Free Statistics

of Irreproducible Research!

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, 08 Dec 2008 11:56:56 -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/08/t1228762647ns4vsg133e6a0ec.htm/, Retrieved Thu, 31 Oct 2024 23:20:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30710, Retrieved Thu, 31 Oct 2024 23:20:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact236
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
F RMP   [Variance Reduction Matrix] [step1.1] [2008-12-08 18:32:25] [922d8ae7bd2fd460a62d9020ccd4931a]
F RMP     [(Partial) Autocorrelation Function] [step2] [2008-12-08 18:51:58] [922d8ae7bd2fd460a62d9020ccd4931a]
-   P       [(Partial) Autocorrelation Function] [step22] [2008-12-08 18:54:38] [922d8ae7bd2fd460a62d9020ccd4931a]
F   P           [(Partial) Autocorrelation Function] [step23] [2008-12-08 18:56:56] [89a49ebb3ece8e9a225c7f9f53a14c57] [Current]
Feedback Forum
2008-12-16 17:38:42 [Lana Van Wesemael] [reply
Hier had de studente ook nog de lambda transformatie moeten toepassen. Deze moest gelijk gesteld worden aan 0.5.
Wanneer er dan geen lage termijn trend of seizoenaliteit meer aanwezig is dan zijn de voorwaarden vervuld.
2008-12-16 17:41:15 [Lana Van Wesemael] [reply
Goed besproken.
Om een AR proces te ontdekken moeten we naar de eerste coëfficiënten van de ACF kijken en deze dan vergelijken met de patronen die op moodle beschikbaar staan. Om een SAR proces te ontdekken kijken we naar de seizoenale coëfficiënten van de ACF en vergelijken deze met de patronen met andere woorden we kijken naar de streepjes op lag 12,24,36,… Om dan de orde te ontdekken van deze processen kijken we naar de PCAF en tellen hier het aantal significante streepjes die zich bevinden tussen de eerste 5 coëfficiënten (AR) of de seizoenale coëfficiënten (SAR).
Om een MA proces te ontdekken moeten we naar de eerste coëfficiënten van de PACF kijken en deze dan vergelijken met de patronen die op moodle beschikbaar staan. Om een SMA proces te ontdekken kijken we naar de seizoenale coëfficiënten van de PCFA en vergelijken deze met de patronen met andere woorden we kijken naar de streepjes op lag 12,24,36,… Om dan de orde te ontdekken van deze processen kijken we naar de ACF en tellen hier het aantal significante streepjes die zich bevinden tussen de eerste 5 coëfficiënten (AR) of de seizoenale coëfficiënten (SAR).
  2008-12-16 17:43:15 [Lana Van Wesemael] [reply
Deze feedback hoort niet bij deze link. Sorry

Post a new message
Dataseries X:
235.1
280.7
264.6
240.7
201.4
240.8
241.1
223.8
206.1
174.7
203.3
220.5
299.5
347.4
338.3
327.7
351.6
396.6
438.8
395.6
363.5
378.8
357
369
464.8
479.1
431.3
366.5
326.3
355.1
331.6
261.3
249
205.5
235.6
240.9
264.9
253.8
232.3
193.8
177
213.2
207.2
180.6
188.6
175.4
199
179.6
225.8
234
200.2
183.6
178.2
203.2
208.5
191.8
172.8
148
159.4
154.5
213.2
196.4
182.8
176.4
153.6
173.2
171
151.2
161.9
157.2
201.7
236.4
356.1
398.3
403.7
384.6
365.8
368.1
367.9
347
343.3
292.9
311.5
300.9
366.9
356.9
329.7
316.2
269
289.3
266.2
253.6
233.8
228.4
253.6
260.1
306.6
309.2
309.5
271
279.9
317.9
298.4
246.7
227.3
209.1
259.9
266
320.6
308.5
282.2
262.7
263.5
313.1
284.3
252.6
250.3
246.5
312.7
