def cochrans(conf, prop):
z_critical = z_critical = scipy.stats.norm.ppf(1 - (1 - conf) / 2)
z_critical_squared = squaring(z_critical)
e = (1-conf)
e_squared = squaring(e)
cochrans_n = ceil((z_critical_squared * prop * (1-prop)) / e_squared)
return cochrans_n
def variance(num):
try:
pop_mean = populationmean(num)
num_values = len(num)
x = 0
for i in num:
x = x + squaring(i - pop_mean)
return round(division(x, num_values), 3)
except ValueError:
print("Error with data")
def known_pop_sample(data, seed):
z_s = Z_Score.zscore(data, seed)
m_e = MarginError.margin(data, seed)
s_d = StandardDeviation.standard_deviance(data)
value = (z_s * s_d) / m_e
popSample = squaring(value)
return popSample
def unknown_pop_sample(data, seed, percent):
z_s = Z_Score.zscore(data, seed)
m_e = MarginError.margin(data, seed)
p = percent
q = subtraction(1, p)
val = division(z_s, m_e)
samplePop = squaring(val) * p * q
return samplePop
def variance(a):
try:
a_mean = mean(a)
n = len(a)
x = 0
for i in a:
x = x + squaring(i - a_mean)
return division(x, n)
except ZeroDivisionError:
print("Error: Can't Divide by 0")
except ValueError:
print("Error: Check your data inputs")
def samplevariance(num):
try:
pop_mean = populationmean(num)
num_values = len(num)
x = 0
for i in num:
x = x + squaring(i - pop_mean)
return round(division(x, num_values), 7)
except ZeroDivisionError:
print("Error: Can't Divide by 0")
except ValueError:
print("Error: Check your data inputs")
def findsamplesize(conf, width):
z_critical = z_critical = scipy.stats.norm.ppf(1 - (1 - conf) / 2)
z_critical_squared = squarerooting(z_critical)
moe = width / 2
p_hat = .5
q_hat = 1 - p_hat
p_times_q = p_hat * q_hat
z_div_moe = z_critical / moe
z_div_moe_squared = squaring(z_div_moe)
n = ceil(p_times_q * z_div_moe_squared)
return n
def square(self, a):
self.result = squaring(a)
return self.result
