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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 computationThu, 15 Dec 2016 20:40:05 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/15/t1481831040f237gkvd3epwooe.htm/, Retrieved Fri, 01 Nov 2024 03:33:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299983, Retrieved Fri, 01 Nov 2024 03:33:36 +0000
QR Codes:

Original text written by user:d=0 D=0 s=6
IsPrivate?No (this computation is public)
User-defined keywordsF1competitie d=0 D=0 s=6
Estimated Impact53
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [(Partial) Autocorrelation Function] [Autocorrelation f...] [2016-12-15 19:40:05] [d92250bd36540c2281a4ec15b45df1dd] [Current]
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Dataseries X:
649
655
618
640
707
730
768
753
773
797
810
794
809
828
828
849
865
879
908
961




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299983&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=299983&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299983&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.8034063.59290.000909
20.6383922.8550.004894
30.46072.06030.026309
40.2981641.33340.09869
50.188070.84110.205125
60.098330.43970.332418
70.0243480.10890.457188
8-0.04595-0.20550.419631
9-0.087681-0.39210.349558
10-0.154883-0.69270.248245
11-0.212662-0.95110.176465
12-0.271125-1.21250.119723
13-0.309587-1.38450.090727

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.803406 & 3.5929 & 0.000909 \tabularnewline
2 & 0.638392 & 2.855 & 0.004894 \tabularnewline
3 & 0.4607 & 2.0603 & 0.026309 \tabularnewline
4 & 0.298164 & 1.3334 & 0.09869 \tabularnewline
5 & 0.18807 & 0.8411 & 0.205125 \tabularnewline
6 & 0.09833 & 0.4397 & 0.332418 \tabularnewline
7 & 0.024348 & 0.1089 & 0.457188 \tabularnewline
8 & -0.04595 & -0.2055 & 0.419631 \tabularnewline
9 & -0.087681 & -0.3921 & 0.349558 \tabularnewline
10 & -0.154883 & -0.6927 & 0.248245 \tabularnewline
11 & -0.212662 & -0.9511 & 0.176465 \tabularnewline
12 & -0.271125 & -1.2125 & 0.119723 \tabularnewline
13 & -0.309587 & -1.3845 & 0.090727 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299983&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]0.803406[/C][C]3.5929[/C][C]0.000909[/C][/ROW]
[ROW][C]2[/C][C]0.638392[/C][C]2.855[/C][C]0.004894[/C][/ROW]
[ROW][C]3[/C][C]0.4607[/C][C]2.0603[/C][C]0.026309[/C][/ROW]
[ROW][C]4[/C][C]0.298164[/C][C]1.3334[/C][C]0.09869[/C][/ROW]
[ROW][C]5[/C][C]0.18807[/C][C]0.8411[/C][C]0.205125[/C][/ROW]
[ROW][C]6[/C][C]0.09833[/C][C]0.4397[/C][C]0.332418[/C][/ROW]
[ROW][C]7[/C][C]0.024348[/C][C]0.1089[/C][C]0.457188[/C][/ROW]
[ROW][C]8[/C][C]-0.04595[/C][C]-0.2055[/C][C]0.419631[/C][/ROW]
[ROW][C]9[/C][C]-0.087681[/C][C]-0.3921[/C][C]0.349558[/C][/ROW]
[ROW][C]10[/C][C]-0.154883[/C][C]-0.6927[/C][C]0.248245[/C][/ROW]
[ROW][C]11[/C][C]-0.212662[/C][C]-0.9511[/C][C]0.176465[/C][/ROW]
[ROW][C]12[/C][C]-0.271125[/C][C]-1.2125[/C][C]0.119723[/C][/ROW]
[ROW][C]13[/C][C]-0.309587[/C][C]-1.3845[/C][C]0.090727[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299983&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299983&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
10.8034063.59290.000909
20.6383922.8550.004894
30.46072.06030.026309
40.2981641.33340.09869
50.188070.84110.205125
60.098330.43970.332418
70.0243480.10890.457188
8-0.04595-0.20550.419631
9-0.087681-0.39210.349558
10-0.154883-0.69270.248245
11-0.212662-0.95110.176465
12-0.271125-1.21250.119723
13-0.309587-1.38450.090727







