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 08:45:16 -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/t12604599630ojqhc5kxplakn4.htm/, Retrieved Fri, 29 Mar 2024 02:08:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65500, Retrieved Fri, 29 Mar 2024 02:08:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115
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] [ARIMA Parameter E...] [2009-12-10 15:45:16] [2622964eb3e61db9b0dfd11950e3a18c] [Current]
Feedback Forum

Post a new message
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65500&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]9 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=65500&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65500&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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11
Estimates ( 1 )0.4718-0.10170.1173-0.07360.0092-0.0977-0.0125-7e-040.0398-0.06250.0039
(p-val)(0 )(0.0833 )(0.0478 )(0.2163 )(0.8787 )(0.1053 )(0.8366 )(0.9904 )(0.508 )(0.2959 )(0.9421 )
Estimates ( 2 )0.4718-0.10160.1173-0.07360.0091-0.0975-0.012800.0395-0.06240.0039
(p-val)(0 )(0.0826 )(0.0476 )(0.2154 )(0.8787 )(0.1013 )(0.8144 )(NA )(0.467 )(0.291 )(0.9431 )
Estimates ( 3 )0.4715-0.10150.1173-0.07360.0088-0.0975-0.012900.0392-0.06070
(p-val)(0 )(0.0829 )(0.0476 )(0.2149 )(0.8825 )(0.1014 )(0.8126 )(NA )(0.4689 )(0.2608 )(NA )
Estimates ( 4 )0.471-0.10050.1161-0.070-0.0939-0.013800.0391-0.06070
(p-val)(0 )(0.0839 )(0.0478 )(0.1948 )(NA )(0.0828 )(0.7997 )(NA )(0.4701 )(0.2611 )(NA )
Estimates ( 5 )0.4722-0.10060.1167-0.0710-0.0994000.0386-0.06180
(p-val)(0 )(0.0836 )(0.0463 )(0.1868 )(NA )(0.045 )(NA )(NA )(0.4755 )(0.2508 )(NA )
Estimates ( 6 )0.472-0.10280.1145-0.07050-0.0956000-0.0460
(p-val)(0 )(0.0769 )(0.0504 )(0.1906 )(NA )(0.0525 )(NA )(NA )(NA )(0.3487 )(NA )
Estimates ( 7 )0.4717-0.10240.1172-0.06690-0.096200000
(p-val)(0 )(0.0787 )(0.0453 )(0.2135 )(NA )(0.0513 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0.4669-0.09550.08800-0.101500000
(p-val)(0 )(0.0999 )(0.1011 )(NA )(NA )(0.0395 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )0.4611-0.0558000-0.094500000
(p-val)(0 )(0.2912 )(NA )(NA )(NA )(0.0553 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )0.43680000-0.095300000
(p-val)(0 )(NA )(NA )(NA )(NA )(0.0536 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )0.44220000000000
(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 ) & 0.4718 & -0.1017 & 0.1173 & -0.0736 & 0.0092 & -0.0977 & -0.0125 & -7e-04 & 0.0398 & -0.0625 & 0.0039 \tabularnewline
(p-val) & (0 ) & (0.0833 ) & (0.0478 ) & (0.2163 ) & (0.8787 ) & (0.1053 ) & (0.8366 ) & (0.9904 ) & (0.508 ) & (0.2959 ) & (0.9421 ) \tabularnewline
Estimates ( 2 ) & 0.4718 & -0.1016 & 0.1173 & -0.0736 & 0.0091 & -0.0975 & -0.0128 & 0 & 0.0395 & -0.0624 & 0.0039 \tabularnewline
(p-val) & (0 ) & (0.0826 ) & (0.0476 ) & (0.2154 ) & (0.8787 ) & (0.1013 ) & (0.8144 ) & (NA ) & (0.467 ) & (0.291 ) & (0.9431 ) \tabularnewline
Estimates ( 3 ) & 0.4715 & -0.1015 & 0.1173 & -0.0736 & 0.0088 & -0.0975 & -0.0129 & 0 & 0.0392 & -0.0607 & 0 \tabularnewline
(p-val) & (0 ) & (0.0829 ) & (0.0476 ) & (0.2149 ) & (0.8825 ) & (0.1014 ) & (0.8126 ) & (NA ) & (0.4689 ) & (0.2608 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0.471 & -0.1005 & 0.1161 & -0.07 & 0 & -0.0939 & -0.0138 & 0 & 0.0391 & -0.0607 & 0 \tabularnewline
(p-val) & (0 ) & (0.0839 ) & (0.0478 ) & (0.1948 ) & (NA ) & (0.0828 ) & (0.7997 ) & (NA ) & (0.4701 ) & (0.2611 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0.4722 & -0.1006 & 0.1167 & -0.071 & 0 & -0.0994 & 0 & 0 & 0.0386 & -0.0618 & 0 \tabularnewline
(p-val) & (0 ) & (0.0836 ) & (0.0463 ) & (0.1868 ) & (NA ) & (0.045 ) & (NA ) & (NA ) & (0.4755 ) & (0.2508 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0.472 & -0.1028 & 0.1145 & -0.0705 & 0 & -0.0956 & 0 & 0 & 0 & -0.046 & 0 \tabularnewline
(p-val) & (0 ) & (0.0769 ) & (0.0504 ) & (0.1906 ) & (NA ) & (0.0525 ) & (NA ) & (NA ) & (NA ) & (0.3487 ) & (NA ) \tabularnewline
Estimates ( 7 ) & 0.4717 & -0.1024 & 0.1172 & -0.0669 & 0 & -0.0962 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.0787 ) & (0.0453 ) & (0.2135 ) & (NA ) & (0.0513 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & 0.4669 & -0.0955 & 0.088 & 0 & 0 & -0.1015 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.0999 ) & (0.1011 ) & (NA ) & (NA ) & (0.0395 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & 0.4611 & -0.0558 & 0 & 0 & 0 & -0.0945 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.2912 ) & (NA ) & (NA ) & (NA ) & (0.0553 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & 0.4368 & 0 & 0 & 0 & 0 & -0.0953 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0536 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & 0.4422 & 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=65500&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]0.4718[/C][C]-0.1017[/C][C]0.1173[/C][C]-0.0736[/C][C]0.0092[/C][C]-0.0977[/C][C]-0.0125[/C][C]-7e-04[/C][C]0.0398[/C][C]-0.0625[/C][C]0.0039[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0833 )[/C][C](0.0478 )[/C][C](0.2163 )[/C][C](0.8787 )[/C][C](0.1053 )[/C][C](0.8366 )[/C][C](0.9904 )[/C][C](0.508 )[/C][C](0.2959 )[/C][C](0.9421 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4718[/C][C]-0.1016[/C][C]0.1173[/C][C]-0.0736[/C][C]0.0091[/C][C]-0.0975[/C][C]-0.0128[/C][C]0[/C][C]0.0395[/C][C]-0.0624[/C][C]0.0039[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0826 )[/C][C](0.0476 )[/C][C](0.2154 )[/C][C](0.8787 )[/C][C](0.1013 )[/C][C](0.8144 )[/C][C](NA )[/C][C](0.467 )[/C][C](0.291 )[/C][C](0.9431 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4715[/C][C]-0.1015[/C][C]0.1173[/C][C]-0.0736[/C][C]0.0088[/C][C]-0.0975[/C][C]-0.0129[/C][C]0[/C][C]0.0392[/C][C]-0.0607[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0829 )[/C][C](0.0476 )[/C][C](0.2149 )[/C][C](0.8825 )[/C][C](0.1014 )[/C][C](0.8126 )[/C][C](NA )[/C][C](0.4689 )[/C][C](0.2608 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.471[/C][C]-0.1005[/C][C]0.1161[/C][C]-0.07[/C][C]0[/C][C]-0.0939[/C][C]-0.0138[/C][C]0[/C][C]0.0391[/C][C]-0.0607[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0839 )[/C][C](0.0478 )[/C][C](0.1948 )[/C][C](NA )[/C][C](0.0828 )[/C][C](0.7997 )[/C][C](NA )[/C][C](0.4701 )[/C][C](0.2611 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.4722[/C][C]-0.1006[/C][C]0.1167[/C][C]-0.071[/C][C]0[/C][C]-0.0994[/C][C]0[/C][C]0[/C][C]0.0386[/C][C]-0.0618[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0836 )[/C][C](0.0463 )[/C][C](0.1868 )[/C][C](NA )[/C][C](0.045 )[/C][C](NA )[/C][C](NA )[/C][C](0.4755 )[/C][C](0.2508 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.472[/C][C]-0.1028[/C][C]0.1145[/C][C]-0.0705[/C][C]0[/C][C]-0.0956[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.046[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0769 )[/C][C](0.0504 )[/C][C](0.1906 )[/C][C](NA )[/C][C](0.0525 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.3487 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0.4717[/C][C]-0.1024[/C][C]0.1172[/C][C]-0.0669[/C][C]0[/C][C]-0.0962[/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](0.0787 )[/C][C](0.0453 )[/C][C](0.2135 )[/C][C](NA )[/C][C](0.0513 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]0.4669[/C][C]-0.0955[/C][C]0.088[/C][C]0[/C][C]0[/C][C]-0.1015[/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](0.0999 )[/C][C](0.1011 )[/C][C](NA )[/C][C](NA )[/C][C](0.0395 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]0.4611[/C][C]-0.0558[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0945[/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](0.2912 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0553 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]0.4368[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.0953[/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](0.0536 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]0.4422[/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=65500&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65500&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 )0.4718-0.10170.1173-0.07360.0092-0.0977-0.0125-7e-040.0398-0.06250.0039
(p-val)(0 )(0.0833 )(0.0478 )(0.2163 )(0.8787 )(0.1053 )(0.8366 )(0.9904 )(0.508 )(0.2959 )(0.9421 )
Estimates ( 2 )0.4718-0.10160.1173-0.07360.0091-0.0975-0.012800.0395-0.06240.0039
(p-val)(0 )(0.0826 )(0.0476 )(0.2154 )(0.8787 )(0.1013 )(0.8144 )(NA )(0.467 )(0.291 )(0.9431 )
Estimates ( 3 )0.4715-0.10150.1173-0.07360.0088-0.0975-0.012900.0392-0.06070
(p-val)(0 )(0.0829 )(0.0476 )(0.2149 )(0.8825 )(0.1014 )(0.8126 )(NA )(0.4689 )(0.2608 )(NA )
Estimates ( 4 )0.471-0.10050.1161-0.070-0.0939-0.013800.0391-0.06070
(p-val)(0 )(0.0839 )(0.0478 )(0.1948 )(NA )(0.0828 )(0.7997 )(NA )(0.4701 )(0.2611 )(NA )
Estimates ( 5 )0.4722-0.10060.1167-0.0710-0.0994000.0386-0.06180
(p-val)(0 )(0.0836 )(0.0463 )(0.1868 )(NA )(0.045 )(NA )(NA )(0.4755 )(0.2508 )(NA )
Estimates ( 6 )0.472-0.10280.1145-0.07050-0.0956000-0.0460
(p-val)(0 )(0.0769 )(0.0504 )(0.1906 )(NA )(0.0525 )(NA )(NA )(NA )(0.3487 )(NA )
Estimates ( 7 )0.4717-0.10240.1172-0.06690-0.096200000
(p-val)(0 )(0.0787 )(0.0453 )(0.2135 )(NA )(0.0513 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )0.4669-0.09550.08800-0.101500000
(p-val)(0 )(0.0999 )(0.1011 )(NA )(NA )(0.0395 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )0.4611-0.0558000-0.094500000
(p-val)(0 )(0.2912 )(NA )(NA )(NA )(0.0553 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )0.43680000-0.095300000
(p-val)(0 )(NA )(NA )(NA )(NA )(0.0536 )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )0.44220000000000
(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
0.254999840058752
22.4994972712462
8.55870582186404
30.5152642507268
17.7456867592395
4.28798837240887
-11.6959537697560
-3.22064159991203
-1.34685327123145
0.383797236448743
-5.5569488227664
1.60599627528308
-3.69455517173844
-1.86840234301388
-1.64676284828533
-11.4549989204009
-1.26339991380394
-6.74615228900365
-2.17691265395092
-12.8841778234258
-2.13911061334341
-1.87406283756991
0.507499999921379
-1.92626241642756
-4.15927742395053
-3.64133340550018
-6.00938323267542
-0.861489419208624
-0.456881882985897
5.38703897643859
19.5372468149104
16.5993200712925
-1.48636039730536
-0.151130695314919
1.97855268840033
0.569486612033074
-10.677499459898
13.3325796266944
-8.41717663557029
-1.77539381340034
4.72484506589069
-2.98308140787276
6.77086428402583
-5.58049368377942
-10.3128122976523
-1.59996140152697
-11.0322049773254
-3.99102866559241
1.63825982513549
-14.4538598575302
0.266318829441730
-0.459637767309403
-2.91016600103183
1.30199632896367
-4.19492280288446
-1.60116659488091
-0.725123353556882
0.573995897184517
-3.62245357377077
10.5026303714110
-6.05532714317181
12.0338290865652
-3.9654969228792
-0.142390142509157
0.0640609479235081
-1.16089802407890
-2.28522673291451
3.65295913421744
-1.86305873459702
-1.65986854891122
0.152325091770166
0.721635445883862
1.20528071687610
0.981269164348703
-0.632302418494476
-4.40650588428414
1.30520999781515
-0.577632530766607
-0.788569693354049
2.22031229757366
-2.83617388252293
1.57035926360243
1.92506855970538
-3.32459080112329
5.23714073631891
3.72091152022361
-4.81212292162047
2.05628266033602
1.09185003242186
2.72252024398711
1.29035872388158
-1.22433248751128
1.39615984665738
0.427413355663134
-1.36927499458329
1.81397862236423
-0.208149967578152
1.93330463182087
-0.349776776051897
-0.888976732875108
-0.55535116607058
0.944734938466866
0.081590099332459
-5.14048774560314
1.10957460590214
1.54796048368320
-1.65672505938767
-7.27185866983194
23.7357781796335
74.5371067264236
-16.3635162488951
-15.6063169400978
-1.03443235805980
-2.23957973450320
-3.96801095853323
-2.31729917849941
-11.7592390950416
6.21377024385697
3.97100518241461
-2.47744439648244
-3.53344553020975
-7.83212643074404
-4.01659387846786
-7.11578357810117
-5.35088911681441
2.92366659464494
-2.72749946020042
-9.14224546544466
7.60358912751775
3.80077925924485
-15.7293818830524
2.01797478940307
11.1683040919427
-5.03608723825249
-6.25945746054543
11.1491149320302
-6.30686163906529
-1.00908955953287
7.81754858572992
-6.97969903905806
2.15901101264916
1.84051122876872
2.87326792267959
1.54084808896198
3.04922505946627
6.68670265597848
-5.05826306409023
8.31078438783379
1.8327021702284
-1.38231402499275
5.99686055932108
-7.09509879063478
-5.06503589940871
-2.08709431002319
-10.4217415244754
9.33517895698361
-2.9325685596267
5.15913247671705
-10.1922724574193
2.06079194548744
4.27582406591813
1.90880398407955
5.80584457999555
-5.60617280276648
0.274348142854194
0.530196501827788
-8.21343068445424
3.68455679120365
1.30837697050430
2.16801851648938
-8.85478730295114
6.4111520189798
-6.62033173189462
0.627577467048582
-5.49402261930487
-5.18767355861905
-0.395408659155805
3.08366065646862
-4.3403036601635
-3.48235748221248
5.8155325522763
-0.65008583467079
-6.87853271434619
0.723263603968405
5.31553660112093
-6.05407471392255
4.63856942348747
-4.88885526880733
-6.43949362996585
-1.36119979491068
-7.676971226478
1.37840315258913
0.236667026581387
10.1525952817471
-4.09231915358163
0.634424260370565
2.08245141427008
0.0265131720834404
0.555707460584784
-4.50243872801531
1.49201414380732
-7.35130749296732
5.55972711078928
4.09475464265438
3.08986557982305
-2.74143867418954
-1.42301338804560
-0.0316292378410026
2.02782039518422
5.91785413523735
72.0244307385825
81.4347608510607
-52.0871661084284
0.170531472677112
3.7304861261257
-10.7975650506603
13.0474349500774
-6.24662248841986
-5.64576927238119
3.02770621890346
-0.346739365119731
-1.01019731171772
-2.69809760309391
2.23524616693311
-7.74888415042841
-10.9261609264138
0.460364931912181
-4.04844229110984
-15.0966103433983
9.17301635712158
-5.09190077742869
-1.80087697058303
-7.78123785366279
2.43487394720938
-7.20529232367687
3.65741767436202
-6.51793619093007
3.37073094359306
-7.3713182898781
5.00743332970495
-2.30098628811697
2.00105295833333
18.3579974626909
29.7431389009703
4.23228109486610
22.7370441050519
5.99875296923051
-4.48394947086439
-5.12349087645873
-16.5888755124255
2.97694612411311
-7.27290056109763
-5.49157687306655
1.13139359749249
7.77601166046918
-12.1646984994824
-5.71551743696887
-7.0147227922476
1.22294644766026
-6.54022160447079
-4.90163274666224
-8.73577898953567
2.23903098669109
-3.55924611326458
0.32963263870829
-0.857868171102893
4.30542944278517
-4.21150048597383
3.13673369679776
-9.00978217450728
3.02765952274126
0.776986611954385
-3.39211617355414
10.3335902072741
-18.1113868494262
6.62882179860856
18.8536522889250
5.34560111215114
-10.7179081191867
-2.13000890727676
-4.54112988573956
-7.58957595552516
7.70708459314687
0.450093932360005
-10.8318640682874
1.91974033676496
-6.94397565327603
-0.820324444107541
2.69706003023697
-1.24323958108187
-1.89465666175215
-7.38543025267506
-0.441552850470259
6.95877618220283
-8.3629019109988
-2.58945854030185
0.0134460699305805
-0.816327467166502
-2.55854540060096
-5.7584398617426
5.01400183536083
-0.49060893980436
-9.33690590587253
15.4050531742169
-1.86804523859752
-9.54346253516348
7.30792647376347
-6.40831812780823
0.467806899039601
-3.43577871936674
3.47389386743771
-5.53235721234591
18.3935956057296
-14.9901384690094
4.09992523209445
6.63999460154457
-10.2808694126147
2.08219256092497
3.09608534892072
-3.58245897222616
0.890436730729562
-8.59804280924237
-1.17037545898097
11.5522036277023
-2.36960726619884
-4.56173558628672
9.15349546532661
-6.61545400569503
-7.08991632482991
-3.8218081946917
16.5002148563923
-5.93429820772502
0.324121140230375
-3.45394191322288
-5.44846172543089
1.73652207934799
28.6069364069708
-22.8251805764488
11.6232241955933
19.7619944396320
-1.78126781471349
4.79324794863771
-9.39921291263005
17.3433802093018
-9.30750728770846
3.72981645458378
-14.5520160331512
1.75595308792981
1.45226624907406
-4.25882341809813
14.9000780068482
-6.44761147695607
4.62583972126106
-22.1424916325229
7.91467447628122
3.82210915562138
-2.92459862864354
-2.58106564458808

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.254999840058752 \tabularnewline
22.4994972712462 \tabularnewline
8.55870582186404 \tabularnewline
30.5152642507268 \tabularnewline
17.7456867592395 \tabularnewline
4.28798837240887 \tabularnewline
-11.6959537697560 \tabularnewline
-3.22064159991203 \tabularnewline
-1.34685327123145 \tabularnewline
0.383797236448743 \tabularnewline
-5.5569488227664 \tabularnewline
1.60599627528308 \tabularnewline
-3.69455517173844 \tabularnewline
-1.86840234301388 \tabularnewline
-1.64676284828533 \tabularnewline
-11.4549989204009 \tabularnewline
-1.26339991380394 \tabularnewline
-6.74615228900365 \tabularnewline
-2.17691265395092 \tabularnewline
-12.8841778234258 \tabularnewline
-2.13911061334341 \tabularnewline
-1.87406283756991 \tabularnewline
0.507499999921379 \tabularnewline
-1.92626241642756 \tabularnewline
-4.15927742395053 \tabularnewline
-3.64133340550018 \tabularnewline
-6.00938323267542 \tabularnewline
-0.861489419208624 \tabularnewline
-0.456881882985897 \tabularnewline
5.38703897643859 \tabularnewline
19.5372468149104 \tabularnewline
16.5993200712925 \tabularnewline
-1.48636039730536 \tabularnewline
-0.151130695314919 \tabularnewline
1.97855268840033 \tabularnewline
0.569486612033074 \tabularnewline
-10.677499459898 \tabularnewline
13.3325796266944 \tabularnewline
-8.41717663557029 \tabularnewline
-1.77539381340034 \tabularnewline
4.72484506589069 \tabularnewline
-2.98308140787276 \tabularnewline
6.77086428402583 \tabularnewline
-5.58049368377942 \tabularnewline
-10.3128122976523 \tabularnewline
-1.59996140152697 \tabularnewline
-11.0322049773254 \tabularnewline
-3.99102866559241 \tabularnewline
1.63825982513549 \tabularnewline
-14.4538598575302 \tabularnewline
0.266318829441730 \tabularnewline
-0.459637767309403 \tabularnewline
-2.91016600103183 \tabularnewline
1.30199632896367 \tabularnewline
-4.19492280288446 \tabularnewline
-1.60116659488091 \tabularnewline
-0.725123353556882 \tabularnewline
0.573995897184517 \tabularnewline
-3.62245357377077 \tabularnewline
10.5026303714110 \tabularnewline
-6.05532714317181 \tabularnewline
12.0338290865652 \tabularnewline
-3.9654969228792 \tabularnewline
-0.142390142509157 \tabularnewline
0.0640609479235081 \tabularnewline
-1.16089802407890 \tabularnewline
-2.28522673291451 \tabularnewline
3.65295913421744 \tabularnewline
-1.86305873459702 \tabularnewline
-1.65986854891122 \tabularnewline
0.152325091770166 \tabularnewline
0.721635445883862 \tabularnewline
1.20528071687610 \tabularnewline
0.981269164348703 \tabularnewline
-0.632302418494476 \tabularnewline
-4.40650588428414 \tabularnewline
1.30520999781515 \tabularnewline
-0.577632530766607 \tabularnewline
-0.788569693354049 \tabularnewline
2.22031229757366 \tabularnewline
-2.83617388252293 \tabularnewline
1.57035926360243 \tabularnewline
1.92506855970538 \tabularnewline
-3.32459080112329 \tabularnewline
5.23714073631891 \tabularnewline
3.72091152022361 \tabularnewline
-4.81212292162047 \tabularnewline
2.05628266033602 \tabularnewline
1.09185003242186 \tabularnewline
2.72252024398711 \tabularnewline
1.29035872388158 \tabularnewline
-1.22433248751128 \tabularnewline
1.39615984665738 \tabularnewline
0.427413355663134 \tabularnewline
-1.36927499458329 \tabularnewline
1.81397862236423 \tabularnewline
-0.208149967578152 \tabularnewline
1.93330463182087 \tabularnewline
-0.349776776051897 \tabularnewline
-0.888976732875108 \tabularnewline
-0.55535116607058 \tabularnewline
0.944734938466866 \tabularnewline
0.081590099332459 \tabularnewline
-5.14048774560314 \tabularnewline
1.10957460590214 \tabularnewline
1.54796048368320 \tabularnewline
-1.65672505938767 \tabularnewline
-7.27185866983194 \tabularnewline
23.7357781796335 \tabularnewline
74.5371067264236 \tabularnewline
-16.3635162488951 \tabularnewline
-15.6063169400978 \tabularnewline
-1.03443235805980 \tabularnewline
-2.23957973450320 \tabularnewline
-3.96801095853323 \tabularnewline
-2.31729917849941 \tabularnewline
-11.7592390950416 \tabularnewline
6.21377024385697 \tabularnewline
3.97100518241461 \tabularnewline
-2.47744439648244 \tabularnewline
-3.53344553020975 \tabularnewline
-7.83212643074404 \tabularnewline
-4.01659387846786 \tabularnewline
-7.11578357810117 \tabularnewline
-5.35088911681441 \tabularnewline
2.92366659464494 \tabularnewline
-2.72749946020042 \tabularnewline
-9.14224546544466 \tabularnewline
7.60358912751775 \tabularnewline
3.80077925924485 \tabularnewline
-15.7293818830524 \tabularnewline
2.01797478940307 \tabularnewline
11.1683040919427 \tabularnewline
-5.03608723825249 \tabularnewline
-6.25945746054543 \tabularnewline
11.1491149320302 \tabularnewline
-6.30686163906529 \tabularnewline
-1.00908955953287 \tabularnewline
7.81754858572992 \tabularnewline
-6.97969903905806 \tabularnewline
2.15901101264916 \tabularnewline
1.84051122876872 \tabularnewline
2.87326792267959 \tabularnewline
1.54084808896198 \tabularnewline
3.04922505946627 \tabularnewline
6.68670265597848 \tabularnewline
-5.05826306409023 \tabularnewline
8.31078438783379 \tabularnewline
1.8327021702284 \tabularnewline
-1.38231402499275 \tabularnewline
5.99686055932108 \tabularnewline
-7.09509879063478 \tabularnewline
-5.06503589940871 \tabularnewline
-2.08709431002319 \tabularnewline
-10.4217415244754 \tabularnewline
9.33517895698361 \tabularnewline
-2.9325685596267 \tabularnewline
5.15913247671705 \tabularnewline
-10.1922724574193 \tabularnewline
2.06079194548744 \tabularnewline
4.27582406591813 \tabularnewline
1.90880398407955 \tabularnewline
5.80584457999555 \tabularnewline
-5.60617280276648 \tabularnewline
0.274348142854194 \tabularnewline
0.530196501827788 \tabularnewline
-8.21343068445424 \tabularnewline
3.68455679120365 \tabularnewline
1.30837697050430 \tabularnewline
2.16801851648938 \tabularnewline
-8.85478730295114 \tabularnewline
6.4111520189798 \tabularnewline
-6.62033173189462 \tabularnewline
0.627577467048582 \tabularnewline
-5.49402261930487 \tabularnewline
-5.18767355861905 \tabularnewline
-0.395408659155805 \tabularnewline
3.08366065646862 \tabularnewline
-4.3403036601635 \tabularnewline
-3.48235748221248 \tabularnewline
5.8155325522763 \tabularnewline
-0.65008583467079 \tabularnewline
-6.87853271434619 \tabularnewline
0.723263603968405 \tabularnewline
5.31553660112093 \tabularnewline
-6.05407471392255 \tabularnewline
4.63856942348747 \tabularnewline
-4.88885526880733 \tabularnewline
-6.43949362996585 \tabularnewline
-1.36119979491068 \tabularnewline
-7.676971226478 \tabularnewline
1.37840315258913 \tabularnewline
0.236667026581387 \tabularnewline
10.1525952817471 \tabularnewline
-4.09231915358163 \tabularnewline
0.634424260370565 \tabularnewline
2.08245141427008 \tabularnewline
0.0265131720834404 \tabularnewline
0.555707460584784 \tabularnewline
-4.50243872801531 \tabularnewline
1.49201414380732 \tabularnewline
-7.35130749296732 \tabularnewline
5.55972711078928 \tabularnewline
4.09475464265438 \tabularnewline
3.08986557982305 \tabularnewline
-2.74143867418954 \tabularnewline
-1.42301338804560 \tabularnewline
-0.0316292378410026 \tabularnewline
2.02782039518422 \tabularnewline
5.91785413523735 \tabularnewline
72.0244307385825 \tabularnewline
81.4347608510607 \tabularnewline
-52.0871661084284 \tabularnewline
0.170531472677112 \tabularnewline
3.7304861261257 \tabularnewline
-10.7975650506603 \tabularnewline
13.0474349500774 \tabularnewline
-6.24662248841986 \tabularnewline
-5.64576927238119 \tabularnewline
3.02770621890346 \tabularnewline
-0.346739365119731 \tabularnewline
-1.01019731171772 \tabularnewline
-2.69809760309391 \tabularnewline
2.23524616693311 \tabularnewline
-7.74888415042841 \tabularnewline
-10.9261609264138 \tabularnewline
0.460364931912181 \tabularnewline
-4.04844229110984 \tabularnewline
-15.0966103433983 \tabularnewline
9.17301635712158 \tabularnewline
-5.09190077742869 \tabularnewline
-1.80087697058303 \tabularnewline
-7.78123785366279 \tabularnewline
2.43487394720938 \tabularnewline
-7.20529232367687 \tabularnewline
3.65741767436202 \tabularnewline
-6.51793619093007 \tabularnewline
3.37073094359306 \tabularnewline
-7.3713182898781 \tabularnewline
5.00743332970495 \tabularnewline
-2.30098628811697 \tabularnewline
2.00105295833333 \tabularnewline
18.3579974626909 \tabularnewline
29.7431389009703 \tabularnewline
4.23228109486610 \tabularnewline
22.7370441050519 \tabularnewline
5.99875296923051 \tabularnewline
-4.48394947086439 \tabularnewline
-5.12349087645873 \tabularnewline
-16.5888755124255 \tabularnewline
2.97694612411311 \tabularnewline
-7.27290056109763 \tabularnewline
-5.49157687306655 \tabularnewline
1.13139359749249 \tabularnewline
7.77601166046918 \tabularnewline
-12.1646984994824 \tabularnewline
-5.71551743696887 \tabularnewline
-7.0147227922476 \tabularnewline
1.22294644766026 \tabularnewline
-6.54022160447079 \tabularnewline
-4.90163274666224 \tabularnewline
-8.73577898953567 \tabularnewline
2.23903098669109 \tabularnewline
-3.55924611326458 \tabularnewline
0.32963263870829 \tabularnewline
-0.857868171102893 \tabularnewline
4.30542944278517 \tabularnewline
-4.21150048597383 \tabularnewline
3.13673369679776 \tabularnewline
-9.00978217450728 \tabularnewline
3.02765952274126 \tabularnewline
0.776986611954385 \tabularnewline
-3.39211617355414 \tabularnewline
10.3335902072741 \tabularnewline
-18.1113868494262 \tabularnewline
6.62882179860856 \tabularnewline
18.8536522889250 \tabularnewline
5.34560111215114 \tabularnewline
-10.7179081191867 \tabularnewline
-2.13000890727676 \tabularnewline
-4.54112988573956 \tabularnewline
-7.58957595552516 \tabularnewline
7.70708459314687 \tabularnewline
0.450093932360005 \tabularnewline
-10.8318640682874 \tabularnewline
1.91974033676496 \tabularnewline
-6.94397565327603 \tabularnewline
-0.820324444107541 \tabularnewline
2.69706003023697 \tabularnewline
-1.24323958108187 \tabularnewline
-1.89465666175215 \tabularnewline
-7.38543025267506 \tabularnewline
-0.441552850470259 \tabularnewline
6.95877618220283 \tabularnewline
-8.3629019109988 \tabularnewline
-2.58945854030185 \tabularnewline
0.0134460699305805 \tabularnewline
-0.816327467166502 \tabularnewline
-2.55854540060096 \tabularnewline
-5.7584398617426 \tabularnewline
5.01400183536083 \tabularnewline
-0.49060893980436 \tabularnewline
-9.33690590587253 \tabularnewline
15.4050531742169 \tabularnewline
-1.86804523859752 \tabularnewline
-9.54346253516348 \tabularnewline
7.30792647376347 \tabularnewline
-6.40831812780823 \tabularnewline
0.467806899039601 \tabularnewline
-3.43577871936674 \tabularnewline
3.47389386743771 \tabularnewline
-5.53235721234591 \tabularnewline
18.3935956057296 \tabularnewline
-14.9901384690094 \tabularnewline
4.09992523209445 \tabularnewline
6.63999460154457 \tabularnewline
-10.2808694126147 \tabularnewline
2.08219256092497 \tabularnewline
3.09608534892072 \tabularnewline
-3.58245897222616 \tabularnewline
0.890436730729562 \tabularnewline
-8.59804280924237 \tabularnewline
-1.17037545898097 \tabularnewline
11.5522036277023 \tabularnewline
-2.36960726619884 \tabularnewline
-4.56173558628672 \tabularnewline
9.15349546532661 \tabularnewline
-6.61545400569503 \tabularnewline
-7.08991632482991 \tabularnewline
-3.8218081946917 \tabularnewline
16.5002148563923 \tabularnewline
-5.93429820772502 \tabularnewline
0.324121140230375 \tabularnewline
-3.45394191322288 \tabularnewline
-5.44846172543089 \tabularnewline
1.73652207934799 \tabularnewline
28.6069364069708 \tabularnewline
-22.8251805764488 \tabularnewline
11.6232241955933 \tabularnewline
19.7619944396320 \tabularnewline
-1.78126781471349 \tabularnewline
4.79324794863771 \tabularnewline
-9.39921291263005 \tabularnewline
17.3433802093018 \tabularnewline
-9.30750728770846 \tabularnewline
3.72981645458378 \tabularnewline
-14.5520160331512 \tabularnewline
1.75595308792981 \tabularnewline
1.45226624907406 \tabularnewline
-4.25882341809813 \tabularnewline
14.9000780068482 \tabularnewline
-6.44761147695607 \tabularnewline
4.62583972126106 \tabularnewline
-22.1424916325229 \tabularnewline
7.91467447628122 \tabularnewline
3.82210915562138 \tabularnewline
-2.92459862864354 \tabularnewline
-2.58106564458808 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65500&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.254999840058752[/C][/ROW]
[ROW][C]22.4994972712462[/C][/ROW]
[ROW][C]8.55870582186404[/C][/ROW]
[ROW][C]30.5152642507268[/C][/ROW]
[ROW][C]17.7456867592395[/C][/ROW]
[ROW][C]4.28798837240887[/C][/ROW]
[ROW][C]-11.6959537697560[/C][/ROW]
[ROW][C]-3.22064159991203[/C][/ROW]
[ROW][C]-1.34685327123145[/C][/ROW]
[ROW][C]0.383797236448743[/C][/ROW]
[ROW][C]-5.5569488227664[/C][/ROW]
[ROW][C]1.60599627528308[/C][/ROW]
[ROW][C]-3.69455517173844[/C][/ROW]
[ROW][C]-1.86840234301388[/C][/ROW]
[ROW][C]-1.64676284828533[/C][/ROW]
[ROW][C]-11.4549989204009[/C][/ROW]
[ROW][C]-1.26339991380394[/C][/ROW]
[ROW][C]-6.74615228900365[/C][/ROW]
[ROW][C]-2.17691265395092[/C][/ROW]
[ROW][C]-12.8841778234258[/C][/ROW]
[ROW][C]-2.13911061334341[/C][/ROW]
[ROW][C]-1.87406283756991[/C][/ROW]
[ROW][C]0.507499999921379[/C][/ROW]
[ROW][C]-1.92626241642756[/C][/ROW]
[ROW][C]-4.15927742395053[/C][/ROW]
[ROW][C]-3.64133340550018[/C][/ROW]
[ROW][C]-6.00938323267542[/C][/ROW]
[ROW][C]-0.861489419208624[/C][/ROW]
[ROW][C]-0.456881882985897[/C][/ROW]
[ROW][C]5.38703897643859[/C][/ROW]
[ROW][C]19.5372468149104[/C][/ROW]
[ROW][C]16.5993200712925[/C][/ROW]
[ROW][C]-1.48636039730536[/C][/ROW]
[ROW][C]-0.151130695314919[/C][/ROW]
[ROW][C]1.97855268840033[/C][/ROW]
[ROW][C]0.569486612033074[/C][/ROW]
[ROW][C]-10.677499459898[/C][/ROW]
[ROW][C]13.3325796266944[/C][/ROW]
[ROW][C]-8.41717663557029[/C][/ROW]
[ROW][C]-1.77539381340034[/C][/ROW]
[ROW][C]4.72484506589069[/C][/ROW]
[ROW][C]-2.98308140787276[/C][/ROW]
[ROW][C]6.77086428402583[/C][/ROW]
[ROW][C]-5.58049368377942[/C][/ROW]
[ROW][C]-10.3128122976523[/C][/ROW]
[ROW][C]-1.59996140152697[/C][/ROW]
[ROW][C]-11.0322049773254[/C][/ROW]
[ROW][C]-3.99102866559241[/C][/ROW]
[ROW][C]1.63825982513549[/C][/ROW]
[ROW][C]-14.4538598575302[/C][/ROW]
[ROW][C]0.266318829441730[/C][/ROW]
[ROW][C]-0.459637767309403[/C][/ROW]
[ROW][C]-2.91016600103183[/C][/ROW]
[ROW][C]1.30199632896367[/C][/ROW]
[ROW][C]-4.19492280288446[/C][/ROW]
[ROW][C]-1.60116659488091[/C][/ROW]
[ROW][C]-0.725123353556882[/C][/ROW]
[ROW][C]0.573995897184517[/C][/ROW]
[ROW][C]-3.62245357377077[/C][/ROW]
[ROW][C]10.5026303714110[/C][/ROW]
[ROW][C]-6.05532714317181[/C][/ROW]
[ROW][C]12.0338290865652[/C][/ROW]
[ROW][C]-3.9654969228792[/C][/ROW]
[ROW][C]-0.142390142509157[/C][/ROW]
[ROW][C]0.0640609479235081[/C][/ROW]
[ROW][C]-1.16089802407890[/C][/ROW]
[ROW][C]-2.28522673291451[/C][/ROW]
[ROW][C]3.65295913421744[/C][/ROW]
[ROW][C]-1.86305873459702[/C][/ROW]
[ROW][C]-1.65986854891122[/C][/ROW]
[ROW][C]0.152325091770166[/C][/ROW]
[ROW][C]0.721635445883862[/C][/ROW]
[ROW][C]1.20528071687610[/C][/ROW]
[ROW][C]0.981269164348703[/C][/ROW]
[ROW][C]-0.632302418494476[/C][/ROW]
[ROW][C]-4.40650588428414[/C][/ROW]
[ROW][C]1.30520999781515[/C][/ROW]
[ROW][C]-0.577632530766607[/C][/ROW]
[ROW][C]-0.788569693354049[/C][/ROW]
[ROW][C]2.22031229757366[/C][/ROW]
[ROW][C]-2.83617388252293[/C][/ROW]
[ROW][C]1.57035926360243[/C][/ROW]
[ROW][C]1.92506855970538[/C][/ROW]
[ROW][C]-3.32459080112329[/C][/ROW]
[ROW][C]5.23714073631891[/C][/ROW]
[ROW][C]3.72091152022361[/C][/ROW]
[ROW][C]-4.81212292162047[/C][/ROW]
[ROW][C]2.05628266033602[/C][/ROW]
[ROW][C]1.09185003242186[/C][/ROW]
[ROW][C]2.72252024398711[/C][/ROW]
[ROW][C]1.29035872388158[/C][/ROW]
[ROW][C]-1.22433248751128[/C][/ROW]
[ROW][C]1.39615984665738[/C][/ROW]
[ROW][C]0.427413355663134[/C][/ROW]
[ROW][C]-1.36927499458329[/C][/ROW]
[ROW][C]1.81397862236423[/C][/ROW]
[ROW][C]-0.208149967578152[/C][/ROW]
[ROW][C]1.93330463182087[/C][/ROW]
[ROW][C]-0.349776776051897[/C][/ROW]
[ROW][C]-0.888976732875108[/C][/ROW]
[ROW][C]-0.55535116607058[/C][/ROW]
[ROW][C]0.944734938466866[/C][/ROW]
[ROW][C]0.081590099332459[/C][/ROW]
[ROW][C]-5.14048774560314[/C][/ROW]
[ROW][C]1.10957460590214[/C][/ROW]
[ROW][C]1.54796048368320[/C][/ROW]
[ROW][C]-1.65672505938767[/C][/ROW]
[ROW][C]-7.27185866983194[/C][/ROW]
[ROW][C]23.7357781796335[/C][/ROW]
[ROW][C]74.5371067264236[/C][/ROW]
[ROW][C]-16.3635162488951[/C][/ROW]
[ROW][C]-15.6063169400978[/C][/ROW]
[ROW][C]-1.03443235805980[/C][/ROW]
[ROW][C]-2.23957973450320[/C][/ROW]
[ROW][C]-3.96801095853323[/C][/ROW]
[ROW][C]-2.31729917849941[/C][/ROW]
[ROW][C]-11.7592390950416[/C][/ROW]
[ROW][C]6.21377024385697[/C][/ROW]
[ROW][C]3.97100518241461[/C][/ROW]
[ROW][C]-2.47744439648244[/C][/ROW]
[ROW][C]-3.53344553020975[/C][/ROW]
[ROW][C]-7.83212643074404[/C][/ROW]
[ROW][C]-4.01659387846786[/C][/ROW]
[ROW][C]-7.11578357810117[/C][/ROW]
[ROW][C]-5.35088911681441[/C][/ROW]
[ROW][C]2.92366659464494[/C][/ROW]
[ROW][C]-2.72749946020042[/C][/ROW]
[ROW][C]-9.14224546544466[/C][/ROW]
[ROW][C]7.60358912751775[/C][/ROW]
[ROW][C]3.80077925924485[/C][/ROW]
[ROW][C]-15.7293818830524[/C][/ROW]
[ROW][C]2.01797478940307[/C][/ROW]
[ROW][C]11.1683040919427[/C][/ROW]
[ROW][C]-5.03608723825249[/C][/ROW]
[ROW][C]-6.25945746054543[/C][/ROW]
[ROW][C]11.1491149320302[/C][/ROW]
[ROW][C]-6.30686163906529[/C][/ROW]
[ROW][C]-1.00908955953287[/C][/ROW]
[ROW][C]7.81754858572992[/C][/ROW]
[ROW][C]-6.97969903905806[/C][/ROW]
[ROW][C]2.15901101264916[/C][/ROW]
[ROW][C]1.84051122876872[/C][/ROW]
[ROW][C]2.87326792267959[/C][/ROW]
[ROW][C]1.54084808896198[/C][/ROW]
[ROW][C]3.04922505946627[/C][/ROW]
[ROW][C]6.68670265597848[/C][/ROW]
[ROW][C]-5.05826306409023[/C][/ROW]
[ROW][C]8.31078438783379[/C][/ROW]
[ROW][C]1.8327021702284[/C][/ROW]
[ROW][C]-1.38231402499275[/C][/ROW]
[ROW][C]5.99686055932108[/C][/ROW]
[ROW][C]-7.09509879063478[/C][/ROW]
[ROW][C]-5.06503589940871[/C][/ROW]
[ROW][C]-2.08709431002319[/C][/ROW]
[ROW][C]-10.4217415244754[/C][/ROW]
[ROW][C]9.33517895698361[/C][/ROW]
[ROW][C]-2.9325685596267[/C][/ROW]
[ROW][C]5.15913247671705[/C][/ROW]
[ROW][C]-10.1922724574193[/C][/ROW]
[ROW][C]2.06079194548744[/C][/ROW]
[ROW][C]4.27582406591813[/C][/ROW]
[ROW][C]1.90880398407955[/C][/ROW]
[ROW][C]5.80584457999555[/C][/ROW]
[ROW][C]-5.60617280276648[/C][/ROW]
[ROW][C]0.274348142854194[/C][/ROW]
[ROW][C]0.530196501827788[/C][/ROW]
[ROW][C]-8.21343068445424[/C][/ROW]
[ROW][C]3.68455679120365[/C][/ROW]
[ROW][C]1.30837697050430[/C][/ROW]
[ROW][C]2.16801851648938[/C][/ROW]
[ROW][C]-8.85478730295114[/C][/ROW]
[ROW][C]6.4111520189798[/C][/ROW]
[ROW][C]-6.62033173189462[/C][/ROW]
[ROW][C]0.627577467048582[/C][/ROW]
[ROW][C]-5.49402261930487[/C][/ROW]
[ROW][C]-5.18767355861905[/C][/ROW]
[ROW][C]-0.395408659155805[/C][/ROW]
[ROW][C]3.08366065646862[/C][/ROW]
[ROW][C]-4.3403036601635[/C][/ROW]
[ROW][C]-3.48235748221248[/C][/ROW]
[ROW][C]5.8155325522763[/C][/ROW]
[ROW][C]-0.65008583467079[/C][/ROW]
[ROW][C]-6.87853271434619[/C][/ROW]
[ROW][C]0.723263603968405[/C][/ROW]
[ROW][C]5.31553660112093[/C][/ROW]
[ROW][C]-6.05407471392255[/C][/ROW]
[ROW][C]4.63856942348747[/C][/ROW]
[ROW][C]-4.88885526880733[/C][/ROW]
[ROW][C]-6.43949362996585[/C][/ROW]
[ROW][C]-1.36119979491068[/C][/ROW]
[ROW][C]-7.676971226478[/C][/ROW]
[ROW][C]1.37840315258913[/C][/ROW]
[ROW][C]0.236667026581387[/C][/ROW]
[ROW][C]10.1525952817471[/C][/ROW]
[ROW][C]-4.09231915358163[/C][/ROW]
[ROW][C]0.634424260370565[/C][/ROW]
[ROW][C]2.08245141427008[/C][/ROW]
[ROW][C]0.0265131720834404[/C][/ROW]
[ROW][C]0.555707460584784[/C][/ROW]
[ROW][C]-4.50243872801531[/C][/ROW]
[ROW][C]1.49201414380732[/C][/ROW]
[ROW][C]-7.35130749296732[/C][/ROW]
[ROW][C]5.55972711078928[/C][/ROW]
[ROW][C]4.09475464265438[/C][/ROW]
[ROW][C]3.08986557982305[/C][/ROW]
[ROW][C]-2.74143867418954[/C][/ROW]
[ROW][C]-1.42301338804560[/C][/ROW]
[ROW][C]-0.0316292378410026[/C][/ROW]
