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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 computationWed, 07 Dec 2016 19:58:05 +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/07/t14811371221ujiqjucj1vtyxk.htm/, Retrieved Fri, 01 Nov 2024 03:41:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298306, Retrieved Fri, 01 Nov 2024 03:41:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact69
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [(Partial) Autocorrelation Function] [N2596] [2016-12-07 18:58:05] [85f5800284aab30c091766186b093bb4] [Current]
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Dataseries X:
1819.6
1312.4
2584
1479.6
1742
2639.2
1706
1408
1951.6
1690.4
2288.4
2912
1460.8
1009.6
2410
1603.2
2115.2
2330
1690
1358
1806.8
1973.6
1402
1857.6
1974.4
1438
1923.2
1996.8
2238.8
2540.4
1704.4
1856
2214.8
1948
1802
1431.6
2857.6
1784
2770.8
2313.6
3707.6
4322.4
3297.6
2223.6
2136.4
2459.2
1650.4
2921.2
1979.6
1403.2
2374
2876.4
2500
3888
1508.8
1011.2
1590.8
2076.4
3736
2125.6
982.8
2034.8
2260
1726
2270.4
1951.6
2104.4
2972.8
2834.4
4227.6
3392.4
3069.2
3138.8
3570
4800.4
4769.2
5124.8
3476.8
2866.8
2549.2
2728
2448.8
3286.8
2830
3251.2
4188.8
2747.6
2269.2
2493.2
2147.6
2689.2
3557.2
2840
3979.6
2683.2
2852
3012.8
2950.8
3065.2
3942.4
4272
4564
5222.8
5164.4
3883.6
4103.2
5244
8071.6
5441.6
7496
10100.4
9616
5645.6
10490
5582
7579.2
4023.6
8146.4
8534.4
10113.6
8504.4
9782.4
13110
8192.8
8708.8
9528.8




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=298306&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=298306&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298306&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
1-0.374044-4.18192.7e-05
2-0.107156-1.1980.116584
30.0439220.49110.312123
4-0.103363-1.15560.125018
5-0.021962-0.24550.403219
60.2212162.47330.007366
7-0.164239-1.83620.034349
8-0.0073-0.08160.467543
90.0765150.85550.196965
10-0.119663-1.33790.091684
110.1139141.27360.102585
120.1280281.43140.077406
13-0.149628-1.67290.048425
14-0.078746-0.88040.190163
150.015070.16850.433236
160.001920.02150.491454
170.0571010.63840.262188
180.0777870.86970.193069
19-0.062578-0.69960.242725
20-0.070867-0.79230.21484
21-0.023186-0.25920.397945

