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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationThu, 10 Dec 2009 13:04:24 -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/Dec/10/t1260475518l8yhfmeff6lotib.htm/, Retrieved Thu, 28 Mar 2024 15:54:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65775, Retrieved Thu, 28 Mar 2024 15:54:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [] [2009-12-07 09:20:41] [b98453cac15ba1066b407e146608df68]
-    D    [ARIMA Backward Selection] [] [2009-12-10 20:04:24] [0545e25c765ce26b196961216dc11e13] [Current]
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Dataseries X:
280.2
299.9
339.2
374.2
393.5
389.2
381.7
375.2
369
357.4
352.1
346.5
342.9
340.3
328.3
322.9
314.3
308.9
294
285.6
281.2
280.3
278.8
274.5
270.4
263.4
259.9
258
262.7
284.7
311.3
322.1
327
331.3
333.3
321.4
327
320
314.7
316.7
314.4
321.3
318.2
307.2
301.3
287.5
277.7
274.4
258.8
253.3
251
248.4
249.5
246.1
244.5
243.6
244
240.8
249.8
248
259.4
260.5
260.8
261.3
259.5
256.6
257.9
256.5
254.2
253.3
253.8
255.5
257.1
257.3
253.2
252.8
252
250.7
252.2
250
251
253.4
251.2
255.6
261.1
258.9
259.9
261.2
264.7
267.1
266.4
267.7
268.6
267.5
268.5
268.5
270.5
270.9
270.1
269.3
269.8
270.1
264.9
263.7
264.8
263.7
255.9
276.2
360.1
380.5
373.7
369.8
366.6
359.3
345.8
326.2
324.5
328.1
327.5
324.4
316.5
310.9
301.5
291.7
290.4
287.4
277.7
281.6
288
276
272.9
283
283.3
276.8
284.5
282.7
281.2
287.4
283.1
284
285.5
289.2
292.5
296.4
305.2
303.9
311.5
316.3
316.7
322.5
317.1
309.8
303.8
290.3
293.7
291.7
296.5
289.1
288.5
293.8
297.7
305.4
302.7
302.5
303
294.5
294.1
294.5
297.1
289.4
292.4
287.9
286.6
280.5
272.4
269.2
270.6
267.3
262.5
266.8
268.8
263.1
261.2
266
262.5
265.2
261.3
253.7
249.2
239.1
236.4
235.2
245.2
246.2
247.7
251.4
253.3
254.8
250
249.3
241.5
243.3
248
253
252.9
251.5
251.6
253.5
259.8
334.1
448
445.8
445
448.2
438.2
439.8
423.4
410.8
408.4
406.7
405.9
402.7
405.1
399.6
386.5
381.4
375.2
357.7
359
355
352.7
344.4
343.8
338
339
333.3
334.4
328.3
330.7
330
331.6
351.2
389.4
410.9
442.8
462.8
466.9
461.7
439.2
430.3
416.1
402.5
397.3
403.3
395.9
387.8
378.6
377.1
370.4
362
350.3
348.2
344.6
343.5
342.8
347.6
346.6
349.5
342.1
342
342.8
339.3
348.2
333.7
334.7
354
367.7
363.3
358.4
353.1
343.1
344.6
344.4
333.9
331.7
324.3
321.2
322.4
321.7
320.5
312.8
309.7
315.6
309.7
304.6
302.5
301.5
298.8
291.3
293.6
294.6
285.9
297.6
301.1
293.8
297.7
292.9
292.1
287.2
288.2
283.8
299.9
292.4
293.3
300.8
293.7
293.1
294.4
292.1
291.9
282.5
277.9
287.5
289.2
285.6
293.2
290.8
283.1
275
287.8
287.8
287.4
284
277.8
277.6
304.9
294
300.9
324
332.9
341.6
333.4
348.2
344.7
344.7
329.3
323.5
323.2
317.4
330.1
329.2
334.9
315.8
315.4
319.6
317.3
313.8
315.8
311.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65775&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]5 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=65775&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65775&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11
Estimates ( 1 )0.4698-0.1110.1108-0.07790.0091-0.0889-0.01650.00280.0366-0.05990.004
(p-val)(0 )(0.0593 )(0.061 )(0.189 )(0.8788 )(0.1338 )(0.7825 )(0.9622 )(0.5382 )(0.3133 )(0.9404 )
Estimates ( 2 )0.4698-0.11110.1109-0.07810.0094-0.0893-0.015300.0378-0.06030.0043
(p-val)(0 )(0.0582 )(0.0605 )(0.1867 )(0.8728 )(0.128 )(0.7774 )(NA )(0.4832 )(0.3046 )(0.9356 )
Estimates ( 3 )0.4695-0.1110.1109-0.07820.0091-0.0893-0.015400.0375-0.05830
(p-val)(0 )(0.0584 )(0.0605 )(0.1863 )(0.8769 )(0.1281 )(0.7754 )(NA )(0.4857 )(0.2768 )(NA )
Estimates ( 4 )0.469-0.10990.1097-0.07440-0.0856-0.016300.0374-0.05830
(p-val)(0 )(0.0591 )(0.0609 )(0.1666 )(NA )(0.1099 )(0.7625 )(NA )(0.4866 )(0.2769 )(NA )
Estimates ( 5 )0.4704-0.11010.1105-0.07570-0.092000.0367-0.05960
(p-val)(0 )(0.0588 )(0.0588 )(0.1581 )(NA )(0.0613 )(NA )(NA )(0.4942 )(0.2653 )(NA )
Estimates ( 6 )0.4703-0.11210.1084-0.0750-0.0887000-0.04460
(p-val)(0 )(0.0542 )(0.0636 )(0.162 )(NA )(0.0698 )(NA )(NA )(NA )(0.3608 )(NA )
Estimates ( 7 )0.47-0.11170.111-0.07150-0.089400000
(p-val)(0 )(0.0554 )(0.0576 )(0.1819 )(NA )(0.0681 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0.4648-0.1040.0800-0.094900000
(p-val)(0 )(0.0735 )(0.1363 )(NA )(NA )(0.0525 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )0.4595-0.0692000-0.088300000
(p-val)(0 )(0.1943 )(NA )(NA )(NA )(0.0709 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )0.43020000-0.089700000
(p-val)(0 )(NA )(NA )(NA )(NA )(0.0671 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )0.43510000000000
