autocorrelatie d0 D0 lambda 1 brandstof
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 3 seconds |
| R Server | 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 7.746 | 0 |
| 1 | 0.956618 | 7.4099 | 0 |
| 2 | 0.900469 | 6.975 | 0 |
| 3 | 0.851864 | 6.5985 | 0 |
| 4 | 0.809579 | 6.271 | 0 |
| 5 | 0.762599 | 5.9071 | 0 |
| 6 | 0.713437 | 5.5263 | 0 |
| 7 | 0.662345 | 5.1305 | 2e-06 |
| 8 | 0.61625 | 4.7735 | 6e-06 |
| 9 | 0.582432 | 4.5115 | 1.5e-05 |
| 10 | 0.553349 | 4.2862 | 3.3e-05 |
| 11 | 0.507104 | 3.928 | 0.000112 |
| 12 | 0.44115 | 3.4171 | 0.000571 |
| 13 | 0.380455 | 2.947 | 0.002282 |
| 14 | 0.32388 | 2.5088 | 0.007418 |
| 15 | 0.260528 | 2.018 | 0.024033 |
| 16 | 0.192915 | 1.4943 | 0.070167 |
| 17 | 0.124769 | 0.9665 | 0.168848 |
| 18 | 0.063539 | 0.4922 | 0.312197 |
| 19 | 0.01605 | 0.1243 | 0.450737 |
| 20 | -0.022039 | -0.1707 | 0.567489 |
| 21 | -0.064383 | -0.4987 | 0.690096 |
| 22 | -0.104997 | -0.8133 | 0.79037 |
| 23 | -0.143568 | -1.1121 | 0.864728 |
| 24 | -0.175321 | -1.358 | 0.91023 |
| 25 | -0.212527 | -1.6462 | 0.947527 |
| 26 | -0.260008 | -2.014 | 0.975751 |
| 27 | -0.30432 | -2.3573 | 0.989155 |
| 28 | -0.334398 | -2.5902 | 0.993989 |
| 29 | -0.350723 | -2.7167 | 0.995698 |
| 30 | -0.36238 | -2.807 | 0.996632 |
| 31 | -0.377008 | -2.9203 | 0.99754 |
| 32 | -0.394058 | -3.0524 | 0.99831 |
| 33 | -0.402655 | -3.119 | 0.998606 |
| 34 | -0.397901 | -3.0821 | 0.998449 |
| 35 | -0.393503 | -3.0481 | 0.998289 |
| 36 | -0.396861 | -3.0741 | 0.998412 |
| 37 | -0.406918 | -3.152 | 0.998734 |
| 38 | -0.407561 | -3.157 | 0.998753 |
| 39 | -0.400801 | -3.1046 | 0.998547 |
| 40 | -0.387994 | -3.0054 | 0.998066 |
| 41 | -0.380092 | -2.9442 | 0.9977 |
| 42 | -0.371262 | -2.8758 | 0.997214 |
| 43 | -0.355722 | -2.7554 | 0.996125 |
| 44 | -0.3315 | -2.5678 | 0.993628 |
| 45 | -0.299879 | -2.3229 | 0.988202 |
| 46 | -0.27586 | -2.1368 | 0.98165 |
| 47 | -0.263917 | -2.0443 | 0.97734 |
| 48 | -0.25246 | -1.9555 | 0.97241 |
| 49 | -0.234489 | -1.8163 | 0.962843 |
| 50 | -0.216804 | -1.6794 | 0.950858 |
| 51 | -0.201191 | -1.5584 | 0.937803 |
| 52 | -0.17951 | -1.3905 | 0.91524 |
| 53 | -0.155772 | -1.2066 | 0.883839 |
| 54 | -0.129677 | -1.0045 | 0.840407 |
| 55 | -0.097868 | -0.7581 | 0.774316 |
| 56 | -0.067345 | -0.5216 | 0.698082 |
| 57 | -0.050738 | -0.393 | 0.652148 |
| 58 | -0.036092 | -0.2796 | 0.609614 |
| 59 | -0.019231 | -0.149 | 0.55896 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.956618 | 7.4099 | 0 |