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.374044 & -4.1819 & 2.7e-05 \tabularnewline
2 & -0.107156 & -1.198 & 0.116584 \tabularnewline
3 & 0.043922 & 0.4911 & 0.312123 \tabularnewline
4 & -0.103363 & -1.1556 & 0.125018 \tabularnewline
5 & -0.021962 & -0.2455 & 0.403219 \tabularnewline
6 & 0.221216 & 2.4733 & 0.007366 \tabularnewline
7 & -0.164239 & -1.8362 & 0.034349 \tabularnewline
8 & -0.0073 & -0.0816 & 0.467543 \tabularnewline
9 & 0.076515 & 0.8555 & 0.196965 \tabularnewline
10 & -0.119663 & -1.3379 & 0.091684 \tabularnewline
11 & 0.113914 & 1.2736 & 0.102585 \tabularnewline
12 & 0.128028 & 1.4314 & 0.077406 \tabularnewline
13 & -0.149628 & -1.6729 & 0.048425 \tabularnewline
14 & -0.078746 & -0.8804 & 0.190163 \tabularnewline
15 & 0.01507 & 0.1685 & 0.433236 \tabularnewline
16 & 0.00192 & 0.0215 & 0.491454 \tabularnewline
17 & 0.057101 & 0.6384 & 0.262188 \tabularnewline
18 & 0.077787 & 0.8697 & 0.193069 \tabularnewline
19 & -0.062578 & -0.6996 & 0.242725 \tabularnewline
20 & -0.070867 & -0.7923 & 0.21484 \tabularnewline
21 & -0.023186 & -0.2592 & 0.397945 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298306&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]-0.374044[/C][C]-4.1819[/C][C]2.7e-05[/C][/ROW]
[ROW][C]2[/C][C]-0.107156[/C][C]-1.198[/C][C]0.116584[/C][/ROW]
[ROW][C]3[/C][C]0.043922[/C][C]0.4911[/C][C]0.312123[/C][/ROW]
[ROW][C]4[/C][C]-0.103363[/C][C]-1.1556[/C][C]0.125018[/C][/ROW]
[ROW][C]5[/C][C]-0.021962[/C][C]-0.2455[/C][C]0.403219[/C][/ROW]
[ROW][C]6[/C][C]0.221216[/C][C]2.4733[/C][C]0.007366[/C][/ROW]
[ROW][C]7[/C][C]-0.164239[/C][C]-1.8362[/C][C]0.034349[/C][/ROW]
[ROW][C]8[/C][C]-0.0073[/C][C]-0.0816[/C][C]0.467543[/C][/ROW]
[ROW][C]9[/C][C]0.076515[/C][C]0.8555[/C][C]0.196965[/C][/ROW]
[ROW][C]10[/C][C]-0.119663[/C][C]-1.3379[/C][C]0.091684[/C][/ROW]
[ROW][C]11[/C][C]0.113914[/C][C]1.2736[/C][C]0.102585[/C][/ROW]
[ROW][C]12[/C][C]0.128028[/C][C]1.4314[/C][C]0.077406[/C][/ROW]
[ROW][C]13[/C][C]-0.149628[/C][C]-1.6729[/C][C]0.048425[/C][/ROW]
[ROW][C]14[/C][C]-0.078746[/C][C]-0.8804[/C][C]0.190163[/C][/ROW]
[ROW][C]15[/C][C]0.01507[/C][C]0.1685[/C][C]0.433236[/C][/ROW]
[ROW][C]16[/C][C]0.00192[/C][C]0.0215[/C][C]0.491454[/C][/ROW]
[ROW][C]17[/C][C]0.057101[/C][C]0.6384[/C][C]0.262188[/C][/ROW]
[ROW][C]18[/C][C]0.077787[/C][C]0.8697[/C][C]0.193069[/C][/ROW]
[ROW][C]19[/C][C]-0.062578[/C][C]-0.6996[/C][C]0.242725[/C][/ROW]
[ROW][C]20[/C][C]-0.070867[/C][C]-0.7923[/C][C]0.21484[/C][/ROW]
[ROW][C]21[/C][C]-0.023186[/C][C]-0.2592[/C][C]0.397945[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298306&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298306&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
1-0.374044-4.18192.7e-05
2-0.107156-1.1980.116584
30.0439220.49110.312123
4-0.103363-1.15560.125018
5-0.021962-0.24550.403219
60.2212162.47330.007366
7-0.164239-1.83620.034349
8-0.0073-0.08160.467543
90.0765150.85550.196965
10-0.119663-1.33790.091684
110.1139141.27360.102585
120.1280281.43140.077406
13-0.149628-1.67290.048425
14-0.078746-0.88040.190163
150.015070.16850.433236
160.001920.02150.491454
170.0571010.63840.262188
180.0777870.86970.193069
19-0.062578-0.69960.242725
20-0.070867-0.79230.21484
21-0.023186-0.25920.397945







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.374044-4.18192.7e-05
2-0.287255-3.21160.000839
3-0.145883-1.6310.052702
4-0.224321-2.5080.006712
5-0.234967-2.6270.004846
60.0669050.7480.227928
7-0.087641-0.97990.164526
8-0.087649-0.97990.164502
9-0.006996-0.07820.468889
10-0.106316-1.18870.118415
110.02850.31860.375264
120.1711241.91320.029003
130.0877430.9810.164245
14-0.064962-0.72630.234508
15-0.108233-1.21010.114266
16-0.047859-0.53510.296773
17-0.057307-0.64070.261441
180.0229610.25670.398913
190.058820.65760.255994
20-0.040247-0.450.326755
21-0.122181-1.3660.087191

