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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 07:47:11 -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/t12604565046j1gm6c7co66o0r.htm/, Retrieved Thu, 28 Mar 2024 18:44:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65440, Retrieved Thu, 28 Mar 2024 18:44:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact131
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 14:47:11] [6dfcce621b31349cab7f0d189e6f8a9d] [Current]
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Dataseries X:
255
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65440&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]6 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=65440&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65440&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11
Estimates ( 1 )0.4714-0.10160.1174-0.07260.0063-0.0956-0.01410.00170.0381-0.06150.0029
(p-val)(0 )(0.0833 )(0.0474 )(0.2214 )(0.9163 )(0.1108 )(0.815 )(0.9775 )(0.5249 )(0.3025 )(0.9575 )
Estimates ( 2 )0.4714-0.10170.1174-0.07280.0065-0.0959-0.013400.0388-0.06180.0031
(p-val)(0 )(0.0821 )(0.047 )(0.2196 )(0.9125 )(0.1058 )(0.8059 )(NA )(0.4741 )(0.295 )(0.9546 )
Estimates ( 3 )0.4712-0.10160.1174-0.07280.0063-0.0958-0.013500.0386-0.06040
(p-val)(0 )(0.0823 )(0.0471 )(0.2191 )(0.9154 )(0.1058 )(0.8046 )(NA )(0.4755 )(0.2624 )(NA )
Estimates ( 4 )0.4708-0.10090.1166-0.07020-0.0933-0.01400.0385-0.06040
(p-val)(0 )(0.0825 )(0.0465 )(0.1928 )(NA )(0.0842 )(0.7952 )(NA )(0.476 )(0.2624 )(NA )
Estimates ( 5 )0.4721-0.1010.1173-0.07130-0.0989000.038-0.06160
(p-val)(0 )(0.0821 )(0.045 )(0.1847 )(NA )(0.0457 )(NA )(NA )(0.4816 )(0.252 )(NA )
Estimates ( 6 )0.4718-0.10310.115-0.07070-0.0952000-0.0460
(p-val)(0 )(0.0757 )(0.049 )(0.1885 )(NA )(0.0531 )(NA )(NA )(NA )(0.348 )(NA )
Estimates ( 7 )0.4715-0.10260.1177-0.06720-0.095800000
(p-val)(0 )(0.0775 )(0.044 )(0.2113 )(NA )(0.0519 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0.4667-0.09580.088400-0.101100000
(p-val)(0 )(0.0985 )(0.099 )(NA )(NA )(0.04 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )0.4609-0.0559000-0.09400000
(p-val)(0 )(0.2899 )(NA )(NA )(NA )(0.0562 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )0.43660000-0.094800000
(p-val)(0 )(NA )(NA )(NA )(NA )(0.0544 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )0.44190000000000
(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 ) & 0.4714 & -0.1016 & 0.1174 & -0.0726 & 0.0063 & -0.0956 & -0.0141 & 0.0017 & 0.0381 & -0.0615 & 0.0029 \tabularnewline
(p-val) & (0 ) & (0.0833 ) & (0.0474 ) & (0.2214 ) & (0.9163 ) & (0.1108 ) & (0.815 ) & (0.9775 ) & (0.5249 ) & (0.3025 ) & (0.9575 ) \tabularnewline
Estimates ( 2 ) & 0.4714 & -0.1017 & 0.1174 & -0.0728 & 0.0065 & -0.0959 & -0.0134 & 0 & 0.0388 & -0.0618 & 0.0031 \tabularnewline
(p-val) & (0 ) & (0.0821 ) & (0.047 ) & (0.2196 ) & (0.9125 ) & (0.1058 ) & (0.8059 ) & (NA ) & (0.4741 ) & (0.295 ) & (0.9546 ) \tabularnewline
Estimates ( 3 ) & 0.4712 & -0.1016 & 0.1174 & -0.0728 & 0.0063 & -0.0958 & -0.0135 & 0 & 0.0386 & -0.0604 & 0 \tabularnewline
(p-val) & (0 ) & (0.0823 ) & (0.0471 ) & (0.2191 ) & (0.9154 ) & (0.1058 ) & (0.8046 ) & (NA ) & (0.4755 ) & (0.2624 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0.4708 & -0.1009 & 0.1166 & -0.0702 & 0 & -0.0933 & -0.014 & 0 & 0.0385 & -0.0604 & 0 \tabularnewline
(p-val) & (0 ) & (0.0825 ) & (0.0465 ) & (0.1928 ) & (NA ) & (0.0842 ) & (0.7952 ) & (NA ) & (0.476 ) & (0.2624 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0.4721 & -0.101 & 0.1173 & -0.0713 & 0 & -0.0989 & 0 & 0 & 0.038 & -0.0616 & 0 \tabularnewline
(p-val) & (0 ) & (0.0821 ) & (0.045 ) & (0.1847 ) & (NA ) & (0.0457 ) & (NA ) & (NA ) & (0.4816 ) & (0.252 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0.4718 & -0.1031 & 0.115 & -0.0707 & 0 & -0.0952 & 0 & 0 & 0 & -0.046 & 0 \tabularnewline
