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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 computationThu, 20 Dec 2012 10:54:45 -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/2012/Dec/20/t13560189065i4drp3dn5prwvk.htm/, Retrieved Fri, 29 Mar 2024 09:40:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202809, Retrieved Fri, 29 Mar 2024 09:40:42 +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)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [(Partial) Autocorrelation Function] [Unemployment] [2010-11-29 09:05:21] [b98453cac15ba1066b407e146608df68]
- R       [(Partial) Autocorrelation Function] [WS9.1] [2012-11-29 11:28:56] [1edfe4f7de973a74350ac08c1294a22c]
-    D      [(Partial) Autocorrelation Function] [ACF partial] [2012-12-20 15:42:29] [77d02b0cf2cecd023ffa9a06f056f18d]
- R P         [(Partial) Autocorrelation Function] [acf 2] [2012-12-20 15:51:28] [77d02b0cf2cecd023ffa9a06f056f18d]
-   P             [(Partial) Autocorrelation Function] [acf 3] [2012-12-20 15:54:45] [eef9f4a55a40721b371cf4577ce601c1] [Current]
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Dataseries X:
9700
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 3 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202809&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202809&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202809&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 time3 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.377551-2.66970.005107
2-0.145785-1.03090.153784
30.0046930.03320.48683
4-0.105814-0.74820.228917
50.2445581.72930.044964
60.01480.10460.458536
7-0.29851-2.11080.019907
80.0908330.64230.261811
90.2236611.58150.060032
10-0.075863-0.53640.297019
110.0835550.59080.278649
12-0.367703-2.60.006111
130.0031120.0220.491266
140.428373.0290.001938
15-0.228894-1.61850.05592
160.0131090.09270.463259
17-0.031101-0.21990.413416
18-0.003995-0.02820.488789

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.377551 & -2.6697 & 0.005107 \tabularnewline
2 & -0.145785 & -1.0309 & 0.153784 \tabularnewline
3 & 0.004693 & 0.0332 & 0.48683 \tabularnewline
4 & -0.105814 & -0.7482 & 0.228917 \tabularnewline
5 & 0.244558 & 1.7293 & 0.044964 \tabularnewline
6 & 0.0148 & 0.1046 & 0.458536 \tabularnewline
7 & -0.29851 & -2.1108 & 0.019907 \tabularnewline
8 & 0.090833 & 0.6423 & 0.261811 \tabularnewline
9 & 0.223661 & 1.5815 & 0.060032 \tabularnewline
10 & -0.075863 & -0.5364 & 0.297019 \tabularnewline
11 & 0.083555 & 0.5908 & 0.278649 \tabularnewline
12 & -0.367703 & -2.6 & 0.006111 \tabularnewline
13 & 0.003112 & 0.022 & 0.491266 \tabularnewline
14 & 0.42837 & 3.029 & 0.001938 \tabularnewline
15 & -0.228894 & -1.6185 & 0.05592 \tabularnewline
16 & 0.013109 & 0.0927 & 0.463259 \tabularnewline
17 & -0.031101 & -0.2199 & 0.413416 \tabularnewline
18 & -0.003995 & -0.0282 & 0.488789 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202809&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]-0.377551[/C][C]-2.6697[/C][C]0.005107[/C][/ROW]
[ROW][C]2[/C][C]-0.145785[/C][C]-1.0309[/C][C]0.153784[/C][/ROW]
[ROW][C]3[/C][C]0.004693[/C][C]0.0332[/C][C]0.48683[/C][/ROW]
[ROW][C]4[/C][C]-0.105814[/C][C]-0.7482[/C][C]0.228917[/C][/ROW]
[ROW][C]5[/C][C]0.244558[/C][C]1.7293[/C][C]0.044964[/C][/ROW]
[ROW][C]6[/C][C]0.0148[/C][C]0.1046[/C][C]0.458536[/C][/ROW]
[ROW][C]7[/C][C]-0.29851[/C][C]-2.1108[/C][C]0.019907[/C][/ROW]
[ROW][C]8[/C][C]0.090833[/C][C]0.6423[/C][C]0.261811[/C][/ROW]
[ROW][C]9[/C][C]0.223661[/C][C]1.5815[/C][C]0.060032[/C][/ROW]
[ROW][C]10[/C][C]-0.075863[/C][C]-0.5364[/C][C]0.297019[/C][/ROW]
[ROW][C]11[/C][C]0.083555[/C][C]0.5908[/C][C]0.278649[/C][/ROW]
[ROW][C]12[/C][C]-0.367703[/C][C]-2.6[/C][C]0.006111[/C][/ROW]
[ROW][C]13[/C][C]0.003112[/C][C]0.022[/C][C]0.491266[/C][/ROW]
[ROW][C]14[/C][C]0.42837[/C][C]3.029[/C][C]0.001938[/C][/ROW]
[ROW][C]15[/C][C]-0.228894[/C][C]-1.6185[/C][C]0.05592[/C][/ROW]
[ROW][C]16[/C][C]0.013109[/C][C]0.0927[/C][C]0.463259[/C][/ROW]
[ROW][C]17[/C][C]-0.031101[/C][C]-0.2199[/C][C]0.413416[/C][/ROW]
[ROW][C]18[/C][C]-0.003995[/C][C]-0.0282[/C][C]0.488789[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202809&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202809&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
1-0.377551-2.66970.005107
2-0.145785-1.03090.153784
30.0046930.03320.48683
4-0.105814-0.74820.228917
50.2445581.72930.044964
60.01480.10460.458536
7-0.29851-2.11080.019907
80.0908330.64230.261811
90.2236611.58150.060032
10-0.075863-0.53640.297019
110.0835550.59080.278649
12-0.367703-2.60.006111
130.0031120.0220.491266
140.428373.0290.001938
15-0.228894-1.61850.05592
160.0131090.09270.463259
17-0.031101-0.21990.413416
18-0.003995-0.02820.488789







