{"id":524,"date":"2023-07-29T14:54:06","date_gmt":"2023-07-29T14:54:06","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d1%96%d0%bd%d1%82%d0%b5%d1%80%d0%b2%d0%b0%d0%bb-%d0%bf%d1%80%d0%be%d0%b3%d0%bd%d0%be%d0%b7%d1%83%d0%b2%d0%b0%d0%bd%d0%bd%d1%8f-r\/"},"modified":"2023-07-29T14:54:06","modified_gmt":"2023-07-29T14:54:06","slug":"%d1%96%d0%bd%d1%82%d0%b5%d1%80%d0%b2%d0%b0%d0%bb-%d0%bf%d1%80%d0%be%d0%b3%d0%bd%d0%be%d0%b7%d1%83%d0%b2%d0%b0%d0%bd%d0%bd%d1%8f-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d1%96%d0%bd%d1%82%d0%b5%d1%80%d0%b2%d0%b0%d0%bb-%d0%bf%d1%80%d0%be%d0%b3%d0%bd%d0%be%d0%b7%d1%83%d0%b2%d0%b0%d0%bd%d0%bd%d1%8f-r\/","title":{"rendered":"\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f \u0432 r"},"content":{"rendered":"

<\/p>\n

\n

\u041c\u043e\u0434\u0435\u043b\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457<\/a> \u043c\u043e\u0436\u0435 \u0431\u0443\u0442\u0438 \u043a\u043e\u0440\u0438\u0441\u043d\u043e\u044e \u0434\u043b\u044f \u0434\u0432\u043e\u0445 \u0440\u0435\u0447\u0435\u0439:<\/span><\/p>\n

(1)<\/strong> \u041a\u0456\u043b\u044c\u043a\u0456\u0441\u043d\u043e \u0432\u0438\u0437\u043d\u0430\u0447\u0438\u0442\u0438 \u0437\u0432\u2019\u044f\u0437\u043e\u043a \u043c\u0456\u0436 \u043e\u0434\u043d\u0456\u0454\u044e \u0430\u0431\u043e \u0434\u0435\u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 \u0437\u043c\u0456\u043d\u043d\u0438\u043c\u0438 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430 \u0442\u0430 \u0437\u043c\u0456\u043d\u043d\u043e\u044e \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456.<\/span><\/p>\n

(2)<\/b> \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f \u043c\u0430\u0439\u0431\u0443\u0442\u043d\u0456\u0445 \u0446\u0456\u043d\u043d\u043e\u0441\u0442\u0435\u0439.<\/span><\/p>\n

\u0429\u043e \u0441\u0442\u043e\u0441\u0443\u0454\u0442\u044c\u0441\u044f (2)<\/strong> , \u043a\u043e\u043b\u0438 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u043c\u043e \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043d\u0443 \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f \u043c\u0430\u0439\u0431\u0443\u0442\u043d\u0456\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c, \u043c\u0438 \u0447\u0430\u0441\u0442\u043e \u0445\u043e\u0447\u0435\u043c\u043e \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0438\u0442\u0438 \u044f\u043a \u0442\u043e\u0447\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f<\/em> , \u0442\u0430\u043a \u0456 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b<\/em> , \u0449\u043e \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d \u0456\u043c\u043e\u0432\u0456\u0440\u043d\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c. \u0426\u0435\u0439 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043d\u0430\u0437\u0438\u0432\u0430\u0454\u0442\u044c\u0441\u044f \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u043e\u043c \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f<\/strong> .<\/span><\/p>\n

