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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 computationFri, 27 Nov 2009 04:03:04 -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/27/t12593200358xrmvr5mn4zblng.htm/, Retrieved Mon, 29 Apr 2024 18:51:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60569, Retrieved Mon, 29 Apr 2024 18:51:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150
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 12:16:10] [b98453cac15ba1066b407e146608df68]
-   PD        [(Partial) Autocorrelation Function] [acf2] [2009-11-26 16:03:04] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D            [(Partial) Autocorrelation Function] [Workshop8] [2009-11-27 11:03:04] [307139c5e328127f586f26d5bcc435d8] [Current]
-    D              [(Partial) Autocorrelation Function] [acf] [2009-12-12 10:11:28] [34b80aeb109c116fd63bf2eb7493a276]
-    D                [(Partial) Autocorrelation Function] [acf] [2009-12-14 08:54:34] [34b80aeb109c116fd63bf2eb7493a276]
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Dataseries X:
5.4
5.4
5.6
5.7
5.8
5.8
5.8
5.9
6.1
6.4
6.4
6.3
6.2
6.2
6.3
6.4
6.5
6.6
6.6
6.6
6.8
7
7.2
7.3
7.5
7.6
7.6
7.7
7.7
7.7
7.7
7.6
7.7
7.9
7.9
7.9
7.8
7.6
7.4
7
7
7.2
7.5
7.8
7.8
7.7
7.6
7.6
7.5
7.5
7.6
7.6
7.9
7.6
7.5
7.5
7.6
7.7
7.8
7.9
7.9




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=60569&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=60569&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60569&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'Gwilym Jenkins' @ 72.249.127.135







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.405033.13740.001321
20.0827880.64130.261894
3-0.234364-1.81540.037232
4-0.333333-2.5820.006141
5-0.11303-0.87550.192389
6-0.036-0.27890.390658
70.1457581.1290.131689
80.1842421.42710.079362
90.1958181.51680.067284
100.011030.08540.466098
11-0.086485-0.66990.252743
12-0.126545-0.98020.165458
13-0.131697-1.02010.155884
140.0187880.14550.44239
150.0085450.06620.473722
160.0506670.39250.348054
170.0178790.13850.445159
180.0360.27890.390658
190.0541210.41920.338276
200.0060610.04690.481356
21-0.066727-0.51690.303574
22-0.049333-0.38210.351856
23-0.03703-0.28680.387613
24-0.029818-0.2310.409062
250.0559390.43330.333174
260.0456970.3540.362302
270.0478180.37040.356195
28-0.016242-0.12580.45015
29-0.14503-1.12340.132871
30-0.193818-1.50130.06926
31-0.172788-1.33840.092906
32-0.065939-0.51080.305695
330.0183640.14220.443682
340.1077580.83470.203603
350.0538790.41730.338958
36-0.034909-0.27040.393888

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.40503 & 3.1374 & 0.001321 \tabularnewline
2 & 0.082788 & 0.6413 & 0.261894 \tabularnewline
3 & -0.234364 & -1.8154 & 0.037232 \tabularnewline
4 & -0.333333 & -2.582 & 0.006141 \tabularnewline
5 & -0.11303 & -0.8755 & 0.192389 \tabularnewline
