Tijdreeks 2: Omzet volgens BTW (D=0;d=0)
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 2 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 | 9 | 0 |
| 1 | 0.764713 | 6.8824 | 0 |
| 2 | 0.677923 | 6.1013 | 0 |
| 3 | 0.758403 | 6.8256 | 0 |
| 4 | 0.630903 | 5.6781 | 0 |
| 5 | 0.66084 | 5.9476 | 0 |
| 6 | 0.769728 | 6.9276 | 0 |
| 7 | 0.594332 | 5.349 | 0 |
| 8 | 0.515501 | 4.6395 | 7e-06 |
| 9 | 0.564841 | 5.0836 | 1e-06 |
| 10 | 0.438093 | 3.9428 | 8.5e-05 |
| 11 | 0.485857 | 4.3727 | 1.8e-05 |
| 12 | 0.601212 | 5.4109 | 0 |
| 13 | 0.410587 | 3.6953 | 0.000199 |
| 14 | 0.327377 | 2.9464 | 0.002099 |
| 15 | 0.390468 | 3.5142 | 0.000362 |
| 16 | 0.261863 | 2.3568 | 0.010425 |
| 17 | 0.278545 | 2.5069 | 0.00709 |
| 18 | 0.374086 | 3.3668 | 0.000583 |
| 19 | 0.197304 | 1.7757 | 0.039766 |
| 20 | 0.139267 | 1.2534 | 0.106832 |
| 21 | 0.169149 | 1.5223 | 0.06591 |
| 22 | 0.055492 | 0.4994 | 0.309415 |
| 23 | 0.108277 | 0.9745 | 0.166357 |
| 24 | 0.208067 | 1.8726 | 0.032366 |
| 25 | 0.048667 | 0.438 | 0.331275 |
| 26 | -0.009165 | -0.0825 | 0.532769 |
| 27 | 0.037089 | 0.3338 | 0.369696 |
| 28 | -0.066216 | -0.5959 | 0.723564 |
| 29 | -0.028517 | -0.2567 | 0.60095 |
| 30 | 0.023846 | 0.2146 | 0.415305 |
| 31 | -0.097515 | -0.8776 | 0.808631 |
| 32 | -0.145946 | -1.3135 | 0.903641 |
| 33 | -0.122669 | -1.104 | 0.863574 |
| 34 | -0.219172 | -1.9726 | 0.974021 |
| 35 | -0.17677 | -1.5909 | 0.94224 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.764713 | 6.8824 | 0 |
| 1 | 0.224312 | 2.0188 | 0.023408 |
| 2 | 0.468497 | 4.2165 | 3.2e-05 |
| 3 | -0.19885 | -1.7897 | 0.961376 |
| 4 | 0.377244 | 3.3952 | 0.000532 |
| 5 | 0.23213 | 2.0892 | 0.019916 |
| 6 | -0.298373 | -2.6854 | 0.995607 |
| 7 | -0.191949 | -1.7275 | 0.956059 |
| 8 | -0.026411 | -0.2377 | 0.593641 |
| 9 | -0.127111 | -1.144 | 0.872004 |
| 10 | 0.241802 | 2.1762 | 0.016226 |
| 11 | 0.19548 | 1.7593 | 0.04115 |
| 12 | -0.249141 | -2.2423 | 0.986161 |
| 13 | -0.207159 | -1.8644 | 0.967058 |
| 14 | 0.064562 | 0.5811 | 0.281406 |
| 15 | -0.078922 | -0.7103 | 0.760221 |
| 16 | -0.035507 | -0.3196 | 0.624938 |
| 17 | 0.01173 | 0.1056 | 0.458093 |
| 18 | -0.084345 | -0.7591 | 0.775002 |
| 19 | 0.038904 | 0.3501 | 0.363574 |
| 20 | -0.093273 | -0.8395 | 0.798159 |
| 21 | 0.039885 | 0.359 | 0.360276 |
| 22 | 0.071526 | 0.6437 | 0.260782 |
| 23 | 0.107847 | 0.9706 | 0.167313 |
| 24 | -0.030068 | -0.2706 | 0.606312 |
| 25 | -0.103856 | -0.9347 | 0.823641 |
| 26 | -0.067996 | -0.612 | 0.728862 |
| 27 | 0.004405 | 0.0396 | 0.484236 |
| 28 | -0.009098 | -0.0819 | 0.532528 |
| 29 | -0.231747 | -2.0857 | 0.979924 |
| 30 | 0.130014 | 1.1701 | 0.122691 |
| 31 | -0.050029 | -0.4503 | 0.673137 |
| 32 | 0.061768 | 0.5559 | 0.289901 |
| 33 | -0.112567 | -1.0131 | 0.842987 |
| 34 | -0.02429 | -0.2186 | 0.586248 |
| 35 | -0.027618 | -0.2486 | 0.597837 |
 |
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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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