{"id":782,"date":"2023-07-28T18:44:38","date_gmt":"2023-07-28T18:44:38","guid":{"rendered":"https:\/\/statorials.org\/my\/%e1%80%9c%e1%80%b1%e1%80%b8%e1%80%91%e1%80%b1%e1%80%ac%e1%80%84%e1%80%b7%e1%80%ba%e1%80%86%e1%80%af%e1%80%90%e1%80%ba%e1%80%9a%e1%80%af%e1%80%90%e1%80%ba-r\/"},"modified":"2023-07-28T18:44:38","modified_gmt":"2023-07-28T18:44:38","slug":"%e1%80%9c%e1%80%b1%e1%80%b8%e1%80%91%e1%80%b1%e1%80%ac%e1%80%84%e1%80%b7%e1%80%ba%e1%80%86%e1%80%af%e1%80%90%e1%80%ba%e1%80%9a%e1%80%af%e1%80%90%e1%80%ba-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/%e1%80%9c%e1%80%b1%e1%80%b8%e1%80%91%e1%80%b1%e1%80%ac%e1%80%84%e1%80%b7%e1%80%ba%e1%80%86%e1%80%af%e1%80%90%e1%80%ba%e1%80%9a%e1%80%af%e1%80%90%e1%80%ba-r\/","title":{"rendered":"R \u1010\u103d\u1004\u103a \u101c\u1031\u1038\u1015\u102f\u1036\u1010\u1015\u102f\u1036 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1014\u100a\u103a\u1038"},"content":{"rendered":"

<\/p>\n

\n

\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1014\u103e\u1005\u103a\u1001\u102f\u101e\u100a\u103a linear \u1006\u1000\u103a\u1006\u1036\u101b\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d\u101e\u1031\u102c\u1021\u1001\u102b\u104a \u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u104f\u1006\u1000\u103a\u1014\u103d\u101a\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c linear regression \u1000\u102d\u102f<\/a> \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span> <\/p>\n

\"linear<\/p>\n

\u101e\u102d\u102f\u1037\u101e\u1031\u102c\u103a\u104a \u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1014\u103e\u1005\u103a\u1001\u102f\u101e\u100a\u103a \u101c\u1031\u1038\u1011\u1031\u102c\u1004\u1037\u103a\u1015\u102f\u1036\u1005\u1036 \u1006\u1000\u103a\u1014\u103d\u101a\u103a\u1019\u103e\u102f\u101b\u103e\u102d\u1015\u102b\u1000\u104a \u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u104f \u1006\u1000\u103a\u1014\u103d\u101a\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a \u101c\u1031\u1038\u1011\u1031\u102c\u1004\u1037\u103a\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1000\u102d\u102f<\/strong> \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<\/span> <\/p>\n

\"\u101c\u1031\u1038\u1011\u1031\u102c\u1004\u1037\u103a\u1015\u102f\u1036<\/p>\n

\u1024\u1000\u103b\u1030\u1010\u102d\u102f\u101b\u102e\u101b\u101a\u103a\u1010\u103d\u1004\u103a R \u1010\u103d\u1004\u103a \u101c\u1031\u1038\u1015\u102f\u1036\u1010\u1015\u102f\u1036 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\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\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1025\u1015\u1019\u102c- R \u1010\u103d\u1004\u103a \u101c\u1031\u1038\u1015\u102f\u1036\u1010\u1005\u103a\u1015\u102f\u1036 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f<\/strong><\/h3>\n

\u1021\u101c\u102f\u1015\u103a\u101c\u102f\u1015\u103a\u1010\u1032\u1037 \u1014\u102c\u101b\u102e\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1014\u1032\u1037 \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1021\u1005\u102e\u101b\u1004\u103a\u1001\u1036\u1010\u1032\u1037\u1000\u103c\u102c\u1038\u1000 \u1006\u1000\u103a\u1014\u103d\u101a\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1014\u102c\u1038\u101c\u100a\u103a\u1001\u103b\u1004\u103a\u1010\u101a\u103a\u1006\u102d\u102f\u1015\u102b\u1005\u102d\u102f\u1037\u104b \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1004\u103a \u1010\u1005\u103a\u1015\u1010\u103a\u101c\u103b\u103e\u1004\u103a \u1021\u101c\u102f\u1015\u103a\u101c\u102f\u1015\u103a\u101e\u100a\u1037\u103a \u1014\u102c\u101b\u102e\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1014\u103e\u1004\u1037\u103a \u1015\u1010\u103a\u101e\u1000\u103a\u101e\u100a\u1037\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u101c\u1030 11 \u1026\u1038\u1021\u1010\u103d\u1000\u103a \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f\u1021\u1006\u1004\u1037\u103a (0 \u1019\u103e 100 \u1021\u1011\u102d) \u1021\u1005\u102e\u101b\u1004\u103a\u1001\u1036\u1010\u1004\u103a\u1015\u103c\u101e\u100a\u103a-<\/span> <\/p>\n

\"\"<\/p>\n

R \u1010\u103d\u1004\u103a \u101c\u1031\u1038\u1015\u102f\u1036\u1010\u1005\u103a\u1015\u102f\u1036 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u1005\u1031\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1006\u1004\u1037\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n

\u1021\u1006\u1004\u1037\u103a 1: \u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1011\u100a\u1037\u103a\u1015\u102b\u104b<\/strong><\/span><\/p>\n

\u1015\u1011\u1019\u1026\u1038\u1005\u103d\u102c \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u104f\u1012\u1031\u1010\u102c\u1019\u103b\u102c\u1038\u1015\u102b\u101d\u1004\u103a\u101e\u1031\u102c \u1012\u1031\u1010\u102c\u1018\u1031\u102c\u1004\u103a\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n

 #createdata<\/span>\ndata <- data.frame(hours=c(6, 9, 12, 14, 30, 35, 40, 47, 51, 55, 60),\n                   happiness=c(14, 28, 50, 70, 89, 94, 90, 75, 59, 44, 27))\n\n#viewdata<\/span> \ndata\n\n   hours happiness\n1 6 14\n2 9 28\n3 12 50\n4 14 70\n5 30 89\n6 35 94\n7 40 90\n8 47 75\n9 51 59\n10 55 44\n11 60 27\n<\/strong><\/pre>\n
 \u1021\u1006\u1004\u1037\u103a 2- \u1012\u1031\u1010\u102c\u1000\u102d\u102f \u1019\u103c\u1004\u103a\u101a\u1031\u102c\u1004\u103a\u1000\u103c\u100a\u1037\u103a\u1015\u102b\u104b<\/strong><\/span><\/p>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1012\u1031\u1010\u102c\u1000\u102d\u102f \u1019\u103c\u1004\u103a\u101a\u1031\u102c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101b\u1014\u103a \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c \u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1021\u1005\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/span> <\/p>\n
 #create scatterplot<\/span>\nplot(data$hours, data$happiness, pch=16)<\/strong> <\/pre>\n
\"R<\/p>\n
 \u1012\u1031\u1010\u102c\u101e\u100a\u103a linear \u1015\u102f\u1036\u1005\u1036\u1021\u1010\u102d\u102f\u1004\u103a\u1038 \u1019\u101c\u102d\u102f\u1000\u103a\u1014\u102c\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101b\u103e\u1004\u103a\u1038\u101b\u103e\u1004\u103a\u1038\u101c\u1004\u103a\u1038\u101c\u1004\u103a\u1038 \u101e\u102d\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1021\u1006\u1004\u1037\u103a 3- \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c linear regression model \u1000\u102d\u102f \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1015\u102b\u104b<\/strong><\/span><\/p>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1019\u100a\u103a\u1019\u103b\u103e \u101c\u102d\u102f\u1000\u103a\u1016\u1000\u103a\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103c\u100a\u1037\u103a\u101b\u1014\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\u1021\u1010\u102d\u102f\u1004\u103a\u1038 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1010\u1015\u103a\u1006\u1004\u103a\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n
 #fit linear model<\/span>\nlinearModel <- lm(happiness ~ hours, data=data)\n\n#view model summary<\/span>\nsummary(linearModel)\n\nCall:\nlm(formula = happiness ~ hours)\n\nResiduals:\n   Min 1Q Median 3Q Max \n-39.34 -21.99 -2.03 23.50 35.11 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)  \n(Intercept) 48.4531 17.3288 2.796 0.0208 *\nhours 0.2981 0.4599 0.648 0.5331  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 28.72 on 9 degrees of freedom\nMultiple R-squared: 0.0446, Adjusted R-squared: -0.06156 \nF-statistic: 0.4201 on 1 and 9 DF, p-value: 0.5331\n<\/strong><\/pre>\n
 \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103e\u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u1031\u102c \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f\u1005\u102f\u1005\u102f\u1015\u1031\u102b\u1004\u103a\u1038\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u101e\u100a\u103a 4.46%<\/strong> \u101e\u102c\u101b\u103e\u102d\u1015\u103c\u102e\u1038\u104a \u1019\u103b\u102d\u102f\u1038\u1005\u102f\u1036 R-squared \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1016\u103c\u1004\u1037\u103a\u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1021\u1006\u1004\u1037\u103a 4- \u101c\u1031\u1038\u1015\u102f\u1036\u1010\u1005\u103a\u1015\u102f\u1036 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1000\u103d\u1000\u103a\u1010\u102d\u1015\u102b\u104b<\/strong><\/span><\/p>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u101c\u1031\u1038\u1011\u1031\u102c\u1004\u1037\u103a\u1015\u102f\u1036\u101e\u100f\u1039\u100d\u102c\u1014\u103a \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n
 #create a new variable for hours 2<\/sup><\/span>\ndata$hours2 <- data$hours^2\n\n#fit quadratic regression model<\/span>\nquadraticModel <- lm(happiness ~ hours + hours2, data=data)\n\n#view model summary\n<\/span>summary(quadraticModel)\n\nCall:\nlm(formula = happiness ~ hours + hours2, data = data)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-6.2484 -3.7429 -0.1812 1.1464 13.6678 \n\nCoefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) -18.25364 6.18507 -2.951 0.0184 *  \nhours 6.74436 0.48551 13.891 6.98e-07 ***\nhours2 -0.10120 0.00746 -13.565 8.38e-07 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 6.218 on 8 degrees of freedom\nMultiple R-squared: 0.9602, Adjusted R-squared: 0.9502 \nF-statistic: 96.49 on 2 and 8 DF, p-value: 2.51e-06\n<\/strong><\/pre>\n
 \u1014\u1019\u1030\u1014\u102c\u1015\u102f\u1036\u1005\u1036\u1016\u103c\u1004\u1037\u103a \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u1031\u102c \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f \u1005\u102f\u1005\u102f\u1015\u1031\u102b\u1004\u103a\u1038\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u101e\u100a\u103a 96.02%<\/b> \u1021\u1011\u102d \u1010\u102d\u102f\u1038\u101c\u102c\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u101e\u100a\u103a \u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1019\u100a\u103a\u1019\u103b\u103e\u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1019\u103c\u1004\u103a\u101e\u102c\u1005\u1031\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span> <\/p>\n
 #create sequence of hour values<\/span>\nhourValues <- seq(0, 60, 0.1)\n\n#create list of predicted happiness levels using quadratic model\n<\/span>happinessPredict <- predict(quadraticModel, list(hours=hourValues, hours2=hourValues^2))\n\n#create scatterplot of original data values\n<\/span>plot(data$hours, data$happiness, pch=16)\n#add predicted lines based on quadratic regression model<\/span>\nlines(hourValues, happinessPredict, col='blue')\n<\/strong><\/pre>\n
\"R<\/p>\n
 quadratic regression line \u101e\u100a\u103a data values \u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1021\u1010\u1031\u102c\u103a\u101c\u1031\u1038 \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1010\u103d\u1031\u1037\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1021\u1006\u1004\u1037\u103a 5- \u101c\u1031\u1038\u1011\u1031\u102c\u1004\u1037\u103a\u1015\u102f\u1036\u1005\u1036 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1021\u1013\u102d\u1015\u102b\u101a\u103a\u1016\u103d\u1004\u1037\u103a\u1006\u102d\u102f\u1015\u102b\u104b<\/strong><\/span><\/p>\n
 \u101a\u1001\u1004\u103a\u1021\u1006\u1004\u1037\u103a\u1010\u103d\u1004\u103a\u104a Quadratic regression model \u104f\u101b\u101c\u1012\u103a\u1019\u103e\u102c-<\/span><\/p>\n
 Coefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) -18.25364 6.18507 -2.951 0.0184 *  \nhours 6.74436 0.48551 13.891 6.98e-07 ***\nhours2 -0.10120 0.00746 -13.565 8.38e-07 ***\n<\/strong><\/pre>\n
 \u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a \u1010\u1004\u103a\u1015\u103c\u1011\u102c\u1038\u101e\u1031\u102c \u1000\u102d\u1014\u103a\u1038\u1002\u100f\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1021\u1015\u1031\u102b\u103a \u1021\u1001\u103c\u1031\u1001\u1036\u104d \u1001\u103b\u102d\u1014\u103a\u100a\u103e\u102d\u1011\u102c\u1038\u101e\u1031\u102c \u101c\u1031\u1038\u1011\u1031\u102c\u1004\u1037\u103a\u1015\u102f\u1036\u101e\u100f\u1039\u100d\u1014\u103a\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u101e\u100a\u103a-<\/span><\/p>\n
 \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f = -0.1012 (\u1014\u102c\u101b\u102e) 2<\/sup> + 6.7444 (\u1014\u102c\u101b\u102e) \u2013 18.2536<\/strong><\/span><\/p>\n
 \u1010\u1005\u103a\u1015\u1010\u103a\u101c\u103b\u103e\u1004\u103a \u1021\u101c\u102f\u1015\u103a\u101c\u102f\u1015\u103a\u101e\u100a\u1037\u103a \u1014\u102c\u101b\u102e\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1016\u103c\u1004\u1037\u103a \u101c\u1030\u1010\u1005\u103a\u1026\u1038\u1001\u103b\u1004\u103a\u1038\u1005\u102e\u104f \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1011\u102c\u1038\u101e\u1031\u102c \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u101b\u103e\u102c\u1016\u103d\u1031\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\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1025\u1015\u1019\u102c\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a\u104a \u1010\u1005\u103a\u1015\u1010\u103a\u101c\u103b\u103e\u1004\u103a \u1014\u102c\u101b\u102e 60 \u1021\u101c\u102f\u1015\u103a\u101c\u102f\u1015\u103a\u101e\u1030\u101e\u100a\u103a \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f\u1021\u1006\u1004\u1037\u103a 22.09<\/strong> \u101b\u103e\u102d\u101c\u102d\u1019\u1037\u103a\u1019\u100a\u103a \u104b<\/span><\/p>\n
 \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f = -0.1012(60) 2<\/sup> + 6.7444(60) \u2013 18.2536 = 22.09<\/span><\/p>\n
 \u1021\u1015\u103c\u1014\u103a\u1021\u101c\u103e\u1014\u103a\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a \u1010\u1005\u103a\u1015\u1010\u103a\u101c\u103b\u103e\u1004\u103a \u1014\u102c\u101b\u102e 30 \u1021\u101c\u102f\u1015\u103a\u101c\u102f\u1015\u103a\u101e\u1030\u101e\u100a\u103a \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f\u1021\u1006\u1004\u1037\u103a 92.99<\/strong> \u101b\u103e\u102d\u101e\u1004\u1037\u103a\u101e\u100a\u103a \u104b<\/span><\/p>\n
 \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f = -0.1012(30) 2<\/sup> + 6.7444(30) \u2013 18.2536 = 92.99<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1014\u103e\u1005\u103a\u1001\u102f\u101e\u100a\u103a linear \u1006\u1000\u103a\u1006\u1036\u101b\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d\u101e\u1031\u102c\u1021\u1001\u102b\u104a \u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u104f\u1006\u1000\u103a\u1014\u103d\u101a\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c linear regression \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b \u101e\u102d\u102f\u1037\u101e\u1031\u102c\u103a\u104a \u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1014\u103e\u1005\u103a\u1001\u102f\u101e\u100a\u103a \u101c\u1031\u1038\u1011\u1031\u102c\u1004\u1037\u103a\u1015\u102f\u1036\u1005\u1036 \u1006\u1000\u103a\u1014\u103d\u101a\u103a\u1019\u103e\u102f\u101b\u103e\u102d\u1015\u102b\u1000\u104a \u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u104f \u1006\u1000\u103a\u1014\u103d\u101a\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a \u101c\u1031\u1038\u1011\u1031\u102c\u1004\u1037\u103a\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1000\u102d\u102f \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 \u1024\u1000\u103b\u1030\u1010\u102d\u102f\u101b\u102e\u101b\u101a\u103a\u1010\u103d\u1004\u103a R \u1010\u103d\u1004\u103a \u101c\u1031\u1038\u1015\u102f\u1036\u1010\u1015\u102f\u1036 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\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\u1011\u102c\u1038\u101e\u100a\u103a\u104b \u1025\u1015\u1019\u102c- R \u1010\u103d\u1004\u103a \u101c\u1031\u1038\u1015\u102f\u1036\u1010\u1005\u103a\u1015\u102f\u1036 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f \u1021\u101c\u102f\u1015\u103a\u101c\u102f\u1015\u103a\u1010\u1032\u1037 \u1014\u102c\u101b\u102e\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1014\u1032\u1037 \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1021\u1005\u102e\u101b\u1004\u103a\u1001\u1036\u1010\u1032\u1037\u1000\u103c\u102c\u1038\u1000 \u1006\u1000\u103a\u1014\u103d\u101a\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1014\u102c\u1038\u101c\u100a\u103a\u1001\u103b\u1004\u103a\u1010\u101a\u103a\u1006\u102d\u102f\u1015\u102b\u1005\u102d\u102f\u1037\u104b \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1004\u103a \u1010\u1005\u103a\u1015\u1010\u103a\u101c\u103b\u103e\u1004\u103a \u1021\u101c\u102f\u1015\u103a\u101c\u102f\u1015\u103a\u101e\u100a\u1037\u103a \u1014\u102c\u101b\u102e\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1014\u103e\u1004\u1037\u103a \u1015\u1010\u103a\u101e\u1000\u103a\u101e\u100a\u1037\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u101c\u1030 11 \u1026\u1038\u1021\u1010\u103d\u1000\u103a \u1015\u103b\u1031\u102c\u103a\u101b\u103d\u103e\u1004\u103a\u1019\u103e\u102f\u1021\u1006\u1004\u1037\u103a (0 \u1019\u103e 100 \u1021\u1011\u102d) \u1021\u1005\u102e\u101b\u1004\u103a\u1001\u1036\u1010\u1004\u103a\u1015\u103c\u101e\u100a\u103a- R \u1010\u103d\u1004\u103a […]<\/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 Quadratic regression \u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b<\/title>\n<meta name=\"description\" content=\"\u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c\u1010\u1005\u103a\u1001\u102f\u1021\u1015\u102b\u1021\u101d\u1004\u103a R \u1010\u103d\u1004\u103a \u101c\u1031\u1038\u1011\u1031\u102c\u1004\u1037\u103a\u1015\u102f\u1036\u101e\u100f\u1039\u100d\u1014\u103a\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1000\u102d\u102f 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