(p-val)(0 )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 18 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 19 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 20 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 21 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ar4 & ar5 & ar6 & ar7 & ar8 & ar9 & ar10 & ar11 \tabularnewline
Estimates ( 1 ) & 0.4698 & -0.111 & 0.1108 & -0.0779 & 0.0091 & -0.0889 & -0.0165 & 0.0028 & 0.0366 & -0.0599 & 0.004 \tabularnewline
(p-val) & (0 ) & (0.0593 ) & (0.061 ) & (0.189 ) & (0.8788 ) & (0.1338 ) & (0.7825 ) & (0.9622 ) & (0.5382 ) & (0.3133 ) & (0.9404 ) \tabularnewline
Estimates ( 2 ) & 0.4698 & -0.1111 & 0.1109 & -0.0781 & 0.0094 & -0.0893 & -0.0153 & 0 & 0.0378 & -0.0603 & 0.0043 \tabularnewline
(p-val) & (0 ) & (0.0582 ) & (0.0605 ) & (0.1867 ) & (0.8728 ) & (0.128 ) & (0.7774 ) & (NA ) & (0.4832 ) & (0.3046 ) & (0.9356 ) \tabularnewline
Estimates ( 3 ) & 0.4695 & -0.111 & 0.1109 & -0.0782 & 0.0091 & -0.0893 & -0.0154 & 0 & 0.0375 & -0.0583 & 0 \tabularnewline
(p-val) & (0 ) & (0.0584 ) & (0.0605 ) & (0.1863 ) & (0.8769 ) & (0.1281 ) & (0.7754 ) & (NA ) & (0.4857 ) & (0.2768 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0.469 & -0.1099 & 0.1097 & -0.0744 & 0 & -0.0856 & -0.0163 & 0 & 0.0374 & -0.0583 & 0 \tabularnewline
(p-val) & (0 ) & (0.0591 ) & (0.0609 ) & (0.1666 ) & (NA ) & (0.1099 ) & (0.7625 ) & (NA ) & (0.4866 ) & (0.2769 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0.4704 & -0.1101 & 0.1105 & -0.0757 & 0 & -0.092 & 0 & 0 & 0.0367 & -0.0596 & 0 \tabularnewline
(p-val) & (0 ) & (0.0588 ) & (0.0588 ) & (0.1581 ) & (NA ) & (0.0613 ) & (NA ) & (NA ) & (0.4942 ) & (0.2653 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0.4703 & -0.1121 & 0.1084 & -0.075 & 0 & -0.0887 & 0 & 0 & 0 & -0.0446 & 0 \tabularnewline
(p-val) & (0 ) & (0.0542 ) & (0.0636 ) & (0.162 ) & (NA ) & (0.0698 ) & (NA ) & (NA ) & (NA ) & (0.3608 ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0.47 & -0.1117 & 0.111 & -0.0715 & 0 & -0.0894 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.0554 ) & (0.0576 ) & (0.1819 ) & (NA ) & (0.0681 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & 0.4648 & -0.104 & 0.08 & 0 & 0 & -0.0949 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.0735 ) & (0.1363 ) & (NA ) & (NA ) & (0.0525 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & 0.4595 & -0.0692 & 0 & 0 & 0 & -0.0883 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.1943 ) & (NA ) & (NA ) & (NA ) & (0.0709 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & 0.4302 & 0 & 0 & 0 & 0 & -0.0897 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0671 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & 0.4351 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 14 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 15 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 16 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 17 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 18 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 19 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 20 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 21 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65775&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ar4[/C][C]ar5[/C][C]ar6[/C][C]ar7[/C][C]ar8[/C][C]ar9[/C][C]ar10[/C][C]ar11[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.4698[/C][C]-0.111[/C][C]0.1108[/C][C]-0.0779[/C][C]0.0091[/C][C]-0.0889[/C][C]-0.0165[/C][C]0.0028[/C][C]0.0366[/C][C]-0.0599[/C][C]0.004[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0593 )[/C][C](0.061 )[/C][C](0.189 )[/C][C](0.8788 )[/C][C](0.1338 )[/C][C](0.7825 )[/C][C](0.9622 )[/C][C](0.5382 )[/C][C](0.3133 )[/C][C](0.9404 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4698[/C][C]-0.1111[/C][C]0.1109[/C][C]-0.0781[/C][C]0.0094[/C][C]-0.0893[/C][C]-0.0153[/C][C]0[/C][C]0.0378[/C][C]-0.0603[/C][C]0.0043[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0582 )[/C][C](0.0605 )[/C][C](0.1867 )[/C][C](0.8728 )[/C][C](0.128 )[/C][C](0.7774 )[/C][C](NA )[/C][C](0.4832 )[/C][C](0.3046 )[/C][C](0.9356 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4695[/C][C]-0.111[/C][C]0.1109[/C][C]-0.0782[/C][C]0.0091[/C][C]-0.0893[/C][C]-0.0154[/C][C]0[/C][C]0.0375[/C][C]-0.0583[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0584 )[/C][C](0.0605 )[/C][C](0.1863 )[/C][C](0.8769 )[/C][C](0.1281 )[/C][C](0.7754 )[/C][C](NA )[/C][C](0.4857 )[/C][C](0.2768 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.469[/C][C]-0.1099[/C][C]0.1097[/C][C]-0.0744[/C][C]0[/C][C]-0.0856[/C][C]-0.0163[/C][C]0[/C][C]0.0374[/C][C]-0.0583[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0591 )[/C][C](0.0609 )[/C][C](0.1666 )[/C][C](NA )[/C][C](0.1099 )[/C][C](0.7625 )[/C][C](NA )[/C][C](0.4866 )[/C][C](0.2769 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.4704[/C][C]-0.1101[/C][C]0.1105[/C][C]-0.0757[/C][C]0[/C][C]-0.092[/C][C]0[/C][C]0[/C][C]0.0367[/C][C]-0.0596[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0588 )[/C][C](0.0588 )[/C][C](0.1581 )[/C][C](NA )[/C][C](0.0613 )[/C][C](NA )[/C][C](NA )[/C][C](0.4942 )[/C][C](0.2653 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.4703[/C][C]-0.1121[/C][C]0.1084[/C][C]-0.075[/C][C]0[/C][C]-0.0887[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0446[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0542 )[/C][C](0.0636 )[/C][C](0.162 )[/C][C](NA )[/C][C](0.0698 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.3608 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0.47[/C][C]-0.1117[/C][C]0.111[/C][C]-0.0715[/C][C]0[/C][C]-0.0894[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0554 )[/C][C](0.0576 )[/C][C](0.1819 )[/C][C](NA )[/C][C](0.0681 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]0.4648[/C][C]-0.104[/C][C]0.08[/C][C]0[/C][C]0[/C][C]-0.0949[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0735 )[/C][C](0.1363 )[/C][C](NA )[/C][C](NA )[/C][C](0.0525 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]0.4595[/C][C]-0.0692[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0883[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.1943 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0709 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]0.4302[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0897[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0671 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]0.4351[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 14 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 15 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 16 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 17 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 18 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 19 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 20 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 21 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65775&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65775&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11
Estimates ( 1 )0.4698-0.1110.1108-0.07790.0091-0.0889-0.01650.00280.0366-0.05990.004
(p-val)(0 )(0.0593 )(0.061 )(0.189 )(0.8788 )(0.1338 )(0.7825 )(0.9622 )(0.5382 )(0.3133 )(0.9404 )
Estimates ( 2 )0.4698-0.11110.1109-0.07810.0094-0.0893-0.015300.0378-0.06030.0043
(p-val)(0 )(0.0582 )(0.0605 )(0.1867 )(0.8728 )(0.128 )(0.7774 )(NA )(0.4832 )(0.3046 )(0.9356 )
Estimates ( 3 )0.4695-0.1110.1109-0.07820.0091-0.0893-0.015400.0375-0.05830
(p-val)(0 )(0.0584 )(0.0605 )(0.1863 )(0.8769 )(0.1281 )(0.7754 )(NA )(0.4857 )(0.2768 )(NA )
Estimates ( 4 )0.469-0.10990.1097-0.07440-0.0856-0.016300.0374-0.05830
(p-val)(0 )(0.0591 )(0.0609 )(0.1666 )(NA )(0.1099 )(0.7625 )(NA )(0.4866 )(0.2769 )(NA )
Estimates ( 5 )0.4704-0.11010.1105-0.07570-0.092000.0367-0.05960
(p-val)(0 )(0.0588 )(0.0588 )(0.1581 )(NA )(0.0613 )(NA )(NA )(0.4942 )(0.2653 )(NA )
Estimates ( 6 )0.4703-0.11210.1084-0.0750-0.0887000-0.04460
(p-val)(0 )(0.0542 )(0.0636 )(0.162 )(NA )(0.0698 )(NA )(NA )(NA )(0.3608 )(NA )
Estimates ( 7 )0.47-0.11170.111-0.07150-0.089400000
(p-val)(0 )(0.0554 )(0.0576 )(0.1819 )(NA )(0.0681 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0.4648-0.1040.0800-0.094900000
(p-val)(0 )(0.0735 )(0.1363 )(NA )(NA )(0.0525 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )0.4595-0.0692000-0.088300000
(p-val)(0 )(0.1943 )(NA )(NA )(NA )(0.0709 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )0.43020000-0.089700000
(p-val)(0 )(NA )(NA )(NA )(NA )(0.0671 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )0.43510000000000
(p-val)(0 )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 18 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 19 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 20 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 21 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.280199825824534
17.6680379950921
30.6147524218351
17.8974005911129
4.21664022360639
-12.2912543616655
-4.8490293216265
-1.50632589970665
0.121522562241523
-5.79324126996516
1.42169038942995
-3.70554111520556
-1.86350692749335
-1.63424632620399
-11.4375554827458
-1.27786840895862
-6.75221656690593
-2.20242705725661
-12.8997311808205
-2.22295964283251
-1.86253335568949
0.508595362179165
-1.88420015093084
