Analyze Factorial Design

Two-Level Split-Plot Designs
Estimated Effects and Coefficients Table - Coefficients

  

For each term in the model, there is a coefficient. Use these coefficients to construct an equation representing the relationship between the response and the factors.

If the VIF values are all close to 1, this indicates that the predictors are not correlated. VIF values greater than 5 suggest that the regression coefficients are poorly estimated.

To use this equation, put in the coded factor values and calculate the predicted response. Because the coefficients are estimated using coded units, putting uncoded factor values into this equation would yield incorrect predictions about strength.

Example Output

Coded Coefficients

 

Term                     Effect    Coef  SE Coef  T-Value  P-Value   VIF

Constant                         50.875    0.811    62.70    0.000

Pretreatment[HTC]        -8.050  -4.025    0.811    -4.96    0.008     *

Stain                    -0.050  -0.025    0.739    -0.03    0.975  1.00

Pretreatment[HTC]*Stain   4.483   2.242    0.739     3.03    0.039  1.00

Interpretation

For the water resistance data, the VIFs are 1.00, which indicates that multicollinearity is not a problem. The regression equation is:

Resistance = 50.875 - 4.025 (Pretreatment [HTC]) - 0.025 (Stain) + 2.242 (Pretreatment [HTC] * Stain)