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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 computationTue, 14 Dec 2010 14:15:36 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/14/t12923360045jenhm1wis2v845.htm/, Retrieved Thu, 02 May 2024 18:55:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109674, Retrieved Thu, 02 May 2024 18:55:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [(Partial) Autocorrelation Function] [] [2010-12-14 14:15:36] [c474a97a96075919a678ad3d2290b00b] [Current]
-   P     [(Partial) Autocorrelation Function] [] [2010-12-14 14:35:40] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   P     [(Partial) Autocorrelation Function] [] [2010-12-19 16:00:34] [acfa3f91ce5598ec4ba98aad4cfba2f0]
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Dataseries X:
1145.11
1176.86
1206.41
1192.72
1214.82
1199.07
1157.47
1100.1
1095.63
1105.63
1137.79
1124.72
1152.6
1211.85
1239.62
1244.13
1198.42
1227.99
1304.92
1340.26
1307.32
1356.51
1383.29
1437.87
1494.56
1521.42
1498.76
1488.75
1524.62
1439.27
1423.11
1466.85
1425.83
1363.45
1389.18
1395.89
1368.43
1349.03
1299.88
1365.41
1451.04
1433.75
1464.65
1475.57
1471.16
1429.12
1452.46
1538.09
1631.59
1665.5
1690.6
1711.74
1734.1
1748.09
1703.45
1745.74
1751.01
1795.65
1852.13
1877.1
1989.31
2097.76
2154.87
2152.18
2250.27
2346.9
2525.56
2409.36
2394.36
2401.33
2354.32
2450.41
2504.67
2661.39
2880.4
3064.42
3141.12
3327.7
3564.95
3403.13
3149.9
3006.84
3230.66
3361.13
3484.74
3411.13
3288.18
3280.37
3173.95
3165.26
3092.71
3053.05
3181.96
2999.93
3249.57
3210.52
3030.29
2803.47
2767.63
2882.6
2863.36
2897.06
3012.61
3142.95
3032.93
3045.78
3110.52
3013.24
2987.1
2995.55
2833.18
2848.96
2794.83
2845.26
2915.02
2892.63
2604.42
2641.65
2659.81
2638.53
2720.25
2745.88
2735.7
2811.7
2799.43
2555.28
2304.98
2214.95
2065.81
1940.49
2042
1995.37
1946.81
1765.9
1635.25
1833.42
1910.43
1959.67
1969.6
2061.41
2093.48
2120.88
2174.56
2196.72
2350.44
2440.25
2408.64
2472.81
2407.6
2454.62
2448.05
2497.84
2645.64
2756.76
2849.27
2921.44
2981.85
3080.58
3106.22
3119.31
3061.26
3097.31
3161.69
3257.16
3277.01
3295.32
3363.99
3494.17
3667.03
3813.06
3917.96
3895.51
3801.06
3570.12
3701.61
3862.27
3970.1
4138.52
4199.75
4290.89
4443.91
4502.64
4356.98
4591.27
4696.96
4621.4
4562.84
4202.52
4296.49
4435.23
4105.18
4116.68
3844.49
3720.98
3674.4
3857.62
3801.06
3504.37
3032.6
3047.03
2962.34
2197.82
2014.45
1862.83
1905.41
1810.99
1670.07
1864.44
2052.02
2029.6
2070.83
2293.41
2443.27
2513.17
2466.92
2502.66




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109674&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109674&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109674&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.98574414.48740
20.96642914.20350
30.94615413.90560
40.9232613.56910
50.89782313.19530
60.8689812.77130
70.84009312.34680
80.81056411.91280
90.77857111.44260
100.74485910.94720
110.712410.47010
120.6781429.96660
130.6429789.44980
140.6089338.94950
150.5749188.44950
160.5418017.96280
170.5068177.44870
180.4715946.9310
190.4380156.43750
200.4047445.94850
210.3728155.47920
220.3425985.03511e-06
230.3147064.62523e-06

