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Titlebook: Business Analysis Using Regression; A Casebook Dean P. Foster,Robert A. Stine,Richard P. Waterman Textbook 1998 Springer Science+Business M

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期刊全称Business Analysis Using Regression
期刊简称A Casebook
影响因子2023Dean P. Foster,Robert A. Stine,Richard P. Waterman
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图书封面Titlebook: Business Analysis Using Regression; A Casebook Dean P. Foster,Robert A. Stine,Richard P. Waterman Textbook 1998 Springer Science+Business M
影响因子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
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Assumptions in Regression Modeling,at are needed for tests of means and confidence intervals (as discussed in the preceding volume of cases .). For example, regression analysis assumes that the observations are independent with equal variance.
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Prediction and confidence intervals in regression,r the mean, these intervals rely on an estimate of variability known as the standard error. Although the interpretation of confidence intervals in regression is similar to that of the confidence intervals developed in ., the regression model itself is more complex and complicates the interpretation
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Collinearity,epends upon which other correlated variables also appear in the regression model. This class considers methods for understanding how the correlations among the explanatory variables affect the regression coefficients.
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Analysis of Variance with two Factors,tomers, and we would like to make sure that we choose the right combination. The problem of choosing the right combination is made more difficult because such factors often interact. In this example, the best color for one style of package might be the worst color for a different style. Two-way anal
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Modeling Time Series,pendent. Such autocorrelation has a powerful effect on a regression model, leading to a spurious impression of accuracy. In addition, several time series often show high correlation, tending to move up and down together. This correlation between series introduces multicollinearity into models using
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