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Exhibit 3.25
Summary output for product E quarter 4

variables. For the quarter 4 regression model in Exhibit 3.25, the value is larger

(0.00845293), yet there is likely to be a signiﬁcant association between X and Y.

The smaller the Signiﬁcance F the better the ﬁt.

There are many other important measures of regression ﬁt that we have not dis-

cussed for time series errors or residuals—e.g. independence or serial correlation,

homoscedasticity, and normality. These are equally important measures to those we

have discussed and deserve attention in a serious regression modeling effort, but are

beyond the scope of this chapter.

Thus far, we have used data analysis to explore and examine our data, taking

what we can from each form of analysis and adding whatever is contributed to our

overall insight. Simply because a model, such as regression, does not ﬁt our data

does not mean that our efforts have been wasted. It is still likely that we
have
gained

insight: this is not an appropriate model and there may be indicators of an alternative

to explore. It may sound odd, but often we may be as well informed by what doesn’t

work, as by what does.

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