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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 03:57:32 -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/t1260442718i0wgvm4c2aveknw.htm/, Retrieved Fri, 29 Mar 2024 07:23:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65269, Retrieved Fri, 29 Mar 2024 07:23:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146
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]
-         [ARIMA Backward Selection] [Bivariate Granger...] [2009-12-10 10:57:32] [4996e0131d5120d29a6e9a8dccb25dc3] [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
311.3




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=65269&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=65269&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65269&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 )0.4706-0.10060.1171-0.07270.0042-0.0899-0.01820.00480.0335-0.05830.0018
(p-val)(0 )(0.086 )(0.0478 )(0.2208 )(0.9437 )(0.1301 )(0.7608 )(0.9367 )(0.574 )(0.3273 )(0.9735 )
Estimates ( 2 )0.4705-0.10050.117-0.07270.004-0.0899-0.01840.0050.0332-0.05740
(p-val)(0 )(0.0861 )(0.0479 )(0.2208 )(0.9457 )(0.1302 )(0.7581 )(0.9329 )(0.5743 )(0.2884 )(NA )
Estimates ( 3 )0.4703-0.10.1165-0.0710-0.0882-0.0190.00550.033-0.05740
(p-val)(0 )(0.0854 )(0.0466 )(0.1875 )(NA )(0.1018 )(0.7484 )(0.9259 )(0.5763 )(0.2887 )(NA )
Estimates ( 4 )0.4701-0.10030.1166-0.07110-0.0887-0.016700.0353-0.05790
(p-val)(0 )(0.084 )(0.0462 )(0.1868 )(NA )(0.0981 )(0.7568 )(NA )(0.5122 )(0.2812 )(NA )
Estimates ( 5 )0.4716-0.10040.1175-0.07240-0.0953000.0346-0.05920
(p-val)(0 )(0.0837 )(0.0445 )(0.1774 )(NA )(0.0529 )(NA )(NA )(0.5203 )(0.2693 )(NA )
Estimates ( 6 )0.4715-0.10240.1154-0.07190-0.0922000-0.04510
(p-val)(0 )(0.0775 )(0.0481 )(0.1812 )(NA )(0.06 )(NA )(NA )(NA )(0.3565 )(NA )
Estimates ( 7 )0.4712-0.1020.1181-0.06830-0.092900000
(p-val)(0 )(0.0792 )(0.0433 )(0.2031 )(NA )(0.0584 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0.4662-0.09490.088300-0.098100000
(p-val)(0 )(0.1012 )(0.0994 )(NA )(NA )(0.0454 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )0.429700.051700-0.096400000
(p-val)(0 )(NA )(0.2898 )(NA )(NA )(0.0499 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )0.43640000-0.091900000
(p-val)(0 )(NA )(NA )(NA )(NA )(0.0611 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )0.44170000000000
(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 ) & 0.4706 & -0.1006 & 0.1171 & -0.0727 & 0.0042 & -0.0899 & -0.0182 & 0.0048 & 0.0335 & -0.0583 & 0.0018 \tabularnewline
(p-val) & (0 ) & (0.086 ) & (0.0478 ) & (0.2208 ) & (0.9437 ) & (0.1301 ) & (0.7608 ) & (0.9367 ) & (0.574 ) & (0.3273 ) & (0.9735 ) \tabularnewline
Estimates ( 2 ) & 0.4705 & -0.1005 & 0.117 & -0.0727 & 0.004 & -0.0899 & -0.0184 & 0.005 & 0.0332 & -0.0574 & 0 \tabularnewline
(p-val) & (0 ) & (0.0861 ) & (0.0479 ) & (0.2208 ) & (0.9457 ) & (0.1302 ) & (0.7581 ) & (0.9329 ) & (0.5743 ) & (0.2884 ) & (NA ) \tabularnewline
Estimates ( 3 ) & 0.4703 & -0.1 & 0.1165 & -0.071 & 0 & -0.0882 & -0.019 & 0.0055 & 0.033 & -0.0574 & 0 \tabularnewline
(p-val) & (0 ) & (0.0854 ) & (0.0466 ) & (0.1875 ) & (NA ) & (0.1018 ) & (0.7484 ) & (0.9259 ) & (0.5763 ) & (0.2887 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0.4701 & -0.1003 & 0.1166 & -0.0711 & 0 & -0.0887 & -0.0167 & 0 & 0.0353 & -0.0579 & 0 \tabularnewline
(p-val) & (0 ) & (0.084 ) & (0.0462 ) & (0.1868 ) & (NA ) & (0.0981 ) & (0.7568 ) & (NA ) & (0.5122 ) & (0.2812 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0.4716 & -0.1004 & 0.1175 & -0.0724 & 0 & -0.0953 & 0 & 0 & 0.0346 & -0.0592 & 0 \tabularnewline
(p-val) & (0 ) & (0.0837 ) & (0.0445 ) & (0.1774 ) & (NA ) & (0.0529 ) & (NA ) & (NA ) & (0.5203 ) & (0.2693 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0.4715 & -0.1024 & 0.1154 & -0.0719 & 0 & -0.0922 & 0 & 0 & 0 & -0.0451 & 0 \tabularnewline