(p-val) & (0 ) & (0.0757 ) & (0.049 ) & (0.1885 ) & (NA ) & (0.0531 ) & (NA ) & (NA ) & (NA ) & (0.348 ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0.4715 & -0.1026 & 0.1177 & -0.0672 & 0 & -0.0958 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.0775 ) & (0.044 ) & (0.2113 ) & (NA ) & (0.0519 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & 0.4667 & -0.0958 & 0.0884 & 0 & 0 & -0.1011 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.0985 ) & (0.099 ) & (NA ) & (NA ) & (0.04 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & 0.4609 & -0.0559 & 0 & 0 & 0 & -0.094 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.2899 ) & (NA ) & (NA ) & (NA ) & (0.0562 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & 0.4366 & 0 & 0 & 0 & 0 & -0.0948 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0544 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & 0.4419 & 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=65440&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]0.4714[/C][C]-0.1016[/C][C]0.1174[/C][C]-0.0726[/C][C]0.0063[/C][C]-0.0956[/C][C]-0.0141[/C][C]0.0017[/C][C]0.0381[/C][C]-0.0615[/C][C]0.0029[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0833 )[/C][C](0.0474 )[/C][C](0.2214 )[/C][C](0.9163 )[/C][C](0.1108 )[/C][C](0.815 )[/C][C](0.9775 )[/C][C](0.5249 )[/C][C](0.3025 )[/C][C](0.9575 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4714[/C][C]-0.1017[/C][C]0.1174[/C][C]-0.0728[/C][C]0.0065[/C][C]-0.0959[/C][C]-0.0134[/C][C]0[/C][C]0.0388[/C][C]-0.0618[/C][C]0.0031[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0821 )[/C][C](0.047 )[/C][C](0.2196 )[/C][C](0.9125 )[/C][C](0.1058 )[/C][C](0.8059 )[/C][C](NA )[/C][C](0.4741 )[/C][C](0.295 )[/C][C](0.9546 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4712[/C][C]-0.1016[/C][C]0.1174[/C][C]-0.0728[/C][C]0.0063[/C][C]-0.0958[/C][C]-0.0135[/C][C]0[/C][C]0.0386[/C][C]-0.0604[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0823 )[/C][C](0.0471 )[/C][C](0.2191 )[/C][C](0.9154 )[/C][C](0.1058 )[/C][C](0.8046 )[/C][C](NA )[/C][C](0.4755 )[/C][C](0.2624 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.4708[/C][C]-0.1009[/C][C]0.1166[/C][C]-0.0702[/C][C]0[/C][C]-0.0933[/C][C]-0.014[/C][C]0[/C][C]0.0385[/C][C]-0.0604[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0825 )[/C][C](0.0465 )[/C][C](0.1928 )[/C][C](NA )[/C][C](0.0842 )[/C][C](0.7952 )[/C][C](NA )[/C][C](0.476 )[/C][C](0.2624 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.4721[/C][C]-0.101[/C][C]0.1173[/C][C]-0.0713[/C][C]0[/C][C]-0.0989[/C][C]0[/C][C]0[/C][C]0.038[/C][C]-0.0616[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0821 )[/C][C](0.045 )[/C][C](0.1847 )[/C][C](NA )[/C][C](0.0457 )[/C][C](NA )[/C][C](NA )[/C][C](0.4816 )[/C][C](0.252 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.4718[/C][C]-0.1031[/C][C]0.115[/C][C]-0.0707[/C][C]0[/C][C]-0.0952[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.046[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0757 )[/C][C](0.049 )[/C][C](0.1885 )[/C][C](NA )[/C][C](0.0531 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.348 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0.4715[/C][C]-0.1026[/C][C]0.1177[/C][C]-0.0672[/C][C]0[/C][C]-0.0958[/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](0.0775 )[/C][C](0.044 )[/C][C](0.2113 )[/C][C](NA )[/C][C](0.0519 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]0.4667[/C][C]-0.0958[/C][C]0.0884[/C][C]0[/C][C]0[/C][C]-0.1011[/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](0.0985 )[/C][C](0.099 )[/C][C](NA )[/C][C](NA )[/C][C](0.04 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]0.4609[/C][C]-0.0559[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.094[/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](0.2899 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0562 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]0.4366[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0948[/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](0.0544 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]0.4419[/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=65440&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65440&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 )0.4714-0.10160.1174-0.07260.0063-0.0956-0.01410.00170.0381-0.06150.0029
