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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 computationThu, 03 Dec 2009 09:44:54 -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/03/t1259858846ywxtyi5y5z9i2ud.htm/, Retrieved Thu, 28 Mar 2024 15:57:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62901, Retrieved Thu, 28 Mar 2024 15:57:00 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact183
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
- R PD      [ARIMA Backward Selection] [Bouwvergunningen] [2009-12-03 16:44:54] [a4292616308a56e4faddaa97386e0403] [Current]
- R PD        [ARIMA Backward Selection] [] [2009-12-04 15:30:07] [639dd97b6eeebe46a3c92d62cb04fb95]
- RMPD          [ARIMA Forecasting] [] [2009-12-11 11:45:13] [639dd97b6eeebe46a3c92d62cb04fb95]
-    D            [ARIMA Forecasting] [] [2009-12-11 13:25:34] [639dd97b6eeebe46a3c92d62cb04fb95]
-   P         [ARIMA Backward Selection] [Bouwvergunningen ...] [2009-12-11 17:54:33] [11ac052cc87d77b9933b02bea117068e]
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Dataseries X:
100
108.1560276
114.0150276
102.1880309
110.3672031
96.8602511
94.1944583
99.51621961
94.06333487
97.5541476
78.15062422
81.2434643
92.36262465
96.06324371
114.0523777
110.6616666
104.9171949
90.00187193
95.7008067
86.02741157
84.85287668
100.04328
80.91713823
74.06539709
77.30281369
97.23043249
90.75515676
100.5614455
92.01293267
99.24012138
105.8672755
90.9920463
93.30624423
91.17419413
77.33295039
91.1277721
85.01249943
83.90390242
104.8626302
110.9039108
95.43714373
111.6238727
108.8925403
96.17511682
101.9740205
99.11953031
86.78158147
118.4195003
118.7441447
106.5296192
134.7772694
104.6778714
105.2954304
139.4139849
103.6060491
99.78182974
103.4610301
120.0594945
96.71377168
107.1308929
105.3608372
111.6942359
132.0519998
126.8037879
154.4824253
141.5570984
109.9506882
127.904198
133.0888617
120.0796299
117.5557142
143.0362309
159.982927
128.5991124
149.7373327
126.8169313
140.9639674
137.6691981
117.9402337
122.3095247
127.7804207
136.1677176
116.2405856
123.1576893
116.3400234
108.6119282
125.8982264
112.8003105
107.5182447
135.0955413
115.5096488
115.8640759
104.5883906
163.7213386
113.4482275
98.0428844
116.7868521
126.5330444
113.0336597
124.3392163
109.8298759
124.4434777
111.5039454
102.0350019
116.8726598
112.2073122
101.1513902
124.4255108




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62901&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]11 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=62901&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62901&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 time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.1565-0.08950.2033-0.80370.1526-0.037-1
(p-val)(0.405 )(0.5243 )(0.1444 )(0 )(0.1963 )(0.7857 )(5e-04 )
Estimates ( 2 )0.1446-0.09390.2031-0.79810.15690-1
(p-val)(0.4335 )(0.5057 )(0.1478 )(0 )(0.1826 )(NA )(1e-04 )
Estimates ( 3 )0.199500.2374-0.85680.15090-1
(p-val)(0.1849 )(NA )(0.0667 )(0 )(0.1995 )(NA )(0 )
Estimates ( 4 )0.175700.238-0.838200-0.836
(p-val)(0.2417 )(NA )(0.064 )(0 )(NA )(NA )(0 )
Estimates ( 5 )000.1738-1.330500-1.2107
(p-val)(NA )(NA )(0.1155 )(0 )(NA )(NA )(0 )
Estimates ( 6 )000-1.397100-1.1771
(p-val)(NA )(NA )(NA )(0 )(NA )(NA )(0 )
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 ) & 0.1565 & -0.0895 & 0.2033 & -0.8037 & 0.1526 & -0.037 & -1 \tabularnewline
(p-val) & (0.405 ) & (0.5243 ) & (0.1444 ) & (0 ) & (0.1963 ) & (0.7857 ) & (5e-04 ) \tabularnewline
