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Author*The author of this computation has been verified*
R Software Modulerwasp_decomposeloess.wasp
Title produced by softwareDecomposition by Loess
Date of computationFri, 09 Dec 2016 14:34:56 +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/09/t14812910015df8oxa20hncrhz.htm/, Retrieved Sat, 18 May 2024 06:15:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298540, Retrieved Sat, 18 May 2024 06:15:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [Autocorr eerste] [2016-12-07 13:39:31] [5f979cb1c6fa86b57093c7542788c28c]
- RM      [Decomposition by Loess] [Loess 1e] [2016-12-09 13:34:56] [4c05fa0998bf98e29c2e453b139976f4] [Current]
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Dataseries X:
2954.4
1769.7
1509.9
2257.2
3433.2
2083.8
1664.7
2463.3
3995.4
2447.4
2042.7
3198.6
4935.3
3024
2573.7
3957.9
5640.6
3630
3028.2
4534.2
6815.1
3962.4
3236.4
4946.1
6911.7
4376.1
3276
5187
7664.1
4283.7
3254.7
5046.6
7470.6
3655.8
2937.3
4923.9
6344.7
2981.7
2114.7
3919.5
5380.8
2661
1935.9
3669.9
5669.7
2508.9
1911.6
3758.1
5597.7
2573.4
1916.7
4160.1
5292.6
2547
1850.4
3855.6




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298540&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]2 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298540&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298540&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal561057
Trend711
Low-pass511

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 561 & 0 & 57 \tabularnewline
Trend & 7 & 1 & 1 \tabularnewline
Low-pass & 5 & 1 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298540&T=1

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Parameters[/C][/ROW]
[ROW][C]Component[/C][C]Window[/C][C]Degree[/C][C]Jump[/C][/ROW]
[ROW][C]Seasonal[/C][C]561[/C][C]0[/C][C]57[/C][/ROW]
[ROW][C]Trend[/C][C]7[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]Low-pass[/C][C]5[/C][C]1[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298540&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298540&T=1

As an alternative you can also use a QR Code:  

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

Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal561057
Trend711
Low-pass511







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
12954.42277.863078374661881.261376366171749.67554525916-676.536921625339
21769.72274.13779017306-691.9763517142761957.23856154122504.437790173057
31509.92273.08365648517-1384.314574599342131.03091811417763.18365648517
42257.22063.32034855544195.0289847436012256.05066670096-193.879651444561
53433.22704.519185770851881.261376366172280.61943786298-728.680814229154
62083.82449.6979282245-691.9763517142762409.87842348977365.897928224501
71664.72178.25417348401-1384.314574599342535.46040111534513.554173484008
82463.32164.70988027978195.0289847436012566.86113497662-298.590119720219
93995.43468.128458206221881.261376366172641.4101654276-527.271541793779
102447.42737.25602130366-691.9763517142762849.52033041062289.856021303659
112042.72393.68899398782-1384.314574599343076.02558061153350.988993987817
