Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationSun, 06 Dec 2009 12:50:50 -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/06/t1260129196nenl1n958khhwwv.htm/, Retrieved Mon, 06 May 2024 08:44:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64486, Retrieved Mon, 06 May 2024 08:44:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W21
Estimated Impact134
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [(Partial) Autocorrelation Function] [] [2009-12-06 19:50:50] [46199ea7e385a69efb178ac615a86e3a] [Current]
-   PD    [(Partial) Autocorrelation Function] [] [2010-01-13 17:40:57] [7d3231f13acd73ba47c9ab8bcf0bcfd9]
Feedback Forum

Post a new message
Dataseries X:
8 357
7 454
8 076
7 248
7 339
7 292
7 359
7 537
7 441
8 057
8 037
8 257
8 692
8 119
8 236
7 432
7 669
7 453
7 566
7 731
7 657
8 130
8 401
8 737
9 009
7 919
8 228
7 903
7 912
7 857
7 965
8 091
8 024
8 772
8 656
8 953
9 014
8 103
8 876
8 231
8 173
8 087
8 296
8 007
8 382
9 168
9 137
9 321
9 234
8 451
9 101
8 279
8 284
8 225
8 597
8 305
8 620
9 102
9 258
9 652
9 522
8 874
9 415
8 525
8 862
8 421
8 626
8 750
8 852
9 412
9 570
9 513
9 986
8 907
9 663
8 799
8 931
8 732
8 936
9 127
9 070
9 773
9 670
9 929
10 095
9 025
9 659
8 954
9 022
8 855
9 034
9 196
9 038
9 650
9 715
10 052
10 436
9 314
9 717
8 997
9 062
8 885
9 058
9 095
9 149
9 857
9 848
10 269
10 341
9 690
10 125
9 349
9 224
9 224
9 454
9 347
9 430
9 933
10 148
10 677
10 735
9 760
10 567
9 333
9 409
9 502
9 348
9 319
9 594
10 160
10 182
10 810
11 105
9 874
10 958
9 311
9 610
9 398
9 784
9 425
9 557
10 166
10 337
10 770
11 265
10 183
10 941
9 628
9 709
9 637
9 579
9 741
9 754
10 508
10 749
11 079
11 608
10 668
10 933
9 703
9 799
9 656
9 648
9 712
9 766
10 540
10 564
10 911
11 218
10 230
10 410
9 227
9 378
9 105
9 128




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=64486&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=64486&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64486&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
10.85167811.26660
20.82696210.93970
30.6959389.20640
40.5745697.60080
50.5172946.84320
60.4514775.97250
70.4844716.40890
80.510476.75290
90.599027.92430
100.7033159.3040
110.7087099.37530
120.8222410.87720
130.69099.13970
140.6695758.85760
150.546387.22790
160.430575.69590
170.3764024.97931e-06
180.3121214.1292.8e-05
190.3385934.47927e-06
200.3597584.75922e-06
210.4331015.72940
220.5275376.97870