(p-val)(0 )(0.0833 )(0.0474 )(0.2214 )(0.9163 )(0.1108 )(0.815 )(0.9775 )(0.5249 )(0.3025 )(0.9575 )
Estimates ( 2 )0.4714-0.10170.1174-0.07280.0065-0.0959-0.013400.0388-0.06180.0031
(p-val)(0 )(0.0821 )(0.047 )(0.2196 )(0.9125 )(0.1058 )(0.8059 )(NA )(0.4741 )(0.295 )(0.9546 )
Estimates ( 3 )0.4712-0.10160.1174-0.07280.0063-0.0958-0.013500.0386-0.06040
(p-val)(0 )(0.0823 )(0.0471 )(0.2191 )(0.9154 )(0.1058 )(0.8046 )(NA )(0.4755 )(0.2624 )(NA )
Estimates ( 4 )0.4708-0.10090.1166-0.07020-0.0933-0.01400.0385-0.06040
(p-val)(0 )(0.0825 )(0.0465 )(0.1928 )(NA )(0.0842 )(0.7952 )(NA )(0.476 )(0.2624 )(NA )
Estimates ( 5 )0.4721-0.1010.1173-0.07130-0.0989000.038-0.06160
(p-val)(0 )(0.0821 )(0.045 )(0.1847 )(NA )(0.0457 )(NA )(NA )(0.4816 )(0.252 )(NA )
Estimates ( 6 )0.4718-0.10310.115-0.07070-0.0952000-0.0460
(p-val)(0 )(0.0757 )(0.049 )(0.1885 )(NA )(0.0531 )(NA )(NA )(NA )(0.348 )(NA )
Estimates ( 7 )0.4715-0.10260.1177-0.06720-0.095800000
(p-val)(0 )(0.0775 )(0.044 )(0.2113 )(NA )(0.0519 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0.4667-0.09580.088400-0.101100000
(p-val)(0 )(0.0985 )(0.099 )(NA )(NA )(0.04 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )0.4609-0.0559000-0.09400000
(p-val)(0 )(0.2899 )(NA )(NA )(NA )(0.0562 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )0.43660000-0.094800000
(p-val)(0 )(NA )(NA )(NA )(NA )(0.0544 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )0.44190000000000
(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
0.254999840129496
22.5044750036747
8.56666662771422
30.5224425186224
17.7574029835131
4.29599552355775
-11.6967564104387
-3.23382517886057
-1.3582203395938
0.363289924781157
-5.57531773752589
1.59376998313809
-3.69385227442251
-1.86624221154045
-1.64456945586153
-11.4526930140069
-1.26091474164383
-6.74499560813354
-2.17643243331952
-12.8838361522639
-2.14168358217944
-1.87046307725319
0.508960961751882
-1.92237013143057
-4.15707307840262
-3.63529234373198
-6.00640711665477
-0.861174356807055
-0.457347594503176
5.38727105814723
19.5405131043552
16.6069251456254
-1.47618585191458
-0.146684270651747
1.98071964374680
0.568335608514019
-10.6875339659314
13.3167766852150
-8.42091884471262
-1.77953733352012
4.7214249729181
-2.98352756439783
6.77597977179363
-5.58140968107278
-10.3102510268844
-1.60024022024908
-11.0346762136322
-3.99346348653688
1.63243972026879
-14.4532195827589
0.267593529950204
-0.458219069875355
-2.90413962006073
1.30602813826920
-4.19305875208141
-1.59455923558826
-0.72289670018273
0.574867852086356
-3.62110455264951
10.5012827267068
-6.05139007083937
12.0341347439427
-3.96214318012437
-0.142299936365134
0.0656719856086738
-1.16508461175118
-2.28482519204198
3.64675039128031
-1.86325285284317
-1.66037124905191
0.151495802842817
0.722267692048007
1.20679919716125
0.981081698292542
-0.631221091288239
-4.4053519562481
1.30459048277018
-0.57797477518946
-0.789589774432585
2.21921198863623
-2.83588525719867
1.57176092898328
1.92551666590845
-3.32359205303203
5.23720003276861
3.72132028654715
-4.80965833750361
2.05523929659222
1.09095576969378
2.72390820340979
1.28914647423488
-1.22635406951514
1.39703512765425
0.42726717916446
-1.36966753200971
1.81201868806608
-0.209044230306233
1.93364022405365
-0.349887340344139
-0.889305672917828
-0.555028878217911
0.944050449621102
0.0817182689389142
-5.14136967879006
1.10804987500495
1.54803641060801
-1.65605955227312
-7.27238035170387
23.7336349085304
74.5448033833175
-16.3414340879630
-15.6016149793778
-1.03564810548448
-2.23684000112524
-3.97856341876542
-2.35939358094083
-11.7724797909141
6.21200603411711
3.97243913390685
-2.47498743939468
-3.53009958616695
-7.82644894638594
-4.00922237573161
-7.11640406798512
-5.35502460832708
2.92144212084384
-2.72634650700343
-9.13922708502753
7.60378737501503
3.806285506444
-15.7230430208308
2.01552196156638
11.1689476928090
-5.02884786269072
-6.26125028693571
11.1443804553037
-6.29913481742108
-1.00806477594227
7.81232196040889
-6.97825356118074
2.16102496762392
1.83705041949958
2.87451538295466
1.54251567026279
3.04709859052122
6.68976387405303
-5.05643875474465
8.30973202064393
1.83287650358733
