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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 computationMon, 06 Dec 2010 17:51:47 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/06/t1291657789vl0k0hwm48xmbjv.htm/, Retrieved Mon, 29 Apr 2024 05:10:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105734, Retrieved Mon, 29 Apr 2024 05:10:49 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133
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]
- RMP   [(Partial) Autocorrelation Function] [Unemployment] [2010-11-29 09:05:21] [b98453cac15ba1066b407e146608df68]
-    D    [(Partial) Autocorrelation Function] [] [2010-12-06 17:46:41] [7f2363d2c77d3bf71367965cc53be730]
-   PD        [(Partial) Autocorrelation Function] [] [2010-12-06 17:51:47] [4dba6678eac10ee5c3460d144a14bd5c] [Current]
-   P           [(Partial) Autocorrelation Function] [] [2010-12-06 17:55:23] [7f2363d2c77d3bf71367965cc53be730]
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Dataseries X:
5.81
5.76
5.99
6.12
6.03
6.25
5.80
5.67
5.89
5.91
5.86
6.07
6.27
6.68
6.77
6.71
6.62
6.50
5.89
6.05
6.43
6.47
6.62
6.77
6.70
6.95
6.73
7.07
7.28
7.32
6.76
6.93
6.99
7.16
7.28
7.08
7.34
7.87
6.28
6.30
6.36
6.28
5.89
6.04
5.96
6.10
6.26
6.02
6.25
6.41
6.22
6.57
6.18
6.26
6.10
6.02
6.06
6.35
6.21
6.48
6.74
6.53
6.80
6.75
6.56
6.66
6.18
6.40
6.43
6.54
6.44
6.64
6.82
6.97
7.00
6.91
6.74
6.98
6.37
6.56
6.63
6.87
6.68
6.75
6.84
7.15
7.09
6.97
7.15




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105734&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105734&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105734&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.243535-2.28460.012372
2-0.063517-0.59580.276404
30.0671230.62970.265271
4-0.003586-0.03360.486622
5-0.127153-1.19280.118077
6-0.011052-0.10370.45883
7-0.25608-2.40220.009198
80.2042751.91630.029288
90.0872660.81860.207606
10-0.165224-1.54990.062372
11-0.052533-0.49280.31169
120.3778043.54410.000317
13-0.253432-2.37740.009801
140.2456382.30430.011781
15-0.159691-1.4980.068851
16-0.022101-0.20730.418119
170.0723420.67860.249577
18-0.159665-1.49780.068884
19-0.215159-2.01840.023299

