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Description of Statistical Computation

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 computationMon, 19 Dec 2016 20:38:16 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/19/t14821763656x8ffk3jb34jen2.htm/, Retrieved Thu, 01 May 2025 08:36:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301469, Retrieved Thu, 01 May 2025 08:36:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Exponential Smoothing] [] [2016-12-18 12:31:12] [683f400e1b95307fc738e729f07c4fce]
- RM D  [ARIMA Backward Selection] [] [2016-12-18 13:30:22] [683f400e1b95307fc738e729f07c4fce]
- R P       [ARIMA Backward Selection] [] [2016-12-19 19:38:16] [404ac5ee4f7301873f6a96ef36861981] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2280
3640
3950
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3500
4740
3690
4810
6150
4530
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3510
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3170
3440
4710
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2810
2670
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3500
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3310
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2490
2310
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2560
2740
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3010
2500
2630
2270
2410
2210
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2690
3150
2330
2260
2330
2240
2230
2270
2220
2290
2240
2110
2240
2230
2320
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2540
2530
2400
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2110
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2170
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Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301469&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]4 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301469&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301469&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.10870.0268-0.7883-0.30670.0221-0.6976-0.0385
(p-val)(0.251 )(0.7386 )(0 )(0.0424 )(0.8664 )(0 )(0.8478 )
Estimates ( 2 )0.10550.0283-0.7906-0.29620-0.6953-0.0134
(p-val)(0.2488 )(0.7202 )(0 )(0.0268 )(NA )(0 )(0.9228 )
Estimates ( 3 )0.10420.0281-0.7959-0.29530-0.69740
(p-val)(0.2426 )(0.7189 )(0 )(0.0261 )(NA )(0 )(NA )
Estimates ( 4 )0.11130-0.7898-0.30080-0.70620
(p-val)(0.2105 )(NA )(0 )(0.0231 )(NA )(0 )(NA )
Estimates ( 5 )00-0.8042-0.20560-0.6960
(p-val)(NA )(NA )(0 )(0.0644 )(NA )(0 )(NA )
Estimates ( 6 )00-0.771300-0.64440
(p-val)(NA )(NA )(0 )(NA )(NA )(0 )(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 ) & 0.1087 & 0.0268 & -0.7883 & -0.3067 & 0.0221 & -0.6976 & -0.0385 \tabularnewline
(p-val) & (0.251 ) & (0.7386 ) & (0 ) & (0.0424 ) & (0.8664 ) & (0 ) & (0.8478 ) \tabularnewline
Estimates ( 2 ) & 0.1055 & 0.0283 & -0.7906 & -0.2962 & 0 & -0.6953 & -0.0134 \tabularnewline
(p-val) & (0.2488 ) & (0.7202 ) & (0 ) & (0.0268 ) & (NA ) & (0 ) & (0.9228 ) \tabularnewline
Estimates ( 3 ) & 0.1042 & 0.0281 & -0.7959 & -0.2953 & 0 & -0.6974 & 0 \tabularnewline
(p-val) & (0.2426 ) & (0.7189 ) & (0 ) & (0.0261 ) & (NA ) & (0 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0.1113 & 0 & -0.7898 & -0.3008 & 0 & -0.7062 & 0 \tabularnewline