123198.62992.22880863817195.0289847436013209.94220661823-206.371191361828
134935.34650.762506389671881.261376366173338.57611724415-284.537493610327
1430243198.24368606156-691.9763517142763541.73266565272174.243686061555
152573.72796.11321199401-1384.314574599343735.60136260533222.413211994014
163957.93857.52481458617195.0289847436013863.24620067023-100.375185413834
175640.65410.210002562271881.261376366173989.72862107155-230.389997437727
1836303805.26012855347-691.9763517142764146.71622316081175.260128553469
193028.23083.41034147017-1384.314574599344357.3042331291755.2103414701687
204534.24333.1145144644195.0289847436014540.256500792-201.085485535597
216815.17122.703439831541881.261376366174626.23518380228307.603439831542
223962.43921.24325959293-691.9763517142764695.53309212135-41.1567404070702
233236.43122.02508505512-1384.314574599344735.08948954423-114.374914944884
244946.14885.85304371554195.0289847436014811.31797154087-60.2469562844653
256911.77051.702544248861881.261376366174890.43607938497140.002544248857
264376.14529.62617619976-691.9763517142764914.55017551451153.526176199762
2732762937.0789822876-1384.314574599344999.23559231174-338.921017712395
2851875063.28112703432195.0289847436015115.68988822208-123.718872965677
297664.18300.568809840741881.261376366175146.36981379309636.468809840739
304283.74173.85702068721-691.9763517142765085.51933102707-109.84297931279
313254.72904.36075576183-1384.314574599344989.35381883751-350.339244238166
325046.64949.93362503956195.0289847436014948.23739021684-96.6663749604431
337470.68199.996306888571881.261376366174859.94231674525729.396306888573
343655.83255.65161730805-691.9763517142764747.92473440623-400.148382691953
352937.32685.14685727422-1384.314574599344573.76771732512-252.153142725777
364923.95240.07515197131195.0289847436014412.69586328508316.175151971314
376344.76589.815581101521881.261376366174218.3230425323245.115581101521
382981.72713.33772852343-691.9763517142763942.03862319085-268.36227147657
392114.71915.56819575655-1384.314574599343698.14637884279-199.131804243451
403919.54077.22613581962195.0289847436013566.74487943678157.726135819619
415380.85378.702283435781881.261376366173501.63634019805-2.0977165642239
4226612582.45666543572-691.9763517142763431.51968627856-78.5433345642818
431935.91823.0582402036-1384.314574599343433.05633439574-112.841759796398
443669.93668.76920754154195.0289847436013476.00180771486-1.13079245846029
455669.75996.864382908011881.261376366173461.27424072581327.164382908012
462508.92270.23663249986-691.9763517142763439.53971921441-238.663367500139
471911.61776.11631230809-1384.314574599343431.39826229126-135.483687691912
483758.13853.30002597039195.0289847436013467.8709892860195.2000259703877
495597.75836.308334377971881.261376366173477.83028925585238.608334377973
502573.42348.82285521857-691.9763517142763489.95349649571-224.577144781435
511916.71702.97816823572-1384.314574599343514.73640636363-213.721831764283
524160.14614.98121049982195.0289847436013510.18980475658454.881210499824
535292.65223.533992175041881.261376366173480.40463145879-69.0660078249648
5425472342.21197918223-691.9763517142763443.76437253205-204.788020817773
