Model Browser User's Guide

The Design Editor

The Design Editor provides prebuilt standard designs to allow a user with a minimal knowledge of the subject to quickly create experiments. You can apply engineering knowledge to define variable ranges and apply constraints to exclude impractical points. You can increase modeling sophistication by altering optimality criteria, forcing or removing specific design points, and optimally augmenting existing designs with additional points.

There is a step-by-step guide to using the Design Editor in the Design of Experiment Tutorial.

The functionality in the Design Editor is covered in the following sections:

Design Styles

Design Editor Displays

The Design Tree

Display Options

Adding Design Points

Fixing, Deleting, and Sorting Design Points

Saving and Importing Designs

Creating a Classical Design

Creating a Space Filling Design

Creating an Optimal Design

Applying Constraints

You can design experiments at both stages, local and global. You can invoke the Design Editor in several ways from the test plan level:

1. First you must select the stage (first/local or second/global) for which you want to design an experiment. Click the appropriate Model block in the test plan diagram.
2. Right-click the model block and select Design Experiment.

1. Alternatively, click the Design Experiment toolbar icon .

   You can also select TestPlan -> Design Experiment.

For an existing design, View -> Design Data also launches the Design Editor (also in the right-click menu on each Model block). This shows the selected data as a design.

Design Styles

The Design Editor provides the interface for building experimental designs. You can make three different styles of design: Classical, Space-Filling, and Optimal.

Optimal designs are best for cases with high system knowledge, where previous studies have given confidence in the best type of model to be fitted, and the constraints of the system are well understood. See Creating an Optimal Design.

Space-filling designs are better when there is low system knowledge. In cases where you are not sure what type of model is appropriate, and the constraints are uncertain, space-filling designs collect data in such as a way as to maximize coverage of the factors' ranges as quickly as possible. See Creating a Space Filling Design.

Classical designs (including full factorial) are very well researched and are suitable for simple regions (hypercube or sphere). See Creating a Classical Design.

Any design can be augmented by optimally adding points. Working in this way allows new experiments to enhance the original, rather than simply being a second attempt to gain the necessary knowledge. See Adding Design Points.

Evaluating Models in the Workspace

Design Editor Displays