6 & -0.036 & -0.2789 & 0.390658 \tabularnewline
7 & 0.145758 & 1.129 & 0.131689 \tabularnewline
8 & 0.184242 & 1.4271 & 0.079362 \tabularnewline
9 & 0.195818 & 1.5168 & 0.067284 \tabularnewline
10 & 0.01103 & 0.0854 & 0.466098 \tabularnewline
11 & -0.086485 & -0.6699 & 0.252743 \tabularnewline
12 & -0.126545 & -0.9802 & 0.165458 \tabularnewline
13 & -0.131697 & -1.0201 & 0.155884 \tabularnewline
14 & 0.018788 & 0.1455 & 0.44239 \tabularnewline
15 & 0.008545 & 0.0662 & 0.473722 \tabularnewline
16 & 0.050667 & 0.3925 & 0.348054 \tabularnewline
17 & 0.017879 & 0.1385 & 0.445159 \tabularnewline
18 & 0.036 & 0.2789 & 0.390658 \tabularnewline
19 & 0.054121 & 0.4192 & 0.338276 \tabularnewline
20 & 0.006061 & 0.0469 & 0.481356 \tabularnewline
21 & -0.066727 & -0.5169 & 0.303574 \tabularnewline
22 & -0.049333 & -0.3821 & 0.351856 \tabularnewline
23 & -0.03703 & -0.2868 & 0.387613 \tabularnewline
24 & -0.029818 & -0.231 & 0.409062 \tabularnewline
25 & 0.055939 & 0.4333 & 0.333174 \tabularnewline
26 & 0.045697 & 0.354 & 0.362302 \tabularnewline
27 & 0.047818 & 0.3704 & 0.356195 \tabularnewline
28 & -0.016242 & -0.1258 & 0.45015 \tabularnewline
29 & -0.14503 & -1.1234 & 0.132871 \tabularnewline
30 & -0.193818 & -1.5013 & 0.06926 \tabularnewline
31 & -0.172788 & -1.3384 & 0.092906 \tabularnewline
32 & -0.065939 & -0.5108 & 0.305695 \tabularnewline
33 & 0.018364 & 0.1422 & 0.443682 \tabularnewline
34 & 0.107758 & 0.8347 & 0.203603 \tabularnewline
35 & 0.053879 & 0.4173 & 0.338958 \tabularnewline
36 & -0.034909 & -0.2704 & 0.393888 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60569&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]0.40503[/C][C]3.1374[/C][C]0.001321[/C][/ROW]
[ROW][C]2[/C][C]0.082788[/C][C]0.6413[/C][C]0.261894[/C][/ROW]
[ROW][C]3[/C][C]-0.234364[/C][C]-1.8154[/C][C]0.037232[/C][/ROW]
[ROW][C]4[/C][C]-0.333333[/C][C]-2.582[/C][C]0.006141[/C][/ROW]
[ROW][C]5[/C][C]-0.11303[/C][C]-0.8755[/C][C]0.192389[/C][/ROW]
[ROW][C]6[/C][C]-0.036[/C][C]-0.2789[/C][C]0.390658[/C][/ROW]
[ROW][C]7[/C][C]0.145758[/C][C]1.129[/C][C]0.131689[/C][/ROW]
[ROW][C]8[/C][C]0.184242[/C][C]1.4271[/C][C]0.079362[/C][/ROW]
[ROW][C]9[/C][C]0.195818[/C][C]1.5168[/C][C]0.067284[/C][/ROW]
[ROW][C]10[/C][C]0.01103[/C][C]0.0854[/C][C]0.466098[/C][/ROW]
[ROW][C]11[/C][C]-0.086485[/C][C]-0.6699[/C][C]0.252743[/C][/ROW]
[ROW][C]12[/C][C]-0.126545[/C][C]-0.9802[/C][C]0.165458[/C][/ROW]
[ROW][C]13[/C][C]-0.131697[/C][C]-1.0201[/C][C]0.155884[/C][/ROW]
[ROW][C]14[/C][C]0.018788[/C][C]0.1455[/C][C]0.44239[/C][/ROW]
[ROW][C]15[/C][C]0.008545[/C][C]0.0662[/C][C]0.473722[/C][/ROW]
[ROW][C]16[/C][C]0.050667[/C][C]0.3925[/C][C]0.348054[/C][/ROW]
[ROW][C]17[/C][C]0.017879[/C][C]0.1385[/C][C]0.445159[/C][/ROW]
[ROW][C]18[/C][C]0.036[/C][C]0.2789[/C][C]0.390658[/C][/ROW]
[ROW][C]19[/C][C]0.054121[/C][C]0.4192[/C][C]0.338276[/C][/ROW]