(p-val) & (0 ) & (0.0775 ) & (0.0481 ) & (0.1812 ) & (NA ) & (0.06 ) & (NA ) & (NA ) & (NA ) & (0.3565 ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0.4712 & -0.102 & 0.1181 & -0.0683 & 0 & -0.0929 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.0792 ) & (0.0433 ) & (0.2031 ) & (NA ) & (0.0584 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & 0.4662 & -0.0949 & 0.0883 & 0 & 0 & -0.0981 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.1012 ) & (0.0994 ) & (NA ) & (NA ) & (0.0454 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & 0.4297 & 0 & 0.0517 & 0 & 0 & -0.0964 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (0.2898 ) & (NA ) & (NA ) & (0.0499 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & 0.4364 & 0 & 0 & 0 & 0 & -0.0919 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0611 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & 0.4417 & 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=65269&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]0.4706[/C][C]-0.1006[/C][C]0.1171[/C][C]-0.0727[/C][C]0.0042[/C][C]-0.0899[/C][C]-0.0182[/C][C]0.0048[/C][C]0.0335[/C][C]-0.0583[/C][C]0.0018[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.086 )[/C][C](0.0478 )[/C][C](0.2208 )[/C][C](0.9437 )[/C][C](0.1301 )[/C][C](0.7608 )[/C][C](0.9367 )[/C][C](0.574 )[/C][C](0.3273 )[/C][C](0.9735 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4705[/C][C]-0.1005[/C][C]0.117[/C][C]-0.0727[/C][C]0.004[/C][C]-0.0899[/C][C]-0.0184[/C][C]0.005[/C][C]0.0332[/C][C]-0.0574[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0861 )[/C][C](0.0479 )[/C][C](0.2208 )[/C][C](0.9457 )[/C][C](0.1302 )[/C][C](0.7581 )[/C][C](0.9329 )[/C][C](0.5743 )[/C][C](0.2884 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4703[/C][C]-0.1[/C][C]0.1165[/C][C]-0.071[/C][C]0[/C][C]-0.0882[/C][C]-0.019[/C][C]0.0055[/C][C]0.033[/C][C]-0.0574[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0854 )[/C][C](0.0466 )[/C][C](0.1875 )[/C][C](NA )[/C][C](0.1018 )[/C][C](0.7484 )[/C][C](0.9259 )[/C][C](0.5763 )[/C][C](0.2887 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.4701[/C][C]-0.1003[/C][C]0.1166[/C][C]-0.0711[/C][C]0[/C][C]-0.0887[/C][C]-0.0167[/C][C]0[/C][C]0.0353[/C][C]-0.0579[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.084 )[/C][C](0.0462 )[/C][C](0.1868 )[/C][C](NA )[/C][C](0.0981 )[/C][C](0.7568 )[/C][C](NA )[/C][C](0.5122 )[/C][C](0.2812 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.4716[/C][C]-0.1004[/C][C]0.1175[/C][C]-0.0724[/C][C]0[/C][C]-0.0953[/C][C]0[/C][C]0[/C][C]0.0346[/C][C]-0.0592[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0837 )[/C][C](0.0445 )[/C][C](0.1774 )[/C][C](NA )[/C][C](0.0529 )[/C][C](NA )[/C][C](NA )[/C][C](0.5203 )[/C][C](0.2693 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.4715[/C][C]-0.1024[/C][C]0.1154[/C][C]-0.0719[/C][C]0[/C][C]-0.0922[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0451[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0775 )[/C][C](0.0481 )[/C][C](0.1812 )[/C][C](NA )[/C][C](0.06 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.3565 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0.4712[/C][C]-0.102[/C][C]0.1181[/C][C]-0.0683[/C][C]0[/C][C]-0.0929[/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](0.0792 )[/C][C](0.0433 )[/C][C](0.2031 )[/C][C](NA )[/C][C](0.0584 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]0.4662[/C][C]-0.0949[/C][C]0.0883[/C][C]0[/C][C]0[/C][C]-0.0981[/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](0.1012 )[/C][C](0.0994 )[/C][C](NA )[/C][C](NA )[/C][C](0.0454 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]0.4297[/C][C]0[/C][C]0.0517[/C][C]0[/C][C]0[/C][C]-0.0964[/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](0.2898 )[/C][C](NA )[/C][C](NA )[/C][C](0.0499 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]0.4364[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0919[/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](0.0611 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]0.4417[/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=65269&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65269&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 )0.4706-0.10060.1171-0.07270.0042-0.0899-0.01820.00480.0335-0.05830.0018