(p-val) & (0.2105 ) & (NA ) & (0 ) & (0.0231 ) & (NA ) & (0 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & -0.8042 & -0.2056 & 0 & -0.696 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0 ) & (0.0644 ) & (NA ) & (0 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & -0.7713 & 0 & 0 & -0.6444 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) & (0 ) & (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=301469&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]0.1087[/C][C]0.0268[/C][C]-0.7883[/C][C]-0.3067[/C][C]0.0221[/C][C]-0.6976[/C][C]-0.0385[/C][/ROW]
[ROW][C](p-val)[/C][C](0.251 )[/C][C](0.7386 )[/C][C](0 )[/C][C](0.0424 )[/C][C](0.8664 )[/C][C](0 )[/C][C](0.8478 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1055[/C][C]0.0283[/C][C]-0.7906[/C][C]-0.2962[/C][C]0[/C][C]-0.6953[/C][C]-0.0134[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2488 )[/C][C](0.7202 )[/C][C](0 )[/C][C](0.0268 )[/C][C](NA )[/C][C](0 )[/C][C](0.9228 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.1042[/C][C]0.0281[/C][C]-0.7959[/C][C]-0.2953[/C][C]0[/C][C]-0.6974[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2426 )[/C][C](0.7189 )[/C][C](0 )[/C][C](0.0261 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.1113[/C][C]0[/C][C]-0.7898[/C][C]-0.3008[/C][C]0[/C][C]-0.7062[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2105 )[/C][C](NA )[/C][C](0 )[/C][C](0.0231 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]-0.8042[/C][C]-0.2056[/C][C]0[/C][C]-0.696[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.0644 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]-0.7713[/C][C]0[/C][C]0[/C][C]-0.6444[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/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=301469&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301469&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 )0.10870.0268-0.7883-0.30670.0221-0.6976-0.0385
(p-val)(0.251 )(0.7386 )(0 )(0.0424 )(0.8664 )(0 )(0.8478 )
Estimates ( 2 )0.10550.0283-0.7906-0.29620-0.6953-0.0134
(p-val)(0.2488 )(0.7202 )(0 )(0.0268 )(NA )(0 )(0.9228 )
Estimates ( 3 )0.10420.0281-0.7959-0.29530-0.69740
(p-val)(0.2426 )(0.7189 )(0 )(0.0261 )(NA )(0 )(NA )
Estimates ( 4 )0.11130-0.7898-0.30080-0.70620
(p-val)(0.2105 )(NA )(0 )(0.0231 )(NA )(0 )(NA )
Estimates ( 5 )00-0.8042-0.20560-0.6960
(p-val)(NA )(NA )(0 )(0.0644 )(NA )(0 )(NA )
Estimates ( 6 )00-0.771300-0.64440
(p-val)(NA )(NA )(0 )(NA )(NA )(0 )(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
-2.5361230066298
-1167.65344382866
409.037833297093
-484.335385279503
462.233836126465
491.91589062608
-620.606733008894
-624.861991159603
-987.123291948008
-290.185696856841
-1438.80942204715
-513.70011518906
-49.6699264223161
25.5802740115895
-240.567492450406
880.261046265739
-133.30678257655
346.612850874244
826.03918715703
-601.153763812746
-738.472822292851
369.263331677508
391.185049215719
-377.568407209139
198.591128893087
-85.4387166899801
-362.695627056203
38.9374268459169
342.955414305597
30.7609074936536
251.026583162897
247.206549565716
-155.684007607392
-156.293510279374
72.1422666020389
101.33897378733
-135.647323207267
-115.124644997036
-128.924206903302
127.857950146494
78.4239489385741
-138.818106618233
157.090224720454
274.451489306187
-353.069574362891
258.96527962996
-354.826813045891
119.573113868617
-39.8267357210079
237.667373294856
389.747127002733
541.764113574675
-824.004022929678
-356.315189820667
-139.762844503442
-22.8717306738063
226.405258152408
-22.4563969556803
236.90758836906
