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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, 29 Dec 2010 15:30:47 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t129363654723tx0fiejia70os.htm/, Retrieved Fri, 03 May 2024 11:33:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116911, Retrieved Fri, 03 May 2024 11:33:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Spectral Analysis] [Paper] [2010-12-29 15:15:23] [f9c71f724b8f3da7e2789afe36ffff39]
- RMPD    [ARIMA Backward Selection] [Paper: ARIMA (18t...] [2010-12-29 15:30:47] [35c3410767ea63f72c8afa35bf7b6164] [Current]
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Dataseries X:
49915
47469
45652
43492
41087
42931
67256
72316
65624
59450
52851
51214
44092
43752
40320
40551
38329
39530
59648
61031
55560
43877
38510
36085
35994
32617
30001
27894
26083
28817
48742
49915
40264
34276
30426
30793
29855
28081
26820
25782
22654
27373
43675
45096
38145
34017
31537
33814
36531
36935
36497
35110
33137
37407
53963
56602
49694
43957
41723
45599
42503
42153
39098
37449
34748
36548
53639
55289
47774
42156
38019




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 13 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116911&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116911&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116911&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 time13 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.18790.2162-0.09860.24060.2383-0.1387-0.9996
(p-val)(0.7501 )(0.1024 )(0.6079 )(0.6762 )(0.1939 )(0.5178 )(0.0158 )
Estimates ( 2 )00.2141-0.13570.05810.2274-0.1326-1.0002
(p-val)(NA )(0.0962 )(0.3069 )(0.6707 )(0.2075 )(0.5356 )(0.0161 )
Estimates ( 3 )00.212-0.133900.2445-0.1409-1.0005
(p-val)(NA )(0.0994 )(0.3144 )(NA )(0.1659 )(0.51 )(0.0183 )
Estimates ( 4 )00.2109-0.10900.28010-1.0002
(p-val)(NA )(0.1012 )(0.3906 )(NA )(0.1099 )(NA )(0.0027 )
Estimates ( 5 )00.205000.28780-1.0002
(p-val)(NA )(0.1115 )(NA )(NA )(0.1058 )(NA )(0.0042 )
Estimates ( 6 )00000.30440-1.0002
(p-val)(NA )(NA )(NA )(NA )(0.0868 )(NA )(0.0046 )
Estimates ( 7 )000000-0.5654
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0.0066 )
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 ) & -0.1879 & 0.2162 & -0.0986 & 0.2406 & 0.2383 & -0.1387 & -0.9996 \tabularnewline
(p-val) & (0.7501 ) & (0.1024 ) & (0.6079 ) & (0.6762 ) & (0.1939 ) & (0.5178 ) & (0.0158 ) \tabularnewline
Estimates ( 2 ) & 0 & 0.2141 & -0.1357 & 0.0581 & 0.2274 & -0.1326 & -1.0002 \tabularnewline
(p-val) & (NA ) & (0.0962 ) & (0.3069 ) & (0.6707 ) & (0.2075 ) & (0.5356 ) & (0.0161 ) \tabularnewline
Estimates ( 3 ) & 0 & 0.212 & -0.1339 & 0 & 0.2445 & -0.1409 & -1.0005 \tabularnewline
(p-val) & (NA ) & (0.0994 ) & (0.3144 ) & (NA ) & (0.1659 ) & (0.51 ) & (0.0183 ) \tabularnewline
Estimates ( 4 ) & 0 & 0.2109 & -0.109 & 0 & 0.2801 & 0 & -1.0002 \tabularnewline
(p-val) & (NA ) & (0.1012 ) & (0.3906 ) & (NA ) & (0.1099 ) & (NA ) & (0.0027 ) \tabularnewline
Estimates ( 5 ) & 0 & 0.205 & 0 & 0 & 0.2878 & 0 & -1.0002 \tabularnewline
(p-val) & (NA ) & (0.1115 ) & (NA ) & (NA ) & (0.1058 ) & (NA ) & (0.0042 ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & 0 & 0.3044 & 0 & -1.0002 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0868 ) & (NA ) & (0.0046 ) \tabularnewline
