After a LOT of searching and testing, I’m going to settle once and for all with one software option to do my (sparse) statistical analysis: matplotlib.
I have a text file with two columns with numeric values I wish to plot and find a correlation coeficient (ie. R-Square). The following script does that and saves the plot to a PNG file.
The script reads columns from the file and stores them in two variables: x,y. Then, it calculates the R-square value. Then it plots the graph and places the R-square value somewhere in there, along with a textbox. Pretty easy and self-explanatory :)
import sys, numpy from pylab import scatter, title, show , legend, text, savefig, xlim, ylim # Data data = open(sys.argv).readlines()[1:] # first line is header x, y = ,  for line in data: x.append(float(line.split())) # My file has 3 columns, I want to plot 2,3 y.append(float(line.split())) # R-Square correlation = numpy.corrcoef(x, y)[0,1] rsq = correlation**2 # matplotlib plotting title('cRMS Correlation') scatter(x,y, marker='o', c='b',) # plot my data points plot(x,x) # to add a x=y line text(0.5, 2.0, r'$R^2$'+'Value = %0.4f' %rsq, horizontalalignment='center', verticalalignment='center') xlim(xmin=0) ylim(ymin=0) #savefig('%s.png' %sys.argv.split('.')) show() # Shows in the screen as well