Free Statistics

of Irreproducible Research!

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 12:26:16 -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/t1260473226932q6vopc63uwr3.htm/, Retrieved Thu, 28 Mar 2024 17:16:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65747, Retrieved Thu, 28 Mar 2024 17:16:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
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] [workshop 10 berek...] [2009-12-10 19:26:16] [78d370e6d5f4594e9982a5085e7604c6] [Current]
Feedback Forum

Post a new message
Dataseries X:
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=65747&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=65747&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65747&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 )0.4686-0.11170.1102-0.07740.0092-0.0887-0.01670.00320.0364-0.05960.0041
(p-val)(0 )(0.0585 )(0.0633 )(0.1935 )(0.8774 )(0.1353 )(0.78 )(0.9575 )(0.5409 )(0.3162 )(0.9401 )
Estimates ( 2 )0.4685-0.11190.1103-0.07760.0096-0.0891-0.015400.0377-0.06010.0044
(p-val)(0 )(0.0574 )(0.0626 )(0.1909 )(0.8708 )(0.1293 )(0.7768 )(NA )(0.4842 )(0.307 )(0.9347 )
Estimates ( 3 )0.4683-0.11170.1103-0.07770.0093-0.0891-0.015500.0374-0.05810
(p-val)(0 )(0.0576 )(0.0626 )(0.1905 )(0.8749 )(0.1293 )(0.7748 )(NA )(0.4868 )(0.2795 )(NA )
Estimates ( 4 )0.4677-0.11070.109-0.07380-0.0853-0.016300.0373-0.05810
(p-val)(0 )(0.0584 )(0.0632 )(0.1714 )(NA )(0.1114 )(0.7618 )(NA )(0.4877 )(0.2796 )(NA )
Estimates ( 5 )0.4692-0.11080.1099-0.07510-0.0918000.0367-0.05940
(p-val)(0 )(0.0581 )(0.061 )(0.1627 )(NA )(0.0622 )(NA )(NA )(0.4953 )(0.2679 )(NA )
Estimates ( 6 )0.469-0.11280.1078-0.07440-0.0885000-0.04440
(p-val)(0 )(0.0535 )(0.0659 )(0.1667 )(NA )(0.0708 )(NA )(NA )(NA )(0.3638 )(NA )
Estimates ( 7 )0.4686-0.11250.1103-0.07090-0.089200000
(p-val)(0 )(0.0545 )(0.0599 )(0.1872 )(NA )(0.0692 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0.463-0.10520.079400-0.094600000
(p-val)(0 )(0.0714 )(0.1399 )(NA )(NA )(0.0538 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )0.4574-0.0709000-0.087900000
(p-val)(0 )(0.1859 )(NA )(NA )(NA )(0.0725 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )0.42840000-0.089500000
(p-val)(0 )(NA )(NA )(NA )(NA )(0.0682 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )0.4330000000000
(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 ) & 0.4686 & -0.1117 & 0.1102 & -0.0774 & 0.0092 & -0.0887 & -0.0167 & 0.0032 & 0.0364 & -0.0596 & 0.0041 \tabularnewline
(p-val) & (0 ) & (0.0585 ) & (0.0633 ) & (0.1935 ) & (0.8774 ) & (0.1353 ) & (0.78 ) & (0.9575 ) & (0.5409 ) & (0.3162 ) & (0.9401 ) \tabularnewline
Estimates ( 2 ) & 0.4685 & -0.1119 & 0.1103 & -0.0776 & 0.0096 & -0.0891 & -0.0154 & 0 & 0.0377 & -0.0601 & 0.0044 \tabularnewline
(p-val) & (0 ) & (0.0574 ) & (0.0626 ) & (0.1909 ) & (0.8708 ) & (0.1293 ) & (0.7768 ) & (NA ) & (0.4842 ) & (0.307 ) & (0.9347 ) \tabularnewline
Estimates ( 3 ) & 0.4683 & -0.1117 & 0.1103 & -0.0777 & 0.0093 & -0.0891 & -0.0155 & 0 & 0.0374 & -0.0581 & 0 \tabularnewline
(p-val) & (0 ) & (0.0576 ) & (0.0626 ) & (0.1905 ) & (0.8749 ) & (0.1293 ) & (0.7748 ) & (NA ) & (0.4868 ) & (0.2795 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0.4677 & -0.1107 & 0.109 & -0.0738 & 0 & -0.0853 & -0.0163 & 0 & 0.0373 & -0.0581 & 0 \tabularnewline
(p-val) & (0 ) & (0.0584 ) & (0.0632 ) & (0.1714 ) & (NA ) & (0.1114 ) & (0.7618 ) & (NA ) & (0.4877 ) & (0.2796 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0.4692 & -0.1108 & 0.1099 & -0.0751 & 0 & -0.0918 & 0 & 0 & 0.0367 & -0.0594 & 0 \tabularnewline
(p-val) & (0 ) & (0.0581 ) & (0.061 ) & (0.1627 ) & (NA ) & (0.0622 ) & (NA ) & (NA ) & (0.4953 ) & (0.2679 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0.469 & -0.1128 & 0.1078 & -0.0744 & 0 & -0.0885 & 0 & 0 & 0 & -0.0444 & 0 \tabularnewline
(p-val) & (0 ) & (0.0535 ) & (0.0659 ) & (0.1667 ) & (NA ) & (0.0708 ) & (NA ) & (NA ) & (NA ) & (0.3638 ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0.4686 & -0.1125 & 0.1103 & -0.0709 & 0 & -0.0892 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.0545 ) & (0.0599 ) & (0.1872 ) & (NA ) & (0.0692 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & 0.463 & -0.1052 & 0.0794 & 0 & 0 & -0.0946 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.0714 ) & (0.1399 ) & (NA ) & (NA ) & (0.0538 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & 0.4574 & -0.0709 & 0 & 0 & 0 & -0.0879 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.1859 ) & (NA ) & (NA ) & (NA ) & (0.0725 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & 0.4284 & 0 & 0 & 0 & 0 & -0.0895 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0682 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & 0.433 & 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=65747&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]0.4686[/C][C]-0.1117[/C][C]0.1102[/C][C]-0.0774[/C][C]0.0092[/C][C]-0.0887[/C][C]-0.0167[/C][C]0.0032[/C][C]0.0364[/C][C]-0.0596[/C][C]0.0041[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0585 )[/C][C](0.0633 )[/C][C](0.1935 )[/C][C](0.8774 )[/C][C](0.1353 )[/C][C](0.78 )[/C][C](0.9575 )[/C][C](0.5409 )[/C][C](0.3162 )[/C][C](0.9401 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4685[/C][C]-0.1119[/C][C]0.1103[/C][C]-0.0776[/C][C]0.0096[/C][C]-0.0891[/C][C]-0.0154[/C][C]0[/C][C]0.0377[/C][C]-0.0601[/C][C]0.0044[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0574 )[/C][C](0.0626 )[/C][C](0.1909 )[/C][C](0.8708 )[/C][C](0.1293 )[/C][C](0.7768 )[/C][C](NA )[/C][C](0.4842 )[/C][C](0.307 )[/C][C](0.9347 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4683[/C][C]-0.1117[/C][C]0.1103[/C][C]-0.0777[/C][C]0.0093[/C][C]-0.0891[/C][C]-0.0155[/C][C]0[/C][C]0.0374[/C][C]-0.0581[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0576 )[/C][C](0.0626 )[/C][C](0.1905 )[/C][C](0.8749 )[/C][C](0.1293 )[/C][C](0.7748 )[/C][C](NA )[/C][C](0.4868 )[/C][C](0.2795 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.4677[/C][C]-0.1107[/C][C]0.109[/C][C]-0.0738[/C][C]0[/C][C]-0.0853[/C][C]-0.0163[/C][C]0[/C][C]0.0373[/C][C]-0.0581[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0584 )[/C][C](0.0632 )[/C][C](0.1714 )[/C][C](NA )[/C][C](0.1114 )[/C][C](0.7618 )[/C][C](NA )[/C][C](0.4877 )[/C][C](0.2796 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.4692[/C][C]-0.1108[/C][C]0.1099[/C][C]-0.0751[/C][C]0[/C][C]-0.0918[/C][C]0[/C][C]0[/C][C]0.0367[/C][C]-0.0594[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0581 )[/C][C](0.061 )[/C][C](0.1627 )[/C][C](NA )[/C][C](0.0622 )[/C][C](NA )[/C][C](NA )[/C][C](0.4953 )[/C][C](0.2679 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.469[/C][C]-0.1128[/C][C]0.1078[/C][C]-0.0744[/C][C]0[/C][C]-0.0885[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0444[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0535 )[/C][C](0.0659 )[/C][C](0.1667 )[/C][C](NA )[/C][C](0.0708 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.3638 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0.4686[/C][C]-0.1125[/C][C]0.1103[/C][C]-0.0709[/C][C]0[/C][C]-0.0892[/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](0.0545 )[/C][C](0.0599 )[/C][C](0.1872 )[/C][C](NA )[/C][C](0.0692 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]0.463[/C][C]-0.1052[/C][C]0.0794[/C][C]0[/C][C]0[/C][C]-0.0946[/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](0.0714 )[/C][C](0.1399 )[/C][C](NA )[/C][C](NA )[/C][C](0.0538 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]0.4574[/C][C]-0.0709[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0879[/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](0.1859 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0725 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]0.4284[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0895[/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](0.0682 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]0.433[/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=65747&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65747&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 )0.4686-0.11170.1102-0.07740.0092-0.0887-0.01670.00320.0364-0.05960.0041
(p-val)(0 )(0.0585 )(0.0633 )(0.1935 )(0.8774 )(0.1353 )(0.78 )(0.9575 )(0.5409 )(0.3162 )(0.9401 )
Estimates ( 2 )0.4685-0.11190.1103-0.07760.0096-0.0891-0.015400.0377-0.06010.0044
(p-val)(0 )(0.0574 )(0.0626 )(0.1909 )(0.8708 )(0.1293 )(0.7768 )(NA )(0.4842 )(0.307 )(0.9347 )
Estimates ( 3 )0.4683-0.11170.1103-0.07770.0093-0.0891-0.015500.0374-0.05810
(p-val)(0 )(0.0576 )(0.0626 )(0.1905 )(0.8749 )(0.1293 )(0.7748 )(NA )(0.4868 )(0.2795 )(NA )
Estimates ( 4 )0.4677-0.11070.109-0.07380-0.0853-0.016300.0373-0.05810
(p-val)(0 )(0.0584 )(0.0632 )(0.1714 )(NA )(0.1114 )(0.7618 )(NA )(0.4877 )(0.2796 )(NA )
Estimates ( 5 )0.4692-0.11080.1099-0.07510-0.0918000.0367-0.05940
(p-val)(0 )(0.0581 )(0.061 )(0.1627 )(NA )(0.0622 )(NA )(NA )(0.4953 )(0.2679 )(NA )
Estimates ( 6 )0.469-0.11280.1078-0.07440-0.0885000-0.04440
(p-val)(0 )(0.0535 )(0.0659 )(0.1667 )(NA )(0.0708 )(NA )(NA )(NA )(0.3638 )(NA )
Estimates ( 7 )0.4686-0.11250.1103-0.07090-0.089200000
(p-val)(0 )(0.0545 )(0.0599 )(0.1872 )(NA )(0.0692 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0.463-0.10520.079400-0.094600000
(p-val)(0 )(0.0714 )(0.1399 )(NA )(NA )(0.0538 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )0.4574-0.0709000-0.087900000
(p-val)(0 )(0.1859 )(NA )(NA )(NA )(0.0725 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )0.42840000-0.089500000
(p-val)(0 )(NA )(NA )(NA )(NA )(0.0682 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )0.4330000000000
(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
0.299899813944721
35.2811604730447
17.9592328190631
4.25761280130688
-12.3057837872709
-4.95932877701646
-1.73482467703338
0.101579479790644
-5.8117305522714
1.39700528279246
-3.71400545178335
-1.87181610248564
-1.63922783774024
-11.4408302569079
-1.29663303160260
-6.76064348483726
-2.21645712341757
-12.9085227967442
-2.24879119749932
-1.87483622200489
0.501960992914348
-1.88394919440526
-4.14053602187255
-3.59096992990078
-5.99502145413459
-0.894593408540516
-0.48096911925785
5.37982563175586
19.6015220834346
16.8072812709019
-1.22307606023321
-0.0404199139211414
2.03059125262331
0.578245801342234
-10.7882738235164
13.0787635879592
-8.43289079709837
-1.86240376191358
4.65554626445237
-2.97792897755278
6.82058385024828
-5.55518255756954
-10.2981928398303
-1.66133890455927
-11.0931957303749
-4.09323834131482
1.51621404362902
-14.4635206354975
0.199466710340118
-0.47148841481058
-2.84943338938521
1.33703181536552
-4.16658507154204
-1.53921038212246
-0.706639572994874
0.579792179116225
-3.60403406861602
10.4694622222023
-5.96026659449012
12.0280326313379
-3.86483309178325
-0.135498868045318
0.0851210014106982
-1.20887880392274
-2.28986392020244
3.5626004525875
-1.85855038694962
-1.67332902761999
0.130170045750816
0.724533557967106
1.22627637942560
0.98796726599943
-0.610791455147705
-4.39149979200985
1.27609922862908
-0.583879722557839
-0.805121514724334
2.20015383274460
-2.82477431817696
1.57570463567461
1.93575965044107
-3.2998595977092
5.22625635723972
3.74905625204863
-4.75332192257423
2.03206669995393
1.08631137200615
2.74615651263429
1.29415877121431
-1.23611822418775
1.40305100559078
0.432501337280144
-1.36927493681804
1.7844816230824
-0.213688627993861
1.93736206960875
-0.340566907944094
-0.890844416659604
-0.555673236289465
0.932240554884402
0.0857763767609185
-5.14956865853986
1.06371878476745
1.54255048961244
-1.64287817728751
-7.28396665002305
23.6687333498406
74.7372105564428
-15.6541032887644
-15.4418927946836
-1.08498975742015
-2.22702124880914
-4.11246882992282
-2.86473172952634
-11.9905139154270
6.08908327860064
3.9793775639759
-2.42875491196742
-3.49615578333641
-7.77983076489204
-3.96912880377783
-7.15281610781506
-5.45045795537828
2.84509336086546
-2.72041512845396
-9.12157204640971
7.5548348477085
3.88791781633785
-15.6189934029384
1.92503937272619
11.1597381909771
-4.8952999391376
-6.27955141890635
11.0575967514000
-6.1728368903041
-1.00619150521487
7.74644672250571
-6.92952810085433
2.16068523479447
1.80341471247363
2.89626016641938
1.58052105147789
3.04091718437314
6.74427988061717
-4.98980128707666
8.29120555697443
1.87488713026232
-1.36125368267943
5.9776038564458
-7.09754576179762
-5.10271245403004
-2.19226614217519
-10.4997992841620
9.21983093053655
-2.93772064335536
5.17368760136662
-10.1097709143187
2.03361307772781
4.34905111891237
1.93347096985161
5.85009022333134
-5.56952656091352
0.294635158497726
0.531999794674562
-8.23996500741947
3.59078435010196
1.26039613289527
2.18701765561383
-8.83185939238376
6.34378517673315
-6.54594517990012
0.592219506071274
-5.50722547649764
-5.25381662645816
-0.418594537830472
3.03947946183575
-4.30249855472772
-3.50245167163428
5.81070196111432
-0.567133497241059
-6.84323931760224
0.667425165708323
5.31875666789261
-5.98606402006419
4.5843412207916
-4.87784205008751
-6.43910745763537
-1.41381816639904
-7.74247015387928
1.31412753747358
0.198411011286083
10.1651539407368
-3.96454142331598
0.668880343863606
2.15355327749432
0.0731417422356913
0.578570922449217
-4.54784329269913
1.44602954079727
-7.36586279091253
5.47297472602682
4.09881219597267
3.12052207810513
-2.6717534693598
-1.41979320574345
0.00186063499529610
2.01822423921547
5.90651919288996
72.0481961356963
82.0574213108985
-51.1254172346511
0.151532218022226
3.71277503681603
-10.8072898152088
12.5330413620274
-6.8934294921279
-5.77032722470125
2.92684909946399
-0.385381783806451
-0.966467258005252
-2.71406979049448
2.30351396242048
-7.65575613859068
-10.9582987628542
0.360538240771518
-4.08650524912258
-15.1299718964809
9.0125854318531
-5.04913658778167
-1.75843513998097
-7.77093339737945
2.40131904801797
-7.10887991189497
3.60132161458603
-6.4863782772855
3.33633896221158
-7.31399886005107
4.95983854889596
-2.24727338621824
1.98939583023656
18.4044326867344
29.9008650024991
4.58747036255113
22.9031428192028
6.26989490695371
-4.32577251724149
-5.20277165960493
-16.8538329741038
2.66394233634895
-7.5323195356558
-5.72639394597297
0.993761858681182
7.76261534163723
-11.9840455271603
-5.72588691960738
-7.00023246289231
1.22474916285483
-6.5226394703322
-4.992506902385
-8.76321553657647
2.18802244641034
-3.52350215325248
0.308185950768802
-0.828242505476112
4.34825790783947
-4.10349504820675
3.14053345530436
-8.96463494251333
2.97207859046677
0.780206794256571
-3.41324056021386
10.3100826049719
-18.0536804963208
6.55031266694061
18.8626044777516
5.50255434913191
-10.5829169287079
-2.21843557194967
-4.49810847893309
-7.63974683596365
7.51148968842728
0.383242910797549
-10.8080346845923
1.86023057528234
-6.93167467356756
-0.824317953079458
2.66241060063521
-1.23203324731423
-1.83965588333433
-7.38272537117012
-0.463128609111266
6.95078991520688
-8.32045944497935
-2.63479917616974
-0.022298352203336
-0.789278016699768
-2.54894930239743
-5.81524416406808
4.9854060781459
-0.441790731179196
-9.31636103765209
15.3380082866587
-1.75443622959006
-9.4706860454374
7.23347524200511
-6.38146150356323
0.478046791089639
-3.51029393770619
3.41258115969964
-5.4816713777015
18.3341506395412
-14.8275179052679
4.04176814242516
6.67593196543066
-10.2238715908848
2.04825131610738
2.99774074688617
-3.52810210318529
0.865963148831497
-8.64318986794069
-1.20792346278773
11.5171676791787
-2.29676598117851
-4.5341706617271
9.12451353578115
-6.49733699563149
-7.08334729388065
-3.94192172818032
16.4225433845667
-5.80626268264768
0.280068958533775
-3.44337971989302
-5.43229659627809
1.73156259078019
28.5310687478789
-22.5966098288552
11.5342818835318
19.8394726231141
-1.55192449139804
4.86892295480357
-9.48461175910086
17.3379053621716
-9.223588219735
3.56661706558577
-14.6036034564538
1.57658758777029
1.45123541641868
-4.34712101206958
14.8718043776173
-6.34128003027337
4.70756805322236
-22.0611492995963
7.75649758042329
3.85237890392079
-2.96304741239544
-2.59510581503173
4.00961708157411
-7.06601516506117

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.299899813944721 \tabularnewline
35.2811604730447 \tabularnewline
17.9592328190631 \tabularnewline
4.25761280130688 \tabularnewline
-12.3057837872709 \tabularnewline
-4.95932877701646 \tabularnewline
-1.73482467703338 \tabularnewline
0.101579479790644 \tabularnewline
-5.8117305522714 \tabularnewline
1.39700528279246 \tabularnewline
-3.71400545178335 \tabularnewline
-1.87181610248564 \tabularnewline
-1.63922783774024 \tabularnewline
-11.4408302569079 \tabularnewline
-1.29663303160260 \tabularnewline
-6.76064348483726 \tabularnewline
-2.21645712341757 \tabularnewline
-12.9085227967442 \tabularnewline
-2.24879119749932 \tabularnewline
-1.87483622200489 \tabularnewline
0.501960992914348 \tabularnewline
-1.88394919440526 \tabularnewline
-4.14053602187255 \tabularnewline
-3.59096992990078 \tabularnewline
-5.99502145413459 \tabularnewline
-0.894593408540516 \tabularnewline
-0.48096911925785 \tabularnewline
5.37982563175586 \tabularnewline
19.6015220834346 \tabularnewline
16.8072812709019 \tabularnewline
-1.22307606023321 \tabularnewline
-0.0404199139211414 \tabularnewline
2.03059125262331 \tabularnewline
0.578245801342234 \tabularnewline
-10.7882738235164 \tabularnewline
13.0787635879592 \tabularnewline
-8.43289079709837 \tabularnewline
-1.86240376191358 \tabularnewline
4.65554626445237 \tabularnewline
-2.97792897755278 \tabularnewline
6.82058385024828 \tabularnewline
-5.55518255756954 \tabularnewline
-10.2981928398303 \tabularnewline
-1.66133890455927 \tabularnewline
-11.0931957303749 \tabularnewline
-4.09323834131482 \tabularnewline
1.51621404362902 \tabularnewline
-14.4635206354975 \tabularnewline
0.199466710340118 \tabularnewline
-0.47148841481058 \tabularnewline
-2.84943338938521 \tabularnewline