Estimates ( 2 ) & 0.1446 & -0.0939 & 0.2031 & -0.7981 & 0.1569 & 0 & -1 \tabularnewline
(p-val) & (0.4335 ) & (0.5057 ) & (0.1478 ) & (0 ) & (0.1826 ) & (NA ) & (1e-04 ) \tabularnewline
Estimates ( 3 ) & 0.1995 & 0 & 0.2374 & -0.8568 & 0.1509 & 0 & -1 \tabularnewline
(p-val) & (0.1849 ) & (NA ) & (0.0667 ) & (0 ) & (0.1995 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0.1757 & 0 & 0.238 & -0.8382 & 0 & 0 & -0.836 \tabularnewline
(p-val) & (0.2417 ) & (NA ) & (0.064 ) & (0 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0.1738 & -1.3305 & 0 & 0 & -1.2107 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.1155 ) & (0 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & -1.3971 & 0 & 0 & -1.1771 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) & (0 ) \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=62901&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]0.1565[/C][C]-0.0895[/C][C]0.2033[/C][C]-0.8037[/C][C]0.1526[/C][C]-0.037[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.405 )[/C][C](0.5243 )[/C][C](0.1444 )[/C][C](0 )[/C][C](0.1963 )[/C][C](0.7857 )[/C][C](5e-04 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1446[/C][C]-0.0939[/C][C]0.2031[/C][C]-0.7981[/C][C]0.1569[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4335 )[/C][C](0.5057 )[/C][C](0.1478 )[/C][C](0 )[/C][C](0.1826 )[/C][C](NA )[/C][C](1e-04 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.1995[/C][C]0[/C][C]0.2374[/C][C]-0.8568[/C][C]0.1509[/C][C]0[/C][C]-1[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1849 )[/C][C](NA )[/C][C](0.0667 )[/C][C](0 )[/C][C](0.1995 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.1757[/C][C]0[/C][C]0.238[/C][C]-0.8382[/C][C]0[/C][C]0[/C][C]-0.836[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2417 )[/C][C](NA )[/C][C](0.064 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0.1738[/C][C]-1.3305[/C][C]0[/C][C]0[/C][C]-1.2107[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.1155 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-1.3971[/C][C]0[/C][C]0[/C][C]-1.1771[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/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=62901&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62901&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 )0.1565-0.08950.2033-0.80370.1526-0.037-1
(p-val)(0.405 )(0.5243 )(0.1444 )(0 )(0.1963 )(0.7857 )(5e-04 )
Estimates ( 2 )0.1446-0.09390.2031-0.79810.15690-1
(p-val)(0.4335 )(0.5057 )(0.1478 )(0 )(0.1826 )(NA )(1e-04 )
Estimates ( 3 )0.199500.2374-0.85680.15090-1
(p-val)(0.1849 )(NA )(0.0667 )(0 )(0.1995 )(NA )(0 )
Estimates ( 4 )0.175700.238-0.838200-0.836
(p-val)(0.2417 )(NA )(0.064 )(0 )(NA )(NA )(0 )
Estimates ( 5 )000.1738-1.330500-1.2107
(p-val)(NA )(NA )(0.1155 )(0 )(NA )(NA )(0 )
Estimates ( 6 )000-1.397100-1.1771
(p-val)(NA )(NA )(NA )(0 )(NA )(NA )(0 )
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
-0.0162546059377794
-0.0148298338621527
0.0429789143056512
0.0676099254775447
-0.00913062407613658
-0.0273148951016960
0.0154314265058987
-0.0550836452812792
-0.0181857545735439
0.039120282650129
0.045609695591456
-0.0257114925670081
-0.0822248655735198
0.0344670951184958
-0.0649480573005924
0.0547880946588895
-0.0298714644311193
0.114486013193493
0.0950182690016939
0.0128897761246238
0.0217338721591169
-0.0585786658314538
-0.00178464667589517
0.0975509395010088
-0.0113550087842068
-0.0883044373725658
0.0179204878624572
0.066164679052732
-0.0099207405260636
0.103607176083683
0.0369264253242576
