eLtronicsVilla

# This is the function formate only to calculate mean on given data set
price = [500,1800,4600,1200,2000]
def mean(x):
	return sum(x) / len(x)

mean(price)

eLtronicsVilla

# This is sample code,function to calculate the median of given dataset

data = [2,3,4,5,6,8,9]
def median(value):
	# find middle most value for even or odd data set
	n = len(value)			# find out the length
	sorted_value = sorted(value)	#sort the value
	mid_p = n // 2 		# find out the mid point
	
	if n % 2 == True:

eLtronicsVilla

# sample code to calculate the mode of given data set

from collections import Counter
d_set = [1,3,3,4,5,7,8,9]
def mode(data):
        # return a list might be more than one mode
        c = Counter(data)       # count occurance of the each item 
        max_count = max (c.values())    # find out max value in data
        return [x_i for x_i , count in c.iteritems() if count == max_count ]

eLtronicsVilla

# Create the data
d_data = [1,3,4,5,6,7,8]

# Define a function
def data_range(data):
        return max(data) - min(data)

print(data_range(d_data))

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# Generalization of median is quantile. Which represent the value less than which a certain percentile of the data lies.

num = [100, 49, 41, 40, 25]

def quantile(x, p):
        """returns the pth-percentile value in x data list"""
        p_index = int(p * len(x))
        return sorted(x)[p_index]

eLtronicsVilla

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"""

eLtronicsVilla

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"""

eLtronicsVilla

import math
import numpy as np

data_X = [5,6,8,12,15,18,10]
data_Y = [1,4,6,8,10,12,14]

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

def de_mean(data):

eLtronicsVilla

import math
import numpy as np

data_X = [5,6,8,12,15,18,10]
data_Y = [1,4,6,8,10,12,14]

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

def de_mean(data):

eLtronicsVilla

import random
def bernoulli_trial(p):
    return 1 if random.random() < p else 0


def binomial(n,p):
    return sum(bernoulli_trial(p) for _ in range(n))

# Suppose  this problem, there are 9 people selected (n = number of trials = 9). The probability of success is 0.62.