1.33703181536552 \tabularnewline
-4.16658507154204 \tabularnewline
-1.53921038212246 \tabularnewline
-0.706639572994874 \tabularnewline
0.579792179116225 \tabularnewline
-3.60403406861602 \tabularnewline
10.4694622222023 \tabularnewline
-5.96026659449012 \tabularnewline
12.0280326313379 \tabularnewline
-3.86483309178325 \tabularnewline
-0.135498868045318 \tabularnewline
0.0851210014106982 \tabularnewline
-1.20887880392274 \tabularnewline
-2.28986392020244 \tabularnewline
3.5626004525875 \tabularnewline
-1.85855038694962 \tabularnewline
-1.67332902761999 \tabularnewline
0.130170045750816 \tabularnewline
0.724533557967106 \tabularnewline
1.22627637942560 \tabularnewline
0.98796726599943 \tabularnewline
-0.610791455147705 \tabularnewline
-4.39149979200985 \tabularnewline
1.27609922862908 \tabularnewline
-0.583879722557839 \tabularnewline
-0.805121514724334 \tabularnewline
2.20015383274460 \tabularnewline
-2.82477431817696 \tabularnewline
1.57570463567461 \tabularnewline
1.93575965044107 \tabularnewline
-3.2998595977092 \tabularnewline
5.22625635723972 \tabularnewline
3.74905625204863 \tabularnewline
-4.75332192257423 \tabularnewline
2.03206669995393 \tabularnewline
1.08631137200615 \tabularnewline
2.74615651263429 \tabularnewline
1.29415877121431 \tabularnewline
-1.23611822418775 \tabularnewline
1.40305100559078 \tabularnewline
0.432501337280144 \tabularnewline
-1.36927493681804 \tabularnewline
1.7844816230824 \tabularnewline
-0.213688627993861 \tabularnewline
1.93736206960875 \tabularnewline
-0.340566907944094 \tabularnewline
-0.890844416659604 \tabularnewline
-0.555673236289465 \tabularnewline
0.932240554884402 \tabularnewline
0.0857763767609185 \tabularnewline
-5.14956865853986 \tabularnewline
1.06371878476745 \tabularnewline
1.54255048961244 \tabularnewline
-1.64287817728751 \tabularnewline
-7.28396665002305 \tabularnewline
23.6687333498406 \tabularnewline
74.7372105564428 \tabularnewline
-15.6541032887644 \tabularnewline
-15.4418927946836 \tabularnewline
-1.08498975742015 \tabularnewline
-2.22702124880914 \tabularnewline
-4.11246882992282 \tabularnewline
-2.86473172952634 \tabularnewline
-11.9905139154270 \tabularnewline
6.08908327860064 \tabularnewline
3.9793775639759 \tabularnewline
-2.42875491196742 \tabularnewline
-3.49615578333641 \tabularnewline
-7.77983076489204 \tabularnewline
-3.96912880377783 \tabularnewline
-7.15281610781506 \tabularnewline
-5.45045795537828 \tabularnewline
2.84509336086546 \tabularnewline
-2.72041512845396 \tabularnewline
-9.12157204640971 \tabularnewline
7.5548348477085 \tabularnewline
3.88791781633785 \tabularnewline
-15.6189934029384 \tabularnewline
1.92503937272619 \tabularnewline
11.1597381909771 \tabularnewline
-4.8952999391376 \tabularnewline
-6.27955141890635 \tabularnewline
11.0575967514000 \tabularnewline
-6.1728368903041 \tabularnewline
-1.00619150521487 \tabularnewline
7.74644672250571 \tabularnewline
-6.92952810085433 \tabularnewline
2.16068523479447 \tabularnewline
1.80341471247363 \tabularnewline
2.89626016641938 \tabularnewline
1.58052105147789 \tabularnewline
3.04091718437314 \tabularnewline
6.74427988061717 \tabularnewline
-4.98980128707666 \tabularnewline
8.29120555697443 \tabularnewline
1.87488713026232 \tabularnewline
-1.36125368267943 \tabularnewline
5.9776038564458 \tabularnewline
-7.09754576179762 \tabularnewline
-5.10271245403004 \tabularnewline
-2.19226614217519 \tabularnewline
-10.4997992841620 \tabularnewline
9.21983093053655 \tabularnewline
-2.93772064335536 \tabularnewline
5.17368760136662 \tabularnewline
-10.1097709143187 \tabularnewline
2.03361307772781 \tabularnewline
4.34905111891237 \tabularnewline
1.93347096985161 \tabularnewline
5.85009022333134 \tabularnewline
-5.56952656091352 \tabularnewline
0.294635158497726 \tabularnewline
0.531999794674562 \tabularnewline
-8.23996500741947 \tabularnewline
3.59078435010196 \tabularnewline
1.26039613289527 \tabularnewline
2.18701765561383 \tabularnewline
-8.83185939238376 \tabularnewline
6.34378517673315 \tabularnewline
-6.54594517990012 \tabularnewline
0.592219506071274 \tabularnewline
-5.50722547649764 \tabularnewline
-5.25381662645816 \tabularnewline
-0.418594537830472 \tabularnewline
3.03947946183575 \tabularnewline
-4.30249855472772 \tabularnewline
-3.50245167163428 \tabularnewline
5.81070196111432 \tabularnewline
-0.567133497241059 \tabularnewline
-6.84323931760224 \tabularnewline
0.667425165708323 \tabularnewline
5.31875666789261 \tabularnewline
-5.98606402006419 \tabularnewline
4.5843412207916 \tabularnewline
-4.87784205008751 \tabularnewline
-6.43910745763537 \tabularnewline
-1.41381816639904 \tabularnewline
-7.74247015387928 \tabularnewline
1.31412753747358 \tabularnewline
0.198411011286083 \tabularnewline
10.1651539407368 \tabularnewline
-3.96454142331598 \tabularnewline
0.668880343863606 \tabularnewline
2.15355327749432 \tabularnewline
0.0731417422356913 \tabularnewline
0.578570922449217 \tabularnewline
-4.54784329269913 \tabularnewline
1.44602954079727 \tabularnewline
-7.36586279091253 \tabularnewline
5.47297472602682 \tabularnewline
4.09881219597267 \tabularnewline
3.12052207810513 \tabularnewline
-2.6717534693598 \tabularnewline
-1.41979320574345 \tabularnewline
0.00186063499529610 \tabularnewline
2.01822423921547 \tabularnewline
5.90651919288996 \tabularnewline
72.0481961356963 \tabularnewline
82.0574213108985 \tabularnewline
-51.1254172346511 \tabularnewline
0.151532218022226 \tabularnewline
3.71277503681603 \tabularnewline
-10.8072898152088 \tabularnewline
12.5330413620274 \tabularnewline
-6.8934294921279 \tabularnewline
-5.77032722470125 \tabularnewline
2.92684909946399 \tabularnewline
-0.385381783806451 \tabularnewline
-0.966467258005252 \tabularnewline
-2.71406979049448 \tabularnewline
2.30351396242048 \tabularnewline
-7.65575613859068 \tabularnewline
-10.9582987628542 \tabularnewline
0.360538240771518 \tabularnewline
-4.08650524912258 \tabularnewline
-15.1299718964809 \tabularnewline
9.0125854318531 \tabularnewline