Estimates ( 7 ) & 0 & 0 & 0 & 0 & 0 & 0 & -0.5654 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0066 ) \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=116911&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]-0.1879[/C][C]0.2162[/C][C]-0.0986[/C][C]0.2406[/C][C]0.2383[/C][C]-0.1387[/C][C]-0.9996[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7501 )[/C][C](0.1024 )[/C][C](0.6079 )[/C][C](0.6762 )[/C][C](0.1939 )[/C][C](0.5178 )[/C][C](0.0158 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0[/C][C]0.2141[/C][C]-0.1357[/C][C]0.0581[/C][C]0.2274[/C][C]-0.1326[/C][C]-1.0002[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0962 )[/C][C](0.3069 )[/C][C](0.6707 )[/C][C](0.2075 )[/C][C](0.5356 )[/C][C](0.0161 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.212[/C][C]-0.1339[/C][C]0[/C][C]0.2445[/C][C]-0.1409[/C][C]-1.0005[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.0994 )[/C][C](0.3144 )[/C][C](NA )[/C][C](0.1659 )[/C][C](0.51 )[/C][C](0.0183 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.2109[/C][C]-0.109[/C][C]0[/C][C]0.2801[/C][C]0[/C][C]-1.0002[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.1012 )[/C][C](0.3906 )[/C][C](NA )[/C][C](0.1099 )[/C][C](NA )[/C][C](0.0027 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0.205[/C][C]0[/C][C]0[/C][C]0.2878[/C][C]0[/C][C]-1.0002[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.1115 )[/C][C](NA )[/C][C](NA )[/C][C](0.1058 )[/C][C](NA )[/C][C](0.0042 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0.3044[/C][C]0[/C][C]-1.0002[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0868 )[/C][C](NA )[/C][C](0.0046 )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.5654[/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](0.0066 )[/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=116911&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116911&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 )-0.18790.2162-0.09860.24060.2383-0.1387-0.9996
(p-val)(0.7501 )(0.1024 )(0.6079 )(0.6762 )(0.1939 )(0.5178 )(0.0158 )
Estimates ( 2 )00.2141-0.13570.05810.2274-0.1326-1.0002
(p-val)(NA )(0.0962 )(0.3069 )(0.6707 )(0.2075 )(0.5356 )(0.0161 )
Estimates ( 3 )00.212-0.133900.2445-0.1409-1.0005
(p-val)(NA )(0.0994 )(0.3144 )(NA )(0.1659 )(0.51 )(0.0183 )
Estimates ( 4 )00.2109-0.10900.28010-1.0002
(p-val)(NA )(0.1012 )(0.3906 )(NA )(0.1099 )(NA )(0.0027 )
Estimates ( 5 )00.205000.28780-1.0002
(p-val)(NA )(0.1115 )(NA )(NA )(0.1058 )(NA )(0.0042 )
Estimates ( 6 )00000.30440-1.0002
(p-val)(NA )(NA )(NA )(NA )(0.0868 )(NA )(0.0046 )
Estimates ( 7 )000000-0.5654
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(0.0066 )
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
-0.0949373188922437
0.263786694178484
-0.244745818872373
0.324667956872568
0.00999349363157334
-0.0812361770612972
-0.317426903218636
-0.34795815007022
0.0533559411618615
-0.806552968394256
-0.0080034947183476
-0.172605967210577
0.903266048163195
-0.436518450122715
-0.0583050414054969
-0.325274342308147
-0.0182534687579758
0.332351835175594
0.376452471513606
-0.132874365077022
-0.708056771749468
0.28435347411258
0.11993898922924
