementary texts such as issues of robustness, the use of transformations to simplify problems, sampling bias, confounding, kernel density, quality control, and scatterplot matrices. Business Analysis with Regression includes a discussion of scatterplot smoothing, prediction intervals for new observations, collinearity, logistic regression, nonlinear models, and multiple comparisons in regression.The text includes directions for data analyses with JMP and an appendix with Minitab commands.Professors Dean P. Foster, Robert A. Stine, and Richard P. Waterman are members of the Department of Statistics at the Wharton School, University of Pennsylvania.

Table of Contents

Fitting Equations to Data

Assumptions in Regression Modeling

Prediction and Confidence Intervals in Regression

Multiple Regression

Collinearity

Modeling Categorical Factors with Two or More Levels

Summary Regression Case

Comparing Many Mean Values

Analysis of Variance with Two Factors

Modeling a Categorical Response

Modeling Time Series

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