Model Summary and Parameter Estimates

When you don't request an ANOVA table in the Curve Estimation main dialog box, summaries for each model are displayed in a single table.

The Linear model states that the expected sales is equal to 6.584 + 1.071*advertising spending. The b1 value greater than 1 suggests that you should spend as much on advertising as you can, because you'll make that investment back and more in sales. Practically, this doesn't make much sense because you know that the market has a saturation point for advertising.

The Quadratic model states that the expected sales is equal to 3.903 + 2.854*advertising spending - 0.245*squared advertising spending. The negative value for b2 means that this model suggests that past a certain point, increased advertising would actually decrease sales. More exactly, increased advertising past 2.854/(2 * 0.245) = 5.824 will decrease expected sales.

The F, df1, df2, and Sig. columns summarize the results of the F test of model fit. The significance value of the F statistic is less than 0.05 for both models, which means that the variation explained by each model is not due to chance. The R Square statistic is a better measure of the strength of relationship.

The R Square statistic is a measure of the strength of association between the observed and model-predicted values of the dependent variable. The large R Square values indicate strong relationships for both models. The R Square for the Quadratic model is larger, though it is not clear whether this is due to the Quadratic model capitalizing on chance with an extra parameter .

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