{"id":4233,"date":"2023-07-12T15:27:26","date_gmt":"2023-07-12T15:27:26","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d1%80%d1%96%d0%b2%d0%bd%d1%8f%d0%bd%d0%bd%d1%8f-%d0%bc%d0%be%d1%80%d1%81%d1%8c%d0%ba%d0%be%d0%b3%d0%be-regplot\/"},"modified":"2023-07-12T15:27:26","modified_gmt":"2023-07-12T15:27:26","slug":"%d1%80%d1%96%d0%b2%d0%bd%d1%8f%d0%bd%d0%bd%d1%8f-%d0%bc%d0%be%d1%80%d1%81%d1%8c%d0%ba%d0%be%d0%b3%d0%be-regplot","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d1%80%d1%96%d0%b2%d0%bd%d1%8f%d0%bd%d0%bd%d1%8f-%d0%bc%d0%be%d1%80%d1%81%d1%8c%d0%ba%d0%be%d0%b3%d0%be-regplot\/","title":{"rendered":"\u042f\u043a \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0437\u0438\u0442\u0438 \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432 seaborn regplot"},"content":{"rendered":"

<\/p>\n

\n

\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e seaborn regplot<\/strong> , \u0449\u043e\u0431 \u043f\u043e\u0431\u0443\u0434\u0443\u0432\u0430\u0442\u0438 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457, \u044f\u043a\u0430 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u0454 \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445.<\/span><\/span><\/p>\n

\u041d\u0430 \u0436\u0430\u043b\u044c, Seaborn \u043d\u0435 \u043c\u0430\u0454 \u0432\u0431\u0443\u0434\u043e\u0432\u0430\u043d\u043e\u0457 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \u0434\u043b\u044f \u043e\u0442\u0440\u0438\u043c\u0430\u043d\u043d\u044f \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0437 \u043b\u0456\u043d\u0456\u0457, \u0430\u043b\u0435 \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e scipy.stats.linregress<\/strong> , \u0449\u043e\u0431 \u0448\u0432\u0438\u0434\u043a\u043e \u0437\u043d\u0430\u0439\u0442\u0438 \u043a\u043e\u0435\u0444\u0456\u0446\u0456\u0454\u043d\u0442\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457:<\/span><\/p>\n

