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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, 01 Dec 2008 14:28:52 -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/01/t1228167015pm69rce2uvtmifh.htm/, Retrieved Sun, 05 May 2024 15:39:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27422, Retrieved Sun, 05 May 2024 15:39:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact182
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:40:39] [b98453cac15ba1066b407e146608df68]
F RMPD    [(Partial) Autocorrelation Function] [Non stationary ti...] [2008-12-01 21:28:52] [c5d6d05aee6be5527ac4a30a8c3b8fe5] [Current]
Feedback Forum
2008-12-06 14:07:42 [Thomas Plasschaert] [reply
Er wordt alleen gekeken naar de seizonaliteit. Spectraal –analyse wordt gebruikt om willekeurige tijdreeksen te ontbinden in regelmatige golfbewegingen. Een lange termijntrend betekent dat er sprake is van een lange periode en een trage frequentie. In de tabel zien we dat op een periode van 144 maanden het spectrum (relatief) groot is. Het spectrum geeft de intensiteit van een golfperiode aan, maw hoe belangrijk is deze golfbeweging. Een (relatief) groot spectrum wijst op een (relatief) belangrijke golfbeweging. Aangezien de periode hier zeer groot is (144) en het spectrum ook is er sprake van een relatief belangrijke lange termijn trend. De seizoenaliteit valt op wanneer we naar de spectrums van periode 4, 6 en 12 kijken. Dit zijn ook zeer hoge sprectums. Op het raw periodogram kunnen we duidelijk zien dat dit het geval is. Er doet zich een langzaam dalend patroon voor. De spectrums zijn groter wanneer er een lage frequentie is (linkerhelft van de grafiek). De steeds wederkerende pieken in het raw periodogram wijzen op seizonaliteit. Het cumulative periodogram kunnen we beschouwen als een soort R^2: 80% (y-as) kan verklaard worden door een zeer lage frequentie. De zeer steile stijging wijst dus op een lange termijn trend. De trap-structuur in het cumulative periodogram wijst op seizonaliteit.

2008-12-08 13:30:10 [Katja van Hek] [reply
De autocorrelatiegrafiek toont een duidelijk patroon dat een langzaam dalend verloop heeft. Er is dus sprake van een trendmatig verloop en dus een lange termijn trend. Het hangmat achtige verloop van de grafiek geeft aan dat er mogelijk sprake is van seizoenaliteit. De trend kun je verwijderen door d=1, wat wel een sterke seizoenaliteit zal gaan tonen die je vervolgens kunt verwijderen door D=1 om op deze manier een stationaire tijdreeks te maken.
2008-12-08 16:32:56 [Jonas Janssens] [reply
Je vermeldt niet waarom je gaat differentiëren. Wel, je ziet een soort 'hangmatten'-patroon, dit zou kunnen wijzen op seizonaliteit. Je moet differentiëren om de langzaam dalende lange termijn trend en de seizonaliteit uit de tijdreeks te halen. Zo kan je de tijdreeks stationair maken.
2008-12-08 19:35:38 [5faab2fc6fb120339944528a32d48a04] [reply
Het is inderdaad zo dat de ACF voor d = 0 en D = 0 enkel positieve waarden weergeeft die langzaam dalend zijn op lange termijn.
