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Data X:
26.012 21.288 28.576 18.023 23.666 51.887 16.642 24.505 6.886 21.614 17.972 20.514 31.325 19.459 35.75 15.787 45.21 27.778 21.489 27.393 28.944 16.318 4.148 27.405 20.413 43.35 19.125 25.031 23.804 20.327 12.455 30.598 6.343 21.453 50.435 14.647 78.187 36.639 69.935 40.63 4.437 14.942 20.483 36.377 28.392 54.545 15.787 20.22 23.05 32.141 218.987 29.965 28.942 51.018 21.637 27.026 24.258 29.872 21.59 40.5 22.056 72.165 14.026 22.125 26.845 22.834 20.719 12.301 343.037 33.655 65.078 60.501 73.215 45.577 150.834 23.14 41.579 17.954 43.676 37.572 12.208 18.504 26.145 13.791 42.453 26.099 33.494 32.651 28.751 20.218 18.734 67.125 27.585 12.143 324.724 17.253 19.001 25.298 23.449 30.245 91.059 48.154 17.748 10.045 12.871 23.727 12.821 6.684 23.341 60.505 123.69 8.687 27.213 27.718 36.96 6.113 14.345 33.53 46.639 19.807 44.131 34.465 12.509 15.263 2.974 12.85 14.459 9.605 30.422 29.966 48.422 38.056 29.639 60.639 40.353 3.636 19.599 12.95 29.676 18.609 10.562 43.283 20.266 155.213 73.938 80.59 23.077 25.44 52.128 17.724 8.431 118.919 26.719 17.591 101.462 18.541 8.059 345.61 26.573 66.183 13.047 69.978 11.313 14.723 60.973 8.507 20.146 24.486 21.939 23.813
Data Y:
162.961 143.843 330.045 185.511 408.589 202.001 127.147 111.288 152.99 153.358 200.304 148.122 201.677 224.896 171.75 204.718 238.903 157.166 212.108 83.178 407.225 126.735 348.082 148.947 182.881 256.367 152.64 171.875 185.806 168.03 152.631 359.107 211.763 177.074 189.696 170.66 272.506 305.783 253.422 207.372 173.177 150.88 129.589 281.863 169.353 232.379 237.947 116.108 264.094 134.779 502.513 196.733 139.64 197.299 150.072 192.42 196.311 254.363 501.749 264.359 200.927 255.593 165.519 108.677 223.899 117.261 163.407 100.313 542.167 253.128 231.782 179.355 246.221 296.323 386.414 173.679 219.625 491.827 219.237 204.517 358.472 49.678 320.58 364.879 202.591 193.035 190.105 413.4 227.963 228.062 142.047 280.549 280.57 87.968 681.944 157.17 189.206 184.88 143.002 210.046 690.992 208.427 168.628 170.154 163.336 146.779 397.704 59.616 202.325 255.614 389.883 130.712 253.638 253.327 431.801 185.353 104.058 67.982 186.906 192.535 157.478 133.281 162.382 258.44 116.406 231.911 221.136 47.923 218.922 115.151 237.187 336.088 178.29 221.478 224.425 54.246 134.99 175.844 684.951 146.311 82.281 205.401 125.25 401.432 428.11 248.945 206.823 251.551 232.807 129.242 168.344 464.963 192.782 105.935 333.543 166.653 45.971 370.407 114.431 228.531 105.719 310.386 53.516 159.619 296.031 258.861 115.844 149.782 315.715 130.141
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R Code
par1 <- as.numeric(par1) library(lattice) z <- as.data.frame(cbind(x,y)) m <- lm(y~x) summary(m) bitmap(file='test1.png') plot(z,main='Scatterplot, lowess, and regression line') lines(lowess(z),col='red') abline(m) grid() dev.off() bitmap(file='test2.png') m2 <- lm(m$fitted.values ~ x) summary(m2) z2 <- as.data.frame(cbind(x,m$fitted.values)) names(z2) <- list('x','Fitted') plot(z2,main='Scatterplot, lowess, and regression line') lines(lowess(z2),col='red') abline(m2) grid() dev.off() bitmap(file='test3.png') m3 <- lm(m$residuals ~ x) summary(m3) z3 <- as.data.frame(cbind(x,m$residuals)) names(z3) <- list('x','Residuals') plot(z3,main='Scatterplot, lowess, and regression line') lines(lowess(z3),col='red') abline(m3) grid() dev.off() bitmap(file='test4.png') m4 <- lm(m$fitted.values ~ m$residuals) summary(m4) z4 <- as.data.frame(cbind(m$residuals,m$fitted.values)) names(z4) <- list('Residuals','Fitted') plot(z4,main='Scatterplot, lowess, and regression line') lines(lowess(z4),col='red') abline(m4) grid() dev.off() bitmap(file='test5.png') myr <- as.ts(m$residuals) z5 <- as.data.frame(cbind(lag(myr,1),myr)) names(z5) <- list('Lagged Residuals','Residuals') plot(z5,main='Lag plot') m5 <- lm(z5) summary(m5) abline(m5) grid() dev.off() bitmap(file='test6.png') hist(m$residuals,main='Residual Histogram',xlab='Residuals') dev.off() bitmap(file='test7.png') if (par1 > 0) { densityplot(~m$residuals,col='black',main=paste('Density Plot bw = ',par1),bw=par1) } else { densityplot(~m$residuals,col='black',main='Density Plot') } dev.off() bitmap(file='test8.png') acf(m$residuals,main='Residual Autocorrelation Function') dev.off() bitmap(file='test9.png') qqnorm(x) qqline(x) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Simple Linear Regression',5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Statistics',1,TRUE) a<-table.element(a,'Estimate',1,TRUE) a<-table.element(a,'S.D.',1,TRUE) a<-table.element(a,'T-STAT (H0: coeff=0)',1,TRUE) a<-table.element(a,'P-value (two-sided)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'constant term',header=TRUE) a<-table.element(a,m$coefficients[[1]]) sd <- sqrt(vcov(m)[1,1]) a<-table.element(a,sd) tstat <- m$coefficients[[1]]/sd a<-table.element(a,tstat) pval <- 2*(1-pt(abs(tstat),length(x)-2)) a<-table.element(a,pval) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'slope',header=TRUE) a<-table.element(a,m$coefficients[[2]]) sd <- sqrt(vcov(m)[2,2]) a<-table.element(a,sd) tstat <- m$coefficients[[2]]/sd a<-table.element(a,tstat) pval <- 2*(1-pt(abs(tstat),length(x)-2)) a<-table.element(a,pval) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Big Analytics Cloud Computing Center
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