 import<\/span> scipy\nimport<\/span> seaborn as<\/span> sns\n\n#create regplot\n<\/span>p = sns. regplot<\/span> (data=df, x=df. x<\/span> , y=df. y<\/span> )\n\n#calculate slope and intercept of regression equation\n<\/span>slope, intercept, r, p, sterr = scipy. stats<\/span> . linregress<\/span> (x= p.get_lines<\/span> ()[0] .get_xdata<\/span> (),\n                                                       y=p. get_lines<\/span> ()[0]. get_ydata<\/span> ())\n<\/strong><\/pre>\n
 \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0446\u0435\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456.<\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u041f\u043e\u043a\u0430\u0437\u0430\u0442\u0438 \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432 Seaborn Regplot<\/strong><\/span><\/h2>\n
 \u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u043c\u0438 \u043c\u0430\u0454\u043c\u043e \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 DataFrame pandas, \u044f\u043a\u0438\u0439 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u044e \u043f\u0440\u043e \u0432\u0438\u0432\u0447\u0435\u043d\u0456 \u0433\u043e\u0434\u0438\u043d\u0438 \u0442\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0438 \u0432\u0438\u043f\u0443\u0441\u043a\u043d\u0438\u0445 \u0456\u0441\u043f\u0438\u0442\u0456\u0432 \u0440\u0456\u0437\u043d\u0438\u0445 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0456\u0432:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                   ' score<\/span> ': [77, 79, 84, 80, 81, 89, 95, 90, 83, 89]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   hours score\n0 1 77\n1 2 79\n2 3 84\n3 4 80\n4 5 81\n5 6 89\n6 7 95\n7 8 90\n8 9 83\n9 10 89\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u043c\u0438 \u0445\u043e\u0447\u0435\u043c\u043e \u043f\u043e\u0431\u0443\u0434\u0443\u0432\u0430\u0442\u0438 \u0442\u043e\u0447\u043a\u0438 \u0434\u0430\u043d\u0438\u0445 \u0456 \u0434\u043e\u0434\u0430\u0442\u0438 \u0434\u043e \u0434\u0430\u043d\u0438\u0445 \u043f\u0456\u0434\u0456\u0433\u043d\u0430\u043d\u0443 \u043b\u0456\u043d\u0456\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457.<\/span><\/p>\n
 \u0414\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441:<\/span> <\/p>\n
 import<\/span> scipy\nimport<\/span> seaborn as<\/span> sns\n\n#create regplot\n<\/span>p = sns. regplot<\/span> (data=df, x=df. hours<\/span> , y=df. score<\/span> )\n\n#calculate slope and intercept of regression equation\n<\/span>slope, intercept, r, p, sterr = scipy. stats<\/span> . linregress<\/span> (x= p.get_lines<\/span> ()[0] .get_xdata<\/span> (),\n                                                       y=p. get_lines<\/span> ()[0]. get_ydata<\/span> ())\n\n#display slope and intercept of regression equation\n<\/span>print<\/span> (intercept, slope)\n\n77.39999999999995 1.3272727272727356\n<\/strong><\/pre>\n
\"\"<\/p>\n
 \u0417 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u043b\u0456\u043d\u0456\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u043c\u0430\u0454 \u0442\u0430\u043a\u0435 \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f:<\/span><\/p>\n
 y = 77,4 + 1,327<\/strong><\/span><\/p>\n
 \u042f\u043a\u0449\u043e \u043c\u0438 \u0445\u043e\u0447\u0435\u043c\u043e \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0437\u0438\u0442\u0438 \u0446\u0435 \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u043d\u0430 \u043c\u043e\u0440\u0441\u044c\u043a\u043e\u043c\u0443 regplot<\/strong> , \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e matplotlib text()<\/strong> :<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt<\/span>\nimport<\/span> scipy\nimport<\/span> seaborn as<\/span> sns\n\n#create regplot\n<\/span>p = sns. regplot<\/span> (data=df, x=df. hours<\/span> , y=df. score<\/span> )\n\n#calculate slope and intercept of regression equation\n<\/span>slope, intercept, r, p, sterr = scipy. stats<\/span> . linregress<\/span> (x= p.get_lines<\/span> ()[0] .get_xdata<\/span> (),\n                                                       y=p. get_lines<\/span> ()[0]. get_ydata<\/span> ())\n\n#add regression equation to plot\n<\/span>plt. text<\/span> (2, 95, ' y =<\/span> ' + str(round(intercept,3)) + ' +<\/span> ' + str(round(slope,3)) + ' x<\/span> ')<\/span><\/strong> <\/pre>\n
\"\u0420\u0456\u0432\u043d\u044f\u043d\u043d\u044f<\/p>\n
 \u0417\u0432\u0435\u0440\u043d\u0456\u0442\u044c \u0443\u0432\u0430\u0433\u0443, \u0449\u043e \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0442\u0435\u043f\u0435\u0440 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454\u0442\u044c\u0441\u044f \u0443 \u0432\u0435\u0440\u0445\u043d\u044c\u043e\u043c\u0443 \u043b\u0456\u0432\u043e\u043c\u0443 \u043a\u0443\u0442\u0456 \u0433\u0440\u0430\u0444\u0456\u043a\u0430.<\/span><\/p>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0443 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 text()<\/strong> \u043c\u0438 \u0432\u043a\u0430\u0437\u0430\u043b\u0438, \u0449\u043e \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u043c\u0430\u0454 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0442\u0438\u0441\u044f \u0437 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442 (x, y) (2, 95).<\/span><\/p>\n
 \u041d\u0435 \u0441\u043e\u0440\u043e\u043c\u0442\u0435\u0441\u044f \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u0446\u0456 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u0438, \u0449\u043e\u0431 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0437\u0438\u0442\u0438 \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457, \u0434\u0435 \u0437\u0430\u0432\u0433\u043e\u0434\u043d\u043e \u043d\u0430 \u0432\u0430\u0448\u043e\u043c\u0443 \u0432\u043b\u0430\u0441\u043d\u043e\u043c\u0443 \u0433\u0440\u0430\u0444\u0456\u043a\u0443.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> : \u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0437\u043d\u0430\u0439\u0442\u0438 \u043f\u043e\u0432\u043d\u0443 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u044e \u0434\u043b\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u0457 seaborn regplot<\/strong> \u0442\u0443\u0442<\/a> .<\/span><\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h2>\n
 \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0445 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a\u0430\u0445 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0432\u0438\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u0456\u043d\u0448\u0456 \u0442\u0438\u043f\u043e\u0432\u0456 \u0437\u0430\u0432\u0434\u0430\u043d\u043d\u044f \u0432 Seaborn:<\/span><\/p>\n
 \u042f\u043a \u0432\u0456\u0434\u043a\u043e\u0440\u0438\u0433\u0443\u0432\u0430\u0442\u0438 \u0440\u043e\u0437\u043c\u0456\u0440 \u0444\u0456\u0433\u0443\u0440\u0438 \u0441\u044e\u0436\u0435\u0442\u0443 Seaborn<\/a>
 \u042f\u043a \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u043f\u043e\u043b\u043e\u0436\u0435\u043d\u043d\u044f \u043b\u0435\u0433\u0435\u043d\u0434\u0438 \u0432 Seaborn<\/a>
 \u042f\u043a \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u043f\u043e\u0437\u043d\u0430\u0447\u043a\u0438 \u043e\u0441\u0435\u0439 \u043d\u0430 \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0456 Seaborn<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e seaborn regplot , \u0449\u043e\u0431 \u043f\u043e\u0431\u0443\u0434\u0443\u0432\u0430\u0442\u0438 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457, \u044f\u043a\u0430 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u0454 \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445. \u041d\u0430 \u0436\u0430\u043b\u044c, Seaborn \u043d\u0435 \u043c\u0430\u0454 \u0432\u0431\u0443\u0434\u043e\u0432\u0430\u043d\u043e\u0457 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \u0434\u043b\u044f \u043e\u0442\u0440\u0438\u043c\u0430\u043d\u043d\u044f \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0437 \u043b\u0456\u043d\u0456\u0457, \u0430\u043b\u0435 \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e scipy.stats.linregress , \u0449\u043e\u0431 \u0448\u0432\u0438\u0434\u043a\u043e \u0437\u043d\u0430\u0439\u0442\u0438 \u043a\u043e\u0435\u0444\u0456\u0446\u0456\u0454\u043d\u0442\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457: import scipy import seaborn as sns #create regplot p = sns. regplot (data=df, x=df. […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"\n\u042f\u043a \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0437\u0438\u0442\u0438 \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432 Seaborn Regplot - Statorials<\/title>\n<meta name=\"description\" content=\"\u0423 \u0446\u044c\u043e\u043c\u0443 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a\u0443 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0437\u0438\u0442\u0438 \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u043d\u0430 \u0441\u0445\u0435\u043c\u0456 \u0421\u0456\u0431\u043e\u0440\u043d\u0430 \u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c.\" \/>\n<meta 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