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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationTue, 02 Dec 2008 10:57:26 -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/t1228240796g5dhevhe6uj0ryx.htm/, Retrieved Fri, 17 May 2024 03:21:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28158, Retrieved Fri, 17 May 2024 03:21:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSeverijns Britt
Estimated Impact187

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD    [(Partial) Autocorrelation Function] [non stationary ti...] [2008-12-02 17:57:26] [78308c9f3efc33d1da821bcd963df161] [Current]
-    D      [(Partial) Autocorrelation Function] [non stationary ti...] [2008-12-02 18:02:47] [9ea94c8297ec7e569f27218c1d8ea30f]
F             [(Partial) Autocorrelation Function] [non stationary ti...] [2008-12-02 18:04:45] [4f5e3fd83f430616bbe7746c57513b8b]
Feedback Forum
2008-12-08 18:00:50 [Jessica Alves Pires] [reply] 
Ik zou eerst de VRM gebruikt hebben om te zien welke d en D ik het best kon gebruiken (diegene bij de kleinste variantie). Erna zou ik de differentiatie met de gevonden waarden voor d en D doorvoeren adhv de ACF. Dan zou men kunnen zien of de differentiatie echt iets uithaalt of niet. Nog een bijkomende controlemiddel is de spectrale analyse.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98,1
101,1
111,1
93,3
100
108
70,4
75,4
105,5
112,3
102,5
93,5
86,7
95,2
103,8
97
95,5
101
67,5
64
106,7
100,6
101,2
93,1
84,2
85,8
91,8
92,4
80,3
79,7
62,5
57,1
100,8
100,7
86,2
83,2
71,7
77,5
89,8
80,3
78,7
93,8
57,6
60,6
91
85,3
77,4
77,3
68,3
69,9
81,7
75,1
69,9
84
54,3
60
89,9
77
85,3
77,6
69,2
75,5
85,7
72,2
79,9
85,3
52,2
61,2
82,4
85,4
78,2
70,2
70,2
69,3
77,5
66,1
69
79,2
56,2
63,3
77,8
92
78,1
65,1
71,1
70,9
72
81,9
70,6
72,5
65,1
54,9

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=28158&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=28158&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28158&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
10.4752114.55818e-06
20.1365461.30970.096778
30.2533922.43040.00851
40.2042541.95910.026562
50.3496963.35420.000579
60.4946574.74464e-06
70.3460463.31920.000647
80.225992.16760.016384
90.2009551.92750.028502
100.0602480.57790.282378
110.3312553.17730.001012
120.6913646.63130
130.3114562.98740.001803
140.0012940.01240.495063
150.1069471.02580.153837
160.0758650.72770.234331
170.1743931.67270.04889
180.3172753.04320.001526
190.1741531.67040.049118

