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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, 30 Nov 2009 12:39:04 -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/30/t125960998669hk2e1ei7l1k8p.htm/, Retrieved Wed, 01 May 2024 22:45:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=61870, Retrieved Wed, 01 May 2024 22:45:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138
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]
-    D        [(Partial) Autocorrelation Function] [ACF D=d=o, lambda=1] [2009-11-30 11:39:52] [005293453b571dbccb80b45226e44173]
-   P           [(Partial) Autocorrelation Function] [ACF d=D=lambda=1] [2009-11-30 11:44:53] [005293453b571dbccb80b45226e44173]
-   P             [(Partial) Autocorrelation Function] [d=2 en D=1] [2009-11-30 19:33:11] [cd6314e7e707a6546bd4604c9d1f2b69]
-   P               [(Partial) Autocorrelation Function] [d=1 en D=2] [2009-11-30 19:37:11] [cd6314e7e707a6546bd4604c9d1f2b69]
-   P                   [(Partial) Autocorrelation Function] [d=D=2] [2009-11-30 19:39:04] [ea241b681aafed79da4b5b99fad98471] [Current]
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Dataseries X:
244.576
241.572
240.541
236.089
236.997
264.579
270.349
269.645
267.037
258.113
262.813
267.413
267.366
264.777
258.863
254.844
254.868
277.267
285.351
286.602
283.042
276.687
277.915
277.128
277.103
275.037
270.150
267.140
264.993
287.259
291.186
292.300
288.186
281.477
282.656
280.190
280.408
276.836
275.216
274.352
271.311
289.802
290.726
292.300
278.506
269.826
265.861
269.034
264.176
255.198
253.353
246.057
235.372
258.556
260.993
254.663
250.643
243.422
247.105
248.541
245.039
237.080
237.085
225.554
226.839
247.934
248.333
246.969
245.098
246.263
255.765
264.319
268.347
273.046
273.963
267.430
271.993
292.710
295.881




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=61870&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=61870&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61870&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
1-0.653897-4.76048e-06
20.1771841.28990.101339
3-0.01001-0.07290.471089
40.0617680.44970.327388
5-0.172922-1.25890.106793
60.1387281.010.158552
7-0.081582-0.59390.277544
80.0878580.63960.262588
90.0072680.05290.479
10-0.266754-1.9420.02873
110.5534834.02949e-05
12-0.55426-4.03518.8e-05
130.240491.75080.042884
14-0.051068-0.37180.355768
150.0820020.5970.276531
16-0.147858-1.07640.143306
170.1651021.2020.117361
18-0.116395-0.84740.2003
190.1111970.80950.210917
20-0.130146-0.94750.173848
210.0285120.20760.41818
220.1525971.11090.135808
23-0.24419-1.77770.040593
240.1574671.14640.128395
25-0.027162-0.19770.422001
26-0.01102-0.08020.46818
27-0.000822-0.0060.497623
280.0094120.06850.472814
29-0.013166-0.09590.461999
300.0277010.20170.420476
31-0.064925-0.47270.319197
320.0816610.59450.277353
33-0.010073-0.07330.47091
34-0.099126-0.72170.23684
350.1347380.98090.165547
36-0.07621-0.55480.290677

