Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
12.9 12.2 12.8 7.4 6.7 12.6 14.8 13.3 11.1 8.2 11.4 6.4 10.6 12 6.3 11.3 11.9 9.3 9.6 10 6.4 13.8 10.8 13.8 11.7 10.9 16.1 13.4 9.9 11.5 8.3 11.7 9 9.7 10.8 10.3 10.4 12.7 9.3 11.8 5.9 11.4 13 10.8 12.3 11.3 11.8 7.9 12.7 12.3 11.6 6.7 10.9 12.1 13.3 10.1 5.7 14.3 8 13.3 9.3 12.5 7.6 15.9 9.2 9.1 11.1 13 14.5 12.2 12.3 11.4 8.8 14.6 12.6 13 12.6 13.2 9.9 7.7 10.5 13.4 10.9 4.3 10.3 11.8 11.2 11.4 8.6 13.2 12.6 5.6 9.9 8.8 7.7 9 7.3 11.4 13.6 7.9 10.7 10.3 8.3 9.6 14.2 8.5 13.5 4.9 6.4 9.6 11.6 11.1 4.35 12.7 18.1 17.85 16.6 12.6 17.1 19.1 16.1 13.35 18.4 14.7 10.6 12.6 16.2 13.6 18.9 14.1 14.5 16.15 14.75 14.8 12.45 12.65 17.35 8.6 18.4 16.1 11.6 17.75 15.25 17.65 16.35 17.65 13.6 14.35 14.75 18.25 9.9 16 18.25 16.85 14.6 13.85 18.95 15.6 14.85 11.75 18.45 15.9 17.1 16.1 19.9 10.95 18.45 15.1 15 11.35 15.95 18.1 14.6 15.4 15.4 17.6 13.35 19.1 15.35 7.6 13.4 13.9 19.1 15.25 12.9 16.1 17.35 13.15 12.15 12.6 10.35 15.4 9.6 18.2 13.6 14.85 14.75 14.1 14.9 16.25 19.25 13.6 13.6 15.65 12.75 14.6 9.85 12.65 19.2 16.6 11.2 15.25 11.9 13.2 16.35 12.4 15.85 18.15 11.15 15.65 17.75 7.65 12.35 15.6 19.3 15.2 17.1 15.6 18.4 19.05 18.55 19.1 13.1 12.85 9.5 4.5 11.85 13.6 11.7 12.4 13.35 11.4 14.9 19.9 11.2 14.6 17.6 14.05 16.1 13.35 11.85 11.95 14.75 15.15 13.2 16.85 7.85 7.7 12.6 7.85 10.95 12.35 9.95 14.9 16.65 13.4 13.95 15.7 16.85 10.95 15.35 12.2 15.1 17.75 15.2 14.6 16.65
Sample Range:
(leave blank to include all observations)
From:
To:
bandwidth of density plot
(?)
# lags (autocorrelation function)
(?)
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
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')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation