Design Efficiency
In the Evaluate Design platform, D, G, and A, efficiency is reported. The descriptions of the efficiency measures use the following notation:
• X is the model matrix
• n is the number of runs in the design
• p is the number of terms, including the intercept, in the model
• 
is the relative prediction variance at the point 
. See “Relative Prediction Variance”.
• 
is the maximum relative prediction variance over the design region
D Efficiency
The efficiency of the design to that of an ideal orthogonal design in terms of the D-optimality criterion. A design is D-optimal if it minimizes the volume of the joint confidence region for the vector of regression coefficients:
G Efficiency
The efficiency of the design to that of an ideal orthogonal design in terms of the G-optimality criterion. A design is G-optimal if it minimizes the maximum prediction variance over the design region:
Letting D denote the design region,
Note: G-Efficiency is calculated using Monte Carlo sampling of the design space. Therefore, calculations for the same design might vary slightly.
A Efficiency
The efficiency of the design to that of an ideal orthogonal design in terms of the A-optimality criterion. A design is A-optimal if it minimizes the sum of the variances of the regression coefficients:
