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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationWed, 04 Dec 2013 13:01:14 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/04/t13861801058gnsjqagps5qok9.htm/, Retrieved Thu, 28 Mar 2024 19:44:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=230732, Retrieved Thu, 28 Mar 2024 19:44:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact59
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2008-12-08 19:22:39] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMP   [ARIMA Backward Selection] [] [2011-12-06 19:59:13] [b98453cac15ba1066b407e146608df68]
- R         [ARIMA Backward Selection] [W9_5] [2013-12-04 18:01:14] [6ca235be63c25d1ede0161784935ca53] [Current]
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Dataseries X:
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time22 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\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 & 22 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230732&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]22 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230732&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230732&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 time22 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.39950.06390.18580.1506-0.7323-0.39710.183
(p-val)(0.4771 )(0.7441 )(0.1433 )(0.7911 )(0.2421 )(0.1575 )(0.7882 )
Estimates ( 2 )-0.25460.09950.17790-0.7335-0.39650.1899
(p-val)(0.0521 )(0.4567 )(0.1662 )(NA )(0.2436 )(0.1556 )(0.7814 )
Estimates ( 3 )-0.24940.10670.16980-0.5607-0.32480
(p-val)(0.0543 )(0.4148 )(0.1758 )(NA )(0 )(0.0447 )(NA )
Estimates ( 4 )-0.27100.14510-0.5702-0.34050
(p-val)(0.0345 )(NA )(0.2359 )(NA )(0 )(0.0326 )(NA )
Estimates ( 5 )-0.2532000-0.574-0.33760
(p-val)(0.0476 )(NA )(NA )(NA )(0 )(0.033 )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.3995 & 0.0639 & 0.1858 & 0.1506 & -0.7323 & -0.3971 & 0.183 \tabularnewline
(p-val) & (0.4771 ) & (0.7441 ) & (0.1433 ) & (0.7911 ) & (0.2421 ) & (0.1575 ) & (0.7882 ) \tabularnewline
Estimates ( 2 ) & -0.2546 & 0.0995 & 0.1779 & 0 & -0.7335 & -0.3965 & 0.1899 \tabularnewline
(p-val) & (0.0521 ) & (0.4567 ) & (0.1662 ) & (NA ) & (0.2436 ) & (0.1556 ) & (0.7814 ) \tabularnewline
Estimates ( 3 ) & -0.2494 & 0.1067 & 0.1698 & 0 & -0.5607 & -0.3248 & 0 \tabularnewline
(p-val) & (0.0543 ) & (0.4148 ) & (0.1758 ) & (NA ) & (0 ) & (0.0447 ) & (NA ) \tabularnewline
Estimates ( 4 ) & -0.271 & 0 & 0.1451 & 0 & -0.5702 & -0.3405 & 0 \tabularnewline
(p-val) & (0.0345 ) & (NA ) & (0.2359 ) & (NA ) & (0 ) & (0.0326 ) & (NA ) \tabularnewline
Estimates ( 5 ) & -0.2532 & 0 & 0 & 0 & -0.574 & -0.3376 & 0 \tabularnewline
(p-val) & (0.0476 ) & (NA ) & (NA ) & (NA ) & (0 ) & (0.033 ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230732&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]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.3995[/C][C]0.0639[/C][C]0.1858[/C][C]0.1506[/C][C]-0.7323[/C][C]-0.3971[/C][C]0.183[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4771 )[/C][C](0.7441 )[/C][C](0.1433 )[/C][C](0.7911 )[/C][C](0.2421 )[/C][C](0.1575 )[/C][C](0.7882 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.2546[/C][C]0.0995[/C][C]0.1779[/C][C]0[/C][C]-0.7335[/C][C]-0.3965[/C][C]0.1899[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0521 )[/C][C](0.4567 )[/C][C](0.1662 )[/C][C](NA )[/C][C](0.2436 )[/C][C](0.1556 )[/C][C](0.7814 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.2494[/C][C]0.1067[/C][C]0.1698[/C][C]0[/C][C]-0.5607[/C][C]-0.3248[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0543 )[/C][C](0.4148 )[/C][C](0.1758 )[/C][C](NA )[/C][C](0 )[/C][C](0.0447 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.271[/C][C]0[/C][C]0.1451[/C][C]0[/C][C]-0.5702[/C][C]-0.3405[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0345 )[/C][C](NA )[/C][C](0.2359 )[/C][C](NA )[/C][C](0 )[/C][C](0.0326 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.2532[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.574[/C][C]-0.3376[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0476 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.033 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/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][/ROW]
