{"id":683,"date":"2023-07-29T02:38:21","date_gmt":"2023-07-29T02:38:21","guid":{"rendered":"https:\/\/statorials.org\/my\/%e1%80%86%e1%80%af%e1%80%90%e1%80%ba%e1%80%9a%e1%80%af%e1%80%90%e1%80%ba%e1%80%99%e1%80%be%e1%80%af%e1%80%99%e1%80%bb%e1%80%89%e1%80%ba%e1%80%b8-r-%e1%80%96%e1%80%bc%e1%80%84%e1%80%b7%e1%80%ba\/"},"modified":"2023-07-29T02:38:21","modified_gmt":"2023-07-29T02:38:21","slug":"%e1%80%86%e1%80%af%e1%80%90%e1%80%ba%e1%80%9a%e1%80%af%e1%80%90%e1%80%ba%e1%80%99%e1%80%be%e1%80%af%e1%80%99%e1%80%bb%e1%80%89%e1%80%ba%e1%80%b8-r-%e1%80%96%e1%80%bc%e1%80%84%e1%80%b7%e1%80%ba","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/%e1%80%86%e1%80%af%e1%80%90%e1%80%ba%e1%80%9a%e1%80%af%e1%80%90%e1%80%ba%e1%80%99%e1%80%be%e1%80%af%e1%80%99%e1%80%bb%e1%80%89%e1%80%ba%e1%80%b8-r-%e1%80%96%e1%80%bc%e1%80%84%e1%80%b7%e1%80%ba\/","title":{"rendered":"R \u1010\u103d\u1004\u103a regression line \u1016\u103c\u1004\u1037\u103a scatterplot \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038"},"content":{"rendered":"

<\/p>\n

\n

\u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c \u1019\u103b\u1009\u103a\u1038\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u103c\u1014\u103a\u1006\u102f\u1010\u103a\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101e\u1031\u102c\u1021\u1001\u102b\u1010\u103d\u1004\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c x \u1014\u103e\u1004\u1037\u103a y \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u104f \u1015\u1031\u102b\u1004\u103a\u1038\u1005\u1015\u103a\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u103c\u1004\u103a\u101e\u102c\u1005\u1031\u101b\u1014\u103a scatterplot \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u101c\u102d\u102f\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1000\u1036\u1021\u102c\u1038\u101c\u103b\u1031\u102c\u103a\u1005\u103d\u102c\u104a R \u101e\u100a\u103a plot()<\/strong> \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 point cloud \u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u101b\u1014\u103a \u101c\u103d\u101a\u103a\u1000\u1030\u1005\u1031\u101e\u100a\u103a\u104b \u1025\u1015\u1019\u102c\u1021\u102c\u1038\u1016\u103c\u1004\u103a\u1037:<\/span> <\/p>\n

 #create some fake data<\/span>\ndata <- 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#create scatterplot of data<\/span>\nplot(data$x, data$y)\n<\/strong><\/pre>\n
\"R<\/p>\n
 abline()<\/strong> \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 scatterplot \u1010\u103d\u1004\u103a regression line \u1010\u1005\u103a\u1001\u102f\u1011\u100a\u1037\u103a\u101b\u1014\u103a\u101c\u100a\u103a\u1038 \u101c\u103d\u101a\u103a\u1000\u1030\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1025\u1015\u1019\u102c\u1021\u102c\u1038\u1016\u103c\u1004\u103a\u1037:<\/span> <\/p>\n
 #fit a simple linear regression model<\/span>\nmodel <- lm(y ~ x, data = data)\n\n#add the fitted regression line to the scatterplot<\/span>\nabline(model)<\/strong> <\/pre>\n
\"R<\/p>\n
 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038()<\/strong> \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 \u1000\u103c\u1036\u1005\u100a\u103a\u1019\u103e\u102f\u101e\u102d\u102f\u1037 \u101a\u102f\u1036\u1000\u103c\u100a\u103a\u1019\u103e\u102f\u1000\u103c\u102c\u1038\u1000\u102c\u101c\u1019\u103b\u1025\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u1031\u102b\u1004\u103a\u1038\u1011\u100a\u1037\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span> <\/p>\n
 #define range of x values<\/span>\nnewx = seq(min(data$x),max(data$x),by = 1)\n\n#find 95% confidence interval for the range of x values<\/span> \nconf_interval <- predict(model, newdata=data.frame(x=newx), interval=\"confidence\",\n                         level = 0.95)\n\n#create scatterplot of values with regression line<\/span>\nplot(data$x, data$y)\nabline(model)\n\n#add dashed lines (lty=2) for the 95% confidence interval<\/span>\nlines(newx, conf_interval[,2], col=\"blue\", lty=2)\nlines(newx, conf_interval[,3], col=\"blue\", lty=2)\n<\/strong><\/pre>\n
\"R<\/p>\n
 \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u101b\u103e\u102d \u1000\u103c\u102c\u1038\u1000\u102c\u101c\u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1000\u102d\u102f \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1004\u1037\u103a \u1000\u103d\u1000\u103a\u1000\u103d\u1000\u103a\u101e\u102d\u102f\u1037 \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u100a\u1037\u103a\u1000\u103c\u102c\u1038\u1000\u102c\u101c\u1019\u103b\u1025\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1011\u1015\u103a\u1011\u100a\u1037\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span> <\/p>\n
 #define range of x values<\/span>\nnewx = seq(min(data$x),max(data$x),by = 1)\n\n#find 95% prediction interval for the range of x values<\/span> \npred_interval <- predict(model, newdata=data.frame(x=newx), interval=\"prediction\"<\/span> ,\n                         level = 0.95)\n\n#create scatterplot of values with regression line<\/span>\nplot(data$x, data$y)\nabline(model)\n\n#add dashed lines (lty=2) for the 95% confidence interval<\/span>\nlines(newx, pred_interval[,2], col=\"red\", lty=2)\nlines(newx, pred_interval[,3], col=\"red\", lty=2)\n<\/strong><\/pre>\n
\"R<\/p>\n
 \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1010\u103d\u1004\u103a\u104a \u1001\u1031\u102b\u1004\u103a\u1038\u1005\u1009\u103a\u1010\u1005\u103a\u1001\u102f\u1011\u100a\u1037\u103a\u1001\u103c\u1004\u103a\u1038\u104a \u101d\u1004\u103a\u101b\u102d\u102f\u1038\u1021\u1019\u100a\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1001\u103c\u1004\u103a\u1038\u1014\u103e\u1004\u1037\u103a \u1010\u1005\u103a\u1026\u1038\u1001\u103b\u1004\u103a\u1038\u1006\u103d\u1032\u1000\u103d\u1000\u103a\u1021\u1019\u103e\u1010\u103a\u1019\u103b\u102c\u1038\u104f\u1015\u102f\u1036\u101e\u100f\u1039\u100d\u102c\u1014\u103a\u1000\u102d\u102f\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1004\u1037\u103a \u1007\u102c\u1010\u103a\u1000\u103d\u1000\u103a\u1000\u102d\u102f \u1015\u102d\u102f\u1019\u102d\u102f\u101e\u102c\u101a\u102c\u101c\u103e\u1015\u1005\u1031\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span> <\/p>\n
 plot(data$x, data$y,\n     main = \"Scatterplot of x vs. y\", #add title<\/span>\n     pch=16, #specify points to be filled in<\/span>\n     xlab='x', #change x-axis name\n<\/span>ylab='y') #change y-axis name<\/span>\n\nabline(model, col='steelblue') #specify color of regression line<\/span><\/span>\n<\/strong><\/pre>\n
\"R<\/p>\n
 \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><\/h2>\n
 \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a R \u1010\u103d\u1004\u103a \u1021\u1001\u103c\u102c\u1038\u1018\u102f\u1036\u1021\u101c\u102f\u1015\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u101e\u100a\u103a-<\/span><\/p>\n
 R \u1016\u103c\u1004\u1037\u103a \u1001\u103d\u1032\u1001\u103c\u1019\u103a\u1038\u1005\u102d\u1010\u103a\u1016\u103c\u102c\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1010\u103d\u1004\u103a \u1021\u1019\u103e\u1010\u103a\u1021\u101e\u102c\u1038\u1015\u103c\u102f\u1014\u100a\u103a\u1038<\/a>
 Point clouds \u1021\u1010\u103d\u1000\u103a R \u1010\u103d\u1004\u103a Jitter \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\u100a\u103a\u1038<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c \u1019\u103b\u1009\u103a\u1038\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u103c\u1014\u103a\u1006\u102f\u1010\u103a\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101e\u1031\u102c\u1021\u1001\u102b\u1010\u103d\u1004\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c x \u1014\u103e\u1004\u1037\u103a y \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u104f \u1015\u1031\u102b\u1004\u103a\u1038\u1005\u1015\u103a\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u103c\u1004\u103a\u101e\u102c\u1005\u1031\u101b\u1014\u103a scatterplot \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u101c\u102d\u102f\u1015\u102b\u101e\u100a\u103a\u104b \u1000\u1036\u1021\u102c\u1038\u101c\u103b\u1031\u102c\u103a\u1005\u103d\u102c\u104a R \u101e\u100a\u103a plot() \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 point cloud \u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u101b\u1014\u103a \u101c\u103d\u101a\u103a\u1000\u1030\u1005\u1031\u101e\u100a\u103a\u104b \u1025\u1015\u1019\u102c\u1021\u102c\u1038\u1016\u103c\u1004\u103a\u1037: #create some fake data data <- data.frame(x = c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 11, 11), y = c(13, 14, 17, 12, […]<\/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":"\nR - Statology \u1010\u103d\u1004\u103a Regression Line \u1016\u103c\u1004\u1037\u103a Scatter Plot \u1010\u1005\u103a\u1001\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038<\/title>\n<meta name=\"description\" content=\"\u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u1005\u103d\u102c \u1021\u1015\u102b\u1021\u101d\u1004\u103a R \u101b\u103e\u102d \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1019\u103b\u1009\u103a\u1038\u1016\u103c\u1004\u1037\u103a \u1001\u103d\u1032\u1001\u103c\u1019\u103a\u1038\u1005\u102d\u1015\u103a\u1016\u103c\u102c\u1015\u102f\u1036\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038 \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c \u101b\u103e\u1004\u103a\u1038\u101c\u1004\u103a\u1038\u1001\u103b\u1000\u103a\u104b\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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