(p-val)(0 )(0.086 )(0.0478 )(0.2208 )(0.9437 )(0.1301 )(0.7608 )(0.9367 )(0.574 )(0.3273 )(0.9735 )
Estimates ( 2 )0.4705-0.10050.117-0.07270.004-0.0899-0.01840.0050.0332-0.05740
(p-val)(0 )(0.0861 )(0.0479 )(0.2208 )(0.9457 )(0.1302 )(0.7581 )(0.9329 )(0.5743 )(0.2884 )(NA )
Estimates ( 3 )0.4703-0.10.1165-0.0710-0.0882-0.0190.00550.033-0.05740
(p-val)(0 )(0.0854 )(0.0466 )(0.1875 )(NA )(0.1018 )(0.7484 )(0.9259 )(0.5763 )(0.2887 )(NA )
Estimates ( 4 )0.4701-0.10030.1166-0.07110-0.0887-0.016700.0353-0.05790
(p-val)(0 )(0.084 )(0.0462 )(0.1868 )(NA )(0.0981 )(0.7568 )(NA )(0.5122 )(0.2812 )(NA )
Estimates ( 5 )0.4716-0.10040.1175-0.07240-0.0953000.0346-0.05920
(p-val)(0 )(0.0837 )(0.0445 )(0.1774 )(NA )(0.0529 )(NA )(NA )(0.5203 )(0.2693 )(NA )
Estimates ( 6 )0.4715-0.10240.1154-0.07190-0.0922000-0.04510
(p-val)(0 )(0.0775 )(0.0481 )(0.1812 )(NA )(0.06 )(NA )(NA )(NA )(0.3565 )(NA )
Estimates ( 7 )0.4712-0.1020.1181-0.06830-0.092900000
(p-val)(0 )(0.0792 )(0.0433 )(0.2031 )(NA )(0.0584 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0.4662-0.09490.088300-0.098100000
(p-val)(0 )(0.1012 )(0.0994 )(NA )(NA )(0.0454 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )0.429700.051700-0.096400000
(p-val)(0 )(NA )(0.2898 )(NA )(NA )(0.0499 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )0.43640000-0.091900000
(p-val)(0 )(NA )(NA )(NA )(NA )(0.0611 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )0.44170000000000
(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
0.254999840302621
22.5166702853635
8.57874214848219
30.5380400622327
17.7729740433548
4.29660328451066
-11.7262048035173
-3.30798652293415
-1.41667708624885
0.247657500356354
-5.67834610056246
1.53600552840999
-3.68189963319816
-1.84497067144952
-1.62598832707943
-11.4348724786057
-1.22840313013421
-6.73013153804482
-2.16106368491415
-12.8739412864996
-2.13581686556023
-1.83636347109137
0.524233663006612
-1.89733649619819
-4.14146303456312
-3.59223892698503
-5.98233033378943
-0.849127161995682
-0.455123976874972
5.39143463264259
19.5536289958721
16.6214688478747
-1.45263017656555
-0.13519724555033
1.98684345298796
0.555097417713512
-10.7516086659533
13.2376389849969
-8.45183648731557
-1.79467460942459
4.70823928638373
-2.98914103707392
6.81049665449711
-5.59697618202784
-10.2901502049040
-1.58602757032412
-11.0412086540394
-3.98834573079574
1.61113061154782
-14.4445419651168
0.297932534534141
-0.441612795469155
-2.86406892075937
1.33437169459265
-4.18328469203718
-1.54935498335792
-0.707005982709404
0.58148620070483
-3.61345806308287
10.4976951191092
-6.04040473692734
12.0386022570777
-3.95818631956038
-0.143341203379862
0.0750606794928217
-1.19133400154226
-2.27977306663342
3.6130901491083
-1.86631699780918
-1.66141102250305
0.149766724591899
0.727424509982825
1.21533423764100
0.977479471064783
-0.626942685327208
-4.39860565009749
1.30674430538085
-0.579482835884818
-0.794652038780384
2.21438386791053
-2.83629538192824
1.58348956552481
1.92680221548463
-3.32097442386799
5.24074409748175
3.7174475246249
-4.80258781483468
2.05206032016059
1.08405674744148
2.73049016095587
1.27669214182112
-1.24215172696506
1.40338443514185
0.42449534033517
-1.37336281926088
1.80166001574838
-0.215943252558475
1.93568636701076
-0.353454670063172
-0.891889898136697
-0.551906570737287
0.941034328230444
0.0817764314535339
-5.1471809040159
1.10627576030595
1.55023526966676
-1.65359314564711
-7.27396983991969
23.7318506548920
74.5623647005211
-16.3281667443684
-15.6024573162852
-1.03322374817935
-2.21449379007441
-4.03827380461507
-2.60548760237032
-11.8336806307017
6.22960288084147
3.98364132068957
-2.46521487291409
-3.50883103320325
-7.78734822551814
-3.95284934066365
-7.11208628382451
-5.3666410845243
2.92205597237762
-2.7174362393028
-9.11648387531369
7.61902816588815
3.83421595091068
-15.6936525392442
2.01792604099290
11.1773562693202
-4.99931928463258
-6.2726153287594
11.1249167498630
-6.26316237828831
-0.999212670756322
7.78262455305537
-6.97840926440961
2.17952466888556
1.81464753949774
2.87995138095965
1.54733066492196
3.02935948264053
6.70278670554649
-5.05804584971781
8.30519620605492
1.82294524675063