-1.38266567443975
5.99509336685207
-7.09783089698362
-5.06579688844033
-2.09260915909044
-10.4255807636347
9.33152661061939
-2.93447762766027
5.1612086526585
-10.1875422816269
2.0617715401645
4.28214239830851
1.90853256249147
5.80780313787665
-5.60650019470921
0.277203716297436
0.530432884470429
-8.21584342746746
3.68050817973995
1.30458292025844
2.16941547935818
-8.85402493750252
6.40893849830286
-6.61548766571497
0.626615707580754
-5.4945476272718
-5.1904837132538
-0.393793492219515
3.08140211856113
-4.33778740662609
-3.48258015889695
5.81722657065444
-0.645100294504516
-6.87648589999907
0.721131285989316
5.31663163428527
-6.05054308181866
4.63561074109811
-4.88912198788336
-6.43776067328628
-1.36223707972536
-7.68042595681803
1.37749208770288
0.234680483522965
10.1541574028457
-4.08611218863956
0.636837978222815
2.08767803959111
0.0287560543547727
0.556769806059776
-4.50684839395012
1.49030429811003
-7.35220605662946
5.55595382026971
4.09430516003428
3.09035124791052
-2.73785577424309
-1.42270342973413
-0.0282486564308044
2.02698307764976
5.9160879176074
72.0236485882466
81.453854796326
-52.0572978876145
0.169919584661216
3.72937016155208
-10.7997650952742
13.0092508395253
-6.30081799612901
-5.64891851702669
3.02485987880135
-0.348888715151986
-1.00583891362544
-2.69906974242491
2.24228832804863
-7.7422282761272
-10.9264201901437
0.457821897931524
-4.04936608711529
-15.0966655105968
9.167379818955
-5.08893074033716
-1.79562195850667
-7.77938240472781
2.43571872008994
-7.19705632138488
3.65530766420926
-6.51576218181549
3.37037247027303
-7.36705715169802
5.00615731099157
-2.29759045264859
2.00039410340889
18.3611402850415
29.74763579032
4.24499769939860
22.7414047961881
6.00726578235458
-4.47958975456731
-5.13183646820397
-16.6085222238934
2.96087101610163
-7.29047539755811
-5.50480523939791
1.12594177254795
7.77717166743429
-12.1523735710082
-5.71314753161306
-7.00999141172116
1.22710820456729
-6.53811314241801
-4.90622672424109
-8.73438454960586
2.23991509945114
-3.55537378483785
0.329428943754976
-0.85493804715179
4.30927711212962
-4.20466087328958
3.13748413428323
-9.00731288787841
3.0262899717888
0.777296570265946
-3.39421230606587
10.3331724375045
-18.1104957411102
6.62865256933969
18.8539565698854
5.35016492497965
-10.7127183108062
-2.13540361534467
-4.53543439448919
-7.59141397082891
7.69526844374246
0.443910421766134
-10.8298058992382
1.91939792065915
-6.94199868692471
-0.817427179528806
2.69554625246388
-1.24283609053134
-1.88980221570932
-7.38468314128465
-0.439978973091456
6.95946772481665
-8.36196481061319
-2.59063534942516
0.0127140409153412
-0.81317426495292
-2.55731554564005
-5.76196054072238
5.01490785436914
-0.487574330490077
-9.33564278996124
15.4033024405401
-1.86375164262307
-9.53896971685276
7.3049525373159
-6.40779782235336
0.470747402853817
-3.44159297519934
3.47095984413050
-5.52860112556243
18.3905979850387
-14.9837102037998
4.0983862219781
6.64257445246568
-10.2794262859934
2.08248198940521
3.08821292403883
-3.5785301001838
0.889415674812085
-8.60168970815062
-1.16938118350669
11.5513121178083
-2.3677696524731
-4.56019714943142
9.1526685276553
-6.60899930340815
-7.08832621855396
-3.82838441439492
16.4973234990597
-5.92929116288849
0.320477567417242
-3.45289384696707
-5.44564176419402
1.73881605777888
28.6007485954430
-22.8181825159368
11.6206218651629
19.7653931996173
-1.77237399054815
4.79562525112755
-9.4100708585567
17.3465038782381
-9.30702144793804
3.71784472365658
-14.5562828486824
1.74783453201519
1.45471070493767
-4.26599569849816
14.9002692005773
-6.44435596895232
4.63299204509082
-22.1382498776520
7.9099222225575
3.82478724129368
-2.92961060588715
-2.58122374904985
4.06833029299077

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.254999840129496 \tabularnewline
22.5044750036747 \tabularnewline
8.56666662771422 \tabularnewline
30.5224425186224 \tabularnewline
17.7574029835131 \tabularnewline
4.29599552355775 \tabularnewline
-11.6967564104387 \tabularnewline
-3.23382517886057 \tabularnewline
-1.3582203395938 \tabularnewline
0.363289924781157 \tabularnewline
-5.57531773752589 \tabularnewline
1.59376998313809 \tabularnewline
