{"id":868,"date":"2023-07-28T11:48:31","date_gmt":"2023-07-28T11:48:31","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%b1%d8%b3%d9%85-%d8%a7%d9%84%d8%a8%d9%8a%d8%a7%d9%86%d9%8a-%d8%a7%d9%84%d9%85%d8%aa%d8%a8%d9%82%d9%8a-%d9%84%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/"},"modified":"2023-07-28T11:48:31","modified_gmt":"2023-07-28T11:48:31","slug":"%d8%a7%d9%84%d8%b1%d8%b3%d9%85-%d8%a7%d9%84%d8%a8%d9%8a%d8%a7%d9%86%d9%8a-%d8%a7%d9%84%d9%85%d8%aa%d8%a8%d9%82%d9%8a-%d9%84%d8%a8%d9%8a%d8%ab%d9%88%d9%86","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%b1%d8%b3%d9%85-%d8%a7%d9%84%d8%a8%d9%8a%d8%a7%d9%86%d9%8a-%d8%a7%d9%84%d9%85%d8%aa%d8%a8%d9%82%d9%8a-%d9%84%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0645\u0624\u0627\u0645\u0631\u0629 \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629 \u0641\u064a \u0628\u064a\u062b\u0648\u0646"},"content":{"rendered":"

<\/p>\n

\n

\u0627\u0644\u0645\u062e\u0637\u0637 \u0627\u0644\u0645\u062a\u0628\u0642\u064a<\/strong> \u0647\u0648 \u0646\u0648\u0639 \u0645\u0646 \u0627\u0644\u0645\u062e\u0637\u0637 \u0627\u0644\u0630\u064a \u064a\u0639\u0631\u0636 \u0627\u0644\u0642\u064a\u0645 \u0627\u0644\u0645\u062c\u0647\u0632\u0629 \u0645\u0642\u0627\u0628\u0644 \u0628\u0642\u0627\u064a\u0627 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631<\/a> .<\/span><\/p>\n

\u063a\u0627\u0644\u0628\u064b\u0627 \u0645\u0627 \u064a\u0633\u062a\u062e\u062f\u0645 \u0647\u0630\u0627 \u0627\u0644\u0646\u0648\u0639 \u0645\u0646 \u0627\u0644\u0645\u062e\u0637\u0637\u0627\u062a \u0644\u062a\u0642\u064a\u064a\u0645 \u0645\u0627 \u0625\u0630\u0627 \u0643\u0627\u0646 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0645\u0646\u0627\u0633\u0628\u064b\u0627 \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0645\u0639\u064a\u0646\u0629 \u0623\u0645 \u0644\u0627 \u0648\u0644\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0627\u0644\u0645\u062a\u0628\u0642\u064a<\/a> .<\/span><\/p>\n

\u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0645\u062e\u0637\u0637 \u0645\u062a\u0628\u0642\u064a \u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646.<\/span><\/p>\n

\u0645\u062b\u0627\u0644: \u0627\u0644\u0645\u0624\u0627\u0645\u0631\u0629 \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/strong><\/span><\/h3>\n

