Hello,
I am displaying the input necessary for the script:
![12769_pastedImage_2.png 12769_pastedImage_2.png](https://community.jmp.com/t5/image/serverpage/image-id/3733iB1D2C24B47970AD4/image-size/medium?v=v2&px=400)
I see that if I was performing a One-Way ANOVA that I would simply place the means of each of setting of X1 in the boxes.
e.g.
Temperature (X1) : [ 90, 100, 110, 120]
I would input the average response at Temperature 90, 100, 110, 120 into the boxes. Let's say the average response was 9, 10, 11, 12.
![](data:image/png;base64,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)
However, what does the Extra Parameters represent? Would this be if I had two factors instead of a one factor ANOVA?
Thank you,
Nate