Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationSat, 06 Dec 2008 03:52:06 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/06/t1228560916433rz9u4vrufqa5.htm/, Retrieved Thu, 31 Oct 2024 22:54:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29493, Retrieved Thu, 31 Oct 2024 22:54:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact233
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
F RMP   [Standard Deviation-Mean Plot] [sdm ] [2008-12-05 13:33:27] [de72ca3f4fcfd0997c84e1ac92aea119]
F RM D    [Variance Reduction Matrix] [Q2 eigen tijdreeks] [2008-12-06 10:45:14] [de72ca3f4fcfd0997c84e1ac92aea119]
F RMP         [(Partial) Autocorrelation Function] [Q2 eigen tijdreeks] [2008-12-06 10:52:06] [56fd94b954e08a6655cb7790b21ee404] [Current]
F RMP           [Spectral Analysis] [Q2 eigen tijdreeks] [2008-12-06 10:56:27] [de72ca3f4fcfd0997c84e1ac92aea119]
-   P             [Spectral Analysis] [Q3 Eigen tijdreeks] [2008-12-06 11:07:10] [de72ca3f4fcfd0997c84e1ac92aea119]
-   P               [Spectral Analysis] [Q3 Eigen tijdreeks] [2008-12-06 13:01:00] [de72ca3f4fcfd0997c84e1ac92aea119]
-   P           [(Partial) Autocorrelation Function] [Q3 Eigen tijdreeks] [2008-12-06 13:06:08] [de72ca3f4fcfd0997c84e1ac92aea119]
-   P             [(Partial) Autocorrelation Function] [Q3 Eigen tijdreeks] [2008-12-06 13:14:50] [de72ca3f4fcfd0997c84e1ac92aea119]
Feedback Forum
2008-12-14 14:20:07 [Hannes Van Hoof] [reply
De ACF bevestigt wat we gevonden hebben in de VRM Er is enkel een LT trend aanwezig en moeten dus enkel niet seizoenaal differentiëren.
Seizoenaliteit zien we niet op deze ACF

Post a new message
Dataseries X:
0.9059
0.8883
0.8924
0.8833
0.87
0.8758
0.8858
0.917
0.9554
0.9922
0.9778
0.9808
0.9811
1.0014
1.0183
1.0622
1.0773
1.0807
1.0848
1.1582
1.1663
1.1372
1.1139
1.1222
1.1692
1.1702
1.2286
1.2613
1.2646
1.2262
1.1985
1.2007
1.2138
1.2266
1.2176
1.2218
1.249
1.2991
1.3408
1.3119
1.3014
1.3201
1.2938
1.2694
1.2165
1.2037
1.2292
1.2256
1.2015
1.1786
1.1856
1.2103
1.1938
1.202
1.2271
1.277
1.265
1.2684
1.2811
1.2727
1.2611
1.2881
1.3213
1.2999
1.3074
1.3242
1.3516
1.3511
1.3419
1.3716
1.3622
1.3896
1.4227
1.4684




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29493&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29493&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29493&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.9359648.05150
20.8672517.46040
30.8062516.93560
40.7501876.45340
50.6848075.89090
60.6228985.35840
70.563884.85073e-06
80.5079394.36952e-05
90.4604773.96128.5e-05
100.4191143.60540.000281
110.3734583.21260.000974
120.3198832.75170.003725
130.2703462.32560.011391
140.2285151.96580.026539
150.1856551.59710.057257
160.1498181.28880.100744
170.1218921.04860.148899
180.0926480.7970.214004

