def simulation(self):
sample = bt.sample_distr(self.nums)
print("Simulation results: ")
print(
"Theoretical mean and simulated mean: {},{}\nTheoretical SE and simulated SE: {},{}"
.format(float('%.4f' % round(st.s_mean(self.nums), 4)),
float('%.4f' % round(st.s_mean(sample), 4)),
bt.sample_stand_err(self.nums),
float('%.4f' % round(st.s_std(sample), 4))))
bt.b_plot(sample)
def sd(self):
try:
if type(self.nums) != np.ndarray:
raise (NonNumpy)
if self.nums.shape[0] < 50:
raise (LengthError)
return float('%.4f' % round(st.s_std(self.nums), 4))
except LengthError:
print("The array of size {} too short".format(self.nums.shape[0]))
except NonNumpy:
print("Inapropriate type, pass numpy array")
def p_se(n, values):
try:
if values <= 0:
raise (nonNegative)
if n <= 0:
raise (nonNegative)
se = st.s_std(p_obs(n, values)) / (len(p_obs(n, values))**(1 / 2))
return se
except nonNegative:
print("Error: values and n should be greater than 0")
except:
print("Error: unknown")
def p_zscore(x, n, values):
try:
if values <= 0:
raise (nonNegative)
if n <= 0:
raise (nonNegative)
if x == str(x):
raise (nonText)
z = (x - st.s_mean(p_obs(n, values))) / st.s_std(p_obs(n, values))
return z
except nonNegative:
print("Error: values and n should be greater than 0")
except nonText:
print("Error: not supporting string, please type a number")
def test_std(self):
self.assertEqual(st.s_std(self.nums1), 1.4142135623730951)
self.assertEqual(st.s_std(self.nums2), 1.4142135623730951)
self.assertEqual(st.s_std(self.nums3), 1.7435595774162693)
self.assertEqual(st.s_std(self.nums4), 2.8284271247461903)
self.assertEqual(st.s_std(self.nums5), 2.1540659228538015)
def stand_err(nums):
return float('%.4f' % round((st.s_std(nums) / len(nums)**0.5), 4))
def sd(self):
return float('%.4f' % round(st.s_std(self.nums), 4))
def p_se(n, values):
se = st.s_std(p_obs(n, values)) / (len(p_obs(n, values))**(1 / 2))
return se
def p_zscore(x, n, values):
z = (x - st.s_mean(p_obs(n, values))) / st.s_std(p_obs(n, values))
return z