\u041d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434, \u043f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u043c\u043e \u043f\u0440\u043e\u0441\u0442\u0443 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u0432\u0438\u0432\u0447\u0435\u043d\u0456 \u0433\u043e\u0434\u0438\u043d\u0438<\/em> \u044f\u043a \u0437\u043c\u0456\u043d\u043d\u0443 \u043f\u0440\u043e\u0433\u043d\u043e\u0441\u0442\u0438\u0447\u043d\u0443 \u0432\u0435\u043b\u0438\u0447\u0438\u043d\u0443 \u0442\u0430 \u043e\u0446\u0456\u043d\u043a\u0438 \u0437\u0430 \u0456\u0441\u043f\u0438\u0442<\/em> \u044f\u043a \u0437\u043c\u0456\u043d\u043d\u0443 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456. \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u0446\u044e \u043c\u043e\u0434\u0435\u043b\u044c, \u043c\u0438 \u043c\u043e\u0433\u043b\u0438 \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0438\u0442\u0438, \u0449\u043e \u0441\u0442\u0443\u0434\u0435\u043d\u0442, \u044f\u043a\u0438\u0439 \u043d\u0430\u0432\u0447\u0430\u0454\u0442\u044c\u0441\u044f 6 \u0433\u043e\u0434\u0438\u043d, \u043e\u0442\u0440\u0438\u043c\u0430\u0454 91 \u0431\u0430\u043b<\/strong> \u043d\u0430 \u0456\u0441\u043f\u0438\u0442\u0456.<\/span><\/p>\n

\u041e\u0434\u043d\u0430\u043a, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0456\u0441\u043d\u0443\u0454 \u043d\u0435\u0432\u0438\u0437\u043d\u0430\u0447\u0435\u043d\u0456\u0441\u0442\u044c \u043d\u0430\u0432\u043a\u043e\u043b\u043e \u0446\u044c\u043e\u0433\u043e \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443, \u044f\u043a\u0438\u0439 \u0432\u043a\u0430\u0437\u0443\u0454 \u043d\u0430 95% \u0439\u043c\u043e\u0432\u0456\u0440\u043d\u0456\u0441\u0442\u044c \u0442\u043e\u0433\u043e, \u0449\u043e \u0441\u0442\u0443\u0434\u0435\u043d\u0442, \u044f\u043a\u0438\u0439 \u043d\u0430\u0432\u0447\u0430\u0454\u0442\u044c\u0441\u044f \u043f\u0440\u043e\u0442\u044f\u0433\u043e\u043c 6 \u0433\u043e\u0434\u0438\u043d, \u043e\u0442\u0440\u0438\u043c\u0430\u0454 \u043e\u0446\u0456\u043d\u043a\u0443 \u043d\u0430 \u0456\u0441\u043f\u0438\u0442\u0456 \u0432\u0456\u0434 85<\/strong> \u0434\u043e 97<\/strong> . \u0426\u0435\u0439 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u0432\u0456\u0434\u043e\u043c\u0438\u0439 \u044f\u043a 95% \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f, \u0456 \u0432\u0456\u043d \u0447\u0430\u0441\u0442\u043e \u043a\u043e\u0440\u0438\u0441\u043d\u0456\u0448\u0438\u0439 \u0434\u043b\u044f \u043d\u0430\u0441, \u043d\u0456\u0436 \u043f\u0440\u043e\u0441\u0442\u043e \u0437\u043d\u0430\u0442\u0438 \u0442\u043e\u0447\u043d\u0435 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432\u0430\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f.<\/span><\/p>\n

\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f \u0432 R<\/strong><\/span><\/h2>\n

\u0429\u043e\u0431 \u043f\u0440\u043e\u0456\u043b\u044e\u0441\u0442\u0440\u0443\u0432\u0430\u0442\u0438, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f \u0432 R, \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0454\u043c\u043e \u0432\u0431\u0443\u0434\u043e\u0432\u0430\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 mtcars<\/em> , \u044f\u043a\u0438\u0439 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u044e \u043f\u0440\u043e \u0445\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a\u0438 \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0440\u0456\u0437\u043d\u0438\u0445 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0456\u043b\u0456\u0432:<\/span><\/p>\n

