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Data X:
1.655 0.3865 0.4795 0.272 0.675 0.563 0.3575 0.0295 -0.2805 -0.16 1.126 1.037 0.7825 0.1565 0.0885 -0.163 -0.6965 0.467 0.253 0.8025 0.3095 0.3495 0.393 0.4175 0.1375 0.01 0.5545 0.4635 0.5255 0.6455 0.4 0.374 0.2165 -0.8805 -0.7445 -0.5665 -0.5405 -0.9515 -0.316 -0.228 -0.606 -0.8315 -0.9275 -1.417 -1.2295 -1.616 -0.521 -0.256 -0.9795 -0.7295 -0.4265 -0.35 0.311 0.203 -0.3045 -0.5585 -0.9585 -1.5165
Data Y:
-478.349 -478.7143 -478.4909 -478.4344 -478.233 -478.2786 -478.4745 -478.7409 -478.8389 -479.176 -478.6092 -478.6974 -478.7695 -478.8283 -478.7667 -478.6774 -478.7197 -478.0234 -477.9766 -477.6735 -478.0569 -478.3449 -478.8246 -479.1665 -479.2505 -479.21 -478.7559 -478.7917 -478.6561 -478.6001 -478.768 -478.6868 -478.7203 -479.3989 -479.1421 -479.1657 -479.3069 -479.6387 -479.3888 -479.0944 -478.9708 -478.9827 -479.1875 -479.5226 -479.4951 -479.6688 -478.8378 -478.7608 -479.3051 -479.5951 -479.5537 -479.258 -478.3362 -477.9866 -477.8301 -477.9153 -478.1153 -478.3757
Sample Range:
(leave blank to include all observations)
From:
To:
bandwidth of density plot
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# lags (autocorrelation function)
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Chart options
Label y-axis:
Label x-axis:
R Code
par1 <- as.numeric(par1) par2 <- as.numeric(par2) x <- as.ts(x) y <- as.ts(y) mylm <- lm(y~x) cbind(mylm$resid) library(lattice) bitmap(file='pic1.png') plot(y,type='l',main='Run Sequence Plot of Y[t]',xlab='time or index',ylab='value') grid() dev.off() bitmap(file='pic1a.png') plot(x,type='l',main='Run Sequence Plot of X[t]',xlab='time or index',ylab='value') grid() dev.off() bitmap(file='pic1b.png') plot(x,y,main='Scatter Plot',xlab='X[t]',ylab='Y[t]') grid() dev.off() bitmap(file='pic1c.png') plot(mylm$resid,type='l',main='Run Sequence Plot of e[t]',xlab='time or index',ylab='value') grid() dev.off() bitmap(file='pic2.png') hist(mylm$resid,main='Histogram of e[t]') dev.off() bitmap(file='pic3.png') if (par1 > 0) { densityplot(~mylm$resid,col='black',main=paste('Density Plot of e[t] bw = ',par1),bw=par1) } else { densityplot(~mylm$resid,col='black',main='Density Plot of e[t]') } dev.off() bitmap(file='pic4.png') qqnorm(mylm$resid,main='QQ plot of e[t]') qqline(mylm$resid) grid() dev.off() if (par2 > 0) { bitmap(file='pic5.png') acf(mylm$resid,lag.max=par2,main='Residual Autocorrelation Function') grid() dev.off() } summary(x) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Model: Y[t] = c + b X[t] + e[t]',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'c',1,TRUE) a<-table.element(a,mylm$coeff[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'b',1,TRUE) a<-table.element(a,mylm$coeff[[2]]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Descriptive Statistics about e[t]',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'# observations',header=TRUE) a<-table.element(a,length(mylm$resid)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'minimum',header=TRUE) a<-table.element(a,min(mylm$resid)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Q1',header=TRUE) a<-table.element(a,quantile(mylm$resid,0.25)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) a<-table.element(a,median(mylm$resid)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,mean(mylm$resid)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Q3',header=TRUE) a<-table.element(a,quantile(mylm$resid,0.75)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'maximum',header=TRUE) a<-table.element(a,max(mylm$resid)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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