PACF Steenkolen d=1
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 1 seconds |
| R Server | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 7.6811 | 0 |
| 1 | 0.379254 | 2.9131 | 0.002523 |
| 2 | 0.245703 | 1.8873 | 0.032022 |
| 3 | 0.082799 | 0.636 | 0.263622 |
| 4 | -0.062008 | -0.4763 | 0.682187 |
| 5 | -0.190108 | -1.4602 | 0.925237 |
| 6 | 0.059208 | 0.4548 | 0.325466 |
| 7 | -0.228903 | -1.7582 | 0.958053 |
| 8 | -0.213638 | -1.641 | 0.946939 |
| 9 | -0.060671 | -0.466 | 0.678542 |
| 10 | -0.030995 | -0.2381 | 0.593677 |
| 11 | 0.033759 | 0.2593 | 0.398149 |
| 12 | 0.158872 | 1.2203 | 0.1136 |
| 13 | -0.085507 | -0.6568 | 0.743065 |
| 14 | -0.097962 | -0.7525 | 0.772616 |
| 15 | -0.033731 | -0.2591 | 0.601768 |
| 16 | -0.097243 | -0.7469 | 0.770967 |
| 17 | -0.145161 | -1.115 | 0.865315 |
| 18 | -0.057571 | -0.4422 | 0.670022 |
| 19 | -0.127161 | -0.9767 | 0.833657 |
| 20 | -0.115695 | -0.8887 | 0.811105 |
| 21 | 0.057518 | 0.4418 | 0.330124 |
| 22 | -0.071885 | -0.5522 | 0.708536 |
| 23 | 0.032987 | 0.2534 | 0.400428 |
| 24 | 0.090161 | 0.6925 | 0.245657 |
| 25 | 0.10679 | 0.8203 | 0.207683 |
| 26 | -0.000463 | -0.0036 | 0.501413 |
| 27 | 0.034246 | 0.263 | 0.396715 |
| 28 | -0.148717 | -1.1423 | 0.871032 |
| 29 | -0.118802 | -0.9125 | 0.8174 |
| 30 | -0.053919 | -0.4142 | 0.659872 |
| 31 | -0.075834 | -0.5825 | 0.718772 |
| 32 | -0.066816 | -0.5132 | 0.695144 |
| 33 | -0.032965 | -0.2532 | 0.599505 |
| 34 | -0.172133 | -1.3222 | 0.904394 |
| 35 | -0.032957 | -0.2531 | 0.599482 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.379254 | 2.9131 | 0.002523 |
| 1 | 0.118983 | 0.9139 | 0.182238 |
| 2 | -0.052631 | -0.4043 | 0.65626 |
| 3 | -0.123305 | -0.9471 | 0.82628 |
| 4 | -0.160117 | -1.2299 | 0.888189 |
| 5 | 0.25391 | 1.9503 | 0.027947 |
| 6 | -0.324833 | -2.4951 | 0.992295 |
| 7 | -0.12191 | -0.9364 | 0.823561 |
| 8 | 0.158844 | 1.2201 | 0.11364 |
| 9 | 0.022535 | 0.1731 | 0.431584 |
| 10 | 0.077458 | 0.595 | 0.277072 |
| 11 | -0.066253 | -0.5089 | 0.693638 |
| 12 | -0.192024 | -1.475 | 0.927231 |
| 13 | 0.023376 | 0.1796 | 0.429058 |
| 14 | -0.01096 | -0.0842 | 0.533404 |
| 15 | -0.058393 | -0.4485 | 0.672291 |
| 16 | -0.140459 | -1.0789 | 0.857486 |
| 17 | -0.037423 | -0.2875 | 0.612613 |
| 18 | 0.096764 | 0.7433 | 0.230137 |
| 19 | -0.115303 | -0.8857 | 0.810301 |
| 20 | 0.020198 | 0.1551 | 0.43862 |
| 21 | -0.212223 | -1.6301 | 0.945798 |
| 22 | 0.154068 | 1.1834 | 0.120695 |
| 23 | 0.083339 | 0.6401 | 0.262281 |
| 24 | -0.005598 | -0.043 | 0.517077 |
| 25 | -0.142884 | -1.0975 | 0.861562 |
| 26 | -0.178546 | -1.3714 | 0.912284 |
| 27 | 0.046345 | 0.356 | 0.361562 |
| 28 | 0.002065 | 0.0159 | 0.493699 |
| 29 | -0.06172 | -0.4741 | 0.681403 |
| 30 | -0.156001 | -1.1983 | 0.882198 |
| 31 | 0.004086 | 0.0314 | 0.487535 |
| 32 | 0.019793 | 0.152 | 0.439841 |
| 33 | -0.237137 | -1.8215 | 0.963198 |
| 34 | -0.057508 | -0.4417 | 0.66985 |
| 35 | -0.031524 | -0.2421 | 0.595245 |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/28/t1196259597wthmtawj0rsdkhg/1jic01196260153.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/28/t1196259597wthmtawj0rsdkhg/1jic01196260153.ps (open in new window) |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/28/t1196259597wthmtawj0rsdkhg/257o61196260153.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/28/t1196259597wthmtawj0rsdkhg/257o61196260153.ps (open in new window) |
par1 = 10*log10(length(x))
} else {
par1 <- as.numeric(par1)
}
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='pic1.png')
racf <- acf(x,par1,main='Autocorrelation',xlab='lags',ylab='ACF')
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')
dev.off()
(myacf <- c(racf$acf))
(mypacf <- c(rpacf$acf))
lengthx <- length(x)
sqrtn <- sqrt(lengthx)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:par1) {
a<-table.row.start(a)
a<-table.element(a,i-1,header=TRUE)
a<-table.element(a,round(myacf[i],6))
mytstat <- myacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(mytstat,lengthx),6))
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,'Partial Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:par1) {
a<-table.row.start(a)
a<-table.element(a,i-1,header=TRUE)
a<-table.element(a,round(mypacf[i],6))
mytstat <- mypacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(mytstat,lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
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