nav[aria-label="Primary Navigation"] { padding: 0; & ul { list-style: none; width: 100%; display: flex; flex-direction: row; justify-content: start; align-items: start; gap: 30px; padding: 0; & li { margin: 0; } & ul li { list-style: none; } } }

How to compute coefficient of variation (CV) when have used randomized complete block design with 5 reps using JMP. When i compute it using JMP table -summary it comes 17 while calculating it manually through using error mean sum of square it comes t

manphoolfageria

File is attached please

For CV.xlsx

Accepted answers

All comments

Mark_Bailey

I do not know how you might have used Table > Summary to estimate the CV. I do not know how you might have manually calculated the CV. I cannot assess either one of these results.

I assume that you want to estimate the CV of the response. The estimate as RMSE/mean would be the best estimate. The fixed effects have been accounted for in the model sum of squares leaving the random effect alone in the RMSE.

Here is the analysis of variance using the Oneway platform:

Screen Shot 2020-02-02 at 5.53.54 AM.png

So the estimated CV is about 7%:

Screen Shot 2020-02-02 at 5.57.30 AM.png

manphoolfageria

Thank you so much

Another questions- Since I have used blocks/reps, why we not used reps in the model.

Also, should I use fit model or fit Y by X? I have analyzed the data and look the attached table and advise.

I have two more questions.docx

Mark_Bailey

I did not see any blocking structure in your data set. If you actually blocked the runs then you have to decide if their effect is fixed or random before adding to add a term to estimate this effect.

Do not enter replicate in the model. It is assumed to be the random error in the response. It is assumed to be normally distributed with constant variance. It is used to estimate the RMSE, a model-dependent estimate of the model errors. It should represent the normal variation run-to-run under complete randomization.

Both the Oneway platform and the Fit Least Squares platform can estimate the model parameters but they differ with respect to many available options. Also, if you decide to add a block effect, then you cannot use Oneway.

Mark_Bailey

You do not seem to understand the basics of linear statistical models or design of experiments from your use of terminology, for example, adding replicates as a term or referring to blocks and blocking. Your use of specific terms is confusing.

Where did you get the design? What effects were you trying to estimate? It seems as though you collected the data and then thought about how to analyze it. The optimal design is based on the intended model in the analysis.

Also, you did not state the purpose of your study. What are you trying to learn?

manphoolfageria

Thank you. We have a lot of field variation. Therefore, the design used was randomized block design where blocks/reps were made against the field slop. The randomization of the treatments were done block wise. The blocks/reps were constructed to minimize the experimental error. Now, our main interest is to determine the treatment effect. So, should it be one way ANOVA or two way?

Mark_Bailey

If I understand what you say correctly, then you might have selected a RCBD, but you ran a split-plot design. So the replicate indicates the observations from the same field?