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6

a bit of an extended comment, Note there is no need for a delayed defintion of your pdf: pdf[r_] = Simplify[2 (Piecewise[{{0, r <= 0.59}, {1.36814, Inequality[0.59, Less, r, LessEqual, 0.7]}, {0, r > 0.7}}, Indeterminate] + Piecewise[{{0, r <= 0.7}, {1.99139, Inequality[0.7, Less, r, LessEqual, 0.85]}, {0, r > 0.85}}, ...


3

The distribution of the means of sufficiently large sets of random drawings from a given distribution (well, many of them) approaches a normal distribution with a mean equal to the mean of the given distribution and a standard deviation equal to the standard deviation of the given distribution divided by the square root of the sample size -1. Using this ...


1

You might fix the garbage you are getting with ToExpression: ratevector = Import[file, "CSV"][[1]] // ToExpression I'd consider that a bit of a hack and prefer to understand why you are getting strings in the first place though. edit I think I figured out whats going on -- It seems if your file contains tabs, ie. ...


1

It seems like Quantile doesn't work with a symbolic MultinormalDistribution. You can get an approximate numerical result for a specific {rc1, rc2, rc3, rc4} {rc1, rc2, rc3, rc4} = RandomReal[1, {4, 4}] using Quantile[ RandomVariate[ MultinormalDistribution[{0, 0, 0, 0}, Covariance[{rc1, rc2, rc3, rc4}]], 10^5], 0.01]


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using the 2d form of BinLists ( note the first dimension binspec is cooked up so there is just one bin ) list = RandomReal[{0, 1}, {20}]; binspec = {0, 1, .1}; (#[[All, 1]] & /@ First@BinLists[ MapIndexed[{First@#2, #} &, #] , {0, Length@# + 1, Length@# + 1}, binspec]) &@list {{1, 10, 20}, {6}, {7, 15}, {4}, {5, 12, 17}, {11}, ...



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