2008-12-09 22:26:57 [Gert-Jan Geudens] [reply
We zien hier duidelijk een hangmatpatroon met de 'palen' op 12, 24 en 36... Alles wijst hier dus op seizonaliteit. Tevens zien we een langzaam dalende trend want dan op zijn beurt weer wijst op een lineaire trend. We zullen dus zowel seizonaal als niet-seizonaal moeten differentiëren ( d=1, D=1)

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Dataseries X:
112
118
132
129
121
135
148
148
136
119
104
118
115
126
141
135
125
149
170
170
158
133
114
140
145
150
178
163
172
178
199
199
184
162
146
166
171
180
193
181
183
218
230
242
209
191
172
194
196
196
236
235
229
243
264
272
237
211
180
201
204
188
235
227
234
264
302
293
259
229
203
229
242
233
267
269
270
315
364
347
312
274
237
278
284
277
317
313
318
374
413
405
355
306
271
306
315
301
356
348
355
422
465
467
404
347
305
336
340
318
362
348
363
435
491
505
404
359
310
337
360
342
406
396
420
472
548
559
463
407
362
405
417
391
419
461
472
535
622
606
508
461
390
432




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27422&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]2 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=27422&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27422&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.94804711.37660
20.87557510.50690
30.8066819.68020
40.7526259.03150
50.713778.56520
60.6817348.18080
70.6629047.95490
80.655617.86730
90.6709488.05140
100.702728.43260
110.743248.91890
120.7603959.12470
130.7126618.55190
140.6463427.75610
150.5859237.03110
160.5379556.45550
170.4997485.9970
180.4687345.62480
190.4498715.39840
200.4416295.29950
210.4572245.48670
220.4824825.78980
230.5171276.20550
240.532196.38630
250.4939765.92770
260.4377215.25270
270.3876034.65124e-06
280.3480254.17632.6e-05
290.3149843.77980.000115
300.2884973.4620.000353
310.2708023.24960.000719
320.264293.17150.000927
330.2767993.32160.000567
340.2985213.58230.000233
350.3255873.9077.2e-05
360.3370244.04434.3e-05

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.948047 & 11.3766 & 0 \tabularnewline
2 & 0.875575 & 10.5069 & 0 \tabularnewline
3 & 0.806681 & 9.6802 & 0 \tabularnewline
4 & 0.752625 & 9.0315 & 0 \tabularnewline
5 & 0.71377 & 8.5652 & 0 \tabularnewline
6 & 0.681734 & 8.1808 & 0 \tabularnewline
7 & 0.662904 & 7.9549 & 0 \tabularnewline
8 & 0.65561 & 7.8673 & 0 \tabularnewline
9 & 0.670948 & 8.0514 & 0 \tabularnewline
10 & 0.70272 & 8.4326 & 0 \tabularnewline
11 & 0.74324 & 8.9189 & 0 \tabularnewline
12 & 0.760395 & 9.1247 & 0 \tabularnewline
13 & 0.712661 & 8.5519 & 0 \tabularnewline
14 & 0.646342 & 7.7561 & 0 \tabularnewline
15 & 0.585923 & 7.0311 & 0 \tabularnewline
16 & 0.537955 & 6.4555 & 0 \tabularnewline
17 & 0.499748 & 5.997 & 0 \tabularnewline
18 & 0.468734 & 5.6248 & 0 \tabularnewline
19 & 0.449871 & 5.3984 & 0 \tabularnewline
20 & 0.441629 & 5.2995 & 0 \tabularnewline
21 & 0.457224 & 5.4867 & 0 \tabularnewline
22 & 0.482482 & 5.7898 & 0 \tabularnewline
23 & 0.517127 & 6.2055 & 0 \tabularnewline
24 & 0.53219 & 6.3863 & 0 \tabularnewline
25 & 0.493976 & 5.9277 & 0 \tabularnewline
26 & 0.437721 & 5.2527 & 0 \tabularnewline
27 & 0.387603 & 4.6512 & 4e-06 \tabularnewline
28 & 0.348025 & 4.1763 & 2.6e-05 \tabularnewline
29 & 0.314984 & 3.7798 & 0.000115 \tabularnewline
