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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 computationMon, 19 Dec 2016 15:20:38 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/19/t1482157272kotn5qbxmrtl1fy.htm/, Retrieved Fri, 01 Nov 2024 03:37:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301366, Retrieved Fri, 01 Nov 2024 03:37:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [(Partial) Autocorrelation Function] [auto 1 0] [2016-12-19 14:20:38] [06fd994a2f2098873ec640c3e39346e5] [Current]
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Dataseries X:
4738.4
4687.2
5930.8
5532
5429.8
6107.4
5960.8
5541.8
5362.2
5237
4827
4781.6
4983.2
4718.4
5523.8
5286.6
5389
5810.4
5057.4
5604.4
5285
5215.2
4625.4
4270.4
4685.4
4233.8
5278.4
4978.8
5333.4
5451
5224
5790.2
5079.4
4705.8
4139.6
3720.8
4594
4638.8
4969.4
4764.4
5010.8
5267.8
5312.2
5723.2
4579.6
5015.2
4282.4
3834.2
4523.4
3884.2
3897.8
4845.6
4929
4955.4
5198.4
5122.2
4643.2
4789.8
3950.8
3824.4
4511.8
4262.4
4616.6
5139.6
4972.8
5222
5242
4979.8
4691.8
4821.6
4123.6
4027.4
4365.2
4333.6
4930
5053
5031.4
5342
5191.4
4852.2
4675.6
4689.2
3809.4
4054.2
4409.6
4210.2
4566.4
4907
5021.8
5215.2
4933.6
5197.8
4734.6
4681.8
4172
4037.8
4462.6
4282.6
4962.4
4969.2
5214.6
5416.8
4764.2
5326.2
4545.4
4797.2
4259
4117
4469.2
4203.2
5033.8
4883
5361.6
5044.6
5005.6
5382
4565.4
4825
4290.2
3933.6
4177.6
3949.4
4492.6
4894.2
5224.4
5071
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301366&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301366&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301366&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Autocorrelation Function
Time lag kACF(k)T-STATP-value
1-0.307512-3.43810.000398
20.1622681.81420.036022
30.1558891.74290.041906
4-0.382321-4.27451.9e-05
50.0975381.09050.138794
6-0.371164-4.14973.1e-05
70.0412350.4610.322791
8-0.338943-3.78950.000117
90.1449571.62070.053804
100.1071661.19820.116562
11-0.107487-1.20170.115868
120.667847.46670
13-0.294132-3.28850.000654
140.2859893.19750.000878
150.0792220.88570.188731
16-0.332141-3.71340.000153
170.1370941.53280.063932
18-0.383251-4.28491.8e-05
190.000610.00680.497285
20-0.209465-2.34190.010384
210.036830.41180.340604

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.307512 & -3.4381 & 0.000398 \tabularnewline
2 & 0.162268 & 1.8142 & 0.036022 \tabularnewline
3 & 0.155889 & 1.7429 & 0.041906 \tabularnewline
4 & -0.382321 & -4.2745 & 1.9e-05 \tabularnewline
5 & 0.097538 & 1.0905 & 0.138794 \tabularnewline
6 & -0.371164 & -4.1497 & 3.1e-05 \tabularnewline
7 & 0.041235 & 0.461 & 0.322791 \tabularnewline
8 & -0.338943 & -3.7895 & 0.000117 \tabularnewline
9 & 0.144957 & 1.6207 & 0.053804 \tabularnewline
10 & 0.107166 & 1.1982 & 0.116562 \tabularnewline
11 & -0.107487 & -1.2017 & 0.115868 \tabularnewline
12 & 0.66784 & 7.4667 & 0 \tabularnewline
13 & -0.294132 & -3.2885 & 0.000654 \tabularnewline
14 & 0.285989 & 3.1975 & 0.000878 \tabularnewline
15 & 0.079222 & 0.8857 & 0.188731 \tabularnewline
16 & -0.332141 & -3.7134 & 0.000153 \tabularnewline
17 & 0.137094 & 1.5328 & 0.063932 \tabularnewline
18 & -0.383251 & -4.2849 & 1.8e-05 \tabularnewline
19 & 0.00061 & 0.0068 & 0.497285 \tabularnewline
