Free Statistics

of Irreproducible Research!

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 13:24:35 -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/t1259871944ffj3q6mvzg4avm0.htm/, Retrieved Thu, 28 Mar 2024 08:42:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63112, Retrieved Thu, 28 Mar 2024 08:42:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsBasisjaar 2000 = 100
Estimated Impact101
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] [Grondstofprijsind...] [2009-12-03 20:24:35] [c483349466b1550829c7523719d2d027] [Current]
Feedback Forum

Post a new message
Dataseries X:
117.1
118.7
126.5
127.5
134.6
131.8
135.9
142.7
141.7
153.4
145
137.7
148.3
152.2
169.4
168.6
161.1
174.1
179
190.6
190
181.6
174.8
180.5
196.8
193.8
197
216.3
221.4
217.9
229.7
227.4
204.2
196.6
198.8
207.5
190.7
201.6
210.5
223.5
223.8
231.2
244
234.7
250.2
265.7
287.6
283.3
295.4
312.3
333.8
347.7
383.2
407.1
413.6
362.7
321.9
239.4
191
159.7
163.4




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=63112&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=63112&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63112&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
10.9461597.38970
20.8494946.63480
30.7314535.71280
40.6136454.79275e-06
50.5120083.99898.7e-05
60.4309623.36590.000662
70.3735132.91720.002469
80.324982.53820.006856
90.2788152.17760.016656
100.2415241.88640.032004
110.2023231.58020.059617
120.1597761.24790.108421
130.123270.96280.169731
140.0951760.74330.230063
150.0756690.5910.278353
160.0521630.40740.342569
170.0293350.22910.409774

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.946159 & 7.3897 & 0 \tabularnewline
2 & 0.849494 & 6.6348 & 0 \tabularnewline
3 & 0.731453 & 5.7128 & 0 \tabularnewline
4 & 0.613645 & 4.7927 & 5e-06 \tabularnewline
5 & 0.512008 & 3.9989 & 8.7e-05 \tabularnewline
6 & 0.430962 & 3.3659 & 0.000662 \tabularnewline
7 & 0.373513 & 2.9172 & 0.002469 \tabularnewline
8 & 0.32498 & 2.5382 & 0.006856 \tabularnewline
9 & 0.278815 & 2.1776 & 0.016656 \tabularnewline
10 & 0.241524 & 1.8864 & 0.032004 \tabularnewline
11 & 0.202323 & 1.5802 & 0.059617 \tabularnewline
12 & 0.159776 & 1.2479 & 0.108421 \tabularnewline
13 & 0.12327 & 0.9628 & 0.169731 \tabularnewline
14 & 0.095176 & 0.7433 & 0.230063 \tabularnewline
15 & 0.075669 & 0.591 & 0.278353 \tabularnewline
16 & 0.052163 & 0.4074 & 0.342569 \tabularnewline
17 & 0.029335 & 0.2291 & 0.409774 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63112&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]0.946159[/C][C]7.3897[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.849494[/C][C]6.6348[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.731453[/C][C]5.7128[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.613645[/C][C]4.7927[/C][C]5e-06[/C][/ROW]
[ROW][C]5[/C][C]0.512008[/C][C]3.9989[/C][C]8.7e-05[/C][/ROW]
[ROW][C]6[/C][C]0.430962[/C][C]3.3659[/C][C]0.000662[/C][/ROW]
[ROW][C]7[/C][C]0.373513[/C][C]2.9172[/C][C]0.002469[/C][/ROW]
[ROW][C]8[/C][C]0.32498[/C][C]2.5382[/C][C]0.006856[/C][/ROW]
[ROW][C]9[/C][C]0.278815[/C][C]2.1776[/C][C]0.016656[/C][/ROW]
[ROW][C]10[/C][C]0.241524[/C][C]1.8864[/C][C]0.032004[/C][/ROW]
[ROW][C]11[/C][C]0.202323[/C][C]1.5802[/C][C]0.059617[/C][/ROW]
[ROW][C]12[/C][C]0.159776[/C][C]1.2479[/C][C]0.108421[/C][/ROW]
[ROW][C]13[/C][C]0.12327[/C][C]0.9628[/C][C]0.169731[/C][/ROW]
[ROW][C]14[/C][C]0.095176[/C][C]0.7433[/C][C]0.230063[/C][/ROW]
[ROW][C]15[/C][C]0.075669[/C][C]0.591[/C][C]0.278353[/C][/ROW]
[ROW][C]16[/C][C]0.052163[/C][C]0.4074[/C][C]0.342569[/C][/ROW]
[ROW][C]17[/C][C]0.029335[/C][C]0.2291[/C][C]0.409774[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63112&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63112&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
10.9461597.38970
20.8494946.63480
30.7314535.71280
40.6136454.79275e-06
50.5120083.99898.7e-05
60.4309623.36590.000662
70.3735132.91720.002469
80.324982.53820.006856
90.2788152.17760.016656
100.2415241.88640.032004
110.2023231.58020.059617
120.1597761.24790.108421
130.123270.96280.169731
140.0951760.74330.230063
150.0756690.5910.278353
160.0521630.40740.342569
170.0293350.22910.409774