| 1 | -0.172562 | -1.3367 | 0.90681 |
| 2 | 0.083653 | 0.648 | 0.259736 |
| 3 | 0.019666 | 0.1523 | 0.439717 |
| 4 | -0.090594 | -0.7017 | 0.757223 |
| 5 | -0.022377 | -0.1733 | 0.568512 |
| 6 | -0.055997 | -0.4338 | 0.666988 |
| 7 | 0.029056 | 0.2251 | 0.411347 |
| 8 | 0.102649 | 0.7951 | 0.214839 |
| 9 | -0.000173 | -0.0013 | 0.500531 |
| 10 | -0.213921 | -1.657 | 0.948632 |
| 11 | -0.205843 | -1.5945 | 0.941955 |
| 12 | 0.041662 | 0.3227 | 0.374018 |
| 13 | -0.071483 | -0.5537 | 0.70908 |
| 14 | -0.130796 | -1.0131 | 0.84247 |
| 15 | -0.039409 | -0.3053 | 0.619389 |
| 16 | -0.050809 | -0.3936 | 0.652353 |
| 17 | 0.021103 | 0.1635 | 0.435352 |
| 18 | 0.048284 | 0.374 | 0.354858 |
| 19 | -0.015501 | -0.1201 | 0.547586 |
| 20 | -0.069454 | -0.538 | 0.703713 |
| 21 | 0.085705 | 0.6639 | 0.254658 |
| 22 | -0.049143 | -0.3807 | 0.6476 |
| 23 | -0.014822 | -0.1148 | 0.545512 |
| 24 | -0.089849 | -0.696 | 0.755433 |
| 25 | -0.111183 | -0.8612 | 0.803728 |
| 26 | 0.051191 | 0.3965 | 0.346562 |
| 27 | 0.103192 | 0.7993 | 0.213629 |
| 28 | 0.043306 | 0.3354 | 0.369229 |
| 29 | -0.034958 | -0.2708 | 0.606257 |
| 30 | -0.044261 | -0.3428 | 0.633544 |
| 31 | -0.060457 | -0.4683 | 0.679365 |
| 32 | -0.004277 | -0.0331 | 0.513159 |
| 33 | 0.07127 | 0.5521 | 0.291481 |
| 34 | -0.05668 | -0.439 | 0.668895 |
| 35 | -0.00418 | -0.0324 | 0.512861 |
| 36 | -0.02887 | -0.2236 | 0.588096 |
| 37 | 0.023208 | 0.1798 | 0.42897 |
| 38 | -0.047092 | -0.3648 | 0.641719 |
| 39 | 0.03626 | 0.2809 | 0.389888 |
| 40 | -0.057716 | -0.4471 | 0.671781 |
| 41 | 0.031626 | 0.245 | 0.403656 |
| 42 | 0.03877 | 0.3003 | 0.38249 |
| 43 | -0.055128 | -0.427 | 0.664553 |
| 44 | 0.068803 | 0.5329 | 0.29802 |
| 45 | -0.036961 | -0.2863 | 0.612182 |
| 46 | -0.095038 | -0.7362 | 0.76775 |
| 47 | 0.003474 | 0.0269 | 0.489312 |
| 48 | 0.003154 | 0.0244 | 0.490296 |
| 49 | -0.037221 | -0.2883 | 0.612948 |
| 50 | 0.023168 | 0.1795 | 0.429092 |
| 51 | 0.122519 | 0.949 | 0.173207 |
| 52 | -0.077505 | -0.6004 | 0.724734 |
| 53 | -0.015469 | -0.1198 | 0.547487 |
| 54 | 0.083096 | 0.6437 | 0.261124 |
| 55 | -0.046677 | -0.3616 | 0.640523 |
| 56 | -0.081741 | -0.6332 | 0.735483 |
| 57 | 0.021453 | 0.1662 | 0.43429 |
| 58 | -0.061703 | -0.4779 | 0.682788 |
| 59 | NA | NA | NA |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/18/t1197985395dfwxkg6flptlg3f/2lnvi1197986316.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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