{"id":500,"date":"2023-07-29T16:49:12","date_gmt":"2023-07-29T16:49:12","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d0%b0%d0%b1%d0%bb%d0%b8%d0%bd-%d0%b2-%d1%80\/"},"modified":"2023-07-29T16:49:12","modified_gmt":"2023-07-29T16:49:12","slug":"%d0%b0%d0%b1%d0%bb%d0%b8%d0%bd-%d0%b2-%d1%80","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d0%b0%d0%b1%d0%bb%d0%b8%d0%bd-%d0%b2-%d1%80\/","title":{"rendered":"\u041a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c aline() \u0432 r \u0434\u043b\u044f \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043f\u0440\u044f\u043c\u044b\u0445 \u043b\u0438\u043d\u0438\u0439 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u043a\u0438"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0424\u0443\u043d\u043a\u0446\u0438\u044e <strong>abline()<\/strong> \u0432 R \u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0434\u043b\u044f \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043e\u0434\u043d\u043e\u0439 \u0438\u043b\u0438 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u043f\u0440\u044f\u043c\u044b\u0445 \u043b\u0438\u043d\u0438\u0439 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u043a \u0432 R.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u042d\u0442\u0430 \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>abline(a=NULL, b=NULL, h=NULL, v=NULL, \u2026)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0417\u043e\u043b\u043e\u0442\u043e:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>a, b:<\/strong> \u0443\u043d\u0438\u043a\u0430\u043b\u044c\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f, \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u044e\u0449\u0438\u0435 \u043d\u0430\u0447\u0430\u043b\u043e \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442 \u0438 \u043d\u0430\u043a\u043b\u043e\u043d \u043b\u0438\u043d\u0438\u0438.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>h:<\/strong> \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 y \u0434\u043b\u044f \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u0430\u043b\u044c\u043d\u043e\u0439 \u043b\u0438\u043d\u0438\u0438<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>v:<\/strong> \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 x \u0434\u043b\u044f \u0432\u0435\u0440\u0442\u0438\u043a\u0430\u043b\u044c\u043d\u043e\u0439 \u043b\u0438\u043d\u0438\u0438<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u043f\u0440\u0438\u043c\u0435\u0440\u044b \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u044d\u0442\u0443 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u041a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u0430\u043b\u044c\u043d\u044b\u0435 \u043b\u0438\u043d\u0438\u0438<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u0411\u0430\u0437\u043e\u0432\u044b\u0439 \u043a\u043e\u0434 \u0434\u043b\u044f \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u0430\u043b\u044c\u043d\u043e\u0439 \u043b\u0438\u043d\u0438\u0438 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u043a \u0432 R: <strong>abline(h = \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0435\u0434\u043f\u043e\u043b\u043e\u0436\u0438\u043c, \u0443 \u043d\u0430\u0441 \u0435\u0441\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0430\u044f \u0434\u0438\u0430\u0433\u0440\u0430\u043c\u043c\u0430 \u0440\u0430\u0441\u0441\u0435\u044f\u043d\u0438\u044f, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u0435\u0442 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f <em>x<\/em> \u0438 <em>y<\/em> \u0432 \u043d\u0430\u0431\u043e\u0440\u0435 \u0434\u0430\u043d\u043d\u044b\u0445:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define dataset\n<\/span>data &lt;- data.frame(x = c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 11, 11),\n                   y = c(13, 14, 17, 12, 23, 24, 25, 25, 24, 28, 32, 33, 35, 40, 41))\n\n<span style=\"color: #008080;\">#plot <em>x<\/em> and <em>y<\/em> values in dataset\n<\/span>plot(data$x, data$y, pch = 16)<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0427\u0442\u043e\u0431\u044b \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u0430\u043b\u044c\u043d\u0443\u044e \u043b\u0438\u043d\u0438\u044e \u0441\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c y = 20, \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong>abline(h = 20, col = 'coral2', lwd = 2)<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u043a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0441\u043f\u043b\u043e\u0448\u043d\u0443\u044e \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u0430\u043b\u044c\u043d\u0443\u044e \u043b\u0438\u043d\u0438\u044e \u043a \u0441\u0440\u0435\u0434\u043d\u0435\u043c\u0443 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044e <em>y<\/em> \u0438 \u0434\u0432\u0435 \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u0430\u043b\u044c\u043d\u044b\u0435 \u043f\u0443\u043d\u043a\u0442\u0438\u0440\u043d\u044b\u0435 \u043b\u0438\u043d\u0438\u0438 \u043d\u0430 \u043e\u0434\u043d\u043e \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u043e\u0435 \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435 \u0432\u044b\u0448\u0435 \u0438 \u043d\u0438\u0436\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create scatterplot for <em>x<\/em> and <em>y<\/em><\/span>\nplot(data$x, data$y, pch = 16)\n\n<span style=\"color: #008080;\">#create horizontal line at mean value of <em>y<\/em>\n<\/span>abline(h = mean(data$y), lwd = 2)\n\n<span style=\"color: #008080;\">#create horizontal lines at one standard deviation above and below the mean value\n<\/span>abline(h = mean(data$y) + sd(data$y), col = 'steelblue', lwd = 3, lty = 2)\nabline(h = mean(data$y) - sd(data$y), col = 'steelblue', lwd = 3, lty = 2)<\/strong><\/pre>\n<h2> <span style=\"color: #000000;\"><strong>\u041a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0432\u0435\u0440\u0442\u0438\u043a\u0430\u043b\u044c\u043d\u044b\u0435 \u043b\u0438\u043d\u0438\u0438<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u0411\u0430\u0437\u043e\u0432\u044b\u0439 \u043a\u043e\u0434 \u0434\u043b\u044f \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0432\u0435\u0440\u0442\u0438\u043a\u0430\u043b\u044c\u043d\u043e\u0439 \u043b\u0438\u043d\u0438\u0438 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u043a \u0432 R: <strong>abline(v = \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0438\u0440\u0443\u0435\u0442, \u043a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0432\u0435\u0440\u0442\u0438\u043a\u0430\u043b\u044c\u043d\u0443\u044e \u043b\u0438\u043d\u0438\u044e \u043a \u0441\u0440\u0435\u0434\u043d\u0435\u043c\u0443 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044e \u043d\u0430 \u0433\u0438\u0441\u0442\u043e\u0433\u0440\u0430\u043c\u043c\u0435:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(0)\n\n<span style=\"color: #008080;\">#create dataset with 1000 random values normally distributed with mean = 10, sd = 2\n<\/span>data &lt;- rnorm(1000, mean = 10, sd = 2)\n\n<span style=\"color: #008080;\">#create histogram of data values\n<\/span>hist(data, col = 'steelblue')\n\n<span style=\"color: #008080;\">#draw a vertical dashed line at the mean value\n<\/span>abline(v = mean(data), lwd = 3, lty = 2)<\/strong><\/pre>\n<h2> <span style=\"color: #000000;\"><strong>\u041a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u043b\u0438\u043d\u0438\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u0411\u0430\u0437\u043e\u0432\u044b\u0439 \u043a\u043e\u0434 \u0434\u043b\u044f \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u043b\u0438\u043d\u0438\u0438 \u043b\u0438\u043d\u0435\u0439\u043d\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u043a \u0432 R: <strong>abline(model)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0438\u0440\u0443\u0435\u0442, \u043a\u0430\u043a \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u043f\u043e\u0434\u043e\u0431\u0440\u0430\u043d\u043d\u0443\u044e \u043b\u0438\u043d\u0438\u044e \u043b\u0438\u043d\u0435\u0439\u043d\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u043a \u0434\u0438\u0430\u0433\u0440\u0430\u043c\u043c\u0435 \u0440\u0430\u0441\u0441\u0435\u044f\u043d\u0438\u044f:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define dataset\n<\/span>data &lt;- data.frame(x = c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 11, 11),\n                   y = c(13, 14, 17, 12, 23, 24, 25, 25, 24, 28, 32, 33, 35, 40, 41))\n\n<span style=\"color: #008080;\">#create scatterplot of <em>x<\/em> and <em>y<\/em> values\n<\/span>plot(data$x, data$y, pch = 16)\n\n<span style=\"color: #008080;\">#fit a linear regression model to the data\n<\/span>reg_model &lt;- lm(y ~ x, data = data)\n\n<span style=\"color: #008080;\">#add the fitted regression line to the scatterplot\n<\/span>abline(reg_model, col=\"steelblue\")<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u043d\u0430\u043c \u043f\u0440\u043e\u0441\u0442\u043e \u043d\u0443\u0436\u043d\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0442\u043e\u0447\u043a\u0438 \u043f\u0435\u0440\u0435\u0441\u0435\u0447\u0435\u043d\u0438\u044f \u0438 \u043d\u0430\u043a\u043b\u043e\u043d\u0430, \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u0434\u043e\u0433\u043d\u0430\u0442\u044c \u043a \u0434\u0430\u043d\u043d\u044b\u043c \u043f\u0440\u043e\u0441\u0442\u0443\u044e \u043b\u0438\u043d\u0438\u044e \u043b\u0438\u043d\u0435\u0439\u043d\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u0438 abline().