\u0641\u064a \u0647\u0630\u0627 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0633\u0646\u0633\u062a\u062e\u062f\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u062a\u0635\u0641 \u0633\u0645\u0627\u062a 10 \u0645\u0646 \u0644\u0627\u0639\u0628\u064a \u0643\u0631\u0629 \u0627\u0644\u0633\u0644\u0629:<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#create dataset<\/span>\ndf = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#view dataset\n<\/span>df\n\n\trating points assists rebounds\n0 90 25 5 11\n1 85 20 7 8\n2 82 14 7 10\n3 88 16 8 6\n4 94 27 5 6\n5 90 20 7 9\n6 76 12 6 6\n7 75 15 9 10\n8 87 14 9 10\n9 86 19 5 7<\/strong><\/pre>\n
 \u0627\u0644\u0645\u0624\u0627\u0645\u0631\u0629 \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629 \u0644\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0628\u0633\u064a\u0637<\/span><\/strong><\/h3>\n
 \u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646\u0646\u0627 \u0646\u0644\u0627\u0626\u0645 \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 \u062e\u0637\u064a \u0628\u0633\u064a\u0637 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0646\u0642\u0627\u0637<\/em> \u0643\u0645\u062a\u063a\u064a\u0631 \u0645\u062a\u0648\u0642\u0639 \u0648\u0627\u0644\u062f\u0631\u062c\u0629<\/em> \u0643\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629:<\/span><\/p>\n
 #import necessary libraries<\/span>\nimport<\/span> matplotlib.pyplot as<\/span> plt\nimport<\/span> statsmodels.api as<\/span> sm\nfrom<\/span> statsmodels.formula.api import<\/span> ols\n\n#fit simple linear regression model\n<\/span>model = ols('rating ~ points', data=df). fit<\/span> ()\n\n#view model summary\n<\/span>print(model.summary())\n<\/strong><\/pre>\n
 #define figure size\n<\/span>fig = plt.figure(figsize=(12,8))\n\n#produce regression plots<\/span>\nfig = sm.graphics.plot_regress_exog(model, ' points<\/span> ', fig=fig)\n<\/strong><\/pre>\n
\"\u0645\u0624\u0627\u0645\u0631\u0629<\/p>\n
 \u064a\u062a\u0645 \u0625\u0646\u062a\u0627\u062c \u0623\u0631\u0628\u0639 \u0642\u0637\u0639. \u0627\u0644\u0642\u0637\u0639\u0629 \u0627\u0644\u0645\u0648\u062c\u0648\u062f\u0629 \u0641\u064a \u0627\u0644\u0632\u0627\u0648\u064a\u0629 \u0627\u0644\u064a\u0645\u0646\u0649 \u0627\u0644\u0639\u0644\u064a\u0627 \u0647\u064a \u0642\u0637\u0639\u0629 \u0627\u0644\u0623\u0631\u0636 \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629 \u0645\u0642\u0627\u0628\u0644 \u0642\u0637\u0639\u0629 \u0627\u0644\u0623\u0631\u0636 \u0627\u0644\u0645\u0639\u062f\u0644\u0629. \u064a\u064f\u0638\u0647\u0631 \u0627\u0644\u0645\u062d\u0648\u0631 \u0627\u0644\u0633\u064a\u0646\u064a \u0641\u064a \u0647\u0630\u0627 \u0627\u0644\u0631\u0633\u0645 \u0627\u0644\u0642\u064a\u0645 \u0627\u0644\u0641\u0639\u0644\u064a\u0629 \u0644\u0646\u0642\u0627\u0637<\/em> \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u062a\u0648\u0642\u0639 \u0648\u064a\u0638\u0647\u0631 \u0627\u0644\u0645\u062d\u0648\u0631 \u0627\u0644\u0635\u0627\u062f\u064a \u0627\u0644\u0645\u062a\u0628\u0642\u064a \u0644\u062a\u0644\u0643 \u0627\u0644\u0642\u064a\u0645\u0629.<\/span><\/p>\n
 \u0648\u0628\u0645\u0627 \u0623\u0646 \u0627\u0644\u0628\u0642\u0627\u064a\u0627 \u062a\u0628\u062f\u0648 \u0645\u0646\u062a\u0634\u0631\u0629 \u0628\u0634\u0643\u0644 \u0639\u0634\u0648\u0627\u0626\u064a \u062d\u0648\u0644 \u0627\u0644\u0635\u0641\u0631\u060c \u0641\u0625\u0646 \u0647\u0630\u0627 \u064a\u0634\u064a\u0631 \u0625\u0644\u0649 \u0623\u0646 \u0627\u0644\u062a\u063a\u0627\u064a\u0631\u064a\u0629 \u0644\u064a\u0633\u062a \u0645\u0634\u0643\u0644\u0629 \u0645\u0639 \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u062a\u0648\u0642\u0639.<\/span><\/p>\n
 \u0627\u0644\u0645\u0624\u0627\u0645\u0631\u0627\u062a \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629 \u0644\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f<\/strong><\/span><\/h3>\n
 \u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646\u0646\u0627 \u0628\u062f\u0644\u0627\u064b \u0645\u0646 \u0630\u0644\u0643 \u0646\u0644\u0627\u0626\u0645 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0645\u0633\u0627\u0639\u062f\u0629<\/em> \u0648\u0627\u0644\u0627\u0631\u062a\u062f\u0627\u062f\u0627\u062a<\/em> \u0643\u0645\u062a\u063a\u064a\u0631 \u0645\u062a\u0646\u0628\u0626 \u0648\u0627\u0644\u062a\u0642\u064a\u064a\u0645<\/em> \u0643\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629:<\/span><\/p>\n