333.2
446.4
511.6
515.5
506.4
483.2
522.3
509.8
460.7
405.8
375
378.5
406.8
467.8
469.8
429.8
355.8
332.7
378
360.5
334.7
319.5
323.1
363.6
352.1
411.9
388.6
416.4
360.7
338
417.2
388.4
371.1
331.5
353.7
396.7
447
533.5
565.4
542.3
488.7
467.1
531.3
496.1
444
403.4
386.3
394.1
404.1
462.1
448.1
432.3
386.3
395.2
421.9
382.9
384.2
345.5
323.4
372.6
376
462.7
487
444.2
399.3
394.9
455.4
414
375.5
347
339.4
385.8
378.8
451.8
446.1
422.5
383.1
352.8
445.3
367.5
355.1
326.2
319.8
331.8
340.9
394.1
417.2
369.9
349.2
321.4
405.7
342.9
316.5
284.2
270.9
288.8
278.8
324.4
310.9
299
273
279.3
359.2
305
282.1
250.3
246.5
257.9
266.5
315.9
318.4
295.4
266.4
245.8
362.8
324.9
294.2
289.5
295.2
290.3
272
307.4
328.7
292.9
249.1
230.4
361.5
321.7
277.2
260.7
251
257.6
241.8
287.5
292.3
274.7
254.2
230
339
318.2
287
295.8
284
271
262.7
340.6
379.4
373.3
355.2
338.4
466.9
451
422
429.2
425.9
460.7
463.6
541.4
544.2
517.5
469.4
439.4
549
533
506.1
484
457
481.5
469.5
544.7
541.2
521.5
469.7
434.4
542.6
517.3
485.7
465.8
447
426.6
411.6
467.5
484.5
451.2
417.4
379.9
484.7
455
420.8
416.5
376.3
405.6
405.8
500.8
514
475.5
430.1
414.4
538
526
488.5
520.2
504.4
568.5
610.6
818
830.9
835.9
782
762.3
856.9
820.9
769.6
752.2
724.4
723.1
719.5
817.4
803.3
752.5
689
630.4
765.5
757.7
732.2
702.6
683.3
709.5
702.2
784.8
810.9
755.6
656.8
615.1
745.3
694.1
675.7
643.7
622.1
634.6
588
689.7
673.9
647.9
568.8
545.7
632.6
643.8
593.1
579.7
546
562.9
572.5




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=30710&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=30710&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30710&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
10.2101413.98164.1e-05
20.3251316.16030
30.1532752.90410.001955
40.1672523.1690.000831
50.0991561.87870.030545
60.0645131.22230.111191
7-0.057203-1.08380.13958
8-0.023244-0.44040.329956
9-0.085115-1.61270.053845
10-0.168424-3.19120.000771
11-0.075939-1.43880.075533
12-0.475334-9.00630
13-0.179215-3.39560.000381
14-0.162819-3.0850.001097
15-0.106644-2.02060.02203
16-0.147945-2.80320.002668
17-0.091478-1.73330.041954
18-0.089745-1.70040.044958
190.0273280.51780.302458
20-0.012913-0.24470.403425
210.0241990.45850.323437
22-0.018741-0.35510.361367
230.0095670.18130.42813
24-0.022226-0.42110.336957
250.0919741.74270.041125
26-0.045751-0.86680.193302
27-0.014979-0.28380.388362
280.0344830.65340.256968
290.0814741.54370.061769
300.0121170.22960.409275
310.0255520.48410.31429
32-0.054477-1.03220.151339
330.0086490.16390.434964
340.0161160.30540.380134
35-0.058412-1.10670.134573
36-0.022491-0.42610.335132
37-0.120582-2.28470.011456
380.0149740.28370.388394
39-0.025852-0.48980.312276
40-0.027437-0.51990.301741
41-0.10329-1.95710.025557
42-0.019216-0.36410.358002
43-0.134731-2.55280.00555
440.0030170.05720.477223
45-0.055783-1.05690.145624
460.0122860.23280.408029
470.0203670.38590.3499
480.1078962.04430.020825
490.1324432.50940.006266
500.1007761.90940.028501
510.0743631.4090.079853
520.0990641.8770.030665
530.1190782.25620.01233
540.0610261.15630.12417
550.1373932.60320.004809
560.0528911.00210.158474
570.0429540.81390.208131
580.039930.75660.224901
590.0675731.28030.100629
60-0.029187-0.5530.2903