[ROW][C]2.02782039518422[/C][/ROW]
[ROW][C]5.91785413523735[/C][/ROW]
[ROW][C]72.0244307385825[/C][/ROW]
[ROW][C]81.4347608510607[/C][/ROW]
[ROW][C]-52.0871661084284[/C][/ROW]
[ROW][C]0.170531472677112[/C][/ROW]
[ROW][C]3.7304861261257[/C][/ROW]
[ROW][C]-10.7975650506603[/C][/ROW]
[ROW][C]13.0474349500774[/C][/ROW]
[ROW][C]-6.24662248841986[/C][/ROW]
[ROW][C]-5.64576927238119[/C][/ROW]
[ROW][C]3.02770621890346[/C][/ROW]
[ROW][C]-0.346739365119731[/C][/ROW]
[ROW][C]-1.01019731171772[/C][/ROW]
[ROW][C]-2.69809760309391[/C][/ROW]
[ROW][C]2.23524616693311[/C][/ROW]
[ROW][C]-7.74888415042841[/C][/ROW]
[ROW][C]-10.9261609264138[/C][/ROW]
[ROW][C]0.460364931912181[/C][/ROW]
[ROW][C]-4.04844229110984[/C][/ROW]
[ROW][C]-15.0966103433983[/C][/ROW]
[ROW][C]9.17301635712158[/C][/ROW]
[ROW][C]-5.09190077742869[/C][/ROW]
[ROW][C]-1.80087697058303[/C][/ROW]
[ROW][C]-7.78123785366279[/C][/ROW]
[ROW][C]2.43487394720938[/C][/ROW]
[ROW][C]-7.20529232367687[/C][/ROW]
[ROW][C]3.65741767436202[/C][/ROW]
[ROW][C]-6.51793619093007[/C][/ROW]
[ROW][C]3.37073094359306[/C][/ROW]
[ROW][C]-7.3713182898781[/C][/ROW]
[ROW][C]5.00743332970495[/C][/ROW]
[ROW][C]-2.30098628811697[/C][/ROW]
[ROW][C]2.00105295833333[/C][/ROW]
[ROW][C]18.3579974626909[/C][/ROW]
[ROW][C]29.7431389009703[/C][/ROW]
[ROW][C]4.23228109486610[/C][/ROW]
[ROW][C]22.7370441050519[/C][/ROW]
[ROW][C]5.99875296923051[/C][/ROW]
[ROW][C]-4.48394947086439[/C][/ROW]
[ROW][C]-5.12349087645873[/C][/ROW]
[ROW][C]-16.5888755124255[/C][/ROW]
[ROW][C]2.97694612411311[/C][/ROW]
[ROW][C]-7.27290056109763[/C][/ROW]
[ROW][C]-5.49157687306655[/C][/ROW]
[ROW][C]1.13139359749249[/C][/ROW]
[ROW][C]7.77601166046918[/C][/ROW]
[ROW][C]-12.1646984994824[/C][/ROW]
[ROW][C]-5.71551743696887[/C][/ROW]
[ROW][C]-7.0147227922476[/C][/ROW]
[ROW][C]1.22294644766026[/C][/ROW]
[ROW][C]-6.54022160447079[/C][/ROW]
[ROW][C]-4.90163274666224[/C][/ROW]
[ROW][C]-8.73577898953567[/C][/ROW]
[ROW][C]2.23903098669109[/C][/ROW]
[ROW][C]-3.55924611326458[/C][/ROW]
[ROW][C]0.32963263870829[/C][/ROW]
[ROW][C]-0.857868171102893[/C][/ROW]
[ROW][C]4.30542944278517[/C][/ROW]
[ROW][C]-4.21150048597383[/C][/ROW]
[ROW][C]3.13673369679776[/C][/ROW]
[ROW][C]-9.00978217450728[/C][/ROW]
[ROW][C]3.02765952274126[/C][/ROW]
[ROW][C]0.776986611954385[/C][/ROW]
[ROW][C]-3.39211617355414[/C][/ROW]
[ROW][C]10.3335902072741[/C][/ROW]
[ROW][C]-18.1113868494262[/C][/ROW]
[ROW][C]6.62882179860856[/C][/ROW]
[ROW][C]18.8536522889250[/C][/ROW]
[ROW][C]5.34560111215114[/C][/ROW]
[ROW][C]-10.7179081191867[/C][/ROW]
[ROW][C]-2.13000890727676[/C][/ROW]
[ROW][C]-4.54112988573956[/C][/ROW]
[ROW][C]-7.58957595552516[/C][/ROW]
[ROW][C]7.70708459314687[/C][/ROW]
[ROW][C]0.450093932360005[/C][/ROW]
[ROW][C]-10.8318640682874[/C][/ROW]
[ROW][C]1.91974033676496[/C][/ROW]
[ROW][C]-6.94397565327603[/C][/ROW]
[ROW][C]-0.820324444107541[/C][/ROW]
[ROW][C]2.69706003023697[/C][/ROW]
[ROW][C]-1.24323958108187[/C][/ROW]
[ROW][C]-1.89465666175215[/C][/ROW]
[ROW][C]-7.38543025267506[/C][/ROW]
[ROW][C]-0.441552850470259[/C][/ROW]
[ROW][C]6.95877618220283[/C][/ROW]
[ROW][C]-8.3629019109988[/C][/ROW]
[ROW][C]-2.58945854030185[/C][/ROW]
[ROW][C]0.0134460699305805[/C][/ROW]
[ROW][C]-0.816327467166502[/C][/ROW]
[ROW][C]-2.55854540060096[/C][/ROW]
[ROW][C]-5.7584398617426[/C][/ROW]
[ROW][C]5.01400183536083[/C][/ROW]
[ROW][C]-0.49060893980436[/C][/ROW]
[ROW][C]-9.33690590587253[/C][/ROW]
[ROW][C]15.4050531742169[/C][/ROW]
[ROW][C]-1.86804523859752[/C][/ROW]
[ROW][C]-9.54346253516348[/C][/ROW]
[ROW][C]7.30792647376347[/C][/ROW]
[ROW][C]-6.40831812780823[/C][/ROW]
[ROW][C]0.467806899039601[/C][/ROW]
[ROW][C]-3.43577871936674[/C][/ROW]
[ROW][C]3.47389386743771[/C][/ROW]
[ROW][C]-5.53235721234591[/C][/ROW]
[ROW][C]18.3935956057296[/C][/ROW]
[ROW][C]-14.9901384690094[/C][/ROW]
[ROW][C]4.09992523209445[/C][/ROW]
[ROW][C]6.63999460154457[/C][/ROW]
[ROW][C]-10.2808694126147[/C][/ROW]
[ROW][C]2.08219256092497[/C][/ROW]
[ROW][C]3.09608534892072[/C][/ROW]
[ROW][C]-3.58245897222616[/C][/ROW]
[ROW][C]0.890436730729562[/C][/ROW]
[ROW][C]-8.59804280924237[/C][/ROW]
[ROW][C]-1.17037545898097[/C][/ROW]
[ROW][C]11.5522036277023[/C][/ROW]
[ROW][C]-2.36960726619884[/C][/ROW]
[ROW][C]-4.56173558628672[/C][/ROW]
[ROW][C]9.15349546532661[/C][/ROW]
[ROW][C]-6.61545400569503[/C][/ROW]
[ROW][C]-7.08991632482991[/C][/ROW]
[ROW][C]-3.8218081946917[/C][/ROW]
[ROW][C]16.5002148563923[/C][/ROW]
[ROW][C]-5.93429820772502[/C][/ROW]
[ROW][C]0.324121140230375[/C][/ROW]
[ROW][C]-3.45394191322288[/C][/ROW]
[ROW][C]-5.44846172543089[/C][/ROW]
[ROW][C]1.73652207934799[/C][/ROW]
[ROW][C]28.6069364069708[/C][/ROW]
[ROW][C]-22.8251805764488[/C][/ROW]
[ROW][C]11.6232241955933[/C][/ROW]
[ROW][C]19.7619944396320[/C][/ROW]
[ROW][C]-1.78126781471349[/C][/ROW]
[ROW][C]4.79324794863771[/C][/ROW]
[ROW][C]-9.39921291263005[/C][/ROW]
[ROW][C]17.3433802093018[/C][/ROW]
[ROW][C]-9.30750728770846[/C][/ROW]
[ROW][C]3.72981645458378[/C][/ROW]
[ROW][C]-14.5520160331512[/C][/ROW]
[ROW][C]1.75595308792981[/C][/ROW]
[ROW][C]1.45226624907406[/C][/ROW]
[ROW][C]-4.25882341809813[/C][/ROW]
[ROW][C]14.9000780068482[/C][/ROW]
[ROW][C]-6.44761147695607[/C][/ROW]
[ROW][C]4.62583972126106[/C][/ROW]
[ROW][C]-22.1424916325229[/C][/ROW]
[ROW][C]7.91467447628122[/C][/ROW]
[ROW][C]3.82210915562138[/C][/ROW]
[ROW][C]-2.92459862864354[/C][/ROW]
[ROW][C]-2.58106564458808[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65500&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65500&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
0.254999840058752
22.4994972712462
8.55870582186404
30.5152642507268
17.7456867592395
4.28798837240887
-11.6959537697560
-3.22064159991203
-1.34685327123145
0.383797236448743
-5.5569488227664
1.60599627528308
-3.69455517173844
-1.86840234301388
-1.64676284828533
-11.4549989204009
-1.26339991380394
-6.74615228900365
-2.17691265395092
-12.8841778234258
-2.13911061334341
-1.87406283756991
0.507499999921379
-1.92626241642756