-3.69385227442251 \tabularnewline
-1.86624221154045 \tabularnewline
-1.64456945586153 \tabularnewline
-11.4526930140069 \tabularnewline
-1.26091474164383 \tabularnewline
-6.74499560813354 \tabularnewline
-2.17643243331952 \tabularnewline
-12.8838361522639 \tabularnewline
-2.14168358217944 \tabularnewline
-1.87046307725319 \tabularnewline
0.508960961751882 \tabularnewline
-1.92237013143057 \tabularnewline
-4.15707307840262 \tabularnewline
-3.63529234373198 \tabularnewline
-6.00640711665477 \tabularnewline
-0.861174356807055 \tabularnewline
-0.457347594503176 \tabularnewline
5.38727105814723 \tabularnewline
19.5405131043552 \tabularnewline
16.6069251456254 \tabularnewline
-1.47618585191458 \tabularnewline
-0.146684270651747 \tabularnewline
1.98071964374680 \tabularnewline
0.568335608514019 \tabularnewline
-10.6875339659314 \tabularnewline
13.3167766852150 \tabularnewline
-8.42091884471262 \tabularnewline
-1.77953733352012 \tabularnewline
4.7214249729181 \tabularnewline
-2.98352756439783 \tabularnewline
6.77597977179363 \tabularnewline
-5.58140968107278 \tabularnewline
-10.3102510268844 \tabularnewline
-1.60024022024908 \tabularnewline
-11.0346762136322 \tabularnewline
-3.99346348653688 \tabularnewline
1.63243972026879 \tabularnewline
-14.4532195827589 \tabularnewline
0.267593529950204 \tabularnewline
-0.458219069875355 \tabularnewline
-2.90413962006073 \tabularnewline
1.30602813826920 \tabularnewline
-4.19305875208141 \tabularnewline
-1.59455923558826 \tabularnewline
-0.72289670018273 \tabularnewline
0.574867852086356 \tabularnewline
-3.62110455264951 \tabularnewline
10.5012827267068 \tabularnewline
-6.05139007083937 \tabularnewline
12.0341347439427 \tabularnewline
-3.96214318012437 \tabularnewline
-0.142299936365134 \tabularnewline
0.0656719856086738 \tabularnewline
-1.16508461175118 \tabularnewline
-2.28482519204198 \tabularnewline
3.64675039128031 \tabularnewline
-1.86325285284317 \tabularnewline
-1.66037124905191 \tabularnewline
0.151495802842817 \tabularnewline
0.722267692048007 \tabularnewline
1.20679919716125 \tabularnewline
0.981081698292542 \tabularnewline
-0.631221091288239 \tabularnewline
-4.4053519562481 \tabularnewline
1.30459048277018 \tabularnewline
-0.57797477518946 \tabularnewline
-0.789589774432585 \tabularnewline
2.21921198863623 \tabularnewline
-2.83588525719867 \tabularnewline
1.57176092898328 \tabularnewline
1.92551666590845 \tabularnewline
-3.32359205303203 \tabularnewline
5.23720003276861 \tabularnewline
3.72132028654715 \tabularnewline
-4.80965833750361 \tabularnewline
2.05523929659222 \tabularnewline
1.09095576969378 \tabularnewline
2.72390820340979 \tabularnewline
1.28914647423488 \tabularnewline
-1.22635406951514 \tabularnewline
1.39703512765425 \tabularnewline
0.42726717916446 \tabularnewline
-1.36966753200971 \tabularnewline
1.81201868806608 \tabularnewline
-0.209044230306233 \tabularnewline
1.93364022405365 \tabularnewline
-0.349887340344139 \tabularnewline
-0.889305672917828 \tabularnewline
-0.555028878217911 \tabularnewline
0.944050449621102 \tabularnewline
0.0817182689389142 \tabularnewline
-5.14136967879006 \tabularnewline
1.10804987500495 \tabularnewline
1.54803641060801 \tabularnewline
-1.65605955227312 \tabularnewline
-7.27238035170387 \tabularnewline
23.7336349085304 \tabularnewline
74.5448033833175 \tabularnewline
-16.3414340879630 \tabularnewline
-15.6016149793778 \tabularnewline
-1.03564810548448 \tabularnewline
-2.23684000112524 \tabularnewline
-3.97856341876542 \tabularnewline
-2.35939358094083 \tabularnewline
-11.7724797909141 \tabularnewline
6.21200603411711 \tabularnewline
3.97243913390685 \tabularnewline
-2.47498743939468 \tabularnewline
-3.53009958616695 \tabularnewline
-7.82644894638594 \tabularnewline
-4.00922237573161 \tabularnewline
-7.11640406798512 \tabularnewline
-5.35502460832708 \tabularnewline
2.92144212084384 \tabularnewline
-2.72634650700343 \tabularnewline
-9.13922708502753 \tabularnewline
7.60378737501503 \tabularnewline
3.806285506444 \tabularnewline
-15.7230430208308 \tabularnewline
2.01552196156638 \tabularnewline
11.1689476928090 \tabularnewline
-5.02884786269072 \tabularnewline