551850.41659.22788957959-1384.314574599343425.88668501975-191.17211042041
563855.64095.06520746221195.0289847436013421.10580779419239.465207462213

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 2954.4 & 2277.86307837466 & 1881.26137636617 & 1749.67554525916 & -676.536921625339 \tabularnewline
2 & 1769.7 & 2274.13779017306 & -691.976351714276 & 1957.23856154122 & 504.437790173057 \tabularnewline
3 & 1509.9 & 2273.08365648517 & -1384.31457459934 & 2131.03091811417 & 763.18365648517 \tabularnewline
4 & 2257.2 & 2063.32034855544 & 195.028984743601 & 2256.05066670096 & -193.879651444561 \tabularnewline
5 & 3433.2 & 2704.51918577085 & 1881.26137636617 & 2280.61943786298 & -728.680814229154 \tabularnewline
6 & 2083.8 & 2449.6979282245 & -691.976351714276 & 2409.87842348977 & 365.897928224501 \tabularnewline
7 & 1664.7 & 2178.25417348401 & -1384.31457459934 & 2535.46040111534 & 513.554173484008 \tabularnewline
8 & 2463.3 & 2164.70988027978 & 195.028984743601 & 2566.86113497662 & -298.590119720219 \tabularnewline
9 & 3995.4 & 3468.12845820622 & 1881.26137636617 & 2641.4101654276 & -527.271541793779 \tabularnewline
10 & 2447.4 & 2737.25602130366 & -691.976351714276 & 2849.52033041062 & 289.856021303659 \tabularnewline
11 & 2042.7 & 2393.68899398782 & -1384.31457459934 & 3076.02558061153 & 350.988993987817 \tabularnewline
12 & 3198.6 & 2992.22880863817 & 195.028984743601 & 3209.94220661823 & -206.371191361828 \tabularnewline
13 & 4935.3 & 4650.76250638967 & 1881.26137636617 & 3338.57611724415 & -284.537493610327 \tabularnewline
14 & 3024 & 3198.24368606156 & -691.976351714276 & 3541.73266565272 & 174.243686061555 \tabularnewline
15 & 2573.7 & 2796.11321199401 & -1384.31457459934 & 3735.60136260533 & 222.413211994014 \tabularnewline
16 & 3957.9 & 3857.52481458617 & 195.028984743601 & 3863.24620067023 & -100.375185413834 \tabularnewline
17 & 5640.6 & 5410.21000256227 & 1881.26137636617 & 3989.72862107155 & -230.389997437727 \tabularnewline
18 & 3630 & 3805.26012855347 & -691.976351714276 & 4146.71622316081 & 175.260128553469 \tabularnewline
19 & 3028.2 & 3083.41034147017 & -1384.31457459934 & 4357.30423312917 & 55.2103414701687 \tabularnewline
20 & 4534.2 & 4333.1145144644 & 195.028984743601 & 4540.256500792 & -201.085485535597 \tabularnewline
21 & 6815.1 & 7122.70343983154 & 1881.26137636617 & 4626.23518380228 & 307.603439831542 \tabularnewline
22 & 3962.4 & 3921.24325959293 & -691.976351714276 & 4695.53309212135 & -41.1567404070702 \tabularnewline
23 & 3236.4 & 3122.02508505512 & -1384.31457459934 & 4735.08948954423 & -114.374914944884 \tabularnewline
24 & 4946.1 & 4885.85304371554 & 195.028984743601 & 4811.31797154087 & -60.2469562844653 \tabularnewline
25 & 6911.7 & 7051.70254424886 & 1881.26137636617 & 4890.43607938497 & 140.002544248857 \tabularnewline
26 & 4376.1 & 4529.62617619976 & -691.976351714276 & 4914.55017551451 & 153.526176199762 \tabularnewline
27 & 3276 & 2937.0789822876 & -1384.31457459934 & 4999.23559231174 & -338.921017712395 \tabularnewline
28 & 5187 & 5063.28112703432 & 195.028984743601 & 5115.68988822208 & -123.718872965677 \tabularnewline