30 & 0.288497 & 3.462 & 0.000353 \tabularnewline
31 & 0.270802 & 3.2496 & 0.000719 \tabularnewline
32 & 0.26429 & 3.1715 & 0.000927 \tabularnewline
33 & 0.276799 & 3.3216 & 0.000567 \tabularnewline
34 & 0.298521 & 3.5823 & 0.000233 \tabularnewline
35 & 0.325587 & 3.907 & 7.2e-05 \tabularnewline
36 & 0.337024 & 4.0443 & 4.3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27422&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]0.948047[/C][C]11.3766[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.875575[/C][C]10.5069[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.806681[/C][C]9.6802[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.752625[/C][C]9.0315[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.71377[/C][C]8.5652[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.681734[/C][C]8.1808[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.662904[/C][C]7.9549[/C][C]0[/C][/ROW]
[ROW][C]8[/C][C]0.65561[/C][C]7.8673[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.670948[/C][C]8.0514[/C][C]0[/C][/ROW]
[ROW][C]10[/C][C]0.70272[/C][C]8.4326[/C][C]0[/C][/ROW]
[ROW][C]11[/C][C]0.74324[/C][C]8.9189[/C][C]0[/C][/ROW]
[ROW][C]12[/C][C]0.760395[/C][C]9.1247[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.712661[/C][C]8.5519[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]0.646342[/C][C]7.7561[/C][C]0[/C][/ROW]
[ROW][C]15[/C][C]0.585923[/C][C]7.0311[/C][C]0[/C][/ROW]
[ROW][C]16[/C][C]0.537955[/C][C]6.4555[/C][C]0[/C][/ROW]
[ROW][C]17[/C][C]0.499748[/C][C]5.997[/C][C]0[/C][/ROW]
[ROW][C]18[/C][C]0.468734[/C][C]5.6248[/C][C]0[/C][/ROW]
[ROW][C]19[/C][C]0.449871[/C][C]5.3984[/C][C]0[/C][/ROW]
[ROW][C]20[/C][C]0.441629[/C][C]5.2995[/C][C]0[/C][/ROW]
[ROW][C]21[/C][C]0.457224[/C][C]5.4867[/C][C]0[/C][/ROW]
[ROW][C]22[/C][C]0.482482[/C][C]5.7898[/C][C]0[/C][/ROW]
[ROW][C]23[/C][C]0.517127[/C][C]6.2055[/C][C]0[/C][/ROW]
[ROW][C]24[/C][C]0.53219[/C][C]6.3863[/C][C]0[/C][/ROW]
[ROW][C]25[/C][C]0.493976[/C][C]5.9277[/C][C]0[/C][/ROW]
[ROW][C]26[/C][C]0.437721[/C][C]5.2527[/C][C]0[/C][/ROW]
[ROW][C]27[/C][C]0.387603[/C][C]4.6512[/C][C]4e-06[/C][/ROW]
[ROW][C]28[/C][C]0.348025[/C][C]4.1763[/C][C]2.6e-05[/C][/ROW]
[ROW][C]29[/C][C]0.314984[/C][C]3.7798[/C][C]0.000115[/C][/ROW]
[ROW][C]30[/C][C]0.288497[/C][C]3.462[/C][C]0.000353[/C][/ROW]
[ROW][C]31[/C][C]0.270802[/C][C]3.2496[/C][C]0.000719[/C][/ROW]
[ROW][C]32[/C][C]0.26429[/C][C]3.1715[/C][C]0.000927[/C][/ROW]
[ROW][C]33[/C][C]0.276799[/C][C]3.3216[/C][C]0.000567[/C][/ROW]
[ROW][C]34[/C][C]0.298521[/C][C]3.5823[/C][C]0.000233[/C][/ROW]
[ROW][C]35[/C][C]0.325587[/C][C]3.907[/C][C]7.2e-05[/C][/ROW]
[ROW][C]36[/C][C]0.337024[/C][C]4.0443[/C][C]4.3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27422&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27422&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
10.94804711.37660
20.87557510.50690
30.8066819.68020
40.7526259.03150
50.713778.56520
60.6817348.18080
70.6629047.95490
80.655617.86730
90.6709488.05140
100.702728.43260
110.743248.91890
120.7603959.12470
130.7126618.55190
140.6463427.75610
150.5859237.03110
160.5379556.45550
170.4997485.9970
180.4687345.62480
190.4498715.39840
200.4416295.29950
210.4572245.48670
220.4824825.78980
230.5171276.20550
240.532196.38630
250.4939765.92770
260.4377215.25270
