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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 computationTue, 02 Dec 2008 13:04:35 -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/2008/Dec/02/t1228248365mzxqnw3lwy136li.htm/, Retrieved Fri, 17 May 2024 06:59:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28321, Retrieved Fri, 17 May 2024 06:59:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsPartial correlation function : Q8
Estimated Impact137

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [(Partial) Autocorrelation Function] [Partial correlati...] [2008-12-02 20:04:35] [0cdfeda4aa2f9e551c2e529c44a404df] [Current]
- RMPD    [Cross Correlation Function] [Cross correlatie ...] [2008-12-02 20:15:37] [12d343c4448a5f9e527bb31caeac580b]
-   PD      [Cross Correlation Function] [Partial correlati...] [2008-12-02 20:18:21] [12d343c4448a5f9e527bb31caeac580b]
- RMPD      [Variance Reduction Matrix] [Variance reductio...] [2008-12-02 20:34:12] [12d343c4448a5f9e527bb31caeac580b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98,6
98
106,8
96,6
100,1
107,7
91,5
97,8
107,4
117,5
105,6
97,4
99,5
98
104,3
100,6
101,1
103,9
96,9
95,5
108,4
117
103,8
100,8
110,6
104
112,6
107,3
98,9
109,8
104,9
102,2
123,9
124,9
112,7
121,9
100,6
104,3
120,4
107,5
102,9
125,6
107,5
108,8
128,4
121,1
119,5
128,7
108,7
105,5
119,8
111,3
110,6
120,1
97,5
107,7
127,3
117,2
119,8
116,2
111
112,4
130,6
109,1
118,8
123,9
101,6
112,8
128
129,6
125,8
119,5
115,7
113,6
129,7
112
116,8
127
112,1
114,2
121,1
131,6
125
120,4
117,7

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'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28321&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28321&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28321&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'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.1458241.24590.108388
20.0602230.51450.304212
30.2591682.21430.014964
4-0.03346-0.28590.387889
50.0628340.53690.296501
60.2001591.71020.045742
7-0.129072-1.10280.136871
80.1249351.06740.144645
90.2054151.75510.041721
10-0.210361-1.79730.03821
11-0.077267-0.66020.255611
12-0.205864-1.75890.041392
13-0.25341-2.16510.016823
140.0874890.74750.228581
15-0.131524-1.12370.132402
16-0.150556-1.28630.101193
170.2105071.79860.03811
18-0.163627-1.3980.083169
19-0.193823-1.6560.051004

