Design & Analysis of Taguchi Robust Design Experiments - Analyze Design Tab
Select the Analyze design tab of the Design & Analysis of Taguchi Robust Design Experiments dialog to analyze a Taguchi robust design experiment. Note that the transformation facilities available in Statistica allow you to compute all possible S/N ratios; thus, if your design does not follow the standard orthogonal array notation, or if it contains partial replications of runs, or is blocked, etc., you should use the standard design facilities available from the Startup Panel (i.e., analyze your design as a 2(k-p), screening, 3(k-p), Box-Behnken, central composite, or response surface design).
Summary box
The Summary box at the top of the dialog displays a brief explanation of the problem type specified in the Problem type box. As explained in the Introductory Overview, an important aspect of the Taguchi robust design methodology is the use of signal-to-noise (S/N) ratios. In short, you may consider a product as being of ideal quality if it responds reliably to the signals of the operator, while being immune to extraneous noise conditions. Taguchi has proposed different S/N ratios to address different types of engineering problems.
Problem type
In the Problem type box, choose the appropriate S/N ratio; a brief explanation of the respective ratio appears in the Summary box. For a more detailed description of available S/N ratios, refer to the listing in the Introductory Overview. For additional specialized ratios (e.g., applicable to dynamic engineering problems), refer to Phadke (1989, Chapter 5). Most S/N ratios require transformations of the dependent variables within each observation or run. However, note that untransformed variables can also be analyzed (choose Maximize average S/N ratio as the problem type); customized S/N ratios can also be created using the spreadsheet formulas.
Variables
Click the Variables button to display the standard variable selection dialog, in which you specify the dependent variable list and the independent variable list (list of factors in the design). Valid codes that can be used in the independent variables are successive positive integers, starting with 1 (as produced when generating the design via the Experimental Design module).