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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, 03 Dec 2009 10:57:16 -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/2009/Dec/03/t12598631160v8xfms3ehnf9r3.htm/, Retrieved Fri, 29 Mar 2024 09:34:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62997, Retrieved Fri, 29 Mar 2024 09:34:43 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
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] [] [2009-11-27 14:46:03] [b98453cac15ba1066b407e146608df68]
- R PD    [(Partial) Autocorrelation Function] [Partiele autocorr...] [2009-12-03 17:25:34] [863a41223bd4bb97f4e5094488ffff34]
-   P         [(Partial) Autocorrelation Function] [Partial correlati...] [2009-12-03 17:57:16] [b1ac221d009d6e5c29a4ef1869874933] [Current]
-   P           [(Partial) Autocorrelation Function] [Autocorrelation e...] [2009-12-04 17:50:40] [863a41223bd4bb97f4e5094488ffff34]
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Dataseries X:
89.6
92.8
107.6
104.6
103
106.9
56.3
93.4
109.1
113.8
97.4
72.5
82.7
88.9
105.9
100.8
94
105
58.5
87.6
113.1
112.5
89.6
74.5
82.7
90.1
109.4
96
89.2
109.1
49.1
92.9
107.7
103.5
91.1
79.8
71.9
82.9
90.1
100.7
90.7
108.8
44.1
93.6
107.4
96.5
93.6
76.5
76.7
84
103.3
88.5
99
105.9
44.7
94
107.1
104.8
102.5
77.7
85.2
91.3
106.5
92.4
97.5
107
51.1
98.6
102.2
114.3
99.4
72.5
92.3




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=62997&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=62997&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62997&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
1-0.085806-0.67020.252642
20.1639291.28030.102639
30.1142110.8920.187944
40.1003090.78340.218201
50.1533671.19780.117807
60.2102141.64180.052887
7-0.072759-0.56830.285969
80.1461981.14180.12899
90.1472161.14980.127359
10-0.00015-0.00120.499534
110.1852651.4470.076513
12-0.147174-1.14950.127426
130.0216570.16910.433121
14-0.033124-0.25870.398365
150.1416441.10630.136476
16-0.092068-0.71910.237421
170.1442021.12630.132236
18-0.099474-0.77690.220105

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.085806 & -0.6702 & 0.252642 \tabularnewline
2 & 0.163929 & 1.2803 & 0.102639 \tabularnewline
3 & 0.114211 & 0.892 & 0.187944 \tabularnewline
4 & 0.100309 & 0.7834 & 0.218201 \tabularnewline
5 & 0.153367 & 1.1978 & 0.117807 \tabularnewline
6 & 0.210214 & 1.6418 & 0.052887 \tabularnewline
7 & -0.072759 & -0.5683 & 0.285969 \tabularnewline
8 & 0.146198 & 1.1418 & 0.12899 \tabularnewline
9 & 0.147216 & 1.1498 & 0.127359 \tabularnewline
10 & -0.00015 & -0.0012 & 0.499534 \tabularnewline
11 & 0.185265 & 1.447 & 0.076513 \tabularnewline
12 & -0.147174 & -1.1495 & 0.127426 \tabularnewline
13 & 0.021657 & 0.1691 & 0.433121 \tabularnewline
14 & -0.033124 & -0.2587 & 0.398365 \tabularnewline
15 & 0.141644 & 1.1063 & 0.136476 \tabularnewline
16 & -0.092068 & -0.7191 & 0.237421 \tabularnewline
17 & 0.144202 & 1.1263 & 0.132236 \tabularnewline
18 & -0.099474 & -0.7769 & 0.220105 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62997&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]-0.085806[/C][C]-0.6702[/C][C]0.252642[/C][/ROW]
[ROW][C]2[/C][C]0.163929[/C][C]1.2803[/C][C]0.102639[/C][/ROW]
[ROW][C]3[/C][C]0.114211[/C][C]0.892[/C][C]0.187944[/C][/ROW]
[ROW][C]4[/C][C]0.100309[/C][C]0.7834[/C][C]0.218201[/C][/ROW]
[ROW][C]5[/C][C]0.153367[/C][C]1.1978[/C][C]0.117807[/C][/ROW]
[ROW][C]6[/C][C]0.210214[/C][C]1.6418[/C][C]0.052887[/C][/ROW]
[ROW][C]7[/C][C]-0.072759[/C][C]-0.5683[/C][C]0.285969[/C][/ROW]
[ROW][C]8[/C][C]0.146198[/C][C]1.1418[/C][C]0.12899[/C][/ROW]
[ROW][C]9[/C][C]0.147216[/C][C]1.1498[/C][C]0.127359[/C][/ROW]
[ROW][C]10[/C][C]-0.00015[/C][C]-0.0012[/C][C]0.499534[/C][/ROW]
[ROW][C]11[/C][C]0.185265[/C][C]1.447[/C][C]0.076513[/C][/ROW]
[ROW][C]12[/C][C]-0.147174[/C][C]-1.1495[/C][C]0.127426[/C][/ROW]
[ROW][C]13[/C][C]0.021657[/C][C]0.1691[/C][C]0.433121[/C][/ROW]
[ROW][C]14[/C][C]-0.033124[/C][C]-0.2587[/C][C]0.398365[/C][/ROW]
[ROW][C]15[/C][C]0.141644[/C][C]1.1063[/C][C]0.136476[/C][/ROW]
[ROW][C]16[/C][C]-0.092068[/C][C]-0.7191[/C][C]0.237421[/C][/ROW]
[ROW][C]17[/C][C]0.144202[/C][C]1.1263[/C][C]0.132236[/C][/ROW]
[ROW][C]18[/C][C]-0.099474[/C][C]-0.7769[/C][C]0.220105[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62997&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62997&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
1-0.085806-0.67020.252642
20.1639291.28030.102639
30.1142110.8920.187944
40.1003090.78340.218201
50.1533671.19780.117807
60.2102141.64180.052887
7-0.072759-0.56830.285969
80.1461981.14180.12899
90.1472161.14980.127359
10-0.00015-0.00120.499534
110.1852651.4470.076513
12-0.147174-1.14950.127426
130.0216570.16910.433121
14-0.033124-0.25870.398365
150.1416441.10630.136476
16-0.092068-0.71910.237421
170.1442021.12630.132236
18-0.099474-0.77690.220105