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.851678 & 11.2666 & 0 \tabularnewline
2 & 0.826962 & 10.9397 & 0 \tabularnewline
3 & 0.695938 & 9.2064 & 0 \tabularnewline
4 & 0.574569 & 7.6008 & 0 \tabularnewline
5 & 0.517294 & 6.8432 & 0 \tabularnewline
6 & 0.451477 & 5.9725 & 0 \tabularnewline
7 & 0.484471 & 6.4089 & 0 \tabularnewline
8 & 0.51047 & 6.7529 & 0 \tabularnewline
9 & 0.59902 & 7.9243 & 0 \tabularnewline
10 & 0.703315 & 9.304 & 0 \tabularnewline
11 & 0.708709 & 9.3753 & 0 \tabularnewline
12 & 0.82224 & 10.8772 & 0 \tabularnewline
13 & 0.6909 & 9.1397 & 0 \tabularnewline
14 & 0.669575 & 8.8576 & 0 \tabularnewline
15 & 0.54638 & 7.2279 & 0 \tabularnewline
16 & 0.43057 & 5.6959 & 0 \tabularnewline
17 & 0.376402 & 4.9793 & 1e-06 \tabularnewline
18 & 0.312121 & 4.129 & 2.8e-05 \tabularnewline
19 & 0.338593 & 4.4792 & 7e-06 \tabularnewline
20 & 0.359758 & 4.7592 & 2e-06 \tabularnewline
21 & 0.433101 & 5.7294 & 0 \tabularnewline
22 & 0.527537 & 6.9787 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64486&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]0.851678[/C][C]11.2666[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.826962[/C][C]10.9397[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.695938[/C][C]9.2064[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.574569[/C][C]7.6008[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.517294[/C][C]6.8432[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.451477[/C][C]5.9725[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.484471[/C][C]6.4089[/C][C]0[/C][/ROW]
[ROW][C]8[/C][C]0.51047[/C][C]6.7529[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.59902[/C][C]7.9243[/C][C]0[/C][/ROW]
[ROW][C]10[/C][C]0.703315[/C][C]9.304[/C][C]0[/C][/ROW]
[ROW][C]11[/C][C]0.708709[/C][C]9.3753[/C][C]0[/C][/ROW]
[ROW][C]12[/C][C]0.82224[/C][C]10.8772[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.6909[/C][C]9.1397[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]0.669575[/C][C]8.8576[/C][C]0[/C][/ROW]
[ROW][C]15[/C][C]0.54638[/C][C]7.2279[/C][C]0[/C][/ROW]
[ROW][C]16[/C][C]0.43057[/C][C]5.6959[/C][C]0[/C][/ROW]
[ROW][C]17[/C][C]0.376402[/C][C]4.9793[/C][C]1e-06[/C][/ROW]
[ROW][C]18[/C][C]0.312121[/C][C]4.129[/C][C]2.8e-05[/C][/ROW]
[ROW][C]19[/C][C]0.338593[/C][C]4.4792[/C][C]7e-06[/C][/ROW]
[ROW][C]20[/C][C]0.359758[/C][C]4.7592[/C][C]2e-06[/C][/ROW]
[ROW][C]21[/C][C]0.433101[/C][C]5.7294[/C][C]0[/C][/ROW]
[ROW][C]22[/C][C]0.527537[/C][C]6.9787[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64486&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64486&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
10.85167811.26660
20.82696210.93970
30.6959389.20640
40.5745697.60080
50.5172946.84320
60.4514775.97250
70.4844716.40890
80.510476.75290
90.599027.92430
100.7033159.3040
110.7087099.37530
120.8222410.87720
130.69099.13970
140.6695758.85760
150.546387.22790
160.430575.69590
170.3764024.97931e-06
180.3121214.1292.8e-05
190.3385934.47927e-06
200.3597584.75922e-06
210.4331015.72940
220.5275376.97870







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.85167811.26660
20.3699574.89411e-06
3-0.26529-3.50950.000285
4-0.294458-3.89537e-05
50.2561843.3890.000433
60.2551093.37480.000455
70.3039954.02154.3e-05
80.1307591.72980.042717
90.1969062.60480.004992
100.3609764.77532e-06
11-0.266989-3.53190.000264
120.3003073.97275.2e-05
13-0.567779-7.5110
14-0.090648-1.19920.116042
150.0227160.30050.382072
160.0187840.24850.402026
170.0352920.46690.320589
180.0710350.93970.174333
19-0.033542-0.44370.328899
20-0.026749-0.35390.361935
21-0.045043-0.59590.276017
220.0358560.47430.31793

