Welch's t-test: Difference between revisions
m
not wise to put fist a solution which is buggy, not written following common coding conv, and more complicated than necessary (numy is actually used, especially for stats using pandas
m (→Using Burkardt's betain: added output) |
m (not wise to put fist a solution which is buggy, not written following common coding conv, and more complicated than necessary (numy is actually used, especially for stats using pandas) |
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Line 1,217:
=={{header|Python}}==
=== Using NumPy & SciPy ===▼
<lang python>import numpy as np▼
import scipy as sp▼
import scipy.stats▼
def welch_ttest(x1, x2):▼
n1 = x1.size▼
n2 = x2.size▼
m1 = np.mean(x1)▼
m2 = np.mean(x2)▼
v1 = np.var(x1, ddof=1)▼
v2 = np.var(x2, ddof=1)▼
t = (m1 - m2) / np.sqrt(v1 / n1 + v2 / n2)▼
df = (v1 / n1 + v2 / n2)**2 / (v1**2 / (n1**2 * (n1 - 1)) + v2**2 / (n2**2 * (n2 - 1)))▼
p = 2 * sp.stats.t.cdf(-abs(t), df)▼
return t, df, p▼
welch_ttest(np.array([3.0, 4.0, 1.0, 2.1]), np.array([490.2, 340.0, 433.9]))▼
(-9.559497721932658, 2.0008523488562844, 0.01075156114978449)</lang>▼
=== Using Burkardt's betain ===
{{trans|Perl}}
Line 1,371 ⟶ 1,390:
the cumulative error is 1.13104e-14
</pre>
▲=== Using NumPy & SciPy ===
▲<lang python>import numpy as np
▲import scipy as sp
▲import scipy.stats
▲def welch_ttest(x1, x2):
▲ n1 = x1.size
▲ n2 = x2.size
▲ m1 = np.mean(x1)
▲ m2 = np.mean(x2)
▲ v1 = np.var(x1, ddof=1)
▲ v2 = np.var(x2, ddof=1)
▲ t = (m1 - m2) / np.sqrt(v1 / n1 + v2 / n2)
▲ df = (v1 / n1 + v2 / n2)**2 / (v1**2 / (n1**2 * (n1 - 1)) + v2**2 / (n2**2 * (n2 - 1)))
▲ p = 2 * sp.stats.t.cdf(-abs(t), df)
▲ return t, df, p
▲welch_ttest(np.array([3.0, 4.0, 1.0, 2.1]), np.array([490.2, 340.0, 433.9]))
▲(-9.559497721932658, 2.0008523488562844, 0.01075156114978449)</lang>
=={{header|R}}==
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