-4.13903783055360
-3.58655068841267
-5.98956199831514
-0.883139661851999
-0.474963359213632
5.38286892151217
19.5922754615782
16.7674375966006
-1.27168890728342
-0.0602985978376864
2.02150527935214
0.571638371900178
-10.7870963941262
13.1055460794344
-8.4404920289291
-1.84895490267894
4.66585554381476
-2.98104211277808
6.82210448474194
-5.56620176079673
-10.2942041403311
-1.64299384990483
-11.0823172084372
-4.06929063360656
1.53505102768685
-14.4583410567296
0.224735994958792
-0.463013104233340
-2.84832035853006
1.33953428789681
-4.16924122013137
-1.53653529665493
-0.704985724914678
0.58089275044452
-3.60530028035339
10.4753657099550
-5.97693585306547
12.0308773586868
-3.88521665980585
-0.137361262180718
0.0839031823192045
-1.20783359776749
-2.28706209849969
3.57018225236419
-1.86061683049218
-1.67078513496998
0.134350830306062
0.725741393410772
1.22476869963467
0.985235289765626
-0.613925548020234
-4.39234742610932
1.28316764617938
-0.58306402010507
-0.803339940057299
2.20279950251026
-2.82738805629592
1.57872148078394
1.93390274330534
-3.30428185031801
5.22987388139524
3.74158522268377
-4.76353491182073
2.03617763904020
1.08505514391663
2.74338212593017
1.28890385997778
-1.23918894894268
1.40381881057345
0.430413440914492
-1.37059003637711
1.78718067731785
-0.214944856083378
1.93721189984717
-0.343830212930925
-0.891359779589266
-0.55449234066856
0.933871959789883
0.0848908288392067
-5.14967093083135
1.0730142944455
1.54450418203993
-1.64499800529990
-7.28191118047988
23.6826122558884
74.7001417640216
-15.8029556639036
-15.4777871688353
-1.07318228673876
-2.22178729521988
-4.10244640013917
-2.83380381130752
-11.9622277456331
6.12233796516222
3.98155176680967
-2.43581734734221
-3.49665918191494
-7.77723649889293
-3.95934189993807
-7.14326266908427
-5.43303735281938
2.86232138319235
-2.71877774137295
-9.11795353190257
7.57081311929727
3.87899397717922
-15.6324307929978
1.94601363614709
11.1645850033993
-4.91526893099456
-6.27924608755939
11.0704818550591
-6.18904866706765
-1.00366857021208
7.75127010140159
-6.94044453661422
2.16690651342105
1.80347259359155
2.89321737183889
1.57364620451108
3.03640264312071
6.73645013543552
-5.00519385509097
8.29382977391703
1.86222055630623
-1.36904699956625
5.97773207820848
-7.10593026925858
-5.09342742317563
-2.17770672796439
-10.4881429735938
9.24382653571496
-2.94249810548445
5.17607134060722
-10.1198382304517
2.04543201758446
4.34721764530298
1.92481355786617
5.84275389308044
-5.58213426340006
0.297829608367238
0.532225296904755
-8.23971355571808
3.60667532487099
1.26275643860959
2.18572999105339
-8.83650714722279
6.35752987884285
-6.55308195739195
0.600103626074315
-5.50483724060888
-5.24245516841336
-0.405900528875804
3.04579055322705
-4.305943465947
-3.49688594205099
5.81789459895498
-0.576486888036982
-6.84746799962755
0.677820751538889
5.32141380683356
-5.99559501562027
4.59146252763583
-4.88219495240315
-6.43342299476149
-1.40076544162756
-7.73347048707623
1.33126475668436
0.203772196286366
10.1664425956488
-3.98388279630399
0.666143870981699
2.14872989859839
0.0660094613919853
0.574948406469787
-4.5483546541565
1.45474532907642
-7.36430123147599
5.4875830280593
4.09603182709299
3.11251971998726
-2.68163868408459
-1.41976626592066
0.00266684897604819
2.01843328044652
5.90416239347212
72.0381108730367
81.9258074369099
-51.3274453907388
0.155450081700792
3.71459951712922
-10.8116057940538
12.566691768008
-6.87182847999219
-5.741753214977
2.94899328450634
-0.380444663443768
-0.965601677379198
-2.71230966865051
2.30566320613468
-7.66270982432258
-10.9490726034693
0.383374898327872
-4.07764428290574
-15.1196775925902
9.04409447686669
-5.05261891764741
-1.75416107643048
-7.7679539709165
2.41468906848746
-7.11157149842745
3.61187285717779
-6.489007486052
3.34594079358828
-7.31772764979428
4.97051351660235
-2.25276827998096
1.99085012555776
18.4003761224696
29.866387505022
4.51850587912509
22.8655791263233
6.21324677978731
-4.36085118894039
-5.20582839923981
-16.8364282973025
2.70840434978737
-7.50971333208776
-5.69695382038105
1.01872832789775
7.77070949322297
-11.9994989874132
-5.71469011161997
-6.98893288743767
1.23812566067568
-6.52109837336712
-4.9793533908495
-8.74992584039956
2.20700658910897
-3.52175651170438
0.314240103458928
-0.827731639194553
4.34769563779133
-4.11450628855516
3.14185404186316
-8.97054342209003
2.98494871865415
0.780233734079388
-3.41362770138085
10.3160669121931
-18.068821117458
6.57440604776241
18.8608119290851
5.46854382193868
-10.6079317905703
-2.20873344898479
-4.49254076864662
-7.63014549976282
7.53334104171535
0.383525303794045
-10.8086243896391
1.87777589330739
-6.92891526233518
