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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, 04 Dec 2009 07:24:14 -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/04/t1259936712e63mdjvk7spiund.htm/, Retrieved Sat, 27 Apr 2024 17:37:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63598, Retrieved Sat, 27 Apr 2024 17:37:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
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   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
-    D    [ARIMA Backward Selection] [BBWS9-Arimabackward1] [2009-12-01 20:26:03] [408e92805dcb18620260f240a7fb9d53]
- RM D      [Harrell-Davis Quantiles] [BBWS9-Harolddavis] [2009-12-01 20:39:11] [408e92805dcb18620260f240a7fb9d53]
-   PD        [Harrell-Davis Quantiles] [W9: Harrell Davis] [2009-12-02 10:38:10] [03d5b865e91ca35b5a5d21b8d6da5aba]
- RMPD            [(Partial) Autocorrelation Function] [W9.2] [2009-12-04 14:24:14] [852eae237d08746109043531619a60c9] [Current]
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Dataseries X:
474605
470390
461251
454724
455626
516847
525192
522975
518585
509239
512238
519164
517009
509933
509127
500875
506971
569323
579714
577992
565644
547344
554788
562325
560854
555332
543599
536662
542722
593530
610763
612613
611324
594167
595454
590865
589379
584428
573100
567456
569028
620735
628884
628232
612117
595404
597141
593408
590072
579799
574205
572775
572942
619567
625809
619916
587625
565724
557274
560576
548854
531673
525919
511038
498662
555362
564591
541667
527070
509846
514258
516922
507561
492622
490243
469357
477580
528379
533590
517945
506174
501866
516441
528222
532638




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=63598&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=63598&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63598&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
10.054440.46190.322759
20.1368761.16140.124651
30.1495131.26870.104325
40.031690.26890.394387
50.0455930.38690.349997
60.0919810.78050.218832
70.0151920.12890.448893
80.2035581.72720.044205
90.027380.23230.408472
10-0.079682-0.67610.250565
110.1901931.61380.055469
12-0.180006-1.52740.065521
13-0.013888-0.11780.45326
140.1946591.65170.051471
150.0214930.18240.4279
160.0059170.05020.480047
17-0.074964-0.63610.263368
180.0163190.13850.445127
190.0044580.03780.484967

