def covariance(list_x, list_y):
sum = 0
N = len(list_x)
x_mean = zmean.mean(list_x)
y_mean = zmean.mean(list_y)
x_dev = zdev.stddev(list_x)
y_dev = zdev.stddev(list_y)
for i in range(0, len(list_y)):
sum += (list_x[i] - x_mean) * (list_y[i] - y_mean)
covar = sum / (N - 1)
cor = round(covar / (x_dev * y_dev), 3)
return cor
#print(f'Covariance is {covar} and Correlation is {cor}')
# x = [10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0]
# y = [9.14, 8.14, 8.74, 8.77, 9.26, 8.1, 6.13, 3.1, 9.13, 7.26, 4.74]
#
# covariance(x,y)
def stderr(in_list):
"""
Calculates standard error of mean of given list
:param in_list: list of values
:return: float rounded to 5 decimal places
"""
std_dev = zstddev.stddev(in_list)
n = zcount.count(in_list)
std_err = std_dev / sqrt(n)
return round(std_err, 5)
def csv_reader(file):
reader = csv.DictReader(open(file))
result = {}
for row in reader:
for column, value in row.items():
result.setdefault(column, []).append(float(value))
return result
test = csv_reader(dataOne)
x = [10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0]
y = [9.14, 8.14, 8.74, 8.77, 9.26, 8.1, 6.13, 3.1, 9.13, 7.26, 4.74]
print("Count")
print(zcount.count(x))
print("Mean")
print(zmean.mean(x))
print("Mode")
print(zmode.mode(x))
print("Median")
print(zmedian.median(x))
print("Variance")
print(zvariance.variance(x))
print("Standard Deviation")
print(zstderr.stdderr(x))
print("Standard Error")
print(zstddev.stddev(x))
print("Correlation")
print(zcorr.covariance(x, y))
print('----------------------')
print('File: ', f)
print('----------------------')
data = csvReader.read_csv(f)
x = data[0]
y = data[1]
# x list
print('x list')
print('------')
print('Count X: ', zcount.count(x))
print('Mean X: ', zmean.mean(x))
print('Median X ', zmedian.median(x))
print('Mode X: ', zmode.mode(x))
print('Variance X: ', zvariance.variance(x))
print('Standard Deviation X: ', zstddev.stddev(x))
print('Standard Error X: ', zstderr.stderr(x))
# y list
print('-------')
print('y list:')
print('-------')
print('Count Y: ', zcount.count(y))
print('Mean Y: ', zmean.mean(y))
print('Median Y ', zmedian.median(y))
print('Mode Y: ', zmode.mode(y))
print('Variance Y: ', zvariance.variance(y))
print('Standard Deviation Y: ', zstddev.stddev(y))
print('Standard Error Y: ', zstderr.stderr(y))
# Correlation
def stdderr(list_in):
dev = zs.stddev(list_in)
err = dev / math.sqrt(len(list_in))
return round(err, 2)
def test_stddev1(self):
test_data = [1, 2, 3, 4, 5]
self.assertEqual(round(stdev(test_data), 5), zstddev.stddev(test_data))
def test_stddev3(self):
test_data = [-10, -20, -30, -40, -50]
self.assertEqual(round(stdev(test_data), 5), zstddev.stddev(test_data))
def test_stddev2(self):
test_data = [6, 7, 8, 9, 10]
self.assertEqual(round(stdev(test_data), 5), zstddev.stddev(test_data))