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Description of Statistical Computation

Author's title

Totale industriële productie index met basis jaar = 2000 (periode 31/1 2004...

Author*Unverified author*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationThu, 26 Nov 2009 02:05:54 -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/Nov/26/t1259226412dazij3v4p5zvjjz.htm/, Retrieved Sun, 28 Apr 2024 21:24:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59716, Retrieved Sun, 28 Apr 2024 21:24:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact184

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [(Partial) Autocorrelation Function] [Identifying Integ...] [2009-11-22 12:26:39] [b98453cac15ba1066b407e146608df68]
- R PD          [(Partial) Autocorrelation Function] [Totale industriël...] [2009-11-26 09:05:54] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
95.1
97
112.7
102.9
97.4
111.4
87.4
96.8
114.1
110.3
103.9
101.6
94.6
95.9
104.7
102.8
98.1
113.9
80.9
95.7
113.2
105.9
108.8
102.3
99
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102
106
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100
110.7
112.8
109.8
117.3
109.1
115.9
96
99.8
116.8
115.7
99.4
94.3
91

Tables (Output of Computation)





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=59716&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=59716&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59716&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.627811-4.34963.5e-05
20.1007610.69810.244245
30.2495641.7290.045115
4-0.240973-1.66950.050764
50.1070150.74140.231024
60.0598580.41470.340102
7-0.135462-0.93850.17634
80.1132270.78450.218313
9-0.020367-0.14110.444189
10-0.06666-0.46180.323144
110.1038030.71920.237761
12-0.082875-0.57420.284266
130.0195490.13540.446415
14-0.006082-0.04210.483282
150.0520190.36040.360066
16-0.100508-0.69630.244786
170.0823090.57030.285582

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.627811 & -4.3496 & 3.5e-05 \tabularnewline
2 & 0.100761 & 0.6981 & 0.244245 \tabularnewline
3 & 0.249564 & 1.729 & 0.045115 \tabularnewline
4 & -0.240973 & -1.6695 & 0.050764 \tabularnewline
5 & 0.107015 & 0.7414 & 0.231024 \tabularnewline
6 & 0.059858 & 0.4147 & 0.340102 \tabularnewline
7 & -0.135462 & -0.9385 & 0.17634 \tabularnewline
8 & 0.113227 & 0.7845 & 0.218313 \tabularnewline
9 & -0.020367 & -0.1411 & 0.444189 \tabularnewline
10 & -0.06666 & -0.4618 & 0.323144 \tabularnewline
11 & 0.103803 & 0.7192 & 0.237761 \tabularnewline
12 & -0.082875 & -0.5742 & 0.284266 \tabularnewline
13 & 0.019549 & 0.1354 & 0.446415 \tabularnewline
14 & -0.006082 & -0.0421 & 0.483282 \tabularnewline
15 & 0.052019 & 0.3604 & 0.360066 \tabularnewline
16 & -0.100508 & -0.6963 & 0.244786 \tabularnewline
17 & 0.082309 & 0.5703 & 0.285582 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59716&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]-0.627811[/C][C]-4.3496[/C][C]3.5e-05[/C][/ROW]
[ROW][C]2[/C][C]0.100761[/C][C]0.6981[/C][C]0.244245[/C][/ROW]
[ROW][C]3[/C][C]0.249564[/C][C]1.729[/C][C]0.045115[/C][/ROW]
[ROW][C]4[/C][C]-0.240973[/C][C]-1.6695[/C][C]0.050764[/C][/ROW]
[ROW][C]5[/C][C]0.107015[/C][C]0.7414[/C][C]0.231024[/C][/ROW]
[ROW][C]6[/C][C]0.059858[/C][C]0.4147[/C][C]0.340102[/C][/ROW]
[ROW][C]7[/C][C]-0.135462[/C][C]-0.9385[/C][C]0.17634[/C][/ROW]
[ROW][C]8[/C][C]0.113227[/C][C]0.7845[/C][C]0.218313[/C][/ROW]
[ROW][C]9[/C][C]-0.020367[/C][C]-0.1411[/C][C]0.444189[/C][/ROW]
[ROW][C]10[/C][C]-0.06666[/C][C]-0.4618[/C][C]0.323144[/C][/ROW]
[ROW][C]11[/C][C]0.103803[/C][C]0.7192[/C][C]0.237761[/C][/ROW]
[ROW][C]12[/C][C]-0.082875[/C][C]-0.5742[/C][C]0.284266[/C][/ROW]
[ROW][C]13[/C][C]0.019549[/C][C]0.1354[/C][C]0.446415[/C][/ROW]
[ROW][C]14[/C][C]-0.006082[/C][C]-0.0421[/C][C]0.483282[/C][/ROW]
[ROW][C]15[/C][C]0.052019[/C][C]0.3604[/C][C]0.360066[/C][/ROW]
[ROW][C]16[/C][C]-0.100508[/C][C]-0.6963[/C][C]0.244786[/C][/ROW]
[ROW][C]17[/C][C]0.082309[/C][C]0.5703[/C][C]0.285582[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59716&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59716&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
1-0.627811-4.34963.5e-05
20.1007610.69810.244245
30.2495641.7290.045115
4-0.240973-1.66950.050764
50.1070150.74140.231024
60.0598580.41470.340102
7-0.135462-0.93850.17634
80.1132270.78450.218313
9-0.020367-0.14110.444189
10-0.06666-0.46180.323144
110.1038030.71920.237761
12-0.082875-0.57420.284266
130.0195490.13540.446415
14-0.006082-0.04210.483282
150.0520190.36040.360066
16-0.100508-0.69630.244786
170.0823090.57030.285582