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.985744 & 14.4874 & 0 \tabularnewline
2 & 0.966429 & 14.2035 & 0 \tabularnewline
3 & 0.946154 & 13.9056 & 0 \tabularnewline
4 & 0.92326 & 13.5691 & 0 \tabularnewline
5 & 0.897823 & 13.1953 & 0 \tabularnewline
6 & 0.86898 & 12.7713 & 0 \tabularnewline
7 & 0.840093 & 12.3468 & 0 \tabularnewline
8 & 0.810564 & 11.9128 & 0 \tabularnewline
9 & 0.778571 & 11.4426 & 0 \tabularnewline
10 & 0.744859 & 10.9472 & 0 \tabularnewline
11 & 0.7124 & 10.4701 & 0 \tabularnewline
12 & 0.678142 & 9.9666 & 0 \tabularnewline
13 & 0.642978 & 9.4498 & 0 \tabularnewline
14 & 0.608933 & 8.9495 & 0 \tabularnewline
15 & 0.574918 & 8.4495 & 0 \tabularnewline
16 & 0.541801 & 7.9628 & 0 \tabularnewline
17 & 0.506817 & 7.4487 & 0 \tabularnewline
18 & 0.471594 & 6.931 & 0 \tabularnewline
19 & 0.438015 & 6.4375 & 0 \tabularnewline
20 & 0.404744 & 5.9485 & 0 \tabularnewline
21 & 0.372815 & 5.4792 & 0 \tabularnewline
22 & 0.342598 & 5.0351 & 1e-06 \tabularnewline
23 & 0.314706 & 4.6252 & 3e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109674&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]0.985744[/C][C]14.4874[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.966429[/C][C]14.2035[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.946154[/C][C]13.9056[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.92326[/C][C]13.5691[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.897823[/C][C]13.1953[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.86898[/C][C]12.7713[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.840093[/C][C]12.3468[/C][C]0[/C][/ROW]
[ROW][C]8[/C][C]0.810564[/C][C]11.9128[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.778571[/C][C]11.4426[/C][C]0[/C][/ROW]
[ROW][C]10[/C][C]0.744859[/C][C]10.9472[/C][C]0[/C][/ROW]
[ROW][C]11[/C][C]0.7124[/C][C]10.4701[/C][C]0[/C][/ROW]
[ROW][C]12[/C][C]0.678142[/C][C]9.9666[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.642978[/C][C]9.4498[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]0.608933[/C][C]8.9495[/C][C]0[/C][/ROW]
[ROW][C]15[/C][C]0.574918[/C][C]8.4495[/C][C]0[/C][/ROW]
[ROW][C]16[/C][C]0.541801[/C][C]7.9628[/C][C]0[/C][/ROW]
[ROW][C]17[/C][C]0.506817[/C][C]7.4487[/C][C]0[/C][/ROW]
[ROW][C]18[/C][C]0.471594[/C][C]6.931[/C][C]0[/C][/ROW]
[ROW][C]19[/C][C]0.438015[/C][C]6.4375[/C][C]0[/C][/ROW]
[ROW][C]20[/C][C]0.404744[/C][C]5.9485[/C][C]0[/C][/ROW]
[ROW][C]21[/C][C]0.372815[/C][C]5.4792[/C][C]0[/C][/ROW]
[ROW][C]22[/C][C]0.342598[/C][C]5.0351[/C][C]1e-06[/C][/ROW]
[ROW][C]23[/C][C]0.314706[/C][C]4.6252[/C][C]3e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109674&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109674&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
10.98574414.48740
20.96642914.20350
30.94615413.90560
40.9232613.56910
50.89782313.19530
60.8689812.77130
70.84009312.34680
80.81056411.91280
90.77857111.44260
100.74485910.94720
110.712410.47010
120.6781429.96660
130.6429789.44980
140.6089338.94950
150.5749188.44950
160.5418017.96280
170.5068177.44870
180.4715946.9310
190.4380156.43750
200.4047445.94850
210.3728155.47920
220.3425985.03511e-06
230.3147064.62523e-06







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.98574414.48740
2-0.1859-2.73220.003406
3-0.012627-0.18560.426474
4-0.099807-1.46690.071934
5-0.073512-1.08040.140584
6-0.112183-1.64870.050327
70.0273820.40240.343883
8-0.035615-0.52340.300607
9-0.080002-1.17580.120486
10-0.048081-0.70660.240276
110.0530620.77980.218169
12-0.100531-1.47750.0705
13-0.012077-0.17750.429645
140.0350960.51580.30326
15-0.027922-0.41040.340973
160.0071870.10560.457991
17-0.084163-1.23690.108726
18-0.010983-0.16140.435959
190.0152750.22450.411294
20-0.016309-0.23970.405399
210.0367240.53970.294967
220.0192480.28290.388768
230.0375140.55130.290985