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.243535 & -2.2846 & 0.012372 \tabularnewline
2 & -0.063517 & -0.5958 & 0.276404 \tabularnewline
3 & 0.067123 & 0.6297 & 0.265271 \tabularnewline
4 & -0.003586 & -0.0336 & 0.486622 \tabularnewline
5 & -0.127153 & -1.1928 & 0.118077 \tabularnewline
6 & -0.011052 & -0.1037 & 0.45883 \tabularnewline
7 & -0.25608 & -2.4022 & 0.009198 \tabularnewline
8 & 0.204275 & 1.9163 & 0.029288 \tabularnewline
9 & 0.087266 & 0.8186 & 0.207606 \tabularnewline
10 & -0.165224 & -1.5499 & 0.062372 \tabularnewline
11 & -0.052533 & -0.4928 & 0.31169 \tabularnewline
12 & 0.377804 & 3.5441 & 0.000317 \tabularnewline
13 & -0.253432 & -2.3774 & 0.009801 \tabularnewline
14 & 0.245638 & 2.3043 & 0.011781 \tabularnewline
15 & -0.159691 & -1.498 & 0.068851 \tabularnewline
16 & -0.022101 & -0.2073 & 0.418119 \tabularnewline
17 & 0.072342 & 0.6786 & 0.249577 \tabularnewline
18 & -0.159665 & -1.4978 & 0.068884 \tabularnewline
19 & -0.215159 & -2.0184 & 0.023299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105734&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]-0.243535[/C][C]-2.2846[/C][C]0.012372[/C][/ROW]
[ROW][C]2[/C][C]-0.063517[/C][C]-0.5958[/C][C]0.276404[/C][/ROW]
[ROW][C]3[/C][C]0.067123[/C][C]0.6297[/C][C]0.265271[/C][/ROW]
[ROW][C]4[/C][C]-0.003586[/C][C]-0.0336[/C][C]0.486622[/C][/ROW]
[ROW][C]5[/C][C]-0.127153[/C][C]-1.1928[/C][C]0.118077[/C][/ROW]
[ROW][C]6[/C][C]-0.011052[/C][C]-0.1037[/C][C]0.45883[/C][/ROW]
[ROW][C]7[/C][C]-0.25608[/C][C]-2.4022[/C][C]0.009198[/C][/ROW]
[ROW][C]8[/C][C]0.204275[/C][C]1.9163[/C][C]0.029288[/C][/ROW]
[ROW][C]9[/C][C]0.087266[/C][C]0.8186[/C][C]0.207606[/C][/ROW]
[ROW][C]10[/C][C]-0.165224[/C][C]-1.5499[/C][C]0.062372[/C][/ROW]
[ROW][C]11[/C][C]-0.052533[/C][C]-0.4928[/C][C]0.31169[/C][/ROW]
[ROW][C]12[/C][C]0.377804[/C][C]3.5441[/C][C]0.000317[/C][/ROW]
[ROW][C]13[/C][C]-0.253432[/C][C]-2.3774[/C][C]0.009801[/C][/ROW]
[ROW][C]14[/C][C]0.245638[/C][C]2.3043[/C][C]0.011781[/C][/ROW]
[ROW][C]15[/C][C]-0.159691[/C][C]-1.498[/C][C]0.068851[/C][/ROW]
[ROW][C]16[/C][C]-0.022101[/C][C]-0.2073[/C][C]0.418119[/C][/ROW]
[ROW][C]17[/C][C]0.072342[/C][C]0.6786[/C][C]0.249577[/C][/ROW]
[ROW][C]18[/C][C]-0.159665[/C][C]-1.4978[/C][C]0.068884[/C][/ROW]
[ROW][C]19[/C][C]-0.215159[/C][C]-2.0184[/C][C]0.023299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105734&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105734&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
1-0.243535-2.28460.012372
2-0.063517-0.59580.276404
30.0671230.62970.265271
4-0.003586-0.03360.486622
5-0.127153-1.19280.118077
6-0.011052-0.10370.45883
7-0.25608-2.40220.009198
80.2042751.91630.029288
90.0872660.81860.207606
10-0.165224-1.54990.062372
11-0.052533-0.49280.31169
120.3778043.54410.000317
13-0.253432-2.37740.009801
140.2456382.30430.011781
15-0.159691-1.4980.068851
16-0.022101-0.20730.418119
170.0723420.67860.249577
18-0.159665-1.49780.068884
19-0.215159-2.01840.023299







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.243535-2.28460.012372
2-0.13057-1.22490.111949
30.019290.1810.42841
40.0124070.11640.453804
5-0.124833-1.1710.122372
6-0.086035-0.80710.210898
7-0.335526-3.14750.001124
80.052090.48860.313153
90.1317281.23570.109927
10-0.093107-0.87340.192405
11-0.182791-1.71470.044957
120.2663062.49820.007171
13-0.118854-1.1150.133954
140.2693712.52690.006647
15-0.08752-0.8210.20693
16-0.04083-0.3830.351314
17-0.006188-0.0580.476921
18-0.165594-1.55340.061957
19-0.084218-0.790.215814