[ROW][C]20[/C][C]0.006061[/C][C]0.0469[/C][C]0.481356[/C][/ROW]
[ROW][C]21[/C][C]-0.066727[/C][C]-0.5169[/C][C]0.303574[/C][/ROW]
[ROW][C]22[/C][C]-0.049333[/C][C]-0.3821[/C][C]0.351856[/C][/ROW]
[ROW][C]23[/C][C]-0.03703[/C][C]-0.2868[/C][C]0.387613[/C][/ROW]
[ROW][C]24[/C][C]-0.029818[/C][C]-0.231[/C][C]0.409062[/C][/ROW]
[ROW][C]25[/C][C]0.055939[/C][C]0.4333[/C][C]0.333174[/C][/ROW]
[ROW][C]26[/C][C]0.045697[/C][C]0.354[/C][C]0.362302[/C][/ROW]
[ROW][C]27[/C][C]0.047818[/C][C]0.3704[/C][C]0.356195[/C][/ROW]
[ROW][C]28[/C][C]-0.016242[/C][C]-0.1258[/C][C]0.45015[/C][/ROW]
[ROW][C]29[/C][C]-0.14503[/C][C]-1.1234[/C][C]0.132871[/C][/ROW]
[ROW][C]30[/C][C]-0.193818[/C][C]-1.5013[/C][C]0.06926[/C][/ROW]
[ROW][C]31[/C][C]-0.172788[/C][C]-1.3384[/C][C]0.092906[/C][/ROW]
[ROW][C]32[/C][C]-0.065939[/C][C]-0.5108[/C][C]0.305695[/C][/ROW]
[ROW][C]33[/C][C]0.018364[/C][C]0.1422[/C][C]0.443682[/C][/ROW]
[ROW][C]34[/C][C]0.107758[/C][C]0.8347[/C][C]0.203603[/C][/ROW]
[ROW][C]35[/C][C]0.053879[/C][C]0.4173[/C][C]0.338958[/C][/ROW]
[ROW][C]36[/C][C]-0.034909[/C][C]-0.2704[/C][C]0.393888[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60569&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60569&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
10.405033.13740.001321
20.0827880.64130.261894
3-0.234364-1.81540.037232
4-0.333333-2.5820.006141
5-0.11303-0.87550.192389
6-0.036-0.27890.390658
70.1457581.1290.131689
80.1842421.42710.079362
90.1958181.51680.067284
100.011030.08540.466098
11-0.086485-0.66990.252743
12-0.126545-0.98020.165458
13-0.131697-1.02010.155884
140.0187880.14550.44239
150.0085450.06620.473722
160.0506670.39250.348054
170.0178790.13850.445159
180.0360.27890.390658
190.0541210.41920.338276
200.0060610.04690.481356
21-0.066727-0.51690.303574
22-0.049333-0.38210.351856
23-0.03703-0.28680.387613
24-0.029818-0.2310.409062
250.0559390.43330.333174
260.0456970.3540.362302
270.0478180.37040.356195
28-0.016242-0.12580.45015
29-0.14503-1.12340.132871
30-0.193818-1.50130.06926
31-0.172788-1.33840.092906
32-0.065939-0.51080.305695
330.0183640.14220.443682
340.1077580.83470.203603
350.0538790.41730.338958
36-0.034909-0.27040.393888







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.405033.13740.001321
2-0.097209-0.7530.227205
3-0.279913-2.16820.01706
4-0.163051-1.2630.10574
50.1370511.06160.146337
6-0.08194-0.63470.264017
70.0793110.61430.270656
80.0743110.57560.283517
90.1210450.93760.176102
10-0.135591-1.05030.148899
110.0272470.21110.416781
120.0096220.07450.470416
13-0.050392-0.39030.348834
140.0388610.3010.382222
15-0.046812-0.36260.359087
16-0.026588-0.2060.418763
17-0.027037-0.20940.417412
180.078770.61020.272033
190.0359310.27830.390861
20-0.017846-0.13820.445258
21-0.08654-0.67030.252608
220.0839460.65020.259008
23-0.044732-0.34650.365092
24-0.042713-0.33090.370953
250.0750670.58150.281553
260.0142220.11020.456323
27-0.047821-0.37040.356187
