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Data X:
103.65 93 103.85 104.13 118.36 112.13 107.35 104.48 95.18 93.48 115.35 119.61 109.6 99.8 135.7 162.8 150.3 148.5 147.7 147.3 146.3 145.9 146.7 146.7 148.7 149.8 149.39 149.87 150.69 150.28 148.21 144.43 144.52 145.99 145.60 171.79 190.87 172.46 163.39 151.40 150.17 148.09 146.82 146.90 147.33 147.23 147.46 145.78 145.70 145.29 145.32 145.92 145.03 145.36 145.03 145.48 145.53 145.48 145.94 145.35 145.37 145.41 145.40 130.84 129.67 128.88 143.63 148.78 161.06 161.17 167.27 165.70 164.09 166.24 172.68 166.38 168.06 165.61
Data Y:
8.16 8.36 8.49 8.66 9.00 9.17 9.22 9.34 9.39 9.30 9.37 9.55 9.62 11.38 12.77 13.72 15.58 16.48 17.13 17.61 17.85 18.33 18.48 18.55 18.72 17.78 17.26 17.24 16.25 16.10 16.05 16.01 15.98 15.91 15.90 15.37 15.13 14.33 13.34 12.48 11.61 11.23 11.14 11.23 11.28 11.26 11.28 11.44 11.37 11.31 11.25 11.37 11.40 11.22 11.08 11.02 11.24 11.61 11.85 11.98 12.13 12.20 12.26 12.34 12.40 12.56 12.82 13.22 13.71 14.23 14.89 15.75 16.47 17.33 18.17 18.12 17.93 17.75
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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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