-1.39175341681141
5.98373995753133
-7.12287915184618
-5.06262506382075
-2.11567359819543
-10.4403094083731
9.32878699817763
-2.95103587849115
5.17676053398344
-10.1656455735053
2.07844910315185
4.3215339317494
1.89921067649766
5.81410292822534
-5.61963518654699
0.298520292836429
0.532163456284991
-8.23127749019955
3.66811947765888
1.28202881771858
2.17735427506159
-8.85313788015304
6.40658126489399
-6.59029266900518
0.627261469496034
-5.49586807435674
-5.19879325548715
-0.372228152428249
3.07226069436564
-4.3244707754323
-3.479164051716
5.83449888485455
-0.620923299803678
-6.86689945356522
0.71637594740838
5.32605671924182
-6.03595402711528
4.62263443961638
-4.89465403303899
-6.42155289110676
-1.35756733334981
-7.69498011401262
1.38654791969302
0.226474140252321
10.1654177521431
-4.06273367195564
0.650108079404873
2.11737544694464
0.0370787226546838
0.560498497256106
-4.53590452007919
1.48682287660236
-7.35667207620088
5.54423115798258
4.08896072848907
3.08651338349705
-2.72324345453384
-1.42066891927993
-0.00561163280687538
2.02173319969157
5.90257054673668
72.0097661290944
81.4627900521359
-52.039956180869
0.169371363460129
3.72372328493083
-10.8178081417946
12.7909041296429
-6.63356856581697
-5.6443955124983
3.02573263252668
-0.358521691597502
-0.97680605250224
-2.70383970063597
2.2898542943787
-7.70511852282914
-10.9200446305231
0.461267244372777
-4.04762089567060
-15.0880329294028
9.15832878366166
-5.07270268027906
-1.75779515471208
-7.76474233932191
2.45287620282443
-7.1459725424748
3.65083299926215
-6.50395361131712
3.37643245875108
-7.34266778481725
5.00720156513353
-2.28035751664811
1.99738961452113
18.3779878548818
29.7467003933892
4.26727198985634
22.7368904370354
6.01302269374463
-4.48194015217001
-5.188651538383
-16.7207880582762
2.89540788354674
-7.38475634793508
-5.56491828215911
1.11237520054431
7.79176669639213
-12.0859067400686
-5.68799309066054
-6.96942617304251
1.26579164889341
-6.52308771085194
-4.9245444701408
-8.71373102573358
2.26223089368403
-3.52872590282016
0.333394765700632
-0.835481493523105
4.33374940009446
-4.16990269515185
3.14350623812527
-8.9964525243713
3.02864453407642
0.779331080720112
-3.40814994060548
10.3356883612694
-18.1179373263150
6.64859651053354
18.8543652010515
5.35007154895027
-10.7008939431199
-2.16193069164228
-4.49361999730672
-7.5949535548512
7.63769010906117
0.404038968578107
-10.8169676942279
1.93249950855176
-6.92676237674237
-0.78905737107226
2.69080105282228
-1.24211188822278
-1.85919149887320
-7.37839199631168
-0.419244021698717
6.96816860746458
-8.36478616658138
-2.58927552414059
0.0156284569069385
-0.790910974986161
-2.54837038043070
-5.77952068036836
5.03128147871678
-0.472399169949597
-9.32938803606072
15.4052134741530
-1.85449837408919
-9.51663961899567
7.29738032345762
-6.4102672161066
0.495619676608896
-3.47588585322012
3.46015913670175
-5.50714645255198
18.3786862155776
-14.9678066762659
4.09985223335258
6.65700214569176
-10.2814769096414
2.09451755171369
3.04108184100778
-3.55645591739102
0.886517372014225
-8.62363593341115
-1.14972090307384
11.5525308594941
-2.37045291196983
-4.5532763557369
9.1528343698235
-6.5806384563333
-7.07515931633458
-3.8573415062462
16.4914120619984
-5.91727918159188
0.298262301025773
-3.44592502969721
-5.42352969676529
1.76177163967265
28.5633101449357
-22.8150068426384
11.6205231468911
19.7761342509680
-1.75156390189528
4.7972451561613
-9.4888584061294
17.3774113819018
-9.32546896093953
3.64991486846975
-14.5822980948513
1.72061249266932
1.47800512297982
-4.30929190424285
14.9098252301932
-6.44287864108094
4.67790249762083
-22.1206330690549
7.90857733290909
3.84169446721228
-2.96624492012899
-2.57886054138658
4.05126170559481
-7.12773768860615

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.254999840302621 \tabularnewline
22.5166702853635 \tabularnewline
8.57874214848219 \tabularnewline
30.5380400622327 \tabularnewline
17.7729740433548 \tabularnewline
4.29660328451066 \tabularnewline
-11.7262048035173 \tabularnewline
-3.30798652293415 \tabularnewline
-1.41667708624885 \tabularnewline
0.247657500356354 \tabularnewline
