def central_limit_theorem():
y = []
n=100
for i in range(1000):
r = expon.rvs(scale=1, size=n)
rsum=np.sum(r)
z=(rsum-n)/np.sqrt(n)
y.append(z)
plt.hist(y,color='grey')
plt.savefig('central_limit_theorem.png')
def first_year():
X=sta.norm(loc=950, scale=20)#generate random data in normal distribution whose expectation is 950 and standard deviation is 20
wbread=[]
for i in range(365):
x=X.rvs(size=100)
wbread.append(x[0])#get the random data for one day
log(numpy.mean(wbread))#print mean value
log(sta.skew(wbread))#print skew value
plt.hist(wbread,color='grey')
plt.savefig('first_year.png')
def second_year():
X=sta.norm(loc=950, scale=20)#generate random data in normal distribution whose expectation is 950 and standard deviation is 20
wbread=[]
for i in range(365):
x=X.rvs(size=100)
wbread.append(max(x))#get the random data for one day
log(numpy.mean(wbread))#print mean value
log(sta.skew(wbread))#print skew value
plt.hist(wbread,color='grey')
plt.savefig('second_year.png')
def central_limit_theorem():
y = []
n=100
for i in range(1000):
r = binom.rvs(n, 0.3)
rsum=np.sum(r)
z=(rsum-n*0.3)/np.sqrt(n*0.3*0.7)
y.append(z)
plt.hist(y,color='grey')
plt.savefig('central_limit_theorem.png')