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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationSat, 13 Dec 2008 08:32:40 -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/2008/Dec/13/t1229182414scmg0hkuo5dpx8y.htm/, Retrieved Thu, 16 May 2024 01:57:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33149, Retrieved Thu, 16 May 2024 01:57:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact202

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [ARIMA bel20] [2008-12-13 15:32:40] [d41d8cd98f00b204e9800998ecf8427e] [Current]
F RMP     [ARIMA Forecasting] [] [2008-12-13 15:36:11] [74be16979710d4c4e7c6647856088456]
-   PD      [ARIMA Forecasting] [ARIMA p=1 BEL20] [2008-12-16 16:17:08] [74be16979710d4c4e7c6647856088456]
-  MPD      [ARIMA Forecasting] [forecasting] [2009-12-14 13:22:22] [960f506a46b790b06fab7ca57984a121]
-  MPD      [ARIMA Forecasting] [] [2009-12-15 15:40:27] [2f674a53c3d7aaa1bcf80e66074d3c9b]
-    D        [ARIMA Forecasting] [] [2009-12-15 15:48:35] [2f674a53c3d7aaa1bcf80e66074d3c9b]
-   PD        [ARIMA Forecasting] [paper] [2010-12-25 14:06:37] [960f506a46b790b06fab7ca57984a121]
-  MPD    [ARIMA Backward Selection] [residuals] [2009-12-14 12:37:59] [960f506a46b790b06fab7ca57984a121]
-  MPD    [ARIMA Backward Selection] [] [2009-12-15 15:31:01] [2f674a53c3d7aaa1bcf80e66074d3c9b]
-    D      [ARIMA Backward Selection] [] [2009-12-15 15:46:26] [2f674a53c3d7aaa1bcf80e66074d3c9b]
-   PD      [ARIMA Backward Selection] [paper] [2010-12-24 14:13:09] [960f506a46b790b06fab7ca57984a121]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3230,66
3361,13
3484,74
3411,13
3288,18
3280,37
3173,95
3165,26
3092,71
3053,05
3181,96
2999,93
3249,57
3210,52
3030,29
2803,47
2767,63
2882,6
2863,36
2897,06
3012,61
3142,95
3032,93
3045,78
3110,52
3013,24
2987,1
2995,55
2833,18
2848,96
2794,83
2845,26
2915,02
2892,63
2604,42
2641,65
2659,81
2638,53
2720,25
2745,88
2735,7
2811,7
2799,43
2555,28
2304,98
2214,95
2065,81
1940,49
2042
1995,37
1946,81
1765,9
1635,25
1833,42
1910,43
1959,67
1969,6
2061,41
2093,48
2120,88
2174,56
2196,72
2350,44
2440,25
2408,64
2472,81
2407,6
2454,62
2448,05
2497,84
2645,64
2756,76
2849,27
2921,44
2981,85
3080,58
3106,22
3119,31
3061,26
3097,31
3161,69
3257,16
3277,01
3295,32
3363,99
3494,17
3667,03
3813,06
3917,96
3895,51
3801,06
3570,12
3701,61
3862,27
3970,1
4138,52
4199,75
4290,89
4443,91
4502,64
4356,98
4591,27
4696,96
4621,4
4562,84
4202,52
4296,49
4435,23
4105,18
4116,68
3844,49
3720,98
3674,4
3857,62
3801,06
3504,37
3032,6
3047,03
2962,34
2197,82
2014,45

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 14 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33149&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33149&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33149&T=0

As an alternative you can also use a QR Code:  
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 time14 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.9649-0.24560.21-0.7602-0.3473-0.17330.3969
(p-val)(0 )(0.0724 )(0.0512 )(0 )(0.4183 )(0.1903 )(0.3444 )
Estimates ( 2 )0.9496-0.24090.215-0.75060-0.18220.0541
(p-val)(0 )(0.077 )(0.047 )(0 )(NA )(0.1504 )(0.6532 )
Estimates ( 3 )0.9461-0.23210.2113-0.74640-0.17390
(p-val)(0 )(0.0828 )(0.0509 )(0 )(NA )(0.1677 )(NA )
