Experimental Design

Experimental design techniques are designed to discover what factors or interactions have a significant impact on a response variable.  Our SPC for Excel provides an easy-to-use design of experiments (DOE) methodology in the Excel environment you know. 

The software contains two-level full factorial designs (up to 7 factors), fractional factorial designs (29 different designs, up to 15 factors), and Plackett-Burman designs (up to 27 factors). 

The output includes the ANOVA table for the factors and interactions, the model in both coded and uncoded format, normal and half-normal plot of effects and much more.  A complete list of features is given below.

Experimental Design

Watch a Video Highlighting SPC for Excel's Experimental Design Capabilities!

Join those in over 80 countries using SPC for Excel!

Experimental Design Features

Full factorials

Fractional factorials

Plackett- Burman

Design table analysis

ANOVA Table for factors and interactions

ANOVA for model

Model for coded and actual factors

Design statistics


Standard deviation

Coefficient of variation

R square

Adjusted R square


R square prediction

Normal plot of effects

Half-normal plot of effects

Effects charts

Two factor charts

Residuals analysis



Standardized residuals

Internally studentized residuals

Externally studentized residuals


Cook's distance

Potential outliers in red

Residuals charts (raw, standardized, internally or externally studentized residuals)

Normal plot

Versus predicted values

Versus actual run number

Other charts

Predicted vs actual

DFFITS, Cook's distance and leverage versus actual run number

DOE optimization chart

Easily select effects to include/exclude in the analysis

p values < 0.05 in red