-0.813357126145945
2.66822279009591
-1.23420146797247
-1.8406686626671
-7.38107201826574
-0.451078680024693
6.955615274806
-8.33065147657851
-2.62449988045518
-0.0135231972788574
-0.787210582805528
-2.54784324406944
-5.8091964887293
4.99742358040987
-0.446958345595931
-9.31858264278014
15.3532022922656
-1.77573727718107
-9.47849384262014
7.24689765659281
-6.38815424912173
0.484681655530267
-3.50636708073142
3.42201037814004
-5.48500852962951
18.3427801213522
-14.8570622838546
4.05487973866616
6.67328679084079
-10.2369402814796
2.05988217238018
3.0022573089077
-3.53201348951251
0.870229744679023
-8.64122668704118
-1.19279831229818
11.5251860031200
-2.31348961457525
-4.53767493959168
9.13084657517129
-6.51281388941072
-7.080083493718
-3.92622481917061
16.4372539588906
-5.82970501107417
0.281699373087690
-3.44318614930955
-5.42792673778746
1.74080570634010
28.5341689284015
-22.6449607453803
11.5535010169326
19.82652266581
-1.59416688041165
4.85311729615108
-9.4941636722384
17.3500899903721
-9.24832019342517
3.57777150316861
-14.6016941551999
1.60572885936659
1.45974864081813
-4.34341466550114
14.8813258847014
-6.36377294748473
4.70585830472754
-22.0724888096423
7.79026115256323
3.85184307851966
-2.96776150640693
-2.59122536999956
4.01703872794144
-7.07365484595579

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.280199825824534 \tabularnewline
17.6680379950921 \tabularnewline
30.6147524218351 \tabularnewline
17.8974005911129 \tabularnewline
4.21664022360639 \tabularnewline
-12.2912543616655 \tabularnewline
-4.8490293216265 \tabularnewline
-1.50632589970665 \tabularnewline
0.121522562241523 \tabularnewline
-5.79324126996516 \tabularnewline
1.42169038942995 \tabularnewline
-3.70554111520556 \tabularnewline
-1.86350692749335 \tabularnewline
-1.63424632620399 \tabularnewline
-11.4375554827458 \tabularnewline
-1.27786840895862 \tabularnewline
-6.75221656690593 \tabularnewline
-2.20242705725661 \tabularnewline
-12.8997311808205 \tabularnewline
-2.22295964283251 \tabularnewline
-1.86253335568949 \tabularnewline
0.508595362179165 \tabularnewline
-1.88420015093084 \tabularnewline
-4.13903783055360 \tabularnewline
-3.58655068841267 \tabularnewline
-5.98956199831514 \tabularnewline
-0.883139661851999 \tabularnewline
-0.474963359213632 \tabularnewline
5.38286892151217 \tabularnewline
19.5922754615782 \tabularnewline
16.7674375966006 \tabularnewline
-1.27168890728342 \tabularnewline
-0.0602985978376864 \tabularnewline
2.02150527935214 \tabularnewline
0.571638371900178 \tabularnewline
-10.7870963941262 \tabularnewline
13.1055460794344 \tabularnewline
-8.4404920289291 \tabularnewline
-1.84895490267894 \tabularnewline
4.66585554381476 \tabularnewline
-2.98104211277808 \tabularnewline
6.82210448474194 \tabularnewline
-5.56620176079673 \tabularnewline
-10.2942041403311 \tabularnewline
-1.64299384990483 \tabularnewline
-11.0823172084372 \tabularnewline
-4.06929063360656 \tabularnewline
1.53505102768685 \tabularnewline
-14.4583410567296 \tabularnewline
0.224735994958792 \tabularnewline
-0.463013104233340 \tabularnewline
-2.84832035853006 \tabularnewline
1.33953428789681 \tabularnewline
-4.16924122013137 \tabularnewline
-1.53653529665493 \tabularnewline
-0.704985724914678 \tabularnewline
0.58089275044452 \tabularnewline
-3.60530028035339 \tabularnewline
10.4753657099550 \tabularnewline
-5.97693585306547 \tabularnewline
12.0308773586868 \tabularnewline
-3.88521665980585 \tabularnewline
-0.137361262180718 \tabularnewline
0.0839031823192045 \tabularnewline
-1.20783359776749 \tabularnewline
-2.28706209849969 \tabularnewline
3.57018225236419 \tabularnewline
-1.86061683049218 \tabularnewline
-1.67078513496998 \tabularnewline
0.134350830306062 \tabularnewline
0.725741393410772 \tabularnewline
1.22476869963467 \tabularnewline
0.985235289765626 \tabularnewline
-0.613925548020234 \tabularnewline
-4.39234742610932 \tabularnewline
1.28316764617938 \tabularnewline
-0.58306402010507 \tabularnewline
-0.803339940057299 \tabularnewline
2.20279950251026 \tabularnewline
-2.82738805629592 \tabularnewline
1.57872148078394 \tabularnewline
1.93390274330534 \tabularnewline
-3.30428185031801 \tabularnewline
5.22987388139524 \tabularnewline
3.74158522268377 \tabularnewline
-4.76353491182073 \tabularnewline
2.03617763904020 \tabularnewline
1.08505514391663 \tabularnewline
2.74338212593017 \tabularnewline
1.28890385997778 \tabularnewline
-1.23918894894268 \tabularnewline
1.40381881057345 \tabularnewline
0.430413440914492 \tabularnewline
-1.37059003637711 \tabularnewline
1.78718067731785 \tabularnewline
-0.214944856083378 \tabularnewline
1.93721189984717 \tabularnewline
-0.343830212930925 \tabularnewline
