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Data X:
5 3 0 7 4 1 6 3 12 0 5 6 6 6 2 1 5 7 3 3 3 7 8 6 3 5 5 10 2 6 4 6 8 4 5 10 6 7 4 10 4 3 3 3 3 7 15 0 0 4 5 5 2 3 0 9 2 7 7 0 0 10 2 1 8 6 11 3 8 6 9 9 8 8 7 6 5 4 6 3 2 12 8 5 9 6 5 2 4 7 5 6 7 8 6 0 1 5 5 5 7 7 1 3 4 8 6 6 2 2 3 3 0 2 8 8 0 5 9 6 6 3 9 7 8 0 7 0 5 0 14 5 2 8 4 2 6 3 5 9 3 3 0 10 4 2 3 10 7 0 6 8 0 4 10 5
Data Y:
13 12 15 12 10 12 15 9 12 11 11 11 15 7 11 11 10 14 10 6 11 15 11 12 14 15 9 13 13 16 13 12 14 11 9 16 12 10 13 16 14 15 5 8 11 16 17 9 9 13 10 6 12 8 14 12 11 16 8 15 7 16 14 16 9 14 11 13 15 5 15 13 11 11 12 12 12 12 14 6 7 14 14 10 13 12 9 12 16 10 14 10 16 15 12 10 8 8 11 13 16 16 14 11 4 14 9 14 8 8 11 12 11 14 15 16 16 11 14 14 12 14 8 13 16 12 16 12 11 4 16 15 10 13 15 12 14 7 19 12 12 13 15 8 12 10 8 10 15 16 13 16 9 14 14 12
Sample Range:
(leave blank to include all observations)
From:
To:
bandwidth of density plot
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Chart options
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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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Raw Output
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R Server
Big Analytics Cloud Computing Center
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