-5.04913658778167 \tabularnewline
-1.75843513998097 \tabularnewline
-7.77093339737945 \tabularnewline
2.40131904801797 \tabularnewline
-7.10887991189497 \tabularnewline
3.60132161458603 \tabularnewline
-6.4863782772855 \tabularnewline
3.33633896221158 \tabularnewline
-7.31399886005107 \tabularnewline
4.95983854889596 \tabularnewline
-2.24727338621824 \tabularnewline
1.98939583023656 \tabularnewline
18.4044326867344 \tabularnewline
29.9008650024991 \tabularnewline
4.58747036255113 \tabularnewline
22.9031428192028 \tabularnewline
6.26989490695371 \tabularnewline
-4.32577251724149 \tabularnewline
-5.20277165960493 \tabularnewline
-16.8538329741038 \tabularnewline
2.66394233634895 \tabularnewline
-7.5323195356558 \tabularnewline
-5.72639394597297 \tabularnewline
0.993761858681182 \tabularnewline
7.76261534163723 \tabularnewline
-11.9840455271603 \tabularnewline
-5.72588691960738 \tabularnewline
-7.00023246289231 \tabularnewline
1.22474916285483 \tabularnewline
-6.5226394703322 \tabularnewline
-4.992506902385 \tabularnewline
-8.76321553657647 \tabularnewline
2.18802244641034 \tabularnewline
-3.52350215325248 \tabularnewline
0.308185950768802 \tabularnewline
-0.828242505476112 \tabularnewline
4.34825790783947 \tabularnewline
-4.10349504820675 \tabularnewline
3.14053345530436 \tabularnewline
-8.96463494251333 \tabularnewline
2.97207859046677 \tabularnewline
0.780206794256571 \tabularnewline
-3.41324056021386 \tabularnewline
10.3100826049719 \tabularnewline
-18.0536804963208 \tabularnewline
6.55031266694061 \tabularnewline
18.8626044777516 \tabularnewline
5.50255434913191 \tabularnewline
-10.5829169287079 \tabularnewline
-2.21843557194967 \tabularnewline
-4.49810847893309 \tabularnewline
-7.63974683596365 \tabularnewline
7.51148968842728 \tabularnewline
0.383242910797549 \tabularnewline
-10.8080346845923 \tabularnewline
1.86023057528234 \tabularnewline
-6.93167467356756 \tabularnewline
-0.824317953079458 \tabularnewline
2.66241060063521 \tabularnewline
-1.23203324731423 \tabularnewline
-1.83965588333433 \tabularnewline
-7.38272537117012 \tabularnewline
-0.463128609111266 \tabularnewline
6.95078991520688 \tabularnewline
-8.32045944497935 \tabularnewline
-2.63479917616974 \tabularnewline
-0.022298352203336 \tabularnewline
-0.789278016699768 \tabularnewline
-2.54894930239743 \tabularnewline
-5.81524416406808 \tabularnewline
4.9854060781459 \tabularnewline
-0.441790731179196 \tabularnewline
-9.31636103765209 \tabularnewline
15.3380082866587 \tabularnewline
-1.75443622959006 \tabularnewline
-9.4706860454374 \tabularnewline
7.23347524200511 \tabularnewline
-6.38146150356323 \tabularnewline
0.478046791089639 \tabularnewline
-3.51029393770619 \tabularnewline
3.41258115969964 \tabularnewline
-5.4816713777015 \tabularnewline
18.3341506395412 \tabularnewline
-14.8275179052679 \tabularnewline
4.04176814242516 \tabularnewline
6.67593196543066 \tabularnewline
-10.2238715908848 \tabularnewline
2.04825131610738 \tabularnewline
2.99774074688617 \tabularnewline
-3.52810210318529 \tabularnewline
0.865963148831497 \tabularnewline
-8.64318986794069 \tabularnewline
-1.20792346278773 \tabularnewline
11.5171676791787 \tabularnewline
-2.29676598117851 \tabularnewline
-4.5341706617271 \tabularnewline
9.12451353578115 \tabularnewline
-6.49733699563149 \tabularnewline
-7.08334729388065 \tabularnewline
-3.94192172818032 \tabularnewline
16.4225433845667 \tabularnewline
-5.80626268264768 \tabularnewline
0.280068958533775 \tabularnewline
-3.44337971989302 \tabularnewline
-5.43229659627809 \tabularnewline
1.73156259078019 \tabularnewline
28.5310687478789 \tabularnewline
-22.5966098288552 \tabularnewline
11.5342818835318 \tabularnewline
19.8394726231141 \tabularnewline
-1.55192449139804 \tabularnewline
4.86892295480357 \tabularnewline
-9.48461175910086 \tabularnewline
17.3379053621716 \tabularnewline
-9.223588219735 \tabularnewline
3.56661706558577 \tabularnewline
-14.6036034564538 \tabularnewline
1.57658758777029 \tabularnewline
1.45123541641868 \tabularnewline
-4.34712101206958 \tabularnewline
14.8718043776173 \tabularnewline
-6.34128003027337 \tabularnewline
4.70756805322236 \tabularnewline
-22.0611492995963 \tabularnewline
7.75649758042329 \tabularnewline
3.85237890392079 \tabularnewline
-2.96304741239544 \tabularnewline
-2.59510581503173 \tabularnewline
4.00961708157411 \tabularnewline
-7.06601516506117 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65747&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.299899813944721[/C][/ROW]
[ROW][C]35.2811604730447[/C][/ROW]
[ROW][C]17.9592328190631[/C][/ROW]
[ROW][C]4.25761280130688[/C][/ROW]
[ROW][C]-12.3057837872709[/C][/ROW]
[ROW][C]-4.95932877701646[/C][/ROW]
[ROW][C]-1.73482467703338[/C][/ROW]
[ROW][C]0.101579479790644[/C][/ROW]
[ROW][C]-5.8117305522714[/C][/ROW]
[ROW][C]1.39700528279246[/C][/ROW]
[ROW][C]-3.71400545178335[/C][/ROW]
[ROW][C]-1.87181610248564[/C][/ROW]
[ROW][C]-1.63922783774024[/C][/ROW]
[ROW][C]-11.4408302569079[/C][/ROW]
[ROW][C]-1.29663303160260[/C][/ROW]
[ROW][C]-6.76064348483726[/C][/ROW]
[ROW][C]-2.21645712341757[/C][/ROW]
[ROW][C]-12.9085227967442[/C][/ROW]
[ROW][C]-2.24879119749932[/C][/ROW]
[ROW][C]-1.87483622200489[/C][/ROW]
[ROW][C]0.501960992914348[/C][/ROW]
[ROW][C]-1.88394919440526[/C][/ROW]
[ROW][C]-4.14053602187255[/C][/ROW]
[ROW][C]-3.59096992990078[/C][/ROW]
[ROW][C]-5.99502145413459[/C][/ROW]
[ROW][C]-0.894593408540516[/C][/ROW]
[ROW][C]-0.48096911925785[/C][/ROW]
[ROW][C]5.37982563175586[/C][/ROW]
[ROW][C]19.6015220834346[/C][/ROW]
[ROW][C]16.8072812709019[/C][/ROW]
[ROW][C]-1.22307606023321[/C][/ROW]
[ROW][C]-0.0404199139211414[/C][/ROW]
[ROW][C]2.03059125262331[/C][/ROW]
[ROW][C]0.578245801342234[/C][/ROW]
[ROW][C]-10.7882738235164[/C][/ROW]
[ROW][C]13.0787635879592[/C][/ROW]
[ROW][C]-8.43289079709837[/C][/ROW]
[ROW][C]-1.86240376191358[/C][/ROW]
[ROW][C]4.65554626445237[/C][/ROW]
[ROW][C]-2.97792897755278[/C][/ROW]
[ROW][C]6.82058385024828[/C][/ROW]