-0.891359779589266 \tabularnewline
-0.55449234066856 \tabularnewline
0.933871959789883 \tabularnewline
0.0848908288392067 \tabularnewline
-5.14967093083135 \tabularnewline
1.0730142944455 \tabularnewline
1.54450418203993 \tabularnewline
-1.64499800529990 \tabularnewline
-7.28191118047988 \tabularnewline
23.6826122558884 \tabularnewline
74.7001417640216 \tabularnewline
-15.8029556639036 \tabularnewline
-15.4777871688353 \tabularnewline
-1.07318228673876 \tabularnewline
-2.22178729521988 \tabularnewline
-4.10244640013917 \tabularnewline
-2.83380381130752 \tabularnewline
-11.9622277456331 \tabularnewline
6.12233796516222 \tabularnewline
3.98155176680967 \tabularnewline
-2.43581734734221 \tabularnewline
-3.49665918191494 \tabularnewline
-7.77723649889293 \tabularnewline
-3.95934189993807 \tabularnewline
-7.14326266908427 \tabularnewline
-5.43303735281938 \tabularnewline
2.86232138319235 \tabularnewline
-2.71877774137295 \tabularnewline
-9.11795353190257 \tabularnewline
7.57081311929727 \tabularnewline
3.87899397717922 \tabularnewline
-15.6324307929978 \tabularnewline
1.94601363614709 \tabularnewline
11.1645850033993 \tabularnewline
-4.91526893099456 \tabularnewline
-6.27924608755939 \tabularnewline
11.0704818550591 \tabularnewline
-6.18904866706765 \tabularnewline
-1.00366857021208 \tabularnewline
7.75127010140159 \tabularnewline
-6.94044453661422 \tabularnewline
2.16690651342105 \tabularnewline
1.80347259359155 \tabularnewline
2.89321737183889 \tabularnewline
1.57364620451108 \tabularnewline
3.03640264312071 \tabularnewline
6.73645013543552 \tabularnewline
-5.00519385509097 \tabularnewline
8.29382977391703 \tabularnewline
1.86222055630623 \tabularnewline
-1.36904699956625 \tabularnewline
5.97773207820848 \tabularnewline
-7.10593026925858 \tabularnewline
-5.09342742317563 \tabularnewline
-2.17770672796439 \tabularnewline
-10.4881429735938 \tabularnewline
9.24382653571496 \tabularnewline
-2.94249810548445 \tabularnewline
5.17607134060722 \tabularnewline
-10.1198382304517 \tabularnewline
2.04543201758446 \tabularnewline
4.34721764530298 \tabularnewline
1.92481355786617 \tabularnewline
5.84275389308044 \tabularnewline
-5.58213426340006 \tabularnewline
0.297829608367238 \tabularnewline
0.532225296904755 \tabularnewline
-8.23971355571808 \tabularnewline
3.60667532487099 \tabularnewline
1.26275643860959 \tabularnewline
2.18572999105339 \tabularnewline
-8.83650714722279 \tabularnewline
6.35752987884285 \tabularnewline
-6.55308195739195 \tabularnewline
0.600103626074315 \tabularnewline
-5.50483724060888 \tabularnewline
-5.24245516841336 \tabularnewline
-0.405900528875804 \tabularnewline
3.04579055322705 \tabularnewline
-4.305943465947 \tabularnewline
-3.49688594205099 \tabularnewline
5.81789459895498 \tabularnewline
-0.576486888036982 \tabularnewline
-6.84746799962755 \tabularnewline
0.677820751538889 \tabularnewline
5.32141380683356 \tabularnewline
-5.99559501562027 \tabularnewline
4.59146252763583 \tabularnewline
-4.88219495240315 \tabularnewline
-6.43342299476149 \tabularnewline
-1.40076544162756 \tabularnewline
-7.73347048707623 \tabularnewline
1.33126475668436 \tabularnewline
0.203772196286366 \tabularnewline
10.1664425956488 \tabularnewline
-3.98388279630399 \tabularnewline
0.666143870981699 \tabularnewline
2.14872989859839 \tabularnewline
0.0660094613919853 \tabularnewline
0.574948406469787 \tabularnewline
-4.5483546541565 \tabularnewline
1.45474532907642 \tabularnewline
-7.36430123147599 \tabularnewline
5.4875830280593 \tabularnewline
4.09603182709299 \tabularnewline
3.11251971998726 \tabularnewline
-2.68163868408459 \tabularnewline
-1.41976626592066 \tabularnewline
0.00266684897604819 \tabularnewline
2.01843328044652 \tabularnewline
5.90416239347212 \tabularnewline
72.0381108730367 \tabularnewline
81.9258074369099 \tabularnewline
-51.3274453907388 \tabularnewline
0.155450081700792 \tabularnewline
3.71459951712922 \tabularnewline
-10.8116057940538 \tabularnewline
12.566691768008 \tabularnewline
-6.87182847999219 \tabularnewline
-5.741753214977 \tabularnewline
2.94899328450634 \tabularnewline
-0.380444663443768 \tabularnewline
-0.965601677379198 \tabularnewline
-2.71230966865051 \tabularnewline
2.30566320613468 \tabularnewline
-7.66270982432258 \tabularnewline
-10.9490726034693 \tabularnewline
0.383374898327872 \tabularnewline
-4.07764428290574 \tabularnewline
-15.1196775925902 \tabularnewline
9.04409447686669 \tabularnewline
-5.05261891764741 \tabularnewline
-1.75416107643048 \tabularnewline
-7.7679539709165 \tabularnewline
2.41468906848746 \tabularnewline
-7.11157149842745 \tabularnewline
3.61187285717779 \tabularnewline
-6.489007486052 \tabularnewline
