I have plotted the probability density bar diagram for wind data. Then I have estimated the Weibull parameters in different methods and do the same again. But I can not understand why the sum of the relative frequencies of estimated data becomes two where it should be one as that of the measured data.
"The p-value is defined as the probability that another sample will be as unusual as the current sample given that the fit is appropriate. A small p-value indicates that the current sample is highly unlikely, and, therefore, the fit should be rejected. Conversely, a high p-value indicates that the sample is likely and would be repeated, and, therefore, the fit should not be rejected. Thus, the higher the p-value, the more likely that the fit is appropriate. While comparing two different fitted distributions, the distribution with the higher p value is likely to be the better fit regardless of the level of significance (If p >α , accept H0)."
If you are adding the probability of all the discrete velocity points you have chosen, it fundamentally gives you the incorrect result. The cumulative density is the area under the curve, so you should be sure to multiply the probability of a particular velocity with the delta-v you are using for discretization. If you do that, irrespective of the selection of delta-v, shape, and scale parameter, you should always get the results somewhere less than 1. If not, the Weibull equation you have entered in your code has some typo or mathematical/sequencing error.
Dear Ayush,
Thanks for your support.
May I send you the excel file so that you can check and pass your comments? If you agree please send your email address?
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