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Data:
5260.04 1438.04 -5923.96 -8842.96 -12720.96 -13688.96 16625.04 30271.04 28726.04 19948.04 1134.04 732.04 2221.04 868.04 -1626.96 -7282.96 -9602.96 -7668.96 26184.04 29489.04 27475.04 22342.04 17072.04 18279.04 20790.04 18811.04 14739.04 14964.04 11146.04 16352.04 51122.04 55743.04 54725.04 44805.04 35609.04 38353.04 41290.04 39866.04 36933.04 31114.04 28196.04 34232.04 62641.04 71790.04 72389.04 74660.04 63858.04 63917.04 60115.04 58654.04 55769.04 49328.04 46694.04 50413.04 79854.04 84076.04 82310.04 70309.04 60305.04 60863.04 59596.04 56042.04 49341.04 45367.04 44801.04 48009.04 76143.04 81461.04 73994.04 55497.04 42294.04 37791.04 37920.04 31056.04 22853.04 18944.04 11359.04 9668.04 43184.04 49976.04 33372.04 22805.04 12802.04 13531.04 14759.04 8900.04 1920.04 -463.96 -9818.96 -2880.96 26823.04 31635.04 17354.04 7454.04 1981.04 6754.04 10281.04 10669.04 9654.04 8950.04 2545.04 10599.04 40238.04
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
R Code
par1 <- as.numeric(par1) par2 <- as.numeric(par2) x <- as.ts(x) library(lattice) bitmap(file='pic1.png') plot(x,type='l',main='Run Sequence Plot',xlab='time or index',ylab='value') grid() dev.off() bitmap(file='pic2.png') hist(x) grid() dev.off() bitmap(file='pic3.png') if (par1 > 0) { densityplot(~x,col='black',main=paste('Density Plot bw = ',par1),bw=par1) } else { densityplot(~x,col='black',main='Density Plot') } dev.off() bitmap(file='pic4.png') qqnorm(x) qqline(x) grid() dev.off() if (par2 > 0) { bitmap(file='lagplot1.png') dum <- cbind(lag(x,k=1),x) dum dum1 <- dum[2:length(x),] dum1 z <- as.data.frame(dum1) z plot(z,main='Lag plot (k=1), lowess, and regression line') lines(lowess(z)) abline(lm(z)) dev.off() if (par2 > 1) { bitmap(file='lagplotpar2.png') dum <- cbind(lag(x,k=par2),x) dum dum1 <- dum[(par2+1):length(x),] dum1 z <- as.data.frame(dum1) z mylagtitle <- 'Lag plot (k=' mylagtitle <- paste(mylagtitle,par2,sep='') mylagtitle <- paste(mylagtitle,'), and lowess',sep='') plot(z,main=mylagtitle) lines(lowess(z)) dev.off() } bitmap(file='pic5.png') acf(x,lag.max=par2,main='Autocorrelation Function') grid() dev.off() } summary(x) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Descriptive Statistics',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'# observations',header=TRUE) a<-table.element(a,length(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'minimum',header=TRUE) a<-table.element(a,min(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Q1',header=TRUE) a<-table.element(a,quantile(x,0.25)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) a<-table.element(a,median(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,mean(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Q3',header=TRUE) a<-table.element(a,quantile(x,0.75)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'maximum',header=TRUE) a<-table.element(a,max(x)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Computing time
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R Server
Big Analytics Cloud Computing Center
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