Factorial Reliability Designs: Analysis Results

This item is available for all designs with at least two factors. It describes the alias structure for the design, taking into account only the terms you've selected to include in the analysis. Together with your engineering knowledge, you can use this information to help determine whether any important interaction information was lost due to aliasing. When aliased terms exist, the following areas will be shown:

• Terms selected to be in the model lists all the terms that are considered for inclusion in the regression model (i.e., the selections in the Select Terms window).

• Terms included in the model lists all the selected terms that are included in the model. The alias structure determines which terms are excluded.

• Alias Structure lists the aliased effects based on the selected terms. For example, A • B = A • B + C • D means the interaction effect A • B is aliased because it is indistinguishable from effect C • D. Therefore, the model cannot include both interaction terms; it will include only one (e.g., A • B).

This shows the variance/covariance matrix, which is available for one factor R-DOE designs and all other designs with two or more factors. The diagonal elements in this matrix are used to calculate the coefficients in the MLE or Regression Information table.

This table is available for one factor R-DOE designs and all other designs with two or more factors. It displays various analysis results for each run and highlights significant values. The following columns are included:

• Run Order is the randomized order, generated by DOE++, in which it is recommended to perform the runs to avoid biased results. Note that any changes made to the Run Order column on the Data tab will be reflected here.

• Standard Order is the basic order of runs, as specified in the design type, without randomization. Note that any changes made to the Standard Order column on the Data tab will be reflected here.

• Actual Value (Y) is the observed response value for the run, as entered in the response column on the Data tab.

• Predicted Value (YF) is the response value predicted by the model given the factor settings used in the run.

• Residual (or "regular residual") is the difference between the actual value (Y) and the predicted value (YF) for the run.

• Standardized Residual is the regular residual for the run divided by the constant standard deviation across all runs.

• Studentized Residual is the regular residual for the run divided by an estimate of its standard deviation.

• External Studentized Residual is the regular residual for the run divided by an estimate of its standard deviation, where the run in question is omitted from the estimation.

• Leverage is a measure of how much the run influences the predicted values of the model, stated as a value between 0 and 1, where 1 indicates that the actual response value of the run is exactly equal to the predicted value (i.e. the predicted value is completely dependent upon the observed value).

• Cook’s Distance is a measure of how much the output is predicted to change if the run is deleted from the analysis.

Values that are considered to be significant, or outliers, are displayed in red. For the residual columns, significant or critical values are those that fall outside the residual’s upper or lower bounds, calculated based on the specified alpha (risk) value.

The ReliaWiki resource portal has more information on how significant values are determined for the Leverage and Cook's Distance columns at: http://ReliaWiki.org/index.php/Multiple_Linear_Regression_Analysis.

This table shows the predicted response values for the given factor levels. It includes the following columns:

• Effect is the main effect or interaction used to predict the response. The coefficients for effects not used in the prediction are set to zero.

• Level is the combination of factor levels used to predict the response.

• Mean is the predicted response value.