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.05444 & 0.4619 & 0.322759 \tabularnewline
2 & 0.136876 & 1.1614 & 0.124651 \tabularnewline
3 & 0.149513 & 1.2687 & 0.104325 \tabularnewline
4 & 0.03169 & 0.2689 & 0.394387 \tabularnewline
5 & 0.045593 & 0.3869 & 0.349997 \tabularnewline
6 & 0.091981 & 0.7805 & 0.218832 \tabularnewline
7 & 0.015192 & 0.1289 & 0.448893 \tabularnewline
8 & 0.203558 & 1.7272 & 0.044205 \tabularnewline
9 & 0.02738 & 0.2323 & 0.408472 \tabularnewline
10 & -0.079682 & -0.6761 & 0.250565 \tabularnewline
11 & 0.190193 & 1.6138 & 0.055469 \tabularnewline
12 & -0.180006 & -1.5274 & 0.065521 \tabularnewline
13 & -0.013888 & -0.1178 & 0.45326 \tabularnewline
14 & 0.194659 & 1.6517 & 0.051471 \tabularnewline
15 & 0.021493 & 0.1824 & 0.4279 \tabularnewline
16 & 0.005917 & 0.0502 & 0.480047 \tabularnewline
17 & -0.074964 & -0.6361 & 0.263368 \tabularnewline
18 & 0.016319 & 0.1385 & 0.445127 \tabularnewline
19 & 0.004458 & 0.0378 & 0.484967 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63598&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]0.05444[/C][C]0.4619[/C][C]0.322759[/C][/ROW]
[ROW][C]2[/C][C]0.136876[/C][C]1.1614[/C][C]0.124651[/C][/ROW]
[ROW][C]3[/C][C]0.149513[/C][C]1.2687[/C][C]0.104325[/C][/ROW]
[ROW][C]4[/C][C]0.03169[/C][C]0.2689[/C][C]0.394387[/C][/ROW]
[ROW][C]5[/C][C]0.045593[/C][C]0.3869[/C][C]0.349997[/C][/ROW]
[ROW][C]6[/C][C]0.091981[/C][C]0.7805[/C][C]0.218832[/C][/ROW]
[ROW][C]7[/C][C]0.015192[/C][C]0.1289[/C][C]0.448893[/C][/ROW]
[ROW][C]8[/C][C]0.203558[/C][C]1.7272[/C][C]0.044205[/C][/ROW]
[ROW][C]9[/C][C]0.02738[/C][C]0.2323[/C][C]0.408472[/C][/ROW]
[ROW][C]10[/C][C]-0.079682[/C][C]-0.6761[/C][C]0.250565[/C][/ROW]
[ROW][C]11[/C][C]0.190193[/C][C]1.6138[/C][C]0.055469[/C][/ROW]
[ROW][C]12[/C][C]-0.180006[/C][C]-1.5274[/C][C]0.065521[/C][/ROW]
[ROW][C]13[/C][C]-0.013888[/C][C]-0.1178[/C][C]0.45326[/C][/ROW]
[ROW][C]14[/C][C]0.194659[/C][C]1.6517[/C][C]0.051471[/C][/ROW]
[ROW][C]15[/C][C]0.021493[/C][C]0.1824[/C][C]0.4279[/C][/ROW]
[ROW][C]16[/C][C]0.005917[/C][C]0.0502[/C][C]0.480047[/C][/ROW]
[ROW][C]17[/C][C]-0.074964[/C][C]-0.6361[/C][C]0.263368[/C][/ROW]
[ROW][C]18[/C][C]0.016319[/C][C]0.1385[/C][C]0.445127[/C][/ROW]
[ROW][C]19[/C][C]0.004458[/C][C]0.0378[/C][C]0.484967[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63598&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63598&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
10.054440.46190.322759
20.1368761.16140.124651
30.1495131.26870.104325
40.031690.26890.394387
50.0455930.38690.349997
60.0919810.78050.218832
70.0151920.12890.448893
80.2035581.72720.044205
90.027380.23230.408472
10-0.079682-0.67610.250565
110.1901931.61380.055469
12-0.180006-1.52740.065521
13-0.013888-0.11780.45326
140.1946591.65170.051471
150.0214930.18240.4279
160.0059170.05020.480047
17-0.074964-0.63610.263368
180.0163190.13850.445127
190.0044580.03780.484967







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.054440.46190.322759
20.1343111.13970.129102
30.1386551.17650.12163
40.002490.02110.491603
50.006810.05780.477038
60.0681260.57810.28251
7-0.000643-0.00550.49783
80.1839711.5610.061449
9-0.007425-0.0630.474971
10-0.140554-1.19260.118463
110.155121.31620.096134
12-0.199256-1.69070.047605
13-0.015101-0.12810.4492
140.2135061.81170.037104
150.033330.28280.389066
16-0.071837-0.60960.272036
17-0.165311-1.40270.0825
180.0997520.84640.20006
19-0.024497-0.20790.41796