270.3876034.65124e-06
280.3480254.17632.6e-05
290.3149843.77980.000115
300.2884973.4620.000353
310.2708023.24960.000719
320.264293.17150.000927
330.2767993.32160.000567
340.2985213.58230.000233
350.3255873.9077.2e-05
360.3370244.04434.3e-05







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.94804711.37660
2-0.229422-2.75310.003332
30.0381480.45780.323903
40.0937851.12540.131141
50.0736070.88330.189279
60.0077280.09270.463123
70.1255971.50720.066979
80.0899511.07940.141103
90.2324892.78990.002994
100.1660511.99260.024097
110.1712742.05530.020829
12-0.135431-1.62520.053156
13-0.539691-6.47630
14-0.02661-0.31930.374973
150.0907651.08920.138947
160.0249560.29950.382508
170.0325160.39020.348487
180.0734330.88120.189841
190.0484420.58130.280972
20-0.045542-0.54650.292784
210.0457530.5490.291916
22-0.100179-1.20210.11564
230.0524350.62920.265101
240.0480140.57620.2827
25-0.162746-1.9530.026382
26-0.036135-0.43360.332607
270.0664240.79710.213357
280.0061760.07410.470511
290.0075370.09040.464029
300.019350.23220.408354
31-0.010251-0.1230.451132
32-0.01831-0.21970.413199
33-0.029001-0.3480.364168
34-0.014805-0.17770.42962
35-0.047725-0.57270.283872
360.0462040.55440.290068

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.948047 & 11.3766 & 0 \tabularnewline
2 & -0.229422 & -2.7531 & 0.003332 \tabularnewline
3 & 0.038148 & 0.4578 & 0.323903 \tabularnewline
4 & 0.093785 & 1.1254 & 0.131141 \tabularnewline
5 & 0.073607 & 0.8833 & 0.189279 \tabularnewline
6 & 0.007728 & 0.0927 & 0.463123 \tabularnewline
7 & 0.125597 & 1.5072 & 0.066979 \tabularnewline
8 & 0.089951 & 1.0794 & 0.141103 \tabularnewline
9 & 0.232489 & 2.7899 & 0.002994 \tabularnewline
10 & 0.166051 & 1.9926 & 0.024097 \tabularnewline
11 & 0.171274 & 2.0553 & 0.020829 \tabularnewline
12 & -0.135431 & -1.6252 & 0.053156 \tabularnewline
13 & -0.539691 & -6.4763 & 0 \tabularnewline
14 & -0.02661 & -0.3193 & 0.374973 \tabularnewline
15 & 0.090765 & 1.0892 & 0.138947 \tabularnewline
16 & 0.024956 & 0.2995 & 0.382508 \tabularnewline
17 & 0.032516 & 0.3902 & 0.348487 \tabularnewline
18 & 0.073433 & 0.8812 & 0.189841 \tabularnewline
19 & 0.048442 & 0.5813 & 0.280972 \tabularnewline
20 & -0.045542 & -0.5465 & 0.292784 \tabularnewline
21 & 0.045753 & 0.549 & 0.291916 \tabularnewline
22 & -0.100179 & -1.2021 & 0.11564 \tabularnewline
23 & 0.052435 & 0.6292 & 0.265101 \tabularnewline
24 & 0.048014 & 0.5762 & 0.2827 \tabularnewline
25 & -0.162746 & -1.953 & 0.026382 \tabularnewline
26 & -0.036135 & -0.4336 & 0.332607 \tabularnewline
27 & 0.066424 & 0.7971 & 0.213357 \tabularnewline
28 & 0.006176 & 0.0741 & 0.470511 \tabularnewline
29 & 0.007537 & 0.0904 & 0.464029 \tabularnewline
30 & 0.01935 & 0.2322 & 0.408354 \tabularnewline
31 & -0.010251 & -0.123 & 0.451132 \tabularnewline
32 & -0.01831 & -0.2197 & 0.413199 \tabularnewline
33 & -0.029001 & -0.348 & 0.364168 \tabularnewline
34 & -0.014805 & -0.1777 & 0.42962 \tabularnewline
35 & -0.047725 & -0.5727 & 0.283872 \tabularnewline
36 & 0.046204 & 0.5544 & 0.290068 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27422&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]0.948047[/C][C]11.3766[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.229422[/C][C]-2.7531[/C][C]0.003332[/C][/ROW]