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.475211 & 4.5581 & 8e-06 \tabularnewline
2 & 0.136546 & 1.3097 & 0.096778 \tabularnewline
3 & 0.253392 & 2.4304 & 0.00851 \tabularnewline
4 & 0.204254 & 1.9591 & 0.026562 \tabularnewline
5 & 0.349696 & 3.3542 & 0.000579 \tabularnewline
6 & 0.494657 & 4.7446 & 4e-06 \tabularnewline
7 & 0.346046 & 3.3192 & 0.000647 \tabularnewline
8 & 0.22599 & 2.1676 & 0.016384 \tabularnewline
9 & 0.200955 & 1.9275 & 0.028502 \tabularnewline
10 & 0.060248 & 0.5779 & 0.282378 \tabularnewline
11 & 0.331255 & 3.1773 & 0.001012 \tabularnewline
12 & 0.691364 & 6.6313 & 0 \tabularnewline
13 & 0.311456 & 2.9874 & 0.001803 \tabularnewline
14 & 0.001294 & 0.0124 & 0.495063 \tabularnewline
15 & 0.106947 & 1.0258 & 0.153837 \tabularnewline
16 & 0.075865 & 0.7277 & 0.234331 \tabularnewline
17 & 0.174393 & 1.6727 & 0.04889 \tabularnewline
18 & 0.317275 & 3.0432 & 0.001526 \tabularnewline
19 & 0.174153 & 1.6704 & 0.049118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28158&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]0.475211[/C][C]4.5581[/C][C]8e-06[/C][/ROW]
[ROW][C]2[/C][C]0.136546[/C][C]1.3097[/C][C]0.096778[/C][/ROW]
[ROW][C]3[/C][C]0.253392[/C][C]2.4304[/C][C]0.00851[/C][/ROW]
[ROW][C]4[/C][C]0.204254[/C][C]1.9591[/C][C]0.026562[/C][/ROW]
[ROW][C]5[/C][C]0.349696[/C][C]3.3542[/C][C]0.000579[/C][/ROW]
[ROW][C]6[/C][C]0.494657[/C][C]4.7446[/C][C]4e-06[/C][/ROW]
[ROW][C]7[/C][C]0.346046[/C][C]3.3192[/C][C]0.000647[/C][/ROW]
[ROW][C]8[/C][C]0.22599[/C][C]2.1676[/C][C]0.016384[/C][/ROW]
[ROW][C]9[/C][C]0.200955[/C][C]1.9275[/C][C]0.028502[/C][/ROW]
[ROW][C]10[/C][C]0.060248[/C][C]0.5779[/C][C]0.282378[/C][/ROW]
[ROW][C]11[/C][C]0.331255[/C][C]3.1773[/C][C]0.001012[/C][/ROW]
[ROW][C]12[/C][C]0.691364[/C][C]6.6313[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.311456[/C][C]2.9874[/C][C]0.001803[/C][/ROW]
[ROW][C]14[/C][C]0.001294[/C][C]0.0124[/C][C]0.495063[/C][/ROW]
[ROW][C]15[/C][C]0.106947[/C][C]1.0258[/C][C]0.153837[/C][/ROW]
[ROW][C]16[/C][C]0.075865[/C][C]0.7277[/C][C]0.234331[/C][/ROW]
[ROW][C]17[/C][C]0.174393[/C][C]1.6727[/C][C]0.04889[/C][/ROW]
[ROW][C]18[/C][C]0.317275[/C][C]3.0432[/C][C]0.001526[/C][/ROW]
[ROW][C]19[/C][C]0.174153[/C][C]1.6704[/C][C]0.049118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28158&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28158&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
10.4752114.55818e-06
20.1365461.30970.096778
30.2533922.43040.00851
40.2042541.95910.026562
50.3496963.35420.000579
60.4946574.74464e-06
70.3460463.31920.000647
80.225992.16760.016384
90.2009551.92750.028502
100.0602480.57790.282378
110.3312553.17730.001012
120.6913646.63130
130.3114562.98740.001803
140.0012940.01240.495063
150.1069471.02580.153837
160.0758650.72770.234331
170.1743931.67270.04889
180.3172753.04320.001526
190.1741531.67040.049118






Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.4752114.55818e-06
2-0.115322-1.10610.135777
30.3087182.96110.00195
4-0.069001-0.66180.254866
50.4226354.05385.3e-05
60.1638151.57130.059778
70.1313671.260.105423
80.0014230.01360.494571
90.0125260.12010.452316
10-0.249273-2.39090.009421
110.4367194.18893.2e-05
120.340653.26740.000763
13-0.273803-2.62620.005056
14-0.266436-2.55560.006121
150.028010.26870.394396
16-0.077328-0.74170.230077
17-0.069757-0.66910.25256
18-0.098464-0.94440.173711
19-0.024648-0.23640.406818

