Publication date: 08/13/2020

Examine Results

Stage 1: Main Effect Estimates

Stage 1 determines which main effects are likely to be active.

Figure 8.2 Stage 1 Report for Main Effects 

Note: The fake factors do not appear in the design or as factors in the analysis.

A two-degree-of-freedom error sum of squares is computed from the four runs corresponding to the two fake factors. Because the fake factors are, by construction, inactive, this estimate of error variance is unbiased. For each main effect, the main effects response YME is tested against this estimate. In this example, three factors, Methanol, Ethanol, and Time, have p-values smaller than the threshold value and are retained as active. For more information about the threshold values, see Stage 1 Methodology.

The variability from the three inactive factors, Propanol, Butanol, and pH, is pooled with the fake factor sum of squares to produce the five-degree-of-freedom RMSE statistic shown in Figure 8.2.

Stage 2: Even Order Effect Estimates

Stage 2 uses guided subset selection to arrive at a list of second-order effects that are likely to be active. Interactions and quadratic terms are second-order or even order effects.

Figure 8.3 Stage 2 Report for Even-Order Effects 

Because three main effects are identified as active in Stage 1, the guided subset selection procedure for active second-order effects can continue until all second-order effects are included. Because all six second-order effects are reported in Stage 2, it follows that the Stage 2 RMSE remained larger than the Stage 1 RMSE. See Stage 2 Methodology.

The two-degree-of-freedom RMSE given in the Stage 2 report is the error estimate obtained from the final subset of all six second-order effects.

Combined Results

The effects selected for the model are listed in the Combined Model Parameter Estimates report.

Figure 8.4 Combined Model Parameter Estimates Report 

The RMSE and degrees of freedom given at the bottom of the report are the usual standard least squares quantities. Use these effects as potential factors for your final model.

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