28-0.049177-0.38090.352303
29-0.095121-0.73680.232057
30-0.110964-0.85950.196736
31-0.060566-0.46910.320334
32-0.012339-0.09560.462088
33-0.059691-0.46240.322746
340.0025020.01940.492302
35-0.029223-0.22640.410844
36-0.076771-0.59470.277152

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.40503 & 3.1374 & 0.001321 \tabularnewline
2 & -0.097209 & -0.753 & 0.227205 \tabularnewline
3 & -0.279913 & -2.1682 & 0.01706 \tabularnewline
4 & -0.163051 & -1.263 & 0.10574 \tabularnewline
5 & 0.137051 & 1.0616 & 0.146337 \tabularnewline
6 & -0.08194 & -0.6347 & 0.264017 \tabularnewline
7 & 0.079311 & 0.6143 & 0.270656 \tabularnewline
8 & 0.074311 & 0.5756 & 0.283517 \tabularnewline
9 & 0.121045 & 0.9376 & 0.176102 \tabularnewline
10 & -0.135591 & -1.0503 & 0.148899 \tabularnewline
11 & 0.027247 & 0.2111 & 0.416781 \tabularnewline
12 & 0.009622 & 0.0745 & 0.470416 \tabularnewline
13 & -0.050392 & -0.3903 & 0.348834 \tabularnewline
14 & 0.038861 & 0.301 & 0.382222 \tabularnewline
15 & -0.046812 & -0.3626 & 0.359087 \tabularnewline
16 & -0.026588 & -0.206 & 0.418763 \tabularnewline
17 & -0.027037 & -0.2094 & 0.417412 \tabularnewline
18 & 0.07877 & 0.6102 & 0.272033 \tabularnewline
19 & 0.035931 & 0.2783 & 0.390861 \tabularnewline
20 & -0.017846 & -0.1382 & 0.445258 \tabularnewline
21 & -0.08654 & -0.6703 & 0.252608 \tabularnewline
22 & 0.083946 & 0.6502 & 0.259008 \tabularnewline
23 & -0.044732 & -0.3465 & 0.365092 \tabularnewline
24 & -0.042713 & -0.3309 & 0.370953 \tabularnewline
25 & 0.075067 & 0.5815 & 0.281553 \tabularnewline
26 & 0.014222 & 0.1102 & 0.456323 \tabularnewline
27 & -0.047821 & -0.3704 & 0.356187 \tabularnewline
28 & -0.049177 & -0.3809 & 0.352303 \tabularnewline
29 & -0.095121 & -0.7368 & 0.232057 \tabularnewline
30 & -0.110964 & -0.8595 & 0.196736 \tabularnewline
31 & -0.060566 & -0.4691 & 0.320334 \tabularnewline
32 & -0.012339 & -0.0956 & 0.462088 \tabularnewline
33 & -0.059691 & -0.4624 & 0.322746 \tabularnewline
34 & 0.002502 & 0.0194 & 0.492302 \tabularnewline
35 & -0.029223 & -0.2264 & 0.410844 \tabularnewline
36 & -0.076771 & -0.5947 & 0.277152 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60569&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]0.40503[/C][C]3.1374[/C][C]0.001321[/C][/ROW]
[ROW][C]2[/C][C]-0.097209[/C][C]-0.753[/C][C]0.227205[/C][/ROW]
[ROW][C]3[/C][C]-0.279913[/C][C]-2.1682[/C][C]0.01706[/C][/ROW]
[ROW][C]4[/C][C]-0.163051[/C][C]-1.263[/C][C]0.10574[/C][/ROW]
[ROW][C]5[/C][C]0.137051[/C][C]1.0616[/C][C]0.146337[/C][/ROW]
[ROW][C]6[/C][C]-0.08194[/C][C]-0.6347[/C][C]0.264017[/C][/ROW]
[ROW][C]7[/C][C]0.079311[/C][C]0.6143[/C][C]0.270656[/C][/ROW]
[ROW][C]8[/C][C]0.074311[/C][C]0.5756[/C][C]0.283517[/C][/ROW]
[ROW][C]9[/C][C]0.121045[/C][C]0.9376[/C][C]0.176102[/C][/ROW]
[ROW][C]10[/C][C]-0.135591[/C][C]-1.0503[/C][C]0.148899[/C][/ROW]
