arimitramaiti

import math
x = 0 #it would be 0 for 0 heads, 1 for 1 heads, 2 for 2 heads, 3 for 3 heads
n = 3 #number of trials expected to run
prob = 0.5 #probability of success or failure

def combination(n,r):
    fac = math.factorial
    return fac(n) / fac(r) / fac(n-r)

pmf_probability = combination(n,x) * (prob**x) * ( (1-prob)**(n-x) )

arimitramaiti

##import required modules
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import math
from matplotlib.ticker import StrMethodFormatter

###Python-Graph-1 in article titled Jump from discrete to a continuous distribution
##Create the grid
fig, axes = plt.subplots(2, 2, figsize=(10,8))

arimitramaiti

##import required modules
import numpy as np
import scipy.stats as stats
import math

x = round(stats.norm.ppf(0.975),2) #x gives us 1.96 instead of 2.0
b = stats.norm.cdf(x)
a = stats.norm.cdf(-x)
y = round(b-a, 6)
print(y)

arimitramaiti

##import required modules
import numpy as np
import scipy.stats as stats
import math

sample_size = 40 #known from the sample collected by the manager
sample_success = 25 #known from the sample collected by the manager
proportion = sample_success/sample_size #sample proportion who rated more 6 or more
point_estmate = proportion #similar to proportion of the sample
standard_error = np.sqrt((proportion * (1-proportion))/sample_size) #formula is slightly different relative to population mean estimation

arimitramaiti

##import required modules
import numpy as np
import scipy.stats as stats
import math

sample_size = 30 #known from the sample collected by the manager
population_size = 1184 #known in this specific case therefore required for fpc calc

sample_std = 4.46 #standard deviation of all sample means
point_estmate = 55.45 #as we take more and more samples the sample mean would converge to population mean

arimitramaiti

#install.packages("readr")
library(readr)
data <- read.csv("https://raw.githubusercontent.com/arimitramaiti/datasets/master/articles/adjacency_matrix_mumbailocal.csv", header = TRUE, sep = ",")
mat <- as.matrix(data[, -1L])
rownames(mat) <- unlist(data[,1L])

#install.packages("igraph")
library("igraph")

#Create graph object using igraph library

arimitramaiti

##Import required modules
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import scipy.stats as stats
import math

##Read the centrality scores generated from empirical network of mumbai local rail

arimitramaiti

##Import required modules
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import scipy.stats as stats
import math

##Read the centrality scores generated from empirical network of mumbai local rail

arimitramaiti

#install.packages("igraph")
library("igraph")

#Create empty list 
degree_list = list()
closeness_list = list()
betweenness_list = list()
eigen_list = list()

for (i in 1:100) {

arimitramaiti

##Import required modules
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import scipy.stats as stats
import math

##Read the centrality scores generated from empirical network of mumbai local rail