-5.67834610056246 \tabularnewline
1.53600552840999 \tabularnewline
-3.68189963319816 \tabularnewline
-1.84497067144952 \tabularnewline
-1.62598832707943 \tabularnewline
-11.4348724786057 \tabularnewline
-1.22840313013421 \tabularnewline
-6.73013153804482 \tabularnewline
-2.16106368491415 \tabularnewline
-12.8739412864996 \tabularnewline
-2.13581686556023 \tabularnewline
-1.83636347109137 \tabularnewline
0.524233663006612 \tabularnewline
-1.89733649619819 \tabularnewline
-4.14146303456312 \tabularnewline
-3.59223892698503 \tabularnewline
-5.98233033378943 \tabularnewline
-0.849127161995682 \tabularnewline
-0.455123976874972 \tabularnewline
5.39143463264259 \tabularnewline
19.5536289958721 \tabularnewline
16.6214688478747 \tabularnewline
-1.45263017656555 \tabularnewline
-0.13519724555033 \tabularnewline
1.98684345298796 \tabularnewline
0.555097417713512 \tabularnewline
-10.7516086659533 \tabularnewline
13.2376389849969 \tabularnewline
-8.45183648731557 \tabularnewline
-1.79467460942459 \tabularnewline
4.70823928638373 \tabularnewline
-2.98914103707392 \tabularnewline
6.81049665449711 \tabularnewline
-5.59697618202784 \tabularnewline
-10.2901502049040 \tabularnewline
-1.58602757032412 \tabularnewline
-11.0412086540394 \tabularnewline
-3.98834573079574 \tabularnewline
1.61113061154782 \tabularnewline
-14.4445419651168 \tabularnewline
0.297932534534141 \tabularnewline
-0.441612795469155 \tabularnewline
-2.86406892075937 \tabularnewline
1.33437169459265 \tabularnewline
-4.18328469203718 \tabularnewline
-1.54935498335792 \tabularnewline
-0.707005982709404 \tabularnewline
0.58148620070483 \tabularnewline
-3.61345806308287 \tabularnewline
10.4976951191092 \tabularnewline
-6.04040473692734 \tabularnewline
12.0386022570777 \tabularnewline
-3.95818631956038 \tabularnewline
-0.143341203379862 \tabularnewline
0.0750606794928217 \tabularnewline
-1.19133400154226 \tabularnewline
-2.27977306663342 \tabularnewline
3.6130901491083 \tabularnewline
-1.86631699780918 \tabularnewline
-1.66141102250305 \tabularnewline
0.149766724591899 \tabularnewline
0.727424509982825 \tabularnewline
1.21533423764100 \tabularnewline
0.977479471064783 \tabularnewline
-0.626942685327208 \tabularnewline
-4.39860565009749 \tabularnewline
1.30674430538085 \tabularnewline
-0.579482835884818 \tabularnewline
-0.794652038780384 \tabularnewline
2.21438386791053 \tabularnewline
-2.83629538192824 \tabularnewline
1.58348956552481 \tabularnewline
1.92680221548463 \tabularnewline
-3.32097442386799 \tabularnewline
5.24074409748175 \tabularnewline
3.7174475246249 \tabularnewline
-4.80258781483468 \tabularnewline
2.05206032016059 \tabularnewline
1.08405674744148 \tabularnewline
2.73049016095587 \tabularnewline
1.27669214182112 \tabularnewline
-1.24215172696506 \tabularnewline
1.40338443514185 \tabularnewline
0.42449534033517 \tabularnewline
-1.37336281926088 \tabularnewline
1.80166001574838 \tabularnewline
-0.215943252558475 \tabularnewline
1.93568636701076 \tabularnewline
-0.353454670063172 \tabularnewline
-0.891889898136697 \tabularnewline
-0.551906570737287 \tabularnewline
0.941034328230444 \tabularnewline
0.0817764314535339 \tabularnewline
-5.1471809040159 \tabularnewline
1.10627576030595 \tabularnewline
1.55023526966676 \tabularnewline
-1.65359314564711 \tabularnewline
-7.27396983991969 \tabularnewline
23.7318506548920 \tabularnewline
74.5623647005211 \tabularnewline
-16.3281667443684 \tabularnewline
-15.6024573162852 \tabularnewline
-1.03322374817935 \tabularnewline
-2.21449379007441 \tabularnewline
-4.03827380461507 \tabularnewline
-2.60548760237032 \tabularnewline
-11.8336806307017 \tabularnewline
6.22960288084147 \tabularnewline
3.98364132068957 \tabularnewline
-2.46521487291409 \tabularnewline
-3.50883103320325 \tabularnewline
-7.78734822551814 \tabularnewline
-3.95284934066365 \tabularnewline
-7.11208628382451 \tabularnewline
-5.3666410845243 \tabularnewline
2.92205597237762 \tabularnewline
-2.7174362393028 \tabularnewline
-9.11648387531369 \tabularnewline
7.61902816588815 \tabularnewline
3.83421595091068 \tabularnewline
-15.6936525392442 \tabularnewline
2.01792604099290 \tabularnewline
