{"id":1545,"date":"2023-07-25T22:47:19","date_gmt":"2023-07-25T22:47:19","guid":{"rendered":"https:\/\/statorials.org\/ar\/glm-%d9%85%d9%82%d8%a7%d8%a8%d9%84-lm-%d9%81%d9%8a-r\/"},"modified":"2023-07-25T22:47:19","modified_gmt":"2023-07-25T22:47:19","slug":"glm-%d9%85%d9%82%d8%a7%d8%a8%d9%84-lm-%d9%81%d9%8a-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/glm-%d9%85%d9%82%d8%a7%d8%a8%d9%84-lm-%d9%81%d9%8a-r\/","title":{"rendered":"\u0627\u0644\u0641\u0631\u0642 \u0628\u064a\u0646 glm \u0648lm \u0641\u064a r"},"content":{"rendered":"<p><\/p>\n<hr>\n<p style=\";text-align:right;direction:rtl\"><span style=\"color: #000000;\">\u062a\u0648\u0641\u0631 \u0644\u063a\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u0629 R \u0627\u0644\u0648\u0638\u0627\u0626\u0641 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0644\u062a\u0631\u0643\u064a\u0628 \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062e\u0637\u064a\u0629:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>1.lm \u2013 \u064a\u0633\u062a\u062e\u062f\u0645 \u0644\u062a\u0646\u0627\u0633\u0628 \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062e\u0637\u064a\u0629<\/strong><\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u0633\u062a\u062e\u062f\u0645 \u0647\u0630\u0647 \u0627\u0644\u0648\u0638\u064a\u0641\u0629 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>lm(\u0627\u0644\u0635\u064a\u063a\u0629\u060c \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a\u060c&#8230;)<\/strong><\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0630\u0647\u0628:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0635\u064a\u063a\u0629:<\/strong> \u0635\u064a\u063a\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u062e\u0637\u064a (\u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644 y ~ x1 + x2)<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a:<\/strong> \u0627\u0633\u0645 \u0643\u062a\u0644\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>2.glm \u2013 \u064a\u0633\u062a\u062e\u062f\u0645 \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062e\u0637\u064a\u0629 \u0627\u0644\u0645\u0639\u0645\u0645\u0629<\/strong><\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u0633\u062a\u062e\u062f\u0645 \u0647\u0630\u0647 \u0627\u0644\u0648\u0638\u064a\u0641\u0629 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>glm(\u0627\u0644\u0635\u064a\u063a\u0629\u060c \u0627\u0644\u0623\u0633\u0631\u0629=\u063a\u0627\u0648\u0633\u064a\u060c \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a\u060c &#8230;)<\/strong><\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0630\u0647\u0628:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0635\u064a\u063a\u0629:<\/strong> \u0635\u064a\u063a\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u062e\u0637\u064a (\u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644 y ~ x1 + x2)<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0639\u0627\u0626\u0644\u0629:<\/strong> \u0627\u0644\u0639\u0627\u0626\u0644\u0629 \u0627\u0644\u0625\u062d\u0635\u0627\u0626\u064a\u0629 \u0627\u0644\u0645\u0633\u062a\u062e\u062f\u0645\u0629 \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c. \u0627\u0644\u0625\u0639\u062f\u0627\u062f \u0627\u0644\u0627\u0641\u062a\u0631\u0627\u0636\u064a \u0647\u0648 Gaussian\u060c \u0644\u0643\u0646 \u0627\u0644\u062e\u064a\u0627\u0631\u0627\u062a \u0627\u0644\u0623\u062e\u0631\u0649 \u062a\u062a\u0636\u0645\u0646 Binomial \u0648Gamma \u0648Poisson \u0648\u063a\u064a\u0631\u0647\u0627.<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a:<\/strong> \u0627\u0633\u0645 \u0643\u062a\u0644\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0627\u062d\u0638 \u0623\u0646 \u0627\u0644\u0627\u062e\u062a\u0644\u0627\u0641 \u0627\u0644\u0648\u062d\u064a\u062f \u0628\u064a\u0646 \u0647\u0627\u062a\u064a\u0646 \u0627\u0644\u0648\u0638\u064a\u0641\u062a\u064a\u0646 \u0647\u0648 \u0648\u0633\u064a\u0637\u0629 <strong>\u0627\u0644\u0639\u0627\u0626\u0644\u0629<\/strong> \u0627\u0644\u0645\u0636\u0645\u0646\u0629 \u0641\u064a \u0627\u0644\u062f\u0627\u0644\u0629 <strong>glm()<\/strong> .<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0625\u0630\u0627 \u0643\u0646\u062a \u062a\u0633\u062a\u062e\u062f\u0645 lm() \u0623\u0648 glm() \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a\u060c <strong>\u0641\u0633\u0648\u0641 \u064a\u0646\u062a\u062c\u0648\u0646 \u0646\u0641\u0633 \u0627\u0644\u0646\u062a\u0627\u0626\u062c \u062a\u0645\u0627\u0645\u064b\u0627<\/strong> .