<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0418\u0442\u0430\u043a, \u0434\u0440\u0443\u0433\u043e\u0439 \u0441\u043f\u043e\u0441\u043e\u0431 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c <strong>abline()<\/strong> \u0434\u043b\u044f \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043b\u0438\u043d\u0438\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u2014 \u044d\u0442\u043e \u044f\u0432\u043d\u043e \u0443\u043a\u0430\u0437\u0430\u0442\u044c \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0435 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u044b \u0438 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u044b \u043d\u0430\u043a\u043b\u043e\u043d\u0430 \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define dataset\n<\/span>data &lt;- data.frame(x = c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 11, 11),\n                   y = c(13, 14, 17, 12, 23, 24, 25, 25, 24, 28, 32, 33, 35, 40, 41))\n\n<span style=\"color: #008080;\">#create scatterplot of <em>x<\/em> and <em>y<\/em> values\n<\/span>plot(data$x, data$y, pch = 16)\n\n<span style=\"color: #008080;\">#fit a linear regression model to the data\n<\/span>reg_model &lt;- lm(y ~ x, data = data)\n\n<span style=\"color: #008080;\">#define intercept and slope values\n<\/span>a &lt;- coefficients(reg_model)[1] <span style=\"color: #008080;\">#intercept<\/span>\nb &lt;- coefficients(reg_model)[2] <span style=\"color: #008080;\">#slope<\/span>\n\n<span style=\"color: #008080;\">#add the fitted regression line to the scatterplot\n<\/span>abline(a=a, b=b, col=\"steelblue\")<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u044d\u0442\u043e \u0441\u043e\u0437\u0434\u0430\u0435\u0442 \u0442\u0443 \u0436\u0435 \u0441\u0442\u0440\u043e\u043a\u0443, \u0447\u0442\u043e \u0438 \u0440\u0430\u043d\u044c\u0448\u0435.<\/span><\/p>\n<hr>\n<p> <span style=\"color: #000000;\">\u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0430 \u043f\u043e R \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u043d\u0430\u0439\u0442\u0438 \u043d\u0430 \u044d\u0442\u043e\u0439 \u0441\u0442\u0440\u0430\u043d\u0438\u0446\u0435 .<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0424\u0443\u043d\u043a\u0446\u0438\u044e abline() \u0432 R \u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0434\u043b\u044f \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043e\u0434\u043d\u043e\u0439 \u0438\u043b\u0438 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u043f\u0440\u044f\u043c\u044b\u0445 \u043b\u0438\u043d\u0438\u0439 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u043a \u0432 R. \u042d\u0442\u0430 \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441: abline(a=NULL, b=NULL, h=NULL, v=NULL, \u2026) \u0417\u043e\u043b\u043e\u0442\u043e: a, b: \u0443\u043d\u0438\u043a\u0430\u043b\u044c\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f, \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u044e\u0449\u0438\u0435 \u043d\u0430\u0447\u0430\u043b\u043e \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442 \u0438 \u043d\u0430\u043a\u043b\u043e\u043d \u043b\u0438\u043d\u0438\u0438. h: \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 y \u0434\u043b\u044f \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u0430\u043b\u044c\u043d\u043e\u0439 \u043b\u0438\u043d\u0438\u0438 v: \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 x \u0434\u043b\u044f \u0432\u0435\u0440\u0442\u0438\u043a\u0430\u043b\u044c\u043d\u043e\u0439 \u043b\u0438\u043d\u0438\u0438 \u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u043f\u0440\u0438\u043c\u0435\u0440\u044b \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c [&hellip;]<\/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":[],"class_list":["post-500","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u041a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c abline() \u0432 R \u0434\u043b\u044f \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043f\u0440\u044f\u043c\u044b\u0445 \u043b\u0438\u043d\u0438\u0439 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u043a\u0438 - Statorials<\/title>\n<meta name=\"description\" content=\"\u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, 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\u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043f\u0440\u044f\u043c\u044b\u0445 \u043b\u0438\u043d\u0438\u0439 \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u043a\u0438 - Statorials\" \/>\n<meta property=\"og:description\" content=\"\u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e abline() \u0432 R \u0434\u043b\u044f \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043e\u0434\u043d\u043e\u0439 \u0438\u043b\u0438 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u043f\u0440\u044f\u043c\u044b\u0445 \u043b\u0438\u043d\u0438\u0439 \u043a \u043f\u0443\u0442\u0438 \u0432 R.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ru\/\u0430\u0431\u043b\u0438\u043d-\u0432-\u0440\/\" 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