Changing Design Settings

After building a design (i.e., creating a Data tab in the design folio), you may wish to change some of its settings (e.g., the number of replicates that are used, or the number of factors). There are several ways to do this:

In most cases, you can return to the Design tab of the folio, change the settings and then rebuild the design by choosing Design > General > Build Design.

In the window that appears, select whether you wish to create a new folio for the modified design, or whether you wish to overwrite the experiment plan on the Data tab. If you select the Maintain response data check box, the response data from the prior design will be included in the modified design.

Note that when you modify the experiment using the Design tab, two design summaries will become available under the Design Summary heading. The Current Settings summary shows the settings that are currently selected on the Design tab. The Prior Settings summary shows the settings that were originally used to build the experiment on the Data tab.

- If, after changing the settings on the Design tab, you want to revert to the settings that were originally used, choose Design > General > Undo Changes.

- You can also choose Design > General > Clear Settings to put the Design tab in its original state (i.e., using default settings that change depending on the design type you choose).

If you wish to make changes that aren't possible with the Design tab settings, you can customize the experiment by:
- Manually changing any of the values (e.g., factor levels) that are shown on the Data tab. To do this, simply click inside a cell, change the value and press ENTER.
- Using one of the following features to modify the experiment automatically.
- - Use the augment design feature to add new blank rows to the Data tab, or to add replicates of the entire design.
  - Use the fold design feature to add a replicate to the design with runs that use the opposite levels for all the factors you specify.
  - Use the tool for creating an optimal design. The new design will have fewer runs, and when creating the design the software can consider the number of runs you wish to use, the effects you wish to investigate and which factor level combinations you wish to use in the experiment.

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