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.085806-0.67020.252642
20.1577281.23190.111359
30.1442161.12640.132212
40.1012550.79080.216054
50.139191.08710.140633
60.2107271.64580.052471
7-0.098313-0.76780.22277
80.0265120.20710.418324
90.1326591.03610.152123
10-0.040242-0.31430.377182
110.092010.71860.23756
12-0.188648-1.47340.072894
13-0.079197-0.61850.269259
14-0.126024-0.98430.164434
150.1271460.9930.162307
16-0.038757-0.30270.381573
170.1122580.87680.192028
180.0071160.05560.47793

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.085806 & -0.6702 & 0.252642 \tabularnewline
2 & 0.157728 & 1.2319 & 0.111359 \tabularnewline
3 & 0.144216 & 1.1264 & 0.132212 \tabularnewline
4 & 0.101255 & 0.7908 & 0.216054 \tabularnewline
5 & 0.13919 & 1.0871 & 0.140633 \tabularnewline
6 & 0.210727 & 1.6458 & 0.052471 \tabularnewline
7 & -0.098313 & -0.7678 & 0.22277 \tabularnewline
8 & 0.026512 & 0.2071 & 0.418324 \tabularnewline
9 & 0.132659 & 1.0361 & 0.152123 \tabularnewline
10 & -0.040242 & -0.3143 & 0.377182 \tabularnewline
11 & 0.09201 & 0.7186 & 0.23756 \tabularnewline
12 & -0.188648 & -1.4734 & 0.072894 \tabularnewline
13 & -0.079197 & -0.6185 & 0.269259 \tabularnewline
14 & -0.126024 & -0.9843 & 0.164434 \tabularnewline
15 & 0.127146 & 0.993 & 0.162307 \tabularnewline
16 & -0.038757 & -0.3027 & 0.381573 \tabularnewline
17 & 0.112258 & 0.8768 & 0.192028 \tabularnewline
18 & 0.007116 & 0.0556 & 0.47793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62997&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]-0.085806[/C][C]-0.6702[/C][C]0.252642[/C][/ROW]
[ROW][C]2[/C][C]0.157728[/C][C]1.2319[/C][C]0.111359[/C][/ROW]
[ROW][C]3[/C][C]0.144216[/C][C]1.1264[/C][C]0.132212[/C][/ROW]
[ROW][C]4[/C][C]0.101255[/C][C]0.7908[/C][C]0.216054[/C][/ROW]
[ROW][C]5[/C][C]0.13919[/C][C]1.0871[/C][C]0.140633[/C][/ROW]
[ROW][C]6[/C][C]0.210727[/C][C]1.6458[/C][C]0.052471[/C][/ROW]
[ROW][C]7[/C][C]-0.098313[/C][C]-0.7678[/C][C]0.22277[/C][/ROW]
[ROW][C]8[/C][C]0.026512[/C][C]0.2071[/C][C]0.418324[/C][/ROW]
[ROW][C]9[/C][C]0.132659[/C][C]1.0361[/C][C]0.152123[/C][/ROW]
[ROW][C]10[/C][C]-0.040242[/C][C]-0.3143[/C][C]0.377182[/C][/ROW]
[ROW][C]11[/C][C]0.09201[/C][C]0.7186[/C][C]0.23756[/C][/ROW]
[ROW][C]12[/C][C]-0.188648[/C][C]-1.4734[/C][C]0.072894[/C][/ROW]
[ROW][C]13[/C][C]-0.079197[/C][C]-0.6185[/C][C]0.269259[/C][/ROW]
[ROW][C]14[/C][C]-0.126024[/C][C]-0.9843[/C][C]0.164434[/C][/ROW]
[ROW][C]15[/C][C]0.127146[/C][C]0.993[/C][C]0.162307[/C][/ROW]
[ROW][C]16[/C][C]-0.038757[/C][C]-0.3027[/C][C]0.381573[/C][/ROW]
[ROW][C]17[/C][C]0.112258[/C][C]0.8768[/C][C]0.192028[/C][/ROW]
[ROW][C]18[/C][C]0.007116[/C][C]0.0556[/C][C]0.47793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62997&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62997&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
1-0.085806-0.67020.252642
20.1577281.23190.111359
30.1442161.12640.132212
40.1012550.79080.216054
50.139191.08710.140633
60.2107271.64580.052471
7-0.098313-0.76780.22277
80.0265120.20710.418324
90.1326591.03610.152123
10-0.040242-0.31430.377182
110.092010.71860.23756
12-0.188648-1.47340.072894
13-0.079197-0.61850.269259
14-0.126024-0.98430.164434
150.1271460.9930.162307
16-0.038757-0.30270.381573
170.1122580.87680.192028
180.0071160.05560.47793



Parameters (Session):
par1 = Default ; par2 = 2.0 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = Default ; par2 = 2.0 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = MA ; 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')