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Sep 9, 2016 8:30 AM
(1418 views)

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

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Sep 9, 2016 12:52 PM
(1946 views)

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

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Sep 9, 2016 12:52 PM
(1947 views)

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

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Sep 12, 2016 12:36 AM
(1343 views)

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

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Sep 12, 2016 12:38 AM
(1343 views)