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Author's title

Author*Unverified author*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationTue, 29 Nov 2011 08:04:17 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/29/t1322571888lusda2nv4mk5d9f.htm/, Retrieved Thu, 25 Apr 2024 15:07:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148313, Retrieved Thu, 25 Apr 2024 15:07:07 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD  [Central Tendency] [Workshop 8, Robus...] [2010-11-28 19:41:12] [d946de7cca328fbcf207448a112523ab]
-         [Central Tendency] [Workshop 8, Centr...] [2010-11-29 20:08:54] [3635fb7041b1998c5a1332cf9de22bce]
- RMP       [(Partial) Autocorrelation Function] [Workshop 8 autoco...] [2010-11-30 11:00:00] [a9e130f95bad0a0597234e75c6380c5a]
- R  D          [(Partial) Autocorrelation Function] [WS8 - Mini-tutori...] [2011-11-29 13:04:17] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-    D            [(Partial) Autocorrelation Function] [Paper Deel 2 - Da...] [2011-12-20 10:22:05] [95a4a8598e82ac3272c4dca488d0ba38]
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Dataseries X:
9 700
9 081
9 084
9 743
8 587
9 731
9 563
9 998
9 437
10 038
9 918
9 252
9 737
9 035
9 133
9 487
8 700
9 627
8 947
9 283
8 829
9 947
9 628
9 318
9 605
8 640
9 214
9 567
8 547
9 185
9 470
9 123
9 278
10 170
9 434
9 655
9 429
8 739
9 552
9 687
9 019
9 672
9 206
9 069
9 788
10 312
10 105
9 863
9 656
9 295
9 946
9 701
9 049
10 190
9 706
9 765
9 893
9 994
10 433
10 073
10 112
9 266
9 820
10 097
9 115
10 411
9 678
10 408
10 153
10 368
10 581
10 597
10 680
9 738
9 556




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148313&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148313&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148313&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' @ jenkins.wessa.net







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.3058192.64850.004926
20.3998733.4630.000443
30.3767363.26260.000832
40.1227261.06280.145632
50.2117041.83340.035354
60.1514121.31130.096884
70.137031.18670.119543
80.0394290.34150.366853
90.2922972.53140.006728
100.1622331.4050.082079
110.1626121.40830.081593
120.5787335.0122e-06
130.123171.06670.144768
140.2738482.37160.010139
150.1853481.60520.05633
16-0.025095-0.21730.41427
170.0564830.48920.313079
180.0435050.37680.353705

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.305819 & 2.6485 & 0.004926 \tabularnewline
2 & 0.399873 & 3.463 & 0.000443 \tabularnewline
3 & 0.376736 & 3.2626 & 0.000832 \tabularnewline
4 & 0.122726 & 1.0628 & 0.145632 \tabularnewline
5 & 0.211704 & 1.8334 & 0.035354 \tabularnewline
6 & 0.151412 & 1.3113 & 0.096884 \tabularnewline
7 & 0.13703 & 1.1867 & 0.119543 \tabularnewline
8 & 0.039429 & 0.3415 & 0.366853 \tabularnewline
9 & 0.292297 & 2.5314 & 0.006728 \tabularnewline
10 & 0.162233 & 1.405 & 0.082079 \tabularnewline
11 & 0.162612 & 1.4083 & 0.081593 \tabularnewline
12 & 0.578733 & 5.012 & 2e-06 \tabularnewline
13 & 0.12317 & 1.0667 & 0.144768 \tabularnewline
14 & 0.273848 & 2.3716 & 0.010139 \tabularnewline
15 & 0.185348 & 1.6052 & 0.05633 \tabularnewline
16 & -0.025095 & -0.2173 & 0.41427 \tabularnewline
17 & 0.056483 & 0.4892 & 0.313079 \tabularnewline
18 & 0.043505 & 0.3768 & 0.353705 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148313&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]0.305819[/C][C]2.6485[/C][C]0.004926[/C][/ROW]
[ROW][C]2[/C][C]0.399873[/C][C]3.463[/C][C]0.000443[/C][/ROW]
[ROW][C]3[/C][C]0.376736[/C][C]3.2626[/C][C]0.000832[/C][/ROW]
[ROW][C]4[/C][C]0.122726[/C][C]1.0628[/C][C]0.145632[/C][/ROW]
[ROW][C]5[/C][C]0.211704[/C][C]1.8334[/C][C]0.035354[/C][/ROW]
[ROW][C]6[/C][C]0.151412[/C][C]1.3113[/C][C]0.096884[/C][/ROW]
[ROW][C]7[/C][C]0.13703[/C][C]1.1867[/C][C]0.119543[/C][/ROW]
[ROW][C]8[/C][C]0.039429[/C][C]0.3415[/C][C]0.366853[/C][/ROW]
[ROW][C]9[/C][C]0.292297[/C][C]2.5314[/C][C]0.006728[/C][/ROW]
[ROW][C]10[/C][C]0.162233[/C][C]1.405[/C][C]0.082079[/C][/ROW]
[ROW][C]11[/C][C]0.162612[/C][C]1.4083[/C][C]0.081593[/C][/ROW]
[ROW][C]12[/C][C]0.578733[/C][C]5.012[/C][C]2e-06[/C][/ROW]
[ROW][C]13[/C][C]0.12317[/C][C]1.0667[/C][C]0.144768[/C][/ROW]
[ROW][C]14[/C][C]0.273848[/C][C]2.3716[/C][C]0.010139[/C][/ROW]
[ROW][C]15[/C][C]0.185348[/C][C]1.6052[/C][C]0.05633[/C][/ROW]
[ROW][C]16[/C][C]-0.025095[/C][C]-0.2173[/C][C]0.41427[/C][/ROW]
[ROW][C]17[/C][C]0.056483[/C][C]0.4892[/C][C]0.313079[/C][/ROW]
[ROW][C]18[/C][C]0.043505[/C][C]0.3768[/C][C]0.353705[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148313&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148313&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
10.3058192.64850.004926
20.3998733.4630.000443
30.3767363.26260.000832
40.1227261.06280.145632
50.2117041.83340.035354
60.1514121.31130.096884
70.137031.18670.119543
80.0394290.34150.366853
90.2922972.53140.006728
100.1622331.4050.082079
110.1626121.40830.081593
120.5787335.0122e-06
130.123171.06670.144768
140.2738482.37160.010139
150.1853481.60520.05633
16-0.025095-0.21730.41427
170.0564830.48920.313079
180.0435050.37680.353705







