# Tag Info

1

This is an extension of @BrettChampion 's answer, in case you have 2D information already pre-counted or weighted. Haven't yet added all the bells and whistles to propagate the options yet, but it works. It should give the same output as HistogramList. You have to specify the bins yourself at the moment, but the input is the same as in the previous ...

2

f = DynamicModule[{col = Black}, DynamicWrapper[Dynamic@Style[#, col], If[CurrentValue["MouseOver"], col = Red; pos = {#2, #3}, col = Black; pos = {}]]] &; With[{d1 = Rest@data}, ListPlot[{{#2, #3}} & @@@ d1, BaseStyle -> {PointSize@.02, 15, Bold}, ...

1

As mentioned, a list of one single item has no sample variance but it does have a population variance. The population variance has to be defined, though : PopulationVariance[list_] := Mean[(list - Mean[list])^2]; PopulationVariance[{10}] (* 0 *)

1

You are confusing the PDF of the distribution with numbers that are distributed according to it. Skewness works on distribution objects such as NormalDistribution and gives the skewness of that distribution. Skewness also works on a list of data points and gives the skewness of the distribution of the data (or an estimate of it). ...

0

Just to let you know: I finally solved the problem by adding Assuming[q>0, .... ] before the Integrate command. Hope that may serve someone. Thanks a lot for the helpful comment WalkingRandomly as it finally put me on the right track.

7

I was playing around a little with what I've learned from rm-rf. Here's a small package which encapsulates the White and the Breusch-Pagan test. The math is correct, but I wanted to have a kind of statistical distribution behaviour, where you can query certain properties using a string. I'd appreciate to have overloaded the LinearModelFit, but decided to ...

6

As far as I know there's no reference book available yet that is using the new reliability functionality in Mathematica. Two other resources are: Reliability calculations for complex systems, academic thesis Reliability Mathematics, Wolfram Blog Those two focus on RBDs, Fault trees and system structures.

1

You say your data is a list of $(X,Y)$ so it looks like this: ListLinePlot[data, Frame -> True, FrameLabel -> {"X", "Y"}] Far from been a random coil where the definition of persistent length makes sense. Let see, we want to calculate the average $\cos(\theta) = \hat{v_1} \cdot \hat{v_1}$, as a function of the contour distance, so I use Dot[v1, ...

1

One way to do it is: Show[BoxWhiskerChart[data], ListPlot[MapIndexed[{#2[[1]], #1} &, data, {2}]]] MapIndexed operates just like Mapbut gives you a second argument which is the index of the list element you're getting each time.

0

As I commented, nothing wrong with your idea, assuming the behavior of nearest in your example function is what you want just eliminate the overhead of Nearest by utilizing a NearestFunction. Here's a framework to give you an idea, with some honest timings (including the time to create the needed function or distribution). Timings on my cigar-time netbook. ...

0

Let's try to make it interesting using a large array of random reals: rd = RandomReal[10, 1000000]; Timing of your routine: Do[FindQuantile3[rd, 5, 7], {100}] // AbsoluteTiming//First 23.138323 Now, the alternative: e = EmpiricalDistribution[rd]; Do[1+ CDF[e, 5] 7 // Floor, {100}] // AbsoluteTiming//First 0.018001 There can be some slight ...

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