{"id":1038167,"date":"2024-12-31T12:21:31","date_gmt":"2024-12-31T04:21:31","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1038167.html"},"modified":"2024-12-31T12:21:34","modified_gmt":"2024-12-31T04:21:34","slug":"python%e5%a6%82%e4%bd%95%e6%a3%80%e9%aa%8c%e8%af%af%e5%b7%ae%e6%96%b9%e5%b7%ae%e9%bd%90%e6%80%a7","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1038167.html","title":{"rendered":"python\u5982\u4f55\u68c0\u9a8c\u8bef\u5dee\u65b9\u5dee\u9f50\u6027"},"content":{"rendered":"

\"python\u5982\u4f55\u68c0\u9a8c\u8bef\u5dee\u65b9\u5dee\u9f50\u6027\"<\/p>\n

Python\u4e2d\u68c0\u9a8c\u8bef\u5dee\u65b9\u5dee\u9f50\u6027\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u53ef\u4ee5\u4f7f\u7528\u7edf\u8ba1\u68c0\u9a8c\u3001\u56fe\u5f62\u5206\u6790\u3001\u6a21\u578b\u8bca\u65ad\u7b49\u65b9\u6cd5\uff0c\u5176\u4e2d\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ecLevene\u68c0\u9a8c\u3001Bartlett\u68c0\u9a8c\u3001\u56fe\u5f62\u5206\u6790\u7b49\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecdLevene\u68c0\u9a8c\u3002<\/strong><\/p>\n<\/p>\n

Levene\u68c0\u9a8c\u662f\u4e00\u4e2a\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u7528\u4e8e\u68c0\u9a8c\u4e0d\u540c\u6837\u672c\u7ec4\u7684\u8bef\u5dee\u65b9\u5dee\u662f\u5426\u9f50\u6027\u3002Levene\u68c0\u9a8c\u7684\u539f\u7406\u662f\u6bd4\u8f83\u5404\u7ec4\u6570\u636e\u504f\u79bb\u5176\u5747\u503c\u6216\u4e2d\u4f4d\u6570\u7684\u7edd\u5bf9\u504f\u5dee\uff0c\u6765\u5224\u65ad\u65b9\u5dee\u662f\u5426\u76f8\u7b49\u3002Python\u4e2d\u53ef\u4ee5\u4f7f\u7528scipy.stats<\/code>\u6a21\u5757\u4e2d\u7684levene<\/code>\u51fd\u6570\u6765\u8fdb\u884cLevene\u68c0\u9a8c\u3002<\/p>\n<\/p>\n

\u4e00\u3001Levene\u68c0\u9a8c<\/h3>\n<\/p>\n

Levene\u68c0\u9a8c\u662f\u68c0\u9a8c\u65b9\u5dee\u9f50\u6027\u7684\u5e38\u7528\u65b9\u6cd5\uff0c\u5176\u5047\u8bbe\u662f\u5404\u7ec4\u6570\u636e\u7684\u65b9\u5dee\u76f8\u7b49\uff0c\u5373\u8bef\u5dee\u65b9\u5dee\u9f50\u6027\u3002\u5982\u679c\u68c0\u9a8c\u7ed3\u679c\u7684p\u503c\u5927\u4e8e\u663e\u8457\u6027\u6c34\u5e73\uff08\u901a\u5e38\u4e3a0.05\uff09\uff0c\u5219\u4e0d\u80fd\u62d2\u7edd\u65b9\u5dee\u9f50\u6027\u7684\u5047\u8bbe\uff1b\u5982\u679cp\u503c\u5c0f\u4e8e\u663e\u8457\u6027\u6c34\u5e73\uff0c\u5219\u62d2\u7edd\u65b9\u5dee\u9f50\u6027\u7684\u5047\u8bbe\u3002<\/p>\n<\/p>\n

