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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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.

奇思怪想

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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TSH582

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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集合

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

BACLE

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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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