a quick python script for basic experimental statistical analysis.

stats.py

#!/usr/bin/python -tt

# My little script for calculating some statistical analyses.

import sys
import math

def conf_int(filename):
  data_set = ''
  f = open(sys.argv[1], 'rU')
  for item in f:
    data_set += str(item) # adds data to the string 'data set'
  f.close()
  data_set = data_set.split(' ') # splits 'data set' into a list in the form ['a', 'b', ..., 'z']
  data_set = [float(x) for x in data_set] # turns the string items into floating points (i.e. removes the '' lol)
  data_set = sorted(data_set)

  print ''
  print 'For the data set ' + str(data_set) + ':' 
  print ''

  data_set_sum = 0
  for num in data_set:
    data_set_sum += num
  data_set_mean = data_set_sum / len(data_set) # note: not true mean
  print 'Experimental mean = ' + str(data_set_mean)

  data_set_working_deviation = 0
  for element in data_set:
    data_set_working_deviation += (element - data_set_mean) ** 2
  data_set_deviation = data_set_working_deviation / (len(data_set) - 1 )
  print 'Deviation = ' + str(data_set_deviation)

  data_set_standard_deviation = math.sqrt(data_set_deviation)
  print 'Standard deviation = ' + str(data_set_standard_deviation)

  print 'Relative standard deviation = ' + str(data_set_standard_deviation / data_set_mean * 100)

  print ''

  big_list = [1.000, 1.376, 1.963, 3.078, 6.314, 12.71, 31.82, 63.66, 127.3, 318.3, 636.6, 0.816, 1.061, 1.386, 1.886, 2.920, 4.303, 6.965, 9.925, 14.09, 22.33, 31.60, 0.765, 0.978, 1.250, 1.638, 2.353, 3.182, 4.541, 5.841, 7.453, 10.21, 12.92, 0.741, 0.941, 1.190, 1.533, 2.132, 2.776, 3.747, 4.604, 5.598, 7.173, 8.610, 0.727, 0.920, 1.156, 1.476, 2.015, 2.571, 3.365, 4.032, 4.773, 5.893, 6.869, 0.718, 0.906, 1.134, 1.440, 1.943, 2.447, 3.143, 3.707, 4.317, 5.208, 5.959, 0.711, 0.896, 1.119, 1.415, 1.895, 2.365, 2.998, 3.499, 4.029, 4.785, 5.408, 0.706, 0.889, 1.108, 1.397, 1.860, 2.306, 2.896, 3.355, 3.833, 4.501, 5.041, 0.703, 0.883, 1.100, 1.383, 1.833, 2.262, 2.821, 3.250, 3.690, 4.297, 4.781, 0.700, 0.879, 1.093, 1.372, 1.812, 2.228, 2.764, 3.169, 3.581, 4.144, 4.587, 0.697, 0.876, 1.088, 1.363, 1.796, 2.201, 2.718, 3.106, 3.497, 4.025, 4.437, 0.695, 0.873, 1.083, 1.356, 1.782, 2.179, 2.681, 3.055, 3.428, 3.930, 4.318, 0.694, 0.870, 1.079, 1.350, 1.771, 2.160, 2.650, 3.012, 3.372, 3.852, 4.221, 0.692, 0.868, 1.076, 1.345, 1.761, 2.145, 2.624, 2.977, 3.326, 3.787, 4.140, 0.691, 0.866, 1.074, 1.341, 1.753, 2.131, 2.602, 2.947, 3.286, 3.733, 4.073, 0.690, 0.865, 1.071, 1.337, 1.746, 2.120, 2.583, 2.921, 3.252, 3.686, 4.015, 0.689, 0.863, 1.069, 1.333, 1.740, 2.110, 2.567, 2.898, 3.222, 3.646, 3.965, 0.688, 0.862, 1.067, 1.330, 1.734, 2.101, 2.552, 2.878, 3.197, 3.610, 3.922, 0.688, 0.861, 1.066, 1.328, 1.729, 2.093, 2.539, 2.861, 3.174, 3.579, 3.883, 0.687, 0.860, 1.064, 1.325, 1.725, 2.086, 2.528, 2.845, 3.153, 3.552, 3.850, 0.686, 0.859, 1.063, 1.323, 1.721, 2.080, 2.518, 2.831, 3.135, 3.527, 3.819, 0.686, 0.858, 1.061, 1.321, 1.717, 2.074, 2.508, 2.819, 3.119, 3.505, 3.792, 0.685, 0.858, 1.060, 1.319, 1.714, 2.069, 2.500, 2.807, 3.104, 3.485, 3.767, 0.685, 0.857, 1.059, 1.318, 1.711, 2.064, 2.492, 2.797, 3.091, 3.467, 3.745, 0.684, 0.856, 1.058, 1.316, 1.708, 2.060, 2.485, 2.787, 3.078, 3.450, 3.725, 0.684, 0.856, 1.058, 1.315, 1.706, 2.056, 2.479, 2.779, 3.067, 3.435, 3.707, 0.684, 0.855, 1.057, 1.314, 1.703, 2.052, 2.473, 2.771, 3.057, 3.421, 3.690, 0.683, 0.855, 1.056, 1.313, 1.701, 2.048, 2.467, 2.763, 3.047, 3.408, 3.674, 0.683, 0.854, 1.055, 1.311, 1.699, 2.045, 2.462, 2.756, 3.038, 3.396, 3.659, 0.683, 0.854, 1.055, 1.310, 1.697, 2.042, 2.457, 2.750, 3.030, 3.385, 3.646, 0.681, 0.851, 1.050, 1.303, 1.684, 2.021, 2.423, 2.704, 2.971, 3.307, 3.551, 0.679, 0.849, 1.047, 1.299, 1.676, 2.009, 2.403, 2.678, 2.937, 3.261, 3.496, 0.679, 0.848, 1.045, 1.296, 1.671, 2.000, 2.390, 2.660, 2.915, 3.232, 3.460, 0.678, 0.846, 1.043, 1.292, 1.664, 1.990, 2.374, 2.639, 2.887, 3.195, 3.416, 0.677, 0.845, 1.042, 1.290, 1.660, 1.984, 2.364, 2.626, 2.871, 3.174, 3.390, 0.677, 0.845, 1.041, 1.289, 1.658, 1.980, 2.358, 2.617, 2.860, 3.160, 3.373, 0.674, 0.842, 1.036, 1.282, 1.645, 1.960, 2.326, 2.576, 2.807, 3.090, 3.291]