11.1773562693202 \tabularnewline
-4.99931928463258 \tabularnewline
-6.2726153287594 \tabularnewline
11.1249167498630 \tabularnewline
-6.26316237828831 \tabularnewline
-0.999212670756322 \tabularnewline
7.78262455305537 \tabularnewline
-6.97840926440961 \tabularnewline
2.17952466888556 \tabularnewline
1.81464753949774 \tabularnewline
2.87995138095965 \tabularnewline
1.54733066492196 \tabularnewline
3.02935948264053 \tabularnewline
6.70278670554649 \tabularnewline
-5.05804584971781 \tabularnewline
8.30519620605492 \tabularnewline
1.82294524675063 \tabularnewline
-1.39175341681141 \tabularnewline
5.98373995753133 \tabularnewline
-7.12287915184618 \tabularnewline
-5.06262506382075 \tabularnewline
-2.11567359819543 \tabularnewline
-10.4403094083731 \tabularnewline
9.32878699817763 \tabularnewline
-2.95103587849115 \tabularnewline
5.17676053398344 \tabularnewline
-10.1656455735053 \tabularnewline
2.07844910315185 \tabularnewline
4.3215339317494 \tabularnewline
1.89921067649766 \tabularnewline
5.81410292822534 \tabularnewline
-5.61963518654699 \tabularnewline
0.298520292836429 \tabularnewline
0.532163456284991 \tabularnewline
-8.23127749019955 \tabularnewline
3.66811947765888 \tabularnewline
1.28202881771858 \tabularnewline
2.17735427506159 \tabularnewline
-8.85313788015304 \tabularnewline
6.40658126489399 \tabularnewline
-6.59029266900518 \tabularnewline
0.627261469496034 \tabularnewline
-5.49586807435674 \tabularnewline
-5.19879325548715 \tabularnewline
-0.372228152428249 \tabularnewline
3.07226069436564 \tabularnewline
-4.3244707754323 \tabularnewline
-3.479164051716 \tabularnewline
5.83449888485455 \tabularnewline
-0.620923299803678 \tabularnewline
-6.86689945356522 \tabularnewline
0.71637594740838 \tabularnewline
5.32605671924182 \tabularnewline
-6.03595402711528 \tabularnewline
4.62263443961638 \tabularnewline
-4.89465403303899 \tabularnewline
-6.42155289110676 \tabularnewline
-1.35756733334981 \tabularnewline
-7.69498011401262 \tabularnewline
1.38654791969302 \tabularnewline
0.226474140252321 \tabularnewline
10.1654177521431 \tabularnewline
-4.06273367195564 \tabularnewline
0.650108079404873 \tabularnewline
2.11737544694464 \tabularnewline
0.0370787226546838 \tabularnewline
0.560498497256106 \tabularnewline
-4.53590452007919 \tabularnewline
1.48682287660236 \tabularnewline
-7.35667207620088 \tabularnewline
5.54423115798258 \tabularnewline
4.08896072848907 \tabularnewline
3.08651338349705 \tabularnewline
-2.72324345453384 \tabularnewline
-1.42066891927993 \tabularnewline
-0.00561163280687538 \tabularnewline
2.02173319969157 \tabularnewline
5.90257054673668 \tabularnewline
72.0097661290944 \tabularnewline
81.4627900521359 \tabularnewline
-52.039956180869 \tabularnewline
0.169371363460129 \tabularnewline
3.72372328493083 \tabularnewline
-10.8178081417946 \tabularnewline
12.7909041296429 \tabularnewline
-6.63356856581697 \tabularnewline
-5.6443955124983 \tabularnewline
3.02573263252668 \tabularnewline
-0.358521691597502 \tabularnewline
-0.97680605250224 \tabularnewline
-2.70383970063597 \tabularnewline
2.2898542943787 \tabularnewline
-7.70511852282914 \tabularnewline
-10.9200446305231 \tabularnewline
0.461267244372777 \tabularnewline
-4.04762089567060 \tabularnewline
-15.0880329294028 \tabularnewline
9.15832878366166 \tabularnewline
-5.07270268027906 \tabularnewline
-1.75779515471208 \tabularnewline
-7.76474233932191 \tabularnewline
2.45287620282443 \tabularnewline
-7.1459725424748 \tabularnewline
3.65083299926215 \tabularnewline
-6.50395361131712 \tabularnewline
3.37643245875108 \tabularnewline
-7.34266778481725 \tabularnewline
5.00720156513353 \tabularnewline
-2.28035751664811 \tabularnewline
1.99738961452113 \tabularnewline
18.3779878548818 \tabularnewline
29.7467003933892 \tabularnewline
4.26727198985634 \tabularnewline
22.7368904370354 \tabularnewline
6.01302269374463 \tabularnewline
-4.48194015217001 \tabularnewline
-5.188651538383 \tabularnewline
-16.7207880582762 \tabularnewline
2.89540788354674 \tabularnewline
-7.38475634793508 \tabularnewline
-5.56491828215911 \tabularnewline
1.11237520054431 \tabularnewline
7.79176669639213 \tabularnewline
