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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, 04 Dec 2012 15:09:16 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/04/t1354651774pcdhim7jzc62g6w.htm/, Retrieved Thu, 28 Mar 2024 08:54:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196583, Retrieved Thu, 28 Mar 2024 08:54:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110
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]
- RMP   [(Partial) Autocorrelation Function] [Unemployment] [2010-11-29 09:05:21] [b98453cac15ba1066b407e146608df68]
- R         [(Partial) Autocorrelation Function] [Autocorrelatie Un...] [2012-12-04 20:09:16] [09cdab5d933081235930b6410ef38881] [Current]
-   P         [(Partial) Autocorrelation Function] [d=1 en Timelags =...] [2012-12-04 20:17:51] [f8950b13b9b6c1e097d81f3c7491f9a1]
-   P           [(Partial) Autocorrelation Function] [d=1, D=1 en Timel...] [2012-12-04 20:28:52] [f8950b13b9b6c1e097d81f3c7491f9a1]
- RMP           [Spectral Analysis] [Spectral analysis...] [2012-12-04 20:36:51] [f8950b13b9b6c1e097d81f3c7491f9a1]
- R P             [Spectral Analysis] [Spectral analysis...] [2012-12-04 20:40:07] [f8950b13b9b6c1e097d81f3c7491f9a1]
-   P               [Spectral Analysis] [Spectral analysis...] [2012-12-04 20:42:10] [f8950b13b9b6c1e097d81f3c7491f9a1]
- RMP               [Variance Reduction Matrix] [Variance Reductio...] [2012-12-04 20:45:05] [f8950b13b9b6c1e097d81f3c7491f9a1]
- RMP               [Standard Deviation-Mean Plot] [Standard Deviatio...] [2012-12-04 21:10:39] [f8950b13b9b6c1e097d81f3c7491f9a1]
- RMP               [ARIMA Backward Selection] [Arima backward se...] [2012-12-04 22:37:08] [f8950b13b9b6c1e097d81f3c7491f9a1]
- RMP               [ARIMA Forecasting] [Arima Forecasting] [2012-12-04 22:49:28] [f8950b13b9b6c1e097d81f3c7491f9a1]
- R P                 [ARIMA Forecasting] [arima forecasting] [2012-12-20 16:25:23] [77d02b0cf2cecd023ffa9a06f056f18d]
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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 time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196583&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196583&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196583&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'Gertrude Mary Cox' @ cox.wessa.net







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.95829718.4830
20.91521817.65210
30.88341217.03860
40.87008816.78160
50.86173516.62050
60.83236716.05410
70.81215315.66420
80.77509514.94950
90.74559214.38050
100.73465214.16950
110.73915414.25630
120.74370514.34410
130.69318813.36970
140.6441612.42410
150.61139311.79210
160.59979311.56840
170.59740911.52240
180.57795411.14720
190.56895310.97360
200.54377610.4880
210.52623210.14960
220.52570110.13940
230.53771210.3710
240.55135410.63410
250.5128499.89150