-6.26125028693571 \tabularnewline
11.1443804553037 \tabularnewline
-6.29913481742108 \tabularnewline
-1.00806477594227 \tabularnewline
7.81232196040889 \tabularnewline
-6.97825356118074 \tabularnewline
2.16102496762392 \tabularnewline
1.83705041949958 \tabularnewline
2.87451538295466 \tabularnewline
1.54251567026279 \tabularnewline
3.04709859052122 \tabularnewline
6.68976387405303 \tabularnewline
-5.05643875474465 \tabularnewline
8.30973202064393 \tabularnewline
1.83287650358733 \tabularnewline
-1.38266567443975 \tabularnewline
5.99509336685207 \tabularnewline
-7.09783089698362 \tabularnewline
-5.06579688844033 \tabularnewline
-2.09260915909044 \tabularnewline
-10.4255807636347 \tabularnewline
9.33152661061939 \tabularnewline
-2.93447762766027 \tabularnewline
5.1612086526585 \tabularnewline
-10.1875422816269 \tabularnewline
2.0617715401645 \tabularnewline
4.28214239830851 \tabularnewline
1.90853256249147 \tabularnewline
5.80780313787665 \tabularnewline
-5.60650019470921 \tabularnewline
0.277203716297436 \tabularnewline
0.530432884470429 \tabularnewline
-8.21584342746746 \tabularnewline
3.68050817973995 \tabularnewline
1.30458292025844 \tabularnewline
2.16941547935818 \tabularnewline
-8.85402493750252 \tabularnewline
6.40893849830286 \tabularnewline
-6.61548766571497 \tabularnewline
0.626615707580754 \tabularnewline
-5.4945476272718 \tabularnewline
-5.1904837132538 \tabularnewline
-0.393793492219515 \tabularnewline
3.08140211856113 \tabularnewline
-4.33778740662609 \tabularnewline
-3.48258015889695 \tabularnewline
5.81722657065444 \tabularnewline
-0.645100294504516 \tabularnewline
-6.87648589999907 \tabularnewline
0.721131285989316 \tabularnewline
5.31663163428527 \tabularnewline
-6.05054308181866 \tabularnewline
4.63561074109811 \tabularnewline
-4.88912198788336 \tabularnewline
-6.43776067328628 \tabularnewline
-1.36223707972536 \tabularnewline
-7.68042595681803 \tabularnewline
1.37749208770288 \tabularnewline
0.234680483522965 \tabularnewline
10.1541574028457 \tabularnewline
-4.08611218863956 \tabularnewline
0.636837978222815 \tabularnewline
2.08767803959111 \tabularnewline
0.0287560543547727 \tabularnewline
0.556769806059776 \tabularnewline
-4.50684839395012 \tabularnewline
1.49030429811003 \tabularnewline
-7.35220605662946 \tabularnewline
5.55595382026971 \tabularnewline
4.09430516003428 \tabularnewline
3.09035124791052 \tabularnewline
-2.73785577424309 \tabularnewline
-1.42270342973413 \tabularnewline
-0.0282486564308044 \tabularnewline
2.02698307764976 \tabularnewline
5.9160879176074 \tabularnewline
72.0236485882466 \tabularnewline
81.453854796326 \tabularnewline
-52.0572978876145 \tabularnewline
0.169919584661216 \tabularnewline
3.72937016155208 \tabularnewline
-10.7997650952742 \tabularnewline
13.0092508395253 \tabularnewline
-6.30081799612901 \tabularnewline
-5.64891851702669 \tabularnewline
3.02485987880135 \tabularnewline
-0.348888715151986 \tabularnewline
-1.00583891362544 \tabularnewline
-2.69906974242491 \tabularnewline
2.24228832804863 \tabularnewline
-7.7422282761272 \tabularnewline
-10.9264201901437 \tabularnewline
0.457821897931524 \tabularnewline
-4.04936608711529 \tabularnewline
-15.0966655105968 \tabularnewline
9.167379818955 \tabularnewline
-5.08893074033716 \tabularnewline
-1.79562195850667 \tabularnewline
-7.77938240472781 \tabularnewline
2.43571872008994 \tabularnewline
-7.19705632138488 \tabularnewline
3.65530766420926 \tabularnewline
-6.51576218181549 \tabularnewline
3.37037247027303 \tabularnewline
-7.36705715169802 \tabularnewline
5.00615731099157 \tabularnewline
-2.29759045264859 \tabularnewline
2.00039410340889 \tabularnewline
18.3611402850415 \tabularnewline
29.74763579032 \tabularnewline
4.24499769939860 \tabularnewline
22.7414047961881 \tabularnewline
6.00726578235458 \tabularnewline
-4.47958975456731 \tabularnewline
-5.13183646820397 \tabularnewline
-16.6085222238934 \tabularnewline
2.96087101610163 \tabularnewline
-7.29047539755811 \tabularnewline
-5.50480523939791 \tabularnewline
1.12594177254795 \tabularnewline
7.77717166743429 \tabularnewline
-12.1523735710082 \tabularnewline
-5.71314753161306 \tabularnewline
-7.00999141172116 \tabularnewline
