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Data X:
10.47 10.44 10.41 10.37 10.38 10.38 10.37 10.41 10.44 10.43 10.47 10.49 10.53 10.63 10.66 10.66 10.64 10.65 10.61 10.6 10.61 10.63 10.63 10.61 10.7 10.69 10.62 10.62 10.63 10.62 10.53 10.51 10.5 10.52 10.47 10.43 10.35 10.31 10.25 10.26 10.2 10.13 10.06 10.01 9.95 9.92 9.87 9.83 9.7 9.63 9.56 9.53 9.47 9.4 9.32 9.26 9.19 9.1 9.03 8.95 8.85 8.78 8.71 8.61 8.54 8.49 8.42 8.36 8.3 8.19 8.15 8.1 8.04 8.05 8.04 8 8.02 8 8 8.01 8.04 8.1 8.14 8.17 8.17 8.22 8.21 8.29 8.37 8.43 8.47 8.51 8.55 8.59 8.66 8.71 8.78 8.81 8.84 8.81 8.82 8.84 8.83 8.83 8.88 8.88 8.89 8.93 8.95 8.92 8.97 8.99 9.01 8.99 9.03 9.04 9.07 9.04 9.07 9.09 9.04 9.08 9.13 9.09 9.05 9.06 8.99 8.98 8.99 8.94 8.87 8.83 8.8 8.79 8.71 8.6 8.5 8.38 8.26 8.23 8.17 8.1 8.02 7.9 7.82 7.72 7.63 7.53 7.56 7.49 7.53 7.47 7.39 7.37 7.34 7.39 7.32 7.24 7.18 7.31 7.39 7.48 7.51 7.61 7.69 7.86 8.05 8.24 8.55 8.81 9.13 9.24 9.36 9.48 9.61 9.7 9.82 9.86 9.87 9.87
Data Y:
2.4 2.4 2.5 2.6 2.4 2.6 2.4 2.3 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.5 2.1 2.1 2 2 2 1.9 1.9 2 1.8 1.6 1.3 1.4 1.4 1.5 1.7 1.6 1.5 1.6 1.5 1.1 1.1 1.1 1.4 1.3 1.4 1.3 1.1 1 0.9 0.8 0.8 0.8 0.8 1 1.1 1 0.9 1.1 1.2 1.2 1.4 1.5 1.7 1.9 1.9 1.9 1.7 1.7 2.1 2 2 2.5 2.4 2.5 2.5 2 1.9 2.2 2.7 3.1 2.8 2.6 2.3 2.2 2.2 2 2 2.6 2.5 2.5 2.3 2 1.9 2 2.1 2.1 2.3 2.3 2.3 2.1 2.4 2.5 2.1 1.8 1.9 1.9 2.1 2.2 2 2.2 2 1.9 1.6 1.7 2 2.5 2.4 2.3 2.3 2.1 2.4 2.2 2.4 1.9 2.1 2.1 2.1 2 2.1 2.2 2.2 2.6 2.5 2.3 2.2 2.4 2.3 2.2 2.5 2.5 2.5 2.4 2.3 1.7 1.6 1.9 1.9 1.8 1.8 1.9 1.9 1.9 1.9 1.8 1.7 2.1 2.6 3.1 3.1 3.2 3.3 3.6 3.3 3.7 4 4 3.8 3.6 3.2 2.1 1.6 1.1 1.2 0.6 0.6 0 -0.1 -0.6 -0.2 -0.3 -0.1 0.5 0.9
Sample Range:
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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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