3.34594079358828 \tabularnewline
-7.31772764979428 \tabularnewline
4.97051351660235 \tabularnewline
-2.25276827998096 \tabularnewline
1.99085012555776 \tabularnewline
18.4003761224696 \tabularnewline
29.866387505022 \tabularnewline
4.51850587912509 \tabularnewline
22.8655791263233 \tabularnewline
6.21324677978731 \tabularnewline
-4.36085118894039 \tabularnewline
-5.20582839923981 \tabularnewline
-16.8364282973025 \tabularnewline
2.70840434978737 \tabularnewline
-7.50971333208776 \tabularnewline
-5.69695382038105 \tabularnewline
1.01872832789775 \tabularnewline
7.77070949322297 \tabularnewline
-11.9994989874132 \tabularnewline
-5.71469011161997 \tabularnewline
-6.98893288743767 \tabularnewline
1.23812566067568 \tabularnewline
-6.52109837336712 \tabularnewline
-4.9793533908495 \tabularnewline
-8.74992584039956 \tabularnewline
2.20700658910897 \tabularnewline
-3.52175651170438 \tabularnewline
0.314240103458928 \tabularnewline
-0.827731639194553 \tabularnewline
4.34769563779133 \tabularnewline
-4.11450628855516 \tabularnewline
3.14185404186316 \tabularnewline
-8.97054342209003 \tabularnewline
2.98494871865415 \tabularnewline
0.780233734079388 \tabularnewline
-3.41362770138085 \tabularnewline
10.3160669121931 \tabularnewline
-18.068821117458 \tabularnewline
6.57440604776241 \tabularnewline
18.8608119290851 \tabularnewline
5.46854382193868 \tabularnewline
-10.6079317905703 \tabularnewline
-2.20873344898479 \tabularnewline
-4.49254076864662 \tabularnewline
-7.63014549976282 \tabularnewline
7.53334104171535 \tabularnewline
0.383525303794045 \tabularnewline
-10.8086243896391 \tabularnewline
1.87777589330739 \tabularnewline
-6.92891526233518 \tabularnewline
-0.813357126145945 \tabularnewline
2.66822279009591 \tabularnewline
-1.23420146797247 \tabularnewline
-1.8406686626671 \tabularnewline
-7.38107201826574 \tabularnewline
-0.451078680024693 \tabularnewline
6.955615274806 \tabularnewline
-8.33065147657851 \tabularnewline
-2.62449988045518 \tabularnewline
-0.0135231972788574 \tabularnewline
-0.787210582805528 \tabularnewline
-2.54784324406944 \tabularnewline
-5.8091964887293 \tabularnewline
4.99742358040987 \tabularnewline
-0.446958345595931 \tabularnewline
-9.31858264278014 \tabularnewline
15.3532022922656 \tabularnewline
-1.77573727718107 \tabularnewline
-9.47849384262014 \tabularnewline
7.24689765659281 \tabularnewline
-6.38815424912173 \tabularnewline
0.484681655530267 \tabularnewline
-3.50636708073142 \tabularnewline
3.42201037814004 \tabularnewline
-5.48500852962951 \tabularnewline
18.3427801213522 \tabularnewline
-14.8570622838546 \tabularnewline
4.05487973866616 \tabularnewline
6.67328679084079 \tabularnewline
-10.2369402814796 \tabularnewline
2.05988217238018 \tabularnewline
3.0022573089077 \tabularnewline
-3.53201348951251 \tabularnewline
0.870229744679023 \tabularnewline
-8.64122668704118 \tabularnewline
-1.19279831229818 \tabularnewline
11.5251860031200 \tabularnewline
-2.31348961457525 \tabularnewline
-4.53767493959168 \tabularnewline
9.13084657517129 \tabularnewline
-6.51281388941072 \tabularnewline
-7.080083493718 \tabularnewline
-3.92622481917061 \tabularnewline
16.4372539588906 \tabularnewline
-5.82970501107417 \tabularnewline
0.281699373087690 \tabularnewline
-3.44318614930955 \tabularnewline
-5.42792673778746 \tabularnewline
1.74080570634010 \tabularnewline
28.5341689284015 \tabularnewline
-22.6449607453803 \tabularnewline
11.5535010169326 \tabularnewline
19.82652266581 \tabularnewline
-1.59416688041165 \tabularnewline
4.85311729615108 \tabularnewline
-9.4941636722384 \tabularnewline
17.3500899903721 \tabularnewline
-9.24832019342517 \tabularnewline
3.57777150316861 \tabularnewline
-14.6016941551999 \tabularnewline
1.60572885936659 \tabularnewline
1.45974864081813 \tabularnewline
-4.34341466550114 \tabularnewline
14.8813258847014 \tabularnewline
-6.36377294748473 \tabularnewline
4.70585830472754 \tabularnewline
-22.0724888096423 \tabularnewline
7.79026115256323 \tabularnewline
3.85184307851966 \tabularnewline
-2.96776150640693 \tabularnewline
-2.59122536999956 \tabularnewline
4.01703872794144 \tabularnewline
-7.07365484595579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65775&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.280199825824534[/C][/ROW]
[ROW][C]17.6680379950921[/C][/ROW]
[ROW][C]30.6147524218351[/C][/ROW]
[ROW][C]17.8974005911129[/C][/ROW]
[ROW][C]4.21664022360639[/C][/ROW]
[ROW][C]-12.2912543616655[/C][/ROW]
[ROW][C]-4.8490293216265[/C][/ROW]
[ROW][C]-1.50632589970665[/C][/ROW]
[ROW][C]0.121522562241523[/C][/ROW]
[ROW][C]-5.79324126996516[/C][/ROW]
[ROW][C]1.42169038942995[/C][/ROW]
[ROW][C]-3.70554111520556[/C][/ROW]
