def confidence_interval(data):
z_value = 1.05
mean =sample_mean(data)
sd = pop_standard_dev(data)
x = len(data)
y = division(squareroot(x), sd)
margin_of_error = multiplication(z_value, y)
a = subtraction(mean, margin_of_error)
b = addition(mean, margin_of_error)
return a, b
def sample_st_deviation(data, sample_size):
dev = 0
sample = getSample(data, sample_size)
sample_values = len(sample)
x_bar = sample_mean()
x = sample_values
n = subtraction(sample_values, 1)
for dev in sample:
dev = subtraction(x, x_bar)
square_x_bar = square(dev)
add = addition(square_x_bar, square_x_bar)
divide = division(add, n)
return squareroot(divide)
def sample_standard_deviation(number_list, sample_size):
# 1. Calculate the mean of number_list
mean = sample_mean(number_list, sample_size)
# 2. Subtract mean from each data point and then square each value
new_list = []
for x in number_list:
new_val = x - mean
new_val = math.pow(new_val, 2)
new_list.append(new_val)
# 3. Calculate the mean of the squared differences, this is the variance
new_mean = population_mean(new_list)
# 4. pop standard deviation is the square root of the variance
result = math.sqrt(new_mean)
return result
def P_value(P_value_list):
return division(
subtraction(sample_mean(P_value_list), population_mean(P_value_list)),
division(population_standard_deviance(P_value_list),
square_root(num_values)))
def sample_mean(self, sample_size):
self.result = sample_mean(self.data, sample_size)
return self.result
def sample_mean(self):
self.result = sample_mean(self.sample_mean_list)
return self.result
def sample_mean(self, a, b, c):
self.result = sample_mean(a, b, c)
return self.result
def sample_mean(self, val1, val2, val3, val4, val5):
self.result = sample_mean(val1, val2, val3, val4, val5)
return self.result