[ROW][C]-5.55518255756954[/C][/ROW]
[ROW][C]-10.2981928398303[/C][/ROW]
[ROW][C]-1.66133890455927[/C][/ROW]
[ROW][C]-11.0931957303749[/C][/ROW]
[ROW][C]-4.09323834131482[/C][/ROW]
[ROW][C]1.51621404362902[/C][/ROW]
[ROW][C]-14.4635206354975[/C][/ROW]
[ROW][C]0.199466710340118[/C][/ROW]
[ROW][C]-0.47148841481058[/C][/ROW]
[ROW][C]-2.84943338938521[/C][/ROW]
[ROW][C]1.33703181536552[/C][/ROW]
[ROW][C]-4.16658507154204[/C][/ROW]
[ROW][C]-1.53921038212246[/C][/ROW]
[ROW][C]-0.706639572994874[/C][/ROW]
[ROW][C]0.579792179116225[/C][/ROW]
[ROW][C]-3.60403406861602[/C][/ROW]
[ROW][C]10.4694622222023[/C][/ROW]
[ROW][C]-5.96026659449012[/C][/ROW]
[ROW][C]12.0280326313379[/C][/ROW]
[ROW][C]-3.86483309178325[/C][/ROW]
[ROW][C]-0.135498868045318[/C][/ROW]
[ROW][C]0.0851210014106982[/C][/ROW]
[ROW][C]-1.20887880392274[/C][/ROW]
[ROW][C]-2.28986392020244[/C][/ROW]
[ROW][C]3.5626004525875[/C][/ROW]
[ROW][C]-1.85855038694962[/C][/ROW]
[ROW][C]-1.67332902761999[/C][/ROW]
[ROW][C]0.130170045750816[/C][/ROW]
[ROW][C]0.724533557967106[/C][/ROW]
[ROW][C]1.22627637942560[/C][/ROW]
[ROW][C]0.98796726599943[/C][/ROW]
[ROW][C]-0.610791455147705[/C][/ROW]
[ROW][C]-4.39149979200985[/C][/ROW]
[ROW][C]1.27609922862908[/C][/ROW]
[ROW][C]-0.583879722557839[/C][/ROW]
[ROW][C]-0.805121514724334[/C][/ROW]
[ROW][C]2.20015383274460[/C][/ROW]
[ROW][C]-2.82477431817696[/C][/ROW]
[ROW][C]1.57570463567461[/C][/ROW]
[ROW][C]1.93575965044107[/C][/ROW]
[ROW][C]-3.2998595977092[/C][/ROW]
[ROW][C]5.22625635723972[/C][/ROW]
[ROW][C]3.74905625204863[/C][/ROW]
[ROW][C]-4.75332192257423[/C][/ROW]
[ROW][C]2.03206669995393[/C][/ROW]
[ROW][C]1.08631137200615[/C][/ROW]
[ROW][C]2.74615651263429[/C][/ROW]
[ROW][C]1.29415877121431[/C][/ROW]
[ROW][C]-1.23611822418775[/C][/ROW]
[ROW][C]1.40305100559078[/C][/ROW]
[ROW][C]0.432501337280144[/C][/ROW]
[ROW][C]-1.36927493681804[/C][/ROW]
[ROW][C]1.7844816230824[/C][/ROW]
[ROW][C]-0.213688627993861[/C][/ROW]
[ROW][C]1.93736206960875[/C][/ROW]
[ROW][C]-0.340566907944094[/C][/ROW]
[ROW][C]-0.890844416659604[/C][/ROW]
[ROW][C]-0.555673236289465[/C][/ROW]
[ROW][C]0.932240554884402[/C][/ROW]
[ROW][C]0.0857763767609185[/C][/ROW]
[ROW][C]-5.14956865853986[/C][/ROW]
[ROW][C]1.06371878476745[/C][/ROW]
[ROW][C]1.54255048961244[/C][/ROW]
[ROW][C]-1.64287817728751[/C][/ROW]
[ROW][C]-7.28396665002305[/C][/ROW]
[ROW][C]23.6687333498406[/C][/ROW]
[ROW][C]74.7372105564428[/C][/ROW]
[ROW][C]-15.6541032887644[/C][/ROW]
[ROW][C]-15.4418927946836[/C][/ROW]
[ROW][C]-1.08498975742015[/C][/ROW]
[ROW][C]-2.22702124880914[/C][/ROW]
[ROW][C]-4.11246882992282[/C][/ROW]
[ROW][C]-2.86473172952634[/C][/ROW]
[ROW][C]-11.9905139154270[/C][/ROW]
[ROW][C]6.08908327860064[/C][/ROW]
[ROW][C]3.9793775639759[/C][/ROW]
[ROW][C]-2.42875491196742[/C][/ROW]
[ROW][C]-3.49615578333641[/C][/ROW]
[ROW][C]-7.77983076489204[/C][/ROW]
[ROW][C]-3.96912880377783[/C][/ROW]
[ROW][C]-7.15281610781506[/C][/ROW]
[ROW][C]-5.45045795537828[/C][/ROW]
[ROW][C]2.84509336086546[/C][/ROW]
[ROW][C]-2.72041512845396[/C][/ROW]
[ROW][C]-9.12157204640971[/C][/ROW]
[ROW][C]7.5548348477085[/C][/ROW]
[ROW][C]3.88791781633785[/C][/ROW]
[ROW][C]-15.6189934029384[/C][/ROW]
[ROW][C]1.92503937272619[/C][/ROW]
[ROW][C]11.1597381909771[/C][/ROW]
[ROW][C]-4.8952999391376[/C][/ROW]
[ROW][C]-6.27955141890635[/C][/ROW]
[ROW][C]11.0575967514000[/C][/ROW]
[ROW][C]-6.1728368903041[/C][/ROW]
[ROW][C]-1.00619150521487[/C][/ROW]
[ROW][C]7.74644672250571[/C][/ROW]
[ROW][C]-6.92952810085433[/C][/ROW]
[ROW][C]2.16068523479447[/C][/ROW]
[ROW][C]1.80341471247363[/C][/ROW]
[ROW][C]2.89626016641938[/C][/ROW]
[ROW][C]1.58052105147789[/C][/ROW]
[ROW][C]3.04091718437314[/C][/ROW]
[ROW][C]6.74427988061717[/C][/ROW]
[ROW][C]-4.98980128707666[/C][/ROW]
[ROW][C]8.29120555697443[/C][/ROW]
[ROW][C]1.87488713026232[/C][/ROW]
[ROW][C]-1.36125368267943[/C][/ROW]
[ROW][C]5.9776038564458[/C][/ROW]
[ROW][C]-7.09754576179762[/C][/ROW]
[ROW][C]-5.10271245403004[/C][/ROW]
[ROW][C]-2.19226614217519[/C][/ROW]
[ROW][C]-10.4997992841620[/C][/ROW]
[ROW][C]9.21983093053655[/C][/ROW]
[ROW][C]-2.93772064335536[/C][/ROW]
[ROW][C]5.17368760136662[/C][/ROW]
[ROW][C]-10.1097709143187[/C][/ROW]
[ROW][C]2.03361307772781[/C][/ROW]
[ROW][C]4.34905111891237[/C][/ROW]
[ROW][C]1.93347096985161[/C][/ROW]
[ROW][C]5.85009022333134[/C][/ROW]
[ROW][C]-5.56952656091352[/C][/ROW]
[ROW][C]0.294635158497726[/C][/ROW]
[ROW][C]0.531999794674562[/C][/ROW]
[ROW][C]-8.23996500741947[/C][/ROW]
[ROW][C]3.59078435010196[/C][/ROW]
[ROW][C]1.26039613289527[/C][/ROW]
[ROW][C]2.18701765561383[/C][/ROW]
[ROW][C]-8.83185939238376[/C][/ROW]
[ROW][C]6.34378517673315[/C][/ROW]
[ROW][C]-6.54594517990012[/C][/ROW]
[ROW][C]0.592219506071274[/C][/ROW]
[ROW][C]-5.50722547649764[/C][/ROW]
[ROW][C]-5.25381662645816[/C][/ROW]
[ROW][C]-0.418594537830472[/C][/ROW]
[ROW][C]3.03947946183575[/C][/ROW]
[ROW][C]-4.30249855472772[/C][/ROW]
[ROW][C]-3.50245167163428[/C][/ROW]
[ROW][C]5.81070196111432[/C][/ROW]
[ROW][C]-0.567133497241059[/C][/ROW]
[ROW][C]-6.84323931760224[/C][/ROW]
[ROW][C]0.667425165708323[/C][/ROW]
[ROW][C]5.31875666789261[/C][/ROW]
[ROW][C]-5.98606402006419[/C][/ROW]
[ROW][C]4.5843412207916[/C][/ROW]
[ROW][C]-4.87784205008751[/C][/ROW]
[ROW][C]-6.43910745763537[/C][/ROW]
[ROW][C]-1.41381816639904[/C][/ROW]
[ROW][C]-7.74247015387928[/C][/ROW]
[ROW][C]1.31412753747358[/C][/ROW]
[ROW][C]0.198411011286083[/C][/ROW]
[ROW][C]10.1651539407368[/C][/ROW]
[ROW][C]-3.96454142331598[/C][/ROW]
[ROW][C]0.668880343863606[/C][/ROW]
[ROW][C]2.15355327749432[/C][/ROW]
[ROW][C]0.0731417422356913[/C][/ROW]
[ROW][C]0.578570922449217[/C][/ROW]
[ROW][C]-4.54784329269913[/C][/ROW]
[ROW][C]1.44602954079727[/C][/ROW]
[ROW][C]-7.36586279091253[/C][/ROW]
[ROW][C]5.47297472602682[/C][/ROW]
[ROW][C]4.09881219597267[/C][/ROW]
[ROW][C]3.12052207810513[/C][/ROW]
[ROW][C]-2.6717534693598[/C][/ROW]
[ROW][C]-1.41979320574345[/C][/ROW]
[ROW][C]0.00186063499529610[/C][/ROW]
[ROW][C]2.01822423921547[/C][/ROW]
[ROW][C]5.90651919288996[/C][/ROW]