0.833192657066775
-15.977527470925
-69.1829743613444
-88.8020368164003
-289.800544146609
532.498451128331
116.999242258411
206.796847703594
86.1570280886945
-130.298617135054
-14.3568059568465
-145.037059746441
-241.261837746044
-117.777735260283
192.605841915108
-83.8454856723829
222.549922449841
-147.262216870766
96.4987058642123
-117.241987320072
-59.5612061615807
82.1615454104049
109.914089075809
37.8121523966088
-15.4300628974197
-0.674612956086548
-129.987709803081
85.1576050375034
-138.614871153563
83.1200767902337
64.1517289003546
-39.2453952193332
44.1981719056553
-23.1913064905043
28.0638127025568
61.7275206993609
7.21657992872133
-129.082064650182
-27.5300219236974
8.00309690275572
92.0424755653012
-66.8826926135889
47.2634980825451
51.0846926711447
133.536873616195
-39.0402830392595

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-2.5361230066298 \tabularnewline
-1167.65344382866 \tabularnewline
409.037833297093 \tabularnewline
-484.335385279503 \tabularnewline
462.233836126465 \tabularnewline
491.91589062608 \tabularnewline
-620.606733008894 \tabularnewline
-624.861991159603 \tabularnewline
-987.123291948008 \tabularnewline
-290.185696856841 \tabularnewline
-1438.80942204715 \tabularnewline
-513.70011518906 \tabularnewline
-49.6699264223161 \tabularnewline
25.5802740115895 \tabularnewline
-240.567492450406 \tabularnewline
880.261046265739 \tabularnewline
-133.30678257655 \tabularnewline
346.612850874244 \tabularnewline
826.03918715703 \tabularnewline
-601.153763812746 \tabularnewline
-738.472822292851 \tabularnewline
369.263331677508 \tabularnewline
391.185049215719 \tabularnewline
-377.568407209139 \tabularnewline
198.591128893087 \tabularnewline
-85.4387166899801 \tabularnewline
-362.695627056203 \tabularnewline
38.9374268459169 \tabularnewline
342.955414305597 \tabularnewline
30.7609074936536 \tabularnewline
251.026583162897 \tabularnewline
247.206549565716 \tabularnewline
-155.684007607392 \tabularnewline
-156.293510279374 \tabularnewline
72.1422666020389 \tabularnewline
101.33897378733 \tabularnewline
-135.647323207267 \tabularnewline
-115.124644997036 \tabularnewline
-128.924206903302 \tabularnewline
127.857950146494 \tabularnewline
78.4239489385741 \tabularnewline
-138.818106618233 \tabularnewline
157.090224720454 \tabularnewline
274.451489306187 \tabularnewline
-353.069574362891 \tabularnewline
258.96527962996 \tabularnewline
-354.826813045891 \tabularnewline
119.573113868617 \tabularnewline
-39.8267357210079 \tabularnewline
237.667373294856 \tabularnewline
389.747127002733 \tabularnewline
541.764113574675 \tabularnewline
-824.004022929678 \tabularnewline
-356.315189820667 \tabularnewline
-139.762844503442 \tabularnewline
-22.8717306738063 \tabularnewline
226.405258152408 \tabularnewline
-22.4563969556803 \tabularnewline
236.90758836906 \tabularnewline
0.833192657066775 \tabularnewline
-15.977527470925 \tabularnewline
-69.1829743613444 \tabularnewline
-88.8020368164003 \tabularnewline
-289.800544146609 \tabularnewline
532.498451128331 \tabularnewline
116.999242258411 \tabularnewline
206.796847703594 \tabularnewline
86.1570280886945 \tabularnewline
-130.298617135054 \tabularnewline
-14.3568059568465 \tabularnewline
-145.037059746441 \tabularnewline
-241.261837746044 \tabularnewline
-117.777735260283 \tabularnewline
192.605841915108 \tabularnewline
-83.8454856723829 \tabularnewline
222.549922449841 \tabularnewline
-147.262216870766 \tabularnewline
