import sys
from random import randint
from modules.econ_statistics import Econ_Statistics
# Simple application to calculate sample data of a population
# Intended to show difference between population and sample and to
# show how the average of the [sample variance] approaches 0 as
# repetitions increase
data_list = [25, 24, 21, 23, 21, 20, 20, 20, 20, 20, 20, 20, 20, 20, 19, 21, 20, 20, 21, 21, 20, 20, 19, 18, 19, 19, 22, 25, 21, 20, 28, 22, 20, 20, 20, 21, 24]
ec = Econ_Statistics()
print("Population average: {}".format(ec.get_average(data_list)))
print("Population variance: {}".format(ec.get_variance(data_list)))
looped = 0
while 1:
slist = []
slist_avg = []
slist_var = []
try:
ip = "y" if looped == 1 else raw_input("Would you like to sample the current list?\n> y - Yes\n> c - clear data\n> d - Display list\n> ")
if ip.lower() == "y":
while 1:
if len(data_list) == 0:
while 1:
try:
inp = raw_input("Add a number to the list:\n> d - Done\n> ")
if inp == "d":
break
data_list = []
print("Cleared data_list")
else:
try:
data_list.append(int(number))
except:
print("Incorrect input provided.")
data_list.sort()
print("Your number list: " + str(data_list))
print("Length of list: " + str(len(data_list)))
print("Mean: " + str(ec.get_average(data_list)))
print("Median: " + str(ec.get_median(data_list)))
data_variance = ec.get_variance(data_list)
print("Variance: "+ str(data_variance))
data_stdv = int(math.sqrt(data_variance)*10000)/10000
print("Standard Deviation: " + str(data_stdv))
print("Range: " + str(data_list[len(data_list)-1] - data_list[0]))
print("Interquartile range: " + str(data_list[int(len(data_list)*.75)+1] - data_list[int(len(data_list)*.25)+1]))
print("Coefficient of variation: " + str(int(data_stdv/ec.get_average(data_list)*10000)/100) + "%")
outliers = []
for value in data_list:
if value < ec.get_average(data_list):
if value < ec.get_average(data_list) - data_stdv*3:
outliers.append(value)
elif value > ec.get_average(data_list):
if value > ec.get_average(data_list) + data_stdv*3:
outliers.append(value)
if len(outliers) == 0:
