{"id":1641,"date":"2023-07-25T13:26:30","date_gmt":"2023-07-25T13:26:30","guid":{"rendered":"https:\/\/statorials.org\/my\/r-%e1%80%99%e1%80%bb%e1%80%89%e1%80%ba%e1%80%b8%e1%80%80%e1%80%bd%e1%80%b1%e1%80%b8-%e1%80%81%e1%80%bb%e1%80%ad%e1%80%94%e1%80%ba%e1%80%8a%e1%80%be%e1%80%ad%e1%80%99%e1%80%be%e1%80%af\/"},"modified":"2023-07-25T13:26:30","modified_gmt":"2023-07-25T13:26:30","slug":"r-%e1%80%99%e1%80%bb%e1%80%89%e1%80%ba%e1%80%b8%e1%80%80%e1%80%bd%e1%80%b1%e1%80%b8-%e1%80%81%e1%80%bb%e1%80%ad%e1%80%94%e1%80%ba%e1%80%8a%e1%80%be%e1%80%ad%e1%80%99%e1%80%be%e1%80%af","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/r-%e1%80%99%e1%80%bb%e1%80%89%e1%80%ba%e1%80%b8%e1%80%80%e1%80%bd%e1%80%b1%e1%80%b8-%e1%80%81%e1%80%bb%e1%80%ad%e1%80%94%e1%80%ba%e1%80%8a%e1%80%be%e1%80%ad%e1%80%99%e1%80%be%e1%80%af\/","title":{"rendered":"R in curve fitting (\u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a\u1021\u1010\u1030)"},"content":{"rendered":"

<\/p>\n

\n

\u1019\u1000\u103c\u102c\u1001\u100f\u1006\u102d\u102f\u101e\u101c\u102d\u102f R \u104f\u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u1014\u103e\u1004\u1037\u103a\u1021\u101e\u1004\u1037\u103a\u1010\u1031\u102c\u103a\u1006\u102f\u1036\u1038\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f \u101e\u1004\u103a\u101b\u103e\u102c\u101c\u102d\u102f\u1015\u1031\u1019\u100a\u103a\u104b<\/span> <\/p>\n

\"\u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038<\/p>\n

\u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c\u1010\u103d\u1004\u103a poly()<\/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 R \u1010\u103d\u1004\u103a \u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u1021\u1031\u102c\u1004\u103a \u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038\u1014\u103e\u1004\u1037\u103a \u1019\u100a\u103a\u101e\u100a\u1037\u103a\u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u101e\u100a\u103a \u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1006\u102f\u1036\u1038\u1016\u103c\u1010\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1021\u1006\u1004\u1037\u103a 1- \u1012\u1031\u1010\u102c\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u103c\u102e\u1038 \u1019\u103c\u1004\u103a\u101a\u1031\u102c\u1004\u103a\u1000\u103c\u100a\u1037\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n

\u1021\u1010\u102f\u1021\u1015\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u103c\u102e\u1038 \u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1019\u103c\u1004\u103a\u101a\u1031\u102c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101b\u1014\u103a \u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1021\u1005\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1000\u103c\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/span> <\/p>\n