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.05444 & 0.4619 & 0.322759 \tabularnewline
2 & 0.134311 & 1.1397 & 0.129102 \tabularnewline
3 & 0.138655 & 1.1765 & 0.12163 \tabularnewline
4 & 0.00249 & 0.0211 & 0.491603 \tabularnewline
5 & 0.00681 & 0.0578 & 0.477038 \tabularnewline
6 & 0.068126 & 0.5781 & 0.28251 \tabularnewline
7 & -0.000643 & -0.0055 & 0.49783 \tabularnewline
8 & 0.183971 & 1.561 & 0.061449 \tabularnewline
9 & -0.007425 & -0.063 & 0.474971 \tabularnewline
10 & -0.140554 & -1.1926 & 0.118463 \tabularnewline
11 & 0.15512 & 1.3162 & 0.096134 \tabularnewline
12 & -0.199256 & -1.6907 & 0.047605 \tabularnewline
13 & -0.015101 & -0.1281 & 0.4492 \tabularnewline
14 & 0.213506 & 1.8117 & 0.037104 \tabularnewline
15 & 0.03333 & 0.2828 & 0.389066 \tabularnewline
16 & -0.071837 & -0.6096 & 0.272036 \tabularnewline
17 & -0.165311 & -1.4027 & 0.0825 \tabularnewline
18 & 0.099752 & 0.8464 & 0.20006 \tabularnewline
19 & -0.024497 & -0.2079 & 0.41796 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63598&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]0.05444[/C][C]0.4619[/C][C]0.322759[/C][/ROW]
[ROW][C]2[/C][C]0.134311[/C][C]1.1397[/C][C]0.129102[/C][/ROW]
[ROW][C]3[/C][C]0.138655[/C][C]1.1765[/C][C]0.12163[/C][/ROW]
[ROW][C]4[/C][C]0.00249[/C][C]0.0211[/C][C]0.491603[/C][/ROW]
[ROW][C]5[/C][C]0.00681[/C][C]0.0578[/C][C]0.477038[/C][/ROW]
[ROW][C]6[/C][C]0.068126[/C][C]0.5781[/C][C]0.28251[/C][/ROW]
[ROW][C]7[/C][C]-0.000643[/C][C]-0.0055[/C][C]0.49783[/C][/ROW]
[ROW][C]8[/C][C]0.183971[/C][C]1.561[/C][C]0.061449[/C][/ROW]
[ROW][C]9[/C][C]-0.007425[/C][C]-0.063[/C][C]0.474971[/C][/ROW]
[ROW][C]10[/C][C]-0.140554[/C][C]-1.1926[/C][C]0.118463[/C][/ROW]
[ROW][C]11[/C][C]0.15512[/C][C]1.3162[/C][C]0.096134[/C][/ROW]
[ROW][C]12[/C][C]-0.199256[/C][C]-1.6907[/C][C]0.047605[/C][/ROW]
[ROW][C]13[/C][C]-0.015101[/C][C]-0.1281[/C][C]0.4492[/C][/ROW]
[ROW][C]14[/C][C]0.213506[/C][C]1.8117[/C][C]0.037104[/C][/ROW]
[ROW][C]15[/C][C]0.03333[/C][C]0.2828[/C][C]0.389066[/C][/ROW]
[ROW][C]16[/C][C]-0.071837[/C][C]-0.6096[/C][C]0.272036[/C][/ROW]
[ROW][C]17[/C][C]-0.165311[/C][C]-1.4027[/C][C]0.0825[/C][/ROW]
[ROW][C]18[/C][C]0.099752[/C][C]0.8464[/C][C]0.20006[/C][/ROW]
[ROW][C]19[/C][C]-0.024497[/C][C]-0.2079[/C][C]0.41796[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63598&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63598&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
10.054440.46190.322759
20.1343111.13970.129102
30.1386551.17650.12163
40.002490.02110.491603
50.006810.05780.477038
60.0681260.57810.28251
7-0.000643-0.00550.49783
80.1839711.5610.061449
9-0.007425-0.0630.474971
10-0.140554-1.19260.118463
110.155121.31620.096134
12-0.199256-1.69070.047605
13-0.015101-0.12810.4492
140.2135061.81170.037104
150.033330.28280.389066
16-0.071837-0.60960.272036
17-0.165311-1.40270.0825
180.0997520.84640.20006
19-0.024497-0.20790.41796



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