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Data X:
119.992 157.302 122.4 148.65 116.682 131.111 116.676 137.871 116.014 141.781 120.552 131.162 120.267 137.244 107.332 113.84 95.73 132.068 95.056 120.103 88.333 112.24 91.904 115.871 136.926 159.866 139.173 179.139 152.845 163.305 142.167 217.455 144.188 349.259 168.778 232.181 153.046 175.829 156.405 189.398 153.848 165.738 153.88 172.86 167.93 193.221 173.917 192.735 163.656 200.841 104.4 206.002 171.041 208.313 146.845 208.701 155.358 227.383 162.568 198.346 197.076 206.896 199.228 209.512 198.383 215.203 202.266 211.604 203.184 211.526 201.464 210.565 177.876 192.921 176.17 185.604 180.198 201.249 187.733 202.324 186.163 197.724 184.055 196.537 237.226 247.326 241.404 248.834 243.439 250.912 242.852 255.034 245.51 262.09 252.455 261.487 122.188 128.611 122.964 130.049 124.445 135.069 126.344 134.231 128.001 138.052 129.336 139.867 108.807 134.656 109.86 126.358 110.417 131.067 117.274 129.916 116.879 131.897 114.847 271.314 209.144 237.494 223.365 238.987 222.236 231.345 228.832 234.619 229.401 252.221 228.969 239.541 140.341 159.774 136.969 166.607 143.533 162.215 148.09 162.824 142.729 162.408 136.358 176.595 120.08 139.71 112.014 588.518 110.793 128.101 110.707 122.611 112.876 148.826 110.568 125.394 95.385 102.145 100.77 115.697 96.106 108.664 95.605 107.715 100.96 110.019 98.804 102.305 176.858 205.56 180.978 200.125 178.222 202.45 176.281 227.381 173.898 211.35 179.711 225.93 166.605 206.008 151.955 163.335 148.272 164.989 152.125 161.469 157.821 172.975 157.447 163.267 159.116 168.913 125.036 143.946 125.791 140.557 126.512 141.756 125.641 141.068 128.451 150.449 139.224 586.567 150.258 154.609 154.003 160.267 149.689 160.368 155.078 163.736 151.884 157.765 151.989 157.339 193.03 208.9 200.714 223.982 208.519 220.315 204.664 221.3 210.141 232.706 206.327 226.355 151.872 492.892 158.219 442.557 170.756 450.247 178.285 442.824 217.116 233.481 128.94 479.697 176.824 215.293 138.19 203.522 182.018 197.173 156.239 195.107 145.174 198.109 138.145 197.238 166.888 198.966 119.031 127.533 120.078 126.632 120.289 128.143 120.256 125.306 119.056 125.213 118.747 123.723 106.516 112.777 110.453 127.611 113.4 133.344 113.166 130.27 112.239 126.609 116.15 131.731 170.368 268.796 208.083 253.792 198.458 219.29 202.805 231.508 202.544 241.35 223.361 263.872 169.774 191.759 183.52 216.814 188.62 216.302 202.632 565.74 186.695 211.961 192.818 224.429 198.116 233.099 121.345 139.644 119.1 128.442 117.87 127.349 122.336 142.369 117.963 134.209 126.144 154.284 127.93 138.752 114.238 124.393 115.322 135.738 114.554 126.778 112.15 131.669 102.273 142.83 236.2 244.663 237.323 243.709 260.105 264.919 197.569 217.627 240.301 245.135 244.99 272.21 112.547 133.374 110.739 113.597 113.715 116.443 117.004 144.466 115.38 123.109 116.388 129.038 151.737 190.204 148.79 158.359 148.143 155.982 150.44 163.441 148.462 161.078 149.818 163.417 117.226 123.925 116.848 217.552 116.286 177.291 116.556 592.03 116.342 581.289 114.563 119.167 201.774 262.707 174.188 230.978 209.516 253.017 174.688 240.005 198.764 396.961 214.289 260.277
Names of X columns:
MDVP:Fo(Hz) MDVP:Fhi(Hz)
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
TRUE
TRUE
FALSE
Chart options
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Label y-axis:
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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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