0.39987487583707
0.173522723595544
0.0801667500941082
0.180291659034735
0.0915218348985293
-0.338341077739091
0.623896228705103
-0.305668824329196
-0.0513343811010567
0.02690479241007
0.333252734453844
0.313249172691641
0.511278577636403
0.84468706884714
0.423624353592623
0.266289789778293
-0.00568047231326791
0.196251751611384
0.0447014821785861
-0.557874122752944
0.0714189830894485
0.0967444218554443
0.062555664039424
0.29211015962598
0.474157690514643
-0.482450383754869
0.0948130000989215
-0.261353070789484
-0.0329877021527627
-0.0645765060780181
-0.318191620171804
-0.22465916426824
-0.108605691163017
-0.0800452405987342
0.0818538978596268
-0.124253279682036

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0949373188922437 \tabularnewline
0.263786694178484 \tabularnewline
-0.244745818872373 \tabularnewline
0.324667956872568 \tabularnewline
0.00999349363157334 \tabularnewline
-0.0812361770612972 \tabularnewline
-0.317426903218636 \tabularnewline
-0.34795815007022 \tabularnewline
0.0533559411618615 \tabularnewline
-0.806552968394256 \tabularnewline
-0.0080034947183476 \tabularnewline
-0.172605967210577 \tabularnewline
0.903266048163195 \tabularnewline
-0.436518450122715 \tabularnewline
-0.0583050414054969 \tabularnewline
-0.325274342308147 \tabularnewline
-0.0182534687579758 \tabularnewline
0.332351835175594 \tabularnewline
0.376452471513606 \tabularnewline
-0.132874365077022 \tabularnewline
-0.708056771749468 \tabularnewline
0.28435347411258 \tabularnewline
0.11993898922924 \tabularnewline
0.39987487583707 \tabularnewline
0.173522723595544 \tabularnewline
0.0801667500941082 \tabularnewline
0.180291659034735 \tabularnewline
0.0915218348985293 \tabularnewline
-0.338341077739091 \tabularnewline
0.623896228705103 \tabularnewline
-0.305668824329196 \tabularnewline
-0.0513343811010567 \tabularnewline
0.02690479241007 \tabularnewline
0.333252734453844 \tabularnewline
0.313249172691641 \tabularnewline
0.511278577636403 \tabularnewline
0.84468706884714 \tabularnewline
0.423624353592623 \tabularnewline
0.266289789778293 \tabularnewline
-0.00568047231326791 \tabularnewline
0.196251751611384 \tabularnewline
0.0447014821785861 \tabularnewline
-0.557874122752944 \tabularnewline
0.0714189830894485 \tabularnewline
0.0967444218554443 \tabularnewline
0.062555664039424 \tabularnewline
0.29211015962598 \tabularnewline
0.474157690514643 \tabularnewline
-0.482450383754869 \tabularnewline
0.0948130000989215 \tabularnewline
-0.261353070789484 \tabularnewline
-0.0329877021527627 \tabularnewline
-0.0645765060780181 \tabularnewline
-0.318191620171804 \tabularnewline
-0.22465916426824 \tabularnewline
-0.108605691163017 \tabularnewline
-0.0800452405987342 \tabularnewline
0.0818538978596268 \tabularnewline
-0.124253279682036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116911&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0949373188922437[/C][/ROW]
[ROW][C]0.263786694178484[/C][/ROW]
[ROW][C]-0.244745818872373[/C][/ROW]
[ROW][C]0.324667956872568[/C][/ROW]
[ROW][C]0.00999349363157334[/C][/ROW]
[ROW][C]-0.0812361770612972[/C][/ROW]
[ROW][C]-0.317426903218636[/C][/ROW]
[ROW][C]-0.34795815007022[/C][/ROW]
[ROW][C]0.0533559411618615[/C][/ROW]
[ROW][C]-0.806552968394256[/C][/ROW]
