Home » date » 2008 » Dec » 21 »

(P)ACF inschrijvingen nieuwe personenwagens (d=1, D=0)

*The author of this computation has been verified*
R Software Module: rwasp_autocorrelation.wasp (opens new window with default values)
Title produced by software: (Partial) Autocorrelation Function
Date of computation: Sun, 21 Dec 2008 04:35:27 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/21/t1229859449tshwc5jcbiq2og7.htm/, Retrieved Sun, 21 Dec 2008 12:37:31 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/21/t1229859449tshwc5jcbiq2og7.htm/},
    year = {2008},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
11.514 31.514 27.071 29.462 26.105 22.397 23.843 21.705 18.089 20.764 25.316 17704 15548 28029 29383 36438 32034 22679 24319 18004 17537 20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698 31956 29506 34506 27165 26736 23691 18157 17328 18205 20995 17382 9367 31124 26551 30651 25859 25100 25778 20418 18688 20424 24776 19814 12738
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132


Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.303058-2.34750.011109
20.1541411.1940.118594
3-0.094606-0.73280.233261
4-0.000394-0.00310.498787
50.0883190.68410.248267
6-0.405498-3.1410.001307
70.1609681.24680.108649
8-0.090696-0.70250.242534
9-0.036373-0.28170.389554
100.0248460.19250.424017
11-0.235764-1.82620.036397
120.580764.49851.6e-05
13-0.260725-2.01960.023951
140.185731.43870.077722
15-0.114193-0.88450.189968
160.0398650.30880.379275
170.0699140.54150.295067
18-0.288957-2.23830.014464
190.1948641.50940.068221
20-0.162129-1.25580.10702
210.0101340.07850.468847
22-0.015639-0.12110.451993
23-0.144777-1.12140.133285
240.3688482.85710.002934
25-0.167167-1.29490.100163
260.1900991.47250.073056
27-0.085652-0.66350.25479
280.0662740.51340.304794
29-0.044158-0.3420.366757
30-0.114887-0.88990.188535
310.0605320.46890.320427
32-0.106974-0.82860.205303
33-0.004492-0.03480.48618
34-0.006302-0.04880.480615
35-0.050913-0.39440.347354
360.1507461.16770.123779
37-0.098381-0.76210.224506
380.171571.3290.094444
39-0.058168-0.45060.326963
400.0276480.21420.415573
41-0.041918-0.32470.373269
42-0.023096-0.17890.42931
430.0475350.36820.357009
44-0.028658-0.2220.41254
45-0.016827-0.13030.448367
46-0.021382-0.16560.434504
47-0.002095-0.01620.493554
48-0.029822-0.2310.40905
49-0.049274-0.38170.352026
500.0013560.01050.495828
510.0016330.01260.494976
520.0012470.00970.496164
530.0011680.0090.496405
540.0011590.0090.496433
550.0007370.00570.497733
560.000580.00450.498216
570.0007330.00570.497744
580.0010170.00790.49687
590.0005540.00430.498296
60NANANA


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.303058-2.34750.011109
20.0685970.53140.298569
3-0.033531-0.25970.39798
4-0.051124-0.3960.346754
50.0954680.73950.231245
6-0.396106-3.06820.001615
7-0.077983-0.60410.274042
80.028690.22220.412445
9-0.162492-1.25870.106515
10-0.014634-0.11340.455064
11-0.252978-1.95960.027349
120.4121943.19280.001122
130.028630.22180.412625
14-0.01834-0.14210.443755
15-0.03468-0.26860.394569
16-0.061188-0.4740.318625
17-0.011882-0.0920.463487
180.0251460.19480.423112
190.0365120.28280.389145
20-0.078025-0.60440.273936
21-0.084715-0.65620.257102
220.0048770.03780.484995
230.0074670.05780.477033
240.0452710.35070.363533
250.0953870.73890.231434
26-0.009383-0.07270.471152
270.0232120.17980.428957
280.0378420.29310.38522
29-0.153853-1.19170.119028
300.1178460.91280.182493
31-0.119741-0.92750.178689
320.0045320.03510.486057
330.0057680.04470.482255
340.0042330.03280.486976
350.0640010.49570.310942
36-0.081951-0.63480.263989
37-0.050432-0.39060.348722
380.057090.44220.32996
390.0004590.00360.498587
40-0.055984-0.43360.333049
410.0230450.17850.429465
42-0.043538-0.33720.368555
430.076180.59010.278674
440.096270.74570.229379
450.0213690.16550.434544
46-0.069134-0.53550.297138
47-0.006024-0.04670.48147
48-0.087378-0.67680.250558
49-0.066795-0.51740.303392
50-0.133072-1.03080.153393
51-0.075454-0.58450.280549
52-0.025339-0.19630.422529
530.0802330.62150.268318
54-0.050272-0.38940.349177
55-0.064808-0.5020.308752
56-0.089852-0.6960.244562
57-0.007367-0.05710.477341
580.0179680.13920.444886
59-0.026751-0.20720.418272
60NANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229859449tshwc5jcbiq2og7/1jte11229859325.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229859449tshwc5jcbiq2og7/1jte11229859325.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229859449tshwc5jcbiq2og7/2v57o1229859325.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229859449tshwc5jcbiq2og7/2v57o1229859325.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ;
 
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ;
 
R code (references can be found in the software module):
if (par1 == 'Default') {
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 2:(par1+1)) {
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(abs(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,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(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 




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