0.00965354447922407
0.026902902179049
-0.0250603551600334
0.0246542199720085
0.168876809920545
0.112338488941952
-0.036116193707152
0.0280171003434138
-0.131412282540058
-0.0399410829453708
0.124655566251397
-0.0664914145230275
-0.0280685165725154
-0.0325932501163457
0.0760414611727969
0.0299455213065676
0.0122999790464433
-0.0296840699981808
-0.00479604036760547
0.0196873639947141
0.0273047495098025
0.163972076190680
0.0271393922548936
-0.0986837349351024
0.0367475750419005
0.059550337783431
-0.034635410814478
0.0511197750260465
0.0870247693412692
0.139123550697516
-0.0711869967705733
-0.0539550882823555
-0.120829434834710
-0.00399043356082099
-0.0384057230232389
-0.0498123979425045
-0.0102239321937341
0.0132516871224478
0.0386859479280122
0.0182281387500084
-0.0327287832111250
-0.0857238681324117
-0.0983692652186727
-0.0623788777331983
-0.0589630420706581
-0.084951519189121
0.062846014700024
0.0232288817825146
0.0352284612117494
-0.0732760380316094
0.204214186760906
0.0202510008831150
-0.131477217803103
-0.0460660250497657
0.0514761046443304
-0.0909439057760498
0.0235160893016880
-0.0780858631227644
0.00813869562935705
-0.006437489243248
-0.0388345261403087
0.0477437773767919
-0.0602013822220711
0.0188028818969097
0.0871164100684405

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0162546059377794 \tabularnewline
-0.0148298338621527 \tabularnewline
0.0429789143056512 \tabularnewline
0.0676099254775447 \tabularnewline
-0.00913062407613658 \tabularnewline
-0.0273148951016960 \tabularnewline
0.0154314265058987 \tabularnewline
-0.0550836452812792 \tabularnewline
-0.0181857545735439 \tabularnewline
0.039120282650129 \tabularnewline
0.045609695591456 \tabularnewline
-0.0257114925670081 \tabularnewline
-0.0822248655735198 \tabularnewline
0.0344670951184958 \tabularnewline
-0.0649480573005924 \tabularnewline
0.0547880946588895 \tabularnewline
-0.0298714644311193 \tabularnewline
0.114486013193493 \tabularnewline
0.0950182690016939 \tabularnewline
0.0128897761246238 \tabularnewline
0.0217338721591169 \tabularnewline
-0.0585786658314538 \tabularnewline
-0.00178464667589517 \tabularnewline
0.0975509395010088 \tabularnewline
-0.0113550087842068 \tabularnewline
-0.0883044373725658 \tabularnewline
0.0179204878624572 \tabularnewline
0.066164679052732 \tabularnewline
-0.0099207405260636 \tabularnewline
0.103607176083683 \tabularnewline
0.0369264253242576 \tabularnewline
0.00965354447922407 \tabularnewline
0.026902902179049 \tabularnewline
-0.0250603551600334 \tabularnewline
0.0246542199720085 \tabularnewline
0.168876809920545 \tabularnewline
0.112338488941952 \tabularnewline
-0.036116193707152 \tabularnewline
0.0280171003434138 \tabularnewline
-0.131412282540058 \tabularnewline
-0.0399410829453708 \tabularnewline
0.124655566251397 \tabularnewline
-0.0664914145230275 \tabularnewline
-0.0280685165725154 \tabularnewline
-0.0325932501163457 \tabularnewline
0.0760414611727969 \tabularnewline
0.0299455213065676 \tabularnewline
0.0122999790464433 \tabularnewline
-0.0296840699981808 \tabularnewline
-0.00479604036760547 \tabularnewline
0.0196873639947141 \tabularnewline
0.0273047495098025 \tabularnewline
0.163972076190680 \tabularnewline
0.0271393922548936 \tabularnewline
-0.0986837349351024 \tabularnewline
0.0367475750419005 \tabularnewline
0.059550337783431 \tabularnewline
-0.034635410814478 \tabularnewline
0.0511197750260465 \tabularnewline
0.0870247693412692 \tabularnewline
0.139123550697516 \tabularnewline
-0.0711869967705733 \tabularnewline
-0.0539550882823555 \tabularnewline