[ROW][C]72.0481961356963[/C][/ROW]
[ROW][C]82.0574213108985[/C][/ROW]
[ROW][C]-51.1254172346511[/C][/ROW]
[ROW][C]0.151532218022226[/C][/ROW]
[ROW][C]3.71277503681603[/C][/ROW]
[ROW][C]-10.8072898152088[/C][/ROW]
[ROW][C]12.5330413620274[/C][/ROW]
[ROW][C]-6.8934294921279[/C][/ROW]
[ROW][C]-5.77032722470125[/C][/ROW]
[ROW][C]2.92684909946399[/C][/ROW]
[ROW][C]-0.385381783806451[/C][/ROW]
[ROW][C]-0.966467258005252[/C][/ROW]
[ROW][C]-2.71406979049448[/C][/ROW]
[ROW][C]2.30351396242048[/C][/ROW]
[ROW][C]-7.65575613859068[/C][/ROW]
[ROW][C]-10.9582987628542[/C][/ROW]
[ROW][C]0.360538240771518[/C][/ROW]
[ROW][C]-4.08650524912258[/C][/ROW]
[ROW][C]-15.1299718964809[/C][/ROW]
[ROW][C]9.0125854318531[/C][/ROW]
[ROW][C]-5.04913658778167[/C][/ROW]
[ROW][C]-1.75843513998097[/C][/ROW]
[ROW][C]-7.77093339737945[/C][/ROW]
[ROW][C]2.40131904801797[/C][/ROW]
[ROW][C]-7.10887991189497[/C][/ROW]
[ROW][C]3.60132161458603[/C][/ROW]
[ROW][C]-6.4863782772855[/C][/ROW]
[ROW][C]3.33633896221158[/C][/ROW]
[ROW][C]-7.31399886005107[/C][/ROW]
[ROW][C]4.95983854889596[/C][/ROW]
[ROW][C]-2.24727338621824[/C][/ROW]
[ROW][C]1.98939583023656[/C][/ROW]
[ROW][C]18.4044326867344[/C][/ROW]
[ROW][C]29.9008650024991[/C][/ROW]
[ROW][C]4.58747036255113[/C][/ROW]
[ROW][C]22.9031428192028[/C][/ROW]
[ROW][C]6.26989490695371[/C][/ROW]
[ROW][C]-4.32577251724149[/C][/ROW]
[ROW][C]-5.20277165960493[/C][/ROW]
[ROW][C]-16.8538329741038[/C][/ROW]
[ROW][C]2.66394233634895[/C][/ROW]
[ROW][C]-7.5323195356558[/C][/ROW]
[ROW][C]-5.72639394597297[/C][/ROW]
[ROW][C]0.993761858681182[/C][/ROW]
[ROW][C]7.76261534163723[/C][/ROW]
[ROW][C]-11.9840455271603[/C][/ROW]
[ROW][C]-5.72588691960738[/C][/ROW]
[ROW][C]-7.00023246289231[/C][/ROW]
[ROW][C]1.22474916285483[/C][/ROW]
[ROW][C]-6.5226394703322[/C][/ROW]
[ROW][C]-4.992506902385[/C][/ROW]
[ROW][C]-8.76321553657647[/C][/ROW]
[ROW][C]2.18802244641034[/C][/ROW]
[ROW][C]-3.52350215325248[/C][/ROW]
[ROW][C]0.308185950768802[/C][/ROW]
[ROW][C]-0.828242505476112[/C][/ROW]
[ROW][C]4.34825790783947[/C][/ROW]
[ROW][C]-4.10349504820675[/C][/ROW]
[ROW][C]3.14053345530436[/C][/ROW]
[ROW][C]-8.96463494251333[/C][/ROW]
[ROW][C]2.97207859046677[/C][/ROW]
[ROW][C]0.780206794256571[/C][/ROW]
[ROW][C]-3.41324056021386[/C][/ROW]
[ROW][C]10.3100826049719[/C][/ROW]
[ROW][C]-18.0536804963208[/C][/ROW]
[ROW][C]6.55031266694061[/C][/ROW]
[ROW][C]18.8626044777516[/C][/ROW]
[ROW][C]5.50255434913191[/C][/ROW]
[ROW][C]-10.5829169287079[/C][/ROW]
[ROW][C]-2.21843557194967[/C][/ROW]
[ROW][C]-4.49810847893309[/C][/ROW]
[ROW][C]-7.63974683596365[/C][/ROW]
[ROW][C]7.51148968842728[/C][/ROW]
[ROW][C]0.383242910797549[/C][/ROW]
[ROW][C]-10.8080346845923[/C][/ROW]
[ROW][C]1.86023057528234[/C][/ROW]
[ROW][C]-6.93167467356756[/C][/ROW]
[ROW][C]-0.824317953079458[/C][/ROW]
[ROW][C]2.66241060063521[/C][/ROW]
[ROW][C]-1.23203324731423[/C][/ROW]
[ROW][C]-1.83965588333433[/C][/ROW]
[ROW][C]-7.38272537117012[/C][/ROW]
[ROW][C]-0.463128609111266[/C][/ROW]
[ROW][C]6.95078991520688[/C][/ROW]
[ROW][C]-8.32045944497935[/C][/ROW]
[ROW][C]-2.63479917616974[/C][/ROW]
[ROW][C]-0.022298352203336[/C][/ROW]
[ROW][C]-0.789278016699768[/C][/ROW]
[ROW][C]-2.54894930239743[/C][/ROW]
[ROW][C]-5.81524416406808[/C][/ROW]
[ROW][C]4.9854060781459[/C][/ROW]
[ROW][C]-0.441790731179196[/C][/ROW]
[ROW][C]-9.31636103765209[/C][/ROW]
[ROW][C]15.3380082866587[/C][/ROW]
[ROW][C]-1.75443622959006[/C][/ROW]
[ROW][C]-9.4706860454374[/C][/ROW]
[ROW][C]7.23347524200511[/C][/ROW]
[ROW][C]-6.38146150356323[/C][/ROW]
[ROW][C]0.478046791089639[/C][/ROW]
[ROW][C]-3.51029393770619[/C][/ROW]
[ROW][C]3.41258115969964[/C][/ROW]
[ROW][C]-5.4816713777015[/C][/ROW]
[ROW][C]18.3341506395412[/C][/ROW]
[ROW][C]-14.8275179052679[/C][/ROW]
[ROW][C]4.04176814242516[/C][/ROW]
[ROW][C]6.67593196543066[/C][/ROW]
[ROW][C]-10.2238715908848[/C][/ROW]
[ROW][C]2.04825131610738[/C][/ROW]
[ROW][C]2.99774074688617[/C][/ROW]
[ROW][C]-3.52810210318529[/C][/ROW]
[ROW][C]0.865963148831497[/C][/ROW]
[ROW][C]-8.64318986794069[/C][/ROW]
[ROW][C]-1.20792346278773[/C][/ROW]
[ROW][C]11.5171676791787[/C][/ROW]
[ROW][C]-2.29676598117851[/C][/ROW]
[ROW][C]-4.5341706617271[/C][/ROW]
[ROW][C]9.12451353578115[/C][/ROW]
[ROW][C]-6.49733699563149[/C][/ROW]
[ROW][C]-7.08334729388065[/C][/ROW]
[ROW][C]-3.94192172818032[/C][/ROW]
[ROW][C]16.4225433845667[/C][/ROW]
[ROW][C]-5.80626268264768[/C][/ROW]
[ROW][C]0.280068958533775[/C][/ROW]
[ROW][C]-3.44337971989302[/C][/ROW]
[ROW][C]-5.43229659627809[/C][/ROW]
[ROW][C]1.73156259078019[/C][/ROW]
[ROW][C]28.5310687478789[/C][/ROW]
[ROW][C]-22.5966098288552[/C][/ROW]
[ROW][C]11.5342818835318[/C][/ROW]
[ROW][C]19.8394726231141[/C][/ROW]
[ROW][C]-1.55192449139804[/C][/ROW]
[ROW][C]4.86892295480357[/C][/ROW]
[ROW][C]-9.48461175910086[/C][/ROW]
[ROW][C]17.3379053621716[/C][/ROW]
[ROW][C]-9.223588219735[/C][/ROW]
[ROW][C]3.56661706558577[/C][/ROW]
[ROW][C]-14.6036034564538[/C][/ROW]
[ROW][C]1.57658758777029[/C][/ROW]
[ROW][C]1.45123541641868[/C][/ROW]
[ROW][C]-4.34712101206958[/C][/ROW]
[ROW][C]14.8718043776173[/C][/ROW]
[ROW][C]-6.34128003027337[/C][/ROW]
[ROW][C]4.70756805322236[/C][/ROW]
[ROW][C]-22.0611492995963[/C][/ROW]
[ROW][C]7.75649758042329[/C][/ROW]
[ROW][C]3.85237890392079[/C][/ROW]
[ROW][C]-2.96304741239544[/C][/ROW]
[ROW][C]-2.59510581503173[/C][/ROW]
[ROW][C]4.00961708157411[/C][/ROW]
[ROW][C]-7.06601516506117[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65747&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65747&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
0.299899813944721
35.2811604730447
17.9592328190631
4.25761280130688
-12.3057837872709
-4.95932877701646
-1.73482467703338
0.101579479790644
-5.8117305522714
1.39700528279246
-3.71400545178335
-1.87181610248564
-1.63922783774024
-11.4408302569079
-1.29663303160260
-6.76064348483726
-2.21645712341757
-12.9085227967442
-2.24879119749932
-1.87483622200489
0.501960992914348
-1.88394919440526
-4.14053602187255
-3.59096992990078
