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Data:
3.03 3.01 2.47 2.38 2.35 2.46 2.73 3.62 3.28 3.52 3.41 3.25 2.82 2.89 2.73 2.71 2.94 3.36 3.29 2.88 3.33 3.49 3.75 3.56 4.01 3.88 4.16 3.94 3.67 3.37 3.54 4.18 4.15 4.76 4.97 5.27 5.21 5.33 5.46 5.44 6.59 7.09 7.98 7.81 8.57 8.28 6.80 6.78 5.10 5.27 4.78 4.66 4.84 5.00 4.84 5.10 4.97 3.90 3.98 3.68 3.98 4.65 6.16 6.98 5.51 5.00 4.81 5.24 6.05 7.38 6.62 8.17 22.62 12.70 9.12 6.09 4.84 5.60 8.93 22.27 12.07 11.16 15.86 15.42 9.58 9.18 7.24 7.44 10.12 11.22 13.08 9.56 7.06 6.23 5.10 3.97 4.19 3.69 3.01 3.28 2.96 2.29 2.44 2.53 2.11 2.36 2.19 2.65 2.86 2.71 2.92 2.26 2.01 2.05 2.34 2.40 3.01 3.19 3.52 3.49 2.62 2.81 2.73 3.92 5.19 7.73 5.86 6.03 6.19 6.20 7.36 5.99 6.86 4.71 4.55 5.34 7.31 4.69 5.57 4.69 4.67 5.32 4.52 4.94 5.60 6.98 7.35 3.91 2.76 3.27 3.65 4.12 4.58 3.04 3.22 3.66 3.76 3.35 2.77 2.97 3.23 2.75 2.74 2.80 2.75 3.44 3.27 2.94 3.31 3.66 5.09 4.34 3.79 4.28 5.13 4.97 6.64 6.61 5.30 4.82
Sample Range:
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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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Big Analytics Cloud Computing Center
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