Workshop 4 Q4
| 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 | 8.3666 | 0 |
| 1 | -0.004422 | -0.037 | 0.514703 |
| 2 | 0.062229 | 0.5206 | 0.302128 |
| 3 | 0.067491 | 0.5647 | 0.287053 |
| 4 | 0.1678 | 1.4039 | 0.082383 |
| 5 | 0.089299 | 0.7471 | 0.228743 |
| 6 | -0.037477 | -0.3136 | 0.622606 |
| 7 | -0.122897 | -1.0282 | 0.846309 |
| 8 | -0.110888 | -0.9278 | 0.821638 |
| 9 | 0.031845 | 0.2664 | 0.395344 |
| 10 | -0.243941 | -2.041 | 0.977486 |
| 11 | -0.110982 | -0.9285 | 0.821842 |
| 12 | -0.015886 | -0.1329 | 0.552677 |
| 13 | -0.122177 | -1.0222 | 0.844899 |
| 14 | -0.020989 | -0.1756 | 0.569445 |
| 15 | -0.04014 | -0.3358 | 0.631001 |
| 16 | -0.120528 | -1.0084 | 0.841634 |
| 17 | 0.069566 | 0.582 | 0.281207 |
| 18 | 0.010729 | 0.0898 | 0.464363 |
| 19 | 0.014851 | 0.1243 | 0.450735 |
| 20 | 0.03963 | 0.3316 | 0.370604 |
| 21 | 0.031654 | 0.2648 | 0.395957 |
| 22 | -0.011387 | -0.0953 | 0.537814 |
| 23 | 0.269605 | 2.2557 | 0.013609 |
| 24 | 0.002466 | 0.0206 | 0.4918 |
| 25 | -0.181328 | -1.5171 | 0.933127 |
| 26 | 0.037369 | 0.3126 | 0.377738 |
| 27 | 0.089545 | 0.7492 | 0.228127 |
| 28 | 0.001286 | 0.0108 | 0.495722 |
| 29 | -0.046966 | -0.3929 | 0.652225 |
| 30 | -0.154554 | -1.2931 | 0.899885 |
| 31 | 0.095975 | 0.803 | 0.212351 |
| 32 | 0.048611 | 0.4067 | 0.342732 |
| 33 | -0.09508 | -0.7955 | 0.785494 |
| 34 | -0.068962 | -0.577 | 0.717097 |
| 35 | 0.050493 | 0.4225 | 0.336995 |
| 36 | 0.040282 | 0.337 | 0.368555 |
| 37 | -0.050197 | -0.42 | 0.662107 |
| 38 | 0.035295 | 0.2953 | 0.384321 |
| 39 | -0.107097 | -0.896 | 0.813348 |
| 40 | 0.129252 | 1.0814 | 0.141616 |
| 41 | -0.039448 | -0.33 | 0.628825 |
| 42 | -0.067218 | -0.5624 | 0.712176 |
| 43 | 0.011039 | 0.0924 | 0.463338 |
| 44 | 0.031684 | 0.2651 | 0.395861 |
| 45 | -0.028111 | -0.2352 | 0.592629 |
| 46 | -0.037763 | -0.316 | 0.62351 |
| 47 | 0.021915 | 0.1834 | 0.427525 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.004422 | -0.037 | 0.514703 |
| 1 | 0.062211 | 0.5205 | 0.302181 |
| 2 | 0.068289 | 0.5714 | 0.284796 |
| 3 | 0.166198 | 1.3905 | 0.084389 |
| 4 | 0.08868 | 0.7419 | 0.230301 |
| 5 | -0.059127 | -0.4947 | 0.688818 |
| 6 | -0.165467 | -1.3844 | 0.914683 |
| 7 | -0.167737 | -1.4034 | 0.917539 |
| 8 | 0.01295 | 0.1083 | 0.457015 |
| 9 | -0.212911 | -1.7813 | 0.960403 |
| 10 | -0.06238 | -0.5219 | 0.698309 |
| 11 | 0.078964 | 0.6607 | 0.2555 |
| 12 | -0.075341 | -0.6303 | 0.73474 |
| 13 | 0.036729 | 0.3073 | 0.379763 |
| 14 | 0.016172 | 0.1353 | 0.44638 |
| 15 | -0.145692 | -1.2189 | 0.886522 |
| 16 | 0.034989 | 0.2927 | 0.385294 |
| 17 | -0.03978 | -0.3328 | 0.629869 |
| 18 | 0.017261 | 0.1444 | 0.442794 |
| 19 | 0.027948 | 0.2338 | 0.407901 |
| 20 | -0.029253 | -0.2447 | 0.596316 |
| 21 | -0.020646 | -0.1727 | 0.568322 |
| 22 | 0.229424 | 1.9195 | 0.029499 |
| 23 | -0.026227 | -0.2194 | 0.586523 |
| 24 | -0.235577 | -1.971 | 0.973659 |
| 25 | -0.075561 | -0.6322 | 0.735339 |
| 26 | 0.054218 | 0.4536 | 0.325753 |
| 27 | -0.002472 | -0.0207 | 0.508222 |
| 28 | 0.019956 | 0.167 | 0.43394 |
| 29 | -0.057677 | -0.4826 | 0.684543 |
| 30 | 0.152016 | 1.2719 | 0.103816 |
| 31 | -0.007992 | -0.0669 | 0.526561 |
| 32 | -0.048877 | -0.4089 | 0.658082 |
| 33 | -0.004726 | -0.0395 | 0.515715 |
| 34 | -0.076228 | -0.6378 | 0.737147 |
| 35 | 0.011594 | 0.097 | 0.461502 |
| 36 | -0.006934 | -0.058 | 0.523047 |
| 37 | 0.045444 | 0.3802 | 0.35247 |
| 38 | -0.023399 | -0.1958 | 0.577322 |
| 39 | 0.033247 | 0.2782 | 0.390853 |
| 40 | -0.052776 | -0.4416 | 0.669916 |
| 41 | -0.046135 | -0.386 | 0.649665 |
| 42 | -0.036984 | -0.3094 | 0.621044 |
| 43 | -0.001142 | -0.0096 | 0.503799 |
| 44 | -0.004598 | -0.0385 | 0.51529 |
| 45 | -0.087321 | -0.7306 | 0.766263 |
| 46 | 0.057197 | 0.4785 | 0.316878 |
| 47 | -0.031952 | -0.2673 | 0.605 |
 |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/06/t11969489854j4jf33inklrlec/20mh91196949723.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/06/t11969489854j4jf33inklrlec/20mh91196949723.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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