In the Example of selecting a D-optimal response surface design, a materials scientist selects a subset of 20 design points from a candidate set of 30 points.

In this example, the materials scientist wants to determine how reducing the model changes the optimality.
###### Note

Remember that a model that is D-optimal for a given model only.

- Open the sample data, CrystalGrowth_optimal_design.MTW.
- Choose .
- Choose Evaluate design, then enter
`OptPoint`in the box. - Click Terms.
- From the Include the following terms drop-down list, select Linear.
- Click OK in each dialog box.

The output contains several components, as follows:

- Number of experimental runs
- This design has 20 experimental runs.
- Model terms
- D-optimal designs depend on the specified model. In these results, the terms include the linear terms that you chose in the Terms sub-dialog box. The terms are as follows:
- Block A B C D

- Experimental runs in the order that they were chosen
- The numbers shown identify the row of the experimental run in the original worksheet.
- Statistics
- You can use optimality metrics to compare designs, but remember that the optimality of a given D-optimal design is model dependent. That is, optimality is defined for a fixed design size and for a particular model. For instance, when comparing designs, larger D-optimality are better, but smaller A-optimality values better.

Evaluation of Specified Response Surface Design

Number of design points in optimal design: 20

Model terms: Block, A, B, C, D

Row number of selected design points: 1, 3, 4, 6, 8, 9, 10, 13, 15, 16, 17, 19, 22, 23, 24,

25, 26, 27, 28, 30

25, 26, 27, 28, 30

Condition number: | 1.43109 |
---|---|

D-optimality (determinant of XTX): | 47267840 |

A-optimality (trace of inv(XTX)): | 0.320581 |

G-optimality (avg leverage/max leverage): | 0.871492 |

V-optimality (average leverage): | 0.3 |

Maximum leverage: | 0.344237 |