dispersion.py

import math
import numpy as np

data_input = [5,6,8,12,15,18,10]

def mean(x):
        return  sum(x)/len(x)

def sum_of_squares(v):
        """v_1*v_1 +... +v_n*v_n"""
        return  np.dot(v,v)

def dev_mean(data):
# convert x by subtracting its mean ( so the result has mean 0)
        x_bar = mean(data)
        return [x_i - x_bar for x_i in data]

def variance(data):
# assumes x has at least two element
        n = len(data)
        deviations =  dev_mean(data)
        return sum_of_squares(deviations) / (n-1)


def standard_deviation(data):
        return math.sqrt(variance(data))


print(standard_deviation(data_input))