docs: def paired(x, y):
      """
  Interactively determines the type of data and then runs the
  appropriated statistic for paired group data.
  
  Usage:   lpaired(x,y)
  Returns: appropriate statistic name, value, and probability
  """
      samples = ''
      while samples not in ['i', 'r', 'I', 'R', 'c', 'C']:
          print('\nIndependent or related samples, or correlation (i,r,c): ',)
          samples = raw_input()
  
      if samples in ['i', 'I', 'r', 'R']:
          print('\nComparing variances ...',)
          # USE O'BRIEN'S TEST FOR HOMOGENEITY OF VARIANCE, Maxwell & delaney, p.112
          r = obrientransform(x, y)
          f, p = F_oneway(pstat.colex(r, 0), pstat.colex(r, 1))
          if p < 0.05:
              vartype = 'unequal, p=' + str(round(p, 4))
          else:
              vartype = 'equal'
          print(vartype)
          if samples in ['i', 'I']:
              if vartype[0] == 'e':
                  t, p = ttest_ind(x, y, 0)
                  print('\nIndependent samples t-test:  ', round(t, 4), round(p, 4))
              else:
                  if len(x) > 20 or len(y) > 20:
                      z, p = ranksums(x, y)
                      print('\nRank Sums test (NONparametric, n>20):  ', round(z, 4), round(p, 4))
                  else:
                      u, p = mannwhitneyu(x, y)
                      print('\nMann-Whitney U-test (NONparametric, ns<20):  ', round(u, 4), round(p, 4))
          else:  # RELATED SAMPLES
              if vartype[0] == 'e':
                  t, p = ttest_rel(x, y, 0)
                  print('\nRelated samples t-test:  ', round(t, 4), round(p, 4))
              else:
                  t, p = ranksums(x, y)
                  print('\nWilcoxon T-test (NONparametric):  ', round(t, 4), round(p, 4))
      else:  # CORRELATION ANALYSIS
          corrtype = ''
          while corrtype not in ['c', 'C', 'r', 'R', 'd', 'D']:
              print('\nIs the data Continuous, Ranked, or Dichotomous (c,r,d): ',)
              corrtype = raw_input()
          if corrtype in ['c', 'C']:
              m, b, r, p, see = linregress(x, y)
              print('\nLinear regression for continuous variables ...')
              lol = [['Slope', 'Intercept', 'r', 'Prob', 'SEestimate'], [round(m, 4), round(b, 4), round(r, 4), round(p, 4), round(see, 4)]]
              pstat.printcc(lol)
          elif corrtype in ['r', 'R']:
              r, p = spearmanr(x, y)
              print('\nCorrelation for ranked variables ...')
              print("Spearman's r: ", round(r, 4), round(p, 4))
          else: # DICHOTOMOUS
              r, p = pointbiserialr(x, y)
              print('\nAssuming x contains a dichotomous variable ...')
              print('Point Biserial r: ', round(r, 4), round(p, 4))
      print('\n\n')
      return None