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.210141 & 3.9816 & 4.1e-05 \tabularnewline
2 & 0.325131 & 6.1603 & 0 \tabularnewline
3 & 0.153275 & 2.9041 & 0.001955 \tabularnewline
4 & 0.167252 & 3.169 & 0.000831 \tabularnewline
5 & 0.099156 & 1.8787 & 0.030545 \tabularnewline
6 & 0.064513 & 1.2223 & 0.111191 \tabularnewline
7 & -0.057203 & -1.0838 & 0.13958 \tabularnewline
8 & -0.023244 & -0.4404 & 0.329956 \tabularnewline
9 & -0.085115 & -1.6127 & 0.053845 \tabularnewline
10 & -0.168424 & -3.1912 & 0.000771 \tabularnewline
11 & -0.075939 & -1.4388 & 0.075533 \tabularnewline
12 & -0.475334 & -9.0063 & 0 \tabularnewline
13 & -0.179215 & -3.3956 & 0.000381 \tabularnewline
14 & -0.162819 & -3.085 & 0.001097 \tabularnewline
15 & -0.106644 & -2.0206 & 0.02203 \tabularnewline
16 & -0.147945 & -2.8032 & 0.002668 \tabularnewline
17 & -0.091478 & -1.7333 & 0.041954 \tabularnewline
18 & -0.089745 & -1.7004 & 0.044958 \tabularnewline
19 & 0.027328 & 0.5178 & 0.302458 \tabularnewline
20 & -0.012913 & -0.2447 & 0.403425 \tabularnewline
21 & 0.024199 & 0.4585 & 0.323437 \tabularnewline
22 & -0.018741 & -0.3551 & 0.361367 \tabularnewline
23 & 0.009567 & 0.1813 & 0.42813 \tabularnewline
24 & -0.022226 & -0.4211 & 0.336957 \tabularnewline
25 & 0.091974 & 1.7427 & 0.041125 \tabularnewline
26 & -0.045751 & -0.8668 & 0.193302 \tabularnewline
27 & -0.014979 & -0.2838 & 0.388362 \tabularnewline
28 & 0.034483 & 0.6534 & 0.256968 \tabularnewline
29 & 0.081474 & 1.5437 & 0.061769 \tabularnewline
30 & 0.012117 & 0.2296 & 0.409275 \tabularnewline
31 & 0.025552 & 0.4841 & 0.31429 \tabularnewline
32 & -0.054477 & -1.0322 & 0.151339 \tabularnewline
33 & 0.008649 & 0.1639 & 0.434964 \tabularnewline
34 & 0.016116 & 0.3054 & 0.380134 \tabularnewline
35 & -0.058412 & -1.1067 & 0.134573 \tabularnewline
36 & -0.022491 & -0.4261 & 0.335132 \tabularnewline
37 & -0.120582 & -2.2847 & 0.011456 \tabularnewline
38 & 0.014974 & 0.2837 & 0.388394 \tabularnewline
39 & -0.025852 & -0.4898 & 0.312276 \tabularnewline
40 & -0.027437 & -0.5199 & 0.301741 \tabularnewline
41 & -0.10329 & -1.9571 & 0.025557 \tabularnewline
42 & -0.019216 & -0.3641 & 0.358002 \tabularnewline
43 & -0.134731 & -2.5528 & 0.00555 \tabularnewline
44 & 0.003017 & 0.0572 & 0.477223 \tabularnewline
45 & -0.055783 & -1.0569 & 0.145624 \tabularnewline
46 & 0.012286 & 0.2328 & 0.408029 \tabularnewline
47 & 0.020367 & 0.3859 & 0.3499 \tabularnewline
48 & 0.107896 & 2.0443 & 0.020825 \tabularnewline
49 & 0.132443 & 2.5094 & 0.006266 \tabularnewline
50 & 0.100776 & 1.9094 & 0.028501 \tabularnewline
51 & 0.074363 & 1.409 & 0.079853 \tabularnewline
52 & 0.099064 & 1.877 & 0.030665 \tabularnewline
53 & 0.119078 & 2.2562 & 0.01233 \tabularnewline
54 & 0.061026 & 1.1563 & 0.12417 \tabularnewline
55 & 0.137393 & 2.6032 & 0.004809 \tabularnewline
56 & 0.052891 & 1.0021 & 0.158474 \tabularnewline
57 & 0.042954 & 0.8139 & 0.208131 \tabularnewline
58 & 0.03993 & 0.7566 & 0.224901 \tabularnewline
59 & 0.067573 & 1.2803 & 0.100629 \tabularnewline
60 & -0.029187 & -0.553 & 0.2903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30710&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]0.210141[/C][C]3.9816[/C][C]4.1e-05[/C][/ROW]
