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7

First I create a set of data to simulate yours. data = RandomVariate[ExponentialDistribution[1], 10^4]; Now you can take advantage of the EmpiricalDistribution function to define a model-free distribution based on your data. edist = EmpiricalDistribution[data]; The core of what you are asking for is to obtain a TransformedDistribution, i.e starting ...


6

I tried to use the above mentioned codes for plotting error ellipses for 2D-Data. However, I did not get the anticipated results, because an error occured when Mathematica tried to solve the equality in the function Counterplot for my data. I found another solution based on the explicit calculation of the ellipse by means of covariance analysis. The ellipse ...


6

Plot[PDF[WeibullDistribution[2, 3.5], x], {x, 0, 20}] data = {0.4, 0.7, 0.4, 0.8, 0.7, 0.7, 0.3, 0.1, 0.2, 0.3, 0.1, 0.7, 0.4, 0.7, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.3, 0.9, 0.4, 0.4, 0.4, 0.4, 0.7, 0.1, 0.7, 0.7, 0.7, 0.7, 0.5, 0.7, 0.7, 0.4, 0.7, 0.5, 0.3, 0.9}; Show[ Histogram[data, Automatic, "PDF"], Plot[Tooltip[PDF[#, x], #], {x, ...


5

Of course, you can Resterize and Export as it was pointed in comments. However let us do something less trivial. I propose to reduce the number of points and don't lose benefits of the vector format. I don't wont to drink the coffee at evening so I use smaller number of points data = RandomVariate[NormalDistribution[], {100000}]; p = QuantilePlot[data, ...


3

The file size is considerably smaller (factor of 25+ with 300,000 data points) with use of the QuantilePlot option Joined->True data = RandomVariate[NormalDistribution[], {300000}]; fbc1 = FileByteCount[Export[ "/Users/hanlonr/Desktop/Mma Temp/qPlot1.pdf", QuantilePlot[data, data]]] // Timing {15.540274, 5528588} fbc2 = FileByteCount[ ...


3

edistdata = Table[{x, CDF[EmpiricalDistribution[R], x]}, {x, R}]; cdfw[a_, b_, x_] := Simplify[CDF[WeibullDistribution[a, b], x], x > 0]; cdfe[a_, x_] := Simplify[CDF[ExponentialDistribution[a], x], x > 0]; cdfp[a_, b_, x_] := Simplify[CDF[ParetoDistribution[a, b], x], x > 0]; nlmw = NonlinearModelFit[edistdata, cdfw[a, b, x], {a, b}, x]; nlme = ...


2

Clear[custom]; When defining the custom distribution, include the parameter assumptions required for the custom distribution to be a valid distribution. custom[a_, b_] = ProbabilityDistribution[ (a/b) ((x/b)^(a - 1)) E^-(x/b)^a, {x, 0, Infinity}, Assumptions -> a > 0 && b > 0]; DistributionParameterAssumptions[custom[a, b]] a ...


2

Needs["MultivariateStatistics`"] data = Table[RandomVariate[BinormalDistribution[{50, 50}, {5, 7}, .8]], {1000}]; Show[ ListPlot[data, Epilog -> { Text[Framed["1.00", Background -> LightBlue], {45, 25}], Text[Framed["0.98", Background -> LightBlue], {50, 33}], Text[Framed["0.90", Background -> LightBlue], {55, 43}] }, ...


2

FindDistributionParameters and EstimatedDistribution do not provide information to construct confidence intervals conveniently. A possible approach is to use NonlinearModelFit using the empirical cumulative distribution of the data as input and the CDF of WeibullDistribution as the model to be estimated. edistdata = Table[{x, CDF[EmpiricalDistribution[W], ...


1

The last example in the documentation for the ANOVA package ANOVA tutorial: In your example, Model-> Duncan, {4, 5} means (1) you have set the option PostTests->Duncan in your code, and (2) groups 4 and 5 are significantly different at the 100 x % significance level where x the significance level you have specified as the value of the ...



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