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.475211 & 4.5581 & 8e-06 \tabularnewline
2 & -0.115322 & -1.1061 & 0.135777 \tabularnewline
3 & 0.308718 & 2.9611 & 0.00195 \tabularnewline
4 & -0.069001 & -0.6618 & 0.254866 \tabularnewline
5 & 0.422635 & 4.0538 & 5.3e-05 \tabularnewline
6 & 0.163815 & 1.5713 & 0.059778 \tabularnewline
7 & 0.131367 & 1.26 & 0.105423 \tabularnewline
8 & 0.001423 & 0.0136 & 0.494571 \tabularnewline
9 & 0.012526 & 0.1201 & 0.452316 \tabularnewline
10 & -0.249273 & -2.3909 & 0.009421 \tabularnewline
11 & 0.436719 & 4.1889 & 3.2e-05 \tabularnewline
12 & 0.34065 & 3.2674 & 0.000763 \tabularnewline
13 & -0.273803 & -2.6262 & 0.005056 \tabularnewline
14 & -0.266436 & -2.5556 & 0.006121 \tabularnewline
15 & 0.02801 & 0.2687 & 0.394396 \tabularnewline
16 & -0.077328 & -0.7417 & 0.230077 \tabularnewline
17 & -0.069757 & -0.6691 & 0.25256 \tabularnewline
18 & -0.098464 & -0.9444 & 0.173711 \tabularnewline
19 & -0.024648 & -0.2364 & 0.406818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28158&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]0.475211[/C][C]4.5581[/C][C]8e-06[/C][/ROW]
[ROW][C]2[/C][C]-0.115322[/C][C]-1.1061[/C][C]0.135777[/C][/ROW]
[ROW][C]3[/C][C]0.308718[/C][C]2.9611[/C][C]0.00195[/C][/ROW]
[ROW][C]4[/C][C]-0.069001[/C][C]-0.6618[/C][C]0.254866[/C][/ROW]
[ROW][C]5[/C][C]0.422635[/C][C]4.0538[/C][C]5.3e-05[/C][/ROW]
[ROW][C]6[/C][C]0.163815[/C][C]1.5713[/C][C]0.059778[/C][/ROW]
[ROW][C]7[/C][C]0.131367[/C][C]1.26[/C][C]0.105423[/C][/ROW]
[ROW][C]8[/C][C]0.001423[/C][C]0.0136[/C][C]0.494571[/C][/ROW]
[ROW][C]9[/C][C]0.012526[/C][C]0.1201[/C][C]0.452316[/C][/ROW]
[ROW][C]10[/C][C]-0.249273[/C][C]-2.3909[/C][C]0.009421[/C][/ROW]
[ROW][C]11[/C][C]0.436719[/C][C]4.1889[/C][C]3.2e-05[/C][/ROW]
[ROW][C]12[/C][C]0.34065[/C][C]3.2674[/C][C]0.000763[/C][/ROW]
[ROW][C]13[/C][C]-0.273803[/C][C]-2.6262[/C][C]0.005056[/C][/ROW]
[ROW][C]14[/C][C]-0.266436[/C][C]-2.5556[/C][C]0.006121[/C][/ROW]
[ROW][C]15[/C][C]0.02801[/C][C]0.2687[/C][C]0.394396[/C][/ROW]
[ROW][C]16[/C][C]-0.077328[/C][C]-0.7417[/C][C]0.230077[/C][/ROW]
[ROW][C]17[/C][C]-0.069757[/C][C]-0.6691[/C][C]0.25256[/C][/ROW]
[ROW][C]18[/C][C]-0.098464[/C][C]-0.9444[/C][C]0.173711[/C][/ROW]
[ROW][C]19[/C][C]-0.024648[/C][C]-0.2364[/C][C]0.406818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28158&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28158&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
10.4752114.55818e-06
2-0.115322-1.10610.135777
30.3087182.96110.00195
4-0.069001-0.66180.254866
50.4226354.05385.3e-05
60.1638151.57130.059778
70.1313671.260.105423
80.0014230.01360.494571
90.0125260.12010.452316
10-0.249273-2.39090.009421
110.4367194.18893.2e-05
120.340653.26740.000763
13-0.273803-2.62620.005056
14-0.266436-2.55560.006121
150.028010.26870.394396
16-0.077328-0.74170.230077
17-0.069757-0.66910.25256
18-0.098464-0.94440.173711
19-0.024648-0.23640.406818


Figures (Output of Computation)

Input Parameters & R Code

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