[ROW][C]2[/C][C]0.325131[/C][C]6.1603[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.153275[/C][C]2.9041[/C][C]0.001955[/C][/ROW]
[ROW][C]4[/C][C]0.167252[/C][C]3.169[/C][C]0.000831[/C][/ROW]
[ROW][C]5[/C][C]0.099156[/C][C]1.8787[/C][C]0.030545[/C][/ROW]
[ROW][C]6[/C][C]0.064513[/C][C]1.2223[/C][C]0.111191[/C][/ROW]
[ROW][C]7[/C][C]-0.057203[/C][C]-1.0838[/C][C]0.13958[/C][/ROW]
[ROW][C]8[/C][C]-0.023244[/C][C]-0.4404[/C][C]0.329956[/C][/ROW]
[ROW][C]9[/C][C]-0.085115[/C][C]-1.6127[/C][C]0.053845[/C][/ROW]
[ROW][C]10[/C][C]-0.168424[/C][C]-3.1912[/C][C]0.000771[/C][/ROW]
[ROW][C]11[/C][C]-0.075939[/C][C]-1.4388[/C][C]0.075533[/C][/ROW]
[ROW][C]12[/C][C]-0.475334[/C][C]-9.0063[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]-0.179215[/C][C]-3.3956[/C][C]0.000381[/C][/ROW]
[ROW][C]14[/C][C]-0.162819[/C][C]-3.085[/C][C]0.001097[/C][/ROW]
[ROW][C]15[/C][C]-0.106644[/C][C]-2.0206[/C][C]0.02203[/C][/ROW]
[ROW][C]16[/C][C]-0.147945[/C][C]-2.8032[/C][C]0.002668[/C][/ROW]
[ROW][C]17[/C][C]-0.091478[/C][C]-1.7333[/C][C]0.041954[/C][/ROW]
[ROW][C]18[/C][C]-0.089745[/C][C]-1.7004[/C][C]0.044958[/C][/ROW]
[ROW][C]19[/C][C]0.027328[/C][C]0.5178[/C][C]0.302458[/C][/ROW]
[ROW][C]20[/C][C]-0.012913[/C][C]-0.2447[/C][C]0.403425[/C][/ROW]
[ROW][C]21[/C][C]0.024199[/C][C]0.4585[/C][C]0.323437[/C][/ROW]
[ROW][C]22[/C][C]-0.018741[/C][C]-0.3551[/C][C]0.361367[/C][/ROW]
[ROW][C]23[/C][C]0.009567[/C][C]0.1813[/C][C]0.42813[/C][/ROW]
[ROW][C]24[/C][C]-0.022226[/C][C]-0.4211[/C][C]0.336957[/C][/ROW]
[ROW][C]25[/C][C]0.091974[/C][C]1.7427[/C][C]0.041125[/C][/ROW]
[ROW][C]26[/C][C]-0.045751[/C][C]-0.8668[/C][C]0.193302[/C][/ROW]
[ROW][C]27[/C][C]-0.014979[/C][C]-0.2838[/C][C]0.388362[/C][/ROW]
[ROW][C]28[/C][C]0.034483[/C][C]0.6534[/C][C]0.256968[/C][/ROW]
[ROW][C]29[/C][C]0.081474[/C][C]1.5437[/C][C]0.061769[/C][/ROW]
[ROW][C]30[/C][C]0.012117[/C][C]0.2296[/C][C]0.409275[/C][/ROW]
[ROW][C]31[/C][C]0.025552[/C][C]0.4841[/C][C]0.31429[/C][/ROW]
[ROW][C]32[/C][C]-0.054477[/C][C]-1.0322[/C][C]0.151339[/C][/ROW]
[ROW][C]33[/C][C]0.008649[/C][C]0.1639[/C][C]0.434964[/C][/ROW]
[ROW][C]34[/C][C]0.016116[/C][C]0.3054[/C][C]0.380134[/C][/ROW]
[ROW][C]35[/C][C]-0.058412[/C][C]-1.1067[/C][C]0.134573[/C][/ROW]
[ROW][C]36[/C][C]-0.022491[/C][C]-0.4261[/C][C]0.335132[/C][/ROW]
[ROW][C]37[/C][C]-0.120582[/C][C]-2.2847[/C][C]0.011456[/C][/ROW]
[ROW][C]38[/C][C]0.014974[/C][C]0.2837[/C][C]0.388394[/C][/ROW]
[ROW][C]39[/C][C]-0.025852[/C][C]-0.4898[/C][C]0.312276[/C][/ROW]
[ROW][C]40[/C][C]-0.027437[/C][C]-0.5199[/C][C]0.301741[/C][/ROW]
[ROW][C]41[/C][C]-0.10329[/C][C]-1.9571[/C][C]0.025557[/C][/ROW]
[ROW][C]42[/C][C]-0.019216[/C][C]-0.3641[/C][C]0.358002[/C][/ROW]
[ROW][C]43[/C][C]-0.134731[/C][C]-2.5528[/C][C]0.00555[/C][/ROW]
[ROW][C]44[/C][C]0.003017[/C][C]0.0572[/C][C]0.477223[/C][/ROW]
[ROW][C]45[/C][C]-0.055783[/C][C]-1.0569[/C][C]0.145624[/C][/ROW]
[ROW][C]46[/C][C]0.012286[/C][C]0.2328[/C][C]0.408029[/C][/ROW]
[ROW][C]47[/C][C]0.020367[/C][C]0.3859[/C][C]0.3499[/C][/ROW]
[ROW][C]48[/C][C]0.107896[/C][C]2.0443[/C][C]0.020825[/C][/ROW]
[ROW][C]49[/C][C]0.132443[/C][C]2.5094[/C][C]0.006266[/C][/ROW]
[ROW][C]50[/C][C]0.100776[/C][C]1.9094[/C][C]0.028501[/C][/ROW]
