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Data X:
60747 60194 58401 58280 59246 60363 59486 60288 61064 61128 60793 61681 63587 65952 66995 66366 65038 64644 63404 63654 61635 62560 63514 65319 67256 67334 67931 66922 66512 66495 65767 68454 71116 73792 75737 77046 76729 76567 78036 80147 82512 84873 83387 81732 81449 80647 80680 79230 77543 75689 74454 73085 71606 71043 67799 67936 67596 67730 66928 66809 55559 55167 51834 45337 40839 44921 47745 48088 45971 43752 42504 42367 41333 41358 40150 38294 34220 31910 29516 27291 25732 23261 20671 18158 16021 13587 8757 4977 4025 3711 3613 3241 2300 1659 1085 807 470 296 190 102 89 37 16 4 0 2 2 0 0 0 0
Data Y:
57619 57077 56161 55614 57029 57667 57275 57514 58519 58759 58170 58277 61050 63191 64035 63358 62149 61789 60973 61229 60566 61922 62945 64810 66641 67675 66585 66573 66193 65545 64835 67184 69421 72359 74730 75067 74927 74845 75335 78121 79944 82779 81484 79939 80611 79088 79992 77800 76277 75425 74524 72844 70768 70172 67726 68032 67096 68261 67363 67322 57465 57031 54046 47435 43623 48866 53075 53778 51237 50393 49947 50660 50307 52235 51783 51602 47498 45981 43588 42791 41325 38917 36971 33628 31445 28478 19247 12209 10467 10127 10344 10117 8142 6404 4519 3482 2373 1772 1178 746 503 293 161 91 55 20 14 8 2 0 1
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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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