2.43.3.1 The Select Optimal Design Dialog Box

1 Supporting Information
2 Task
3 Method
4 Settings
5 Dialog Theme

Supporting Information

To open the Select Optimal Design dialog

Select Statistics: Quality Improvement: Design of Experiment from the Origin menu
Select the Select Optimal Design icon

Topics for Further Reading:

Select Optimal Design

Task

Task	It includes three options Select Optimal Design Selects the best set of experimental runs from the candidate set based on D-optimality criterion and model. It maximizes the precision of the model's parameter estimates, ensuring you capture the most valuable data possible within a strict run budget. Augment Design Adds additional runs to an existing design. Use this when you have already run some experiments and need to add more points to improve estimation precision, resolve ambiguities (e.g., add runs to de-alias effects), or extend the design space. The software treats existing runs as fixed and selects new runs to optimize the combined design. Evaluate Design Assesses the properties of an existing design (one you already have or generated previously) without generating new runs.
Number of Points in Optimal Design	Specifies the total number of experimental runs the algorithm will select. it should larger than the number of model terms (including intercept) to make the model estimable.
Model Terms	Defines the mathematical model you intend to fit to the data. The optimal design algorithm selects runs specifically to estimate these terms with minimal variance. Click the + button to open a dialog to select model terms The dialog include Generate Terms by Model Type dropdown list with following options Linear Linear +Squares Linear + Interactions Full Quafratic Add or remove the terms from the right panel and click the OK button to define the model.
Include Blocks in the Model	Adds block effects to the model.

Method

Initial Design	Specifies how the starting design is constructed before the improvement algorithm begins Generated by sequential optimization The initial design is built using a sequential (greedy) algorithm that adds points one by one, each time selecting the candidate that most improves the optimality criterion. This tends to produce a better starting point than pure random selection and often leads to faster convergence to a high-quality final design. Percentage of design points to be selected randomly A specified percentage of points in the initial design are chosen randomly from the candidate set, while the remainder may be filled sequentially or by another rule. Higher randomness increases the chance of exploring diverse regions of the design space and escaping local optima, but may start from a less efficient initial design. Number of Random Trials Specifies how many independent initial designs are generated and optimized. Random Seed Enter an integer to control the reproducibility of the random number generator used to create initial designs.
Improvement of Initial Design	Specifies the algorithm used to refine the initial design toward the optimal criterion Exchange method with number of exchange points Iteratively improves the design by adding N candidate points that maximize D-optimality, then removing N existing points that contribute least to the criterion. Fedorov's method A classical optimal design algorithm that uses a full exchange strategy, evaluating all possible point exchanges and selecting the globally best swap at each iteration. None

Initial Design

Specifies how the starting design is constructed before the improvement algorithm begins

Generated by sequential optimization
The initial design is built using a sequential (greedy) algorithm that adds points one by one, each time selecting the candidate that most improves the optimality criterion. This tends to produce a better starting point than pure random selection and often leads to faster convergence to a high-quality final design.
Percentage of design points to be selected randomly
A specified percentage of points in the initial design are chosen randomly from the candidate set, while the remainder may be filled sequentially or by another rule. Higher randomness increases the chance of exploring diverse regions of the design space and escaping local optima, but may start from a less efficient initial design.

Number of Random Trials
Specifies how many independent initial designs are generated and optimized.
Random Seed
Enter an integer to control the reproducibility of the random number generator used to create initial designs.

Improvement of Initial Design

Specifies the algorithm used to refine the initial design toward the optimal criterion

Exchange method with number of exchange points
Iteratively improves the design by adding N candidate points that maximize D-optimality, then removing N existing points that contribute least to the criterion.
Fedorov's method
A classical optimal design algorithm that uses a full exchange strategy, evaluating all possible point exchanges and selecting the globally best swap at each iteration.
None

Settings

Store Selection Indicator in Source Worksheet	Select to add new columns to the source worksheet containing the candidate set.
Improvement of Initial Design	Select to output the selected experimental runs as columns

Dialog Theme

Save and load the settings of the dialog. Use this to store frequently used factor configurations, design options, or output preferences, then recall them for future designs without re-entering all values.