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Data X:
10 20 40 67 38 61 29 30 0 10 39 70 65 77 29 60 45 50 31 91 56 20 65 60 27 47 60 10 60 90 15 65 5 72 70 80 70 40 20 75 30 40 20 82 50 87 26 61 67 24 70 80 10 70 50 82 38 70 76 70 90 70 70 13 35 10 66 30 0 35 80 70 55 74 40 50 63 60 35 25 40 40 56 18 60 52 61 50 0 71 39 80 71 55 80 40 72 40 70 100 93 25 40 68 80 25 91 79 70 76 65 64 70 80 65 82 48 60 60 65 65 60 38 70 75 67 90 71 68 80 18 65 30 76 71 70 0 78 49 40 80 90 62 71 80 80 67 35
Data Y:
5 2 6 6 5 5 6 6 5 5 5 6 5 5 6 6 4 5 5 5 5 7 6 6 6 6 4 5 6 4 5 5 5 7 7 6 7 6 5 6 4 6 5 5 6 6 5 6 5 5 5 6 6 5 7 6 5 5 6 5 5 6 6 3 5 5 6 5 5 4 5 5 2 6 6 6 6 5 5 6 5 5 6 3 6 3 5 6 5 6 6 6 5 3 7 6 6 5 4 6 6 6 5 6 6 2 6 5 3 4 6 6 4 6 4 5 6 5 5 6 6 5 6 5 2 6 6 5 5 5 2 5 6 6 5 5 3 6 6 6 5 7 6 6 6 7 6 6
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
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R Server
Big Analytics Cloud Computing Center
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