1. \u4ee3\u7801\u793a\u4f8b<\/h4>\n<\/p>\n

import numpy as np<\/p>\n
from scipy import stats<\/p>\n
\u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
group1 = np.random.normal(loc=0, scale=1, size=100)<\/p>\n
group2 = np.random.normal(loc=0, scale=1, size=100)<\/p>\n
group3 = np.random.normal(loc=0, scale=1, size=100)<\/p>\n
\u8fdb\u884cLevene\u68c0\u9a8c<\/strong><\/h2>\n
stat, p_value = stats.levene(group1, group2, group3)<\/p>\n
print('Levene\u68c0\u9a8c\u7edf\u8ba1\u91cf:', stat)<\/p>\n
print('p\u503c:', p_value)<\/p>\n
\u7ed3\u679c\u89e3\u91ca<\/strong><\/h2>\n
if p_value > 0.05:<\/p>\n
    print('\u4e0d\u80fd\u62d2\u7edd\u539f\u5047\u8bbe\uff0c\u5404\u7ec4\u6570\u636e\u65b9\u5dee\u9f50\u6027')<\/p>\n
else:<\/p>\n
    print('\u62d2\u7edd\u539f\u5047\u8bbe\uff0c\u5404\u7ec4\u6570\u636e\u65b9\u5dee\u4e0d\u9f50\u6027')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e09\u7ec4\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e\uff0c\u5e76\u4f7f\u7528Levene\u68c0\u9a8c\u6765\u68c0\u9a8c\u5b83\u4eec\u7684\u65b9\u5dee\u9f50\u6027\u3002\u68c0\u9a8c\u7ed3\u679c\u663e\u793aLevene\u68c0\u9a8c\u7edf\u8ba1\u91cf\u548cp\u503c\uff0c\u5e76\u6839\u636ep\u503c\u5224\u65ad\u662f\u5426\u62d2\u7edd\u65b9\u5dee\u9f50\u6027\u7684\u539f\u5047\u8bbe\u3002<\/p>\n<\/p>\n
\u4e8c\u3001Bartlett\u68c0\u9a8c<\/h3>\n<\/p>\n
Bartlett\u68c0\u9a8c\u4e5f\u662f\u68c0\u9a8c\u65b9\u5dee\u9f50\u6027\u7684\u5e38\u7528\u65b9\u6cd5\uff0c\u5176\u5047\u8bbe\u662f\u5404\u7ec4\u6570\u636e\u7684\u65b9\u5dee\u76f8\u7b49\u3002\u4e0eLevene\u68c0\u9a8c\u4e0d\u540c\u7684\u662f\uff0cBartlett\u68c0\u9a8c\u5bf9\u6570\u636e\u7684\u6b63\u6001\u6027\u8981\u6c42\u8f83\u9ad8\uff0c\u56e0\u6b64\u5728\u6570\u636e\u4e0d\u6ee1\u8db3\u6b63\u6001\u6027\u5047\u8bbe\u65f6\u53ef\u80fd\u4f1a\u5931\u6548\u3002<\/p>\n<\/p>\n
1. \u4ee3\u7801\u793a\u4f8b<\/h4>\n<\/p>\n
import numpy as np<\/p>\n
from scipy import stats<\/p>\n
\u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
group1 = np.random.normal(loc=0, scale=1, size=100)<\/p>\n
group2 = np.random.normal(loc=0, scale=1, size=100)<\/p>\n
group3 = np.random.normal(loc=0, scale=1, size=100)<\/p>\n
\u8fdb\u884cBartlett\u68c0\u9a8c<\/strong><\/h2>\n
stat, p_value = stats.bartlett(group1, group2, group3)<\/p>\n
print('Bartlett\u68c0\u9a8c\u7edf\u8ba1\u91cf:', stat)<\/p>\n
print('p\u503c:', p_value)<\/p>\n
\u7ed3\u679c\u89e3\u91ca<\/strong><\/h2>\n
if p_value > 0.05:<\/p>\n
    print('\u4e0d\u80fd\u62d2\u7edd\u539f\u5047\u8bbe\uff0c\u5404\u7ec4\u6570\u636e\u65b9\u5dee\u9f50\u6027')<\/p>\n
else:<\/p>\n