 #create data frame\n<\/span>df <- data. frame<\/span> (x=1:15,\n                 y=c(3, 14, 23, 25, 23, 15, 9, 5, 9, 13, 17, 24, 32, 36, 46))\n\n#create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch= 19<\/span> , xlab=' x<\/span> ', ylab=' y<\/span> ')<\/strong> <\/pre>\n
\"\"<\/p>\n
 \u1021\u1006\u1004\u1037\u103a 2- \u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1000\u102d\u102f \u1001\u103b\u102d\u1014\u103a\u100a\u103e\u102d\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a \u1000\u102d\u1014\u103a\u1038\u1002\u100f\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1000\u102d\u102f \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u1021\u1031\u102c\u1004\u103a \u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1000\u103d\u1000\u103a\u101c\u1015\u103a\u1010\u103d\u1004\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u104f \u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u1000\u102d\u102f \u1019\u103c\u1004\u103a\u101a\u1031\u102c\u1004\u103a\u1000\u103c\u100a\u1037\u103a\u1000\u103c\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/span> <\/p>\n
 #fit polynomial regression models up to degree 5\n<\/span>fit1 <- lm(y~x, data=df)\nfit2 <- lm(y~poly(x,2,raw= TRUE<\/span> ), data=df)\nfit3 <- lm(y~poly(x,3,raw= TRUE<\/span> ), data=df)\nfit4 <- lm(y~poly(x,4,raw= TRUE<\/span> ), data=df)\nfit5 <- lm(y~poly(x,5,raw= TRUE<\/span> ), data=df)<\/span>\n\n#create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch=19, xlab=' x<\/span> ', ylab=' y<\/span> ')\n\n#define x-axis values\n<\/span>x_axis <- seq(1, 15, length= 15<\/span> )\n\n#add curve of each model to plot\n<\/span>lines(x_axis, predict(fit1, data. frame<\/span> (x=x_axis)), col=' green<\/span> ')\nlines(x_axis, predict(fit2, data. frame<\/span> (x=x_axis)), col=' red<\/span> ')\nlines(x_axis, predict(fit3, data. frame<\/span> (x=x_axis)), col=' purple<\/span> ')\nlines(x_axis, predict(fit4, data. frame<\/span> (x=x_axis)), col=' blue<\/span> ')\nlines(x_axis, predict(fit5, data. frame<\/span> (x=x_axis)), col=' orange<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
 \u1019\u100a\u103a\u101e\u100a\u1037\u103a\u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u101e\u100a\u103a \u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1006\u102f\u1036\u1038\u1016\u103c\u1010\u103a\u101b\u1014\u103a\u104a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u104f \u1001\u103b\u102d\u1014\u103a\u100a\u103e\u102d\u1011\u102c\u1038\u101e\u1031\u102c R \u1005\u1010\u102f\u101b\u1014\u103a\u1038<\/a> \u1000\u102d\u102f \u1000\u103c\u100a\u1037\u103a\u101b\u103e\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1024\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u101e\u100a\u103a \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u1030\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1021\u1010\u103d\u1000\u103a \u1001\u103b\u102d\u1014\u103a\u100a\u103e\u102d\u1011\u102c\u1038\u101e\u1031\u102c \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u101b\u103e\u102d \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u1030\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a(\u1019\u103b\u102c\u1038) \u1000 \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u1037\u103a \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1010\u103d\u1004\u103a \u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u101b\u102c\u1001\u102d\u102f\u1004\u103a\u1014\u103e\u102f\u1014\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u1031\u102c\u1015\u103c\u101e\u100a\u103a\u104b<\/span><\/p>\n
 #calculated adjusted R-squared of each model\n<\/span>summary(fit1)$adj. r<\/span> . squared<\/span>\nsummary(fit2)$adj. r<\/span> . squared<\/span>\nsummary(fit3)$adj. r<\/span> . squared<\/span>\nsummary(fit4)$adj. r<\/span> . squared<\/span>\nsummary(fit5)$adj. r<\/span> . squared<\/span>\n\n[1] 0.3144819\n[1] 0.5186706\n[1] 0.7842864\n[1] 0.9590276\n[1] 0.9549709\n<\/strong><\/pre>\n
 \u101b\u101c\u1012\u103a\u1019\u103e\u104a \u1021\u1019\u103c\u1004\u1037\u103a\u1006\u102f\u1036\u1038\u1001\u103b\u102d\u1014\u103a\u100a\u103e\u102d\u1011\u102c\u1038\u101e\u1031\u102c R-squared \u1015\u102b\u101e\u100a\u1037\u103a\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u101e\u100a\u103a \u1001\u103b\u102d\u1014\u103a\u100a\u103e\u102d\u1011\u102c\u1038\u101e\u1031\u102c R-squared 0.959<\/strong> \u101b\u103e\u102d\u101e\u100a\u1037\u103a \u1005\u1010\u102f\u1010\u1039\u1011\u1012\u102e\u1002\u101b\u102e\u1015\u102d\u102f\u101c\u102e\u1014\u102d\u102f\u1019\u102e\u1038\u101a\u102c\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1021\u1006\u1004\u1037\u103a 3- \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u1000\u102d\u102f \u1019\u103c\u1004\u103a\u101a\u1031\u102c\u1004\u103a\u1000\u103c\u100a\u1037\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1010\u103d\u1004\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1005\u1010\u102f\u1010\u1039\u1011\u1012\u102e\u1002\u101b\u102e \u1000\u102d\u1014\u103a\u1038\u1002\u100f\u1014\u103a\u1038\u1015\u102f\u1036\u1005\u1036\u104f \u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u1016\u103c\u1004\u1037\u103a \u1016\u103c\u1014\u1037\u103a\u1001\u103d\u1032\u1000\u103d\u1000\u103a\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span> <\/p>\n
