{"id":1546,"date":"2023-07-25T22:47:19","date_gmt":"2023-07-25T22:47:19","guid":{"rendered":"https:\/\/statorials.org\/my\/glm-%e1%80%94%e1%80%be%e1%80%84%e1%80%b7%e1%80%ba-lm-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-r\/"},"modified":"2023-07-25T22:47:19","modified_gmt":"2023-07-25T22:47:19","slug":"glm-%e1%80%94%e1%80%be%e1%80%84%e1%80%b7%e1%80%ba-lm-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/glm-%e1%80%94%e1%80%be%e1%80%84%e1%80%b7%e1%80%ba-lm-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-r\/","title":{"rendered":"R \u1010\u103d\u1004\u103a glm \u1014\u103e\u1004\u1037\u103a lm \u1000\u103d\u102c\u1001\u103c\u102c\u1038\u1001\u103b\u1000\u103a"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">R programming language \u101e\u100a\u103a linear model \u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u101c\u102d\u102f\u1000\u103a\u1016\u1000\u103a\u101e\u1031\u102c \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u1036\u1037\u1015\u102d\u102f\u1038\u1015\u1031\u1038\u101e\u100a\u103a-<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. lm \u2013 linear \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a-<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>lm(\u1016\u1031\u102c\u103a\u1019\u103c\u1030\u101c\u102c\u104a \u1012\u1031\u1010\u102c\u104a \u2026)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u101b\u103d\u103e\u1031-<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u1016\u1031\u102c\u103a\u1019\u103c\u1030\u101c\u102c-<\/strong> linear model \u1016\u1031\u102c\u103a\u1019\u103c\u1030\u101c\u102c (\u1025\u1015\u1019\u102c y ~ x1 + x2)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u1012\u1031\u1010\u102c-<\/strong> \u1012\u1031\u1010\u102c\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1012\u1031\u1010\u102c\u1018\u101c\u1031\u102c\u1000\u103a\u104f \u1021\u1019\u100a\u103a<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>2. glm \u2013 \u101a\u1031\u1018\u1030\u101a\u103b\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a \u1019\u103b\u1009\u103a\u1038\u1016\u103c\u1031\u102c\u1004\u1037\u103a\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a-<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>glm(\u1016\u1031\u102c\u103a\u1019\u103c\u1030\u101c\u102c\u104a \u1019\u102d\u101e\u102c\u1038\u1005\u102f= Gaussian\u104a \u1012\u1031\u1010\u102c\u104a \u2026)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u101b\u103d\u103e\u1031-<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u1016\u1031\u102c\u103a\u1019\u103c\u1030\u101c\u102c-<\/strong> linear model \u1016\u1031\u102c\u103a\u1019\u103c\u1030\u101c\u102c (\u1025\u1015\u1019\u102c y ~ x1 + x2)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u1019\u102d\u101e\u102c\u1038\u1005\u102f-<\/strong> \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1014\u103e\u1004\u1037\u103a\u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1014\u103a \u1000\u102d\u1014\u103a\u1038\u1002\u100f\u1014\u103a\u1038\u1019\u102d\u101e\u102c\u1038\u1005\u102f\u104b \u1019\u1030\u101b\u1004\u103a\u1038\u1019\u103e\u102c Gaussian \u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104a \u101e\u102d\u102f\u1037\u101e\u1031\u102c\u103a \u1021\u1001\u103c\u102c\u1038\u101b\u103d\u1031\u1038\u1001\u103b\u101a\u103a\u1005\u101b\u102c\u1019\u103b\u102c\u1038\u1019\u103e\u102c Binomial\u104a Gamma \u1014\u103e\u1004\u1037\u103a Poisson \u1010\u102d\u102f\u1037\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u1012\u1031\u1010\u102c-<\/strong> \u1012\u1031\u1010\u102c\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1012\u1031\u1010\u102c\u1018\u101c\u1031\u102c\u1000\u103a\u104f \u1021\u1019\u100a\u103a<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1014\u103e\u1005\u103a\u1001\u102f\u1000\u103c\u102c\u1038\u1019\u103e \u1010\u1005\u103a\u1001\u102f\u1010\u100a\u103a\u1038\u101e\u1031\u102c \u1000\u103d\u102c\u1001\u103c\u102c\u1038\u1001\u103b\u1000\u103a\u1019\u103e\u102c <strong>glm()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1010\u103d\u1004\u103a \u1011\u100a\u1037\u103a\u101e\u103d\u1004\u103a\u1038\u1011\u102c\u1038\u101e\u1031\u102c <strong>\u1019\u102d\u101e\u102c\u1038\u1005\u102f<\/strong> \u1021\u1004\u103c\u1004\u103a\u1038\u1021\u1001\u102f\u1036\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">linear regression model \u1000\u102d\u102f \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u1021\u1031\u102c\u1004\u103a lm() \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a glm() \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1000\u104a <strong>\u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1021\u1010\u102d\u1021\u1000\u103b\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u101b\u101c\u1012\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1011\u102f\u1010\u103a\u1015\u1031\u1038\u1019\u100a\u103a\u1016\u103c\u1005\u103a\u1015\u102b\u101e\u100a\u103a<\/strong> \u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u101e\u102d\u102f\u1037\u101e\u1031\u102c\u103a\u101c\u100a\u103a\u1038\u104a glm() \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u1032\u1037\u101e\u102d\u102f\u1037\u101e\u1031\u102c \u1015\u102d\u102f\u1019\u102d\u102f\u101b\u103e\u102f\u1015\u103a\u1011\u103d\u1031\u1038\u101e\u1031\u102c \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u1011\u1031\u102c\u1000\u103a\u1015\u1036\u1037\u1015\u102d\u102f\u1037\u1006\u1031\u102c\u1004\u103a\u101b\u1031\u1038 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f (family=binomial)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/my\/\u1004\u102b\u1038-\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\/\" target=\"_blank\" rel=\"noopener\">Poisson \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f<\/a> (\u1019\u102d\u101e\u102c\u1038\u1005\u102f = \u1004\u102b\u1038)<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a lm() \u1014\u103e\u1004\u1037\u103a glm() \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>lm() function \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038 \u1025\u1015\u1019\u102c<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a lm() \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d <strong>linear regression model<\/strong> \u1010\u1005\u103a\u1001\u102f\u1021\u102c\u1038 \u1019\u100a\u103a\u1000\u1032\u1037\u101e\u102d\u102f\u1037 \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u1005\u1031\u101b\u1014\u103a \u1016\u1031\u102c\u103a\u1015\u103c\u101e\u100a\u103a-<\/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> <span style=\"color: #000000;\"><strong>glm() \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038 \u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a glm() \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1021\u1010\u102d\u1021\u1000\u103b\u1010\u1030\u100a\u102e\u101e\u1031\u102c <strong>linear regression model \u1000\u102d\u102f<\/strong> \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/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> <span style=\"color: #000000;\">coefficient \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a coefficient \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u104f \u1005\u1036\u1021\u1019\u103e\u102c\u1038\u1019\u103b\u102c\u1038\u101e\u100a\u103a lm() \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1019\u103e \u1011\u102f\u1010\u103a\u101c\u102f\u1015\u103a\u101e\u100a\u1037\u103a\u1021\u101b\u102c\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1021\u1010\u102d\u1021\u1000\u103b\u1010\u1030\u100a\u102e\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">family=binomial \u1000\u102d\u102f \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1010\u102d\u102f\u1004\u103a\u1038 \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1004\u1037\u103a <strong>logistic regression model \u1000\u102d\u102f<\/strong> \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u1005\u1031\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a glm() \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/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> <span style=\"color: #000000;\">\u1019\u102d\u101e\u102c\u1038\u1005\u102f = poisson \u1000\u102d\u102f \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1010\u102d\u102f\u1004\u103a\u1038 \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1004\u1037\u103a <strong>Poisson \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036<\/strong> \u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a glm() \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/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> <span style=\"color: #000000;\"><strong>\u1011\u1015\u103a\u101c\u1031\u102c\u1004\u103a\u1038\u1021\u101b\u1004\u103a\u1038\u1021\u1019\u103c\u1005\u103a\u1019\u103b\u102c\u1038<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/my\/r-\u1010\u103d\u1004\u103a-\u101b\u102d\u102f\u1038\u101b\u102d\u102f\u1038\u1019\u103b\u1009\u103a\u1038\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\/\" target=\"_blank\" rel=\"noopener\">R \u1010\u103d\u1004\u103a \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c linear regression \u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038<\/a><br \/> <a href=\"https:\/\/statorials.org\/my\/multiple-linear-regression-r\/\" target=\"_blank\" rel=\"noopener\">R \u1010\u103d\u1004\u103a linear regression \u1021\u1019\u103b\u102c\u1038\u1021\u1015\u103c\u102c\u1038\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1014\u100a\u103a\u1038<\/a><br \/> <a href=\"https:\/\/statorials.org\/my\/r-glm-\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u100a\u103a\u104b\/\" target=\"_blank\" rel=\"noopener\">R \u1010\u103d\u1004\u103a glm \u1016\u103c\u1004\u1037\u103a \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u100a\u1037\u103a\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>R programming language \u101e\u100a\u103a linear model \u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u101c\u102d\u102f\u1000\u103a\u1016\u1000\u103a\u101e\u1031\u102c \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u1036\u1037\u1015\u102d\u102f\u1038\u1015\u1031\u1038\u101e\u100a\u103a- 1. lm \u2013 linear \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b \u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a- lm(\u1016\u1031\u102c\u103a\u1019\u103c\u1030\u101c\u102c\u104a \u1012\u1031\u1010\u102c\u104a \u2026) \u101b\u103d\u103e\u1031- \u1016\u1031\u102c\u103a\u1019\u103c\u1030\u101c\u102c- linear model \u1016\u1031\u102c\u103a\u1019\u103c\u1030\u101c\u102c (\u1025\u1015\u1019\u102c y ~ x1 + x2) \u1012\u1031\u1010\u102c- \u1012\u1031\u1010\u102c\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1012\u1031\u1010\u102c\u1018\u101c\u1031\u102c\u1000\u103a\u104f \u1021\u1019\u100a\u103a 2. glm \u2013 \u101a\u1031\u1018\u1030\u101a\u103b\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a \u1019\u103b\u1009\u103a\u1038\u1016\u103c\u1031\u102c\u1004\u1037\u103a\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b \u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a- glm(\u1016\u1031\u102c\u103a\u1019\u103c\u1030\u101c\u102c\u104a \u1019\u102d\u101e\u102c\u1038\u1005\u102f= Gaussian\u104a \u1012\u1031\u1010\u102c\u104a \u2026) [&hellip;]<\/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":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R \u1010\u103d\u1004\u103a glm \u1014\u103e\u1004\u1037\u103a lm \u1000\u103d\u102c\u1001\u103c\u102c\u1038\u1001\u103b\u1000\u103a<\/title>\n<meta name=\"description\" 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