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Data X:
100.3 101.9 102.1 103.2 103.7 106.2 107.7 109.9 111.7 114.9 116.0 118.3 120.4 126.0 128.1 130.1 130.8 133.6 134.2 135.5 136.2 139.1 139.0 139.6 138.7 140.9 141.3 141.8 142.0 144.5 144.6 145.5 146.8 149.5 149.9 150.1 150.9 152.8 153.1 154.0 154.9 156.9 158.4 159.7 160.2 163.2 163.7 164.4 163.7 165.5 165.6 166.8 167.5 170.6 170.9 172.0 171.8 173.9 174.0 173.8 173.9 176.0 176.6 178.2 179.2 181.3 181.8 182.9 183.8 186.3 187.4 189.2 189.7 191.9 192.6 193.7 194.2 197.6 199.3 201.4 203.0 206.3 207.1 209.8 211.1 215.3 217.4 215.5 210.9 212.6
Data Y:
1844 1790 1707 1617 1530 1453 1386 1329 1255 1201 1179 1167 1160 1161 1203 1290 1404 1550 1670 1746 1817 1836 1872 1956 2010 1933 1917 1900 1829 1805 1730 1650 1618 1582 1576 1512 1540 1519 1473 1402 1299 1267 1200 1145 1103 1076 1095 1080 1082 1060 1027 1003 987 959 913 912 889 882 901 913 921 914 950 894 930 890 898 880 840 845 819 838 833 837 850 927 932 978 978 974 977 950 935 913 942 990 1075 1181 1336 1492
Sample Range:
(leave blank to include all observations)
From:
To:
bandwidth of density plot
(?)
# lags (autocorrelation function)
(?)
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Chart options
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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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