Building a New Design

The Design tab of the design folio is used to define and then build an experiment design. When you build the design, its "plan" will be shown on the Data tab.

After you create the folio, follow the steps outlined next to build a design from scratch.

Tip: If you wish to use saved settings instead, choose Experiment > Import/Export > Import Template or click the

icon on the control panel. After you import the settings, you can proceed to the last step to review your settings and build the design. (See Importing/Exporting Design Settings.)

If you are working with a standard design folio, choose one design type for the experiment by clicking the Design Type header in the navigation panel on the left and choosing an option in the input panel on the right. If you're using a robust design folio, you need to select two design types (one under the Inner Array heading and another under the Outer Array heading).

When you first create a standard design folio, the current factor settings will change as required depending on the design you select. After you directly make changes to the factors, responses or additional settings, the factor settings will no longer change automatically, and the design types will be color-coded in green or red.

If a design is red, then one of the current factor settings is incompatible with it. When you select a red design type, the factor(s) it is incompatible with will become red as well. To find out why the setting is incompatible, read the design type's description. For example, the red text in the design description below says, "All factors must have exactly 2 levels," which indicates that the Carbonation factor (also shown in red) does not have the required number of levels for a Box-Behnken design.

If you are using a standard design folio, you must configure the response(s) that will be measured in the experiment by selecting the response in the navigation panel and editing its properties in the input panel. (See Adding, Removing and Editing Responses.)

For robust design folios, default response properties will be used to build the design.

You can change the number of factors by clicking the Factors heading in the navigation panel and choosing an appropriate number in the input panel. You can then define individual factors by selecting the factor in the navigation panel and editing its properties. (See Adding, Deleting and Editing Factors.)

The options available when you click the Additional Settings heading will vary depending on which design type is currently selected. To see the settings for a particular design type, click a design below.

- General Settings
- - Replicates allows you to specify the number of test units you want to observe at each combination. In other words, the number of replicates is the number of times you want to perform the complete set of runs required by the design. Using replicates helps to separate error caused by a poor model fit from error caused by natural variation (i.e., experimental error).
  - Block on Replicates allows you to choose whether you will use each replicate of the design as a block. By blocking on replicates, you can account for "nuisance factors" (i.e., effects that are not of interest to the investigator) due to conditions that differ from one replicate of the design to another. For example, if the first replicate of the design is performed on Day 1, and the second is performed on Day 2, you could account for variation in the response due to the day. This option is available only if the number of replicates in the design is greater than 1.
- Block Settings
- - Blocks allows you to specify the total number of blocks that will be used in the design. Blocks / Replicate (shown when you choose to block on replicates) allows you to specify the number of blocks that will be used per replicate. The more blocks you use in the design, the more potential nuisance factors you can account for. Note, however, that using blocks requires aliasing them with one or more effects.
    
    Select a number of blocks from the drop-down list; the options available in this list depend on the type of design and the number of factors. Using 1 block in the entire design is equivalent to not using blocks.
  - Center Points / Block allows you to specify the number of center points that will be used per block. Center points are used primarily to help you determine if there is curvature in the relationship between the factors and the response. In addition, using several center points provides an estimate of pure error.
  - Block Values allows you to enter a name for each block.
  - Block Generators will appear when you have multiple blocks. This field is used to specify the factorial interaction effects that will be aliased with that of the nuisance factor for which you are blocking the design. (See Specifying Generators.)
- Factorial Settings
- - Fraction allows you to select the number of base runs you want to use in the experiment, based on a fraction of the total number of runs that would be required for a full factorial design. For example, if your design includes five factors with two levels each, a full factorial design would require 25 = 32 base runs. For a fractional factorial design in this case, you can select to use a one-half fraction (16 runs) or a one-fourth fraction (8 runs). The total number of runs required for the experiment may be greater than the number of base runs (e.g., if you use replicates and/or center points).
  - Fraction Generators allows you to specify the factorial interaction effects that will be aliased with the main effects of particular factors. (See Specifying Generators.)