96.4987058642123 \tabularnewline
-117.241987320072 \tabularnewline
-59.5612061615807 \tabularnewline
82.1615454104049 \tabularnewline
109.914089075809 \tabularnewline
37.8121523966088 \tabularnewline
-15.4300628974197 \tabularnewline
-0.674612956086548 \tabularnewline
-129.987709803081 \tabularnewline
85.1576050375034 \tabularnewline
-138.614871153563 \tabularnewline
83.1200767902337 \tabularnewline
64.1517289003546 \tabularnewline
-39.2453952193332 \tabularnewline
44.1981719056553 \tabularnewline
-23.1913064905043 \tabularnewline
28.0638127025568 \tabularnewline
61.7275206993609 \tabularnewline
7.21657992872133 \tabularnewline
-129.082064650182 \tabularnewline
-27.5300219236974 \tabularnewline
8.00309690275572 \tabularnewline
92.0424755653012 \tabularnewline
-66.8826926135889 \tabularnewline
47.2634980825451 \tabularnewline
51.0846926711447 \tabularnewline
133.536873616195 \tabularnewline
-39.0402830392595 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301469&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-2.5361230066298[/C][/ROW]
[ROW][C]-1167.65344382866[/C][/ROW]
[ROW][C]409.037833297093[/C][/ROW]
[ROW][C]-484.335385279503[/C][/ROW]
[ROW][C]462.233836126465[/C][/ROW]
[ROW][C]491.91589062608[/C][/ROW]
[ROW][C]-620.606733008894[/C][/ROW]
[ROW][C]-624.861991159603[/C][/ROW]
[ROW][C]-987.123291948008[/C][/ROW]
[ROW][C]-290.185696856841[/C][/ROW]
[ROW][C]-1438.80942204715[/C][/ROW]
[ROW][C]-513.70011518906[/C][/ROW]
[ROW][C]-49.6699264223161[/C][/ROW]
[ROW][C]25.5802740115895[/C][/ROW]
[ROW][C]-240.567492450406[/C][/ROW]
[ROW][C]880.261046265739[/C][/ROW]
[ROW][C]-133.30678257655[/C][/ROW]
[ROW][C]346.612850874244[/C][/ROW]
[ROW][C]826.03918715703[/C][/ROW]
[ROW][C]-601.153763812746[/C][/ROW]
[ROW][C]-738.472822292851[/C][/ROW]
[ROW][C]369.263331677508[/C][/ROW]
[ROW][C]391.185049215719[/C][/ROW]
[ROW][C]-377.568407209139[/C][/ROW]
[ROW][C]198.591128893087[/C][/ROW]
[ROW][C]-85.4387166899801[/C][/ROW]
[ROW][C]-362.695627056203[/C][/ROW]
[ROW][C]38.9374268459169[/C][/ROW]
[ROW][C]342.955414305597[/C][/ROW]
[ROW][C]30.7609074936536[/C][/ROW]
[ROW][C]251.026583162897[/C][/ROW]
[ROW][C]247.206549565716[/C][/ROW]
[ROW][C]-155.684007607392[/C][/ROW]
[ROW][C]-156.293510279374[/C][/ROW]
[ROW][C]72.1422666020389[/C][/ROW]
[ROW][C]101.33897378733[/C][/ROW]
[ROW][C]-135.647323207267[/C][/ROW]
[ROW][C]-115.124644997036[/C][/ROW]
[ROW][C]-128.924206903302[/C][/ROW]
[ROW][C]127.857950146494[/C][/ROW]
[ROW][C]78.4239489385741[/C][/ROW]
[ROW][C]-138.818106618233[/C][/ROW]
[ROW][C]157.090224720454[/C][/ROW]
[ROW][C]274.451489306187[/C][/ROW]
[ROW][C]-353.069574362891[/C][/ROW]
[ROW][C]258.96527962996[/C][/ROW]
[ROW][C]-354.826813045891[/C][/ROW]
[ROW][C]119.573113868617[/C][/ROW]
[ROW][C]-39.8267357210079[/C][/ROW]
[ROW][C]237.667373294856[/C][/ROW]
[ROW][C]389.747127002733[/C][/ROW]
[ROW][C]541.764113574675[/C][/ROW]
[ROW][C]-824.004022929678[/C][/ROW]
[ROW][C]-356.315189820667[/C][/ROW]
[ROW][C]-139.762844503442[/C][/ROW]
[ROW][C]-22.8717306738063[/C][/ROW]
[ROW][C]226.405258152408[/C][/ROW]
[ROW][C]-22.4563969556803[/C][/ROW]
[ROW][C]236.90758836906[/C][/ROW]
[ROW][C]0.833192657066775[/C][/ROW]
[ROW][C]-15.977527470925[/C][/ROW]
[ROW][C]-69.1829743613444[/C][/ROW]
[ROW][C]-88.8020368164003[/C][/ROW]
[ROW][C]-289.800544146609[/C][/ROW]
[ROW][C]532.498451128331[/C][/ROW]
[ROW][C]116.999242258411[/C][/ROW]
[ROW][C]206.796847703594[/C][/ROW]
[ROW][C]86.1570280886945[/C][/ROW]