Estimates ( 4 )0.9479-0.21990.2-0.7446000
(p-val)(0 )(0.1025 )(0.0688 )(0 )(NA )(NA )(NA )
Estimates ( 5 )0.182500.21410.0661000
(p-val)(0.7338 )(NA )(0.0342 )(0.9113 )(NA )(NA )(NA )
Estimates ( 6 )0.240800.21270000
(p-val)(0.0066 )(NA )(0.0331 )(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 ) & 0.9649 & -0.2456 & 0.21 & -0.7602 & -0.3473 & -0.1733 & 0.3969 \tabularnewline
(p-val) & (0 ) & (0.0724 ) & (0.0512 ) & (0 ) & (0.4183 ) & (0.1903 ) & (0.3444 ) \tabularnewline
Estimates ( 2 ) & 0.9496 & -0.2409 & 0.215 & -0.7506 & 0 & -0.1822 & 0.0541 \tabularnewline
(p-val) & (0 ) & (0.077 ) & (0.047 ) & (0 ) & (NA ) & (0.1504 ) & (0.6532 ) \tabularnewline
Estimates ( 3 ) & 0.9461 & -0.2321 & 0.2113 & -0.7464 & 0 & -0.1739 & 0 \tabularnewline
(p-val) & (0 ) & (0.0828 ) & (0.0509 ) & (0 ) & (NA ) & (0.1677 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0.9479 & -0.2199 & 0.2 & -0.7446 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.1025 ) & (0.0688 ) & (0 ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0.1825 & 0 & 0.2141 & 0.0661 & 0 & 0 & 0 \tabularnewline
(p-val) & (0.7338 ) & (NA ) & (0.0342 ) & (0.9113 ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0.2408 & 0 & 0.2127 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0.0066 ) & (NA ) & (0.0331 ) & (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=33149&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]0.9649[/C][C]-0.2456[/C][C]0.21[/C][C]-0.7602[/C][C]-0.3473[/C][C]-0.1733[/C][C]0.3969[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0724 )[/C][C](0.0512 )[/C][C](0 )[/C][C](0.4183 )[/C][C](0.1903 )[/C][C](0.3444 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.9496[/C][C]-0.2409[/C][C]0.215[/C][C]-0.7506[/C][C]0[/C][C]-0.1822[/C][C]0.0541[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.077 )[/C][C](0.047 )[/C][C](0 )[/C][C](NA )[/C][C](0.1504 )[/C][C](0.6532 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.9461[/C][C]-0.2321[/C][C]0.2113[/C][C]-0.7464[/C][C]0[/C][C]-0.1739[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0828 )[/C][C](0.0509 )[/C][C](0 )[/C][C](NA )[/C][C](0.1677 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.9479[/C][C]-0.2199[/C][C]0.2[/C][C]-0.7446[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.1025 )[/C][C](0.0688 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.1825[/C][C]0[/C][C]0.2141[/C][C]0.0661[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.7338 )[/C][C](NA )[/C][C](0.0342 )[/C][C](0.9113 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0.2408[/C][C]0[/C][C]0.2127[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0066 )[/C][C](NA )[/C][C](0.0331 )[/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=33149&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33149&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.9649-0.24560.21-0.7602-0.3473-0.17330.3969
(p-val)(0 )(0.0724 )(0.0512 )(0 )(0.4183 )(0.1903 )(0.3444 )
Estimates ( 2 )0.9496-0.24090.215-0.75060-0.18220.0541
(p-val)(0 )(0.077 )(0.047 )(0 )(NA )(0.1504 )(0.6532 )
Estimates ( 3 )0.9461-0.23210.2113-0.74640-0.17390
(p-val)(0 )(0.0828 )(0.0509 )(0 )(NA )(0.1677 )(NA )
Estimates ( 4 )0.9479-0.21990.2-0.7446000
(p-val)(0 )(0.1025 )(0.0688 )(0 )(NA )(NA )(NA )
Estimates ( 5 )0.182500.21410.0661000