 #create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch=19, xlab=' x<\/span> ', ylab=' y<\/span> ')\n\n#define x-axis values\n<\/span>x_axis <- seq(1, 15, length= 15<\/span> )\n\n#add curve of fourth-degree polynomial model\n<\/span>lines(x_axis, predict(fit4, data. frame<\/span> (x=x_axis)), col=' blue<\/span> ')\n<\/strong><\/pre>\n
\"\u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038<\/p>\n
 summary()<\/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 \u1024\u1005\u102c\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1021\u1010\u103d\u1000\u103a \u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f\u101c\u100a\u103a\u1038 \u101b\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/span><\/p>\n
 summary(fit4)\n\nCall:\nlm(formula = y ~ poly(x, 4, raw = TRUE), data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-3.4490 -1.1732 0.6023 1.4899 3.0351 \n\nCoefficients:\n                         Estimate Std. Error t value Pr(>|t|)    \n(Intercept) -26.51615 4.94555 -5.362 0.000318 ***\npoly(x, 4, raw = TRUE)1 35.82311 3.98204 8.996 4.15e-06 ***\npoly(x, 4, raw = TRUE)2 -8.36486 0.96791 -8.642 5.95e-06 ***\npoly(x, 4, raw = TRUE)3 0.70812 0.08954 7.908 1.30e-05 ***\npoly(x, 4, raw = TRUE)4 -0.01924 0.00278 -6.922 4.08e-05 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.424 on 10 degrees of freedom\nMultiple R-squared: 0.9707, Adjusted R-squared: 0.959 \nF-statistic: 82.92 on 4 and 10 DF, p-value: 1.257e-07\n<\/strong><\/pre>\n
 \u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u104f\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1019\u103e\u102c \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1010\u102d\u102f\u1004\u103a\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n
 y = -0.0192x 4<\/sup> + 0.7081x 3<\/sup> \u2013 8.3649x 2<\/sup> + 35.823x \u2013 26.516<\/span><\/p>\n
 \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u101b\u103e\u102d \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u1001\u103c\u1031\u1001\u1036\u104d \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a<\/a> \u104f\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101b\u1014\u103a \u1024\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1021\u102c\u1038 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b \u1025\u1015\u1019\u102c x<\/em> = 4 \u1006\u102d\u102f\u101b\u1004\u103a y<\/em> = 23.34<\/strong> \u1000\u102d\u102f \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101b\u1015\u102b\u101c\u102d\u1019\u1037\u103a\u1019\u101a\u103a\u104b<\/span><\/p>\n
 y = -0.0192(4) 4<\/sup> + 0.7081(4) 3<\/sup> \u2013 8.3649(4) 2<\/sup> + 35.823(4) \u2013 26.516 = 23.34<\/span><\/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><\/h3>\n
 Polynomial Regression \u1014\u102d\u1012\u102b\u1014\u103a\u1038
<\/a> R \u1010\u103d\u1004\u103a Polynomial Regression (\u1010\u1005\u103a\u1006\u1004\u1037\u103a\u1015\u103c\u102e\u1038\u1010\u1005\u103a\u1006\u1004\u1037\u103a)<\/a>
 R \u1010\u103d\u1004\u103a seq function \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u100a\u103a\u1038<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u1019\u1000\u103c\u102c\u1001\u100f\u1006\u102d\u102f\u101e\u101c\u102d\u102f R \u104f\u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u1014\u103e\u1004\u1037\u103a\u1021\u101e\u1004\u1037\u103a\u1010\u1031\u102c\u103a\u1006\u102f\u1036\u1038\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f \u101e\u1004\u103a\u101b\u103e\u102c\u101c\u102d\u102f\u1015\u1031\u1019\u100a\u103a\u104b \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c\u1010\u103d\u1004\u103a poly() \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 R \u1010\u103d\u1004\u103a \u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u1021\u1031\u102c\u1004\u103a \u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038\u1014\u103e\u1004\u1037\u103a \u1019\u100a\u103a\u101e\u100a\u1037\u103a\u1019\u103b\u1009\u103a\u1038\u1000\u103d\u1031\u1038\u101e\u100a\u103a \u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1006\u102f\u1036\u1038\u1016\u103c\u1010\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u100a\u103a\u104b \u1021\u1006\u1004\u1037\u103a 1- \u1012\u1031\u1010\u102c\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u103c\u102e\u1038 \u1019\u103c\u1004\u103a\u101a\u1031\u102c\u1004\u103a\u1000\u103c\u100a\u1037\u103a\u1015\u102b\u104b \u1021\u1010\u102f\u1021\u1015\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u103c\u102e\u1038 \u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1019\u103c\u1004\u103a\u101a\u1031\u102c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101b\u1014\u103a \u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1021\u1005\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1000\u103c\u1015\u102b\u1005\u102d\u102f\u1037\u104b #create data frame df <- data. frame (x=1:15, y=c(3, 14, 23, 25, 23, 15, 9, 5, 9, 13, 17, 24, 32, 36, 46)) #create […]<\/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 in Curve Fitting (\u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a\u1021\u1010\u1030) - Statorials<\/title>\n<meta name=\"description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a 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