<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0648\u0645\u0639 \u0630\u0644\u0643\u060c \u064a\u0645\u0643\u0646 \u0623\u064a\u0636\u064b\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 glm()\u200e \u0644\u062a\u0646\u0627\u0633\u0628 \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0623\u0643\u062b\u0631 \u062a\u0639\u0642\u064a\u062f\u064b\u0627 \u0645\u062b\u0644:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a (\u0627\u0644\u0639\u0627\u0626\u0644\u0629 = \u0630\u0648 \u0627\u0644\u062d\u062f\u064a\u0646)<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ar\/\u062a\u0631\u0627\u062c\u0639-\u0627\u0644\u0627\u0654\u0633\u0645\u0627\u0643\/\" target=\"_blank\" rel=\"noopener\">\u0627\u0646\u062d\u062f\u0627\u0631 \u0628\u0648\u0627\u0633\u0648\u0646<\/a> (\u0639\u0627\u0626\u0644\u0629 = \u0633\u0645\u0643\u0629)<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u0648\u0636\u062d \u0627\u0644\u0623\u0645\u062b\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u062a\u064a\u0646 lm() \u0648 glm() \u0639\u0645\u0644\u064a\u064b\u0627.<\/span><\/p>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0645\u062b\u0627\u0644 \u0639\u0644\u0649 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 lm()<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0645\u0644\u0627\u0621\u0645\u0629 <strong>\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a<\/strong> \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 lm():<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>model &lt;- lm(mpg ~ disp + hp, data=mtcars)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = mpg ~ disp + hp, data = mtcars)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-4.7945 -2.3036 -0.8246 1.8582 6.9363 \n\nCoefficients:\n             Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 30.735904 1.331566 23.083 &lt; 2nd-16 ***\navailable -0.030346 0.007405 -4.098 0.000306 ***\nhp -0.024840 0.013385 -1.856 0.073679 .  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.127 on 29 degrees of freedom\nMultiple R-squared: 0.7482, Adjusted R-squared: 0.7309 \nF-statistic: 43.09 on 2 and 29 DF, p-value: 2.062e-09<\/strong><\/pre>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0623\u0645\u062b\u0644\u0629 \u0639\u0644\u0649 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 glm()<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0641\u0633 <strong>\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a<\/strong> \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 glm():<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>model &lt;- glm(mpg ~ disp + hp, data=mtcars)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = mpg ~ disp + hp, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-4.7945 -2.3036 -0.8246 1.8582 6.9363  \n\nCoefficients:\n             Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 30.735904 1.331566 23.083 &lt; 2nd-16 ***\navailable -0.030346 0.007405 -4.098 0.000306 ***\nhp -0.024840 0.013385 -1.856 0.073679 .  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for gaussian family taken to be 9.775636)\n\n    Null deviance: 1126.05 on 31 degrees of freedom\nResidual deviance: 283.49 on 29 degrees of freedom\nAIC: 168.62\n\nNumber of Fisher Scoring iterations: 2<\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0627\u062d\u0638 \u0623\u0646 \u062a\u0642\u062f\u064a\u0631\u0627\u062a \u0627\u0644\u0645\u0639\u0627\u0645\u0644 \u0648\u0627\u0644\u0623\u062e\u0637\u0627\u0621 \u0627\u0644\u0645\u0639\u064a\u0627\u0631\u064a\u0629 \u0644\u062a\u0642\u062f\u064a\u0631\u0627\u062a \u0627\u0644\u0645\u0639\u0627\u0645\u0644 \u0647\u064a \u062a\u0645\u0627\u0645\u064b\u0627 \u0646\u0641\u0633 \u062a\u0644\u0643 \u0627\u0644\u062a\u064a \u062a\u0646\u062a\u062c\u0647\u0627 \u0627\u0644\u062f\u0627\u0644\u0629 lm().<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0627\u062d\u0638 \u0623\u0646\u0647 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u064a\u0636\u064b\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 glm()\u200e \u0644\u0645\u0644\u0627\u0621\u0645\u0629 <strong>\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a<\/strong> \u0639\u0646 \u0637\u0631\u064a\u0642 \u062a\u062d\u062f\u064a\u062f Family=binomial \u0643\u0645\u0627 \u064a\u0644\u064a:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit logistic regression model\n<\/span>model &lt;- glm(am ~ disp + hp, data=mtcars, family=binomial)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = am ~ disp + hp, family = binomial, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-1.9665 -0.3090 -0.0017 0.3934 1.3682  \n\nCoefficients:\n            Estimate Std. Error z value Pr(&gt;|z|)  \n(Intercept) 1.40342 1.36757 1.026 0.3048  \navailable -0.09518 0.04800 -1.983 0.0474 *\nhp 0.12170 0.06777 1.796 0.0725 .