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.8034063.59290.000909
2-0.019939-0.08920.464916
3-0.13091-0.58540.282397
4-0.082455-0.36870.358095
50.0318330.14240.444109
6-0.024389-0.10910.457117
7-0.049707-0.22230.413169
8-0.072097-0.32240.375237
90.0073340.03280.487081
10-0.125372-0.56070.290621
11-0.076087-0.34030.3686
12-0.087277-0.39030.350215
13-0.033167-0.14830.441786

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.803406 & 3.5929 & 0.000909 \tabularnewline
2 & -0.019939 & -0.0892 & 0.464916 \tabularnewline
3 & -0.13091 & -0.5854 & 0.282397 \tabularnewline
4 & -0.082455 & -0.3687 & 0.358095 \tabularnewline
5 & 0.031833 & 0.1424 & 0.444109 \tabularnewline
6 & -0.024389 & -0.1091 & 0.457117 \tabularnewline
7 & -0.049707 & -0.2223 & 0.413169 \tabularnewline
8 & -0.072097 & -0.3224 & 0.375237 \tabularnewline
9 & 0.007334 & 0.0328 & 0.487081 \tabularnewline
10 & -0.125372 & -0.5607 & 0.290621 \tabularnewline
11 & -0.076087 & -0.3403 & 0.3686 \tabularnewline
12 & -0.087277 & -0.3903 & 0.350215 \tabularnewline
13 & -0.033167 & -0.1483 & 0.441786 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299983&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]0.803406[/C][C]3.5929[/C][C]0.000909[/C][/ROW]
[ROW][C]2[/C][C]-0.019939[/C][C]-0.0892[/C][C]0.464916[/C][/ROW]
[ROW][C]3[/C][C]-0.13091[/C][C]-0.5854[/C][C]0.282397[/C][/ROW]
[ROW][C]4[/C][C]-0.082455[/C][C]-0.3687[/C][C]0.358095[/C][/ROW]
[ROW][C]5[/C][C]0.031833[/C][C]0.1424[/C][C]0.444109[/C][/ROW]
[ROW][C]6[/C][C]-0.024389[/C][C]-0.1091[/C][C]0.457117[/C][/ROW]
[ROW][C]7[/C][C]-0.049707[/C][C]-0.2223[/C][C]0.413169[/C][/ROW]
[ROW][C]8[/C][C]-0.072097[/C][C]-0.3224[/C][C]0.375237[/C][/ROW]
[ROW][C]9[/C][C]0.007334[/C][C]0.0328[/C][C]0.487081[/C][/ROW]
[ROW][C]10[/C][C]-0.125372[/C][C]-0.5607[/C][C]0.290621[/C][/ROW]
[ROW][C]11[/C][C]-0.076087[/C][C]-0.3403[/C][C]0.3686[/C][/ROW]
[ROW][C]12[/C][C]-0.087277[/C][C]-0.3903[/C][C]0.350215[/C][/ROW]
[ROW][C]13[/C][C]-0.033167[/C][C]-0.1483[/C][C]0.441786[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299983&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299983&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
10.8034063.59290.000909
2-0.019939-0.08920.464916
3-0.13091-0.58540.282397
4-0.082455-0.36870.358095
50.0318330.14240.444109
6-0.024389-0.10910.457117
7-0.049707-0.22230.413169
8-0.072097-0.32240.375237
90.0073340.03280.487081
10-0.125372-0.56070.290621
11-0.076087-0.34030.3686
12-0.087277-0.39030.350215
13-0.033167-0.14830.441786



Parameters (Session):
par4 = 12 ;
Parameters (R input):
par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 6 ; 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)
x <- na.omit(x)
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,'ACF(k)',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,'PACF(k)',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')