-0.120829434834710 \tabularnewline
-0.00399043356082099 \tabularnewline
-0.0384057230232389 \tabularnewline
-0.0498123979425045 \tabularnewline
-0.0102239321937341 \tabularnewline
0.0132516871224478 \tabularnewline
0.0386859479280122 \tabularnewline
0.0182281387500084 \tabularnewline
-0.0327287832111250 \tabularnewline
-0.0857238681324117 \tabularnewline
-0.0983692652186727 \tabularnewline
-0.0623788777331983 \tabularnewline
-0.0589630420706581 \tabularnewline
-0.084951519189121 \tabularnewline
0.062846014700024 \tabularnewline
0.0232288817825146 \tabularnewline
0.0352284612117494 \tabularnewline
-0.0732760380316094 \tabularnewline
0.204214186760906 \tabularnewline
0.0202510008831150 \tabularnewline
-0.131477217803103 \tabularnewline
-0.0460660250497657 \tabularnewline
0.0514761046443304 \tabularnewline
-0.0909439057760498 \tabularnewline
0.0235160893016880 \tabularnewline
-0.0780858631227644 \tabularnewline
0.00813869562935705 \tabularnewline
-0.006437489243248 \tabularnewline
-0.0388345261403087 \tabularnewline
0.0477437773767919 \tabularnewline
-0.0602013822220711 \tabularnewline
0.0188028818969097 \tabularnewline
0.0871164100684405 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62901&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0162546059377794[/C][/ROW]
[ROW][C]-0.0148298338621527[/C][/ROW]
[ROW][C]0.0429789143056512[/C][/ROW]
[ROW][C]0.0676099254775447[/C][/ROW]
[ROW][C]-0.00913062407613658[/C][/ROW]
[ROW][C]-0.0273148951016960[/C][/ROW]
[ROW][C]0.0154314265058987[/C][/ROW]
[ROW][C]-0.0550836452812792[/C][/ROW]
[ROW][C]-0.0181857545735439[/C][/ROW]
[ROW][C]0.039120282650129[/C][/ROW]
[ROW][C]0.045609695591456[/C][/ROW]
[ROW][C]-0.0257114925670081[/C][/ROW]
[ROW][C]-0.0822248655735198[/C][/ROW]
[ROW][C]0.0344670951184958[/C][/ROW]
[ROW][C]-0.0649480573005924[/C][/ROW]
[ROW][C]0.0547880946588895[/C][/ROW]
[ROW][C]-0.0298714644311193[/C][/ROW]
[ROW][C]0.114486013193493[/C][/ROW]
[ROW][C]0.0950182690016939[/C][/ROW]
[ROW][C]0.0128897761246238[/C][/ROW]
[ROW][C]0.0217338721591169[/C][/ROW]
[ROW][C]-0.0585786658314538[/C][/ROW]
[ROW][C]-0.00178464667589517[/C][/ROW]
[ROW][C]0.0975509395010088[/C][/ROW]
[ROW][C]-0.0113550087842068[/C][/ROW]
[ROW][C]-0.0883044373725658[/C][/ROW]
[ROW][C]0.0179204878624572[/C][/ROW]
[ROW][C]0.066164679052732[/C][/ROW]
[ROW][C]-0.0099207405260636[/C][/ROW]
[ROW][C]0.103607176083683[/C][/ROW]
[ROW][C]0.0369264253242576[/C][/ROW]
[ROW][C]0.00965354447922407[/C][/ROW]
[ROW][C]0.026902902179049[/C][/ROW]
[ROW][C]-0.0250603551600334[/C][/ROW]
[ROW][C]0.0246542199720085[/C][/ROW]
[ROW][C]0.168876809920545[/C][/ROW]
[ROW][C]0.112338488941952[/C][/ROW]
[ROW][C]-0.036116193707152[/C][/ROW]
[ROW][C]0.0280171003434138[/C][/ROW]
[ROW][C]-0.131412282540058[/C][/ROW]
[ROW][C]-0.0399410829453708[/C][/ROW]
[ROW][C]0.124655566251397[/C][/ROW]
[ROW][C]-0.0664914145230275[/C][/ROW]
[ROW][C]-0.0280685165725154[/C][/ROW]
[ROW][C]-0.0325932501163457[/C][/ROW]
[ROW][C]0.0760414611727969[/C][/ROW]
[ROW][C]0.0299455213065676[/C][/ROW]
[ROW][C]0.0122999790464433[/C][/ROW]
[ROW][C]-0.0296840699981808[/C][/ROW]
[ROW][C]-0.00479604036760547[/C][/ROW]
[ROW][C]0.0196873639947141[/C][/ROW]
[ROW][C]0.0273047495098025[/C][/ROW]
[ROW][C]0.163972076190680[/C][/ROW]
[ROW][C]0.0271393922548936[/C][/ROW]
[ROW][C]-0.0986837349351024[/C][/ROW]
[ROW][C]0.0367475750419005[/C][/ROW]
[ROW][C]0.059550337783431[/C][/ROW]
[ROW][C]-0.034635410814478[/C][/ROW]
[ROW][C]0.0511197750260465[/C][/ROW]
[ROW][C]0.0870247693412692[/C][/ROW]