[ROW][C]Estimates ( 7 )[/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][/ROW]
[ROW][C]Estimates ( 8 )[/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][/ROW]
[ROW][C]Estimates ( 9 )[/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][/ROW]
[ROW][C]Estimates ( 10 )[/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][/ROW]
[ROW][C]Estimates ( 11 )[/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][/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][/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][/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][/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][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230732&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230732&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
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.39950.06390.18580.1506-0.7323-0.39710.183
(p-val)(0.4771 )(0.7441 )(0.1433 )(0.7911 )(0.2421 )(0.1575 )(0.7882 )
Estimates ( 2 )-0.25460.09950.17790-0.7335-0.39650.1899
(p-val)(0.0521 )(0.4567 )(0.1662 )(NA )(0.2436 )(0.1556 )(0.7814 )
Estimates ( 3 )-0.24940.10670.16980-0.5607-0.32480
(p-val)(0.0543 )(0.4148 )(0.1758 )(NA )(0 )(0.0447 )(NA )
Estimates ( 4 )-0.27100.14510-0.5702-0.34050
(p-val)(0.0345 )(NA )(0.2359 )(NA )(0 )(0.0326 )(NA )
Estimates ( 5 )-0.2532000-0.574-0.33760
(p-val)(0.0476 )(NA )(NA )(NA )(0 )(0.033 )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
867.887509505211
-2250.28069676838
33618.3570412959
9954.34468238836
354.191730842355
18882.406400463
20229.4310915672
268402.416151187
-113346.926055862
-45016.394227939
35069.861367254
58531.0957290091
-77256.3771198791
-31473.594568955
-52391.0075132882
32854.9847569661
101107.732845397
-176275.960398033
79531.884415102
-176414.251561376
151290.579462589
167731.594163443
143237.122434691
80251.9665265577
118735.726273623
75035.8259037494
19198.3085437346
-36364.5639276314
-36170.5787440905
-109567.395064155
-100783.336097857
-149267.403931369
38947.3510583149
58613.0600994635
16074.4602044407
-41563.0049150659
-15970.5964777959
-47563.9548420802
59595.3577179922
65897.8405390448
-166489.283203891
46312.3269884632
-15952.8722863516
-87780.6523566012
134744.172737777
75232.8122408289
24408.7558471514
-15406.1403381955
-3766.75348767364
27197.2239951006
-46777.2890031503
-82472.8212609495
-35154.7184801844
-46946.870011609
-43641.5364941684
-54920.7084991732
54905.4038222157
-10509.5840707596
-13706.8046976985
-42347.6087628011
-28990.4687708701

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
867.887509505211 \tabularnewline
-2250.28069676838 \tabularnewline
33618.3570412959 \tabularnewline
9954.34468238836 \tabularnewline
354.191730842355 \tabularnewline
18882.406400463 \tabularnewline
20229.4310915672 \tabularnewline
268402.416151187 \tabularnewline
-113346.926055862 \tabularnewline
-45016.394227939 \tabularnewline
35069.861367254 \tabularnewline
58531.0957290091 \tabularnewline
-77256.3771198791 \tabularnewline
-31473.594568955 \tabularnewline
-52391.0075132882 \tabularnewline
32854.9847569661 \tabularnewline
101107.732845397 \tabularnewline
-176275.960398033 \tabularnewline
79531.884415102 \tabularnewline
-176414.251561376 \tabularnewline
151290.579462589 \tabularnewline
167731.594163443 \tabularnewline
143237.122434691 \tabularnewline
80251.9665265577 \tabularnewline
118735.726273623 \tabularnewline
75035.8259037494 \tabularnewline
19198.3085437346 \tabularnewline
-36364.5639276314 \tabularnewline
-36170.5787440905 \tabularnewline
-109567.395064155 \tabularnewline
-100783.336097857 \tabularnewline
-149267.403931369 \tabularnewline
38947.3510583149 \tabularnewline
58613.0600994635 \tabularnewline
16074.4602044407 \tabularnewline
-41563.0049150659 \tabularnewline
-15970.5964777959 \tabularnewline
-47563.9548420802 \tabularnewline
59595.3577179922 \tabularnewline
65897.8405390448 \tabularnewline
-166489.283203891 \tabularnewline
46312.3269884632 \tabularnewline
-15952.8722863516 \tabularnewline
-87780.6523566012 \tabularnewline
134744.172737777 \tabularnewline
