Aurora Tiffany-Davis
August 15th, 2007, 05:00 PM
I have 2 sets of data each comprising 90 datapoints.
I am using the Weibull-2 distribution (as a default). Given the large number of samples, I chose the MLE analysis method and the Fisher Matrix confidence bound method. Because there are no intervals, I chose the SRM regression method.
Attempting this calculation, I get 2 error messages. One is that Beta > 50 and thus W2 is not the most appropriate distribution, the other reads, "MLE Method cannot converge to reasonable values. This might be due to unreasonable or insufficient data."
If I run the Distribution Wizard, W2 comes up as #5 preference for distributions. However, if I simply change from MLE to RRX, the calculation is successful. Alternatively, if I change from W2 to either Normal or Logistic, but keep MLE, the calculation is successful. Note: the W2-calculated PDF for either dataset does not look exactly "Normal". For this reason I have chosen to keep W2 and change to RRX.
Ultimately though, I don't know *why*. I just fiddle with the settings until it works. Since the results of my analysis will impact timeline and budget, I would like to be more confident in my own analysis, especially when deviating from the recommended settings for the type of dataset I am working with. Any suggestions?
So, now with W2, RRX, FM, SRM settings in place, I compare the results of the 2 datasets. All looks okay except that the 2nd dataset is unable to produce a contour plot, at any confidence level. The only available explanation is "Error In Calculations". The engineers I will report my findings to are somewhat familiar with contour plots and although the Test of Comparison Tool says that "... will last longer with a probability of 94%..." it would be great to also show dataset divergence with a Contour Plot. Not to mention, the simple fact that I cannot produce one with my dataset and settings further erodes my confidence.
So, I can keep fiddling around and changing this and that until I find settings that, for both datasets, successfully calculate and can produce all plots. But somehow that seems like cheating, and a little less than scientific. Any help?
Raw data is attached.
I am using the Weibull-2 distribution (as a default). Given the large number of samples, I chose the MLE analysis method and the Fisher Matrix confidence bound method. Because there are no intervals, I chose the SRM regression method.
Attempting this calculation, I get 2 error messages. One is that Beta > 50 and thus W2 is not the most appropriate distribution, the other reads, "MLE Method cannot converge to reasonable values. This might be due to unreasonable or insufficient data."
If I run the Distribution Wizard, W2 comes up as #5 preference for distributions. However, if I simply change from MLE to RRX, the calculation is successful. Alternatively, if I change from W2 to either Normal or Logistic, but keep MLE, the calculation is successful. Note: the W2-calculated PDF for either dataset does not look exactly "Normal". For this reason I have chosen to keep W2 and change to RRX.
Ultimately though, I don't know *why*. I just fiddle with the settings until it works. Since the results of my analysis will impact timeline and budget, I would like to be more confident in my own analysis, especially when deviating from the recommended settings for the type of dataset I am working with. Any suggestions?
So, now with W2, RRX, FM, SRM settings in place, I compare the results of the 2 datasets. All looks okay except that the 2nd dataset is unable to produce a contour plot, at any confidence level. The only available explanation is "Error In Calculations". The engineers I will report my findings to are somewhat familiar with contour plots and although the Test of Comparison Tool says that "... will last longer with a probability of 94%..." it would be great to also show dataset divergence with a Contour Plot. Not to mention, the simple fact that I cannot produce one with my dataset and settings further erodes my confidence.
So, I can keep fiddling around and changing this and that until I find settings that, for both datasets, successfully calculate and can produce all plots. But somehow that seems like cheating, and a little less than scientific. Any help?
Raw data is attached.