查看DA的源代码

=== 正态检验 ===

==== QQ 图 ====

<source lang=python>
>>> np.random.seed(12345678)
>>> x = np.random.normal(5,3,100)
>>> stats.probplot(x, plot=plt); plt.show()
</source>

[https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.probplot.html scipy.stats.probplot()]

<br>

==== Shapiro-Wilk ====

Shapiro-Wilk W 检验，基于观测值的排序统计量的协方差矩阵的检验，可以被用于小于等于 50 的样本量下

[https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.shapiro.html scipy.stats.shapiro()]

返回值 [W, p-value]

<source lang=python>
>>> np.random.seed(12345678)
>>> x = np.random.normal(5, 3, 100)
>>> np.random.seed()
>>> y = np.random.normal(5, 3, 100)

>>> stats.shapiro(x)
(0.9772805571556091, 0.08144091814756393)
>>> stats.shapiro(y)
(0.9933551549911499, 0.9085326790809631)
</source>

p-value: for the hypothesis test

<br>

==== Kolmogorov-Smirnov ====

科尔莫戈罗夫检验(Kolmogorov-Smirnov test)，检验样本数据是否服从某一分布，仅适用于连续分布的检验。下例中用它检验正态分布。

<source lang=python>
>>> stats.kstest(x,'norm')
KstestResult(statistic=0.8801115630229508, pvalue=1.7157931366221766e-92)
>>> stats.kstest(y,'norm')
KstestResult(statistic=0.8168376836753909, pvalue=1.7239988712511043e-73)
</source>

[https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kstest.html scipy.stats.kstest()]

p-value: One-tailed or two-tailed p-value

<br>

==== Pearson omnibus ====

D'Agostino-Pearson omnibus 检验

[https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html?highlight=omnibus stats.normaltest()]

<source lang=python>
>>> stats.normaltest(x)
NormaltestResult(statistic=6.528044509508757, pvalue=0.03823430021917039)
>>> stats.normaltest(y)
NormaltestResult(statistic=0.7706971982031684, pvalue=0.6802134730639648)
</source>

p-value: A 2-sided chi squared '''probability for the hypothesis test'''

<br>