<p>使用<a href="https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.OLSResults.outlier_test.html" rel="nofollow noreferrer">OLSRresults.outlier_test()</a>函数生成一个数据集,该数据集包含每个观察的studentized残差</p>
<p>例如:</p>
<p/><div class="snippet" data-lang="js" data-hide="false" data-console="true" data-babel="false">&13;
第13部分,;
<pre class="snippet-code-html lang-html prettyprint-override"><code>#import necessary packages and functions
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.formula.api import ols
#create dataset
df = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],
'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19]})
#fit simple linear regression model
model = ols('rating ~ points', data=df).fit()
#calculate studentized residuals
stud_res = model.outlier_test()
#display studentized residuals
print(stud_res)
student_resid unadj_p bonf(p)
0 -0.486471 0.641494 1.000000
1 -0.491937 0.637814 1.000000
2 0.172006 0.868300 1.000000
3 1.287711 0.238781 1.000000
4 0.106923 0.917850 1.000000
5 0.748842 0.478355 1.000000
6 -0.968124 0.365234 1.000000
7 -2.409911 0.046780 0.467801
8 1.688046 0.135258 1.000000
9 -0.014163 0.989095 1.000000</code></pre>
;
</div>和#13;
</div>和#13;
<p>本教程提供了完整的解释:<a href="https://www.statology.org/studentized-residuals-in-python/" rel="nofollow noreferrer">https://www.statology.org/studentized-residuals-in-python/</a></p>