Notice
You are now leaving support.minitab.com.
Click Continue to proceed to:
Minitab displays the criterion and indicates whether the design was selected or augmented.
For factorial designs, D-optimality is the only criterion that Minitab provides.
The number of candidate design points shows how many design points (worksheet rows) are considered in the search for the optimal design. A design point is an experimental condition or factor level combination at which responses are measured. Each point corresponds to a row in the worksheet that contains the candidate points.
The number of design points to augment/improve shows how many experimental runs are in the design before the augmentation or improvement is complete.
Use the number of design points to see the number of points in the initial design. A point is an experimental condition or factor level combination at which responses are measured. The initial design can have replicated points, so the number of design points to augment/improve can exceed the number of candidate design points.
The number of optimal design points shows how many experimental runs are in the final optimal design.
Use the number of optimal design points to see how many points are in the final design. A point is an experimental condition or factor level combination at which responses are measured. If you store the optimal design, each point corresponds to a row in the worksheet.
The list shows the letters that represent the terms in the model. Higher order terms are represented by multiple letters. For example, the first factor is A and the second factor is B. The interaction between the first two factors in the worksheet is AB. The number of terms must be less than the number of design points in the optimal design.
The degrees of freedom for all the terms in the model must be less than the number of design points in the optimal design. For terms with only continuous variables, the degrees of freedom that the terms use is the same as the number of terms. For categorical terms, the degrees of freedom depend on the number of levels for the categorical factors or process variables.
Use the results to see the terms that Minitab uses to calculate the optimality criteria. Because D-optimality depends on the terms, a design that is D-optimal for one set of terms will most likely not be D-optimal for another set of terms.
When using distance-based optimality, Minitab spreads the design points uniformly over the design space. For a response surface design, you can include all the factors or you can use a subset of the factors. For a mixture design, you must include all the components in the design. You can also add process variables for a mixture design.
For a response surface design, Minitab indicates the number of factors in the design. For a mixture design, Minitab indicates the number of components in the mixture, and the number of process variables in the design.
For example you compare the results using an all sequential selection and the results using a combination of sequential and random selection for the same design.
In these results, by trying different starting points, Minitab found a more D-optimal design by using the combination method with different initial designs.
Compare the results for the exchange method and the Fedorov method. The first set of results uses the exchange method. The second set of results uses the Fedorov method.
In these results, the algorithm found a more D-optimal design with Fedorov's method. Larger D-optimality values indicate a more optimal design.
| Condition number: | 223.585 |
|---|---|
| D-optimality (determinant of XTX): | 6.43729E+28 |
| A-optimality (trace of inv(XTX)): | 11.4062 |
| G-optimality (avg leverage/max leverage): | 0.96875 |
| V-optimality (average leverage): | 0.96875 |
| Maximum leverage: | 1 |
| Condition number: | 213.875 |
|---|---|
| D-optimality (determinant of XTX): | 8.91317E+28 |
| A-optimality (trace of inv(XTX)): | 11.1267 |
| G-optimality (avg leverage/max leverage): | 0.96875 |
| V-optimality (average leverage): | 0.96875 |
| Maximum leverage: | 1 |
The list shows the row numbers of the points in the candidate set in the order that the algorithm adds the points to the design.
Use the list so that you can identify the optimal points in the candidate set. The order corresponds to rows, not to the standard order or run order columns. The order of the points in the candidate set affects how the algorithm proceeds, so if the worksheet order changes then the sequential algorithm will most likely find a different optimal solution.
You are now leaving support.minitab.com.
Click Continue to proceed to: