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Data X:
87.28 255 87.28 280.2 87.09 299.9 86.92 339.2 87.59 374.2 90.72 393.5 90.69 389.2 90.3 381.7 89.55 375.2 88.94 369 88.41 357.4 87.82 352.1 87.07 346.5 86.82 342.9 86.4 340.3 86.02 328.3 85.66 322.9 85.32 314.3 85 308.9 84.67 294 83.94 285.6 82.83 281.2 81.95 280.3 81.19 278.8 80.48 274.5 78.86 270.4 69.47 263.4 68.77 259.9 70.06 258 73.95 262.7 75.8 284.7 77.79 311.3 81.57 322.1 83.07 327 84.34 331.3 85.1 333.3 85.25 321.4 84.26 327 83.63 320 86.44 314.7 85.3 316.7 84.1 314.4 83.36 321.3 82.48 318.2 81.58 307.2 80.47 301.3 79.34 287.5 82.13 277.7 81.69 274.4 80.7 258.8 79.88 253.3 79.16 251 78.38 248.4 77.42 249.5 76.47 246.1 75.46 244.5 74.48 243.6 78.27 244 80.7 240.8 79.91 249.8 78.75 248 77.78 259.4 81.14 260.5 81.08 260.8 80.03 261.3 78.91 259.5 78.01 256.6 76.9 257.9 75.97 256.5 81.93 254.2 80.27 253.3 78.67 253.8 77.42 255.5 76.16 257.1 74.7 257.3 76.39 253.2 76.04 252.8 74.65 252 73.29 250.7 71.79 252.2 74.39 250 74.91 251 74.54 253.4 73.08 251.2 72.75 255.6 71.32 261.1 70.38 258.9 70.35 259.9 70.01 261.2 69.36 264.7 67.77 267.1 69.26 266.4 69.8 267.7 68.38 268.6 67.62 267.5 68.39 268.5 66.95 268.5 65.21 270.5 66.64 270.9 63.45 270.1 60.66 269.3 62.34 269.8 60.32 270.1 58.64 264.9 60.46 263.7 58.59 264.8 61.87 263.7 61.85 255.9 67.44 276.2 77.06 360.1 91.74 380.5 93.15 373.7 94.15 369.8 93.11 366.6 91.51 359.3 89.96 345.8 88.16 326.2 86.98 324.5 88.03 328.1 86.24 327.5 84.65 324.4 83.23 316.5 81.7 310.9 80.25 301.5 78.8 291.7 77.51 290.4 76.2 287.4 75.04 277.7 74 281.6 75.49 288 77.14 276 76.15 272.9 76.27 283 78.19 283.3 76.49 276.8 77.31 284.5 76.65 282.7 74.99 281.2 73.51 287.4 72.07 283.1 70.59 284 71.96 285.5 76.29 289.2 74.86 292.5 74.93 296.4 71.9 305.2 71.01 303.9 77.47 311.5 75.78 316.3 76.6 316.7 76.07 322.5 74.57 317.1 73.02 309.8 72.65 303.8 73.16 290.3 71.53 293.7 69.78 291.7 67.98 296.5 69.96 289.1 72.16 288.5 70.47 293.8 68.86 297.7 67.37 305.4 65.87 302.7 72.16 302.5 71.34 303 69.93 294.5 68.44 294.1 67.16 294.5 66.01 297.1 67.25 289.4 70.91 292.4 69.75 287.9 68.59 286.6 67.48 280.5 66.31 272.4 64.81 269.2 66.58 270.6 65.97 267.3 64.7 262.5 64.7 266.8 60.94 268.8 59.08 263.1 58.42 261.2 57.77 266 57.11 262.5 53.31 265.2 49.96 261.3 49.4 253.7 48.84 249.2 48.3 239.1 47.74 236.4 47.24 235.2 46.76 245.2 46.29 246.2 48.9 247.7 49.23 251.4 48.53 253.3 48.03 254.8 54.34 250 53.79 249.3 53.24 241.5 52.96 243.3 52.17 248 51.7 253 58.55 252.9 78.2 251.5 77.03 251.6 76.19 253.5 77.15 259.8 75.87 334.1 95.47 448 109.67 445.8 112.28 445 112.01 448.2 107.93 438.2 105.96 439.8 105.06 423.4 102.98 410.8 102.2 408.4 105.23 406.7 101.85 405.9 99.89 402.7 96.23 405.1 94.76 399.6 91.51 386.5 91.63 381.4 91.54 375.2 85.23 357.7 87.83 359 87.38 355 84.44 352.7 85.19 344.4 84.03 343.8 86.73 338 102.52 339 104.45 333.3 106.98 334.4 107.02 328.3 99.26 330.7 94.45 330 113.44 331.6 157.33 351.2 147.38 389.4 171.89 410.9 171.95 442.8 132.71 462.8 126.02 466.9 121.18 461.7 115.45 439.2 110.48 430.3 117.85 416.1 117.63 402.5 124.65 397.3 109.59 403.3 111.27 395.9 99.78 387.8 98.21 378.6 99.2 377.1 97.97 370.4 89.55 362 87.91 350.3 93.34 348.2 94.42 344.6 93.2 343.5 90.29 342.8 91.46 347.6 89.98 346.6 88.35 349.5 88.41 342.1 82.44 342 79.89 342.8 75.69 339.3 75.66 348.2 84.5 333.7 96.73 334.7 87.48 354 82.39 367.7 83.48 363.3 79.31 358.4 78.16 353.1 72.77 343.1 72.45 344.6 68.46 344.4 67.62 333.9 68.76 331.7 70.07 324.3 68.55 321.2 65.3 322.4 58.96 321.7 59.17 320.5 62.37 312.8 66.28 309.7 55.62 315.6 55.23 309.7 55.85 304.6 56.75 302.5 50.89 301.5 53.88 298.8 52.95 