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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 computationFri, 11 Dec 2009 03:14:54 -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/11/t12605265478klg03a5gcmbe61.htm/, Retrieved Sun, 28 Apr 2024 22:47:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65949, Retrieved Sun, 28 Apr 2024 22:47:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [] [2009-12-06 10:44:58] [1e83ffa964db6f7ea6ccc4e7b5acbbff]
-   PD  [ARIMA Forecasting] [ws 10 deel 2 prblm] [2009-12-09 19:29:01] [134dc66689e3d457a82860db6471d419]
- RMP       [(Partial) Autocorrelation Function] [WS 10 forum ACF] [2009-12-11 10:14:54] [eba9f01697e64705b70041e6f338cb22] [Current]
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Dataseries X:
100.01
103.84
104.48
95.43
104.80
108.64
105.65
108.42
115.35
113.64
115.24
100.33
101.29
104.48
99.26
100.11
103.52
101.18
96.39
97.56
96.39
85.10
79.77
79.13
80.84
82.75
92.55
96.60
96.92
95.32
98.52
100.22
104.91
103.10
97.13
103.42
111.72
118.11
111.62
100.22
102.03
105.76
107.68
110.77
105.44
112.26
114.07
117.90
124.72
126.42
134.73
135.79
143.36
140.37
144.74
151.98
150.92
163.38
154.43
146.66
157.95
162.10
180.42
179.57
171.58
185.43
190.64
203.00
202.36
193.41
186.17
192.24
209.60
206.41
209.82
230.37
235.80
232.07
244.64
242.19
217.48
209.39
211.73
221.00
203.11
214.71
224.19
238.04
238.36
246.24
259.87
249.97
266.48
282.98
306.31
301.73
314.62
332.62
355.51
370.32
408.13
433.58
440.51
386.29
342.84
254.97
203.42
170.09
174.03
167.85
177.01
188.19
211.20
240.91
230.26
251.25
241.66




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65949&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65949&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65949&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'Gwilym Jenkins' @ 72.249.127.135







Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.325592-3.49160.000341
20.130941.40420.08148
3-0.151697-1.62680.053262
40.0817520.87670.191239
5-0.162015-1.73740.042495
6-0.130393-1.39830.082356
70.0508480.54530.293309
8-0.01695-0.18180.42804
9-0.115431-1.23790.109144
10-0.01256-0.13470.446546
110.1865412.00040.023905
12-0.027828-0.29840.382961
13-0.093015-0.99750.160313
140.0424750.45550.324806
150.1736341.8620.032577
16-0.110285-1.18270.119688
17-0.030635-0.32850.371558
18-0.019854-0.21290.415888
190.0828340.88830.188119
20-0.153193-1.64280.051576