(p-val)(0.7338 )(NA )(0.0342 )(0.9113 )(NA )(NA )(NA )
Estimates ( 6 )0.240800.21270000
(p-val)(0.0066 )(NA )(0.0331 )(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
3.23065817970122
122.904162737121
87.1350519106569
-107.371250369866
-130.506329686731
-3.21686642441482
-89.0201125226468
42.9410004765266
-72.130835427895
1.13459072694013
137.932562145992
-199.135946409625
304.512527723665
-132.336999254977
-125.377513304075
-239.100007747722
29.7168144010193
158.137826725849
-2.10422763049837
45.0242702599039
81.805609126673
107.967172279461
-148.157178569455
17.9774374716981
33.2970234036065
-87.735825627155
-5.34056515709517
-0.289941645244653
-143.062122104984
60.4630365303024
-62.8160396449416
99.2282608302271
50.6189866327099
-26.8747315480332
-293.146363502309
94.2622148803625
9.92925541087288
36.4643688115239
75.2202188687729
1.85704443712893
-10.4227985340481
61.0478888905313
-35.6618339097909
-237.373612043611
-206.330784160254
-28.0895824078889
-78.5752728982407
-39.3148962383871
146.254478692628
-42.8861915830259
-10.381368204713
-193.099269466796
-74.8884408678862
237.358860126963
63.8961508639723
58.9398368818051
-45.385582513331
76.5083757289201
-0.284410144520280
19.4406613288197
27.7357794318532
3.66419659958001
143.567044079432
40.7748068641295
-55.4384513340965
40.686874934127
-98.8400024836164
72.221940038125
-33.665141593789
67.1778879711064
124.205213933134
77.3464009482504
56.4589107536817
19.9086733376653
22.1307812275722
66.4346918500987
-12.2211326687743
-3.71620023266587
-81.3339319429683
46.5291587508732
51.9228983378443
92.7201474624599
-11.4196468704627
1.65722019074019
44.776395834871
110.439067482856
137.884061598755
90.6682163068399
44.3842250168027
-81.539972357632
-116.232302987383
-228.483718118659
193.542180959453
144.096338675680
118.439205346759
112.758126674638
-11.3583574365011
57.6285827785623
96.5159654417157
11.3164342374212
-176.640455034981
239.780277259416
34.5110757652992
-65.9366537178948
-90.5819732167702
-366.277508568259
200.112314320763
120.903142284988
-286.203558144092
70.5236940232335
-308.659206270088
17.2359957243948
-27.6450519919181
251.83120698586
-80.193765882018
-271.093616897989
-438.943570280565
141.644182184449
-33.15696175627
-645.854342493358
-4.2596646764855

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
3.23065817970122 \tabularnewline
122.904162737121 \tabularnewline
87.1350519106569 \tabularnewline
-107.371250369866 \tabularnewline
-130.506329686731 \tabularnewline
-3.21686642441482 \tabularnewline
-89.0201125226468 \tabularnewline
42.9410004765266 \tabularnewline
-72.130835427895 \tabularnewline
1.13459072694013 \tabularnewline
137.932562145992 \tabularnewline
-199.135946409625 \tabularnewline
304.512527723665 \tabularnewline
-132.336999254977 \tabularnewline
-125.377513304075 \tabularnewline
-239.100007747722 \tabularnewline
29.7168144010193 \tabularnewline
158.137826725849 \tabularnewline
-2.10422763049837 \tabularnewline
45.0242702599039 \tabularnewline
81.805609126673 \tabularnewline
107.967172279461 \tabularnewline
-148.157178569455 \tabularnewline
17.9774374716981 \tabularnewline
33.2970234036065 \tabularnewline
-87.735825627155 \tabularnewline
-5.34056515709517 \tabularnewline
-0.289941645244653 \tabularnewline
