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Description of Statistical Computation

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 computationWed, 19 Dec 2012 11:17:43 -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/19/t1355933947a3jjv8gi90g9pkz.htm/, Retrieved Sat, 04 May 2024 12:21:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202146, Retrieved Sat, 04 May 2024 12:21:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94

Tree of Dependent Computations

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 PD    [(Partial) Autocorrelation Function] [] [2012-12-10 15:16:35] [74be16979710d4c4e7c6647856088456]
-   P       [(Partial) Autocorrelation Function] [] [2012-12-19 15:26:35] [74be16979710d4c4e7c6647856088456]
-   P           [(Partial) Autocorrelation Function] [] [2012-12-19 16:17:43] [195a7509fef65339447329cdcf8835cc] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
59.8
60.7
59.7
60.2
61.3
59.8
61.2
59.3
59.4
63.1
68
69.4
70.2
72.6
72.1
69.7
71.5
75.7
76
76.4
83.8
86.2
88.5
95.9
103.1
113.5
115.7
113.1
112.7
121.9
120.3
108.7
102.8
83.4
79.4
77.8
85.7
83.2
82
86.9
95.7
97.9
89.3
91.5
86.8
91
93.8
96.8
95.7
91.4
88.7
88.2
87.7
89.5
95.6
100.5
106.3
112
117.7
125
132.4
138.1
134.7
136.7
134.3
131.6
129.8
131.9
129.8
119.4
116.7
112.8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202146&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202146&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202146&T=0

As an alternative you can also use a QR Code:  
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'George Udny Yule' @ yule.wessa.net






Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.9562668.11420
20.8910997.56120
30.8124066.89350
40.7250846.15250
50.6322465.36480
60.5371114.55751e-05
70.4450923.77670.000162
80.3524972.9910.001902
90.2728762.31540.011721
100.2052651.74170.042913
110.1500151.27290.103571
120.0980680.83210.204042
130.0560060.47520.318033
140.0267760.22720.410455
150.0025350.02150.491447
16-0.022536-0.19120.424443
17-0.049397-0.41910.338179
18-0.071643-0.60790.272578

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.956266 & 8.1142 & 0 \tabularnewline
2 & 0.891099 & 7.5612 & 0 \tabularnewline
3 & 0.812406 & 6.8935 & 0 \tabularnewline
4 & 0.725084 & 6.1525 & 0 \tabularnewline
5 & 0.632246 & 5.3648 & 0 \tabularnewline
6 & 0.537111 & 4.5575 & 1e-05 \tabularnewline
7 & 0.445092 & 3.7767 & 0.000162 \tabularnewline
8 & 0.352497 & 2.991 & 0.001902 \tabularnewline
9 & 0.272876 & 2.3154 & 0.011721 \tabularnewline
10 & 0.205265 & 1.7417 & 0.042913 \tabularnewline
11 & 0.150015 & 1.2729 & 0.103571 \tabularnewline
12 & 0.098068 & 0.8321 & 0.204042 \tabularnewline
13 & 0.056006 & 0.4752 & 0.318033 \tabularnewline
14 & 0.026776 & 0.2272 & 0.410455 \tabularnewline
15 & 0.002535 & 0.0215 & 0.491447 \tabularnewline
16 & -0.022536 & -0.1912 & 0.424443 \tabularnewline
17 & -0.049397 & -0.4191 & 0.338179 \tabularnewline
18 & -0.071643 & -0.6079 & 0.272578 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202146&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]0.956266[/C][C]8.1142[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.891099[/C][C]7.5612[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.812406[/C][C]6.8935[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.725084[/C][C]6.1525[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.632246[/C][C]5.3648[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.537111[/C][C]4.5575[/C][C]1e-05[/C][/ROW]
[ROW][C]7[/C][C]0.445092[/C][C]3.7767[/C][C]0.000162[/C][/ROW]
[ROW][C]8[/C][C]0.352497[/C][C]2.991[/C][C]0.001902[/C][/ROW]
[ROW][C]9[/C][C]0.272876[/C][C]2.3154[/C][C]0.011721[/C][/ROW]
[ROW][C]10[/C][C]0.205265[/C][C]1.7417[/C][C]0.042913[/C][/ROW]
[ROW][C]11[/C][C]0.150015[/C][C]1.2729[/C][C]0.103571[/C][/ROW]
[ROW][C]12[/C][C]0.098068[/C][C]0.8321[/C][C]0.204042[/C][/ROW]
[ROW][C]13[/C][C]0.056006[/C][C]0.4752[/C][C]0.318033[/C][/ROW]
[ROW][C]14[/C][C]0.026776[/C][C]0.2272[/C][C]0.410455[/C][/ROW]
[ROW][C]15[/C][C]0.002535[/C][C]0.0215[/C][C]0.491447[/C][/ROW]
[ROW][C]16[/C][C]-0.022536[/C][C]-0.1912[/C][C]0.424443[/C][/ROW]
[ROW][C]17[/C][C]-0.049397[/C][C]-0.4191[/C][C]0.338179[/C][/ROW]
[ROW][C]18[/C][C]-0.071643[/C][C]-0.6079[/C][C]0.272578[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202146&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202146&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.9562668.11420
20.8910997.56120
30.8124066.89350
40.7250846.15250
50.6322465.36480
60.5371114.55751e-05
70.4450923.77670.000162
80.3524972.9910.001902
90.2728762.31540.011721
100.2052651.74170.042913
110.1500151.27290.103571
120.0980680.83210.204042
130.0560060.47520.318033
140.0267760.22720.410455
150.0025350.02150.491447
16-0.022536-0.19120.424443
17-0.049397-0.41910.338179
18-0.071643-0.60790.272578