29 & 7664.1 & 8300.56880984074 & 1881.26137636617 & 5146.36981379309 & 636.468809840739 \tabularnewline
30 & 4283.7 & 4173.85702068721 & -691.976351714276 & 5085.51933102707 & -109.84297931279 \tabularnewline
31 & 3254.7 & 2904.36075576183 & -1384.31457459934 & 4989.35381883751 & -350.339244238166 \tabularnewline
32 & 5046.6 & 4949.93362503956 & 195.028984743601 & 4948.23739021684 & -96.6663749604431 \tabularnewline
33 & 7470.6 & 8199.99630688857 & 1881.26137636617 & 4859.94231674525 & 729.396306888573 \tabularnewline
34 & 3655.8 & 3255.65161730805 & -691.976351714276 & 4747.92473440623 & -400.148382691953 \tabularnewline
35 & 2937.3 & 2685.14685727422 & -1384.31457459934 & 4573.76771732512 & -252.153142725777 \tabularnewline
36 & 4923.9 & 5240.07515197131 & 195.028984743601 & 4412.69586328508 & 316.175151971314 \tabularnewline
37 & 6344.7 & 6589.81558110152 & 1881.26137636617 & 4218.3230425323 & 245.115581101521 \tabularnewline
38 & 2981.7 & 2713.33772852343 & -691.976351714276 & 3942.03862319085 & -268.36227147657 \tabularnewline
39 & 2114.7 & 1915.56819575655 & -1384.31457459934 & 3698.14637884279 & -199.131804243451 \tabularnewline
40 & 3919.5 & 4077.22613581962 & 195.028984743601 & 3566.74487943678 & 157.726135819619 \tabularnewline
41 & 5380.8 & 5378.70228343578 & 1881.26137636617 & 3501.63634019805 & -2.0977165642239 \tabularnewline
42 & 2661 & 2582.45666543572 & -691.976351714276 & 3431.51968627856 & -78.5433345642818 \tabularnewline
43 & 1935.9 & 1823.0582402036 & -1384.31457459934 & 3433.05633439574 & -112.841759796398 \tabularnewline
44 & 3669.9 & 3668.76920754154 & 195.028984743601 & 3476.00180771486 & -1.13079245846029 \tabularnewline
45 & 5669.7 & 5996.86438290801 & 1881.26137636617 & 3461.27424072581 & 327.164382908012 \tabularnewline
46 & 2508.9 & 2270.23663249986 & -691.976351714276 & 3439.53971921441 & -238.663367500139 \tabularnewline
47 & 1911.6 & 1776.11631230809 & -1384.31457459934 & 3431.39826229126 & -135.483687691912 \tabularnewline
48 & 3758.1 & 3853.30002597039 & 195.028984743601 & 3467.87098928601 & 95.2000259703877 \tabularnewline
49 & 5597.7 & 5836.30833437797 & 1881.26137636617 & 3477.83028925585 & 238.608334377973 \tabularnewline
50 & 2573.4 & 2348.82285521857 & -691.976351714276 & 3489.95349649571 & -224.577144781435 \tabularnewline
51 & 1916.7 & 1702.97816823572 & -1384.31457459934 & 3514.73640636363 & -213.721831764283 \tabularnewline
52 & 4160.1 & 4614.98121049982 & 195.028984743601 & 3510.18980475658 & 454.881210499824 \tabularnewline
53 & 5292.6 & 5223.53399217504 & 1881.26137636617 & 3480.40463145879 & -69.0660078249648 \tabularnewline
54 & 2547 & 2342.21197918223 & -691.976351714276 & 3443.76437253205 & -204.788020817773 \tabularnewline
55 & 1850.4 & 1659.22788957959 & -1384.31457459934 & 3425.88668501975 & -191.17211042041 \tabularnewline
56 & 3855.6 & 4095.06520746221 & 195.028984743601 & 3421.10580779419 & 239.465207462213 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298540&T=2

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Time Series Components[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Seasonal[/C][C]Trend[/C][C]Remainder[/C][/ROW]