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.958297 & 18.483 & 0 \tabularnewline
2 & 0.915218 & 17.6521 & 0 \tabularnewline
3 & 0.883412 & 17.0386 & 0 \tabularnewline
4 & 0.870088 & 16.7816 & 0 \tabularnewline
5 & 0.861735 & 16.6205 & 0 \tabularnewline
6 & 0.832367 & 16.0541 & 0 \tabularnewline
7 & 0.812153 & 15.6642 & 0 \tabularnewline
8 & 0.775095 & 14.9495 & 0 \tabularnewline
9 & 0.745592 & 14.3805 & 0 \tabularnewline
10 & 0.734652 & 14.1695 & 0 \tabularnewline
11 & 0.739154 & 14.2563 & 0 \tabularnewline
12 & 0.743705 & 14.3441 & 0 \tabularnewline
13 & 0.693188 & 13.3697 & 0 \tabularnewline
14 & 0.64416 & 12.4241 & 0 \tabularnewline
15 & 0.611393 & 11.7921 & 0 \tabularnewline
16 & 0.599793 & 11.5684 & 0 \tabularnewline
17 & 0.597409 & 11.5224 & 0 \tabularnewline
18 & 0.577954 & 11.1472 & 0 \tabularnewline
19 & 0.568953 & 10.9736 & 0 \tabularnewline
20 & 0.543776 & 10.488 & 0 \tabularnewline
21 & 0.526232 & 10.1496 & 0 \tabularnewline
22 & 0.525701 & 10.1394 & 0 \tabularnewline
23 & 0.537712 & 10.371 & 0 \tabularnewline
24 & 0.551354 & 10.6341 & 0 \tabularnewline
25 & 0.512849 & 9.8915 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196583&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]0.958297[/C][C]18.483[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.915218[/C][C]17.6521[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.883412[/C][C]17.0386[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]0.870088[/C][C]16.7816[/C][C]0[/C][/ROW]
[ROW][C]5[/C][C]0.861735[/C][C]16.6205[/C][C]0[/C][/ROW]
[ROW][C]6[/C][C]0.832367[/C][C]16.0541[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]0.812153[/C][C]15.6642[/C][C]0[/C][/ROW]
[ROW][C]8[/C][C]0.775095[/C][C]14.9495[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.745592[/C][C]14.3805[/C][C]0[/C][/ROW]
[ROW][C]10[/C][C]0.734652[/C][C]14.1695[/C][C]0[/C][/ROW]
[ROW][C]11[/C][C]0.739154[/C][C]14.2563[/C][C]0[/C][/ROW]
[ROW][C]12[/C][C]0.743705[/C][C]14.3441[/C][C]0[/C][/ROW]
[ROW][C]13[/C][C]0.693188[/C][C]13.3697[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]0.64416[/C][C]12.4241[/C][C]0[/C][/ROW]
[ROW][C]15[/C][C]0.611393[/C][C]11.7921[/C][C]0[/C][/ROW]
[ROW][C]16[/C][C]0.599793[/C][C]11.5684[/C][C]0[/C][/ROW]
[ROW][C]17[/C][C]0.597409[/C][C]11.5224[/C][C]0[/C][/ROW]
[ROW][C]18[/C][C]0.577954[/C][C]11.1472[/C][C]0[/C][/ROW]
[ROW][C]19[/C][C]0.568953[/C][C]10.9736[/C][C]0[/C][/ROW]
[ROW][C]20[/C][C]0.543776[/C][C]10.488[/C][C]0[/C][/ROW]
[ROW][C]21[/C][C]0.526232[/C][C]10.1496[/C][C]0[/C][/ROW]
[ROW][C]22[/C][C]0.525701[/C][C]10.1394[/C][C]0[/C][/ROW]
[ROW][C]23[/C][C]0.537712[/C][C]10.371[/C][C]0[/C][/ROW]
[ROW][C]24[/C][C]0.551354[/C][C]10.6341[/C][C]0[/C][/ROW]
[ROW][C]25[/C][C]0.512849[/C][C]9.8915[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196583&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196583&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
10.95829718.4830
20.91521817.65210
30.88341217.03860
40.87008816.78160
50.86173516.62050
60.83236716.05410
70.81215315.66420
80.77509514.94950
90.74559214.38050
100.73465214.16950
110.73915414.25630
120.74370514.34410
130.69318813.36970
140.6441612.42410
150.61139311.79210
160.59979311.56840
170.59740911.52240
180.57795411.14720
190.56895310.97360
200.54377610.4880
210.52623210.14960
220.52570110.13940
230.53771210.3710
240.55135410.63410
250.5128499.89150







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.95829718.4830
2-0.038138-0.73560.231227
30.1160082.23750.012923
40.2073453.99913.8e-05
50.0712951.37510.084967
6-0.216629-4.17821.8e-05
70.1778433.43010.000336
8-0.280159-5.40350
90.0515850.99490.160206
100.2214434.2711.2e-05
110.1890493.64620.000152
12-0.045031-0.86850.192833
13-0.550158-10.61110
140.0486850.9390.17417
150.1391522.68390.003802
160.0589661.13730.128074
170.1450152.7970.002713
180.0564151.08810.13863
190.0891681.71980.04315
20-0.085304-1.64530.050378
210.0107060.20650.418258
220.0204530.39450.346725
23-0.016396-0.31620.376
240.0564891.08950.138315
25-0.235006-4.53264e-06