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.851678 & 11.2666 & 0 \tabularnewline
2 & 0.369957 & 4.8941 & 1e-06 \tabularnewline
3 & -0.26529 & -3.5095 & 0.000285 \tabularnewline
4 & -0.294458 & -3.8953 & 7e-05 \tabularnewline
5 & 0.256184 & 3.389 & 0.000433 \tabularnewline
6 & 0.255109 & 3.3748 & 0.000455 \tabularnewline
7 & 0.303995 & 4.0215 & 4.3e-05 \tabularnewline
8 & 0.130759 & 1.7298 & 0.042717 \tabularnewline
9 & 0.196906 & 2.6048 & 0.004992 \tabularnewline
10 & 0.360976 & 4.7753 & 2e-06 \tabularnewline
11 & -0.266989 & -3.5319 & 0.000264 \tabularnewline
12 & 0.300307 & 3.9727 & 5.2e-05 \tabularnewline
13 & -0.567779 & -7.511 & 0 \tabularnewline
14 & -0.090648 & -1.1992 & 0.116042 \tabularnewline
15 & 0.022716 & 0.3005 & 0.382072 \tabularnewline
16 & 0.018784 & 0.2485 & 0.402026 \tabularnewline
17 & 0.035292 & 0.4669 & 0.320589 \tabularnewline
18 & 0.071035 & 0.9397 & 0.174333 \tabularnewline
19 & -0.033542 & -0.4437 & 0.328899 \tabularnewline
20 & -0.026749 & -0.3539 & 0.361935 \tabularnewline
21 & -0.045043 & -0.5959 & 0.276017 \tabularnewline
22 & 0.035856 & 0.4743 & 0.31793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64486&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]0.851678[/C][C]11.2666[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.369957[/C][C]4.8941[/C][C]1e-06[/C][/ROW]
[ROW][C]3[/C][C]-0.26529[/C][C]-3.5095[/C][C]0.000285[/C][/ROW]
[ROW][C]4[/C][C]-0.294458[/C][C]-3.8953[/C][C]7e-05[/C][/ROW]
[ROW][C]5[/C][C]0.256184[/C][C]3.389[/C][C]0.000433[/C][/ROW]
[ROW][C]6[/C][C]0.255109[/C][C]3.3748[/C][C]0.000455[/C][/ROW]
[ROW][C]7[/C][C]0.303995[/C][C]4.0215[/C][C]4.3e-05[/C][/ROW]
[ROW][C]8[/C][C]0.130759[/C][C]1.7298[/C][C]0.042717[/C][/ROW]
[ROW][C]9[/C][C]0.196906[/C][C]2.6048[/C][C]0.004992[/C][/ROW]
[ROW][C]10[/C][C]0.360976[/C][C]4.7753[/C][C]2e-06[/C][/ROW]
[ROW][C]11[/C][C]-0.266989[/C][C]-3.5319[/C][C]0.000264[/C][/ROW]
[ROW][C]12[/C][C]0.300307[/C][C]3.9727[/C][C]5.2e-05[/C][/ROW]
[ROW][C]13[/C][C]-0.567779[/C][C]-7.511[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]-0.090648[/C][C]-1.1992[/C][C]0.116042[/C][/ROW]
[ROW][C]15[/C][C]0.022716[/C][C]0.3005[/C][C]0.382072[/C][/ROW]
[ROW][C]16[/C][C]0.018784[/C][C]0.2485[/C][C]0.402026[/C][/ROW]
[ROW][C]17[/C][C]0.035292[/C][C]0.4669[/C][C]0.320589[/C][/ROW]
[ROW][C]18[/C][C]0.071035[/C][C]0.9397[/C][C]0.174333[/C][/ROW]
[ROW][C]19[/C][C]-0.033542[/C][C]-0.4437[/C][C]0.328899[/C][/ROW]
[ROW][C]20[/C][C]-0.026749[/C][C]-0.3539[/C][C]0.361935[/C][/ROW]
[ROW][C]21[/C][C]-0.045043[/C][C]-0.5959[/C][C]0.276017[/C][/ROW]
[ROW][C]22[/C][C]0.035856[/C][C]0.4743[/C][C]0.31793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64486&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64486&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
10.85167811.26660
20.3699574.89411e-06
3-0.26529-3.50950.000285
4-0.294458-3.89537e-05
50.2561843.3890.000433
60.2551093.37480.000455
70.3039954.02154.3e-05
80.1307591.72980.042717
90.1969062.60480.004992
100.3609764.77532e-06
11-0.266989-3.53190.000264
120.3003073.97275.2e-05
13-0.567779-7.5110
14-0.090648-1.19920.116042
150.0227160.30050.382072
160.0187840.24850.402026
170.0352920.46690.320589
180.0710350.93970.174333
19-0.033542-0.44370.328899
20-0.026749-0.35390.361935
21-0.045043-0.59590.276017
220.0358560.47430.31793



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