    print('\u62d2\u7edd\u539f\u5047\u8bbe\uff0c\u5404\u7ec4\u6570\u636e\u65b9\u5dee\u4e0d\u9f50\u6027')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0eLevene\u68c0\u9a8c\u7c7b\u4f3c\uff0c\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\u6211\u4eec\u751f\u6210\u4e86\u4e09\u7ec4\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e\uff0c\u5e76\u4f7f\u7528Bartlett\u68c0\u9a8c\u6765\u68c0\u9a8c\u5b83\u4eec\u7684\u65b9\u5dee\u9f50\u6027\u3002\u68c0\u9a8c\u7ed3\u679c\u663e\u793aBartlett\u68c0\u9a8c\u7edf\u8ba1\u91cf\u548cp\u503c\uff0c\u5e76\u6839\u636ep\u503c\u5224\u65ad\u662f\u5426\u62d2\u7edd\u65b9\u5dee\u9f50\u6027\u7684\u539f\u5047\u8bbe\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u56fe\u5f62\u5206\u6790<\/h3>\n<\/p>\n
\u9664\u4e86\u4f7f\u7528\u7edf\u8ba1\u68c0\u9a8c\u65b9\u6cd5\u5916\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7\u56fe\u5f62\u5206\u6790\u6765\u68c0\u9a8c\u8bef\u5dee\u65b9\u5dee\u9f50\u6027\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u7ed8\u5236\u6b8b\u5dee\u56fe\u6216\u7bb1\u7ebf\u56fe\u6765\u89c2\u5bdf\u4e0d\u540c\u7ec4\u6570\u636e\u7684\u65b9\u5dee\u662f\u5426\u76f8\u7b49\u3002<\/p>\n<\/p>\n
1. \u6b8b\u5dee\u56fe<\/h4>\n<\/p>\n
\u6b8b\u5dee\u56fe\u662f\u4e00\u79cd\u5e38\u7528\u7684\u56fe\u5f62\u5206\u6790\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u6765\u89c2\u5bdf\u8bef\u5dee\u7684\u5206\u5e03\u60c5\u51b5\u3002\u901a\u8fc7\u7ed8\u5236\u6b8b\u5dee\u4e0e\u9884\u6d4b\u503c\u6216\u81ea\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdf\u8bef\u5dee\u65b9\u5dee\u662f\u5426\u9f50\u6027\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
\u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
X = np.random.normal(loc=0, scale=1, size=100).reshape(-1, 1)<\/p>\n
y = 3 * X.flatten() + np.random.normal(loc=0, scale=1, size=100)<\/p>\n
\u8fdb\u884c\u7ebf\u6027\u56de\u5f52<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
model.fit(X, y)<\/p>\n
y_pred = model.predict(X)<\/p>\n
residuals = y - y_pred<\/p>\n
\u7ed8\u5236\u6b8b\u5dee\u56fe<\/strong><\/h2>\n
plt.scatter(y_pred, residuals)<\/p>\n
plt.axhline(0, color='red', linestyle='--')<\/p>\n
plt.xlabel('\u9884\u6d4b\u503c')<\/p>\n
plt.ylabel('\u6b8b\u5dee')<\/p>\n
plt.title('\u6b8b\u5dee\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u4e2a\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u6570\u636e\uff0c\u5e76\u7ed8\u5236\u4e86\u6b8b\u5dee\u56fe\u3002\u901a\u8fc7\u89c2\u5bdf\u6b8b\u5dee\u56fe\uff0c\u53ef\u4ee5\u5224\u65ad\u8bef\u5dee\u65b9\u5dee\u662f\u5426\u9f50\u6027\u3002\u5982\u679c\u6b8b\u5dee\u5728\u9884\u6d4b\u503c\u7684\u5404\u4e2a\u8303\u56f4\u5185\u968f\u673a\u5206\u5e03\u4e14\u6ca1\u6709\u660e\u663e\u7684\u6a21\u5f0f\uff0c\u5219\u8bef\u5dee\u65b9\u5dee\u662f\u9f50\u6027\u7684\u3002<\/p>\n<\/p>\n
2. \u7bb1\u7ebf\u56fe<\/h4>\n<\/p>\n
\u7bb1\u7ebf\u56fe\u662f\u4e00\u79cd\u5e38\u7528\u7684\u56fe\u5f62\u5206\u6790\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u6765\u89c2\u5bdf\u4e0d\u540c\u7ec4\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u901a\u8fc7\u7ed8\u5236\u4e0d\u540c\u7ec4\u6570\u636e\u7684\u7bb1\u7ebf\u56fe\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdf\u5404\u7ec4\u6570\u636e\u7684\u65b9\u5dee\u662f\u5426\u76f8\u7b49\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