[ROW][C]-130.298617135054[/C][/ROW]
[ROW][C]-14.3568059568465[/C][/ROW]
[ROW][C]-145.037059746441[/C][/ROW]
[ROW][C]-241.261837746044[/C][/ROW]
[ROW][C]-117.777735260283[/C][/ROW]
[ROW][C]192.605841915108[/C][/ROW]
[ROW][C]-83.8454856723829[/C][/ROW]
[ROW][C]222.549922449841[/C][/ROW]
[ROW][C]-147.262216870766[/C][/ROW]
[ROW][C]96.4987058642123[/C][/ROW]
[ROW][C]-117.241987320072[/C][/ROW]
[ROW][C]-59.5612061615807[/C][/ROW]
[ROW][C]82.1615454104049[/C][/ROW]
[ROW][C]109.914089075809[/C][/ROW]
[ROW][C]37.8121523966088[/C][/ROW]
[ROW][C]-15.4300628974197[/C][/ROW]
[ROW][C]-0.674612956086548[/C][/ROW]
[ROW][C]-129.987709803081[/C][/ROW]
[ROW][C]85.1576050375034[/C][/ROW]
[ROW][C]-138.614871153563[/C][/ROW]
[ROW][C]83.1200767902337[/C][/ROW]
[ROW][C]64.1517289003546[/C][/ROW]
[ROW][C]-39.2453952193332[/C][/ROW]
[ROW][C]44.1981719056553[/C][/ROW]
[ROW][C]-23.1913064905043[/C][/ROW]
[ROW][C]28.0638127025568[/C][/ROW]
[ROW][C]61.7275206993609[/C][/ROW]
[ROW][C]7.21657992872133[/C][/ROW]
[ROW][C]-129.082064650182[/C][/ROW]
[ROW][C]-27.5300219236974[/C][/ROW]
[ROW][C]8.00309690275572[/C][/ROW]
[ROW][C]92.0424755653012[/C][/ROW]
[ROW][C]-66.8826926135889[/C][/ROW]
[ROW][C]47.2634980825451[/C][/ROW]
[ROW][C]51.0846926711447[/C][/ROW]
[ROW][C]133.536873616195[/C][/ROW]
[ROW][C]-39.0402830392595[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301469&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301469&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
-2.5361230066298
-1167.65344382866
409.037833297093
-484.335385279503
462.233836126465
491.91589062608
-620.606733008894
-624.861991159603
-987.123291948008
-290.185696856841
-1438.80942204715
-513.70011518906
-49.6699264223161
25.5802740115895
-240.567492450406
880.261046265739
-133.30678257655
346.612850874244
826.03918715703
-601.153763812746
-738.472822292851
369.263331677508
391.185049215719
-377.568407209139
198.591128893087
-85.4387166899801
-362.695627056203
38.9374268459169
342.955414305597
30.7609074936536
251.026583162897
247.206549565716
-155.684007607392
-156.293510279374
72.1422666020389
101.33897378733
-135.647323207267
-115.124644997036
-128.924206903302
127.857950146494
78.4239489385741
-138.818106618233
157.090224720454
274.451489306187
-353.069574362891
258.96527962996
-354.826813045891
119.573113868617
-39.8267357210079
237.667373294856
389.747127002733
541.764113574675
-824.004022929678
-356.315189820667
-139.762844503442
-22.8717306738063
226.405258152408
-22.4563969556803
236.90758836906
0.833192657066775
-15.977527470925
-69.1829743613444
-88.8020368164003
-289.800544146609
532.498451128331
116.999242258411
206.796847703594
86.1570280886945
-130.298617135054
-14.3568059568465
-145.037059746441
-241.261837746044
-117.777735260283
192.605841915108
-83.8454856723829
222.549922449841
-147.262216870766
96.4987058642123
-117.241987320072
-59.5612061615807
82.1615454104049
109.914089075809
37.8121523966088
-15.4300628974197
-0.674612956086548
-129.987709803081
85.1576050375034
-138.614871153563
83.1200767902337
64.1517289003546
-39.2453952193332
44.1981719056553
-23.1913064905043
28.0638127025568
61.7275206993609
7.21657992872133
-129.082064650182
-27.5300219236974
8.00309690275572
92.0424755653012
-66.8826926135889
47.2634980825451
51.0846926711447
133.536873616195
-39.0402830392595


Figures (Output of Computation)

Input Parameters & R Code

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