Analysis of a Screening Experiment with Two-Level Factors - Model Tab

Analyzing the Results of a 2(k-p) Experiment

Select the Model tab in the Analysis of a Screening Experiment with Two-Level Factors dialog box to access the options described here.

Include in model

Use the options in the Include in model group box to specify the effects to be included in the model. All ANOVA results, effect estimates, predicted and residual values, etc., are computed based on this model.

No interactions

When the No interactions option button is selected, the model will only contain main effects.

2-way interactions

When the 2-way interactions option button is selected, the model will include main effects and two-way interaction effects.

3-way interactions

When this option button is selected, the model will include main effects and three-way interaction effects.

Curvature check

When the Curvature check check box is selected, the model will include a new (added to the design) variable for the curvature check. Specifically, the new variable C is computed as:

C = 1	if the point is a center point
0	otherwise

Thus, the parameter estimate for this new variable pertains to the degree of curvature or non-linearity in the relationship between the factors and the dependent variable. Note that the Curvature check option is only available if the current experimental design contains center points (see also the Introductory Overview).

Ignore some effects/Effects to ignore

Select this check box or click the Effects to ignore button to display the Customized (pooled) error term dialog box, which contains a list of all factor effects and interactions in the current model. Highlight the factors or interactions that you want to ignore, that is, that you want to pool into the error term.

ANOVA error term

The error term specified in the ANOVA error term group box will be used in all tests for statistical significance and in the computation of standard errors.

SS residual

When you select the SS residual option button, the error term used for the ANOVA table and for computing the standard errors for the parameter estimates will be computed as the sum-of-squares residual for the dependent variable, after controlling for all effects in the current model.

Pure error

If some runs in the current design were replicated, select the Pure error option button to compute the variability of measurements within each unique combination of factor levels. That variability will give an indication of the random error in the measurements (e.g., due to uncontrolled factors, unreliability of the measurement instrument, etc.), because the replicated observations were taken under identical conditions (settings of factor levels). If you select the estimate of Pure error for the error term, then the ANOVA table will also include a Lack of fit test (see the Introductory Overview). This is a test of the residual variance, after controlling for all effects in the model, against the estimate of pure error. If significant, there is indication of additional significant effects, or differences between means of the design that cannot be accounted for by the parameters currently in the model. The Pure error option is only available if at least some runs in the current design were replicated (see the Introductory Overview).

Model Profiler. Click this button to display the Model Profiler, where you can run simulations based on the specified model.