20 & -0.209465 & -2.3419 & 0.010384 \tabularnewline
21 & 0.03683 & 0.4118 & 0.340604 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301366&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]-0.307512[/C][C]-3.4381[/C][C]0.000398[/C][/ROW]
[ROW][C]2[/C][C]0.162268[/C][C]1.8142[/C][C]0.036022[/C][/ROW]
[ROW][C]3[/C][C]0.155889[/C][C]1.7429[/C][C]0.041906[/C][/ROW]
[ROW][C]4[/C][C]-0.382321[/C][C]-4.2745[/C][C]1.9e-05[/C][/ROW]
[ROW][C]5[/C][C]0.097538[/C][C]1.0905[/C][C]0.138794[/C][/ROW]
[ROW][C]6[/C][C]-0.371164[/C][C]-4.1497[/C][C]3.1e-05[/C][/ROW]
[ROW][C]7[/C][C]0.041235[/C][C]0.461[/C][C]0.322791[/C][/ROW]
[ROW][C]8[/C][C]-0.338943[/C][C]-3.7895[/C][C]0.000117[/C][/ROW]
[ROW][C]9[/C][C]0.144957[/C][C]1.6207[/C][C]0.053804[/C][/ROW]
[ROW][C]10[/C][C]0.107166[/C][C]1.1982[/C][C]0.116562[/C][/ROW]
[ROW][C]11[/C][C]-0.107487[/C][C]-1.2017[/C][C]0.115868[/C][/ROW]
[ROW][C]12[/C][C]0.66784[/C][C]7.4667[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]-0.294132[/C][C]-3.2885[/C][C]0.000654[/C][/ROW]
[ROW][C]14[/C][C]0.285989[/C][C]3.1975[/C][C]0.000878[/C][/ROW]
[ROW][C]15[/C][C]0.079222[/C][C]0.8857[/C][C]0.188731[/C][/ROW]
[ROW][C]16[/C][C]-0.332141[/C][C]-3.7134[/C][C]0.000153[/C][/ROW]
[ROW][C]17[/C][C]0.137094[/C][C]1.5328[/C][C]0.063932[/C][/ROW]
[ROW][C]18[/C][C]-0.383251[/C][C]-4.2849[/C][C]1.8e-05[/C][/ROW]
[ROW][C]19[/C][C]0.00061[/C][C]0.0068[/C][C]0.497285[/C][/ROW]
[ROW][C]20[/C][C]-0.209465[/C][C]-2.3419[/C][C]0.010384[/C][/ROW]
[ROW][C]21[/C][C]0.03683[/C][C]0.4118[/C][C]0.340604[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301366&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301366&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
1-0.307512-3.43810.000398
20.1622681.81420.036022
30.1558891.74290.041906
4-0.382321-4.27451.9e-05
50.0975381.09050.138794
6-0.371164-4.14973.1e-05
70.0412350.4610.322791
8-0.338943-3.78950.000117
90.1449571.62070.053804
100.1071661.19820.116562
11-0.107487-1.20170.115868
120.667847.46670
13-0.294132-3.28850.000654
140.2859893.19750.000878
150.0792220.88570.188731
16-0.332141-3.71340.000153
170.1370941.53280.063932
18-0.383251-4.28491.8e-05
190.000610.00680.497285
20-0.209465-2.34190.010384
210.036830.41180.340604







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.307512-3.43810.000398
20.0747750.8360.202373
30.2499532.79460.003008
4-0.333914-3.73330.000143
5-0.1915-2.1410.017106
6-0.394564-4.41141.1e-05
7-0.044985-0.50290.307945
8-0.543388-6.07530
9-0.10054-1.12410.131569
10-0.169503-1.89510.030194
11-0.164604-1.84030.034046
120.3301433.69110.000166
13-0.053573-0.5990.275141
140.0422080.47190.318911
150.0633130.70790.240175
16-0.040863-0.45690.324282
170.0011660.0130.49481
18-0.044602-0.49870.309446
19-0.054267-0.60670.272568
200.0356290.39830.345528
21-0.146062-1.6330.052491

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.307512 & -3.4381 & 0.000398 \tabularnewline
2 & 0.074775 & 0.836 & 0.202373 \tabularnewline
3 & 0.249953 & 2.7946 & 0.003008 \tabularnewline
4 & -0.333914 & -3.7333 & 0.000143 \tabularnewline
5 & -0.1915 & -2.141 & 0.017106 \tabularnewline
6 & -0.394564 & -4.4114 & 1.1e-05 \tabularnewline
7 & -0.044985 & -0.5029 & 0.307945 \tabularnewline