For more information on this design type, see Two Level Factorial Designs.

- General Settings
- - Replicates allows you to specify the number of test units you want to observe at each combination. In other words, the number of replicates is the number of times you want to perform the complete set of runs required by the design. Using replicates helps to separate error caused by a poor model fit from error caused by natural variation (i.e., experimental error).
  - Block on Replicates allows you to choose whether you will use each replicate of the design as a block. By blocking on replicates, you can account for "nuisance factors" (i.e., effects that are not of interest to the investigator) due to conditions that differ from one replicate of the design to another. For example, if the first replicate of the design is performed on Day 1, and the second is performed on Day 2, you could account for variation in the response due to the day. This option is available only if the number of replicates in the design is greater than 1.
- Block Settings
- - Blocks allows you to specify the total number of blocks that will be used in the design. Blocks / Replicate (shown when you choose to block on replicates) allows you to specify the number of blocks that will be used per replicate. The more blocks you use in the design, the more potential nuisance factors you can account for. Note, however, that using blocks requires aliasing them with one or more effects.
    
    Select a number of blocks from the drop-down list; the options available in this list depend on the type of design and the number of factors. Using 1 block in the entire design is equivalent to not using blocks.
  - Center Points / Block allows you to specify the number of center points that will be used per block. Center points are used primarily to help you determine if there is curvature in the relationship between the factors and the response. In addition, using several center points provides an estimate of pure error.
  - Block Values allows you to enter a name for each block.
- Placket-Burman Settings
- - Base # of Runs allows you to specify the number of runs you want to use in the experiment. The available choices are determined by the number of factors you specify and the design matrix developed by Robin L. Plackett and J. P. Burman.

For more information on this design type, see Plackett-Burman Designs.

- General Settings
- - Replicates allows you to specify the number of test units you want to observe at each combination. In other words, the number of replicates is the number of times you want to perform the complete set of runs required by the design. Using replicates helps to separate error caused by a poor model fit from error caused by natural variation (i.e., experimental error).
  - Block on Replicates allows you to choose whether you will use each replicate of the design as a block. By blocking on replicates, you can account for "nuisance factors" (i.e., effects that are not of interest to the investigator) due to conditions that differ from one replicate of the design to another. For example, if the first replicate of the design is performed on Day 1, and the second is performed on Day 2, you could account for variation in the response due to the day. This option is available only if the number of replicates in the design is greater than 1.
- Block Settings
- - Blocks allows you to specify the total number of blocks that will be used in the design. Blocks / Replicate (shown when you choose to block on replicates) allows you to specify the number of blocks that will be used per replicate. The more blocks you use in the design, the more potential nuisance factors you can account for. Note, however, that using blocks requires aliasing them with one or more effects.
    
    Select a number of blocks from the drop-down list; the options available in this list depend on the type of design and the number of factors. Using 1 block in the entire design is equivalent to not using blocks.
  - Center Points / Block allows you to specify the number of center points that will be used per block. Center points are used primarily to help you determine if there is curvature in the relationship between the factors and the response. In addition, using several center points provides an estimate of pure error.
  - Block Values allows you to enter a name for each block.
- Factorial Settings
- - Fraction allows you to select the number of base runs you want to use in the experiment, based on a fraction of the total number of runs that would be required for a full factorial design. For example, if your design includes five factors with two levels each, a full factorial design would require 25 = 32 base runs. For a fractional factorial design in this case, you can select to use a one-half fraction (16 runs) or a one-fourth fraction (8 runs). The total number of runs required for the experiment may be greater than the number of base runs (e.g., if you use replicates and/or center points).
  - Fraction Generators allows you to specify the factorial interaction effects that will be aliased with the main effects of particular factors. (See Specifying Generators.)

For more information on this design type, see General Full Factorial Designs.