[ROW][C]0.139123550697516[/C][/ROW]
[ROW][C]-0.0711869967705733[/C][/ROW]
[ROW][C]-0.0539550882823555[/C][/ROW]
[ROW][C]-0.120829434834710[/C][/ROW]
[ROW][C]-0.00399043356082099[/C][/ROW]
[ROW][C]-0.0384057230232389[/C][/ROW]
[ROW][C]-0.0498123979425045[/C][/ROW]
[ROW][C]-0.0102239321937341[/C][/ROW]
[ROW][C]0.0132516871224478[/C][/ROW]
[ROW][C]0.0386859479280122[/C][/ROW]
[ROW][C]0.0182281387500084[/C][/ROW]
[ROW][C]-0.0327287832111250[/C][/ROW]
[ROW][C]-0.0857238681324117[/C][/ROW]
[ROW][C]-0.0983692652186727[/C][/ROW]
[ROW][C]-0.0623788777331983[/C][/ROW]
[ROW][C]-0.0589630420706581[/C][/ROW]
[ROW][C]-0.084951519189121[/C][/ROW]
[ROW][C]0.062846014700024[/C][/ROW]
[ROW][C]0.0232288817825146[/C][/ROW]
[ROW][C]0.0352284612117494[/C][/ROW]
[ROW][C]-0.0732760380316094[/C][/ROW]
[ROW][C]0.204214186760906[/C][/ROW]
[ROW][C]0.0202510008831150[/C][/ROW]
[ROW][C]-0.131477217803103[/C][/ROW]
[ROW][C]-0.0460660250497657[/C][/ROW]
[ROW][C]0.0514761046443304[/C][/ROW]
[ROW][C]-0.0909439057760498[/C][/ROW]
[ROW][C]0.0235160893016880[/C][/ROW]
[ROW][C]-0.0780858631227644[/C][/ROW]
[ROW][C]0.00813869562935705[/C][/ROW]
[ROW][C]-0.006437489243248[/C][/ROW]
[ROW][C]-0.0388345261403087[/C][/ROW]
[ROW][C]0.0477437773767919[/C][/ROW]
[ROW][C]-0.0602013822220711[/C][/ROW]
[ROW][C]0.0188028818969097[/C][/ROW]
[ROW][C]0.0871164100684405[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62901&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62901&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
-0.0162546059377794
-0.0148298338621527
0.0429789143056512
0.0676099254775447
-0.00913062407613658
-0.0273148951016960
0.0154314265058987
-0.0550836452812792
-0.0181857545735439
0.039120282650129
0.045609695591456
-0.0257114925670081
-0.0822248655735198
0.0344670951184958
-0.0649480573005924
0.0547880946588895
-0.0298714644311193
0.114486013193493
0.0950182690016939
0.0128897761246238
0.0217338721591169
-0.0585786658314538
-0.00178464667589517
0.0975509395010088
-0.0113550087842068
-0.0883044373725658
0.0179204878624572
0.066164679052732
-0.0099207405260636
0.103607176083683
0.0369264253242576
0.00965354447922407
0.026902902179049
-0.0250603551600334
0.0246542199720085
0.168876809920545
0.112338488941952
-0.036116193707152
0.0280171003434138
-0.131412282540058
-0.0399410829453708
0.124655566251397
-0.0664914145230275
-0.0280685165725154
-0.0325932501163457
0.0760414611727969
0.0299455213065676
0.0122999790464433
-0.0296840699981808
-0.00479604036760547
0.0196873639947141
0.0273047495098025
0.163972076190680
0.0271393922548936
-0.0986837349351024
0.0367475750419005
0.059550337783431
-0.034635410814478
0.0511197750260465
0.0870247693412692
0.139123550697516
-0.0711869967705733
-0.0539550882823555
-0.120829434834710
-0.00399043356082099
-0.0384057230232389
-0.0498123979425045
-0.0102239321937341
0.0132516871224478
0.0386859479280122
0.0182281387500084
-0.0327287832111250
-0.0857238681324117
-0.0983692652186727
-0.0623788777331983
-0.0589630420706581
-0.084951519189121
0.062846014700024
0.0232288817825146
0.0352284612117494
-0.0732760380316094
0.204214186760906
0.0202510008831150
-0.131477217803103
-0.0460660250497657
0.0514761046443304
-0.0909439057760498
0.0235160893016880
-0.0780858631227644
0.00813869562935705
-0.006437489243248
-0.0388345261403087
0.0477437773767919
-0.0602013822220711
0.0188028818969097
0.0871164100684405



Parameters (Session):
par1 = FALSE ; par2 = 0.0 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.0 ; 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')