 #view first six rows of<\/span> mtcars<\/em><\/span>\nhead(mtcars)\n\n# mpg cyl disp hp drat wt qsec vs am gear carb\n#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\n#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\n#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\n#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\n#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\n#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1\n<\/strong><\/pre>\n
 \u041f\u043e-\u043f\u0435\u0440\u0448\u0435, \u043c\u0438 \u043f\u0456\u0434\u0431\u0435\u0440\u0435\u043c\u043e \u043f\u0440\u043e\u0441\u0442\u0443 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 disp<\/em> \u044f\u043a \u0437\u043c\u0456\u043d\u043d\u0443 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430 \u0442\u0430 mpg<\/em> \u044f\u043a \u0437\u043c\u0456\u043d\u043d\u0443 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456.<\/span><\/p>\n
 #fit simple linear regression model<\/span>\nmodel <- lm(mpg ~ disp, data = mtcars)\n\n#view summary of fitted model<\/span>\nsummary(model)\n\n#Call:\n#lm(formula = mpg ~ availability, data = mtcars)\n#\n#Residuals:\n# Min 1Q Median 3Q Max \n#-4.8922 -2.2022 -0.9631 1.6272 7.2305 \n#\n#Coefficients:\n#Estimate Std. Error t value Pr(>|t|)    \n#(Intercept) 29.599855 1.229720 24.070 < 2e-16 ***\n#disp -0.041215 0.004712 -8.747 9.38e-10 ***\n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n#\n#Residual standard error: 3.251 on 30 degrees of freedom\n#Multiple R-squared: 0.7183, Adjusted R-squared: 0.709 \n#F-statistic: 76.51 on 1 and 30 DF, p-value: 9.38e-10\n<\/strong><\/pre>\n
 \u0414\u0430\u043b\u0456 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0454\u043c\u043e \u043f\u0456\u0434\u0456\u0433\u043d\u0430\u043d\u0443 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043d\u0443 \u043c\u043e\u0434\u0435\u043b\u044c, \u0449\u043e\u0431 \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f mpg<\/em> \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0456 \u0442\u0440\u044c\u043e\u0445 \u043d\u043e\u0432\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c disp<\/em> .<\/span><\/p>\n
 #create data frame with three new values for<\/span> avail\n<\/span><\/em>new_disp <- data.frame(disp= c(150, 200, 250))\n\n#use the fitted model to predict the value for mpg<\/em><\/span> based on the three new values<\/span>\n#for<\/span> avail<\/span>\n<\/em>predict(model, newdata = new_disp)\n\n#1 2 3 \n#23.41759 21.35683 19.29607 \n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 \u0456\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0430\u0446\u0456\u0457 \u0446\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439:<\/span><\/p>\n
    \n
  •  \u0414\u043b\u044f \u043d\u043e\u0432\u043e\u0433\u043e \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0456\u043b\u044f \u0437 EPA<\/em> 150 \u043c\u0438 \u043e\u0447\u0456\u043a\u0443\u0454\u043c\u043e, \u0449\u043e \u0432\u0456\u043d \u043c\u0430\u0442\u0438\u043c\u0435 23,41759<\/strong> \u043c\u0438\u043b\u044c \u043d\u0430 \u0433\u0430\u043b\u043e\u043d<\/em> .<\/span><\/li>\n
  •  \u0414\u043b\u044f \u043d\u043e\u0432\u043e\u0433\u043e \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0456\u043b\u044f \u0437 EPA<\/em> 200 \u043c\u0438 \u043e\u0447\u0456\u043a\u0443\u0454\u043c\u043e, \u0449\u043e \u0432\u0456\u043d \u043c\u0430\u0442\u0438\u043c\u0435 21,35683<\/strong> \u043c\u0438\u043b\u0456 \u043d\u0430 \u0433\u0430\u043b\u043e\u043d<\/em> .<\/span><\/li>\n
  •  \u0414\u043b\u044f \u043d\u043e\u0432\u043e\u0433\u043e \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0456\u043b\u044f \u0437 EPA<\/em> 250 \u043c\u0438 \u043e\u0447\u0456\u043a\u0443\u0454\u043c\u043e, \u0449\u043e \u0432\u0456\u043d \u043c\u0430\u0442\u0438\u043c\u0435 19,29607<\/strong> \u043c\u0438\u043b\u044c \u043d\u0430 \u0433\u0430\u043b\u043e\u043d<\/em> .<\/span><\/li>\n<\/ul>\n
