Finding Bestfit Distribution [image]

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To find the best fit distribution using Excel, users recommend utilizing CurveExpert for model evaluation. However, difficulties arise when there are no Y values available, as this can lead to errors and a score of 0.0 for best fit models. A cumulative histogram is suggested, with errors on the x-axis and frequency on the y-axis, to better visualize the data. Properly structuring the data is crucial for accurate results. Overall, addressing the lack of Y values and employing a histogram may enhance the fitting process.
hoodrych
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I need to find the bestfit distribution for this. I'm using excel, so I need to be able to use xcel to graph it.


[PLAIN]http://img831.imageshack.us/img831/8770/unledcwx.png
 
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I use CurveExpert to find best fit models; give that a try. Then simply evaluate a few points from the model and plot them as separate data.
 
adoado said:
I use CurveExpert to find best fit models; give that a try. Then simply evaluate a few points from the model and plot them as separate data.


Maybe I'm using the program wrong (I don't think I am), but it's not coming up with any results.


I have no Y values, only X's, so that may be the problem? There are lots of errors for almost every test. And when I click the "scan for bestfit" button, the 20 or so different best fit models it tests all come up with a Score of 0.0.
 
As I said in the other thread, you should use a cumulative histogram of the data. The x-axis should be the errors and the y-axis should be the frequency.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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