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & -0.653897 & -4.7604 & 8e-06 \tabularnewline
2 & 0.177184 & 1.2899 & 0.101339 \tabularnewline
3 & -0.01001 & -0.0729 & 0.471089 \tabularnewline
4 & 0.061768 & 0.4497 & 0.327388 \tabularnewline
5 & -0.172922 & -1.2589 & 0.106793 \tabularnewline
6 & 0.138728 & 1.01 & 0.158552 \tabularnewline
7 & -0.081582 & -0.5939 & 0.277544 \tabularnewline
8 & 0.087858 & 0.6396 & 0.262588 \tabularnewline
9 & 0.007268 & 0.0529 & 0.479 \tabularnewline
10 & -0.266754 & -1.942 & 0.02873 \tabularnewline
11 & 0.553483 & 4.0294 & 9e-05 \tabularnewline
12 & -0.55426 & -4.0351 & 8.8e-05 \tabularnewline
13 & 0.24049 & 1.7508 & 0.042884 \tabularnewline
14 & -0.051068 & -0.3718 & 0.355768 \tabularnewline
15 & 0.082002 & 0.597 & 0.276531 \tabularnewline
16 & -0.147858 & -1.0764 & 0.143306 \tabularnewline
17 & 0.165102 & 1.202 & 0.117361 \tabularnewline
18 & -0.116395 & -0.8474 & 0.2003 \tabularnewline
19 & 0.111197 & 0.8095 & 0.210917 \tabularnewline
20 & -0.130146 & -0.9475 & 0.173848 \tabularnewline
21 & 0.028512 & 0.2076 & 0.41818 \tabularnewline
22 & 0.152597 & 1.1109 & 0.135808 \tabularnewline
23 & -0.24419 & -1.7777 & 0.040593 \tabularnewline
24 & 0.157467 & 1.1464 & 0.128395 \tabularnewline
25 & -0.027162 & -0.1977 & 0.422001 \tabularnewline
26 & -0.01102 & -0.0802 & 0.46818 \tabularnewline
27 & -0.000822 & -0.006 & 0.497623 \tabularnewline
28 & 0.009412 & 0.0685 & 0.472814 \tabularnewline
29 & -0.013166 & -0.0959 & 0.461999 \tabularnewline
30 & 0.027701 & 0.2017 & 0.420476 \tabularnewline
31 & -0.064925 & -0.4727 & 0.319197 \tabularnewline
32 & 0.081661 & 0.5945 & 0.277353 \tabularnewline
33 & -0.010073 & -0.0733 & 0.47091 \tabularnewline
34 & -0.099126 & -0.7217 & 0.23684 \tabularnewline
35 & 0.134738 & 0.9809 & 0.165547 \tabularnewline
36 & -0.07621 & -0.5548 & 0.290677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61870&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]-0.653897[/C][C]-4.7604[/C][C]8e-06[/C][/ROW]
[ROW][C]2[/C][C]0.177184[/C][C]1.2899[/C][C]0.101339[/C][/ROW]
[ROW][C]3[/C][C]-0.01001[/C][C]-0.0729[/C][C]0.471089[/C][/ROW]
[ROW][C]4[/C][C]0.061768[/C][C]0.4497[/C][C]0.327388[/C][/ROW]
[ROW][C]5[/C][C]-0.172922[/C][C]-1.2589[/C][C]0.106793[/C][/ROW]
[ROW][C]6[/C][C]0.138728[/C][C]1.01[/C][C]0.158552[/C][/ROW]
[ROW][C]7[/C][C]-0.081582[/C][C]-0.5939[/C][C]0.277544[/C][/ROW]
[ROW][C]8[/C][C]0.087858[/C][C]0.6396[/C][C]0.262588[/C][/ROW]
[ROW][C]9[/C][C]0.007268[/C][C]0.0529[/C][C]0.479[/C][/ROW]
[ROW][C]10[/C][C]-0.266754[/C][C]-1.942[/C][C]0.02873[/C][/ROW]
[ROW][C]11[/C][C]0.553483[/C][C]4.0294[/C][C]9e-05[/C][/ROW]
[ROW][C]12[/C][C]-0.55426[/C][C]-4.0351[/C][C]8.8e-05[/C][/ROW]
[ROW][C]13[/C][C]0.24049[/C][C]1.7508[/C][C]0.042884[/C][/ROW]
[ROW][C]14[/C][C]-0.051068[/C][C]-0.3718[/C][C]0.355768[/C][/ROW]
[ROW][C]15[/C][C]0.082002[/C][C]0.597[/C][C]0.276531[/C][/ROW]
[ROW][C]16[/C][C]-0.147858[/C][C]-1.0764[/C][C]0.143306[/C][/ROW]
[ROW][C]17[/C][C]0.165102[/C][C]1.202[/C][C]0.117361[/C][/ROW]