[ROW][C]-0.0080034947183476[/C][/ROW]
[ROW][C]-0.172605967210577[/C][/ROW]
[ROW][C]0.903266048163195[/C][/ROW]
[ROW][C]-0.436518450122715[/C][/ROW]
[ROW][C]-0.0583050414054969[/C][/ROW]
[ROW][C]-0.325274342308147[/C][/ROW]
[ROW][C]-0.0182534687579758[/C][/ROW]
[ROW][C]0.332351835175594[/C][/ROW]
[ROW][C]0.376452471513606[/C][/ROW]
[ROW][C]-0.132874365077022[/C][/ROW]
[ROW][C]-0.708056771749468[/C][/ROW]
[ROW][C]0.28435347411258[/C][/ROW]
[ROW][C]0.11993898922924[/C][/ROW]
[ROW][C]0.39987487583707[/C][/ROW]
[ROW][C]0.173522723595544[/C][/ROW]
[ROW][C]0.0801667500941082[/C][/ROW]
[ROW][C]0.180291659034735[/C][/ROW]
[ROW][C]0.0915218348985293[/C][/ROW]
[ROW][C]-0.338341077739091[/C][/ROW]
[ROW][C]0.623896228705103[/C][/ROW]
[ROW][C]-0.305668824329196[/C][/ROW]
[ROW][C]-0.0513343811010567[/C][/ROW]
[ROW][C]0.02690479241007[/C][/ROW]
[ROW][C]0.333252734453844[/C][/ROW]
[ROW][C]0.313249172691641[/C][/ROW]
[ROW][C]0.511278577636403[/C][/ROW]
[ROW][C]0.84468706884714[/C][/ROW]
[ROW][C]0.423624353592623[/C][/ROW]
[ROW][C]0.266289789778293[/C][/ROW]
[ROW][C]-0.00568047231326791[/C][/ROW]
[ROW][C]0.196251751611384[/C][/ROW]
[ROW][C]0.0447014821785861[/C][/ROW]
[ROW][C]-0.557874122752944[/C][/ROW]
[ROW][C]0.0714189830894485[/C][/ROW]
[ROW][C]0.0967444218554443[/C][/ROW]
[ROW][C]0.062555664039424[/C][/ROW]
[ROW][C]0.29211015962598[/C][/ROW]
[ROW][C]0.474157690514643[/C][/ROW]
[ROW][C]-0.482450383754869[/C][/ROW]
[ROW][C]0.0948130000989215[/C][/ROW]
[ROW][C]-0.261353070789484[/C][/ROW]
[ROW][C]-0.0329877021527627[/C][/ROW]
[ROW][C]-0.0645765060780181[/C][/ROW]
[ROW][C]-0.318191620171804[/C][/ROW]
[ROW][C]-0.22465916426824[/C][/ROW]
[ROW][C]-0.108605691163017[/C][/ROW]
[ROW][C]-0.0800452405987342[/C][/ROW]
[ROW][C]0.0818538978596268[/C][/ROW]
[ROW][C]-0.124253279682036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116911&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116911&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
-0.0949373188922437
0.263786694178484
-0.244745818872373
0.324667956872568
0.00999349363157334
-0.0812361770612972
-0.317426903218636
-0.34795815007022
0.0533559411618615
-0.806552968394256
-0.0080034947183476
-0.172605967210577
0.903266048163195
-0.436518450122715
-0.0583050414054969
-0.325274342308147
-0.0182534687579758
0.332351835175594
0.376452471513606
-0.132874365077022
-0.708056771749468
0.28435347411258
0.11993898922924
0.39987487583707
0.173522723595544
0.0801667500941082
0.180291659034735
0.0915218348985293
-0.338341077739091
0.623896228705103
-0.305668824329196
-0.0513343811010567
0.02690479241007
0.333252734453844
0.313249172691641
0.511278577636403
0.84468706884714
0.423624353592623
0.266289789778293
-0.00568047231326791
0.196251751611384
0.0447014821785861
-0.557874122752944
0.0714189830894485
0.0967444218554443
0.062555664039424
0.29211015962598
0.474157690514643
-0.482450383754869
0.0948130000989215
-0.261353070789484
-0.0329877021527627
-0.0645765060780181
-0.318191620171804
-0.22465916426824
-0.108605691163017
-0.0800452405987342
0.0818538978596268
-0.124253279682036



Parameters (Session):
par1 = 12 ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = FALSE ; par2 = 0.3 ; par3 = 1 ; 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')