 #fit multiple linear regression model\n<\/span>model = ols('rating ~ assists + rebounds', data=df). fit<\/span> ()\n\n#view model summary\n<\/span>print(model.summary())\n<\/strong><\/pre>\n
 \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0647\u0630\u0627 \u0647\u0648 \u0627\u0644\u0634\u0643\u0644 \u0627\u0644\u0630\u064a \u062a\u0628\u062f\u0648 \u0639\u0644\u064a\u0647 \u0627\u0644\u0645\u062e\u0637\u0637\u0627\u062a \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629\/\u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u062a\u0648\u0642\u0639 \u0627\u0644\u0645\u0633\u0627\u0639\u062f\u0629<\/em> :<\/span><\/p>\n
 #create residual vs. predictor plot for 'assists'\n<\/span>fig = plt.figure(figsize=(12,8))\nfig = sm.graphics.plot_regress_exog(model, ' assists<\/span> ', fig=fig)\n<\/strong><\/pre>\n
 \"\u0627\u0644\u0623\u0631\u0627\u0636\u064a<\/span><\/p>\n
 \u0648\u0647\u0630\u0627 \u0647\u0648 \u0627\u0644\u0634\u0643\u0644 \u0627\u0644\u0630\u064a \u062a\u0628\u062f\u0648 \u0639\u0644\u064a\u0647 \u0627\u0644\u0645\u062e\u0637\u0637\u0627\u062a \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629\/\u0627\u0644\u0645\u062a\u0646\u0628\u0626\u0629 \u0628\u0627\u0644\u0646\u0633\u0628\u0629 \u0644\u0627\u0631\u062a\u062f\u0627\u062f\u0627\u062a<\/em> \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u062a\u0648\u0642\u0639:<\/span> <\/p>\n
 #create residual vs. predictor plot for 'assists'\n<\/span>fig = plt.figure(figsize=(12,8))\nfig = sm.graphics.plot_regress_exog(model, ' rebounds<\/span> ', fig=fig)\n<\/strong><\/pre>\n
\"\u0627\u0644\u0645\u0624\u0627\u0645\u0631\u0629<\/p>\n
 \u0641\u064a \u0643\u0644\u0627 \u0627\u0644\u0645\u062e\u0637\u0637\u064a\u0646\u060c \u064a\u0628\u062f\u0648 \u0623\u0646 \u0627\u0644\u0628\u0642\u0627\u064a\u0627 \u0645\u062a\u0646\u0627\u062b\u0631\u0629 \u0628\u0634\u0643\u0644 \u0639\u0634\u0648\u0627\u0626\u064a \u062d\u0648\u0644 \u0627\u0644\u0635\u0641\u0631\u060c \u0645\u0645\u0627 \u064a\u0634\u064a\u0631 \u0625\u0644\u0649 \u0623\u0646 \u0627\u0644\u062a\u063a\u0627\u064a\u0631\u064a\u0629 \u0644\u0627 \u062a\u0645\u062b\u0644 \u0645\u0634\u0643\u0644\u0629 \u0645\u0639 \u0623\u064a \u0645\u0646 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0641\u064a \u0627\u0644\u0646\u0645\u0648\u0630\u062c.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0627\u0644\u0645\u062e\u0637\u0637 \u0627\u0644\u0645\u062a\u0628\u0642\u064a \u0647\u0648 \u0646\u0648\u0639 \u0645\u0646 \u0627\u0644\u0645\u062e\u0637\u0637 \u0627\u0644\u0630\u064a \u064a\u0639\u0631\u0636 \u0627\u0644\u0642\u064a\u0645 \u0627\u0644\u0645\u062c\u0647\u0632\u0629 \u0645\u0642\u0627\u0628\u0644 \u0628\u0642\u0627\u064a\u0627 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 . \u063a\u0627\u0644\u0628\u064b\u0627 \u0645\u0627 \u064a\u0633\u062a\u062e\u062f\u0645 \u0647\u0630\u0627 \u0627\u0644\u0646\u0648\u0639 \u0645\u0646 \u0627\u0644\u0645\u062e\u0637\u0637\u0627\u062a \u0644\u062a\u0642\u064a\u064a\u0645 \u0645\u0627 \u0625\u0630\u0627 \u0643\u0627\u0646 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0645\u0646\u0627\u0633\u0628\u064b\u0627 \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0645\u0639\u064a\u0646\u0629 \u0623\u0645 \u0644\u0627 \u0648\u0644\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0627\u0644\u0645\u062a\u0628\u0642\u064a . \u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0645\u062e\u0637\u0637 \u0645\u062a\u0628\u0642\u064a \u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646. \u0645\u062b\u0627\u0644: \u0627\u0644\u0645\u0624\u0627\u0645\u0631\u0629 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"\n\u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0642\u0637\u0639\u0629 \u0623\u0631\u0636 \u0645\u062a\u0628\u0642\u064a\u0629 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646 - 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