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.958297 & 18.483 & 0 \tabularnewline
2 & -0.038138 & -0.7356 & 0.231227 \tabularnewline
3 & 0.116008 & 2.2375 & 0.012923 \tabularnewline
4 & 0.207345 & 3.9991 & 3.8e-05 \tabularnewline
5 & 0.071295 & 1.3751 & 0.084967 \tabularnewline
6 & -0.216629 & -4.1782 & 1.8e-05 \tabularnewline
7 & 0.177843 & 3.4301 & 0.000336 \tabularnewline
8 & -0.280159 & -5.4035 & 0 \tabularnewline
9 & 0.051585 & 0.9949 & 0.160206 \tabularnewline
10 & 0.221443 & 4.271 & 1.2e-05 \tabularnewline
11 & 0.189049 & 3.6462 & 0.000152 \tabularnewline
12 & -0.045031 & -0.8685 & 0.192833 \tabularnewline
13 & -0.550158 & -10.6111 & 0 \tabularnewline
14 & 0.048685 & 0.939 & 0.17417 \tabularnewline
15 & 0.139152 & 2.6839 & 0.003802 \tabularnewline
16 & 0.058966 & 1.1373 & 0.128074 \tabularnewline
17 & 0.145015 & 2.797 & 0.002713 \tabularnewline
18 & 0.056415 & 1.0881 & 0.13863 \tabularnewline
19 & 0.089168 & 1.7198 & 0.04315 \tabularnewline
20 & -0.085304 & -1.6453 & 0.050378 \tabularnewline
21 & 0.010706 & 0.2065 & 0.418258 \tabularnewline
22 & 0.020453 & 0.3945 & 0.346725 \tabularnewline
23 & -0.016396 & -0.3162 & 0.376 \tabularnewline
24 & 0.056489 & 1.0895 & 0.138315 \tabularnewline
25 & -0.235006 & -4.5326 & 4e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196583&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]0.958297[/C][C]18.483[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.038138[/C][C]-0.7356[/C][C]0.231227[/C][/ROW]
[ROW][C]3[/C][C]0.116008[/C][C]2.2375[/C][C]0.012923[/C][/ROW]
[ROW][C]4[/C][C]0.207345[/C][C]3.9991[/C][C]3.8e-05[/C][/ROW]
[ROW][C]5[/C][C]0.071295[/C][C]1.3751[/C][C]0.084967[/C][/ROW]
[ROW][C]6[/C][C]-0.216629[/C][C]-4.1782[/C][C]1.8e-05[/C][/ROW]
[ROW][C]7[/C][C]0.177843[/C][C]3.4301[/C][C]0.000336[/C][/ROW]
[ROW][C]8[/C][C]-0.280159[/C][C]-5.4035[/C][C]0[/C][/ROW]
[ROW][C]9[/C][C]0.051585[/C][C]0.9949[/C][C]0.160206[/C][/ROW]
[ROW][C]10[/C][C]0.221443[/C][C]4.271[/C][C]1.2e-05[/C][/ROW]
[ROW][C]11[/C][C]0.189049[/C][C]3.6462[/C][C]0.000152[/C][/ROW]
[ROW][C]12[/C][C]-0.045031[/C][C]-0.8685[/C][C]0.192833[/C][/ROW]
[ROW][C]13[/C][C]-0.550158[/C][C]-10.6111[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]0.048685[/C][C]0.939[/C][C]0.17417[/C][/ROW]
[ROW][C]15[/C][C]0.139152[/C][C]2.6839[/C][C]0.003802[/C][/ROW]
[ROW][C]16[/C][C]0.058966[/C][C]1.1373[/C][C]0.128074[/C][/ROW]
[ROW][C]17[/C][C]0.145015[/C][C]2.797[/C][C]0.002713[/C][/ROW]
[ROW][C]18[/C][C]0.056415[/C][C]1.0881[/C][C]0.13863[/C][/ROW]
[ROW][C]19[/C][C]0.089168[/C][C]1.7198[/C][C]0.04315[/C][/ROW]
[ROW][C]20[/C][C]-0.085304[/C][C]-1.6453[/C][C]0.050378[/C][/ROW]
[ROW][C]21[/C][C]0.010706[/C][C]0.2065[/C][C]0.418258[/C][/ROW]
[ROW][C]22[/C][C]0.020453[/C][C]0.3945[/C][C]0.346725[/C][/ROW]
[ROW][C]23[/C][C]-0.016396[/C][C]-0.3162[/C][C]0.376[/C][/ROW]
[ROW][C]24[/C][C]0.056489[/C][C]1.0895[/C][C]0.138315[/C][/ROW]
[ROW][C]25[/C][C]-0.235006[/C][C]-4.5326[/C][C]4e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196583&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196583&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
10.95829718.4830
2-0.038138-0.73560.231227
30.1160082.23750.012923
40.2073453.99913.8e-05
50.0712951.37510.084967
6-0.216629-4.17821.8e-05
70.1778433.43010.000336
8-0.280159-5.40350
90.0515850.99490.160206
100.2214434.2711.2e-05
110.1890493.64620.000152
12-0.045031-0.86850.192833
13-0.550158-10.61110
140.0486850.9390.17417
150.1391522.68390.003802
160.0589661.13730.128074
170.1450152.7970.002713
180.0564151.08810.13863
190.0891681.71980.04315
20-0.085304-1.64530.050378
210.0107060.20650.418258
220.0204530.39450.346725
23-0.016396-0.31620.376
240.0564891.08950.138315
25-0.235006-4.53264e-06



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 ; par8 = ;
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')