75232.8122408289 \tabularnewline
24408.7558471514 \tabularnewline
-15406.1403381955 \tabularnewline
-3766.75348767364 \tabularnewline
27197.2239951006 \tabularnewline
-46777.2890031503 \tabularnewline
-82472.8212609495 \tabularnewline
-35154.7184801844 \tabularnewline
-46946.870011609 \tabularnewline
-43641.5364941684 \tabularnewline
-54920.7084991732 \tabularnewline
54905.4038222157 \tabularnewline
-10509.5840707596 \tabularnewline
-13706.8046976985 \tabularnewline
-42347.6087628011 \tabularnewline
-28990.4687708701 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230732&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]867.887509505211[/C][/ROW]
[ROW][C]-2250.28069676838[/C][/ROW]
[ROW][C]33618.3570412959[/C][/ROW]
[ROW][C]9954.34468238836[/C][/ROW]
[ROW][C]354.191730842355[/C][/ROW]
[ROW][C]18882.406400463[/C][/ROW]
[ROW][C]20229.4310915672[/C][/ROW]
[ROW][C]268402.416151187[/C][/ROW]
[ROW][C]-113346.926055862[/C][/ROW]
[ROW][C]-45016.394227939[/C][/ROW]
[ROW][C]35069.861367254[/C][/ROW]
[ROW][C]58531.0957290091[/C][/ROW]
[ROW][C]-77256.3771198791[/C][/ROW]
[ROW][C]-31473.594568955[/C][/ROW]
[ROW][C]-52391.0075132882[/C][/ROW]
[ROW][C]32854.9847569661[/C][/ROW]
[ROW][C]101107.732845397[/C][/ROW]
[ROW][C]-176275.960398033[/C][/ROW]
[ROW][C]79531.884415102[/C][/ROW]
[ROW][C]-176414.251561376[/C][/ROW]
[ROW][C]151290.579462589[/C][/ROW]
[ROW][C]167731.594163443[/C][/ROW]
[ROW][C]143237.122434691[/C][/ROW]
[ROW][C]80251.9665265577[/C][/ROW]
[ROW][C]118735.726273623[/C][/ROW]
[ROW][C]75035.8259037494[/C][/ROW]
[ROW][C]19198.3085437346[/C][/ROW]
[ROW][C]-36364.5639276314[/C][/ROW]
[ROW][C]-36170.5787440905[/C][/ROW]
[ROW][C]-109567.395064155[/C][/ROW]
[ROW][C]-100783.336097857[/C][/ROW]
[ROW][C]-149267.403931369[/C][/ROW]
[ROW][C]38947.3510583149[/C][/ROW]
[ROW][C]58613.0600994635[/C][/ROW]
[ROW][C]16074.4602044407[/C][/ROW]
[ROW][C]-41563.0049150659[/C][/ROW]
[ROW][C]-15970.5964777959[/C][/ROW]
[ROW][C]-47563.9548420802[/C][/ROW]
[ROW][C]59595.3577179922[/C][/ROW]
[ROW][C]65897.8405390448[/C][/ROW]
[ROW][C]-166489.283203891[/C][/ROW]
[ROW][C]46312.3269884632[/C][/ROW]
[ROW][C]-15952.8722863516[/C][/ROW]
[ROW][C]-87780.6523566012[/C][/ROW]
[ROW][C]134744.172737777[/C][/ROW]
[ROW][C]75232.8122408289[/C][/ROW]
[ROW][C]24408.7558471514[/C][/ROW]
[ROW][C]-15406.1403381955[/C][/ROW]
[ROW][C]-3766.75348767364[/C][/ROW]
[ROW][C]27197.2239951006[/C][/ROW]
[ROW][C]-46777.2890031503[/C][/ROW]
[ROW][C]-82472.8212609495[/C][/ROW]
[ROW][C]-35154.7184801844[/C][/ROW]
[ROW][C]-46946.870011609[/C][/ROW]
[ROW][C]-43641.5364941684[/C][/ROW]
[ROW][C]-54920.7084991732[/C][/ROW]
[ROW][C]54905.4038222157[/C][/ROW]
[ROW][C]-10509.5840707596[/C][/ROW]
[ROW][C]-13706.8046976985[/C][/ROW]
[ROW][C]-42347.6087628011[/C][/ROW]
[ROW][C]-28990.4687708701[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230732&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230732&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
867.887509505211
-2250.28069676838
33618.3570412959
9954.34468238836
354.191730842355
18882.406400463
20229.4310915672
268402.416151187
-113346.926055862
-45016.394227939
35069.861367254
58531.0957290091
-77256.3771198791
-31473.594568955
-52391.0075132882
32854.9847569661
101107.732845397
-176275.960398033
79531.884415102
-176414.251561376
151290.579462589
167731.594163443
143237.122434691
80251.9665265577
118735.726273623
75035.8259037494
19198.3085437346
-36364.5639276314
-36170.5787440905
-109567.395064155
-100783.336097857
-149267.403931369
38947.3510583149
58613.0600994635
16074.4602044407
-41563.0049150659
-15970.5964777959
-47563.9548420802
59595.3577179922
65897.8405390448
-166489.283203891
46312.3269884632
-15952.8722863516
-87780.6523566012
134744.172737777
75232.8122408289
24408.7558471514
-15406.1403381955
-3766.75348767364
27197.2239951006
-46777.2890031503
-82472.8212609495
-35154.7184801844
-46946.870011609
-43641.5364941684
-54920.7084991732
54905.4038222157
-10509.5840707596
-13706.8046976985
-42347.6087628011
-28990.4687708701



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
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
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')