291.3 55.08 293.6 53.61 294.6 58.78 285.9 61.85 297.6 55.91 301.1 53.32 293.8 46.41 297.7 44.57 292.9 50 292.1 50 287.2 53.36 288.2 46.23 283.8 50.45 299.9 49.07 292.4 45.85 293.3 48.45 300.8 49.96 293.7 46.53 293.1 50.51 294.4 47.58 292.1 48.05 291.9 46.84 282.5 47.67 277.9 49.16 287.5 55.54 289.2 55.82 285.6 58.22 293.2 56.19 290.8 57.77 283.1 63.19 275 54.76 287.8 55.74 287.8 62.54 287.4 61.39 284 69.6 277.8 79.23 277.6 80 304.9 93.68 294 107.63 300.9 100.18 324 97.3 332.9 90.45 341.6 80.64 333.4 80.58 348.2 75.82 344.7 85.59 344.7 89.35 329.3 89.42 323.5 104.73 323.2 95.32 317.4 89.27 330.1 90.44 329.2 86.97 334.9 79.98 315.8 81.22 315.4 87.35 319.6 83.64 317.3 82.22 313.8 94.4 315.8 102.18 311.3
Names of X columns:
X_t Y_t
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
TRUE
TRUE
FALSE
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
cat1 <- as.numeric(par1) cat2<- as.numeric(par2) intercept<-as.logical(par3) x <- t(x) xdf<-data.frame(t(y)) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) xdf <- data.frame(xdf[[cat1]], xdf[[cat2]]) names(xdf)<-c('Y', 'X') if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) ) sumlmxdf<-summary(lmxdf) (aov.xdf<-aov(lmxdf) ) (anova.xdf<-anova(lmxdf) ) load(file='createtable') a<-table.start() nc <- ncol(sumlmxdf$'coefficients') nr <- nrow(sumlmxdf$'coefficients') a<-table.row.start(a) a<-table.element(a,'Linear Regression Model', nc+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, lmxdf$call['formula'],nc+1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'coefficients:',1,TRUE) a<-table.element(a, ' ',nc,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',1,TRUE) for(i in 1 : nc){ a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE) }#end header a<-table.row.end(a) for(i in 1: nr){ a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE) for(j in 1 : nc){ a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE) } a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a, '- - - ',1,TRUE) a<-table.element(a, ' ',nc,FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Std. Err. ',1,TRUE) a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'ANOVA Statistics', 5+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',1,TRUE) a<-table.element(a, 'Df',1,TRUE) a<-table.element(a, 'Sum Sq',1,TRUE) a<-table.element(a, 'Mean Sq',1,TRUE) a<-table.element(a, 'F value',1,TRUE) a<-table.element(a, 'Pr(>F)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, V2,1,TRUE) a<-table.element(a, anova.xdf$Df[1]) a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3)) a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3)) a<-table.element(a, round(anova.xdf$'F value'[1], digits=3)) a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residuals',1,TRUE) a<-table.element(a, anova.xdf$Df[2]) a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3)) a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3)) a<-table.element(a, ' ') a<-table.element(a, ' ') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') bitmap(file='regressionplot.png') plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution') if(intercept == TRUE) abline(coef(lmxdf), col='red') if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red') dev.off() library(car) bitmap(file='residualsQQplot.png') qq.plot(resid(lmxdf), main='QQplot of Residuals of Fit') dev.off() bitmap(file='residualsplot.png') plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit') dev.off() bitmap(file='cooksDistanceLmplot.png') plot.lm(lmxdf, which=4) dev.off()
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