group1 = np.random.normal(loc=0, scale=1, size=100)<\/p>\n
group2 = np.random.normal(loc=0, scale=1, size=100)<\/p>\n
group3 = np.random.normal(loc=0, scale=1, size=100)<\/p>\n
\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
data = [group1, group2, group3]<\/p>\n
plt.boxplot(data, labels=['\u7ec41', '\u7ec42', '\u7ec43'])<\/p>\n
plt.xlabel('\u7ec4\u522b')<\/p>\n
plt.ylabel('\u6570\u636e\u503c')<\/p>\n
plt.title('\u7bb1\u7ebf\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e09\u7ec4\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e\uff0c\u5e76\u7ed8\u5236\u4e86\u5b83\u4eec\u7684\u7bb1\u7ebf\u56fe\u3002\u901a\u8fc7\u89c2\u5bdf\u7bb1\u7ebf\u56fe\uff0c\u53ef\u4ee5\u5224\u65ad\u5404\u7ec4\u6570\u636e\u7684\u65b9\u5dee\u662f\u5426\u76f8\u7b49\u3002\u5982\u679c\u5404\u7ec4\u6570\u636e\u7684\u7bb1\u4f53\u9ad8\u5ea6\u76f8\u4f3c\uff0c\u5219\u65b9\u5dee\u662f\u9f50\u6027\u7684\u3002<\/p>\n<\/p>\n
\u56db\u3001\u6a21\u578b\u8bca\u65ad<\/h3>\n<\/p>\n
\u5728\u5efa\u6a21\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u6a21\u578b\u8bca\u65ad\u6765\u68c0\u9a8c\u8bef\u5dee\u65b9\u5dee\u9f50\u6027\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528\u5e7f\u4e49\u7ebf\u6027\u6a21\u578b\uff08GLM\uff09\u4e2d\u7684\u8bca\u65ad\u65b9\u6cd5\u6765\u68c0\u67e5\u8bef\u5dee\u65b9\u5dee\u662f\u5426\u9f50\u6027\u3002<\/p>\n<\/p>\n
1. \u4ee3\u7801\u793a\u4f8b<\/h4>\n<\/p>\n
import numpy as np<\/p>\n
import statsmodels.api as sm<\/p>\n
\u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
X = np.random.normal(loc=0, scale=1, size=100)<\/p>\n
y = 3 * X + np.random.normal(loc=0, scale=1, size=100)<\/p>\n
\u8fdb\u884c\u5e7f\u4e49\u7ebf\u6027\u6a21\u578b\u62df\u5408<\/strong><\/h2>\n
X = sm.add_constant(X)<\/p>\n
model = sm.OLS(y, X).fit()<\/p>\n
\u7ed8\u5236\u6a21\u578b\u8bca\u65ad\u56fe<\/strong><\/h2>\n
sm.graphics.plot_regress_exog(model, 'x1')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u4e2a\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u6570\u636e\uff0c\u5e76\u4f7f\u7528\u5e7f\u4e49\u7ebf\u6027\u6a21\u578b\uff08GLM\uff09\u62df\u5408\u6570\u636e\u3002\u901a\u8fc7\u7ed8\u5236\u6a21\u578b\u8bca\u65ad\u56fe\uff0c\u53ef\u4ee5\u68c0\u67e5\u8bef\u5dee\u65b9\u5dee\u662f\u5426\u9f50\u6027\u3002\u5982\u679c\u6a21\u578b\u8bca\u65ad\u56fe\u4e2d\u6b8b\u5dee\u7684\u5206\u5e03\u6ca1\u6709\u660e\u663e\u7684\u6a21\u5f0f\u4e14\u65b9\u5dee\u662f\u6052\u5b9a\u7684\uff0c\u5219\u8bef\u5dee\u65b9\u5dee\u662f\u9f50\u6027\u7684\u3002<\/p>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
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