-5.99502145413459
-0.894593408540516
-0.48096911925785
5.37982563175586
19.6015220834346
16.8072812709019
-1.22307606023321
-0.0404199139211414
2.03059125262331
0.578245801342234
-10.7882738235164
13.0787635879592
-8.43289079709837
-1.86240376191358
4.65554626445237
-2.97792897755278
6.82058385024828
-5.55518255756954
-10.2981928398303
-1.66133890455927
-11.0931957303749
-4.09323834131482
1.51621404362902
-14.4635206354975
0.199466710340118
-0.47148841481058
-2.84943338938521
1.33703181536552
-4.16658507154204
-1.53921038212246
-0.706639572994874
0.579792179116225
-3.60403406861602
10.4694622222023
-5.96026659449012
12.0280326313379
-3.86483309178325
-0.135498868045318
0.0851210014106982
-1.20887880392274
-2.28986392020244
3.5626004525875
-1.85855038694962
-1.67332902761999
0.130170045750816
0.724533557967106
1.22627637942560
0.98796726599943
-0.610791455147705
-4.39149979200985
1.27609922862908
-0.583879722557839
-0.805121514724334
2.20015383274460
-2.82477431817696
1.57570463567461
1.93575965044107
-3.2998595977092
5.22625635723972
3.74905625204863
-4.75332192257423
2.03206669995393
1.08631137200615
2.74615651263429
1.29415877121431
-1.23611822418775
1.40305100559078
0.432501337280144
-1.36927493681804
1.7844816230824
-0.213688627993861
1.93736206960875
-0.340566907944094
-0.890844416659604
-0.555673236289465
0.932240554884402
0.0857763767609185
-5.14956865853986
1.06371878476745
1.54255048961244
-1.64287817728751
-7.28396665002305
23.6687333498406
74.7372105564428
-15.6541032887644
-15.4418927946836
-1.08498975742015
-2.22702124880914
-4.11246882992282
-2.86473172952634
-11.9905139154270
6.08908327860064
3.9793775639759
-2.42875491196742
-3.49615578333641
-7.77983076489204
-3.96912880377783
-7.15281610781506
-5.45045795537828
2.84509336086546
-2.72041512845396
-9.12157204640971
7.5548348477085
3.88791781633785
-15.6189934029384
1.92503937272619
11.1597381909771
-4.8952999391376
-6.27955141890635
11.0575967514000
-6.1728368903041
-1.00619150521487
7.74644672250571
-6.92952810085433
2.16068523479447
1.80341471247363
2.89626016641938
1.58052105147789
3.04091718437314
6.74427988061717
-4.98980128707666
8.29120555697443
1.87488713026232
-1.36125368267943
5.9776038564458
-7.09754576179762
-5.10271245403004
-2.19226614217519
-10.4997992841620
9.21983093053655
-2.93772064335536
5.17368760136662
-10.1097709143187
2.03361307772781
4.34905111891237
1.93347096985161
5.85009022333134
-5.56952656091352
0.294635158497726
0.531999794674562
-8.23996500741947
3.59078435010196
1.26039613289527
2.18701765561383
-8.83185939238376
6.34378517673315
-6.54594517990012
0.592219506071274
-5.50722547649764
-5.25381662645816
-0.418594537830472
3.03947946183575
-4.30249855472772
-3.50245167163428
5.81070196111432
-0.567133497241059
-6.84323931760224
0.667425165708323
5.31875666789261
-5.98606402006419
4.5843412207916
-4.87784205008751
-6.43910745763537
-1.41381816639904
-7.74247015387928
1.31412753747358
0.198411011286083
10.1651539407368
-3.96454142331598
0.668880343863606
2.15355327749432
0.0731417422356913
0.578570922449217
-4.54784329269913
1.44602954079727
-7.36586279091253
5.47297472602682
4.09881219597267
3.12052207810513
-2.6717534693598
-1.41979320574345
0.00186063499529610
2.01822423921547
5.90651919288996
72.0481961356963
82.0574213108985
-51.1254172346511
0.151532218022226
3.71277503681603
-10.8072898152088
12.5330413620274
-6.8934294921279
-5.77032722470125
2.92684909946399
-0.385381783806451
-0.966467258005252
-2.71406979049448
2.30351396242048
-7.65575613859068
-10.9582987628542
0.360538240771518
-4.08650524912258
-15.1299718964809
9.0125854318531
-5.04913658778167
-1.75843513998097
-7.77093339737945
2.40131904801797
-7.10887991189497
3.60132161458603
-6.4863782772855
3.33633896221158
-7.31399886005107
4.95983854889596
-2.24727338621824
1.98939583023656
18.4044326867344
29.9008650024991
4.58747036255113
22.9031428192028
6.26989490695371
-4.32577251724149
-5.20277165960493
-16.8538329741038
2.66394233634895
-7.5323195356558
-5.72639394597297
0.993761858681182
7.76261534163723
-11.9840455271603
-5.72588691960738
-7.00023246289231
1.22474916285483
-6.5226394703322
-4.992506902385
-8.76321553657647
2.18802244641034
-3.52350215325248
0.308185950768802
-0.828242505476112
4.34825790783947
-4.10349504820675
3.14053345530436
-8.96463494251333
2.97207859046677
0.780206794256571
-3.41324056021386
10.3100826049719
-18.0536804963208
6.55031266694061
18.8626044777516
5.50255434913191
-10.5829169287079
-2.21843557194967
-4.49810847893309
-7.63974683596365
7.51148968842728
0.383242910797549
-10.8080346845923
1.86023057528234
-6.93167467356756
-0.824317953079458
2.66241060063521
-1.23203324731423
-1.83965588333433
-7.38272537117012
-0.463128609111266
6.95078991520688
-8.32045944497935
-2.63479917616974
-0.022298352203336
-0.789278016699768
-2.54894930239743
-5.81524416406808
4.9854060781459
-0.441790731179196
-9.31636103765209
15.3380082866587
-1.75443622959006
-9.4706860454374
7.23347524200511
-6.38146150356323
0.478046791089639
-3.51029393770619
3.41258115969964
-5.4816713777015
18.3341506395412
-14.8275179052679
4.04176814242516
6.67593196543066
-10.2238715908848
2.04825131610738
2.99774074688617
-3.52810210318529
0.865963148831497
-8.64318986794069
-1.20792346278773
11.5171676791787
-2.29676598117851
-4.5341706617271
9.12451353578115
-6.49733699563149
-7.08334729388065
-3.94192172818032
16.4225433845667
-5.80626268264768
0.280068958533775
-3.44337971989302
-5.43229659627809
1.73156259078019
28.5310687478789
-22.5966098288552
11.5342818835318
19.8394726231141
-1.55192449139804
4.86892295480357
-9.48461175910086
17.3379053621716
-9.223588219735
3.56661706558577
-14.6036034564538
1.57658758777029
1.45123541641868
-4.34712101206958
14.8718043776173
-6.34128003027337
4.70756805322236
-22.0611492995963
7.75649758042329
3.85237890392079
-2.96304741239544
-2.59510581503173
4.00961708157411
-7.06601516506117



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')