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.243535 & -2.2846 & 0.012372 \tabularnewline
2 & -0.13057 & -1.2249 & 0.111949 \tabularnewline
3 & 0.01929 & 0.181 & 0.42841 \tabularnewline
4 & 0.012407 & 0.1164 & 0.453804 \tabularnewline
5 & -0.124833 & -1.171 & 0.122372 \tabularnewline
6 & -0.086035 & -0.8071 & 0.210898 \tabularnewline
7 & -0.335526 & -3.1475 & 0.001124 \tabularnewline
8 & 0.05209 & 0.4886 & 0.313153 \tabularnewline
9 & 0.131728 & 1.2357 & 0.109927 \tabularnewline
10 & -0.093107 & -0.8734 & 0.192405 \tabularnewline
11 & -0.182791 & -1.7147 & 0.044957 \tabularnewline
12 & 0.266306 & 2.4982 & 0.007171 \tabularnewline
13 & -0.118854 & -1.115 & 0.133954 \tabularnewline
14 & 0.269371 & 2.5269 & 0.006647 \tabularnewline
15 & -0.08752 & -0.821 & 0.20693 \tabularnewline
16 & -0.04083 & -0.383 & 0.351314 \tabularnewline
17 & -0.006188 & -0.058 & 0.476921 \tabularnewline
18 & -0.165594 & -1.5534 & 0.061957 \tabularnewline
19 & -0.084218 & -0.79 & 0.215814 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105734&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]-0.243535[/C][C]-2.2846[/C][C]0.012372[/C][/ROW]
[ROW][C]2[/C][C]-0.13057[/C][C]-1.2249[/C][C]0.111949[/C][/ROW]
[ROW][C]3[/C][C]0.01929[/C][C]0.181[/C][C]0.42841[/C][/ROW]
[ROW][C]4[/C][C]0.012407[/C][C]0.1164[/C][C]0.453804[/C][/ROW]
[ROW][C]5[/C][C]-0.124833[/C][C]-1.171[/C][C]0.122372[/C][/ROW]
[ROW][C]6[/C][C]-0.086035[/C][C]-0.8071[/C][C]0.210898[/C][/ROW]
[ROW][C]7[/C][C]-0.335526[/C][C]-3.1475[/C][C]0.001124[/C][/ROW]
[ROW][C]8[/C][C]0.05209[/C][C]0.4886[/C][C]0.313153[/C][/ROW]
[ROW][C]9[/C][C]0.131728[/C][C]1.2357[/C][C]0.109927[/C][/ROW]
[ROW][C]10[/C][C]-0.093107[/C][C]-0.8734[/C][C]0.192405[/C][/ROW]
[ROW][C]11[/C][C]-0.182791[/C][C]-1.7147[/C][C]0.044957[/C][/ROW]
[ROW][C]12[/C][C]0.266306[/C][C]2.4982[/C][C]0.007171[/C][/ROW]
[ROW][C]13[/C][C]-0.118854[/C][C]-1.115[/C][C]0.133954[/C][/ROW]
[ROW][C]14[/C][C]0.269371[/C][C]2.5269[/C][C]0.006647[/C][/ROW]
[ROW][C]15[/C][C]-0.08752[/C][C]-0.821[/C][C]0.20693[/C][/ROW]
[ROW][C]16[/C][C]-0.04083[/C][C]-0.383[/C][C]0.351314[/C][/ROW]
[ROW][C]17[/C][C]-0.006188[/C][C]-0.058[/C][C]0.476921[/C][/ROW]
[ROW][C]18[/C][C]-0.165594[/C][C]-1.5534[/C][C]0.061957[/C][/ROW]
[ROW][C]19[/C][C]-0.084218[/C][C]-0.79[/C][C]0.215814[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105734&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105734&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
1-0.243535-2.28460.012372
2-0.13057-1.22490.111949
30.019290.1810.42841
40.0124070.11640.453804
5-0.124833-1.1710.122372
6-0.086035-0.80710.210898
7-0.335526-3.14750.001124
80.052090.48860.313153
90.1317281.23570.109927
10-0.093107-0.87340.192405
11-0.182791-1.71470.044957
120.2663062.49820.007171
13-0.118854-1.1150.133954
140.2693712.52690.006647
15-0.08752-0.8210.20693
16-0.04083-0.3830.351314
17-0.006188-0.0580.476921
18-0.165594-1.55340.061957
19-0.084218-0.790.215814



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
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 (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma'
par7 <- as.numeric(par7)
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='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))
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')