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.985744 & 14.4874 & 0 \tabularnewline
2 & -0.1859 & -2.7322 & 0.003406 \tabularnewline
3 & -0.012627 & -0.1856 & 0.426474 \tabularnewline
4 & -0.099807 & -1.4669 & 0.071934 \tabularnewline
5 & -0.073512 & -1.0804 & 0.140584 \tabularnewline
6 & -0.112183 & -1.6487 & 0.050327 \tabularnewline
7 & 0.027382 & 0.4024 & 0.343883 \tabularnewline
8 & -0.035615 & -0.5234 & 0.300607 \tabularnewline
9 & -0.080002 & -1.1758 & 0.120486 \tabularnewline
10 & -0.048081 & -0.7066 & 0.240276 \tabularnewline
11 & 0.053062 & 0.7798 & 0.218169 \tabularnewline
12 & -0.100531 & -1.4775 & 0.0705 \tabularnewline
13 & -0.012077 & -0.1775 & 0.429645 \tabularnewline
14 & 0.035096 & 0.5158 & 0.30326 \tabularnewline
15 & -0.027922 & -0.4104 & 0.340973 \tabularnewline
16 & 0.007187 & 0.1056 & 0.457991 \tabularnewline
17 & -0.084163 & -1.2369 & 0.108726 \tabularnewline
18 & -0.010983 & -0.1614 & 0.435959 \tabularnewline
19 & 0.015275 & 0.2245 & 0.411294 \tabularnewline
20 & -0.016309 & -0.2397 & 0.405399 \tabularnewline
21 & 0.036724 & 0.5397 & 0.294967 \tabularnewline
22 & 0.019248 & 0.2829 & 0.388768 \tabularnewline
23 & 0.037514 & 0.5513 & 0.290985 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109674&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]0.985744[/C][C]14.4874[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.1859[/C][C]-2.7322[/C][C]0.003406[/C][/ROW]
[ROW][C]3[/C][C]-0.012627[/C][C]-0.1856[/C][C]0.426474[/C][/ROW]
[ROW][C]4[/C][C]-0.099807[/C][C]-1.4669[/C][C]0.071934[/C][/ROW]
[ROW][C]5[/C][C]-0.073512[/C][C]-1.0804[/C][C]0.140584[/C][/ROW]
[ROW][C]6[/C][C]-0.112183[/C][C]-1.6487[/C][C]0.050327[/C][/ROW]
[ROW][C]7[/C][C]0.027382[/C][C]0.4024[/C][C]0.343883[/C][/ROW]
[ROW][C]8[/C][C]-0.035615[/C][C]-0.5234[/C][C]0.300607[/C][/ROW]
[ROW][C]9[/C][C]-0.080002[/C][C]-1.1758[/C][C]0.120486[/C][/ROW]
[ROW][C]10[/C][C]-0.048081[/C][C]-0.7066[/C][C]0.240276[/C][/ROW]
[ROW][C]11[/C][C]0.053062[/C][C]0.7798[/C][C]0.218169[/C][/ROW]
[ROW][C]12[/C][C]-0.100531[/C][C]-1.4775[/C][C]0.0705[/C][/ROW]
[ROW][C]13[/C][C]-0.012077[/C][C]-0.1775[/C][C]0.429645[/C][/ROW]
[ROW][C]14[/C][C]0.035096[/C][C]0.5158[/C][C]0.30326[/C][/ROW]
[ROW][C]15[/C][C]-0.027922[/C][C]-0.4104[/C][C]0.340973[/C][/ROW]
[ROW][C]16[/C][C]0.007187[/C][C]0.1056[/C][C]0.457991[/C][/ROW]
[ROW][C]17[/C][C]-0.084163[/C][C]-1.2369[/C][C]0.108726[/C][/ROW]
[ROW][C]18[/C][C]-0.010983[/C][C]-0.1614[/C][C]0.435959[/C][/ROW]
[ROW][C]19[/C][C]0.015275[/C][C]0.2245[/C][C]0.411294[/C][/ROW]
[ROW][C]20[/C][C]-0.016309[/C][C]-0.2397[/C][C]0.405399[/C][/ROW]
[ROW][C]21[/C][C]0.036724[/C][C]0.5397[/C][C]0.294967[/C][/ROW]
[ROW][C]22[/C][C]0.019248[/C][C]0.2829[/C][C]0.388768[/C][/ROW]
[ROW][C]23[/C][C]0.037514[/C][C]0.5513[/C][C]0.290985[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109674&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109674&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
10.98574414.48740
2-0.1859-2.73220.003406
3-0.012627-0.18560.426474
4-0.099807-1.46690.071934
5-0.073512-1.08040.140584
6-0.112183-1.64870.050327
70.0273820.40240.343883
8-0.035615-0.52340.300607
9-0.080002-1.17580.120486
10-0.048081-0.70660.240276
110.0530620.77980.218169
12-0.100531-1.47750.0705
13-0.012077-0.17750.429645
140.0350960.51580.30326
15-0.027922-0.41040.340973
160.0071870.10560.457991
17-0.084163-1.23690.108726
18-0.010983-0.16140.435959
190.0152750.22450.411294
20-0.016309-0.23970.405399
210.0367240.53970.294967
220.0192480.28290.388768
230.0375140.55130.290985



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 ;
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')