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.145824 & 1.2459 & 0.108388 \tabularnewline
2 & 0.060223 & 0.5145 & 0.304212 \tabularnewline
3 & 0.259168 & 2.2143 & 0.014964 \tabularnewline
4 & -0.03346 & -0.2859 & 0.387889 \tabularnewline
5 & 0.062834 & 0.5369 & 0.296501 \tabularnewline
6 & 0.200159 & 1.7102 & 0.045742 \tabularnewline
7 & -0.129072 & -1.1028 & 0.136871 \tabularnewline
8 & 0.124935 & 1.0674 & 0.144645 \tabularnewline
9 & 0.205415 & 1.7551 & 0.041721 \tabularnewline
10 & -0.210361 & -1.7973 & 0.03821 \tabularnewline
11 & -0.077267 & -0.6602 & 0.255611 \tabularnewline
12 & -0.205864 & -1.7589 & 0.041392 \tabularnewline
13 & -0.25341 & -2.1651 & 0.016823 \tabularnewline
14 & 0.087489 & 0.7475 & 0.228581 \tabularnewline
15 & -0.131524 & -1.1237 & 0.132402 \tabularnewline
16 & -0.150556 & -1.2863 & 0.101193 \tabularnewline
17 & 0.210507 & 1.7986 & 0.03811 \tabularnewline
18 & -0.163627 & -1.398 & 0.083169 \tabularnewline
19 & -0.193823 & -1.656 & 0.051004 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28321&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]0.145824[/C][C]1.2459[/C][C]0.108388[/C][/ROW]
[ROW][C]2[/C][C]0.060223[/C][C]0.5145[/C][C]0.304212[/C][/ROW]
[ROW][C]3[/C][C]0.259168[/C][C]2.2143[/C][C]0.014964[/C][/ROW]
[ROW][C]4[/C][C]-0.03346[/C][C]-0.2859[/C][C]0.387889[/C][/ROW]
[ROW][C]5[/C][C]0.062834[/C][C]0.5369[/C][C]0.296501[/C][/ROW]
[ROW][C]6[/C][C]0.200159[/C][C]1.7102[/C][C]0.045742[/C][/ROW]
[ROW][C]7[/C][C]-0.129072[/C][C]-1.1028[/C][C]0.136871[/C][/ROW]
[ROW][C]8[/C][C]0.124935[/C][C]1.0674[/C][C]0.144645[/C][/ROW]
[ROW][C]9[/C][C]0.205415[/C][C]1.7551[/C][C]0.041721[/C][/ROW]
[ROW][C]10[/C][C]-0.210361[/C][C]-1.7973[/C][C]0.03821[/C][/ROW]
[ROW][C]11[/C][C]-0.077267[/C][C]-0.6602[/C][C]0.255611[/C][/ROW]
[ROW][C]12[/C][C]-0.205864[/C][C]-1.7589[/C][C]0.041392[/C][/ROW]
[ROW][C]13[/C][C]-0.25341[/C][C]-2.1651[/C][C]0.016823[/C][/ROW]
[ROW][C]14[/C][C]0.087489[/C][C]0.7475[/C][C]0.228581[/C][/ROW]
[ROW][C]15[/C][C]-0.131524[/C][C]-1.1237[/C][C]0.132402[/C][/ROW]
[ROW][C]16[/C][C]-0.150556[/C][C]-1.2863[/C][C]0.101193[/C][/ROW]
[ROW][C]17[/C][C]0.210507[/C][C]1.7986[/C][C]0.03811[/C][/ROW]
[ROW][C]18[/C][C]-0.163627[/C][C]-1.398[/C][C]0.083169[/C][/ROW]
[ROW][C]19[/C][C]-0.193823[/C][C]-1.656[/C][C]0.051004[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28321&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28321&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
10.1458241.24590.108388
20.0602230.51450.304212
30.2591682.21430.014964
4-0.03346-0.28590.387889
50.0628340.53690.296501
60.2001591.71020.045742
7-0.129072-1.10280.136871
80.1249351.06740.144645
90.2054151.75510.041721
10-0.210361-1.79730.03821
11-0.077267-0.66020.255611
12-0.205864-1.75890.041392
13-0.25341-2.16510.016823
140.0874890.74750.228581
15-0.131524-1.12370.132402
16-0.150556-1.28630.101193
170.2105071.79860.03811
18-0.163627-1.3980.083169
19-0.193823-1.6560.051004






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.1458241.24590.108388
20.0398050.34010.367382
30.250652.14160.017783
4-0.11356-0.97030.167561
50.0745080.63660.263189
60.130351.11370.134529
7-0.161972-1.38390.085304
80.1513841.29340.099971
90.1099760.93960.175253
10-0.220501-1.8840.031776
11-0.119569-1.02160.155172
12-0.281429-2.40450.009365
13-0.053219-0.45470.325336
140.1302771.11310.134661
15-0.099903-0.85360.198066
160.045550.38920.349138
170.191371.63510.05317
18-0.155216-1.32620.094459
19-0.08565-0.73180.233319

