Q2 Tijdreeks 4 Methode 2
| 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 | 7.746 | 0 |
| 1 | 0.838096 | 6.4919 | 0 |
| 2 | 0.657426 | 5.0924 | 2e-06 |
| 3 | 0.53532 | 4.1466 | 5.4e-05 |
| 4 | 0.449018 | 3.4781 | 0.000473 |
| 5 | 0.350597 | 2.7157 | 0.004313 |
| 6 | 0.238971 | 1.8511 | 0.034542 |
| 7 | 0.138566 | 1.0733 | 0.143711 |
| 8 | 0.083438 | 0.6463 | 0.260272 |
| 9 | 0.046523 | 0.3604 | 0.35992 |
| 10 | -0.016797 | -0.1301 | 0.551543 |
| 11 | -0.102936 | -0.7973 | 0.785802 |
| 12 | -0.205815 | -1.5942 | 0.941931 |
| 13 | -0.288337 | -2.2334 | 0.98537 |
| 14 | -0.311221 | -2.4107 | 0.990499 |
| 15 | -0.332959 | -2.5791 | 0.993812 |
| 16 | -0.370769 | -2.872 | 0.997185 |
| 17 | -0.352918 | -2.7337 | 0.995891 |
| 18 | -0.316652 | -2.4528 | 0.99145 |
| 19 | -0.306549 | -2.3745 | 0.989606 |
| 20 | -0.290986 | -2.254 | 0.986068 |
| 21 | -0.230584 | -1.7861 | 0.960432 |
| 22 | -0.1544 | -1.196 | 0.881795 |
| 23 | -0.075815 | -0.5873 | 0.720385 |
| 24 | -0.058321 | -0.4518 | 0.673462 |
| 25 | -0.015078 | -0.1168 | 0.546294 |
| 26 | 0.053412 | 0.4137 | 0.340275 |
| 27 | 0.113838 | 0.8818 | 0.190706 |
| 28 | 0.161288 | 1.2493 | 0.108198 |
| 29 | 0.193955 | 1.5024 | 0.069123 |
| 30 | 0.229455 | 1.7773 | 0.04029 |
| 31 | 0.249217 | 1.9304 | 0.029142 |
| 32 | 0.279085 | 2.1618 | 0.017317 |
| 33 | 0.305835 | 2.369 | 0.010537 |
| 34 | 0.275671 | 2.1353 | 0.018413 |
| 35 | 0.23306 | 1.8053 | 0.038025 |
| 36 | 0.186952 | 1.4481 | 0.076394 |
| 37 | 0.125725 | 0.9739 | 0.167018 |
| 38 | 0.036305 | 0.2812 | 0.389757 |
| 39 | -0.011414 | -0.0884 | 0.535079 |
| 40 | -0.043354 | -0.3358 | 0.630911 |
| 41 | -0.06075 | -0.4706 | 0.680173 |
| 42 | -0.106761 | -0.827 | 0.794233 |
| 43 | -0.151467 | -1.1733 | 0.877336 |
| 44 | -0.169665 | -1.3142 | 0.903112 |
| 45 | -0.154211 | -1.1945 | 0.88151 |
| 46 | -0.166862 | -1.2925 | 0.899431 |
| 47 | -0.21272 | -1.6477 | 0.947681 |
| 48 | -0.234704 | -1.818 | 0.962972 |
| 49 | -0.211323 | -1.6369 | 0.946557 |
| 50 | -0.162048 | -1.2552 | 0.892867 |
| 51 | -0.13412 | -1.0389 | 0.848487 |
| 52 | -0.161365 | -1.2499 | 0.89191 |
| 53 | -0.178882 | -1.3856 | 0.914503 |
| 54 | -0.172971 | -1.3398 | 0.907323 |
| 55 | -0.149792 | -1.1603 | 0.874736 |
| 56 | -0.12019 | -0.931 | 0.822204 |
| 57 | -0.11654 | -0.9027 | 0.814857 |
| 58 | -0.08646 | -0.6697 | 0.747197 |
| 59 | -0.046017 | -0.3564 | 0.63862 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.838096 | 6.4919 | 0 |
| 1 | -0.151144 | -1.1708 | 0.876837 |
