Hi folks, I apologise for my level of unfamiliarity. I'm diving through the manuals trying to discover the tools I need, and I'm positive what I'm trying to do must be easy. But I'm just new and struggling a little bit.
I have here some data representing different bin classes of a manufacturing process, a metric scoring the value of each outcome. In this particular process each bin class has equal probability of occurring.
What I want to do is turn this into a 2nd order distribution representing the likely value of a larger run. Say, I want to take 10 results from the list using simple random sampling with replacement. Now, I will sum all the chosen results. Call this distribution G{k}, the profitability for a run of k units.
How do I turn my first order distribution into this second order distribution using jmp? I'm a bit lost in all the documentation. Like I said I'm happy to read, so if you point me to a tool within JMP or some function in the macro language I'll be able to hunt it down in the manuals.
Thanks for any guidance!
What is the goal behind the request? Are you trying to calculate some percent above a certain value (Percent out of spec)?
@pauldeen wrote:What is the goal behind the request? Are you trying to calculate some percent above a certain value (Percent out of spec)?
I want to see visually a cumulative distribution graph for a run of k product, to determine how large runs must become before we can count on profitability more than ~90% of the time. I will vary k until 90% or more of outcomes yield more value than their input cost.
I expect to need k around 10 but it would be better to develop some evidence rather than operating on a guess.
er what i meant to say: I want to develop the cumulative probability function (graphically is good enough) for this second order distribution G.
I think what you will need is to use the Resample Freq() function.
Resample Freq Generates a random selection with replacement frequency counts, suitable for use in bootstrapping. For example, it supports a second Freq Column argument, enabling it to do bootstrap samples relating to a pre-existing frequency column specified in the second argument. Resample Freq() generates a 100% resample. ResampleFreq(rate) generates a rate frequency sample. Resample(rate, column) generates a sample that is calculated by the rate multiplied by the sum of the specified column.
You can find an example in the Scripting Index
Help==>Scripting Indes
Thank you so much for the answer. I recall this term bootstrap simulation from statistics class, and I think it'll turn out to be what I need. My memory is a bit fuzzy. I'm going to have to use this toolset more so that I become more familiar.
Anyway I'll go get to reading and let you know how it turned out!
Thanks,