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11

Updated with working code (tnx @rasher @mfvonh) Let’s start by importing Fisher’s classic dataset on Iris flower measurements… Fisher’s classic paper can be found here…. Needs["MultivariateStatistics"] (*Import Data*) irisData = Import["http://aima.cs.berkeley.edu/data/iris.csv", "CSV"]; plotLabels = {"Sepal.Length", "Sepal.Width", "Petal.Length", ...

8

With the data beeing data = RandomVariate[NormalDistribution[0, 1], 200]; the range of the box specified to be one sigma (approx. 68.3 %tile range) by sigma=Erf[1/Sqrt[2]] and a limit for the fences defined to be 10 % fencesLimit = 0.1 we can plot a BoxWhiskerChart using: BoxWhiskerChart[data, "Median", Method -> "BoxRange" -> (Quantile[#, ...

4

You need to square your argument: ZTest[data, 20^2, 540., "TestStatistic"] (* 2.19469 *)

2

I don't think there is anything unusual here that can't be found in the documentation. Normalization parameters in the x and y dimensions (b, and a, respectively) can be added into the model, as can the x-axis shift (parameter c). Parameter estimates are taken from a look at the original data. nlm = NonlinearModelFit[data, a Erfc[b (x + c)], {{a, 8 ...

2

I am a little unsure what the aim is here. Looking at the data only the first third of the plot could be fitted to Erfc like function. I post this, perhaps, as motivation. d = Transpose@data; ListPlot[ds = SortBy[d, #[[1]] &], Joined -> True] Extracting the part of plot I referred to: fd = ds[[1 ;; 35]] fdlp = ListPlot[fd] Now as a rather ...

1

p1 = Histogram[data, {0, 9, 1}, "Count", ChartLegends -> {"Experimental Result"}, ChartStyle -> "Pastel"]; p2 = DiscretePlot[ Length@data*PDF[PoissonDistribution[2.2766917293233084], x], {x, 0, 10}, PlotStyle -> {Red, Medium}, PlotLegends -> {"Theoretical Poisson"}]; Show[{p1, p2}, AxesLabel -> {"Time (s)", "Counts"}] ...

1

I suspect there may not be a closed form expression (I have not looked at it hard enough). If the aim is to not analytical but numerical, I post the following for illustration (apologies if not the intent of question): f[x_, y_] := NIntegrate[ Log2[t + 1] Exp[-(x - Log[t])^2/(2 y^2)]/(Sqrt[2 Pi] t y), {t, 0, Infinity}] rv[a_, b_, n_] := ...

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