No time to look at it right now, but note the uniform distribution is tricky. Add lower and upper limits for it.(or switch to normal) By default it goes +/- 6 (a six sigma thing?). The deviation typed in has an effect on this, but needs to be quite small. So leaving that big and defining the limits works best. (There is a std dev mathematical definition for a uniform distribution, but it sure doesn't work here. So I've never really figured out what mathcad is doing).
I'm hoping Werner can explain it. 🙂
The question is about how Mathcad uses the uniform distribution within the montecarlo built-in function. The input is the distribution (Uniform) and a standard deviation (and optional limits). (maybe std dev is the wrong term here, but it goes in the same column as the other std devs) The distribution responds to the deviation input typed (i.e. upper and lower limits move), but I'm stumped as to what the mathematical relationship actually is. So I just set the optional hard limits and I seem to get a distribution that looks appropriate - but without understanding the math maybe I'm doing something wrong. Seems like the kind of thing you're normally knowledgeable about. For examples just see my files in this thread - 3rd row in the inputs. Thanks.
Sorry, I never used the built-in Montecarlo function so I am lacking the experience here.
From the syntax in he help it looks that you will have to provide the limits-vector anyway unless all variables are normal distributed. Or does the function recognize the dist vector correctly even when the limits matrix is not provided?
OK, I played around with montecarlo and the uniform distribution and here is what a found:
If a and b are lower and upper limit of the uniform distribution, the standard deviation is sigma=(b-a)/sqrt(12).
Someone at PTC might have made an mistake as the value of sigma in montecarlo is interpreted simply as (b-a)/12.
So, if you want a uniform distribution with limits [3;5] you would provide mean value mu=4 and (the wrong) standard deviation sigma=1/6 (the true standard deviation of this distribution is 1/sqrt 3 ).
This strange effect is also present in Mathcad 15 (which is the version where DOE and montecarlo were introduced the first time if I remember correct).