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.935964 & 8.0515 & 0 \tabularnewline
2 & 0.867251 & 7.4604 & 0 \tabularnewline
3 & 0.806251 & 6.9356 & 0 \tabularnewline
4 & 0.750187 & 6.4534 & 0 \tabularnewline
5 & 0.684807 & 5.8909 & 0 \tabularnewline
6 & 0.622898 & 5.3584 & 0 \tabularnewline
7 & 0.56388 & 4.8507 & 3e-06 \tabularnewline
8 & 0.507939 & 4.3695 & 2e-05 \tabularnewline
9 & 0.460477 & 3.9612 & 8.5e-05 \tabularnewline
10 & 0.419114 & 3.6054 & 0.000281 \tabularnewline
11 & 0.373458 & 3.2126 & 0.000974 \tabularnewline
12 & 0.319883 & 2.7517 & 0.003725 \tabularnewline
13 & 0.270346 & 2.3256 & 0.011391 \tabularnewline
14 & 0.228515 & 1.9658 & 0.026539 \tabularnewline
15 & 0.185655 & 1.5971 & 0.057257 \tabularnewline
16 & 0.149818 & 1.2888 & 0.100744 \tabularnewline
17 & 0.121892 & 1.0486 & 0.148899 \tabularnewline
18 & 0.092648 & 0.797 & 0.214004 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29493&T=1

[TABLE]
[ROW][C]Autocorrelation Function[/C][/ROW]
[ROW][C]Time lag k[/C][C]ACF(k)[/C][C]T-STAT[/C][C]P-value[/C][/ROW]
[ROW][C]1[/C][C]0.935964[/C][C]8.0515[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.867251[/C][C]7.4604[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.806251[/C][C]6.9356[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.750187[/C][C]6.4534[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.684807[/C][C]5.8909[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.622898[/C][C]5.3584[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.56388[/C][C]4.8507[/C][C]3e-06[/C][/ROW]
[ROW][C]8[/C][C]0.507939[/C][C]4.3695[/C][C]2e-05[/C][/ROW]
[ROW][C]9[/C][C]0.460477[/C][C]3.9612[/C][C]8.5e-05[/C][/ROW]
[ROW][C]10[/C][C]0.419114[/C][C]3.6054[/C][C]0.000281[/C][/ROW]
[ROW][C]11[/C][C]0.373458[/C][C]3.2126[/C][C]0.000974[/C][/ROW]
[ROW][C]12[/C][C]0.319883[/C][C]2.7517[/C][C]0.003725[/C][/ROW]
[ROW][C]13[/C][C]0.270346[/C][C]2.3256[/C][C]0.011391[/C][/ROW]
[ROW][C]14[/C][C]0.228515[/C][C]1.9658[/C][C]0.026539[/C][/ROW]
[ROW][C]15[/C][C]0.185655[/C][C]1.5971[/C][C]0.057257[/C][/ROW]
[ROW][C]16[/C][C]0.149818[/C][C]1.2888[/C][C]0.100744[/C][/ROW]
[ROW][C]17[/C][C]0.121892[/C][C]1.0486[/C][C]0.148899[/C][/ROW]
[ROW][C]18[/C][C]0.092648[/C][C]0.797[/C][C]0.214004[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29493&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29493&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.9359648.05150
20.8672517.46040
30.8062516.93560
40.7501876.45340
50.6848075.89090
60.6228985.35840
70.563884.85073e-06
80.5079394.36952e-05
90.4604773.96128.5e-05
100.4191143.60540.000281
110.3734583.21260.000974
120.3198832.75170.003725
130.2703462.32560.011391
140.2285151.96580.026539
150.1856551.59710.057257
160.1498181.28880.100744
170.1218921.04860.148899
180.0926480.7970.214004







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9359648.05150
2-0.070802-0.60910.272175
30.0270140.23240.40844
40.001570.01350.49463
5-0.107086-0.92120.179973
6-0.000186-0.00160.499363
7-0.023967-0.20620.418611
8-0.016244-0.13970.444624
90.0402520.34630.365063
100.0098050.08430.466503
11-0.062147-0.53460.29726
12-0.088526-0.76150.224381
13-0.01139-0.0980.461106
140.0112810.0970.461477
15-0.039942-0.34360.366063
160.0408580.35150.363117
170.0275610.23710.406622
18-0.044526-0.3830.3514

