def test_Sample_random_Sampling(self):
self.assertNotEqual(population(10, 30),
generator_int_and_float(10, 30))
def setUp(self) -> None:
self.sample = generator_int_and_float(10, 35)
def setUp(self) -> None:
seed(5)
self.testData = randint(0, 10, 20)
self.statistics = Statistics()
self.x = 1
self.sample = generator_int_and_float(10, 35)
def generator_int_and_float(self, data, x):
self.result = generator_int_and_float(data, x)
return self.result
from Calculator.Subtraction import subtraction
from Calculator.Division import division
from Calculator.Multipule import mul
from Calculator.Square_root import sqr
from Statistics.Standard_Deviation import stddev
from Statistics.NListWithSeed import generator_int_and_float
from Statistics.Mean import mean
import math
import statistics
sample = generator_int_and_float(10, 35)
def confidence_interval_bottom(probability, data):
num = data
num_values = len(num)
sd = stddev(num)
avg = mean(num)
p = probability
if p == 80:
z = 1.282
return round(subtraction(division(sqr(num_values), mul(z, sd)), avg),
1)
elif p == 85:
z = 1.440
return round(subtraction(division(sqr(num_values), mul(z, sd)), avg),
1)
elif p == 90:
z = 1.645
return round(subtraction(division(sqr(num_values), mul(z, sd)), avg),
def population(data, sample_size):
pp = random.choices(generator_int_and_float(data, sample_size), k=sample_size)
return pp