def pop_standard_deviation(number_list):
# 1. Calculate the mean of number_list
mean = population_mean(number_list)
# 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 pop_stand_dev(data):
u = population_mean(data)
data = [num for elem in data for num in elem]
new_data = [float(x) for x in data]
leng = len(new_data)
return round(
square_root(sum([(element - u)**2 for element in new_data]) / leng), 3)
def z_score(data):
u = population_mean(data)
new_data = [float(x) for x in data]
x = new_data[1]
pop_sd = pop_stand_dev(new_data)
y = subtraction(x, u)
z = division(pop_sd, y)
return z
def population_variance(data):
u = population_mean(data)
deviations = subtraction(data, u)
sq_deviations = square(deviations)
x = len(data)
y = sum(sq_deviations)
d = division(x, y)
return d
def z_score(data):
data = [num for elem in data for num in elem]
new_data = [float(x) for x in data]
x = new_data[1]
u = population_mean(new_data)
sample_sd = sample_st_dev(new_data)
y = subtraction(x, u)
return division(sample_sd, y)
def z_score(data):
x = 62
u = population_mean(data)
sample_sd = sample_st_deviation(data)
y = subtraction(x, u)
return division(sample_sd, y)
#this may not work
def pop_correlation_coefficient(data_x, data_y):
x = pop_stand_dev(data_x)
y = pop_stand_dev(data_y)
divisor = multiplication(x, y)
# Covariance calculation:
d = population_mean(data_x)
e = population_mean(data_y)
a = [(element - d) for element in data_x]
b = [(element - e) for element in data_y]
size = len(a)
product = [a[i] * b[i] for i in range(size)]
total = sum(product)
covariance = division(size, total)
# Population Correlation Coefficient calculation:
d = division(divisor, covariance)
return d
def population_variance(data):
data = [num for elem in data for num in elem]
new_data = [float(x) for x in data]
u = population_mean(new_data)
deviations = subtraction(new_data, u)
sq_deviations = square(deviations)
x = len(new_data)
y = sum(sq_deviations)
d = division(x, y)
return d
def pop_correlation_coefficient(data):
# x_data = CsvReader('Tests/Data/female_height.csv').data
# y_data = CsvReader('Tests/Data/male_height.csv').data
x_data = [num for elem in data for num in elem]
y_data = [num for elem in data for num in elem]
new_x_data = [float(x) for x in x_data]
new_y_data = [float(x) for x in y_data]
x = pop_stand_dev(new_x_data)
y = pop_stand_dev(new_y_data)
divisor = multiplication(x, y)
z = len(new_x_data)
# Covariance calculation:
a = subtraction(new_x_data, population_mean(new_x_data))
b = subtraction(new_y_data, population_mean(new_y_data))
c = multiplication(a, b)
covariance = division(z, (sum(c)))
# Population Correlation Coefficient calculation:
d = division(divisor, covariance)
return d
def confidence_interval(data):
# For a Confidence Interval of 95%
z_value = 1.960
mean = population_mean(data)
sd = pop_stand_dev(data)
x = len(data)
y = division(square_root(x), sd)
margin_of_error = multiplication(z_value, y)
a = [subtraction(mean, margin_of_error)]
b = [addition(mean, margin_of_error)]
size = len(a)
# c = [(a[i], b[i]) for i in range(size)]
lower = a[0]
upper = b[0]
# print(lower, upper)
return lower, upper
def zscore(zscore_list):
return division((subtraction(zscore_list, population_mean(zscore_list))),
population_standard_deviance(zscore_list))
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 z_score(data):
x = 64
u = population_mean(data)
sample_sd = sample_st_dev(data)
y = subtraction(x, u)
return division(sample_sd, y)
def sample_mean(sample_mean_list):
return population_mean(getSample(sample_mean_list, 5))
def pop_mean(self, data):
self.result = population_mean(data)
return self.result
def confidence_interval_SUB(confidence_interval_SUB_list):
return subtraction(
population_mean(confidence_interval_SUB_list),
(multiplication(zscore(confidence_interval_SUB_list)),
division(population_standard_deviance(confidence_interval_SUB_list),
square_root(num_values))))
def population_mean(self):
self.result = population_mean(self.mean_list)
return self.result
def population_mean(self, number_list):
self.result = population_mean(number_list)
return self.result
def population_mean(self):
self.result = population_mean(self.data)
return self.result
def population_variance(data):
u = population_mean(data)
leng = len(data)
return round(division(leng, sum([(element - u)**2 for element in data])),
3)
def variance_population_proportion(variance_population_proportion_list):
return square_root(division(multiplication(subtraction(population_mean(variance_population_proportion_list), 1)), population_mean(variance_population_proportion_list), num_values))
def pop_stand_dev(data):
u = population_mean(data)
leng = len(data)
return round(square_root(sum([(element - u) ** 2 for element in data]) / leng), 3)
def pop_standard_dev(data):
n = len(data)
u = population_mean(data)
return squareroot(
sum([(element - u)**2 for element in data]) / (len(data) - 1))