     \u0414\u0430\u043b\u0456 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0454\u043c\u043e \u043f\u0456\u0434\u0456\u0433\u043d\u0430\u043d\u0443 \u043c\u043e\u0434\u0435\u043b\u044c \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457, \u0449\u043e\u0431 \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u0438 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443 \u043d\u0430\u0432\u043a\u043e\u043b\u043e \u0446\u0438\u0445 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432\u0430\u043d\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c:<\/span><\/p>\n
     #create prediction intervals around the predicted values<\/span>\npredict(model, newdata = new_disp, interval = \" predict<\/span> \")<\/span>\n\n# fit lwr upr\n#1 23.41759 16.62968 30.20549\n#2 21.35683 14.60704 28.10662\n#3 19.29607 12.55021 26.04194\n<\/strong><\/pre>\n
     \u0421\u043f\u043e\u0441\u0456\u0431 \u0456\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0430\u0446\u0456\u0457 \u0446\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439:<\/span><\/p>\n
      \n
    •  \u0406\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f 95% \u043c\u0438\u043b\u044c \u043d\u0430 \u0433\u0430\u043b\u043e\u043d<\/em> \u0434\u043b\u044f \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0456\u043b\u044f \u0437 EPA<\/em> 150 \u0437\u043d\u0430\u0445\u043e\u0434\u0438\u0442\u044c\u0441\u044f \u043c\u0456\u0436 16,62968<\/strong> \u0456 30,20549<\/strong> .<\/span><\/li>\n
    •  \u0406\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f 95% \u043c\u0438\u043b\u044c \u043d\u0430 \u0433\u0430\u043b\u043b\u043e\u043d<\/em> \u0434\u043b\u044f \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0456\u043b\u044f \u0437 EPA<\/em> 200 \u0437\u043d\u0430\u0445\u043e\u0434\u0438\u0442\u044c\u0441\u044f \u043c\u0456\u0436 14,60704<\/b> \u0456 28,10662<\/strong> .<\/span><\/li>\n
    •  \u0406\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f 95% \u043c\u0438\u043b\u044c \u043d\u0430 \u0433\u0430\u043b\u043e\u043d<\/em> \u0434\u043b\u044f \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0456\u043b\u044f \u0437 EPA<\/em> 250 \u0437\u043d\u0430\u0445\u043e\u0434\u0438\u0442\u044c\u0441\u044f \u043c\u0456\u0436 12,55021<\/b> \u0456 26,04194<\/strong> .<\/span><\/li>\n<\/ul>\n
       \u0417\u0430 \u0437\u0430\u043c\u043e\u0432\u0447\u0443\u0432\u0430\u043d\u043d\u044f\u043c R \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 95% \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f. \u041e\u0434\u043d\u0430\u043a \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u0446\u0435 \u044f\u043a \u0437\u0430\u0431\u0430\u0436\u0430\u0454\u043c\u043e \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u043a\u043e\u043c\u0430\u043d\u0434\u0438 level<\/strong> . \u041d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434, \u043d\u0430\u0432\u0435\u0434\u0435\u043d\u0438\u0439 \u043d\u0438\u0436\u0447\u0435 \u043a\u043e\u0434 \u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0443\u0454, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 99%-\u0439 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f:<\/span><\/p>\n
       #create 99% prediction intervals around the predicted values\n<\/span>predict(model, newdata = new_disp, interval = \" predict<\/span> \", level = 0.99<\/span><\/span><\/span> )<\/span>\n\n# fit lwr upr\n#1 23.41759 14.27742 32.55775\n#2 21.35683 12.26799 30.44567\n#3 19.29607 10.21252 28.37963\n<\/strong><\/pre>\n
