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Data X:
265.563 187.07 299.256 241.34 273.941 444.295 161.93 165.479 206.776 239.511 239.745 242.754 179.838 225.461 196.268 172.547 171.014 177.49 173.437 274.655 284.46 182.315 173.285 299.614 210.206 199.094 262.88 263.541 274.238 202.129 334.315 308.94 252.318 185.563 413.511 1106.398 273.259 574.372 759.868 327.407 256.36 240.008 173.67 238.118 235.992 408.621 179.122 255.652 268.266 530.302 173.219 232.34 192.501 183.531 350.53 328.007 290.481 379.511 377.958 400.172 217.748 295.181 211.378 316.197 379.669 258.469 240.495 284.281 187.215 280.35 370.187 531.922 191.488 134.172 190.977 260.172 206.12 215.08 290.83 309.294 382.906 258.801 349.659 271.266 299.364 413.597 257.665 254.641 188.567 287.861 228.466 246.064 149.793 507.767 247.852 183.211 2066.436 231.218 392.645 249.121 221.141 250.407 75.519 308.224 224.196 276.864 190.497 237.699 262.694 260.526 172.548 225.083 766.972 269.769 129.247 204.574 248.89 218.497 276.094 186.915 320.408 225.897 284.475 209.491 185.969 259.326 220.919 250.521 271.423 497.847 200.453 315.686 214.901 231.418 223.284 267.548 464.313 266.963 191.439 188.014 271.659 265.295 277.424 195.734 4964.628 165.909 227.169 207.74 285.945 325.672 205.476 256.9 219.281 187.339 207.837 192.052 272.044 256.234 102.331
Data Y:
15.34 24.164 17.038 14.91 23.744 13.87 14.931 12.594 16.616 20.545 17.471 16.896 13.838 8.925 13.148 13.943 16.35 10.29 14.06 80.415 19.579 15.117 13.998 20.441 15.822 19.506 20.781 29.42 16.055 24.878 41.172 17.072 17.83 19.282 20.233 18.766 22.029 304.004 30.275 18.185 16.406 12.776 11.845 14.661 21.061 17.206 11.501 17.741 17.276 40.198 15.25 14.127 29.77 16.781 44.172 19.75 22.806 18.983 56.465 11.375 20.045 20.876 15.952 15.253 34.067 31.238 27.412 23.828 15.829 24.081 33.328 35.75 25.047 12.203 18.803 15.942 21.309 23.886 22.061 20.723 15.029 19.707 12.928 17.163 15.753 18.303 35.821 16.61 15.232 15.819 15.008 17.921 14.248 13.789 21.033 19.363 35.832 18.316 18.284 13.12 15.094 16.241 14.742 36.378 19.566 15.272 15.343 16.533 13.447 16.603 10.718 13.351 305.45 16.068 15.088 20.528 18.828 14.005 18.032 12.964 29.277 10.908 16.47 14.473 13.604 19.186 25.847 30.249 23.725 304.038 15.984 16.152 18.029 12.933 17.922 17.477 32.225 14.375 15.79 13.088 36.608 21.043 13.384 23.969 102.863 18.508 15.889 15.065 12.534 17.703 14.857 23.793 34.532 11.392 14.916 14.579 25.62 14.94 13.322
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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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