-143.062122104984 \tabularnewline
60.4630365303024 \tabularnewline
-62.8160396449416 \tabularnewline
99.2282608302271 \tabularnewline
50.6189866327099 \tabularnewline
-26.8747315480332 \tabularnewline
-293.146363502309 \tabularnewline
94.2622148803625 \tabularnewline
9.92925541087288 \tabularnewline
36.4643688115239 \tabularnewline
75.2202188687729 \tabularnewline
1.85704443712893 \tabularnewline
-10.4227985340481 \tabularnewline
61.0478888905313 \tabularnewline
-35.6618339097909 \tabularnewline
-237.373612043611 \tabularnewline
-206.330784160254 \tabularnewline
-28.0895824078889 \tabularnewline
-78.5752728982407 \tabularnewline
-39.3148962383871 \tabularnewline
146.254478692628 \tabularnewline
-42.8861915830259 \tabularnewline
-10.381368204713 \tabularnewline
-193.099269466796 \tabularnewline
-74.8884408678862 \tabularnewline
237.358860126963 \tabularnewline
63.8961508639723 \tabularnewline
58.9398368818051 \tabularnewline
-45.385582513331 \tabularnewline
76.5083757289201 \tabularnewline
-0.284410144520280 \tabularnewline
19.4406613288197 \tabularnewline
27.7357794318532 \tabularnewline
3.66419659958001 \tabularnewline
143.567044079432 \tabularnewline
40.7748068641295 \tabularnewline
-55.4384513340965 \tabularnewline
40.686874934127 \tabularnewline
-98.8400024836164 \tabularnewline
72.221940038125 \tabularnewline
-33.665141593789 \tabularnewline
67.1778879711064 \tabularnewline
124.205213933134 \tabularnewline
77.3464009482504 \tabularnewline
56.4589107536817 \tabularnewline
19.9086733376653 \tabularnewline
22.1307812275722 \tabularnewline
66.4346918500987 \tabularnewline
-12.2211326687743 \tabularnewline
-3.71620023266587 \tabularnewline
-81.3339319429683 \tabularnewline
46.5291587508732 \tabularnewline
51.9228983378443 \tabularnewline
92.7201474624599 \tabularnewline
-11.4196468704627 \tabularnewline
1.65722019074019 \tabularnewline
44.776395834871 \tabularnewline
110.439067482856 \tabularnewline
137.884061598755 \tabularnewline
90.6682163068399 \tabularnewline
44.3842250168027 \tabularnewline
-81.539972357632 \tabularnewline
-116.232302987383 \tabularnewline
-228.483718118659 \tabularnewline
193.542180959453 \tabularnewline
144.096338675680 \tabularnewline
118.439205346759 \tabularnewline
112.758126674638 \tabularnewline
-11.3583574365011 \tabularnewline
57.6285827785623 \tabularnewline
96.5159654417157 \tabularnewline
11.3164342374212 \tabularnewline
-176.640455034981 \tabularnewline
239.780277259416 \tabularnewline
34.5110757652992 \tabularnewline
-65.9366537178948 \tabularnewline
-90.5819732167702 \tabularnewline
-366.277508568259 \tabularnewline
200.112314320763 \tabularnewline
120.903142284988 \tabularnewline
-286.203558144092 \tabularnewline
70.5236940232335 \tabularnewline
-308.659206270088 \tabularnewline
17.2359957243948 \tabularnewline
-27.6450519919181 \tabularnewline
251.83120698586 \tabularnewline
-80.193765882018 \tabularnewline
-271.093616897989 \tabularnewline
-438.943570280565 \tabularnewline
141.644182184449 \tabularnewline
-33.15696175627 \tabularnewline
-645.854342493358 \tabularnewline
-4.2596646764855 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33149&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]3.23065817970122[/C][/ROW]
[ROW][C]122.904162737121[/C][/ROW]
[ROW][C]87.1350519106569[/C][/ROW]