[ROW][C]18[/C][C]-0.116395[/C][C]-0.8474[/C][C]0.2003[/C][/ROW]
[ROW][C]19[/C][C]0.111197[/C][C]0.8095[/C][C]0.210917[/C][/ROW]
[ROW][C]20[/C][C]-0.130146[/C][C]-0.9475[/C][C]0.173848[/C][/ROW]
[ROW][C]21[/C][C]0.028512[/C][C]0.2076[/C][C]0.41818[/C][/ROW]
[ROW][C]22[/C][C]0.152597[/C][C]1.1109[/C][C]0.135808[/C][/ROW]
[ROW][C]23[/C][C]-0.24419[/C][C]-1.7777[/C][C]0.040593[/C][/ROW]
[ROW][C]24[/C][C]0.157467[/C][C]1.1464[/C][C]0.128395[/C][/ROW]
[ROW][C]25[/C][C]-0.027162[/C][C]-0.1977[/C][C]0.422001[/C][/ROW]
[ROW][C]26[/C][C]-0.01102[/C][C]-0.0802[/C][C]0.46818[/C][/ROW]
[ROW][C]27[/C][C]-0.000822[/C][C]-0.006[/C][C]0.497623[/C][/ROW]
[ROW][C]28[/C][C]0.009412[/C][C]0.0685[/C][C]0.472814[/C][/ROW]
[ROW][C]29[/C][C]-0.013166[/C][C]-0.0959[/C][C]0.461999[/C][/ROW]
[ROW][C]30[/C][C]0.027701[/C][C]0.2017[/C][C]0.420476[/C][/ROW]
[ROW][C]31[/C][C]-0.064925[/C][C]-0.4727[/C][C]0.319197[/C][/ROW]
[ROW][C]32[/C][C]0.081661[/C][C]0.5945[/C][C]0.277353[/C][/ROW]
[ROW][C]33[/C][C]-0.010073[/C][C]-0.0733[/C][C]0.47091[/C][/ROW]
[ROW][C]34[/C][C]-0.099126[/C][C]-0.7217[/C][C]0.23684[/C][/ROW]
[ROW][C]35[/C][C]0.134738[/C][C]0.9809[/C][C]0.165547[/C][/ROW]
[ROW][C]36[/C][C]-0.07621[/C][C]-0.5548[/C][C]0.290677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61870&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61870&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
1-0.653897-4.76048e-06
20.1771841.28990.101339
3-0.01001-0.07290.471089
40.0617680.44970.327388
5-0.172922-1.25890.106793
60.1387281.010.158552
7-0.081582-0.59390.277544
80.0878580.63960.262588
90.0072680.05290.479
10-0.266754-1.9420.02873
110.5534834.02949e-05
12-0.55426-4.03518.8e-05
130.240491.75080.042884
14-0.051068-0.37180.355768
150.0820020.5970.276531
16-0.147858-1.07640.143306
170.1651021.2020.117361
18-0.116395-0.84740.2003
190.1111970.80950.210917
20-0.130146-0.94750.173848
210.0285120.20760.41818
220.1525971.11090.135808
23-0.24419-1.77770.040593
240.1574671.14640.128395
25-0.027162-0.19770.422001
26-0.01102-0.08020.46818
27-0.000822-0.0060.497623
280.0094120.06850.472814
29-0.013166-0.09590.461999
300.0277010.20170.420476
31-0.064925-0.47270.319197
320.0816610.59450.277353
33-0.010073-0.07330.47091
34-0.099126-0.72170.23684
350.1347380.98090.165547
36-0.07621-0.55480.290677







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
1-0.653897-4.76048e-06
2-0.437436-3.18460.001214
3-0.279781-2.03680.023338
4-0.018076-0.13160.447903
5-0.171096-1.24560.109195
6-0.192518-1.40150.083439
7-0.247991-1.80540.038347
8-0.105802-0.77020.222286
90.171561.2490.108582
10-0.397556-2.89430.002753
110.3030212.2060.01587
120.0068320.04970.480259
13-0.113664-0.82750.205836
14-0.200631-1.46060.075012
15-0.183101-1.3330.094118
160.0804280.58550.280341
17-0.092619-0.67430.251532
18-0.039557-0.2880.387242
19-0.016856-0.12270.451398
20-0.128855-0.93810.17623
210.0968770.70530.241863
22-0.165905-1.20780.116241
230.077480.56410.287546
24-0.025545-0.1860.426588