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.935964 & 8.0515 & 0 \tabularnewline
2 & -0.070802 & -0.6091 & 0.272175 \tabularnewline
3 & 0.027014 & 0.2324 & 0.40844 \tabularnewline
4 & 0.00157 & 0.0135 & 0.49463 \tabularnewline
5 & -0.107086 & -0.9212 & 0.179973 \tabularnewline
6 & -0.000186 & -0.0016 & 0.499363 \tabularnewline
7 & -0.023967 & -0.2062 & 0.418611 \tabularnewline
8 & -0.016244 & -0.1397 & 0.444624 \tabularnewline
9 & 0.040252 & 0.3463 & 0.365063 \tabularnewline
10 & 0.009805 & 0.0843 & 0.466503 \tabularnewline
11 & -0.062147 & -0.5346 & 0.29726 \tabularnewline
12 & -0.088526 & -0.7615 & 0.224381 \tabularnewline
13 & -0.01139 & -0.098 & 0.461106 \tabularnewline
14 & 0.011281 & 0.097 & 0.461477 \tabularnewline
15 & -0.039942 & -0.3436 & 0.366063 \tabularnewline
16 & 0.040858 & 0.3515 & 0.363117 \tabularnewline
17 & 0.027561 & 0.2371 & 0.406622 \tabularnewline
18 & -0.044526 & -0.383 & 0.3514 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29493&T=2

[TABLE]
[ROW][C]Partial Autocorrelation Function[/C][/ROW]
[ROW][C]Time lag k[/C][C]PACF(k)[/C][C]T-STAT[/C][C]P-value[/C][/ROW]
[ROW][C]1[/C][C]0.935964[/C][C]8.0515[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.070802[/C][C]-0.6091[/C][C]0.272175[/C][/ROW]
[ROW][C]3[/C][C]0.027014[/C][C]0.2324[/C][C]0.40844[/C][/ROW]
[ROW][C]4[/C][C]0.00157[/C][C]0.0135[/C][C]0.49463[/C][/ROW]
[ROW][C]5[/C][C]-0.107086[/C][C]-0.9212[/C][C]0.179973[/C][/ROW]
[ROW][C]6[/C][C]-0.000186[/C][C]-0.0016[/C][C]0.499363[/C][/ROW]
[ROW][C]7[/C][C]-0.023967[/C][C]-0.2062[/C][C]0.418611[/C][/ROW]
[ROW][C]8[/C][C]-0.016244[/C][C]-0.1397[/C][C]0.444624[/C][/ROW]
[ROW][C]9[/C][C]0.040252[/C][C]0.3463[/C][C]0.365063[/C][/ROW]
[ROW][C]10[/C][C]0.009805[/C][C]0.0843[/C][C]0.466503[/C][/ROW]
[ROW][C]11[/C][C]-0.062147[/C][C]-0.5346[/C][C]0.29726[/C][/ROW]
[ROW][C]12[/C][C]-0.088526[/C][C]-0.7615[/C][C]0.224381[/C][/ROW]
[ROW][C]13[/C][C]-0.01139[/C][C]-0.098[/C][C]0.461106[/C][/ROW]
[ROW][C]14[/C][C]0.011281[/C][C]0.097[/C][C]0.461477[/C][/ROW]
[ROW][C]15[/C][C]-0.039942[/C][C]-0.3436[/C][C]0.366063[/C][/ROW]
[ROW][C]16[/C][C]0.040858[/C][C]0.3515[/C][C]0.363117[/C][/ROW]
[ROW][C]17[/C][C]0.027561[/C][C]0.2371[/C][C]0.406622[/C][/ROW]
[ROW][C]18[/C][C]-0.044526[/C][C]-0.383[/C][C]0.3514[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29493&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29493&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9359648.05150
2-0.070802-0.60910.272175
30.0270140.23240.40844
40.001570.01350.49463
5-0.107086-0.92120.179973
6-0.000186-0.00160.499363
7-0.023967-0.20620.418611
8-0.016244-0.13970.444624
90.0402520.34630.365063
100.0098050.08430.466503
11-0.062147-0.53460.29726
12-0.088526-0.76150.224381
13-0.01139-0.0980.461106
140.0112810.0970.461477
15-0.039942-0.34360.366063
160.0408580.35150.363117
170.0275610.23710.406622
18-0.044526-0.3830.3514



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
Parameters (R input):
par1 = Default ; par2 = 1 ; par3 = 0 ; 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('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('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')