[ROW][C]-107.371250369866[/C][/ROW]
[ROW][C]-130.506329686731[/C][/ROW]
[ROW][C]-3.21686642441482[/C][/ROW]
[ROW][C]-89.0201125226468[/C][/ROW]
[ROW][C]42.9410004765266[/C][/ROW]
[ROW][C]-72.130835427895[/C][/ROW]
[ROW][C]1.13459072694013[/C][/ROW]
[ROW][C]137.932562145992[/C][/ROW]
[ROW][C]-199.135946409625[/C][/ROW]
[ROW][C]304.512527723665[/C][/ROW]
[ROW][C]-132.336999254977[/C][/ROW]
[ROW][C]-125.377513304075[/C][/ROW]
[ROW][C]-239.100007747722[/C][/ROW]
[ROW][C]29.7168144010193[/C][/ROW]
[ROW][C]158.137826725849[/C][/ROW]
[ROW][C]-2.10422763049837[/C][/ROW]
[ROW][C]45.0242702599039[/C][/ROW]
[ROW][C]81.805609126673[/C][/ROW]
[ROW][C]107.967172279461[/C][/ROW]
[ROW][C]-148.157178569455[/C][/ROW]
[ROW][C]17.9774374716981[/C][/ROW]
[ROW][C]33.2970234036065[/C][/ROW]
[ROW][C]-87.735825627155[/C][/ROW]
[ROW][C]-5.34056515709517[/C][/ROW]
[ROW][C]-0.289941645244653[/C][/ROW]
[ROW][C]-143.062122104984[/C][/ROW]
[ROW][C]60.4630365303024[/C][/ROW]
[ROW][C]-62.8160396449416[/C][/ROW]
[ROW][C]99.2282608302271[/C][/ROW]
[ROW][C]50.6189866327099[/C][/ROW]
[ROW][C]-26.8747315480332[/C][/ROW]
[ROW][C]-293.146363502309[/C][/ROW]
[ROW][C]94.2622148803625[/C][/ROW]
[ROW][C]9.92925541087288[/C][/ROW]
[ROW][C]36.4643688115239[/C][/ROW]
[ROW][C]75.2202188687729[/C][/ROW]
[ROW][C]1.85704443712893[/C][/ROW]
[ROW][C]-10.4227985340481[/C][/ROW]
[ROW][C]61.0478888905313[/C][/ROW]
[ROW][C]-35.6618339097909[/C][/ROW]
[ROW][C]-237.373612043611[/C][/ROW]
[ROW][C]-206.330784160254[/C][/ROW]
[ROW][C]-28.0895824078889[/C][/ROW]
[ROW][C]-78.5752728982407[/C][/ROW]
[ROW][C]-39.3148962383871[/C][/ROW]
[ROW][C]146.254478692628[/C][/ROW]
[ROW][C]-42.8861915830259[/C][/ROW]
[ROW][C]-10.381368204713[/C][/ROW]
[ROW][C]-193.099269466796[/C][/ROW]
[ROW][C]-74.8884408678862[/C][/ROW]
[ROW][C]237.358860126963[/C][/ROW]
[ROW][C]63.8961508639723[/C][/ROW]
[ROW][C]58.9398368818051[/C][/ROW]
[ROW][C]-45.385582513331[/C][/ROW]
[ROW][C]76.5083757289201[/C][/ROW]
[ROW][C]-0.284410144520280[/C][/ROW]
[ROW][C]19.4406613288197[/C][/ROW]
[ROW][C]27.7357794318532[/C][/ROW]
[ROW][C]3.66419659958001[/C][/ROW]
[ROW][C]143.567044079432[/C][/ROW]
[ROW][C]40.7748068641295[/C][/ROW]
[ROW][C]-55.4384513340965[/C][/ROW]
[ROW][C]40.686874934127[/C][/ROW]
[ROW][C]-98.8400024836164[/C][/ROW]
[ROW][C]72.221940038125[/C][/ROW]
[ROW][C]-33.665141593789[/C][/ROW]
[ROW][C]67.1778879711064[/C][/ROW]
[ROW][C]124.205213933134[/C][/ROW]
[ROW][C]77.3464009482504[/C][/ROW]
[ROW][C]56.4589107536817[/C][/ROW]
[ROW][C]19.9086733376653[/C][/ROW]
[ROW][C]22.1307812275722[/C][/ROW]
[ROW][C]66.4346918500987[/C][/ROW]
[ROW][C]-12.2211326687743[/C][/ROW]
[ROW][C]-3.71620023266587[/C][/ROW]
[ROW][C]-81.3339319429683[/C][/ROW]
[ROW][C]46.5291587508732[/C][/ROW]
[ROW][C]51.9228983378443[/C][/ROW]
[ROW][C]92.7201474624599[/C][/ROW]
[ROW][C]-11.4196468704627[/C][/ROW]
[ROW][C]1.65722019074019[/C][/ROW]
[ROW][C]44.776395834871[/C][/ROW]
[ROW][C]110.439067482856[/C][/ROW]
[ROW][C]137.884061598755[/C][/ROW]
[ROW][C]90.6682163068399[/C][/ROW]
[ROW][C]44.3842250168027[/C][/ROW]
[ROW][C]-81.539972357632[/C][/ROW]
[ROW][C]-116.232302987383[/C][/ROW]
[ROW][C]-228.483718118659[/C][/ROW]
[ROW][C]193.542180959453[/C][/ROW]
[ROW][C]144.096338675680[/C][/ROW]
[ROW][C]118.439205346759[/C][/ROW]