[ROW][C]11[/C][C]0.027247[/C][C]0.2111[/C][C]0.416781[/C][/ROW]
[ROW][C]12[/C][C]0.009622[/C][C]0.0745[/C][C]0.470416[/C][/ROW]
[ROW][C]13[/C][C]-0.050392[/C][C]-0.3903[/C][C]0.348834[/C][/ROW]
[ROW][C]14[/C][C]0.038861[/C][C]0.301[/C][C]0.382222[/C][/ROW]
[ROW][C]15[/C][C]-0.046812[/C][C]-0.3626[/C][C]0.359087[/C][/ROW]
[ROW][C]16[/C][C]-0.026588[/C][C]-0.206[/C][C]0.418763[/C][/ROW]
[ROW][C]17[/C][C]-0.027037[/C][C]-0.2094[/C][C]0.417412[/C][/ROW]
[ROW][C]18[/C][C]0.07877[/C][C]0.6102[/C][C]0.272033[/C][/ROW]
[ROW][C]19[/C][C]0.035931[/C][C]0.2783[/C][C]0.390861[/C][/ROW]
[ROW][C]20[/C][C]-0.017846[/C][C]-0.1382[/C][C]0.445258[/C][/ROW]
[ROW][C]21[/C][C]-0.08654[/C][C]-0.6703[/C][C]0.252608[/C][/ROW]
[ROW][C]22[/C][C]0.083946[/C][C]0.6502[/C][C]0.259008[/C][/ROW]
[ROW][C]23[/C][C]-0.044732[/C][C]-0.3465[/C][C]0.365092[/C][/ROW]
[ROW][C]24[/C][C]-0.042713[/C][C]-0.3309[/C][C]0.370953[/C][/ROW]
[ROW][C]25[/C][C]0.075067[/C][C]0.5815[/C][C]0.281553[/C][/ROW]
[ROW][C]26[/C][C]0.014222[/C][C]0.1102[/C][C]0.456323[/C][/ROW]
[ROW][C]27[/C][C]-0.047821[/C][C]-0.3704[/C][C]0.356187[/C][/ROW]
[ROW][C]28[/C][C]-0.049177[/C][C]-0.3809[/C][C]0.352303[/C][/ROW]
[ROW][C]29[/C][C]-0.095121[/C][C]-0.7368[/C][C]0.232057[/C][/ROW]
[ROW][C]30[/C][C]-0.110964[/C][C]-0.8595[/C][C]0.196736[/C][/ROW]
[ROW][C]31[/C][C]-0.060566[/C][C]-0.4691[/C][C]0.320334[/C][/ROW]
[ROW][C]32[/C][C]-0.012339[/C][C]-0.0956[/C][C]0.462088[/C][/ROW]
[ROW][C]33[/C][C]-0.059691[/C][C]-0.4624[/C][C]0.322746[/C][/ROW]
[ROW][C]34[/C][C]0.002502[/C][C]0.0194[/C][C]0.492302[/C][/ROW]
[ROW][C]35[/C][C]-0.029223[/C][C]-0.2264[/C][C]0.410844[/C][/ROW]
[ROW][C]36[/C][C]-0.076771[/C][C]-0.5947[/C][C]0.277152[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60569&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60569&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
10.405033.13740.001321
2-0.097209-0.7530.227205
3-0.279913-2.16820.01706
4-0.163051-1.2630.10574
50.1370511.06160.146337
6-0.08194-0.63470.264017
70.0793110.61430.270656
80.0743110.57560.283517
90.1210450.93760.176102
10-0.135591-1.05030.148899
110.0272470.21110.416781
120.0096220.07450.470416
13-0.050392-0.39030.348834
140.0388610.3010.382222
15-0.046812-0.36260.359087
16-0.026588-0.2060.418763
17-0.027037-0.20940.417412
180.078770.61020.272033
190.0359310.27830.390861
20-0.017846-0.13820.445258
21-0.08654-0.67030.252608
220.0839460.65020.259008
23-0.044732-0.34650.365092
24-0.042713-0.33090.370953
250.0750670.58150.281553
260.0142220.11020.456323
27-0.047821-0.37040.356187
28-0.049177-0.38090.352303
29-0.095121-0.73680.232057
30-0.110964-0.85950.196736
31-0.060566-0.46910.320334
32-0.012339-0.09560.462088
33-0.059691-0.46240.322746
340.0025020.01940.492302
35-0.029223-0.22640.410844
36-0.076771-0.59470.277152



Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; 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')