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.145824 & 1.2459 & 0.108388 \tabularnewline
2 & 0.039805 & 0.3401 & 0.367382 \tabularnewline
3 & 0.25065 & 2.1416 & 0.017783 \tabularnewline
4 & -0.11356 & -0.9703 & 0.167561 \tabularnewline
5 & 0.074508 & 0.6366 & 0.263189 \tabularnewline
6 & 0.13035 & 1.1137 & 0.134529 \tabularnewline
7 & -0.161972 & -1.3839 & 0.085304 \tabularnewline
8 & 0.151384 & 1.2934 & 0.099971 \tabularnewline
9 & 0.109976 & 0.9396 & 0.175253 \tabularnewline
10 & -0.220501 & -1.884 & 0.031776 \tabularnewline
11 & -0.119569 & -1.0216 & 0.155172 \tabularnewline
12 & -0.281429 & -2.4045 & 0.009365 \tabularnewline
13 & -0.053219 & -0.4547 & 0.325336 \tabularnewline
14 & 0.130277 & 1.1131 & 0.134661 \tabularnewline
15 & -0.099903 & -0.8536 & 0.198066 \tabularnewline
16 & 0.04555 & 0.3892 & 0.349138 \tabularnewline
17 & 0.19137 & 1.6351 & 0.05317 \tabularnewline
18 & -0.155216 & -1.3262 & 0.094459 \tabularnewline
19 & -0.08565 & -0.7318 & 0.233319 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28321&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]0.145824[/C][C]1.2459[/C][C]0.108388[/C][/ROW]
[ROW][C]2[/C][C]0.039805[/C][C]0.3401[/C][C]0.367382[/C][/ROW]
[ROW][C]3[/C][C]0.25065[/C][C]2.1416[/C][C]0.017783[/C][/ROW]
[ROW][C]4[/C][C]-0.11356[/C][C]-0.9703[/C][C]0.167561[/C][/ROW]
[ROW][C]5[/C][C]0.074508[/C][C]0.6366[/C][C]0.263189[/C][/ROW]
[ROW][C]6[/C][C]0.13035[/C][C]1.1137[/C][C]0.134529[/C][/ROW]
[ROW][C]7[/C][C]-0.161972[/C][C]-1.3839[/C][C]0.085304[/C][/ROW]
[ROW][C]8[/C][C]0.151384[/C][C]1.2934[/C][C]0.099971[/C][/ROW]
[ROW][C]9[/C][C]0.109976[/C][C]0.9396[/C][C]0.175253[/C][/ROW]
[ROW][C]10[/C][C]-0.220501[/C][C]-1.884[/C][C]0.031776[/C][/ROW]
[ROW][C]11[/C][C]-0.119569[/C][C]-1.0216[/C][C]0.155172[/C][/ROW]
[ROW][C]12[/C][C]-0.281429[/C][C]-2.4045[/C][C]0.009365[/C][/ROW]
[ROW][C]13[/C][C]-0.053219[/C][C]-0.4547[/C][C]0.325336[/C][/ROW]
[ROW][C]14[/C][C]0.130277[/C][C]1.1131[/C][C]0.134661[/C][/ROW]
[ROW][C]15[/C][C]-0.099903[/C][C]-0.8536[/C][C]0.198066[/C][/ROW]
[ROW][C]16[/C][C]0.04555[/C][C]0.3892[/C][C]0.349138[/C][/ROW]
[ROW][C]17[/C][C]0.19137[/C][C]1.6351[/C][C]0.05317[/C][/ROW]
[ROW][C]18[/C][C]-0.155216[/C][C]-1.3262[/C][C]0.094459[/C][/ROW]
[ROW][C]19[/C][C]-0.08565[/C][C]-0.7318[/C][C]0.233319[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28321&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28321&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
10.1458241.24590.108388
20.0398050.34010.367382
30.250652.14160.017783
4-0.11356-0.97030.167561
50.0745080.63660.263189
60.130351.11370.134529
7-0.161972-1.38390.085304
80.1513841.29340.099971
90.1099760.93960.175253
10-0.220501-1.8840.031776
11-0.119569-1.02160.155172
12-0.281429-2.40450.009365
13-0.053219-0.45470.325336
140.1302771.11310.134661
15-0.099903-0.85360.198066
160.045550.38920.349138
170.191371.63510.05317
18-0.155216-1.32620.094459
19-0.08565-0.73180.233319


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ;
Parameters (R input):
par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ;
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 (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='lags',ylab='ACF')
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')