8 & -0.543388 & -6.0753 & 0 \tabularnewline
9 & -0.10054 & -1.1241 & 0.131569 \tabularnewline
10 & -0.169503 & -1.8951 & 0.030194 \tabularnewline
11 & -0.164604 & -1.8403 & 0.034046 \tabularnewline
12 & 0.330143 & 3.6911 & 0.000166 \tabularnewline
13 & -0.053573 & -0.599 & 0.275141 \tabularnewline
14 & 0.042208 & 0.4719 & 0.318911 \tabularnewline
15 & 0.063313 & 0.7079 & 0.240175 \tabularnewline
16 & -0.040863 & -0.4569 & 0.324282 \tabularnewline
17 & 0.001166 & 0.013 & 0.49481 \tabularnewline
18 & -0.044602 & -0.4987 & 0.309446 \tabularnewline
19 & -0.054267 & -0.6067 & 0.272568 \tabularnewline
20 & 0.035629 & 0.3983 & 0.345528 \tabularnewline
21 & -0.146062 & -1.633 & 0.052491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301366&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]-0.307512[/C][C]-3.4381[/C][C]0.000398[/C][/ROW]
[ROW][C]2[/C][C]0.074775[/C][C]0.836[/C][C]0.202373[/C][/ROW]
[ROW][C]3[/C][C]0.249953[/C][C]2.7946[/C][C]0.003008[/C][/ROW]
[ROW][C]4[/C][C]-0.333914[/C][C]-3.7333[/C][C]0.000143[/C][/ROW]
[ROW][C]5[/C][C]-0.1915[/C][C]-2.141[/C][C]0.017106[/C][/ROW]
[ROW][C]6[/C][C]-0.394564[/C][C]-4.4114[/C][C]1.1e-05[/C][/ROW]
[ROW][C]7[/C][C]-0.044985[/C][C]-0.5029[/C][C]0.307945[/C][/ROW]
[ROW][C]8[/C][C]-0.543388[/C][C]-6.0753[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]-0.10054[/C][C]-1.1241[/C][C]0.131569[/C][/ROW]
[ROW][C]10[/C][C]-0.169503[/C][C]-1.8951[/C][C]0.030194[/C][/ROW]
[ROW][C]11[/C][C]-0.164604[/C][C]-1.8403[/C][C]0.034046[/C][/ROW]
[ROW][C]12[/C][C]0.330143[/C][C]3.6911[/C][C]0.000166[/C][/ROW]
[ROW][C]13[/C][C]-0.053573[/C][C]-0.599[/C][C]0.275141[/C][/ROW]
[ROW][C]14[/C][C]0.042208[/C][C]0.4719[/C][C]0.318911[/C][/ROW]
[ROW][C]15[/C][C]0.063313[/C][C]0.7079[/C][C]0.240175[/C][/ROW]
[ROW][C]16[/C][C]-0.040863[/C][C]-0.4569[/C][C]0.324282[/C][/ROW]
[ROW][C]17[/C][C]0.001166[/C][C]0.013[/C][C]0.49481[/C][/ROW]
[ROW][C]18[/C][C]-0.044602[/C][C]-0.4987[/C][C]0.309446[/C][/ROW]
[ROW][C]19[/C][C]-0.054267[/C][C]-0.6067[/C][C]0.272568[/C][/ROW]
[ROW][C]20[/C][C]0.035629[/C][C]0.3983[/C][C]0.345528[/C][/ROW]
[ROW][C]21[/C][C]-0.146062[/C][C]-1.633[/C][C]0.052491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301366&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301366&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
1-0.307512-3.43810.000398
20.0747750.8360.202373
30.2499532.79460.003008
4-0.333914-3.73330.000143
5-0.1915-2.1410.017106
6-0.394564-4.41141.1e-05
7-0.044985-0.50290.307945
8-0.543388-6.07530
9-0.10054-1.12410.131569
10-0.169503-1.89510.030194
11-0.164604-1.84030.034046
120.3301433.69110.000166
13-0.053573-0.5990.275141
140.0422080.47190.318911
150.0633130.70790.240175
16-0.040863-0.45690.324282
170.0011660.0130.49481
18-0.044602-0.49870.309446
19-0.054267-0.60670.272568
200.0356290.39830.345528
21-0.146062-1.6330.052491



Parameters (Session):
Parameters (R input):
par1 = Default ; par2 = 1 ; par3 = 1 ; 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 (par8 != '') par8 <- as.numeric(par8)
x <- na.omit(x)
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,'ACF(k)',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,'PACF(k)',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')