25-0.074134-0.53970.295832
26-0.13937-1.01460.157446
27-0.078255-0.56970.285644
28-0.008039-0.05850.476776
29-0.044615-0.32480.373305
30-0.102782-0.74830.228805
31-0.029823-0.21710.414477
320.0185480.1350.446549
330.0135750.09880.460823
34-0.127473-0.9280.178802
350.0273590.19920.421444
36-0.008465-0.06160.475547

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & -0.653897 & -4.7604 & 8e-06 \tabularnewline
2 & -0.437436 & -3.1846 & 0.001214 \tabularnewline
3 & -0.279781 & -2.0368 & 0.023338 \tabularnewline
4 & -0.018076 & -0.1316 & 0.447903 \tabularnewline
5 & -0.171096 & -1.2456 & 0.109195 \tabularnewline
6 & -0.192518 & -1.4015 & 0.083439 \tabularnewline
7 & -0.247991 & -1.8054 & 0.038347 \tabularnewline
8 & -0.105802 & -0.7702 & 0.222286 \tabularnewline
9 & 0.17156 & 1.249 & 0.108582 \tabularnewline
10 & -0.397556 & -2.8943 & 0.002753 \tabularnewline
11 & 0.303021 & 2.206 & 0.01587 \tabularnewline
12 & 0.006832 & 0.0497 & 0.480259 \tabularnewline
13 & -0.113664 & -0.8275 & 0.205836 \tabularnewline
14 & -0.200631 & -1.4606 & 0.075012 \tabularnewline
15 & -0.183101 & -1.333 & 0.094118 \tabularnewline
16 & 0.080428 & 0.5855 & 0.280341 \tabularnewline
17 & -0.092619 & -0.6743 & 0.251532 \tabularnewline
18 & -0.039557 & -0.288 & 0.387242 \tabularnewline
19 & -0.016856 & -0.1227 & 0.451398 \tabularnewline
20 & -0.128855 & -0.9381 & 0.17623 \tabularnewline
21 & 0.096877 & 0.7053 & 0.241863 \tabularnewline
22 & -0.165905 & -1.2078 & 0.116241 \tabularnewline
23 & 0.07748 & 0.5641 & 0.287546 \tabularnewline
24 & -0.025545 & -0.186 & 0.426588 \tabularnewline
25 & -0.074134 & -0.5397 & 0.295832 \tabularnewline
26 & -0.13937 & -1.0146 & 0.157446 \tabularnewline
27 & -0.078255 & -0.5697 & 0.285644 \tabularnewline
28 & -0.008039 & -0.0585 & 0.476776 \tabularnewline
29 & -0.044615 & -0.3248 & 0.373305 \tabularnewline
30 & -0.102782 & -0.7483 & 0.228805 \tabularnewline
31 & -0.029823 & -0.2171 & 0.414477 \tabularnewline
32 & 0.018548 & 0.135 & 0.446549 \tabularnewline
33 & 0.013575 & 0.0988 & 0.460823 \tabularnewline
34 & -0.127473 & -0.928 & 0.178802 \tabularnewline
35 & 0.027359 & 0.1992 & 0.421444 \tabularnewline
36 & -0.008465 & -0.0616 & 0.475547 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61870&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]-0.653897[/C][C]-4.7604[/C][C]8e-06[/C][/ROW]
[ROW][C]2[/C][C]-0.437436[/C][C]-3.1846[/C][C]0.001214[/C][/ROW]
[ROW][C]3[/C][C]-0.279781[/C][C]-2.0368[/C][C]0.023338[/C][/ROW]
[ROW][C]4[/C][C]-0.018076[/C][C]-0.1316[/C][C]0.447903[/C][/ROW]
[ROW][C]5[/C][C]-0.171096[/C][C]-1.2456[/C][C]0.109195[/C][/ROW]
[ROW][C]6[/C][C]-0.192518[/C][C]-1.4015[/C][C]0.083439[/C][/ROW]
[ROW][C]7[/C][C]-0.247991[/C][C]-1.8054[/C][C]0.038347[/C][/ROW]
[ROW][C]8[/C][C]-0.105802[/C][C]-0.7702[/C][C]0.222286[/C][/ROW]
[ROW][C]9[/C][C]0.17156[/C][C]1.249[/C][C]0.108582[/C][/ROW]
[ROW][C]10[/C][C]-0.397556[/C][C]-2.8943[/C][C]0.002753[/C][/ROW]
[ROW][C]11[/C][C]0.303021[/C][C]2.206[/C][C]0.01587[/C][/ROW]
