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Data X:
38 32 35 33 37 29 31 36 35 38 31 34 35 38 37 33 32 38 38 32 33 31 38 39 32 32 35 37 33 33 28 32 31 37 30 33 31 33 31 33 32 33 32 33 28 35 39 34 38 32 38 30 33 38 32 32 34 34 36 34 28 34 35 35 31 37 35 27 40 37 36 38 39 41 27 30 37 31 31 27 36 38 37 33 34 31 39 34 32 33 36 32 41 28 30 36 35 31 34 36 36 35 37 28 39 32 35 39 35 42 34 33 41 33 34 32 40 40 35 36 37 27 39 38 31 33 32 39 36 33 33 32 37 30 38 29 22 35 35 34 35 34 34 35 23 31 27 36 31 32 39 37 38 39 34 31 32 37 36 32 35 36
Data Y:
12 11 14 12 21 12 22 11 10 13 10 8 15 14 10 14 14 11 10 13 7 14 12 14 11 9 11 15 14 13 9 15 10 11 13 8 20 12 10 10 9 14 8 14 11 13 9 11 15 11 10 14 18 14 11 12 13 9 10 15 20 12 12 14 13 11 17 12 13 14 13 15 13 10 11 19 13 17 13 9 11 10 9 12 12 13 13 12 15 22 13 15 13 15 10 11 16 11 11 10 10 16 12 11 16 19 11 16 15 24 14 15 11 15 12 10 14 13 9 15 15 14 11 8 11 11 8 10 11 13 11 20 10 15 12 14 23 14 16 11 12 10 14 12 12 11 12 13 11 19 12 17 9 12 19 18 15 14 11 9 18 16
Sample Range:
(leave blank to include all observations)
From:
To:
bandwidth of density plot
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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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