I wrote the test according to an approach I found. When looking in Stack Overflow I saw another approach (can be seen here) which was a little more complicated, and made me wonder if I chose the right one. I’m looking for ways to check if my calculation is correct. Here is the relevant code: Any suggestions will be welcomed.
Tag: scipy.stats
Fit data with a lognormal function via Maximum Likelihood estimators
Could someone help me in fitting the data collapse_fractions with a lognormal function, which has median and standard deviation derived via the maximum likelihood method? I tried scipy.stats.lognormal.fit(data), but I did not obtain the data I retrieved with Excel. The excel file can be downloaded: https://stacks.stanford.edu/file/druid:sw589ts9300/p_collapse_from_msa.xlsx Also, any reference is really welcomed. Answer I couldn’t figure out how to get
Why doesn’t Johnson-SU distribution give positive skewness in scipy.stats?
The code below maps the statistical moments (mean, variance, skewness, excess kurtosis) generated by corresponding parameters (a, b, loc, scale) of the Johnson-SU distribution (johnsonsu). For the range of loop values specified in my code below, no parameter configuration results in positive skewness, only negative skewness, even though it should be possible to parameterize the Johnson-SU distribution to be positively-skewed.