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.3058192.64850.004926
20.3379552.92680.002265
30.2396462.07540.020689
4-0.14807-1.28230.10184
50.0128570.11130.45582
60.0513960.44510.328763
70.0780590.6760.250558
8-0.12924-1.11930.133301
90.2958042.56170.006211
100.0869510.7530.226898
11-0.023225-0.20110.420569
120.4883934.22963.3e-05
13-0.178637-1.5470.063031
14-0.147488-1.27730.102721
15-0.127635-1.10540.13627
16-0.121525-1.05240.14799
17-0.05138-0.4450.328814
180.0751290.65060.258634

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.305819 & 2.6485 & 0.004926 \tabularnewline
2 & 0.337955 & 2.9268 & 0.002265 \tabularnewline
3 & 0.239646 & 2.0754 & 0.020689 \tabularnewline
4 & -0.14807 & -1.2823 & 0.10184 \tabularnewline
5 & 0.012857 & 0.1113 & 0.45582 \tabularnewline
6 & 0.051396 & 0.4451 & 0.328763 \tabularnewline
7 & 0.078059 & 0.676 & 0.250558 \tabularnewline
8 & -0.12924 & -1.1193 & 0.133301 \tabularnewline
9 & 0.295804 & 2.5617 & 0.006211 \tabularnewline
10 & 0.086951 & 0.753 & 0.226898 \tabularnewline
11 & -0.023225 & -0.2011 & 0.420569 \tabularnewline
12 & 0.488393 & 4.2296 & 3.3e-05 \tabularnewline
13 & -0.178637 & -1.547 & 0.063031 \tabularnewline
14 & -0.147488 & -1.2773 & 0.102721 \tabularnewline
15 & -0.127635 & -1.1054 & 0.13627 \tabularnewline
16 & -0.121525 & -1.0524 & 0.14799 \tabularnewline
17 & -0.05138 & -0.445 & 0.328814 \tabularnewline
18 & 0.075129 & 0.6506 & 0.258634 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148313&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]0.305819[/C][C]2.6485[/C][C]0.004926[/C][/ROW]
[ROW][C]2[/C][C]0.337955[/C][C]2.9268[/C][C]0.002265[/C][/ROW]
[ROW][C]3[/C][C]0.239646[/C][C]2.0754[/C][C]0.020689[/C][/ROW]
[ROW][C]4[/C][C]-0.14807[/C][C]-1.2823[/C][C]0.10184[/C][/ROW]
[ROW][C]5[/C][C]0.012857[/C][C]0.1113[/C][C]0.45582[/C][/ROW]
[ROW][C]6[/C][C]0.051396[/C][C]0.4451[/C][C]0.328763[/C][/ROW]
[ROW][C]7[/C][C]0.078059[/C][C]0.676[/C][C]0.250558[/C][/ROW]
[ROW][C]8[/C][C]-0.12924[/C][C]-1.1193[/C][C]0.133301[/C][/ROW]
[ROW][C]9[/C][C]0.295804[/C][C]2.5617[/C][C]0.006211[/C][/ROW]
[ROW][C]10[/C][C]0.086951[/C][C]0.753[/C][C]0.226898[/C][/ROW]
[ROW][C]11[/C][C]-0.023225[/C][C]-0.2011[/C][C]0.420569[/C][/ROW]
[ROW][C]12[/C][C]0.488393[/C][C]4.2296[/C][C]3.3e-05[/C][/ROW]
[ROW][C]13[/C][C]-0.178637[/C][C]-1.547[/C][C]0.063031[/C][/ROW]
[ROW][C]14[/C][C]-0.147488[/C][C]-1.2773[/C][C]0.102721[/C][/ROW]
[ROW][C]15[/C][C]-0.127635[/C][C]-1.1054[/C][C]0.13627[/C][/ROW]
[ROW][C]16[/C][C]-0.121525[/C][C]-1.0524[/C][C]0.14799[/C][/ROW]
[ROW][C]17[/C][C]-0.05138[/C][C]-0.445[/C][C]0.328814[/C][/ROW]
[ROW][C]18[/C][C]0.075129[/C][C]0.6506[/C][C]0.258634[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148313&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148313&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
10.3058192.64850.004926
20.3379552.92680.002265
30.2396462.07540.020689
4-0.14807-1.28230.10184
50.0128570.11130.45582
60.0513960.44510.328763
70.0780590.6760.250558
8-0.12924-1.11930.133301
90.2958042.56170.006211
100.0869510.7530.226898
11-0.023225-0.20110.420569
120.4883934.22963.3e-05
13-0.178637-1.5470.063031
14-0.147488-1.27730.102721
15-0.127635-1.10540.13627
16-0.121525-1.05240.14799
17-0.05138-0.4450.328814
180.0751290.65060.258634



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 ; par8 = ;
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')