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.627811-4.34963.5e-05
2-0.484251-3.3550.000779
30.0850350.58910.279264
40.1639721.1360.130792
50.1187280.82260.207409
60.113270.78480.218225
7-0.019234-0.13330.447273
8-0.035303-0.24460.40391
9-0.00138-0.00960.496205
10-0.0351-0.24320.404451
110.0213550.1480.4415
12-0.011183-0.07750.469283
13-0.018799-0.13020.448459
14-0.102622-0.7110.240266
150.0630960.43710.331984
16-0.005163-0.03580.485806
17-0.000218-0.00150.499399

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.627811 & -4.3496 & 3.5e-05 \tabularnewline
2 & -0.484251 & -3.355 & 0.000779 \tabularnewline
3 & 0.085035 & 0.5891 & 0.279264 \tabularnewline
4 & 0.163972 & 1.136 & 0.130792 \tabularnewline
5 & 0.118728 & 0.8226 & 0.207409 \tabularnewline
6 & 0.11327 & 0.7848 & 0.218225 \tabularnewline
7 & -0.019234 & -0.1333 & 0.447273 \tabularnewline
8 & -0.035303 & -0.2446 & 0.40391 \tabularnewline
9 & -0.00138 & -0.0096 & 0.496205 \tabularnewline
10 & -0.0351 & -0.2432 & 0.404451 \tabularnewline
11 & 0.021355 & 0.148 & 0.4415 \tabularnewline
12 & -0.011183 & -0.0775 & 0.469283 \tabularnewline
13 & -0.018799 & -0.1302 & 0.448459 \tabularnewline
14 & -0.102622 & -0.711 & 0.240266 \tabularnewline
15 & 0.063096 & 0.4371 & 0.331984 \tabularnewline
16 & -0.005163 & -0.0358 & 0.485806 \tabularnewline
17 & -0.000218 & -0.0015 & 0.499399 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59716&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]-0.627811[/C][C]-4.3496[/C][C]3.5e-05[/C][/ROW]
[ROW][C]2[/C][C]-0.484251[/C][C]-3.355[/C][C]0.000779[/C][/ROW]
[ROW][C]3[/C][C]0.085035[/C][C]0.5891[/C][C]0.279264[/C][/ROW]
[ROW][C]4[/C][C]0.163972[/C][C]1.136[/C][C]0.130792[/C][/ROW]
[ROW][C]5[/C][C]0.118728[/C][C]0.8226[/C][C]0.207409[/C][/ROW]
[ROW][C]6[/C][C]0.11327[/C][C]0.7848[/C][C]0.218225[/C][/ROW]
[ROW][C]7[/C][C]-0.019234[/C][C]-0.1333[/C][C]0.447273[/C][/ROW]
[ROW][C]8[/C][C]-0.035303[/C][C]-0.2446[/C][C]0.40391[/C][/ROW]
[ROW][C]9[/C][C]-0.00138[/C][C]-0.0096[/C][C]0.496205[/C][/ROW]
[ROW][C]10[/C][C]-0.0351[/C][C]-0.2432[/C][C]0.404451[/C][/ROW]
[ROW][C]11[/C][C]0.021355[/C][C]0.148[/C][C]0.4415[/C][/ROW]
[ROW][C]12[/C][C]-0.011183[/C][C]-0.0775[/C][C]0.469283[/C][/ROW]
[ROW][C]13[/C][C]-0.018799[/C][C]-0.1302[/C][C]0.448459[/C][/ROW]
[ROW][C]14[/C][C]-0.102622[/C][C]-0.711[/C][C]0.240266[/C][/ROW]
[ROW][C]15[/C][C]0.063096[/C][C]0.4371[/C][C]0.331984[/C][/ROW]
[ROW][C]16[/C][C]-0.005163[/C][C]-0.0358[/C][C]0.485806[/C][/ROW]
[ROW][C]17[/C][C]-0.000218[/C][C]-0.0015[/C][C]0.499399[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59716&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59716&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
1-0.627811-4.34963.5e-05
2-0.484251-3.3550.000779
30.0850350.58910.279264
40.1639721.1360.130792
50.1187280.82260.207409
60.113270.78480.218225
7-0.019234-0.13330.447273
8-0.035303-0.24460.40391
9-0.00138-0.00960.496205
10-0.0351-0.24320.404451
110.0213550.1480.4415
12-0.011183-0.07750.469283
13-0.018799-0.13020.448459
14-0.102622-0.7110.240266
150.0630960.43710.331984
16-0.005163-0.03580.485806
17-0.000218-0.00150.499399


Figures (Output of Computation)

Input Parameters & R Code

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