[ROW][C]51[/C][C]0.074363[/C][C]1.409[/C][C]0.079853[/C][/ROW]
[ROW][C]52[/C][C]0.099064[/C][C]1.877[/C][C]0.030665[/C][/ROW]
[ROW][C]53[/C][C]0.119078[/C][C]2.2562[/C][C]0.01233[/C][/ROW]
[ROW][C]54[/C][C]0.061026[/C][C]1.1563[/C][C]0.12417[/C][/ROW]
[ROW][C]55[/C][C]0.137393[/C][C]2.6032[/C][C]0.004809[/C][/ROW]
[ROW][C]56[/C][C]0.052891[/C][C]1.0021[/C][C]0.158474[/C][/ROW]
[ROW][C]57[/C][C]0.042954[/C][C]0.8139[/C][C]0.208131[/C][/ROW]
[ROW][C]58[/C][C]0.03993[/C][C]0.7566[/C][C]0.224901[/C][/ROW]
[ROW][C]59[/C][C]0.067573[/C][C]1.2803[/C][C]0.100629[/C][/ROW]
[ROW][C]60[/C][C]-0.029187[/C][C]-0.553[/C][C]0.2903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30710&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30710&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
10.2101413.98164.1e-05
20.3251316.16030
30.1532752.90410.001955
40.1672523.1690.000831
50.0991561.87870.030545
60.0645131.22230.111191
7-0.057203-1.08380.13958
8-0.023244-0.44040.329956
9-0.085115-1.61270.053845
10-0.168424-3.19120.000771
11-0.075939-1.43880.075533
12-0.475334-9.00630
13-0.179215-3.39560.000381
14-0.162819-3.0850.001097
15-0.106644-2.02060.02203
16-0.147945-2.80320.002668
17-0.091478-1.73330.041954
18-0.089745-1.70040.044958
190.0273280.51780.302458
20-0.012913-0.24470.403425
210.0241990.45850.323437
22-0.018741-0.35510.361367
230.0095670.18130.42813
24-0.022226-0.42110.336957
250.0919741.74270.041125
26-0.045751-0.86680.193302
27-0.014979-0.28380.388362
280.0344830.65340.256968
290.0814741.54370.061769
300.0121170.22960.409275
310.0255520.48410.31429
32-0.054477-1.03220.151339
330.0086490.16390.434964
340.0161160.30540.380134
35-0.058412-1.10670.134573
36-0.022491-0.42610.335132
37-0.120582-2.28470.011456
380.0149740.28370.388394
39-0.025852-0.48980.312276
40-0.027437-0.51990.301741
41-0.10329-1.95710.025557
42-0.019216-0.36410.358002
43-0.134731-2.55280.00555
440.0030170.05720.477223
45-0.055783-1.05690.145624
460.0122860.23280.408029
470.0203670.38590.3499
480.1078962.04430.020825
490.1324432.50940.006266
500.1007761.90940.028501
510.0743631.4090.079853
520.0990641.8770.030665
530.1190782.25620.01233
540.0610261.15630.12417
550.1373932.60320.004809
560.0528911.00210.158474
570.0429540.81390.208131
580.039930.75660.224901
590.0675731.28030.100629
60-0.029187-0.5530.2903







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.2101413.98164.1e-05
20.2939525.56960
30.0495440.93870.174253
40.0495340.93850.174302
50.0127410.24140.404686
6-0.02095-0.39690.34582
7-0.124687-2.36250.009343
8-0.032709-0.61970.267912
9-0.045438-0.86090.194928
10-0.146092-2.7680.002966
110.0236310.44770.327306
12-0.42914-8.1310
13-0.0237-0.4490.326836
140.1508892.85890.002249
150.0297910.56450.286395
16-0.044612-0.84530.199257
17-0.012037-0.22810.40986
180.0046480.08810.464938
190.0113030.21420.415271
200.0098230.18610.426229
21-0.009818-0.1860.426268
22-0.15669-2.96890.001595
230.0121730.23060.408859
24-0.273087-5.17430
250.0532161.00830.156993
26-0.005528-0.10470.458322
27-0.053682-1.01710.15489
280.0462750.87680.190593
290.0910761.72560.042637
30-0.041044-0.77770.218636
310.0234520.44440.328528
32-0.066214-1.25460.105224
33-0.006987-0.13240.447376