       \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e 99%-\u0439 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f \u0454 \u0448\u0438\u0440\u0448\u0438\u043c, \u043d\u0456\u0436 95%-\u0438\u0439 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f. \u0426\u0435 \u043c\u0430\u0454 \u0441\u0435\u043d\u0441, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0447\u0438\u043c \u0448\u0438\u0440\u0448\u0438\u0439 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b, \u0442\u0438\u043c \u0431\u0456\u043b\u044c\u0448\u0430 \u0439\u043c\u043e\u0432\u0456\u0440\u043d\u0456\u0441\u0442\u044c \u0442\u043e\u0433\u043e, \u0449\u043e \u0432\u0456\u043d \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432\u0430\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f.<\/span><\/p>\n
       \u042f\u043a \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0443\u0432\u0430\u0442\u0438 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f \u0432 R<\/strong><\/span><\/h2>\n
       \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0443\u0454, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0443 \u0437 \u0442\u0430\u043a\u0438\u043c\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044f\u043c\u0438:<\/span><\/p>\n
        \n
      •  \u0414\u0456\u0430\u0433\u0440\u0430\u043c\u0430 \u0440\u043e\u0437\u0441\u0456\u044e\u0432\u0430\u043d\u043d\u044f \u0442\u043e\u0447\u043e\u043a \u0434\u0430\u043d\u0438\u0445 \u0434\u043b\u044f \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u043e\u0441\u0442\u0456<\/em> \u0442\u0430 \u043c\u0438\u043b\u044c \u043d\u0430 \u0433\u0430\u043b\u043e\u043d<\/em><\/span><\/li>\n
      •  \u0421\u0438\u043d\u044f \u043b\u0456\u043d\u0456\u044f \u0434\u043b\u044f \u043f\u0456\u0434\u0456\u0433\u043d\u0430\u043d\u043e\u0457 \u043b\u0456\u043d\u0456\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457<\/span><\/li>\n
      •  \u0421\u0456\u0440\u0456 \u0441\u043c\u0443\u0433\u0438 \u0434\u043e\u0432\u0456\u0440\u0438<\/span><\/li>\n
      •  \u0427\u0435\u0440\u0432\u043e\u043d\u0456 \u0441\u043c\u0443\u0433\u0438 \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f<\/span><\/li>\n<\/ul>\n
         #define dataset<\/span>\ndata <- mtcars[, c(\"mpg\", \"disp\")]\n\n#create simple linear regression model\n<\/span>model <- lm(mpg ~ disp, data = mtcars)\n\n#use model to create prediction intervals\n<\/span>predictions <- predict(model, interval = \" predict<\/span> \")\n\n#create dataset that contains original data along with prediction intervals\n<\/span>all_data <- cbind(data, predictions)\n\n#load ggplot2<\/em> library\n<\/span>library(ggplot2)\n\n#createplot\n<\/span>ggplot(all_data, aes(x = disp, y = mpg)) + #define x and y axis variables<\/span>\n  geom_point() + #add scatterplot points<\/span>\n  stat_smooth(method = lm) + #confidence bands<\/span>\n  geom_line(aes(y = lwr), col = \"coral2\", linetype = \"dashed\") + #lwr pred interval<\/span>\n  geom_line(aes(y = upr), col = \"coral2\", linetype = \"dashed\") #upr pred interval<\/span><\/strong><\/pre>\n
         \u041a\u043e\u043b\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0434\u043e\u0432\u0456\u0440\u0447\u0438\u0439 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u0447\u0438 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f<\/span><\/strong><\/h2>\n
         \u0406\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f<\/strong> \u0444\u0456\u043a\u0441\u0443\u0454 \u043d\u0435\u0432\u0438\u0437\u043d\u0430\u0447\u0435\u043d\u0456\u0441\u0442\u044c \u043d\u0430\u0432\u043a\u043e\u043b\u043e \u043e\u0434\u043d\u043e\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f. \u0414\u043e\u0432\u0456\u0440\u0447\u0438\u0439 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b<\/strong> \u0444\u0456\u043a\u0441\u0443\u0454 \u043d\u0435\u0432\u0438\u0437\u043d\u0430\u0447\u0435\u043d\u0456\u0441\u0442\u044c \u043d\u0430\u0432\u043a\u043e\u043b\u043e \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432\u0430\u043d\u0438\u0445 \u0441\u0435\u0440\u0435\u0434\u043d\u0456\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c. \u0422\u0430\u043a\u0438\u043c \u0447\u0438\u043d\u043e\u043c, \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f \u0437\u0430\u0432\u0436\u0434\u0438 \u0431\u0443\u0434\u0435 \u0448\u0438\u0440\u0448\u0438\u043c, \u043d\u0456\u0436 \u0434\u043e\u0432\u0456\u0440\u0447\u0438\u0439 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u0434\u043b\u044f \u0442\u043e\u0433\u043e \u0441\u0430\u043c\u043e\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f.<\/span><\/p>\n
