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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 computationTue, 06 Dec 2011 03:29:14 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/06/t1323160170c9cuq32bw4ltl0j.htm/, Retrieved Mon, 29 Apr 2024 06:44:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=151384, Retrieved Mon, 29 Apr 2024 06:44:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact145
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Variance Reduction Matrix] [] [2011-12-06 08:04:41] [80bca13c5f9401fbb753952fd2952f4a]
- RMP     [(Partial) Autocorrelation Function] [] [2011-12-06 08:29:14] [204816f6f70a8d342ddc2b9d4f4a80d3] [Current]
-   P       [(Partial) Autocorrelation Function] [] [2011-12-06 08:31:19] [80bca13c5f9401fbb753952fd2952f4a]
- RMP         [Variance Reduction Matrix] [] [2011-12-06 08:45:18] [80bca13c5f9401fbb753952fd2952f4a]
- RM            [ARIMA Backward Selection] [] [2011-12-06 09:03:08] [80bca13c5f9401fbb753952fd2952f4a]
- RM              [ARIMA Forecasting] [] [2011-12-06 09:14:29] [80bca13c5f9401fbb753952fd2952f4a]
-   PD              [ARIMA Forecasting] [Paper arima forec...] [2011-12-23 12:03:11] [805a2cd4f7b6665cd8870eed4006f53c]
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Dataseries X:
12.008
9.169
8.788
8.417
8.247
8.197
8.236
8.253
7.733
8.366
8.626
8.863
10.102
8.463
9.114
8.563
8.872
8.301
8.301
8.278
7.736
7.973
8.268
9.476
11.100
8.962
9.173
8.738
8.459
8.078
8.411
8.291
7.810
8.616
8.312
9.692
9.911
8.915
9.452
9.112
8.472
8.230
8.384
8.625
8.221
8.649
8.625
10.443
10.357
8.586
8.892
8.329
8.101
7.922
8.120
7.838
7.735
8.406
8.209
9.451
10.041
9.411
10.405
8.467
8.464
8.102
7.627
7.513
7.510
8.291
8.064
9.383
9.706
8.579
9.474
8.318
8.213
8.059
9.111
7.708
7.680
8.014
8.007
8.718
9.486
9.113
9.025
8.476
7.952
7.759
7.835
7.600
7.651
8.319
8.812
8.630




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'AstonUniversity' @ aston.wessa.net

\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 & 2 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151384&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151384&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151384&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 time2 seconds
R Server'AstonUniversity' @ aston.wessa.net







Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.200333-1.82510.03579
2-0.067335-0.61340.270629
3-0.110482-1.00650.15854
4-0.005098-0.04640.481533
5-0.150417-1.37040.087134
6-0.019136-0.17430.431013
70.1473151.34210.091612
8-0.088189-0.80340.212008
90.1968331.79320.038289
100.0867030.78990.215917
11-0.092256-0.84050.201524
12-0.387897-3.53390.000336
130.0334290.30460.380734
140.1717661.56490.06071
15-0.011596-0.10560.45806
160.1292471.17750.121182
170.0477830.43530.33223
18-0.029536-0.26910.394264
19-0.028214-0.2570.39889