34-0.075688-1.43410.076211
35-0.076212-1.4440.074803
36-0.234257-4.43856e-06
37-0.079056-1.49790.06752
380.0580531.09990.136046
39-0.065149-1.23440.108931
40-0.007132-0.13510.44629
410.0263770.49980.308768
42-0.0345-0.65370.256866
43-0.105199-1.99320.023496
44-0.043345-0.82130.20602
450.0086850.16460.434693
46-0.002039-0.03860.484604
47-0.029957-0.56760.285328
48-0.043935-0.83250.202852
490.0209810.39750.345603
500.0335180.63510.26289
51-0.048425-0.91750.17974
520.0202570.38380.350674
530.0172980.32770.371649
54-0.079249-1.50160.067046
550.0036890.06990.472155
56-0.027055-0.51260.304266
57-0.068417-1.29630.097849
580.0466520.88390.188663
590.0414090.78460.216604
60-0.038067-0.72130.235608

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.210141 & 3.9816 & 4.1e-05 \tabularnewline
2 & 0.293952 & 5.5696 & 0 \tabularnewline
3 & 0.049544 & 0.9387 & 0.174253 \tabularnewline
4 & 0.049534 & 0.9385 & 0.174302 \tabularnewline
5 & 0.012741 & 0.2414 & 0.404686 \tabularnewline
6 & -0.02095 & -0.3969 & 0.34582 \tabularnewline
7 & -0.124687 & -2.3625 & 0.009343 \tabularnewline
8 & -0.032709 & -0.6197 & 0.267912 \tabularnewline
9 & -0.045438 & -0.8609 & 0.194928 \tabularnewline
10 & -0.146092 & -2.768 & 0.002966 \tabularnewline
11 & 0.023631 & 0.4477 & 0.327306 \tabularnewline
12 & -0.42914 & -8.131 & 0 \tabularnewline
13 & -0.0237 & -0.449 & 0.326836 \tabularnewline
14 & 0.150889 & 2.8589 & 0.002249 \tabularnewline
15 & 0.029791 & 0.5645 & 0.286395 \tabularnewline
16 & -0.044612 & -0.8453 & 0.199257 \tabularnewline
17 & -0.012037 & -0.2281 & 0.40986 \tabularnewline
18 & 0.004648 & 0.0881 & 0.464938 \tabularnewline
19 & 0.011303 & 0.2142 & 0.415271 \tabularnewline
20 & 0.009823 & 0.1861 & 0.426229 \tabularnewline
21 & -0.009818 & -0.186 & 0.426268 \tabularnewline
22 & -0.15669 & -2.9689 & 0.001595 \tabularnewline
23 & 0.012173 & 0.2306 & 0.408859 \tabularnewline
24 & -0.273087 & -5.1743 & 0 \tabularnewline
25 & 0.053216 & 1.0083 & 0.156993 \tabularnewline
26 & -0.005528 & -0.1047 & 0.458322 \tabularnewline
27 & -0.053682 & -1.0171 & 0.15489 \tabularnewline
28 & 0.046275 & 0.8768 & 0.190593 \tabularnewline
29 & 0.091076 & 1.7256 & 0.042637 \tabularnewline
30 & -0.041044 & -0.7777 & 0.218636 \tabularnewline
31 & 0.023452 & 0.4444 & 0.328528 \tabularnewline
32 & -0.066214 & -1.2546 & 0.105224 \tabularnewline
33 & -0.006987 & -0.1324 & 0.447376 \tabularnewline
34 & -0.075688 & -1.4341 & 0.076211 \tabularnewline
35 & -0.076212 & -1.444 & 0.074803 \tabularnewline
36 & -0.234257 & -4.4385 & 6e-06 \tabularnewline
37 & -0.079056 & -1.4979 & 0.06752 \tabularnewline
38 & 0.058053 & 1.0999 & 0.136046 \tabularnewline
39 & -0.065149 & -1.2344 & 0.108931 \tabularnewline
40 & -0.007132 & -0.1351 & 0.44629 \tabularnewline
41 & 0.026377 & 0.4998 & 0.308768 \tabularnewline
42 & -0.0345 & -0.6537 & 0.256866 \tabularnewline
43 & -0.105199 & -1.9932 & 0.023496 \tabularnewline
44 & -0.043345 & -0.8213 & 0.20602 \tabularnewline
45 & 0.008685 & 0.1646 & 0.434693 \tabularnewline
46 & -0.002039 & -0.0386 & 0.484604 \tabularnewline
47 & -0.029957 & -0.5676 & 0.285328 \tabularnewline
48 & -0.043935 & -0.8325 & 0.202852 \tabularnewline
49 & 0.020981 & 0.3975 & 0.345603 \tabularnewline
