Dear Rene, Probably the following features exist in root. I just have not yet figured out how to get at them. 1) It is often useful to plot the data as a "normal quantile plot" or sometimes called a "normal plot". Description of "normal plot" from the book by Tamhane and Dunlop, Statistics and Data Analysis, p. 123, is given here: Suppose the data follow an N( mu, sigma^2 ) distribution, then the percentiles of that normal distribution should plot linearly against the sample percentiles, except for sampling variations. For the sample percentiles one uses normally the ordered data values themselves, the i'th ordered data value being the 100( i/(n+1)) th sample percentile, where n is the sample size. The corresponding standard normal percentiles are called normal scores. The plot of these normal scores against the ordered data values is the normal plot. Is there some tool in root to plot out this normal plot? 2) It also would be useful to calculate the inverse cummulative density function (c.d.f.), i.e. the inverse fnct of cdf( z ) = ( 1 + TMath::Erf( z / TMath::Sqrt(2) ) ) Is such a fnct easily obtainable inside root? 3) Also, is there a way to plot out data as a "box plot" or sometimes called a "box and whiskers plot", which plots out the following five important numbers [either bin by bin, or for each sample separately]: { x_min, Q_1, Q_2, Q_3, x_max }, where x_min, x_max are the minimum/maximum in the current sample and Q_k are the k'th quartile (25percentiles). Please, see, for instance, p. 121, Tamhane and Dunlop's book. Thank you for your guidance. Cheers, Isi
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