completely randomized block design: How to include the blocks in JMP?

[#1]

Hello,

Let's say I got an experimental wheat field where I want to investigate how different treatments influence my YIELD. The treatments are:

1) PLOUGH

A: ploughing at 20cm

B: ploughing at 40cm

C: no ploughing

2) FERTILIZATION

1: 100 Kg/ha

0: no fertilization

Unfortunately my field is inclined, therefore I need to add blocks (I, II, III)

Block I	A1	B1	C1	A0	B0	C0
Block II	B1	A0	A1	B0	C0	C1
Block III	C1	B0	C0	A1	A0	B1

If I go to "Fit Model" and put my YELD as Y and use MACRO -> Fullfactorial with PLOUGH and FERTILIZATION I think I can get results just as a completely randomised design. So my question is: How to include the blocks in Fit Model?

Maybe I should use "Fit Y by X" (there is the block option). But then how to include the full-factorial?

Thank you,

Matteo

[#2 · Accepted solution]

I would recommend using Custom Design.

I quickly just chose one three level categorical for Plough, one two-level categorical for Fertilizer and then chose block of size 6

Simulating that experiment gives this output. (I had to reset factor grid to show the block in the Prediction Profiler.

Hope this helps,

PS Perhaps you might want to investigate utilizing a Split-plot design

Lou

[#3]

Thank you very much Lou. That's just an excellent explantation.

[#4]

And what if I want to use repeated measure ove time within the same experiment? For example if I want to monitor CO2 efflux every month? Does somedoby have some idea?