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Data X:
5.3 5.6 3.8 4.0 4.0 3.6 4.4 3.6 4.0 3.8 5.1 6.7 5.1 4.0 3.3 2.7 4.7 3.3 4.4 6.9 6.0 7.6 4.7 6.9 4.2 3.6 4.4 4.7 4.9 3.8 5.3 5.6 5.8 5.6 3.8 7.1 7.3 2.9 7.1 5.6 6.4 4.9 4.0 3.8 4.4 3.3 4.4 7.3 6.4 5.1 5.8 4.0 4.4 2.4 6.2 5.8 4.9 3.8 2.7 3.1 3.8 4.7 4.2 4.0 2.2 6.4 6.9 4.2 2.0 4.4 6.2 4.2 6.7 6.4 5.8 5.1 2.9 4.7 4.2 6.2 5.1 4.0 4.7 4.4 5.1 4.7 4.7 3.3 6.2 4.2 5.8 2.2 3.6 4.9 4.2 6.9 6.9 6.4 4.2 4.9 5.1 3.3 4.4 4.0 5.1 5.6 4.7 5.3 5.6 3.8 2.9 6.2 4.7 5.6 2.0 3.6 4.2 3.8 5.6 4.4 6.4 3.1 4.9 3.3 4.2 4.4 3.3 4.4 4.0 7.3 4.9 3.6 3.8 3.6 4.7 5.8 4.0 4.0 3.8 4.9 6.7 6.7 5.3 4.7 4.7 6.4 6.9 4.4 3.6 4.9 4.4 6.2 8.4 4.9 4.4 3.8 6.2 4.9 6.9
Data Y:
6.0 4.0 4.0 4.0 4.5 3.5 2.0 5.5 3.5 3.5 6.0 5.0 5.0 4.0 4.0 2.0 4.5 4.0 3.5 5.5 4.5 5.5 6.5 4.0 4.0 4.5 3.0 4.5 4.5 3.0 3.0 8.0 2.5 3.5 4.5 3.0 3.0 2.5 6.0 3.5 5.0 4.5 4.0 2.5 4.0 4.0 5.0 3.0 4.0 3.5 2.0 4.0 4.0 2.0 10.0 4.0 4.0 3.0 2.0 4.0 4.5 3.0 3.5 4.5 2.5 2.5 4.0 4.0 3.0 4.0 3.5 3.5 4.5 5.5 3.0 4.0 3.0 4.5 4.0 3.0 5.0 4.0 4.0 5.0 2.5 3.5 2.5 4.0 7.0 3.5 4.0 3.0 2.5 3.0 5.0 6.0 4.5 6.0 3.5 4.0 5.0 3.0 5.0 5.0 5.0 2.5 3.5 5.0 5.5 3.0 3.5 6.0 5.5 5.5 5.5 2.5 4.0 3.0 4.5 2.0 2.0 3.5 5.5 3.0 3.5 4.0 2.0 4.0 4.5 4.0 5.5 4.0 2.5 2.0 4.0 5.0 3.0 4.5 4.5 6.5 4.5 5.0 10.0 2.5 5.5 3.0 4.5 3.5 4.5 5.0 4.5 4.0 3.5 3.0 6.5 3.0 4.0 5.0 8.0
Sample Range:
(leave blank to include all observations)
From:
To:
bandwidth of density plot
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Chart options
Label y-axis:
Label x-axis:
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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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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