- General Settings
- - Replicates allows you to specify the number of test units you want to observe at each combination. In other words, the number of replicates is the number of times you want to perform the complete set of runs required by the design. Using replicates helps to separate error caused by a poor model fit from error caused by natural variation (i.e., experimental error).
  - Block on Replicates allows you to choose whether you will use each replicate of the design as a block. By blocking on replicates, you can account for "nuisance factors" (i.e., effects that are not of interest to the investigator) due to conditions that differ from one replicate of the design to another. For example, if the first replicate of the design is performed on Day 1, and the second is performed on Day 2, you could account for variation in the response due to the day. This option is available only if the number of replicates in the design is greater than 1.
- Block Settings
- - Blocks allows you to specify the total number of blocks that will be used in the design. Blocks / Replicate (shown when you choose to block on replicates) allows you to specify the number of blocks that will be used per replicate. The more blocks you use in the design, the more potential nuisance factors you can account for. Note, however, that using blocks requires aliasing them with one or more effects.
    
    Select a number of blocks from the drop-down list; the options available in this list depend on the type of design and the number of factors. Using 1 block in the entire design is equivalent to not using blocks.
  - Center Points / Block allows you to specify the number of center points that will be used per block. Center points are used primarily to help you determine if there is curvature in the relationship between the factors and the response. In addition, using several center points provides an estimate of pure error.
  - Block Values allows you to enter a name for each block.
- Taguchi OA Settings

The options available in the Taguchi Design Type drop-down list will depend on the numbers of levels for each factor. For example, if all your factors have three levels, then you could choose the L9 (3^4) design, which uses nine runs to investigate up to 4 three-level factors. If some factors have two levels and the rest have four, then you could choose the L16 (2^9*4^2) design, which uses 16 runs to investigate up to 9 two-level factors and up to 2 four-level factors.

Click the View Alias Table icon to see the alias table for the selected Taguchi design type. The table shows what aliases result from assigning factors to particular columns in the orthogonal array.

See Taguchi OA Alias Table.

Taguchi Column Indices allows you assign factors to columns of the selected orthogonal array. No two factors can have the same index.

The Specify Interaction Terms link opens a window that helps you assign factors to columns without aliasing any main effects or any interaction terms of interest. (See Specify Interaction Terms Window.)

For more information on this design type, see Taguchi Orthogonal Array (OA) Factorial Designs.

- General Settings
- - Replicates allows you to specify the number of test units you want to observe at each combination. In other words, the number of replicates is the number of times you want to perform the complete set of runs required by the design. Using replicates helps to separate error caused by a poor model fit from error caused by natural variation (i.e., experimental error).
  - Block on Replicates allows you to choose whether you will use each replicate of the design as a block. By blocking on replicates, you can account for "nuisance factors" (i.e., effects that are not of interest to the investigator) due to conditions that differ from one replicate of the design to another. For example, if the first replicate of the design is performed on Day 1, and the second is performed on Day 2, you could account for variation in the response due to the day. This option is available only if the number of replicates in the design is greater than 1.
- Block Settings
- - Blocks allows you to specify the total number of blocks that will be used in the design. Blocks / Replicate (shown when you choose to block on replicates) allows you to specify the number of blocks that will be used per replicate. The more blocks you use in the design, the more potential nuisance factors you can account for. Note, however, that using blocks requires aliasing them with one or more effects.
    
    Select a number of blocks from the drop-down list; the options available in this list depend on the type of design and the number of factors. Using 1 block in the entire design is equivalent to not using blocks.
  - Center Points / Block allows you to specify the number of center points that will be used per block. Center points are used primarily to help you determine if there is curvature in the relationship between the factors and the response. In addition, using several center points provides an estimate of pure error.
  - Block Values allows you to enter a name for each block.
  - Block Generators will appear when you have multiple blocks. This field is used to specify the factorial interaction effects that will be aliased with that of the nuisance factor for which you are blocking the design. (See Specifying Generators.)
- Factorial Settings
- - Fraction allows you to select the number of base runs you want to use in the experiment. The options available will vary depending on the number of factors included in the design. (See Available Central Composite Designs.)
  - Fraction Generators allows you to specify the factorial interaction effects that will be aliased with the main effects of particular factors. (See Specifying Generators.)