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9461597.38970
2-0.436366-3.40810.000582
3-0.119815-0.93580.176537
40.0424520.33160.370679
50.0878450.68610.247628
60.0311660.24340.404249
70.0566160.44220.329961
8-0.107358-0.83850.202514
9-0.051077-0.39890.345671
100.1049870.820.207711
11-0.087844-0.68610.247631
12-0.061274-0.47860.316978
130.0866720.67690.250505
140.0205830.16080.436406
15-0.011979-0.09360.462883
16-0.123027-0.96090.170205
170.0108640.08490.466328

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.946159 & 7.3897 & 0 \tabularnewline
2 & -0.436366 & -3.4081 & 0.000582 \tabularnewline
3 & -0.119815 & -0.9358 & 0.176537 \tabularnewline
4 & 0.042452 & 0.3316 & 0.370679 \tabularnewline
5 & 0.087845 & 0.6861 & 0.247628 \tabularnewline
6 & 0.031166 & 0.2434 & 0.404249 \tabularnewline
7 & 0.056616 & 0.4422 & 0.329961 \tabularnewline
8 & -0.107358 & -0.8385 & 0.202514 \tabularnewline
9 & -0.051077 & -0.3989 & 0.345671 \tabularnewline
10 & 0.104987 & 0.82 & 0.207711 \tabularnewline
11 & -0.087844 & -0.6861 & 0.247631 \tabularnewline
12 & -0.061274 & -0.4786 & 0.316978 \tabularnewline
13 & 0.086672 & 0.6769 & 0.250505 \tabularnewline
14 & 0.020583 & 0.1608 & 0.436406 \tabularnewline
15 & -0.011979 & -0.0936 & 0.462883 \tabularnewline
16 & -0.123027 & -0.9609 & 0.170205 \tabularnewline
17 & 0.010864 & 0.0849 & 0.466328 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63112&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]0.946159[/C][C]7.3897[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.436366[/C][C]-3.4081[/C][C]0.000582[/C][/ROW]
[ROW][C]3[/C][C]-0.119815[/C][C]-0.9358[/C][C]0.176537[/C][/ROW]
[ROW][C]4[/C][C]0.042452[/C][C]0.3316[/C][C]0.370679[/C][/ROW]
[ROW][C]5[/C][C]0.087845[/C][C]0.6861[/C][C]0.247628[/C][/ROW]
[ROW][C]6[/C][C]0.031166[/C][C]0.2434[/C][C]0.404249[/C][/ROW]
[ROW][C]7[/C][C]0.056616[/C][C]0.4422[/C][C]0.329961[/C][/ROW]
[ROW][C]8[/C][C]-0.107358[/C][C]-0.8385[/C][C]0.202514[/C][/ROW]
[ROW][C]9[/C][C]-0.051077[/C][C]-0.3989[/C][C]0.345671[/C][/ROW]
[ROW][C]10[/C][C]0.104987[/C][C]0.82[/C][C]0.207711[/C][/ROW]
[ROW][C]11[/C][C]-0.087844[/C][C]-0.6861[/C][C]0.247631[/C][/ROW]
[ROW][C]12[/C][C]-0.061274[/C][C]-0.4786[/C][C]0.316978[/C][/ROW]
[ROW][C]13[/C][C]0.086672[/C][C]0.6769[/C][C]0.250505[/C][/ROW]
[ROW][C]14[/C][C]0.020583[/C][C]0.1608[/C][C]0.436406[/C][/ROW]
[ROW][C]15[/C][C]-0.011979[/C][C]-0.0936[/C][C]0.462883[/C][/ROW]
[ROW][C]16[/C][C]-0.123027[/C][C]-0.9609[/C][C]0.170205[/C][/ROW]
[ROW][C]17[/C][C]0.010864[/C][C]0.0849[/C][C]0.466328[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63112&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63112&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
10.9461597.38970
2-0.436366-3.40810.000582
3-0.119815-0.93580.176537
40.0424520.33160.370679
50.0878450.68610.247628
60.0311660.24340.404249
70.0566160.44220.329961
8-0.107358-0.83850.202514
9-0.051077-0.39890.345671
100.1049870.820.207711
11-0.087844-0.68610.247631
12-0.061274-0.47860.316978
130.0866720.67690.250505
140.0205830.16080.436406
15-0.011979-0.09360.462883
16-0.123027-0.96090.170205
170.0108640.08490.466328



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