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Example of how to calculate the Standard Deviation of a dataset using Python 2.7
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#!/usr/bin/env python2 | |
import math | |
# An imaginary dataset of 50 respondents graded from 1-9, so we have an example to play with: | |
dataset = [1,1,1,2,3,1,1,4,4,5,6,1,4,9,9,7,7,6,7,5,4,3,6,8,9,5,5,6,6,6,5,5,5,5,4,4,4,6,6,4,7,3,7,3,7,4,4,4,4,9] | |
# Amounts of each: 1*6,2*1,3*4,4*12,5*8,6*8,7*6,8*1,9*4 | |
# Control: 6+1+4+12+8+8+6+1+4=50 | |
# the mean average (the arithmetic mean) is the sum of all the grades added together, divided by the amount of grades given | |
mean = sum(dataset)/len(dataset) | |
# the deviation is calculated as (grade-mean)^2 for each grade in the dataset | |
deviation = [ (grade-mean)**2 for grade in dataset ] | |
# the variance is simply the mean of the deviations | |
variance = sum(deviation)/len(deviation) | |
# lastly, the standard deviation is the square root of the variance | |
standard_deviation = math.sqrt(variance) | |
print (standard_deviation) | |
# You can now use the standard deviation in the integration of Gaussian distributions |
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