[ROW][C]-1.86350692749335[/C][/ROW]
[ROW][C]-1.63424632620399[/C][/ROW]
[ROW][C]-11.4375554827458[/C][/ROW]
[ROW][C]-1.27786840895862[/C][/ROW]
[ROW][C]-6.75221656690593[/C][/ROW]
[ROW][C]-2.20242705725661[/C][/ROW]
[ROW][C]-12.8997311808205[/C][/ROW]
[ROW][C]-2.22295964283251[/C][/ROW]
[ROW][C]-1.86253335568949[/C][/ROW]
[ROW][C]0.508595362179165[/C][/ROW]
[ROW][C]-1.88420015093084[/C][/ROW]
[ROW][C]-4.13903783055360[/C][/ROW]
[ROW][C]-3.58655068841267[/C][/ROW]
[ROW][C]-5.98956199831514[/C][/ROW]
[ROW][C]-0.883139661851999[/C][/ROW]
[ROW][C]-0.474963359213632[/C][/ROW]
[ROW][C]5.38286892151217[/C][/ROW]
[ROW][C]19.5922754615782[/C][/ROW]
[ROW][C]16.7674375966006[/C][/ROW]
[ROW][C]-1.27168890728342[/C][/ROW]
[ROW][C]-0.0602985978376864[/C][/ROW]
[ROW][C]2.02150527935214[/C][/ROW]
[ROW][C]0.571638371900178[/C][/ROW]
[ROW][C]-10.7870963941262[/C][/ROW]
[ROW][C]13.1055460794344[/C][/ROW]
[ROW][C]-8.4404920289291[/C][/ROW]
[ROW][C]-1.84895490267894[/C][/ROW]
[ROW][C]4.66585554381476[/C][/ROW]
[ROW][C]-2.98104211277808[/C][/ROW]
[ROW][C]6.82210448474194[/C][/ROW]
[ROW][C]-5.56620176079673[/C][/ROW]
[ROW][C]-10.2942041403311[/C][/ROW]
[ROW][C]-1.64299384990483[/C][/ROW]
[ROW][C]-11.0823172084372[/C][/ROW]
[ROW][C]-4.06929063360656[/C][/ROW]
[ROW][C]1.53505102768685[/C][/ROW]
[ROW][C]-14.4583410567296[/C][/ROW]
[ROW][C]0.224735994958792[/C][/ROW]
[ROW][C]-0.463013104233340[/C][/ROW]
[ROW][C]-2.84832035853006[/C][/ROW]
[ROW][C]1.33953428789681[/C][/ROW]
[ROW][C]-4.16924122013137[/C][/ROW]
[ROW][C]-1.53653529665493[/C][/ROW]
[ROW][C]-0.704985724914678[/C][/ROW]
[ROW][C]0.58089275044452[/C][/ROW]
[ROW][C]-3.60530028035339[/C][/ROW]
[ROW][C]10.4753657099550[/C][/ROW]
[ROW][C]-5.97693585306547[/C][/ROW]
[ROW][C]12.0308773586868[/C][/ROW]
[ROW][C]-3.88521665980585[/C][/ROW]
[ROW][C]-0.137361262180718[/C][/ROW]
[ROW][C]0.0839031823192045[/C][/ROW]
[ROW][C]-1.20783359776749[/C][/ROW]
[ROW][C]-2.28706209849969[/C][/ROW]
[ROW][C]3.57018225236419[/C][/ROW]
[ROW][C]-1.86061683049218[/C][/ROW]
[ROW][C]-1.67078513496998[/C][/ROW]
[ROW][C]0.134350830306062[/C][/ROW]
[ROW][C]0.725741393410772[/C][/ROW]
[ROW][C]1.22476869963467[/C][/ROW]
[ROW][C]0.985235289765626[/C][/ROW]
[ROW][C]-0.613925548020234[/C][/ROW]
[ROW][C]-4.39234742610932[/C][/ROW]
[ROW][C]1.28316764617938[/C][/ROW]
[ROW][C]-0.58306402010507[/C][/ROW]
[ROW][C]-0.803339940057299[/C][/ROW]
[ROW][C]2.20279950251026[/C][/ROW]
[ROW][C]-2.82738805629592[/C][/ROW]
[ROW][C]1.57872148078394[/C][/ROW]
[ROW][C]1.93390274330534[/C][/ROW]
[ROW][C]-3.30428185031801[/C][/ROW]
[ROW][C]5.22987388139524[/C][/ROW]
[ROW][C]3.74158522268377[/C][/ROW]
[ROW][C]-4.76353491182073[/C][/ROW]
[ROW][C]2.03617763904020[/C][/ROW]
[ROW][C]1.08505514391663[/C][/ROW]
[ROW][C]2.74338212593017[/C][/ROW]
[ROW][C]1.28890385997778[/C][/ROW]
[ROW][C]-1.23918894894268[/C][/ROW]
[ROW][C]1.40381881057345[/C][/ROW]
[ROW][C]0.430413440914492[/C][/ROW]
[ROW][C]-1.37059003637711[/C][/ROW]
[ROW][C]1.78718067731785[/C][/ROW]
[ROW][C]-0.214944856083378[/C][/ROW]
[ROW][C]1.93721189984717[/C][/ROW]
[ROW][C]-0.343830212930925[/C][/ROW]
[ROW][C]-0.891359779589266[/C][/ROW]
[ROW][C]-0.55449234066856[/C][/ROW]
[ROW][C]0.933871959789883[/C][/ROW]
[ROW][C]0.0848908288392067[/C][/ROW]
[ROW][C]-5.14967093083135[/C][/ROW]
[ROW][C]1.0730142944455[/C][/ROW]
[ROW][C]1.54450418203993[/C][/ROW]
[ROW][C]-1.64499800529990[/C][/ROW]
[ROW][C]-7.28191118047988[/C][/ROW]
[ROW][C]23.6826122558884[/C][/ROW]
[ROW][C]74.7001417640216[/C][/ROW]
[ROW][C]-15.8029556639036[/C][/ROW]
[ROW][C]-15.4777871688353[/C][/ROW]
[ROW][C]-1.07318228673876[/C][/ROW]
[ROW][C]-2.22178729521988[/C][/ROW]
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[ROW][C]7.79026115256323[/C][/ROW]
[ROW][C]3.85184307851966[/C][/ROW]
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[ROW][C]4.01703872794144[/C][/ROW]
[ROW][C]-7.07365484595579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65775&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65775&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
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17.6680379950921
30.6147524218351
17.8974005911129
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5.38286892151217
19.5922754615782
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6.82210448474194
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4.34769563779133
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Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par6 <- 11
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
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,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')