50 & 0.033518 & 0.6351 & 0.26289 \tabularnewline
51 & -0.048425 & -0.9175 & 0.17974 \tabularnewline
52 & 0.020257 & 0.3838 & 0.350674 \tabularnewline
53 & 0.017298 & 0.3277 & 0.371649 \tabularnewline
54 & -0.079249 & -1.5016 & 0.067046 \tabularnewline
55 & 0.003689 & 0.0699 & 0.472155 \tabularnewline
56 & -0.027055 & -0.5126 & 0.304266 \tabularnewline
57 & -0.068417 & -1.2963 & 0.097849 \tabularnewline
58 & 0.046652 & 0.8839 & 0.188663 \tabularnewline
59 & 0.041409 & 0.7846 & 0.216604 \tabularnewline
60 & -0.038067 & -0.7213 & 0.235608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30710&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]0.210141[/C][C]3.9816[/C][C]4.1e-05[/C][/ROW]
[ROW][C]2[/C][C]0.293952[/C][C]5.5696[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.049544[/C][C]0.9387[/C][C]0.174253[/C][/ROW]
[ROW][C]4[/C][C]0.049534[/C][C]0.9385[/C][C]0.174302[/C][/ROW]
[ROW][C]5[/C][C]0.012741[/C][C]0.2414[/C][C]0.404686[/C][/ROW]
[ROW][C]6[/C][C]-0.02095[/C][C]-0.3969[/C][C]0.34582[/C][/ROW]
[ROW][C]7[/C][C]-0.124687[/C][C]-2.3625[/C][C]0.009343[/C][/ROW]
[ROW][C]8[/C][C]-0.032709[/C][C]-0.6197[/C][C]0.267912[/C][/ROW]
[ROW][C]9[/C][C]-0.045438[/C][C]-0.8609[/C][C]0.194928[/C][/ROW]
[ROW][C]10[/C][C]-0.146092[/C][C]-2.768[/C][C]0.002966[/C][/ROW]
[ROW][C]11[/C][C]0.023631[/C][C]0.4477[/C][C]0.327306[/C][/ROW]
[ROW][C]12[/C][C]-0.42914[/C][C]-8.131[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]-0.0237[/C][C]-0.449[/C][C]0.326836[/C][/ROW]
[ROW][C]14[/C][C]0.150889[/C][C]2.8589[/C][C]0.002249[/C][/ROW]
[ROW][C]15[/C][C]0.029791[/C][C]0.5645[/C][C]0.286395[/C][/ROW]
[ROW][C]16[/C][C]-0.044612[/C][C]-0.8453[/C][C]0.199257[/C][/ROW]
[ROW][C]17[/C][C]-0.012037[/C][C]-0.2281[/C][C]0.40986[/C][/ROW]
[ROW][C]18[/C][C]0.004648[/C][C]0.0881[/C][C]0.464938[/C][/ROW]
[ROW][C]19[/C][C]0.011303[/C][C]0.2142[/C][C]0.415271[/C][/ROW]
[ROW][C]20[/C][C]0.009823[/C][C]0.1861[/C][C]0.426229[/C][/ROW]
[ROW][C]21[/C][C]-0.009818[/C][C]-0.186[/C][C]0.426268[/C][/ROW]
[ROW][C]22[/C][C]-0.15669[/C][C]-2.9689[/C][C]0.001595[/C][/ROW]
[ROW][C]23[/C][C]0.012173[/C][C]0.2306[/C][C]0.408859[/C][/ROW]
[ROW][C]24[/C][C]-0.273087[/C][C]-5.1743[/C][C]0[/C][/ROW]
[ROW][C]25[/C][C]0.053216[/C][C]1.0083[/C][C]0.156993[/C][/ROW]
[ROW][C]26[/C][C]-0.005528[/C][C]-0.1047[/C][C]0.458322[/C][/ROW]
[ROW][C]27[/C][C]-0.053682[/C][C]-1.0171[/C][C]0.15489[/C][/ROW]
[ROW][C]28[/C][C]0.046275[/C][C]0.8768[/C][C]0.190593[/C][/ROW]
[ROW][C]29[/C][C]0.091076[/C][C]1.7256[/C][C]0.042637[/C][/ROW]
[ROW][C]30[/C][C]-0.041044[/C][C]-0.7777[/C][C]0.218636[/C][/ROW]
[ROW][C]31[/C][C]0.023452[/C][C]0.4444[/C][C]0.328528[/C][/ROW]
[ROW][C]32[/C][C]-0.066214[/C][C]-1.2546[/C][C]0.105224[/C][/ROW]
[ROW][C]33[/C][C]-0.006987[/C][C]-0.1324[/C][C]0.447376[/C][/ROW]
[ROW][C]34[/C][C]-0.075688[/C][C]-1.4341[/C][C]0.076211[/C][/ROW]
[ROW][C]35[/C][C]-0.076212[/C][C]-1.444[/C][C]0.074803[/C][/ROW]
[ROW][C]36[/C][C]-0.234257[/C][C]-4.4385[/C][C]6e-06[/C][/ROW]
[ROW][C]37[/C][C]-0.079056[/C][C]-1.4979[/C][C]0.06752[/C][/ROW]
[ROW][C]38[/C][C]0.058053[/C][C]1.0999[/C][C]0.136046[/C][/ROW]
[ROW][C]39[/C][C]-0.065149[/C][C]-1.2344[/C][C]0.108931[/C][/ROW]