[ROW][C]1[/C][C]2954.4[/C][C]2277.86307837466[/C][C]1881.26137636617[/C][C]1749.67554525916[/C][C]-676.536921625339[/C][/ROW]
[ROW][C]2[/C][C]1769.7[/C][C]2274.13779017306[/C][C]-691.976351714276[/C][C]1957.23856154122[/C][C]504.437790173057[/C][/ROW]
[ROW][C]3[/C][C]1509.9[/C][C]2273.08365648517[/C][C]-1384.31457459934[/C][C]2131.03091811417[/C][C]763.18365648517[/C][/ROW]
[ROW][C]4[/C][C]2257.2[/C][C]2063.32034855544[/C][C]195.028984743601[/C][C]2256.05066670096[/C][C]-193.879651444561[/C][/ROW]
[ROW][C]5[/C][C]3433.2[/C][C]2704.51918577085[/C][C]1881.26137636617[/C][C]2280.61943786298[/C][C]-728.680814229154[/C][/ROW]
[ROW][C]6[/C][C]2083.8[/C][C]2449.6979282245[/C][C]-691.976351714276[/C][C]2409.87842348977[/C][C]365.897928224501[/C][/ROW]
[ROW][C]7[/C][C]1664.7[/C][C]2178.25417348401[/C][C]-1384.31457459934[/C][C]2535.46040111534[/C][C]513.554173484008[/C][/ROW]
[ROW][C]8[/C][C]2463.3[/C][C]2164.70988027978[/C][C]195.028984743601[/C][C]2566.86113497662[/C][C]-298.590119720219[/C][/ROW]
[ROW][C]9[/C][C]3995.4[/C][C]3468.12845820622[/C][C]1881.26137636617[/C][C]2641.4101654276[/C][C]-527.271541793779[/C][/ROW]
[ROW][C]10[/C][C]2447.4[/C][C]2737.25602130366[/C][C]-691.976351714276[/C][C]2849.52033041062[/C][C]289.856021303659[/C][/ROW]
[ROW][C]11[/C][C]2042.7[/C][C]2393.68899398782[/C][C]-1384.31457459934[/C][C]3076.02558061153[/C][C]350.988993987817[/C][/ROW]
[ROW][C]12[/C][C]3198.6[/C][C]2992.22880863817[/C][C]195.028984743601[/C][C]3209.94220661823[/C][C]-206.371191361828[/C][/ROW]
[ROW][C]13[/C][C]4935.3[/C][C]4650.76250638967[/C][C]1881.26137636617[/C][C]3338.57611724415[/C][C]-284.537493610327[/C][/ROW]
[ROW][C]14[/C][C]3024[/C][C]3198.24368606156[/C][C]-691.976351714276[/C][C]3541.73266565272[/C][C]174.243686061555[/C][/ROW]
[ROW][C]15[/C][C]2573.7[/C][C]2796.11321199401[/C][C]-1384.31457459934[/C][C]3735.60136260533[/C][C]222.413211994014[/C][/ROW]
[ROW][C]16[/C][C]3957.9[/C][C]3857.52481458617[/C][C]195.028984743601[/C][C]3863.24620067023[/C][C]-100.375185413834[/C][/ROW]
[ROW][C]17[/C][C]5640.6[/C][C]5410.21000256227[/C][C]1881.26137636617[/C][C]3989.72862107155[/C][C]-230.389997437727[/C][/ROW]
[ROW][C]18[/C][C]3630[/C][C]3805.26012855347[/C][C]-691.976351714276[/C][C]4146.71622316081[/C][C]175.260128553469[/C][/ROW]
[ROW][C]19[/C][C]3028.2[/C][C]3083.41034147017[/C][C]-1384.31457459934[/C][C]4357.30423312917[/C][C]55.2103414701687[/C][/ROW]
[ROW][C]20[/C][C]4534.2[/C][C]4333.1145144644[/C][C]195.028984743601[/C][C]4540.256500792[/C][C]-201.085485535597[/C][/ROW]
[ROW][C]21[/C][C]6815.1[/C][C]7122.70343983154[/C][C]1881.26137636617[/C][C]4626.23518380228[/C][C]307.603439831542[/C][/ROW]
[ROW][C]22[/C][C]3962.4[/C][C]3921.24325959293[/C][C]-691.976351714276[/C][C]4695.53309212135[/C][C]-41.1567404070702[/C][/ROW]
[ROW][C]23[/C][C]3236.4[/C][C]3122.02508505512[/C][C]-1384.31457459934[/C][C]4735.08948954423[/C][C]-114.374914944884[/C][/ROW]
[ROW][C]24[/C][C]4946.1[/C][C]4885.85304371554[/C][C]195.028984743601[/C][C]4811.31797154087[/C][C]-60.2469562844653[/C][/ROW]
[ROW][C]25[/C][C]6911.7[/C][C]7051.70254424886[/C][C]1881.26137636617[/C][C]4890.43607938497[/C][C]140.002544248857[/C][/ROW]