-4.15927742395053
-3.64133340550018
-6.00938323267542
-0.861489419208624
-0.456881882985897
5.38703897643859
19.5372468149104
16.5993200712925
-1.48636039730536
-0.151130695314919
1.97855268840033
0.569486612033074
-10.677499459898
13.3325796266944
-8.41717663557029
-1.77539381340034
4.72484506589069
-2.98308140787276
6.77086428402583
-5.58049368377942
-10.3128122976523
-1.59996140152697
-11.0322049773254
-3.99102866559241
1.63825982513549
-14.4538598575302
0.266318829441730
-0.459637767309403
-2.91016600103183
1.30199632896367
-4.19492280288446
-1.60116659488091
-0.725123353556882
0.573995897184517
-3.62245357377077
10.5026303714110
-6.05532714317181
12.0338290865652
-3.9654969228792
-0.142390142509157
0.0640609479235081
-1.16089802407890
-2.28522673291451
3.65295913421744
-1.86305873459702
-1.65986854891122
0.152325091770166
0.721635445883862
1.20528071687610
0.981269164348703
-0.632302418494476
-4.40650588428414
1.30520999781515
-0.577632530766607
-0.788569693354049
2.22031229757366
-2.83617388252293
1.57035926360243
1.92506855970538
-3.32459080112329
5.23714073631891
3.72091152022361
-4.81212292162047
2.05628266033602
1.09185003242186
2.72252024398711
1.29035872388158
-1.22433248751128
1.39615984665738
0.427413355663134
-1.36927499458329
1.81397862236423
-0.208149967578152
1.93330463182087
-0.349776776051897
-0.888976732875108
-0.55535116607058
0.944734938466866
0.081590099332459
-5.14048774560314
1.10957460590214
1.54796048368320
-1.65672505938767
-7.27185866983194
23.7357781796335
74.5371067264236
-16.3635162488951
-15.6063169400978
-1.03443235805980
-2.23957973450320
-3.96801095853323
-2.31729917849941
-11.7592390950416
6.21377024385697
3.97100518241461
-2.47744439648244
-3.53344553020975
-7.83212643074404
-4.01659387846786
-7.11578357810117
-5.35088911681441
2.92366659464494
-2.72749946020042
-9.14224546544466
7.60358912751775
3.80077925924485
-15.7293818830524
2.01797478940307
11.1683040919427
-5.03608723825249
-6.25945746054543
11.1491149320302
-6.30686163906529
-1.00908955953287
7.81754858572992
-6.97969903905806
2.15901101264916
1.84051122876872
2.87326792267959
1.54084808896198
3.04922505946627
6.68670265597848
-5.05826306409023
8.31078438783379
1.8327021702284
-1.38231402499275
5.99686055932108
-7.09509879063478
-5.06503589940871
-2.08709431002319
-10.4217415244754
9.33517895698361
-2.9325685596267
5.15913247671705
-10.1922724574193
2.06079194548744
4.27582406591813
1.90880398407955
5.80584457999555
-5.60617280276648
0.274348142854194
0.530196501827788
-8.21343068445424
3.68455679120365
1.30837697050430
2.16801851648938
-8.85478730295114
6.4111520189798
-6.62033173189462
0.627577467048582
-5.49402261930487
-5.18767355861905
-0.395408659155805
3.08366065646862
-4.3403036601635
-3.48235748221248
5.8155325522763
-0.65008583467079
-6.87853271434619
0.723263603968405
5.31553660112093
-6.05407471392255
4.63856942348747
-4.88885526880733
-6.43949362996585
-1.36119979491068
-7.676971226478
1.37840315258913
0.236667026581387
10.1525952817471
-4.09231915358163
0.634424260370565
2.08245141427008
0.0265131720834404
0.555707460584784
-4.50243872801531
1.49201414380732
-7.35130749296732
5.55972711078928
4.09475464265438
3.08986557982305
-2.74143867418954
-1.42301338804560
-0.0316292378410026
2.02782039518422
5.91785413523735
72.0244307385825
81.4347608510607
-52.0871661084284
0.170531472677112
3.7304861261257
-10.7975650506603
13.0474349500774
-6.24662248841986
-5.64576927238119
3.02770621890346
-0.346739365119731
-1.01019731171772
-2.69809760309391
2.23524616693311
-7.74888415042841
-10.9261609264138
0.460364931912181
-4.04844229110984
-15.0966103433983
9.17301635712158
-5.09190077742869
-1.80087697058303
-7.78123785366279
2.43487394720938
-7.20529232367687
3.65741767436202
-6.51793619093007
3.37073094359306
-7.3713182898781
5.00743332970495
-2.30098628811697
2.00105295833333
18.3579974626909
29.7431389009703
4.23228109486610
22.7370441050519
5.99875296923051
-4.48394947086439
-5.12349087645873
-16.5888755124255
2.97694612411311
-7.27290056109763
-5.49157687306655
1.13139359749249
7.77601166046918
-12.1646984994824
-5.71551743696887
-7.0147227922476
1.22294644766026
-6.54022160447079
-4.90163274666224
-8.73577898953567
2.23903098669109
-3.55924611326458
0.32963263870829
-0.857868171102893
4.30542944278517
-4.21150048597383
3.13673369679776
-9.00978217450728
3.02765952274126
0.776986611954385
-3.39211617355414
10.3335902072741
-18.1113868494262
6.62882179860856
18.8536522889250
5.34560111215114
-10.7179081191867
-2.13000890727676
-4.54112988573956
-7.58957595552516
7.70708459314687
0.450093932360005
-10.8318640682874
1.91974033676496
-6.94397565327603
-0.820324444107541
2.69706003023697
-1.24323958108187
-1.89465666175215
-7.38543025267506
-0.441552850470259
6.95877618220283
-8.3629019109988
-2.58945854030185
0.0134460699305805
-0.816327467166502
-2.55854540060096
-5.7584398617426
5.01400183536083
-0.49060893980436
-9.33690590587253
15.4050531742169
-1.86804523859752
-9.54346253516348
7.30792647376347
-6.40831812780823
0.467806899039601
-3.43577871936674
3.47389386743771
-5.53235721234591
18.3935956057296
-14.9901384690094
4.09992523209445
6.63999460154457
-10.2808694126147
2.08219256092497
3.09608534892072
-3.58245897222616
0.890436730729562
-8.59804280924237
-1.17037545898097
11.5522036277023
-2.36960726619884
-4.56173558628672
9.15349546532661
-6.61545400569503
-7.08991632482991
-3.8218081946917
16.5002148563923
-5.93429820772502
0.324121140230375
-3.45394191322288
-5.44846172543089
1.73652207934799
28.6069364069708
-22.8251805764488
11.6232241955933
19.7619944396320
-1.78126781471349
4.79324794863771
-9.39921291263005
17.3433802093018
-9.30750728770846
3.72981645458378
-14.5520160331512
1.75595308792981
1.45226624907406
-4.25882341809813
14.9000780068482
-6.44761147695607
4.62583972126106
-22.1424916325229
7.91467447628122
3.82210915562138
-2.92459862864354
-2.58106564458808



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')