# Difference between revisions of "Eleisha's Segment 10: The Central Limit Theorem"

To Calculate:

1. Take 12 random values, each uniform between 0 and 1. Add them up and subtract 6. Prove that the result is close to a random value drawn from the Normal distribution with mean zero and standard deviation 1.

Below is some code that calculates the sum of these 12 uniform random values and compares it to a normal distribution with mean zero and standard deviation 1.


import random
import math
import numpy as np
import matplotlib.pyplot as plt

def get_uni_sum():
rand_vals = []
for i in xrange(0, 12):
rand_vals.append(random.random())
uni_sum = np.sum(rand_vals)
uni_sum_variate = uni_sum - 6
#print uni_sum_variate
return uni_sum_variate

normal_data = []
uni_data = []
for x in xrange(0, 10000):
normal_data.append(random.normalvariate(0,1))
uni_data.append(get_uni_sum())

print "Summary statstiscs from 5000 normal samples"
print "Mean:" +  str(np.mean(normal_data))
print "Standard Deviation: " + str(np.std(normal_data))

print "Summary statstiscs from 5000 uniform sums samples"
print "Mean:" +  str(np.mean(uni_data))
print "Standard Deviation: " + str(np.std(uni_data))

plt.figure(1)
plt.hist(normal_data, 200)
plt.title("Samples from a Normal Distribution")
plt.xticks([-4, -3, -2, -1, 0, 1, 2, 3, 4], ["-4", "-3", "-2", "-1", "0", "1", "2", "3", "4"])
plt.yticks([0, 25, 50, 75, 100, 125, 150, 175, 200], ["0", "25", "50", "75", "100", "125", "150", "175", "200"])
plt.savefig("Norm_Samples.pdf")

plt.figure(2)
plt.hist(uni_data, 200)
plt.title("Samples from a Uniform Distribution")
plt.xticks([-4, -3, -2, -1, 0, 1, 2, 3, 4], ["-4", "-3", "-2", "-1", "0", "1", "2", "3", "4"])
plt.yticks([0, 25, 50, 75, 100, 125, 150, 175, 200], ["0", "25", "50", "75", "100", "125", "150", "175", "200"])
plt.savefig("Uni_Samples.pdf")
plt.show()



2. Invent a family of functions, each different, that look like those in Slide 3: they all have value 1 at x = 0; they all have zero derivative at x = 0; and they generally (not necessarily monotonically) decrease to zero at large x. Now multiply 10 of them together and graph the result near the origin (i.e., reproduce what Slide 3 was sketching).

3. For what value(s) of $\displaystyle \nu$ does the Student distribution (Segment 8, Slide 4) have a convergent 1st and 2nd moment, but divergent 3rd and higher moments?