Use Analyze Factorial Design to fit a model to data collected using a full or fractional factorial design. You can fit a model with up to 127 terms.

You can also generate effects plots to help you determine which factors are important and residual plots to assess model adequacy and assumptions.

Insulation Data : Two-level factorial designs and Plackett-Burman designs

A building materials manufacturer is developing a new insulation product for construction. They would like to study four factors that are believed to impact the strength (Strength) of the insulation. The four factors are:

·    material type (Material)

·    injection temperature (InjTemp)

·    injection pressure (InjPress)

·    cooling temperature (CoolTemp)

They also recorded the temperature at the time of the strength measurement (MeasTemp), which is included as a covariate in the model.

Two-level split-plot designs

A furniture company wants to test the water resistance of the wood that it uses to make patio furniture. The company wants to test two types of wood pretreatment and two types of stain. The experimenters apply a pretreatment to each board, cut each board into 2 pieces, and then apply a stain to each piece.  The pretreatment is a hard-to-change factor.

Metal Parts Data : General full factorial design

A metals supplier is developing a new part to be incorporated into the production of a motor. They would like to study the effects of three exterior coatings (ExCoat) and three alloys (Alloy) on the corrosion resistance (Resistance) of the new part.

Data: Insulation.MTW, MetalPart.MTW, WaterResistance.MTW (available in the Sample Data folder).