\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 43,230 on 31 degrees of freedom\nResidual deviance: 16,713 on 29 degrees of freedom\nAIC: 22,713\n\nNumber of Fisher Scoring iterations: 8\n<\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u064a\u0636\u064b\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 glm()\u200e \u0644\u062a\u0646\u0627\u0633\u0628 <strong>\u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 Poisson<\/strong> \u0639\u0646 \u0637\u0631\u064a\u0642 \u062a\u062d\u062f\u064a\u062f Family=poisson \u0643\u0645\u0627 \u064a\u0644\u064a:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit Poisson regression model\n<\/span>model &lt;- glm(am ~ disp + hp, data=mtcars, family=fish)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = am ~ disp + hp, family = fish, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-1.1266 -0.4629 -0.2453 0.1797 1.5428  \n\nCoefficients:\n             Estimate Std. Error z value Pr(&gt;|z|)   \n(Intercept) 0.214255 0.593463 0.361 0.71808   \navailable -0.018915 0.007072 -2.674 0.00749 **\nhp 0.016522 0.007163 2.307 0.02107 * \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for fish family taken to be 1)\n\n    Null deviance: 23,420 on 31 degrees of freedom\nResidual deviance: 10,526 on 29 degrees of freedom\nAIC: 42,526\n\nNumber of Fisher Scoring iterations: 6\n<\/strong><\/pre>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <a href=\"https:\/\/statorials.org\/ar\/\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631-\u0627\u0644\u062e\u0637\u064a-\u0627\u0644\u0628\u0633\u064a\u0637-\u0641\u064a-\u0635\/\" target=\"_blank\" rel=\"noopener\">\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0628\u0633\u064a\u0637 \u0641\u064a R<\/a><br \/> <a href=\"https:\/\/statorials.org\/ar\/\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631-\u0627\u0644\u062e\u0637\u064a-\u0627\u0644\u0645\u062a\u0639\u062f\u062f-\u0635\/\" target=\"_blank\" rel=\"noopener\">\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f \u0641\u064a R<\/a><br \/> <a href=\"https:\/\/statorials.org\/ar\/\u062a\u0648\u0642\u0639-r-glm\/\" target=\"_blank\" rel=\"noopener\">\u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 \u0627\u0644\u062a\u0646\u0628\u0624 \u0645\u0639 glm \u0641\u064a R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u062a\u0648\u0641\u0631 \u0644\u063a\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u0629 R \u0627\u0644\u0648\u0638\u0627\u0626\u0641 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0644\u062a\u0631\u0643\u064a\u0628 \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062e\u0637\u064a\u0629: 1.lm \u2013 \u064a\u0633\u062a\u062e\u062f\u0645 \u0644\u062a\u0646\u0627\u0633\u0628 \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062e\u0637\u064a\u0629 \u062a\u0633\u062a\u062e\u062f\u0645 \u0647\u0630\u0647 \u0627\u0644\u0648\u0638\u064a\u0641\u0629 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a: lm(\u0627\u0644\u0635\u064a\u063a\u0629\u060c \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a\u060c&#8230;) \u0630\u0647\u0628: \u0627\u0644\u0635\u064a\u063a\u0629: \u0635\u064a\u063a\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u062e\u0637\u064a (\u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644 y ~ x1 + x2) \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a: \u0627\u0633\u0645 \u0643\u062a\u0644\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a 2.glm \u2013 \u064a\u0633\u062a\u062e\u062f\u0645 \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062e\u0637\u064a\u0629 \u0627\u0644\u0645\u0639\u0645\u0645\u0629 \u062a\u0633\u062a\u062e\u062f\u0645 \u0647\u0630\u0647 \u0627\u0644\u0648\u0638\u064a\u0641\u0629 \u0628\u0646\u0627\u0621 [&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":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u0627\u0644\u0641\u0631\u0642 \u0628\u064a\u0646 glm \u0648lm \u0641\u064a R<\/title>\n<meta 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