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.325592 & -3.4916 & 0.000341 \tabularnewline
2 & 0.13094 & 1.4042 & 0.08148 \tabularnewline
3 & -0.151697 & -1.6268 & 0.053262 \tabularnewline
4 & 0.081752 & 0.8767 & 0.191239 \tabularnewline
5 & -0.162015 & -1.7374 & 0.042495 \tabularnewline
6 & -0.130393 & -1.3983 & 0.082356 \tabularnewline
7 & 0.050848 & 0.5453 & 0.293309 \tabularnewline
8 & -0.01695 & -0.1818 & 0.42804 \tabularnewline
9 & -0.115431 & -1.2379 & 0.109144 \tabularnewline
10 & -0.01256 & -0.1347 & 0.446546 \tabularnewline
11 & 0.186541 & 2.0004 & 0.023905 \tabularnewline
12 & -0.027828 & -0.2984 & 0.382961 \tabularnewline
13 & -0.093015 & -0.9975 & 0.160313 \tabularnewline
14 & 0.042475 & 0.4555 & 0.324806 \tabularnewline
15 & 0.173634 & 1.862 & 0.032577 \tabularnewline
16 & -0.110285 & -1.1827 & 0.119688 \tabularnewline
17 & -0.030635 & -0.3285 & 0.371558 \tabularnewline
18 & -0.019854 & -0.2129 & 0.415888 \tabularnewline
19 & 0.082834 & 0.8883 & 0.188119 \tabularnewline
20 & -0.153193 & -1.6428 & 0.051576 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65949&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]-0.325592[/C][C]-3.4916[/C][C]0.000341[/C][/ROW]
[ROW][C]2[/C][C]0.13094[/C][C]1.4042[/C][C]0.08148[/C][/ROW]
[ROW][C]3[/C][C]-0.151697[/C][C]-1.6268[/C][C]0.053262[/C][/ROW]
[ROW][C]4[/C][C]0.081752[/C][C]0.8767[/C][C]0.191239[/C][/ROW]
[ROW][C]5[/C][C]-0.162015[/C][C]-1.7374[/C][C]0.042495[/C][/ROW]
[ROW][C]6[/C][C]-0.130393[/C][C]-1.3983[/C][C]0.082356[/C][/ROW]
[ROW][C]7[/C][C]0.050848[/C][C]0.5453[/C][C]0.293309[/C][/ROW]
[ROW][C]8[/C][C]-0.01695[/C][C]-0.1818[/C][C]0.42804[/C][/ROW]
[ROW][C]9[/C][C]-0.115431[/C][C]-1.2379[/C][C]0.109144[/C][/ROW]
[ROW][C]10[/C][C]-0.01256[/C][C]-0.1347[/C][C]0.446546[/C][/ROW]
[ROW][C]11[/C][C]0.186541[/C][C]2.0004[/C][C]0.023905[/C][/ROW]
[ROW][C]12[/C][C]-0.027828[/C][C]-0.2984[/C][C]0.382961[/C][/ROW]
[ROW][C]13[/C][C]-0.093015[/C][C]-0.9975[/C][C]0.160313[/C][/ROW]
[ROW][C]14[/C][C]0.042475[/C][C]0.4555[/C][C]0.324806[/C][/ROW]
[ROW][C]15[/C][C]0.173634[/C][C]1.862[/C][C]0.032577[/C][/ROW]
[ROW][C]16[/C][C]-0.110285[/C][C]-1.1827[/C][C]0.119688[/C][/ROW]
[ROW][C]17[/C][C]-0.030635[/C][C]-0.3285[/C][C]0.371558[/C][/ROW]
[ROW][C]18[/C][C]-0.019854[/C][C]-0.2129[/C][C]0.415888[/C][/ROW]
[ROW][C]19[/C][C]0.082834[/C][C]0.8883[/C][C]0.188119[/C][/ROW]
[ROW][C]20[/C][C]-0.153193[/C][C]-1.6428[/C][C]0.051576[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65949&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65949&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
1-0.325592-3.49160.000341
20.130941.40420.08148
3-0.151697-1.62680.053262
40.0817520.87670.191239
5-0.162015-1.73740.042495
6-0.130393-1.39830.082356
70.0508480.54530.293309
8-0.01695-0.18180.42804
9-0.115431-1.23790.109144
10-0.01256-0.13470.446546
110.1865412.00040.023905
12-0.027828-0.29840.382961
13-0.093015-0.99750.160313
140.0424750.45550.324806
150.1736341.8620.032577
16-0.110285-1.18270.119688
17-0.030635-0.32850.371558
18-0.019854-0.21290.415888
190.0828340.88830.188119
20-0.153193-1.64280.051576







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.325592-3.49160.000341
20.0278860.2990.382723
3-0.113259-1.21460.113511
4-0.001833-0.01970.492177
5-0.139802-1.49920.06828
6-0.273259-2.93040.002042
7-0.070416-0.75510.225858
8-0.053499-0.57370.28364
9-0.229371-2.45970.007696
10-0.197085-2.11350.01836
110.0546340.58590.279551
12-0.039361-0.42210.336871
13-0.221723-2.37770.009535
14-0.143489-1.53880.063306
150.0869890.93290.176425
16-0.056848-0.60960.271657
17-0.124579-1.3360.0921
18-0.154615-1.65810.050015
19-0.044371-0.47580.317551
20-0.102635-1.10060.136678