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9562668.11420
2-0.272862-2.31530.011725
3-0.142775-1.21150.114835
4-0.094152-0.79890.213485
5-0.076083-0.64560.260299
6-0.054724-0.46430.321901
7-0.005641-0.04790.480979
8-0.084844-0.71990.236951
90.0985140.83590.202982
100.0291770.24760.402583
110.025040.21250.41617
12-0.095734-0.81230.20964
130.0346710.29420.384729
140.0532010.45140.32652
15-0.044189-0.3750.354396
16-0.107861-0.91520.181563
17-0.059556-0.50540.307428
180.0364450.30920.379013

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.956266 & 8.1142 & 0 \tabularnewline
2 & -0.272862 & -2.3153 & 0.011725 \tabularnewline
3 & -0.142775 & -1.2115 & 0.114835 \tabularnewline
4 & -0.094152 & -0.7989 & 0.213485 \tabularnewline
5 & -0.076083 & -0.6456 & 0.260299 \tabularnewline
6 & -0.054724 & -0.4643 & 0.321901 \tabularnewline
7 & -0.005641 & -0.0479 & 0.480979 \tabularnewline
8 & -0.084844 & -0.7199 & 0.236951 \tabularnewline
9 & 0.098514 & 0.8359 & 0.202982 \tabularnewline
10 & 0.029177 & 0.2476 & 0.402583 \tabularnewline
11 & 0.02504 & 0.2125 & 0.41617 \tabularnewline
12 & -0.095734 & -0.8123 & 0.20964 \tabularnewline
13 & 0.034671 & 0.2942 & 0.384729 \tabularnewline
14 & 0.053201 & 0.4514 & 0.32652 \tabularnewline
15 & -0.044189 & -0.375 & 0.354396 \tabularnewline
16 & -0.107861 & -0.9152 & 0.181563 \tabularnewline
17 & -0.059556 & -0.5054 & 0.307428 \tabularnewline
18 & 0.036445 & 0.3092 & 0.379013 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202146&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]0.956266[/C][C]8.1142[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.272862[/C][C]-2.3153[/C][C]0.011725[/C][/ROW]
[ROW][C]3[/C][C]-0.142775[/C][C]-1.2115[/C][C]0.114835[/C][/ROW]
[ROW][C]4[/C][C]-0.094152[/C][C]-0.7989[/C][C]0.213485[/C][/ROW]
[ROW][C]5[/C][C]-0.076083[/C][C]-0.6456[/C][C]0.260299[/C][/ROW]
[ROW][C]6[/C][C]-0.054724[/C][C]-0.4643[/C][C]0.321901[/C][/ROW]
[ROW][C]7[/C][C]-0.005641[/C][C]-0.0479[/C][C]0.480979[/C][/ROW]
[ROW][C]8[/C][C]-0.084844[/C][C]-0.7199[/C][C]0.236951[/C][/ROW]
[ROW][C]9[/C][C]0.098514[/C][C]0.8359[/C][C]0.202982[/C][/ROW]
[ROW][C]10[/C][C]0.029177[/C][C]0.2476[/C][C]0.402583[/C][/ROW]
[ROW][C]11[/C][C]0.02504[/C][C]0.2125[/C][C]0.41617[/C][/ROW]
[ROW][C]12[/C][C]-0.095734[/C][C]-0.8123[/C][C]0.20964[/C][/ROW]
[ROW][C]13[/C][C]0.034671[/C][C]0.2942[/C][C]0.384729[/C][/ROW]
[ROW][C]14[/C][C]0.053201[/C][C]0.4514[/C][C]0.32652[/C][/ROW]
[ROW][C]15[/C][C]-0.044189[/C][C]-0.375[/C][C]0.354396[/C][/ROW]
[ROW][C]16[/C][C]-0.107861[/C][C]-0.9152[/C][C]0.181563[/C][/ROW]
[ROW][C]17[/C][C]-0.059556[/C][C]-0.5054[/C][C]0.307428[/C][/ROW]
[ROW][C]18[/C][C]0.036445[/C][C]0.3092[/C][C]0.379013[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202146&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202146&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.9562668.11420
2-0.272862-2.31530.011725
3-0.142775-1.21150.114835
4-0.094152-0.79890.213485
5-0.076083-0.64560.260299
6-0.054724-0.46430.321901
7-0.005641-0.04790.480979
8-0.084844-0.71990.236951
90.0985140.83590.202982
100.0291770.24760.402583
110.025040.21250.41617
12-0.095734-0.81230.20964
130.0346710.29420.384729
140.0532010.45140.32652
15-0.044189-0.3750.354396
16-0.107861-0.91520.181563
17-0.059556-0.50540.307428
180.0364450.30920.379013


Figures (Output of Computation)

Input Parameters & R Code

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')