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.200333 & -1.8251 & 0.03579 \tabularnewline
2 & -0.067335 & -0.6134 & 0.270629 \tabularnewline
3 & -0.110482 & -1.0065 & 0.15854 \tabularnewline
4 & -0.005098 & -0.0464 & 0.481533 \tabularnewline
5 & -0.150417 & -1.3704 & 0.087134 \tabularnewline
6 & -0.019136 & -0.1743 & 0.431013 \tabularnewline
7 & 0.147315 & 1.3421 & 0.091612 \tabularnewline
8 & -0.088189 & -0.8034 & 0.212008 \tabularnewline
9 & 0.196833 & 1.7932 & 0.038289 \tabularnewline
10 & 0.086703 & 0.7899 & 0.215917 \tabularnewline
11 & -0.092256 & -0.8405 & 0.201524 \tabularnewline
12 & -0.387897 & -3.5339 & 0.000336 \tabularnewline
13 & 0.033429 & 0.3046 & 0.380734 \tabularnewline
14 & 0.171766 & 1.5649 & 0.06071 \tabularnewline
15 & -0.011596 & -0.1056 & 0.45806 \tabularnewline
16 & 0.129247 & 1.1775 & 0.121182 \tabularnewline
17 & 0.047783 & 0.4353 & 0.33223 \tabularnewline
18 & -0.029536 & -0.2691 & 0.394264 \tabularnewline
19 & -0.028214 & -0.257 & 0.39889 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151384&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]-0.200333[/C][C]-1.8251[/C][C]0.03579[/C][/ROW]
[ROW][C]2[/C][C]-0.067335[/C][C]-0.6134[/C][C]0.270629[/C][/ROW]
[ROW][C]3[/C][C]-0.110482[/C][C]-1.0065[/C][C]0.15854[/C][/ROW]
[ROW][C]4[/C][C]-0.005098[/C][C]-0.0464[/C][C]0.481533[/C][/ROW]
[ROW][C]5[/C][C]-0.150417[/C][C]-1.3704[/C][C]0.087134[/C][/ROW]
[ROW][C]6[/C][C]-0.019136[/C][C]-0.1743[/C][C]0.431013[/C][/ROW]
[ROW][C]7[/C][C]0.147315[/C][C]1.3421[/C][C]0.091612[/C][/ROW]
[ROW][C]8[/C][C]-0.088189[/C][C]-0.8034[/C][C]0.212008[/C][/ROW]
[ROW][C]9[/C][C]0.196833[/C][C]1.7932[/C][C]0.038289[/C][/ROW]
[ROW][C]10[/C][C]0.086703[/C][C]0.7899[/C][C]0.215917[/C][/ROW]
[ROW][C]11[/C][C]-0.092256[/C][C]-0.8405[/C][C]0.201524[/C][/ROW]
[ROW][C]12[/C][C]-0.387897[/C][C]-3.5339[/C][C]0.000336[/C][/ROW]
[ROW][C]13[/C][C]0.033429[/C][C]0.3046[/C][C]0.380734[/C][/ROW]
[ROW][C]14[/C][C]0.171766[/C][C]1.5649[/C][C]0.06071[/C][/ROW]
[ROW][C]15[/C][C]-0.011596[/C][C]-0.1056[/C][C]0.45806[/C][/ROW]
[ROW][C]16[/C][C]0.129247[/C][C]1.1775[/C][C]0.121182[/C][/ROW]
[ROW][C]17[/C][C]0.047783[/C][C]0.4353[/C][C]0.33223[/C][/ROW]
[ROW][C]18[/C][C]-0.029536[/C][C]-0.2691[/C][C]0.394264[/C][/ROW]
[ROW][C]19[/C][C]-0.028214[/C][C]-0.257[/C][C]0.39889[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151384&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151384&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
1-0.200333-1.82510.03579
2-0.067335-0.61340.270629
3-0.110482-1.00650.15854
4-0.005098-0.04640.481533
5-0.150417-1.37040.087134
6-0.019136-0.17430.431013
70.1473151.34210.091612
8-0.088189-0.80340.212008
90.1968331.79320.038289
100.0867030.78990.215917
11-0.092256-0.84050.201524
12-0.387897-3.53390.000336
130.0334290.30460.380734
140.1717661.56490.06071
15-0.011596-0.10560.45806
160.1292471.17750.121182
170.0477830.43530.33223
18-0.029536-0.26910.394264
19-0.028214-0.2570.39889







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.200333-1.82510.03579
2-0.111961-1.020.155343
3-0.156052-1.42170.07943
4-0.078704-0.7170.237685
5-0.214073-1.95030.027258
6-0.154576-1.40830.081395
70.0485720.44250.329636
8-0.130301-1.18710.119288
90.1525771.390.084116
100.1758551.60210.056465
11-0.0091-0.08290.467064
12-0.358682-3.26770.000789
13-0.189175-1.72350.044264
140.106470.970.167435
150.0352640.32130.374408
160.087950.80130.212634
170.0284880.25950.397932
18-0.048859-0.44510.328695
190.0573140.52220.301476

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.200333 & -1.8251 & 0.03579 \tabularnewline
2 & -0.111961 & -1.02 & 0.155343 \tabularnewline
3 & -0.156052 & -1.4217 & 0.07943 \tabularnewline
4 & -0.078704 & -0.717 & 0.237685 \tabularnewline
5 & -0.214073 & -1.9503 & 0.027258 \tabularnewline
6 & -0.154576 & -1.4083 & 0.081395 \tabularnewline
7 & 0.048572 & 0.4425 & 0.329636 \tabularnewline
8 & -0.130301 & -1.1871 & 0.119288 \tabularnewline
9 & 0.152577 & 1.39 & 0.084116 \tabularnewline
10 & 0.175855 & 1.6021 & 0.056465 \tabularnewline
11 & -0.0091 & -0.0829 & 0.467064 \tabularnewline
12 & -0.358682 & -3.2677 & 0.000789 \tabularnewline
13 & -0.189175 & -1.7235 & 0.044264 \tabularnewline
14 & 0.10647 & 0.97 & 0.167435 \tabularnewline
15 & 0.035264 & 0.3213 & 0.374408 \tabularnewline
16 & 0.08795 & 0.8013 & 0.212634 \tabularnewline
17 & 0.028488 & 0.2595 & 0.397932 \tabularnewline
18 & -0.048859 & -0.4451 & 0.328695 \tabularnewline
19 & 0.057314 & 0.5222 & 0.301476 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151384&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]-0.200333[/C][C]-1.8251[/C][C]0.03579[/C][/ROW]
[ROW][C]2[/C][C]-0.111961[/C][C]-1.02[/C][C]0.155343[/C][/ROW]
[ROW][C]3[/C][C]-0.156052[/C][C]-1.4217[/C][C]0.07943[/C][/ROW]
[ROW][C]4[/C][C]-0.078704[/C][C]-0.717[/C][C]0.237685[/C][/ROW]
[ROW][C]5[/C][C]-0.214073[/C][C]-1.9503[/C][C]0.027258[/C][/ROW]
[ROW][C]6[/C][C]-0.154576[/C][C]-1.4083[/C][C]0.081395[/C][/ROW]
[ROW][C]7[/C][C]0.048572[/C][C]0.4425[/C][C]0.329636[/C][/ROW]
[ROW][C]8[/C][C]-0.130301[/C][C]-1.1871[/C][C]0.119288[/C][/ROW]
[ROW][C]9[/C][C]0.152577[/C][C]1.39[/C][C]0.084116[/C][/ROW]
[ROW][C]10[/C][C]0.175855[/C][C]1.6021[/C][C]0.056465[/C][/ROW]
[ROW][C]11[/C][C]-0.0091[/C][C]-0.0829[/C][C]0.467064[/C][/ROW]
[ROW][C]12[/C][C]-0.358682[/C][C]-3.2677[/C][C]0.000789[/C][/ROW]
[ROW][C]13[/C][C]-0.189175[/C][C]-1.7235[/C][C]0.044264[/C][/ROW]
[ROW][C]14[/C][C]0.10647[/C][C]0.97[/C][C]0.167435[/C][/ROW]
[ROW][C]15[/C][C]0.035264[/C][C]0.3213[/C][C]0.374408[/C][/ROW]
[ROW][C]16[/C][C]0.08795[/C][C]0.8013[/C][C]0.212634[/C][/ROW]
[ROW][C]17[/C][C]0.028488[/C][C]0.2595[/C][C]0.397932[/C][/ROW]
[ROW][C]18[/C][C]-0.048859[/C][C]-0.4451[/C][C]0.328695[/C][/ROW]
[ROW][C]19[/C][C]0.057314[/C][C]0.5222[/C][C]0.301476[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151384&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151384&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
1-0.200333-1.82510.03579
2-0.111961-1.020.155343
3-0.156052-1.42170.07943
4-0.078704-0.7170.237685
5-0.214073-1.95030.027258
6-0.154576-1.40830.081395
70.0485720.44250.329636
8-0.130301-1.18710.119288
90.1525771.390.084116
100.1758551.60210.056465
11-0.0091-0.08290.467064
12-0.358682-3.26770.000789
13-0.189175-1.72350.044264
140.106470.970.167435
150.0352640.32130.374408
160.087950.80130.212634
170.0284880.25950.397932
18-0.048859-0.44510.328695
190.0573140.52220.301476



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ; par8 = ;
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 (par8 != '') par8 <- as.numeric(par8)
ox <- x
if (par8 == '') {
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
} else {
x <- log(x,base=par8)
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='picts.png')
op <- par(mfrow=c(2,1))
plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value')
if (par8=='') {
mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='')
mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
} else {
mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='')
mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
}
plot(x,type='l', main=mytitle,xlab='time',ylab='value')
par(op)
dev.off()
bitmap(file='pic1.png')
racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub)
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub)
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')