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Data X:
-4757.94575 -3501.961939 4675.799026 -7958.854315 -15749.64956 -6124.716213 -184.4590898 14345.88187 27460.89236 21294.92474 10550.92664 10201.63045 4614.163773 5480.679012 9449.551401 3259.567589 -2973.602891 5714.73617 5349.964714 19426.52282 32541.6895 23421.48854 18701.25331 15422.67711 10693.59997 12077.42759 18654.9857 6885.427588 12330.61236 12512.78284 10978.979 22258.2714 33638.93903 21010.76665 9861.955217 -1578.057173 4338.06664 -999.9800276 -719.9371594 -11274.35526 -16375.85621 -21643.18668 -15397.97623 -4937.339076 -5575.357165 -2806.125728 -16267.7324 -21705.61908 -12931.76288 -14993.62288 -25009.96574 -19861.60859 -37016.01241 -31067.70192 -23619.74289 -20614.77527 -10320.49907 -12945.85811 -22959.08765 -16954.57051 -10663.64766 -5126.929561 6361.301875 1106.273297 -7004.022896 5033.989494 11966.79233
Data Y:
-1814.938761 -2204.024389 3414.636476 -7742.342918 -13656.14768 5.537168254 15341.93457 29611.03726 39633.20496 39834.37622 25546.34886 24352.55777 16737.71535 15984.81466 17893.10228 10417.1879 5223.136563 17548.26661 29014.43804 41297.8763 49063.98236 33086.73241 24662.33855 21679.84202 18615.35916 15987.33518 17347.77345 5312.335178 9757.499506 15525.55085 27754.55102 41297.39682 41619.4892 24048.46522 8884.57483 -1555.565515 752.2015842 -6493.082658 -7342.743698 -18169.32924 -24011.31556 -21399.25054 -1337.137376 11827.75639 -255.3018794 -5429.853309 -20389.54846 -26904.94906 -19848.74707 -24563.89435 -33177.96967 -31476.78136 -45298.25391 -34656.34985 -15791.71616 -8909.88742 -13931.12725 -24430.2882 -38781.76413 -36786.69218 -35057.17504 -33470.85313 -29093.40456 -34699.63054 -42357.42162 -28366.28127 -9675.585947
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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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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