[ROW][C]112.758126674638[/C][/ROW]
[ROW][C]-11.3583574365011[/C][/ROW]
[ROW][C]57.6285827785623[/C][/ROW]
[ROW][C]96.5159654417157[/C][/ROW]
[ROW][C]11.3164342374212[/C][/ROW]
[ROW][C]-176.640455034981[/C][/ROW]
[ROW][C]239.780277259416[/C][/ROW]
[ROW][C]34.5110757652992[/C][/ROW]
[ROW][C]-65.9366537178948[/C][/ROW]
[ROW][C]-90.5819732167702[/C][/ROW]
[ROW][C]-366.277508568259[/C][/ROW]
[ROW][C]200.112314320763[/C][/ROW]
[ROW][C]120.903142284988[/C][/ROW]
[ROW][C]-286.203558144092[/C][/ROW]
[ROW][C]70.5236940232335[/C][/ROW]
[ROW][C]-308.659206270088[/C][/ROW]
[ROW][C]17.2359957243948[/C][/ROW]
[ROW][C]-27.6450519919181[/C][/ROW]
[ROW][C]251.83120698586[/C][/ROW]
[ROW][C]-80.193765882018[/C][/ROW]
[ROW][C]-271.093616897989[/C][/ROW]
[ROW][C]-438.943570280565[/C][/ROW]
[ROW][C]141.644182184449[/C][/ROW]
[ROW][C]-33.15696175627[/C][/ROW]
[ROW][C]-645.854342493358[/C][/ROW]
[ROW][C]-4.2596646764855[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33149&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33149&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
3.23065817970122
122.904162737121
87.1350519106569
-107.371250369866
-130.506329686731
-3.21686642441482
-89.0201125226468
42.9410004765266
-72.130835427895
1.13459072694013
137.932562145992
-199.135946409625
304.512527723665
-132.336999254977
-125.377513304075
-239.100007747722
29.7168144010193
158.137826725849
-2.10422763049837
45.0242702599039
81.805609126673
107.967172279461
-148.157178569455
17.9774374716981
33.2970234036065
-87.735825627155
-5.34056515709517
-0.289941645244653
-143.062122104984
60.4630365303024
-62.8160396449416
99.2282608302271
50.6189866327099
-26.8747315480332
-293.146363502309
94.2622148803625
9.92925541087288
36.4643688115239
75.2202188687729
1.85704443712893
-10.4227985340481
61.0478888905313
-35.6618339097909
-237.373612043611
-206.330784160254
-28.0895824078889
-78.5752728982407
-39.3148962383871
146.254478692628
-42.8861915830259
-10.381368204713
-193.099269466796
-74.8884408678862
237.358860126963
63.8961508639723
58.9398368818051
-45.385582513331
76.5083757289201
-0.284410144520280
19.4406613288197
27.7357794318532
3.66419659958001
143.567044079432
40.7748068641295
-55.4384513340965
40.686874934127
-98.8400024836164
72.221940038125
-33.665141593789
67.1778879711064
124.205213933134
77.3464009482504
56.4589107536817
19.9086733376653
22.1307812275722
66.4346918500987
-12.2211326687743
-3.71620023266587
-81.3339319429683
46.5291587508732
51.9228983378443
92.7201474624599
-11.4196468704627
1.65722019074019
44.776395834871
110.439067482856
137.884061598755
90.6682163068399
44.3842250168027
-81.539972357632
-116.232302987383
-228.483718118659
193.542180959453
144.096338675680
118.439205346759
112.758126674638
-11.3583574365011
57.6285827785623
96.5159654417157
11.3164342374212
-176.640455034981
239.780277259416
34.5110757652992
-65.9366537178948
-90.5819732167702
-366.277508568259
200.112314320763
120.903142284988
-286.203558144092
70.5236940232335
-308.659206270088
17.2359957243948
-27.6450519919181
251.83120698586
-80.193765882018
-271.093616897989
-438.943570280565
141.644182184449
-33.15696175627
-645.854342493358
-4.2596646764855


Figures (Output of Computation)

Input Parameters & R Code

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