1.22710820456729 \tabularnewline
-6.53811314241801 \tabularnewline
-4.90622672424109 \tabularnewline
-8.73438454960586 \tabularnewline
2.23991509945114 \tabularnewline
-3.55537378483785 \tabularnewline
0.329428943754976 \tabularnewline
-0.85493804715179 \tabularnewline
4.30927711212962 \tabularnewline
-4.20466087328958 \tabularnewline
3.13748413428323 \tabularnewline
-9.00731288787841 \tabularnewline
3.0262899717888 \tabularnewline
0.777296570265946 \tabularnewline
-3.39421230606587 \tabularnewline
10.3331724375045 \tabularnewline
-18.1104957411102 \tabularnewline
6.62865256933969 \tabularnewline
18.8539565698854 \tabularnewline
5.35016492497965 \tabularnewline
-10.7127183108062 \tabularnewline
-2.13540361534467 \tabularnewline
-4.53543439448919 \tabularnewline
-7.59141397082891 \tabularnewline
7.69526844374246 \tabularnewline
0.443910421766134 \tabularnewline
-10.8298058992382 \tabularnewline
1.91939792065915 \tabularnewline
-6.94199868692471 \tabularnewline
-0.817427179528806 \tabularnewline
2.69554625246388 \tabularnewline
-1.24283609053134 \tabularnewline
-1.88980221570932 \tabularnewline
-7.38468314128465 \tabularnewline
-0.439978973091456 \tabularnewline
6.95946772481665 \tabularnewline
-8.36196481061319 \tabularnewline
-2.59063534942516 \tabularnewline
0.0127140409153412 \tabularnewline
-0.81317426495292 \tabularnewline
-2.55731554564005 \tabularnewline
-5.76196054072238 \tabularnewline
5.01490785436914 \tabularnewline
-0.487574330490077 \tabularnewline
-9.33564278996124 \tabularnewline
15.4033024405401 \tabularnewline
-1.86375164262307 \tabularnewline
-9.53896971685276 \tabularnewline
7.3049525373159 \tabularnewline
-6.40779782235336 \tabularnewline
0.470747402853817 \tabularnewline
-3.44159297519934 \tabularnewline
3.47095984413050 \tabularnewline
-5.52860112556243 \tabularnewline
18.3905979850387 \tabularnewline
-14.9837102037998 \tabularnewline
4.0983862219781 \tabularnewline
6.64257445246568 \tabularnewline
-10.2794262859934 \tabularnewline
2.08248198940521 \tabularnewline
3.08821292403883 \tabularnewline
-3.5785301001838 \tabularnewline
0.889415674812085 \tabularnewline
-8.60168970815062 \tabularnewline
-1.16938118350669 \tabularnewline
11.5513121178083 \tabularnewline
-2.3677696524731 \tabularnewline
-4.56019714943142 \tabularnewline
9.1526685276553 \tabularnewline
-6.60899930340815 \tabularnewline
-7.08832621855396 \tabularnewline
-3.82838441439492 \tabularnewline
16.4973234990597 \tabularnewline
-5.92929116288849 \tabularnewline
0.320477567417242 \tabularnewline
-3.45289384696707 \tabularnewline
-5.44564176419402 \tabularnewline
1.73881605777888 \tabularnewline
28.6007485954430 \tabularnewline
-22.8181825159368 \tabularnewline
11.6206218651629 \tabularnewline
19.7653931996173 \tabularnewline
-1.77237399054815 \tabularnewline
4.79562525112755 \tabularnewline
-9.4100708585567 \tabularnewline
17.3465038782381 \tabularnewline
-9.30702144793804 \tabularnewline
3.71784472365658 \tabularnewline
-14.5562828486824 \tabularnewline
1.74783453201519 \tabularnewline
1.45471070493767 \tabularnewline
-4.26599569849816 \tabularnewline
14.9002692005773 \tabularnewline
-6.44435596895232 \tabularnewline
4.63299204509082 \tabularnewline
-22.1382498776520 \tabularnewline
7.9099222225575 \tabularnewline
3.82478724129368 \tabularnewline
-2.92961060588715 \tabularnewline
-2.58122374904985 \tabularnewline
4.06833029299077 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65440&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.254999840129496[/C][/ROW]
[ROW][C]22.5044750036747[/C][/ROW]
[ROW][C]8.56666662771422[/C][/ROW]
[ROW][C]30.5224425186224[/C][/ROW]
[ROW][C]17.7574029835131[/C][/ROW]
[ROW][C]4.29599552355775[/C][/ROW]
[ROW][C]-11.6967564104387[/C][/ROW]
[ROW][C]-3.23382517886057[/C][/ROW]
[ROW][C]-1.3582203395938[/C][/ROW]
[ROW][C]0.363289924781157[/C][/ROW]
[ROW][C]-5.57531773752589[/C][/ROW]
[ROW][C]1.59376998313809[/C][/ROW]
[ROW][C]-3.69385227442251[/C][/ROW]
[ROW][C]-1.86624221154045[/C][/ROW]
[ROW][C]-1.64456945586153[/C][/ROW]
[ROW][C]-11.4526930140069[/C][/ROW]
[ROW][C]-1.26091474164383[/C][/ROW]
[ROW][C]-6.74499560813354[/C][/ROW]