[ROW][C]3[/C][C]0.038148[/C][C]0.4578[/C][C]0.323903[/C][/ROW]
[ROW][C]4[/C][C]0.093785[/C][C]1.1254[/C][C]0.131141[/C][/ROW]
[ROW][C]5[/C][C]0.073607[/C][C]0.8833[/C][C]0.189279[/C][/ROW]
[ROW][C]6[/C][C]0.007728[/C][C]0.0927[/C][C]0.463123[/C][/ROW]
[ROW][C]7[/C][C]0.125597[/C][C]1.5072[/C][C]0.066979[/C][/ROW]
[ROW][C]8[/C][C]0.089951[/C][C]1.0794[/C][C]0.141103[/C][/ROW]
[ROW][C]9[/C][C]0.232489[/C][C]2.7899[/C][C]0.002994[/C][/ROW]
[ROW][C]10[/C][C]0.166051[/C][C]1.9926[/C][C]0.024097[/C][/ROW]
[ROW][C]11[/C][C]0.171274[/C][C]2.0553[/C][C]0.020829[/C][/ROW]
[ROW][C]12[/C][C]-0.135431[/C][C]-1.6252[/C][C]0.053156[/C][/ROW]
[ROW][C]13[/C][C]-0.539691[/C][C]-6.4763[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]-0.02661[/C][C]-0.3193[/C][C]0.374973[/C][/ROW]
[ROW][C]15[/C][C]0.090765[/C][C]1.0892[/C][C]0.138947[/C][/ROW]
[ROW][C]16[/C][C]0.024956[/C][C]0.2995[/C][C]0.382508[/C][/ROW]
[ROW][C]17[/C][C]0.032516[/C][C]0.3902[/C][C]0.348487[/C][/ROW]
[ROW][C]18[/C][C]0.073433[/C][C]0.8812[/C][C]0.189841[/C][/ROW]
[ROW][C]19[/C][C]0.048442[/C][C]0.5813[/C][C]0.280972[/C][/ROW]
[ROW][C]20[/C][C]-0.045542[/C][C]-0.5465[/C][C]0.292784[/C][/ROW]
[ROW][C]21[/C][C]0.045753[/C][C]0.549[/C][C]0.291916[/C][/ROW]
[ROW][C]22[/C][C]-0.100179[/C][C]-1.2021[/C][C]0.11564[/C][/ROW]
[ROW][C]23[/C][C]0.052435[/C][C]0.6292[/C][C]0.265101[/C][/ROW]
[ROW][C]24[/C][C]0.048014[/C][C]0.5762[/C][C]0.2827[/C][/ROW]
[ROW][C]25[/C][C]-0.162746[/C][C]-1.953[/C][C]0.026382[/C][/ROW]
[ROW][C]26[/C][C]-0.036135[/C][C]-0.4336[/C][C]0.332607[/C][/ROW]
[ROW][C]27[/C][C]0.066424[/C][C]0.7971[/C][C]0.213357[/C][/ROW]
[ROW][C]28[/C][C]0.006176[/C][C]0.0741[/C][C]0.470511[/C][/ROW]
[ROW][C]29[/C][C]0.007537[/C][C]0.0904[/C][C]0.464029[/C][/ROW]
[ROW][C]30[/C][C]0.01935[/C][C]0.2322[/C][C]0.408354[/C][/ROW]
[ROW][C]31[/C][C]-0.010251[/C][C]-0.123[/C][C]0.451132[/C][/ROW]
[ROW][C]32[/C][C]-0.01831[/C][C]-0.2197[/C][C]0.413199[/C][/ROW]
[ROW][C]33[/C][C]-0.029001[/C][C]-0.348[/C][C]0.364168[/C][/ROW]
[ROW][C]34[/C][C]-0.014805[/C][C]-0.1777[/C][C]0.42962[/C][/ROW]
[ROW][C]35[/C][C]-0.047725[/C][C]-0.5727[/C][C]0.283872[/C][/ROW]
[ROW][C]36[/C][C]0.046204[/C][C]0.5544[/C][C]0.290068[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27422&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27422&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
10.94804711.37660
2-0.229422-2.75310.003332
30.0381480.45780.323903
40.0937851.12540.131141
50.0736070.88330.189279
60.0077280.09270.463123
70.1255971.50720.066979
80.0899511.07940.141103
90.2324892.78990.002994
100.1660511.99260.024097
110.1712742.05530.020829
12-0.135431-1.62520.053156
13-0.539691-6.47630
14-0.02661-0.31930.374973
150.0907651.08920.138947
160.0249560.29950.382508
170.0325160.39020.348487
180.0734330.88120.189841
190.0484420.58130.280972
20-0.045542-0.54650.292784
210.0457530.5490.291916
22-0.100179-1.20210.11564
230.0524350.62920.265101
240.0480140.57620.2827
25-0.162746-1.9530.026382
26-0.036135-0.43360.332607
270.0664240.79710.213357
280.0061760.07410.470511
290.0075370.09040.464029
300.019350.23220.408354
31-0.010251-0.1230.451132
32-0.01831-0.21970.413199
33-0.029001-0.3480.364168
34-0.014805-0.17770.42962
35-0.047725-0.57270.283872
360.0462040.55440.290068



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