         \u0412\u0438 \u043f\u043e\u0432\u0438\u043d\u043d\u0456 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f, \u043a\u043e\u043b\u0438 \u0432\u0430\u0441 \u0446\u0456\u043a\u0430\u0432\u043b\u044f\u0442\u044c \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u0456 \u043e\u043a\u0440\u0435\u043c\u0456 \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0434\u043e\u0432\u0456\u0440\u0447\u0438\u0439 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u0434\u0430\u0441\u0442\u044c \u0437\u0430\u043d\u0430\u0434\u0442\u043e \u0432\u0443\u0437\u044c\u043a\u0438\u0439 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d \u0437\u043d\u0430\u0447\u0435\u043d\u044c, \u0449\u043e \u043f\u0440\u0438\u0437\u0432\u0435\u0434\u0435 \u0434\u043e \u0431\u0456\u043b\u044c\u0448\u043e\u0457 \u0439\u043c\u043e\u0432\u0456\u0440\u043d\u043e\u0441\u0442\u0456 \u0442\u043e\u0433\u043e, \u0449\u043e \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043d\u0435 \u043c\u0456\u0441\u0442\u0438\u0442\u0438\u043c\u0435 \u0441\u043f\u0440\u0430\u0432\u0436\u043d\u044c\u043e\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
        \u041c\u043e\u0434\u0435\u043b\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u043c\u043e\u0436\u0435 \u0431\u0443\u0442\u0438 \u043a\u043e\u0440\u0438\u0441\u043d\u043e\u044e \u0434\u043b\u044f \u0434\u0432\u043e\u0445 \u0440\u0435\u0447\u0435\u0439: (1) \u041a\u0456\u043b\u044c\u043a\u0456\u0441\u043d\u043e \u0432\u0438\u0437\u043d\u0430\u0447\u0438\u0442\u0438 \u0437\u0432\u2019\u044f\u0437\u043e\u043a \u043c\u0456\u0436 \u043e\u0434\u043d\u0456\u0454\u044e \u0430\u0431\u043e \u0434\u0435\u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 \u0437\u043c\u0456\u043d\u043d\u0438\u043c\u0438 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430 \u0442\u0430 \u0437\u043c\u0456\u043d\u043d\u043e\u044e \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456. (2) \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f \u043c\u0430\u0439\u0431\u0443\u0442\u043d\u0456\u0445 \u0446\u0456\u043d\u043d\u043e\u0441\u0442\u0435\u0439. \u0429\u043e \u0441\u0442\u043e\u0441\u0443\u0454\u0442\u044c\u0441\u044f (2) , \u043a\u043e\u043b\u0438 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u043c\u043e \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043d\u0443 \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f \u043c\u0430\u0439\u0431\u0443\u0442\u043d\u0456\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c, \u043c\u0438 \u0447\u0430\u0441\u0442\u043e \u0445\u043e\u0447\u0435\u043c\u043e \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0438\u0442\u0438 \u044f\u043a \u0442\u043e\u0447\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f , \u0442\u0430\u043a \u0456 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b , \u0449\u043e […]<\/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 \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443 \u0432 R - Statology<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u043b\u0435\u0433\u043a\u043e \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0456\u043d\u0442\u0435\u0440\u0432\u0430\u043b \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0435\u043d\u043d\u044f \u0432 R.\" \/>\n<meta name=\"robots\" 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