[ROW][C]-2.17643243331952[/C][/ROW]
[ROW][C]-12.8838361522639[/C][/ROW]
[ROW][C]-2.14168358217944[/C][/ROW]
[ROW][C]-1.87046307725319[/C][/ROW]
[ROW][C]0.508960961751882[/C][/ROW]
[ROW][C]-1.92237013143057[/C][/ROW]
[ROW][C]-4.15707307840262[/C][/ROW]
[ROW][C]-3.63529234373198[/C][/ROW]
[ROW][C]-6.00640711665477[/C][/ROW]
[ROW][C]-0.861174356807055[/C][/ROW]
[ROW][C]-0.457347594503176[/C][/ROW]
[ROW][C]5.38727105814723[/C][/ROW]
[ROW][C]19.5405131043552[/C][/ROW]
[ROW][C]16.6069251456254[/C][/ROW]
[ROW][C]-1.47618585191458[/C][/ROW]
[ROW][C]-0.146684270651747[/C][/ROW]
[ROW][C]1.98071964374680[/C][/ROW]
[ROW][C]0.568335608514019[/C][/ROW]
[ROW][C]-10.6875339659314[/C][/ROW]
[ROW][C]13.3167766852150[/C][/ROW]
[ROW][C]-8.42091884471262[/C][/ROW]
[ROW][C]-1.77953733352012[/C][/ROW]
[ROW][C]4.7214249729181[/C][/ROW]
[ROW][C]-2.98352756439783[/C][/ROW]
[ROW][C]6.77597977179363[/C][/ROW]
[ROW][C]-5.58140968107278[/C][/ROW]
[ROW][C]-10.3102510268844[/C][/ROW]
[ROW][C]-1.60024022024908[/C][/ROW]
[ROW][C]-11.0346762136322[/C][/ROW]
[ROW][C]-3.99346348653688[/C][/ROW]
[ROW][C]1.63243972026879[/C][/ROW]
[ROW][C]-14.4532195827589[/C][/ROW]
[ROW][C]0.267593529950204[/C][/ROW]
[ROW][C]-0.458219069875355[/C][/ROW]
[ROW][C]-2.90413962006073[/C][/ROW]
[ROW][C]1.30602813826920[/C][/ROW]
[ROW][C]-4.19305875208141[/C][/ROW]
[ROW][C]-1.59455923558826[/C][/ROW]
[ROW][C]-0.72289670018273[/C][/ROW]
[ROW][C]0.574867852086356[/C][/ROW]
[ROW][C]-3.62110455264951[/C][/ROW]
[ROW][C]10.5012827267068[/C][/ROW]
[ROW][C]-6.05139007083937[/C][/ROW]
[ROW][C]12.0341347439427[/C][/ROW]
[ROW][C]-3.96214318012437[/C][/ROW]
[ROW][C]-0.142299936365134[/C][/ROW]
[ROW][C]0.0656719856086738[/C][/ROW]
[ROW][C]-1.16508461175118[/C][/ROW]
[ROW][C]-2.28482519204198[/C][/ROW]
[ROW][C]3.64675039128031[/C][/ROW]
[ROW][C]-1.86325285284317[/C][/ROW]
[ROW][C]-1.66037124905191[/C][/ROW]
[ROW][C]0.151495802842817[/C][/ROW]
[ROW][C]0.722267692048007[/C][/ROW]
[ROW][C]1.20679919716125[/C][/ROW]
[ROW][C]0.981081698292542[/C][/ROW]
[ROW][C]-0.631221091288239[/C][/ROW]
[ROW][C]-4.4053519562481[/C][/ROW]
[ROW][C]1.30459048277018[/C][/ROW]
[ROW][C]-0.57797477518946[/C][/ROW]
[ROW][C]-0.789589774432585[/C][/ROW]
[ROW][C]2.21921198863623[/C][/ROW]
[ROW][C]-2.83588525719867[/C][/ROW]
[ROW][C]1.57176092898328[/C][/ROW]
[ROW][C]1.92551666590845[/C][/ROW]
[ROW][C]-3.32359205303203[/C][/ROW]
[ROW][C]5.23720003276861[/C][/ROW]
[ROW][C]3.72132028654715[/C][/ROW]
[ROW][C]-4.80965833750361[/C][/ROW]
[ROW][C]2.05523929659222[/C][/ROW]
[ROW][C]1.09095576969378[/C][/ROW]
[ROW][C]2.72390820340979[/C][/ROW]
[ROW][C]1.28914647423488[/C][/ROW]
[ROW][C]-1.22635406951514[/C][/ROW]
[ROW][C]1.39703512765425[/C][/ROW]
[ROW][C]0.42726717916446[/C][/ROW]
[ROW][C]-1.36966753200971[/C][/ROW]
[ROW][C]1.81201868806608[/C][/ROW]
[ROW][C]-0.209044230306233[/C][/ROW]
[ROW][C]1.93364022405365[/C][/ROW]
[ROW][C]-0.349887340344139[/C][/ROW]
[ROW][C]-0.889305672917828[/C][/ROW]
[ROW][C]-0.555028878217911[/C][/ROW]
[ROW][C]0.944050449621102[/C][/ROW]
[ROW][C]0.0817182689389142[/C][/ROW]
[ROW][C]-5.14136967879006[/C][/ROW]
[ROW][C]1.10804987500495[/C][/ROW]
[ROW][C]1.54803641060801[/C][/ROW]
[ROW][C]-1.65605955227312[/C][/ROW]
[ROW][C]-7.27238035170387[/C][/ROW]
[ROW][C]23.7336349085304[/C][/ROW]
[ROW][C]74.5448033833175[/C][/ROW]
[ROW][C]-16.3414340879630[/C][/ROW]
[ROW][C]-15.6016149793778[/C][/ROW]
[ROW][C]-1.03564810548448[/C][/ROW]
[ROW][C]-2.23684000112524[/C][/ROW]
[ROW][C]-3.97856341876542[/C][/ROW]
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[ROW][C]4.06833029299077[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65440&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65440&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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22.5044750036747
8.56666662771422
30.5224425186224
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5.38727105814723
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1.98071964374680
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4.7214249729181
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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')