Analyze Factorial Design

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

  

Use the p-values (P) in the analysis of variance table to determine which of the effects in the model are statistically significant. Typically you look at the interaction effects in the model first because a significant interaction will influence how you interpret the main effects. To use the p-value, you need to:

·    identify the p-value for the effect you want to evaluate.

·    compare this p-value to your a-level. A commonly used a-level is 0.05.

-    if the p-value is less than or equal to a, conclude that the effect is significant.

-    if the p-value is greater than a, conclude that the effect is not significant.

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 table shows the following:

·    Interaction effects: The model contains one two-way interaction. The p-value for Pretreatment [HTC]*Stain is 0.039.     

·    Main effects: The model contains two main effects. The main effect for Pretreatment is significant (P = 0.008). Take the interaction between Pretreatment and Stain into account when you interpret the main effects.