[ROW][C]26[/C][C]4376.1[/C][C]4529.62617619976[/C][C]-691.976351714276[/C][C]4914.55017551451[/C][C]153.526176199762[/C][/ROW]
[ROW][C]27[/C][C]3276[/C][C]2937.0789822876[/C][C]-1384.31457459934[/C][C]4999.23559231174[/C][C]-338.921017712395[/C][/ROW]
[ROW][C]28[/C][C]5187[/C][C]5063.28112703432[/C][C]195.028984743601[/C][C]5115.68988822208[/C][C]-123.718872965677[/C][/ROW]
[ROW][C]29[/C][C]7664.1[/C][C]8300.56880984074[/C][C]1881.26137636617[/C][C]5146.36981379309[/C][C]636.468809840739[/C][/ROW]
[ROW][C]30[/C][C]4283.7[/C][C]4173.85702068721[/C][C]-691.976351714276[/C][C]5085.51933102707[/C][C]-109.84297931279[/C][/ROW]
[ROW][C]31[/C][C]3254.7[/C][C]2904.36075576183[/C][C]-1384.31457459934[/C][C]4989.35381883751[/C][C]-350.339244238166[/C][/ROW]
[ROW][C]32[/C][C]5046.6[/C][C]4949.93362503956[/C][C]195.028984743601[/C][C]4948.23739021684[/C][C]-96.6663749604431[/C][/ROW]
[ROW][C]33[/C][C]7470.6[/C][C]8199.99630688857[/C][C]1881.26137636617[/C][C]4859.94231674525[/C][C]729.396306888573[/C][/ROW]
[ROW][C]34[/C][C]3655.8[/C][C]3255.65161730805[/C][C]-691.976351714276[/C][C]4747.92473440623[/C][C]-400.148382691953[/C][/ROW]
[ROW][C]35[/C][C]2937.3[/C][C]2685.14685727422[/C][C]-1384.31457459934[/C][C]4573.76771732512[/C][C]-252.153142725777[/C][/ROW]
[ROW][C]36[/C][C]4923.9[/C][C]5240.07515197131[/C][C]195.028984743601[/C][C]4412.69586328508[/C][C]316.175151971314[/C][/ROW]
[ROW][C]37[/C][C]6344.7[/C][C]6589.81558110152[/C][C]1881.26137636617[/C][C]4218.3230425323[/C][C]245.115581101521[/C][/ROW]
[ROW][C]38[/C][C]2981.7[/C][C]2713.33772852343[/C][C]-691.976351714276[/C][C]3942.03862319085[/C][C]-268.36227147657[/C][/ROW]
[ROW][C]39[/C][C]2114.7[/C][C]1915.56819575655[/C][C]-1384.31457459934[/C][C]3698.14637884279[/C][C]-199.131804243451[/C][/ROW]
[ROW][C]40[/C][C]3919.5[/C][C]4077.22613581962[/C][C]195.028984743601[/C][C]3566.74487943678[/C][C]157.726135819619[/C][/ROW]
[ROW][C]41[/C][C]5380.8[/C][C]5378.70228343578[/C][C]1881.26137636617[/C][C]3501.63634019805[/C][C]-2.0977165642239[/C][/ROW]
[ROW][C]42[/C][C]2661[/C][C]2582.45666543572[/C][C]-691.976351714276[/C][C]3431.51968627856[/C][C]-78.5433345642818[/C][/ROW]
[ROW][C]43[/C][C]1935.9[/C][C]1823.0582402036[/C][C]-1384.31457459934[/C][C]3433.05633439574[/C][C]-112.841759796398[/C][/ROW]
[ROW][C]44[/C][C]3669.9[/C][C]3668.76920754154[/C][C]195.028984743601[/C][C]3476.00180771486[/C][C]-1.13079245846029[/C][/ROW]
[ROW][C]45[/C][C]5669.7[/C][C]5996.86438290801[/C][C]1881.26137636617[/C][C]3461.27424072581[/C][C]327.164382908012[/C][/ROW]
[ROW][C]46[/C][C]2508.9[/C][C]2270.23663249986[/C][C]-691.976351714276[/C][C]3439.53971921441[/C][C]-238.663367500139[/C][/ROW]
[ROW][C]47[/C][C]1911.6[/C][C]1776.11631230809[/C][C]-1384.31457459934[/C][C]3431.39826229126[/C][C]-135.483687691912[/C][/ROW]
[ROW][C]48[/C][C]3758.1[/C][C]3853.30002597039[/C][C]195.028984743601[/C][C]3467.87098928601[/C][C]95.2000259703877[/C][/ROW]
[ROW][C]49[/C][C]5597.7[/C][C]5836.30833437797[/C][C]1881.26137636617[/C][C]3477.83028925585[/C][C]238.608334377973[/C][/ROW]
