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Data X:
25 25 19 18 18 22 29 26 25 23 23 23 24 30 19 24 32 30 29 17 25 26 26 25 23 21 19 35 19 20 21 21 24 23 19 17 24 15 25 27 29 27 18 25 22 26 23 16 27 25 14 19 20 16 18 22 21 22 22 32 23 31 18 23 26 24 19 14 20 22 24 25 21 28 24 20 21 23 13 24 21 21 17 14 29 25 16 25 25 21 23 22 19 24 26 25 20 22 14 20 32 21 22 28 25 17 21 23 27 22 19 20 17 24 21 21 23 24 19 22 26 17 17 19 15 17 27 19 21 25 19 22 18 20 15 20 29 19 29 24 23 22 23 22 29 26 26 21 18 10
Data Y:
14 18 11 12 16 18 14 14 15 15 17 19 10 18 14 14 17 14 16 18 14 12 17 9 16 14 11 16 13 17 15 14 16 9 15 17 13 15 16 16 12 12 11 15 17 13 16 14 11 12 12 15 16 15 12 12 8 13 11 14 15 10 11 12 15 15 14 16 15 15 13 17 13 15 13 15 16 15 16 15 14 15 7 17 13 15 14 13 16 12 14 17 15 17 12 16 11 15 9 16 10 10 15 11 13 14 18 16 14 14 14 14 12 14 15 15 15 13 17 17 19 15 13 9 15 15 16 11 14 11 15 13 15 16 14 15 16 16 11 13 16 12 9 13 13 14 19 13 12 13
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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R Server
Big Analytics Cloud Computing Center
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