  tails = raw_input('Is the confidence interval symmetrical? ')

  def conf(t):
    true_mean_variance = t * data_set_standard_deviation / math.sqrt((len(data_set)))
    print 'The true mean lies within ' + str(data_set_mean) + ' ' + u"\u00B1" + \
    ' ' + str(true_mean_variance) + ' with ' + interval + ' confidence.'

  if tails == 'n':
    interval = raw_input('What is the confidence interval? ')

    if interval == '75%':
      t = big_list[(len(data_set) - 2) * 11 + 0]
      conf(t)
    elif interval == '80%':
      t = big_list[(len(data_set) - 2) * 11 + 1]
      conf(t)
    elif interval == '85%':
      t = big_list[(len(data_set) - 2) * 11 + 2]
      conf(t)
    elif interval == '90%':
      t = big_list[(len(data_set) - 2) * 11 + 3]
      conf(t)
    elif interval == '95%':
      t = big_list[(len(data_set) - 2) * 11 + 4]
      conf(t)
    elif interval == '97.5%':
      t = big_list[(len(data_set) - 2) * 11 + 5]
      conf(t)
    elif interval == '99%':
      t = big_list[(len(data_set) - 2) * 11 + 6]
      conf(t)
    elif interval == '99.5%':
      t = big_list[(len(data_set) - 2) * 11 + 7]
      conf(t)
    elif interval == '99.75%':
      t = big_list[(len(data_set) - 2) * 11 + 8]
      conf(t)
    elif interval == '99.9%':
      t = big_list[(len(data_set) - 2) * 11 + 9]
      conf(t)
    elif interval == '99.95%':
      t = big_list[(len(data_set) - 2) * 11 + 11]
      conf(t)
    else:
      print "Not a valid input."

  elif tails == 'y':
    interval = raw_input('What is the confidence interval? ')

    if interval == '50%':
      t = big_list[(len(data_set) - 2) * 11 + 0]
      conf(t)
    elif interval == '60%':
      t = big_list[(len(data_set) - 2) * 11 + 1]
      conf(t)
    elif interval == '70%':
      t = big_list[(len(data_set) - 2) * 11 + 2]
      conf(t)
    elif interval == '80%':
      t = big_list[(len(data_set) - 2) * 11 + 3]
      conf(t)
    elif interval == '90%':
      t = big_list[(len(data_set) - 2) * 11 + 4]
      conf(t)
    elif interval == '95%':
      t = big_list[(len(data_set) - 2) * 11 + 5]
      conf(t)
    elif interval == '98%':
      t = big_list[(len(data_set) - 2) * 11 + 6]
      conf(t)
    elif interval == '99%':
      t = big_list[(len(data_set) - 2) * 11 + 7]
      conf(t)
    elif interval == '99.5%':
      t = big_list[(len(data_set) - 2) * 11 + 8]
      conf(t)
    elif interval == '99.8%':
      t = big_list[(len(data_set) - 2) * 11 + 9]
      conf(t)
    elif interval == '99.9%':
      t = big_list[(len(data_set) - 2) * 11 + 10]
      conf(t)
    else:
      print 'Not a valid input.'
  else:
    print 'Not a valid input.'

  print ''

  return

def main():

  filename = sys.argv[1]
  if len(sys.argv) < 2:
    print 'Syntax is ./stats.py <file>.'
  elif filename == '--help':
    print ''
    print 'Syntax is ./stats.py <file>.'
    print 'Gives the experimental mean, deviation, standard deviation, relative '
    print 'standard deviation, and true mean to within a certain confidence '
    print 'limit using the "student\'s t distribution".'
  else:
    conf_int(filename)

if __name__ == '__main__':
  main()