[ROW][C]12[/C][C]0.006832[/C][C]0.0497[/C][C]0.480259[/C][/ROW]
[ROW][C]13[/C][C]-0.113664[/C][C]-0.8275[/C][C]0.205836[/C][/ROW]
[ROW][C]14[/C][C]-0.200631[/C][C]-1.4606[/C][C]0.075012[/C][/ROW]
[ROW][C]15[/C][C]-0.183101[/C][C]-1.333[/C][C]0.094118[/C][/ROW]
[ROW][C]16[/C][C]0.080428[/C][C]0.5855[/C][C]0.280341[/C][/ROW]
[ROW][C]17[/C][C]-0.092619[/C][C]-0.6743[/C][C]0.251532[/C][/ROW]
[ROW][C]18[/C][C]-0.039557[/C][C]-0.288[/C][C]0.387242[/C][/ROW]
[ROW][C]19[/C][C]-0.016856[/C][C]-0.1227[/C][C]0.451398[/C][/ROW]
[ROW][C]20[/C][C]-0.128855[/C][C]-0.9381[/C][C]0.17623[/C][/ROW]
[ROW][C]21[/C][C]0.096877[/C][C]0.7053[/C][C]0.241863[/C][/ROW]
[ROW][C]22[/C][C]-0.165905[/C][C]-1.2078[/C][C]0.116241[/C][/ROW]
[ROW][C]23[/C][C]0.07748[/C][C]0.5641[/C][C]0.287546[/C][/ROW]
[ROW][C]24[/C][C]-0.025545[/C][C]-0.186[/C][C]0.426588[/C][/ROW]
[ROW][C]25[/C][C]-0.074134[/C][C]-0.5397[/C][C]0.295832[/C][/ROW]
[ROW][C]26[/C][C]-0.13937[/C][C]-1.0146[/C][C]0.157446[/C][/ROW]
[ROW][C]27[/C][C]-0.078255[/C][C]-0.5697[/C][C]0.285644[/C][/ROW]
[ROW][C]28[/C][C]-0.008039[/C][C]-0.0585[/C][C]0.476776[/C][/ROW]
[ROW][C]29[/C][C]-0.044615[/C][C]-0.3248[/C][C]0.373305[/C][/ROW]
[ROW][C]30[/C][C]-0.102782[/C][C]-0.7483[/C][C]0.228805[/C][/ROW]
[ROW][C]31[/C][C]-0.029823[/C][C]-0.2171[/C][C]0.414477[/C][/ROW]
[ROW][C]32[/C][C]0.018548[/C][C]0.135[/C][C]0.446549[/C][/ROW]
[ROW][C]33[/C][C]0.013575[/C][C]0.0988[/C][C]0.460823[/C][/ROW]
[ROW][C]34[/C][C]-0.127473[/C][C]-0.928[/C][C]0.178802[/C][/ROW]
[ROW][C]35[/C][C]0.027359[/C][C]0.1992[/C][C]0.421444[/C][/ROW]
[ROW][C]36[/C][C]-0.008465[/C][C]-0.0616[/C][C]0.475547[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61870&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61870&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
1-0.653897-4.76048e-06
2-0.437436-3.18460.001214
3-0.279781-2.03680.023338
4-0.018076-0.13160.447903
5-0.171096-1.24560.109195
6-0.192518-1.40150.083439
7-0.247991-1.80540.038347
8-0.105802-0.77020.222286
90.171561.2490.108582
10-0.397556-2.89430.002753
110.3030212.2060.01587
120.0068320.04970.480259
13-0.113664-0.82750.205836
14-0.200631-1.46060.075012
15-0.183101-1.3330.094118
160.0804280.58550.280341
17-0.092619-0.67430.251532
18-0.039557-0.2880.387242
19-0.016856-0.12270.451398
20-0.128855-0.93810.17623
210.0968770.70530.241863
22-0.165905-1.20780.116241
230.077480.56410.287546
24-0.025545-0.1860.426588
25-0.074134-0.53970.295832
26-0.13937-1.01460.157446
27-0.078255-0.56970.285644
28-0.008039-0.05850.476776
29-0.044615-0.32480.373305
30-0.102782-0.74830.228805
31-0.029823-0.21710.414477
320.0185480.1350.446549
330.0135750.09880.460823
34-0.127473-0.9280.178802
350.0273590.19920.421444
36-0.008465-0.06160.475547



Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 2 ; par4 = 2 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
Parameters (R input):
par1 = 36 ; par2 = 1 ; par3 = 2 ; par4 = 2 ; 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')