-12.0859067400686 \tabularnewline
-5.68799309066054 \tabularnewline
-6.96942617304251 \tabularnewline
1.26579164889341 \tabularnewline
-6.52308771085194 \tabularnewline
-4.9245444701408 \tabularnewline
-8.71373102573358 \tabularnewline
2.26223089368403 \tabularnewline
-3.52872590282016 \tabularnewline
0.333394765700632 \tabularnewline
-0.835481493523105 \tabularnewline
4.33374940009446 \tabularnewline
-4.16990269515185 \tabularnewline
3.14350623812527 \tabularnewline
-8.9964525243713 \tabularnewline
3.02864453407642 \tabularnewline
0.779331080720112 \tabularnewline
-3.40814994060548 \tabularnewline
10.3356883612694 \tabularnewline
-18.1179373263150 \tabularnewline
6.64859651053354 \tabularnewline
18.8543652010515 \tabularnewline
5.35007154895027 \tabularnewline
-10.7008939431199 \tabularnewline
-2.16193069164228 \tabularnewline
-4.49361999730672 \tabularnewline
-7.5949535548512 \tabularnewline
7.63769010906117 \tabularnewline
0.404038968578107 \tabularnewline
-10.8169676942279 \tabularnewline
1.93249950855176 \tabularnewline
-6.92676237674237 \tabularnewline
-0.78905737107226 \tabularnewline
2.69080105282228 \tabularnewline
-1.24211188822278 \tabularnewline
-1.85919149887320 \tabularnewline
-7.37839199631168 \tabularnewline
-0.419244021698717 \tabularnewline
6.96816860746458 \tabularnewline
-8.36478616658138 \tabularnewline
-2.58927552414059 \tabularnewline
0.0156284569069385 \tabularnewline
-0.790910974986161 \tabularnewline
-2.54837038043070 \tabularnewline
-5.77952068036836 \tabularnewline
5.03128147871678 \tabularnewline
-0.472399169949597 \tabularnewline
-9.32938803606072 \tabularnewline
15.4052134741530 \tabularnewline
-1.85449837408919 \tabularnewline
-9.51663961899567 \tabularnewline
7.29738032345762 \tabularnewline
-6.4102672161066 \tabularnewline
0.495619676608896 \tabularnewline
-3.47588585322012 \tabularnewline
3.46015913670175 \tabularnewline
-5.50714645255198 \tabularnewline
18.3786862155776 \tabularnewline
-14.9678066762659 \tabularnewline
4.09985223335258 \tabularnewline
6.65700214569176 \tabularnewline
-10.2814769096414 \tabularnewline
2.09451755171369 \tabularnewline
3.04108184100778 \tabularnewline
-3.55645591739102 \tabularnewline
0.886517372014225 \tabularnewline
-8.62363593341115 \tabularnewline
-1.14972090307384 \tabularnewline
11.5525308594941 \tabularnewline
-2.37045291196983 \tabularnewline
-4.5532763557369 \tabularnewline
9.1528343698235 \tabularnewline
-6.5806384563333 \tabularnewline
-7.07515931633458 \tabularnewline
-3.8573415062462 \tabularnewline
16.4914120619984 \tabularnewline
-5.91727918159188 \tabularnewline
0.298262301025773 \tabularnewline
-3.44592502969721 \tabularnewline
-5.42352969676529 \tabularnewline
1.76177163967265 \tabularnewline
28.5633101449357 \tabularnewline
-22.8150068426384 \tabularnewline
11.6205231468911 \tabularnewline
19.7761342509680 \tabularnewline
-1.75156390189528 \tabularnewline
4.7972451561613 \tabularnewline
-9.4888584061294 \tabularnewline
17.3774113819018 \tabularnewline
-9.32546896093953 \tabularnewline
3.64991486846975 \tabularnewline
-14.5822980948513 \tabularnewline
1.72061249266932 \tabularnewline
1.47800512297982 \tabularnewline
-4.30929190424285 \tabularnewline
14.9098252301932 \tabularnewline
-6.44287864108094 \tabularnewline
4.67790249762083 \tabularnewline
-22.1206330690549 \tabularnewline
7.90857733290909 \tabularnewline
3.84169446721228 \tabularnewline
-2.96624492012899 \tabularnewline
-2.57886054138658 \tabularnewline
4.05126170559481 \tabularnewline
-7.12773768860615 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65269&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.254999840302621[/C][/ROW]
[ROW][C]22.5166702853635[/C][/ROW]
[ROW][C]8.57874214848219[/C][/ROW]
[ROW][C]30.5380400622327[/C][/ROW]
[ROW][C]17.7729740433548[/C][/ROW]
[ROW][C]4.29660328451066[/C][/ROW]
[ROW][C]-11.7262048035173[/C][/ROW]
[ROW][C]-3.30798652293415[/C][/ROW]
[ROW][C]-1.41667708624885[/C][/ROW]
[ROW][C]0.247657500356354[/C][/ROW]
[ROW][C]-5.67834610056246[/C][/ROW]
[ROW][C]1.53600552840999[/C][/ROW]
[ROW][C]-3.68189963319816[/C][/ROW]