- Central Composite Settings
  - If you select Yes in the Run axial points in separate block drop-down list, an extra block will be added to the design and used for the axial points. It will appear as the last block under the Block Values header.
  - Center points / axial block allows you to select how many center points will be added to the axial block.
  - Alpha allows you to specify the alpha value that you want to use to determine the axial points in the design. Options include:

For more information on this design type, see Central Composite Designs.

- General Settings
- - Replicates allows you to specify the number of test units you want to observe at each combination. In other words, the number of replicates is the number of times you want to perform the complete set of runs required by the design. Using replicates helps to separate error caused by a poor model fit from error caused by natural variation (i.e., experimental error).
  - Block on Replicates allows you to choose whether you will use each replicate of the design as a block. By blocking on replicates, you can account for "nuisance factors" (i.e., effects that are not of interest to the investigator) due to conditions that differ from one replicate of the design to another. For example, if the first replicate of the design is performed on Day 1, and the second is performed on Day 2, you could account for variation in the response due to the day. This option is available only if the number of replicates in the design is greater than 1.
- Block Settings
- - Blocks allows you to specify the total number of blocks that will be used in the design. Blocks / Replicate (shown when you choose to block on replicates) allows you to specify the number of blocks that will be used per replicate. The more blocks you use in the design, the more potential nuisance factors you can account for. Note, however, that using blocks requires aliasing them with one or more effects.
    
    Select a number of blocks from the drop-down list; the options available in this list depend on the type of design and the number of factors. Using 1 block in the entire design is equivalent to not using blocks.
  - Center Points / Block allows you to specify the number of center points that will be used per block. Center points are used primarily to help you determine if there is curvature in the relationship between the factors and the response. In addition, using several center points provides an estimate of pure error.
  - Block Values allows you to enter a name for each block.
- Factorial Settings
- - Fraction allows you to select the number of base runs you want to use in the experiment, based on a fraction of the total number of runs that would be required for a full factorial design. For example, if your design includes five factors with two levels each, a full factorial design would require 25 = 32 base runs. For a fractional factorial design in this case, you can select to use a one-half fraction (16 runs) or a one-fourth fraction (8 runs). The total number of runs required for the experiment may be greater than the number of base runs (e.g., if you use replicates and/or center points).
  - Fraction Generators allows you to specify the factorial interaction effects that will be aliased with the main effects of particular factors. (See Specifying Generators.)

For more information on this design type, see Box-Behnken Designs.

- General Settings
- - Replicates allows you to specify the number of test units you want to observe at each combination. In other words, the number of replicates is the number of times you want to perform the complete set of runs required by the design. Using replicates helps to separate error caused by a poor model fit from error caused by natural variation (i.e., experimental error).

For more information on this design type, see One Factor Designs.

If you wish to rename the folio, click the heading at the very top of the navigation panel and edit the text in the Name field in the input panel. Alternatively, you can right-click the folio in the project explorer and choose Rename from the shortcut menu.

You can review some properties of your new design in the Design Information area of the control panel, as shown next.

In addition to the Design Type, Responses, Replicates and Factors you chose for the design, the following calculated values are shown:

If you click any link in the first column of this table, you will be able to edit the relevant setting in the input panel. If you wish to see more detailed information about your current design settings, click the Detailed Summary link at the bottom of the table. The Design Summary will provide further information about the design's properties.

You can also use the Design Evaluation tool to help you determine whether a design with the current settings would, for example, be likely to detect the main effects and factorial interactions you are interested in. (See Design Evaluation.)

When you are ready to build the design, choose Design > General > Build Design or click the icon on the control panel.

This will create a new Data tab in the folio that will be used to enter and analyze response data.