| 2 | 0.095355 | 0.7386 | 0.23151 |
| 3 | 0.016561 | 0.1283 | 0.449178 |
| 4 | -0.091193 | -0.7064 | 0.758657 |
| 5 | -0.083794 | -0.6491 | 0.740614 |
| 6 | -0.046661 | -0.3614 | 0.640478 |
| 7 | 0.050842 | 0.3938 | 0.347554 |
| 8 | -0.014988 | -0.1161 | 0.546019 |
| 9 | -0.10902 | -0.8445 | 0.799119 |
| 10 | -0.107499 | -0.8327 | 0.795836 |
| 11 | -0.167571 | -1.298 | 0.900372 |
| 12 | -0.075849 | -0.5875 | 0.720473 |
| 13 | 0.06981 | 0.5407 | 0.295341 |
| 14 | -0.077076 | -0.597 | 0.723632 |
| 15 | -0.07055 | -0.5465 | 0.706618 |
| 16 | 0.127573 | 0.9882 | 0.163517 |
| 17 | -0.054062 | -0.4188 | 0.661557 |
| 18 | -0.118843 | -0.9206 | 0.819516 |
| 19 | 0.033821 | 0.262 | 0.397118 |
| 20 | 0.144785 | 1.1215 | 0.133272 |
| 21 | 0.037524 | 0.2907 | 0.386158 |
| 22 | 0.05375 | 0.4163 | 0.339321 |
| 23 | -0.154152 | -1.1941 | 0.881422 |
| 24 | 0.110569 | 0.8565 | 0.197574 |
| 25 | 0.015679 | 0.1215 | 0.45187 |
| 26 | 0.008636 | 0.0669 | 0.473445 |
| 27 | 0.055298 | 0.4283 | 0.334971 |
| 28 | 0.005388 | 0.0417 | 0.483425 |
| 29 | 0.090215 | 0.6988 | 0.243687 |
| 30 | -0.090976 | -0.7047 | 0.758136 |
| 31 | 0.019545 | 0.1514 | 0.440086 |
| 32 | 0.111977 | 0.8674 | 0.194597 |
| 33 | -0.112371 | -0.8704 | 0.80623 |
| 34 | 0.003724 | 0.0288 | 0.488542 |
| 35 | -0.06075 | -0.4706 | 0.680171 |
| 36 | -0.112951 | -0.8749 | 0.807445 |
| 37 | -0.096615 | -0.7484 | 0.771421 |
| 38 | 0.147489 | 1.1424 | 0.128904 |
| 39 | -0.075967 | -0.5884 | 0.720776 |
| 40 | 0.088403 | 0.6848 | 0.248065 |
| 41 | -0.072196 | -0.5592 | 0.710956 |
| 42 | -0.022469 | -0.174 | 0.568791 |
| 43 | -0.016632 | -0.1288 | 0.55104 |
| 44 | 0.143405 | 1.1108 | 0.135541 |
| 45 | -0.039543 | -0.3063 | 0.619782 |
| 46 | -0.088141 | -0.6827 | 0.751301 |
| 47 | 0.02653 | 0.2055 | 0.418939 |
| 48 | 0.10438 | 0.8085 | 0.210991 |
| 49 | -0.05005 | -0.3877 | 0.650191 |
| 50 | -0.087244 | -0.6758 | 0.749113 |
| 51 | -0.067161 | -0.5202 | 0.697589 |
| 52 | -0.047696 | -0.3694 | 0.643453 |
| 53 | -0.101706 | -0.7878 | 0.783045 |
| 54 | 0.015745 | 0.122 | 0.45167 |
| 55 | -0.03724 | -0.2885 | 0.613006 |
| 56 | -0.00098 | -0.0076 | 0.503017 |
| 57 | 0.061273 | 0.4746 | 0.31839 |
| 58 | -0.080708 | -0.6252 | 0.732882 |
| 59 | NA | NA | NA |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t11963313060o9jnce2o9avdpf/2ipxs1196331924.png (open in new window) |
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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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