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.374044 & -4.1819 & 2.7e-05 \tabularnewline
2 & -0.287255 & -3.2116 & 0.000839 \tabularnewline
3 & -0.145883 & -1.631 & 0.052702 \tabularnewline
4 & -0.224321 & -2.508 & 0.006712 \tabularnewline
5 & -0.234967 & -2.627 & 0.004846 \tabularnewline
6 & 0.066905 & 0.748 & 0.227928 \tabularnewline
7 & -0.087641 & -0.9799 & 0.164526 \tabularnewline
8 & -0.087649 & -0.9799 & 0.164502 \tabularnewline
9 & -0.006996 & -0.0782 & 0.468889 \tabularnewline
10 & -0.106316 & -1.1887 & 0.118415 \tabularnewline
11 & 0.0285 & 0.3186 & 0.375264 \tabularnewline
12 & 0.171124 & 1.9132 & 0.029003 \tabularnewline
13 & 0.087743 & 0.981 & 0.164245 \tabularnewline
14 & -0.064962 & -0.7263 & 0.234508 \tabularnewline
15 & -0.108233 & -1.2101 & 0.114266 \tabularnewline
16 & -0.047859 & -0.5351 & 0.296773 \tabularnewline
17 & -0.057307 & -0.6407 & 0.261441 \tabularnewline
18 & 0.022961 & 0.2567 & 0.398913 \tabularnewline
19 & 0.05882 & 0.6576 & 0.255994 \tabularnewline
20 & -0.040247 & -0.45 & 0.326755 \tabularnewline
21 & -0.122181 & -1.366 & 0.087191 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298306&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]-0.374044[/C][C]-4.1819[/C][C]2.7e-05[/C][/ROW]
[ROW][C]2[/C][C]-0.287255[/C][C]-3.2116[/C][C]0.000839[/C][/ROW]
[ROW][C]3[/C][C]-0.145883[/C][C]-1.631[/C][C]0.052702[/C][/ROW]
[ROW][C]4[/C][C]-0.224321[/C][C]-2.508[/C][C]0.006712[/C][/ROW]
[ROW][C]5[/C][C]-0.234967[/C][C]-2.627[/C][C]0.004846[/C][/ROW]
[ROW][C]6[/C][C]0.066905[/C][C]0.748[/C][C]0.227928[/C][/ROW]
[ROW][C]7[/C][C]-0.087641[/C][C]-0.9799[/C][C]0.164526[/C][/ROW]
[ROW][C]8[/C][C]-0.087649[/C][C]-0.9799[/C][C]0.164502[/C][/ROW]
[ROW][C]9[/C][C]-0.006996[/C][C]-0.0782[/C][C]0.468889[/C][/ROW]
[ROW][C]10[/C][C]-0.106316[/C][C]-1.1887[/C][C]0.118415[/C][/ROW]
[ROW][C]11[/C][C]0.0285[/C][C]0.3186[/C][C]0.375264[/C][/ROW]
[ROW][C]12[/C][C]0.171124[/C][C]1.9132[/C][C]0.029003[/C][/ROW]
[ROW][C]13[/C][C]0.087743[/C][C]0.981[/C][C]0.164245[/C][/ROW]
[ROW][C]14[/C][C]-0.064962[/C][C]-0.7263[/C][C]0.234508[/C][/ROW]
[ROW][C]15[/C][C]-0.108233[/C][C]-1.2101[/C][C]0.114266[/C][/ROW]
[ROW][C]16[/C][C]-0.047859[/C][C]-0.5351[/C][C]0.296773[/C][/ROW]
[ROW][C]17[/C][C]-0.057307[/C][C]-0.6407[/C][C]0.261441[/C][/ROW]
[ROW][C]18[/C][C]0.022961[/C][C]0.2567[/C][C]0.398913[/C][/ROW]
[ROW][C]19[/C][C]0.05882[/C][C]0.6576[/C][C]0.255994[/C][/ROW]
[ROW][C]20[/C][C]-0.040247[/C][C]-0.45[/C][C]0.326755[/C][/ROW]
[ROW][C]21[/C][C]-0.122181[/C][C]-1.366[/C][C]0.087191[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298306&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298306&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
1-0.374044-4.18192.7e-05
2-0.287255-3.21160.000839
3-0.145883-1.6310.052702
4-0.224321-2.5080.006712
5-0.234967-2.6270.004846
60.0669050.7480.227928
7-0.087641-0.97990.164526
8-0.087649-0.97990.164502
9-0.006996-0.07820.468889
10-0.106316-1.18870.118415
110.02850.31860.375264
120.1711241.91320.029003
130.0877430.9810.164245
14-0.064962-0.72630.234508
15-0.108233-1.21010.114266
16-0.047859-0.53510.296773
17-0.057307-0.64070.261441
180.0229610.25670.398913
190.058820.65760.255994
20-0.040247-0.450.326755
21-0.122181-1.3660.087191



Parameters (Session):
par1 = -1.0 ; par2 = 1 ; par3 = 0 ; par4 = 1 ;
Parameters (R input):
par1 = Default ; par2 = 0.0 ; par3 = 1 ; par4 = 0 ; 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)
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')