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.325592 & -3.4916 & 0.000341 \tabularnewline
2 & 0.027886 & 0.299 & 0.382723 \tabularnewline
3 & -0.113259 & -1.2146 & 0.113511 \tabularnewline
4 & -0.001833 & -0.0197 & 0.492177 \tabularnewline
5 & -0.139802 & -1.4992 & 0.06828 \tabularnewline
6 & -0.273259 & -2.9304 & 0.002042 \tabularnewline
7 & -0.070416 & -0.7551 & 0.225858 \tabularnewline
8 & -0.053499 & -0.5737 & 0.28364 \tabularnewline
9 & -0.229371 & -2.4597 & 0.007696 \tabularnewline
10 & -0.197085 & -2.1135 & 0.01836 \tabularnewline
11 & 0.054634 & 0.5859 & 0.279551 \tabularnewline
12 & -0.039361 & -0.4221 & 0.336871 \tabularnewline
13 & -0.221723 & -2.3777 & 0.009535 \tabularnewline
14 & -0.143489 & -1.5388 & 0.063306 \tabularnewline
15 & 0.086989 & 0.9329 & 0.176425 \tabularnewline
16 & -0.056848 & -0.6096 & 0.271657 \tabularnewline
17 & -0.124579 & -1.336 & 0.0921 \tabularnewline
18 & -0.154615 & -1.6581 & 0.050015 \tabularnewline
19 & -0.044371 & -0.4758 & 0.317551 \tabularnewline
20 & -0.102635 & -1.1006 & 0.136678 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65949&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]-0.325592[/C][C]-3.4916[/C][C]0.000341[/C][/ROW]
[ROW][C]2[/C][C]0.027886[/C][C]0.299[/C][C]0.382723[/C][/ROW]
[ROW][C]3[/C][C]-0.113259[/C][C]-1.2146[/C][C]0.113511[/C][/ROW]
[ROW][C]4[/C][C]-0.001833[/C][C]-0.0197[/C][C]0.492177[/C][/ROW]
[ROW][C]5[/C][C]-0.139802[/C][C]-1.4992[/C][C]0.06828[/C][/ROW]
[ROW][C]6[/C][C]-0.273259[/C][C]-2.9304[/C][C]0.002042[/C][/ROW]
[ROW][C]7[/C][C]-0.070416[/C][C]-0.7551[/C][C]0.225858[/C][/ROW]
[ROW][C]8[/C][C]-0.053499[/C][C]-0.5737[/C][C]0.28364[/C][/ROW]
[ROW][C]9[/C][C]-0.229371[/C][C]-2.4597[/C][C]0.007696[/C][/ROW]
[ROW][C]10[/C][C]-0.197085[/C][C]-2.1135[/C][C]0.01836[/C][/ROW]
[ROW][C]11[/C][C]0.054634[/C][C]0.5859[/C][C]0.279551[/C][/ROW]
[ROW][C]12[/C][C]-0.039361[/C][C]-0.4221[/C][C]0.336871[/C][/ROW]
[ROW][C]13[/C][C]-0.221723[/C][C]-2.3777[/C][C]0.009535[/C][/ROW]
[ROW][C]14[/C][C]-0.143489[/C][C]-1.5388[/C][C]0.063306[/C][/ROW]
[ROW][C]15[/C][C]0.086989[/C][C]0.9329[/C][C]0.176425[/C][/ROW]
[ROW][C]16[/C][C]-0.056848[/C][C]-0.6096[/C][C]0.271657[/C][/ROW]
[ROW][C]17[/C][C]-0.124579[/C][C]-1.336[/C][C]0.0921[/C][/ROW]
[ROW][C]18[/C][C]-0.154615[/C][C]-1.6581[/C][C]0.050015[/C][/ROW]
[ROW][C]19[/C][C]-0.044371[/C][C]-0.4758[/C][C]0.317551[/C][/ROW]
[ROW][C]20[/C][C]-0.102635[/C][C]-1.1006[/C][C]0.136678[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65949&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65949&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
1-0.325592-3.49160.000341
20.0278860.2990.382723
3-0.113259-1.21460.113511
4-0.001833-0.01970.492177
5-0.139802-1.49920.06828
6-0.273259-2.93040.002042
7-0.070416-0.75510.225858
8-0.053499-0.57370.28364
9-0.229371-2.45970.007696
10-0.197085-2.11350.01836
110.0546340.58590.279551
12-0.039361-0.42210.336871
13-0.221723-2.37770.009535
14-0.143489-1.53880.063306
150.0869890.93290.176425
16-0.056848-0.60960.271657
17-0.124579-1.3360.0921
18-0.154615-1.65810.050015
19-0.044371-0.47580.317551
20-0.102635-1.10060.136678



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = Default ; par2 = 1 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; 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')