[ROW][C]40[/C][C]-0.007132[/C][C]-0.1351[/C][C]0.44629[/C][/ROW]
[ROW][C]41[/C][C]0.026377[/C][C]0.4998[/C][C]0.308768[/C][/ROW]
[ROW][C]42[/C][C]-0.0345[/C][C]-0.6537[/C][C]0.256866[/C][/ROW]
[ROW][C]43[/C][C]-0.105199[/C][C]-1.9932[/C][C]0.023496[/C][/ROW]
[ROW][C]44[/C][C]-0.043345[/C][C]-0.8213[/C][C]0.20602[/C][/ROW]
[ROW][C]45[/C][C]0.008685[/C][C]0.1646[/C][C]0.434693[/C][/ROW]
[ROW][C]46[/C][C]-0.002039[/C][C]-0.0386[/C][C]0.484604[/C][/ROW]
[ROW][C]47[/C][C]-0.029957[/C][C]-0.5676[/C][C]0.285328[/C][/ROW]
[ROW][C]48[/C][C]-0.043935[/C][C]-0.8325[/C][C]0.202852[/C][/ROW]
[ROW][C]49[/C][C]0.020981[/C][C]0.3975[/C][C]0.345603[/C][/ROW]
[ROW][C]50[/C][C]0.033518[/C][C]0.6351[/C][C]0.26289[/C][/ROW]
[ROW][C]51[/C][C]-0.048425[/C][C]-0.9175[/C][C]0.17974[/C][/ROW]
[ROW][C]52[/C][C]0.020257[/C][C]0.3838[/C][C]0.350674[/C][/ROW]
[ROW][C]53[/C][C]0.017298[/C][C]0.3277[/C][C]0.371649[/C][/ROW]
[ROW][C]54[/C][C]-0.079249[/C][C]-1.5016[/C][C]0.067046[/C][/ROW]
[ROW][C]55[/C][C]0.003689[/C][C]0.0699[/C][C]0.472155[/C][/ROW]
[ROW][C]56[/C][C]-0.027055[/C][C]-0.5126[/C][C]0.304266[/C][/ROW]
[ROW][C]57[/C][C]-0.068417[/C][C]-1.2963[/C][C]0.097849[/C][/ROW]
[ROW][C]58[/C][C]0.046652[/C][C]0.8839[/C][C]0.188663[/C][/ROW]
[ROW][C]59[/C][C]0.041409[/C][C]0.7846[/C][C]0.216604[/C][/ROW]
[ROW][C]60[/C][C]-0.038067[/C][C]-0.7213[/C][C]0.235608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30710&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30710&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
10.2101413.98164.1e-05
20.2939525.56960
30.0495440.93870.174253
40.0495340.93850.174302
50.0127410.24140.404686
6-0.02095-0.39690.34582
7-0.124687-2.36250.009343
8-0.032709-0.61970.267912
9-0.045438-0.86090.194928
10-0.146092-2.7680.002966
110.0236310.44770.327306
12-0.42914-8.1310
13-0.0237-0.4490.326836
140.1508892.85890.002249
150.0297910.56450.286395
16-0.044612-0.84530.199257
17-0.012037-0.22810.40986
180.0046480.08810.464938
190.0113030.21420.415271
200.0098230.18610.426229
21-0.009818-0.1860.426268
22-0.15669-2.96890.001595
230.0121730.23060.408859
24-0.273087-5.17430
250.0532161.00830.156993
26-0.005528-0.10470.458322
27-0.053682-1.01710.15489
280.0462750.87680.190593
290.0910761.72560.042637
30-0.041044-0.77770.218636
310.0234520.44440.328528
32-0.066214-1.25460.105224
33-0.006987-0.13240.447376
34-0.075688-1.43410.076211
35-0.076212-1.4440.074803
36-0.234257-4.43856e-06
37-0.079056-1.49790.06752
380.0580531.09990.136046
39-0.065149-1.23440.108931
40-0.007132-0.13510.44629
410.0263770.49980.308768
42-0.0345-0.65370.256866
43-0.105199-1.99320.023496
44-0.043345-0.82130.20602
450.0086850.16460.434693
46-0.002039-0.03860.484604
47-0.029957-0.56760.285328
48-0.043935-0.83250.202852
490.0209810.39750.345603
500.0335180.63510.26289
51-0.048425-0.91750.17974
520.0202570.38380.350674
530.0172980.32770.371649
54-0.079249-1.50160.067046
550.0036890.06990.472155
56-0.027055-0.51260.304266
57-0.068417-1.29630.097849
580.0466520.88390.188663
590.0414090.78460.216604
60-0.038067-0.72130.235608



Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ;
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; 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')