Descriptive Statistics is that branch of Statistics which analyzes brief descriptive coefficients that summarize a given data set. Those coefficients are called ‘descriptive statistics’.
I’ll be using Python to run build outputs
def median(a, l, r):
n = r - l + 1
n = (n + 1) // 2 - 1
return n + l
# Function to calculate IQR
def IQR(a, n):
a.sort()
# Index of median of entire data
mid_index = median(a, 0, n)
# Median of first half
Q1 = a[median(a, 0, mid_index)]
# Median of second half
Q3 = a[median(a, mid_index + 1, n)]
# IQR calculation
return (Q3 - Q1)
# Driver Function
if __name__=='__main__':
a = [1, 2, 3, 4, 5 , 6,7,8,9,10]
n = len(a)
print("IQR :%.03f"%IQR(a, n))Output : IQR :6.000
Variance calculation
#Import statistics module
import statistics
import random
import pandas as pd
#Create a sample of data
sample = []
num = random.randint(0,10)
for j in range(100):
sample.append(random.randint(0,1900))
print("Variance of sample set is % s" %(statistics.variance(sample)))output :
# Python code to demonstrate the working of
# variance() function of Statistics Module
# Importing Statistics module
import statistics
# Creating a sample of data
sample = [2.74, 1.23, 2.63, 2.22, 3, 1.98]
# Prints variance of the sample set
# Function will automatically calulate
# it's mean and set it as xbar
print("Variance of sample set is % s"
%(statistics.variance(sample)))Here’s my git-hub gist :
