wpm-stats.py

# The MIT License (MIT)
#
# Copyright (c) 2014
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import numpy

#top 50
wpm = [87, 86, 82, 77, 77, 76, 74, 74, 72, 72, 71, 71, 70, 70, 70, 69, 68, 68, 68, 68, 67, 67, 66, 66, 65, 65, 65, 65, 64, 64, 63, 63, 63, 63, 62, 62, 62, 62, 62, 62, 61, 61, 61, 61, 60, 60, 60, 60, 59, 59, ]

population = 2900
distribution_range = 29

# distributes population wpm evenly
# among distribution_range lower than 59
# e.g. if distribution_range == 10, population is
# distributed evenly amongst wpm 49, 50, 51, ... 58
for i in range(59 - distribution_range,59):
    wpm += [i]*(population/distribution_range)

def upper_outlier_bound(data):
    return numpy.mean(data) + 3 * numpy.std(data)

def num_upper_outliers(data):
    uob = upper_outlier_bound(data)
    return sum(1 for d in data if d > uob)

print "Mean: {0:.1f}\nStandard Deviation: {1:.1f}\nOutlier if WPM > {2:.1f}\n# of Upper Outliers: {3}".format(numpy.mean(wpm), numpy.std(wpm), upper_outlier_bound(wpm), num_upper_outliers(wpm))