[ROW][C]-1.84497067144952[/C][/ROW]
[ROW][C]-1.62598832707943[/C][/ROW]
[ROW][C]-11.4348724786057[/C][/ROW]
[ROW][C]-1.22840313013421[/C][/ROW]
[ROW][C]-6.73013153804482[/C][/ROW]
[ROW][C]-2.16106368491415[/C][/ROW]
[ROW][C]-12.8739412864996[/C][/ROW]
[ROW][C]-2.13581686556023[/C][/ROW]
[ROW][C]-1.83636347109137[/C][/ROW]
[ROW][C]0.524233663006612[/C][/ROW]
[ROW][C]-1.89733649619819[/C][/ROW]
[ROW][C]-4.14146303456312[/C][/ROW]
[ROW][C]-3.59223892698503[/C][/ROW]
[ROW][C]-5.98233033378943[/C][/ROW]
[ROW][C]-0.849127161995682[/C][/ROW]
[ROW][C]-0.455123976874972[/C][/ROW]
[ROW][C]5.39143463264259[/C][/ROW]
[ROW][C]19.5536289958721[/C][/ROW]
[ROW][C]16.6214688478747[/C][/ROW]
[ROW][C]-1.45263017656555[/C][/ROW]
[ROW][C]-0.13519724555033[/C][/ROW]
[ROW][C]1.98684345298796[/C][/ROW]
[ROW][C]0.555097417713512[/C][/ROW]
[ROW][C]-10.7516086659533[/C][/ROW]
[ROW][C]13.2376389849969[/C][/ROW]
[ROW][C]-8.45183648731557[/C][/ROW]
[ROW][C]-1.79467460942459[/C][/ROW]
[ROW][C]4.70823928638373[/C][/ROW]
[ROW][C]-2.98914103707392[/C][/ROW]
[ROW][C]6.81049665449711[/C][/ROW]
[ROW][C]-5.59697618202784[/C][/ROW]
[ROW][C]-10.2901502049040[/C][/ROW]
[ROW][C]-1.58602757032412[/C][/ROW]
[ROW][C]-11.0412086540394[/C][/ROW]
[ROW][C]-3.98834573079574[/C][/ROW]
[ROW][C]1.61113061154782[/C][/ROW]
[ROW][C]-14.4445419651168[/C][/ROW]
[ROW][C]0.297932534534141[/C][/ROW]
[ROW][C]-0.441612795469155[/C][/ROW]
[ROW][C]-2.86406892075937[/C][/ROW]
[ROW][C]1.33437169459265[/C][/ROW]
[ROW][C]-4.18328469203718[/C][/ROW]
[ROW][C]-1.54935498335792[/C][/ROW]
[ROW][C]-0.707005982709404[/C][/ROW]
[ROW][C]0.58148620070483[/C][/ROW]
[ROW][C]-3.61345806308287[/C][/ROW]
[ROW][C]10.4976951191092[/C][/ROW]
[ROW][C]-6.04040473692734[/C][/ROW]
[ROW][C]12.0386022570777[/C][/ROW]
[ROW][C]-3.95818631956038[/C][/ROW]
[ROW][C]-0.143341203379862[/C][/ROW]
[ROW][C]0.0750606794928217[/C][/ROW]
[ROW][C]-1.19133400154226[/C][/ROW]
[ROW][C]-2.27977306663342[/C][/ROW]
[ROW][C]3.6130901491083[/C][/ROW]
[ROW][C]-1.86631699780918[/C][/ROW]
[ROW][C]-1.66141102250305[/C][/ROW]
[ROW][C]0.149766724591899[/C][/ROW]
[ROW][C]0.727424509982825[/C][/ROW]
[ROW][C]1.21533423764100[/C][/ROW]
[ROW][C]0.977479471064783[/C][/ROW]
[ROW][C]-0.626942685327208[/C][/ROW]
[ROW][C]-4.39860565009749[/C][/ROW]
[ROW][C]1.30674430538085[/C][/ROW]
[ROW][C]-0.579482835884818[/C][/ROW]
[ROW][C]-0.794652038780384[/C][/ROW]
[ROW][C]2.21438386791053[/C][/ROW]
[ROW][C]-2.83629538192824[/C][/ROW]
[ROW][C]1.58348956552481[/C][/ROW]
[ROW][C]1.92680221548463[/C][/ROW]
[ROW][C]-3.32097442386799[/C][/ROW]
[ROW][C]5.24074409748175[/C][/ROW]
[ROW][C]3.7174475246249[/C][/ROW]
[ROW][C]-4.80258781483468[/C][/ROW]
[ROW][C]2.05206032016059[/C][/ROW]
[ROW][C]1.08405674744148[/C][/ROW]
[ROW][C]2.73049016095587[/C][/ROW]
[ROW][C]1.27669214182112[/C][/ROW]
[ROW][C]-1.24215172696506[/C][/ROW]
[ROW][C]1.40338443514185[/C][/ROW]
[ROW][C]0.42449534033517[/C][/ROW]
[ROW][C]-1.37336281926088[/C][/ROW]
[ROW][C]1.80166001574838[/C][/ROW]
[ROW][C]-0.215943252558475[/C][/ROW]
[ROW][C]1.93568636701076[/C][/ROW]
[ROW][C]-0.353454670063172[/C][/ROW]
[ROW][C]-0.891889898136697[/C][/ROW]
[ROW][C]-0.551906570737287[/C][/ROW]
[ROW][C]0.941034328230444[/C][/ROW]
[ROW][C]0.0817764314535339[/C][/ROW]
[ROW][C]-5.1471809040159[/C][/ROW]
[ROW][C]1.10627576030595[/C][/ROW]
[ROW][C]1.55023526966676[/C][/ROW]
[ROW][C]-1.65359314564711[/C][/ROW]
[ROW][C]-7.27396983991969[/C][/ROW]
[ROW][C]23.7318506548920[/C][/ROW]
[ROW][C]74.5623647005211[/C][/ROW]
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[ROW][C]4.05126170559481[/C][/ROW]
[ROW][C]-7.12773768860615[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65269&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65269&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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22.5166702853635
8.57874214848219
30.5380400622327
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5.39143463264259
19.5536289958721
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4.70823928638373
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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')