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.377551-2.66970.005107
2-0.336262-2.37770.010642
3-0.257479-1.82070.037324
4-0.377718-2.67090.005091
5-0.062351-0.44090.330597
60.0582930.41220.34098
7-0.266014-1.8810.0329
8-0.238973-1.68980.048647
90.1235660.87370.193218
100.0779760.55140.291916
110.2194621.55180.063505
12-0.163753-1.15790.126202
13-0.332193-2.3490.011409
140.0460120.32540.373136
15-0.130809-0.9250.179714
16-0.079062-0.55910.289311
170.0008210.00580.497696
180.0985760.6970.244503

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.377551 & -2.6697 & 0.005107 \tabularnewline
2 & -0.336262 & -2.3777 & 0.010642 \tabularnewline
3 & -0.257479 & -1.8207 & 0.037324 \tabularnewline
4 & -0.377718 & -2.6709 & 0.005091 \tabularnewline
5 & -0.062351 & -0.4409 & 0.330597 \tabularnewline
6 & 0.058293 & 0.4122 & 0.34098 \tabularnewline
7 & -0.266014 & -1.881 & 0.0329 \tabularnewline
8 & -0.238973 & -1.6898 & 0.048647 \tabularnewline
9 & 0.123566 & 0.8737 & 0.193218 \tabularnewline
10 & 0.077976 & 0.5514 & 0.291916 \tabularnewline
11 & 0.219462 & 1.5518 & 0.063505 \tabularnewline
12 & -0.163753 & -1.1579 & 0.126202 \tabularnewline
13 & -0.332193 & -2.349 & 0.011409 \tabularnewline
14 & 0.046012 & 0.3254 & 0.373136 \tabularnewline
15 & -0.130809 & -0.925 & 0.179714 \tabularnewline
16 & -0.079062 & -0.5591 & 0.289311 \tabularnewline
17 & 0.000821 & 0.0058 & 0.497696 \tabularnewline
18 & 0.098576 & 0.697 & 0.244503 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202809&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]-0.377551[/C][C]-2.6697[/C][C]0.005107[/C][/ROW]
[ROW][C]2[/C][C]-0.336262[/C][C]-2.3777[/C][C]0.010642[/C][/ROW]
[ROW][C]3[/C][C]-0.257479[/C][C]-1.8207[/C][C]0.037324[/C][/ROW]
[ROW][C]4[/C][C]-0.377718[/C][C]-2.6709[/C][C]0.005091[/C][/ROW]
[ROW][C]5[/C][C]-0.062351[/C][C]-0.4409[/C][C]0.330597[/C][/ROW]
[ROW][C]6[/C][C]0.058293[/C][C]0.4122[/C][C]0.34098[/C][/ROW]
[ROW][C]7[/C][C]-0.266014[/C][C]-1.881[/C][C]0.0329[/C][/ROW]
[ROW][C]8[/C][C]-0.238973[/C][C]-1.6898[/C][C]0.048647[/C][/ROW]
[ROW][C]9[/C][C]0.123566[/C][C]0.8737[/C][C]0.193218[/C][/ROW]
[ROW][C]10[/C][C]0.077976[/C][C]0.5514[/C][C]0.291916[/C][/ROW]
[ROW][C]11[/C][C]0.219462[/C][C]1.5518[/C][C]0.063505[/C][/ROW]
[ROW][C]12[/C][C]-0.163753[/C][C]-1.1579[/C][C]0.126202[/C][/ROW]
[ROW][C]13[/C][C]-0.332193[/C][C]-2.349[/C][C]0.011409[/C][/ROW]
[ROW][C]14[/C][C]0.046012[/C][C]0.3254[/C][C]0.373136[/C][/ROW]
[ROW][C]15[/C][C]-0.130809[/C][C]-0.925[/C][C]0.179714[/C][/ROW]
[ROW][C]16[/C][C]-0.079062[/C][C]-0.5591[/C][C]0.289311[/C][/ROW]
[ROW][C]17[/C][C]0.000821[/C][C]0.0058[/C][C]0.497696[/C][/ROW]
[ROW][C]18[/C][C]0.098576[/C][C]0.697[/C][C]0.244503[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202809&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202809&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
1-0.377551-2.66970.005107
2-0.336262-2.37770.010642
3-0.257479-1.82070.037324
4-0.377718-2.67090.005091
5-0.062351-0.44090.330597
60.0582930.41220.34098
7-0.266014-1.8810.0329
8-0.238973-1.68980.048647
90.1235660.87370.193218
100.0779760.55140.291916
110.2194621.55180.063505
12-0.163753-1.15790.126202
13-0.332193-2.3490.011409
140.0460120.32540.373136
15-0.130809-0.9250.179714
16-0.079062-0.55910.289311
170.0008210.00580.497696
180.0985760.6970.244503



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 2 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 2 ; 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 (par8 != '') par8 <- as.numeric(par8)
ox <- x
if (par8 == '') {
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
} else {
x <- log(x,base=par8)
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='picts.png')
op <- par(mfrow=c(2,1))
plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value')
if (par8=='') {
mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='')
mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
} else {
mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='')
mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
}
plot(x,type='l', main=mytitle,xlab='time',ylab='value')
par(op)
dev.off()
bitmap(file='pic1.png')
racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub)
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub)
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')