[ROW][C]50[/C][C]2573.4[/C][C]2348.82285521857[/C][C]-691.976351714276[/C][C]3489.95349649571[/C][C]-224.577144781435[/C][/ROW]
[ROW][C]51[/C][C]1916.7[/C][C]1702.97816823572[/C][C]-1384.31457459934[/C][C]3514.73640636363[/C][C]-213.721831764283[/C][/ROW]
[ROW][C]52[/C][C]4160.1[/C][C]4614.98121049982[/C][C]195.028984743601[/C][C]3510.18980475658[/C][C]454.881210499824[/C][/ROW]
[ROW][C]53[/C][C]5292.6[/C][C]5223.53399217504[/C][C]1881.26137636617[/C][C]3480.40463145879[/C][C]-69.0660078249648[/C][/ROW]
[ROW][C]54[/C][C]2547[/C][C]2342.21197918223[/C][C]-691.976351714276[/C][C]3443.76437253205[/C][C]-204.788020817773[/C][/ROW]
[ROW][C]55[/C][C]1850.4[/C][C]1659.22788957959[/C][C]-1384.31457459934[/C][C]3425.88668501975[/C][C]-191.17211042041[/C][/ROW]
[ROW][C]56[/C][C]3855.6[/C][C]4095.06520746221[/C][C]195.028984743601[/C][C]3421.10580779419[/C][C]239.465207462213[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298540&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298540&T=2

As an alternative you can also use a QR Code:  

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

Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
12954.42277.863078374661881.261376366171749.67554525916-676.536921625339
21769.72274.13779017306-691.9763517142761957.23856154122504.437790173057
31509.92273.08365648517-1384.314574599342131.03091811417763.18365648517
42257.22063.32034855544195.0289847436012256.05066670096-193.879651444561
53433.22704.519185770851881.261376366172280.61943786298-728.680814229154
62083.82449.6979282245-691.9763517142762409.87842348977365.897928224501
71664.72178.25417348401-1384.314574599342535.46040111534513.554173484008
82463.32164.70988027978195.0289847436012566.86113497662-298.590119720219
93995.43468.128458206221881.261376366172641.4101654276-527.271541793779
102447.42737.25602130366-691.9763517142762849.52033041062289.856021303659
112042.72393.68899398782-1384.314574599343076.02558061153350.988993987817
123198.62992.22880863817195.0289847436013209.94220661823-206.371191361828
134935.34650.762506389671881.261376366173338.57611724415-284.537493610327
1430243198.24368606156-691.9763517142763541.73266565272174.243686061555
152573.72796.11321199401-1384.314574599343735.60136260533222.413211994014
163957.93857.52481458617195.0289847436013863.24620067023-100.375185413834
175640.65410.210002562271881.261376366173989.72862107155-230.389997437727
1836303805.26012855347-691.9763517142764146.71622316081175.260128553469
193028.23083.41034147017-1384.314574599344357.3042331291755.2103414701687
204534.24333.1145144644195.0289847436014540.256500792-201.085485535597
216815.17122.703439831541881.261376366174626.23518380228307.603439831542
223962.43921.24325959293-691.9763517142764695.53309212135-41.1567404070702
233236.43122.02508505512-1384.314574599344735.08948954423-114.374914944884
244946.14885.85304371554195.0289847436014811.31797154087-60.2469562844653
256911.77051.702544248861881.261376366174890.43607938497140.002544248857
264376.14529.62617619976-691.9763517142764914.55017551451153.526176199762
2732762937.0789822876-1384.314574599344999.23559231174-338.921017712395
2851875063.28112703432195.0289847436015115.68988822208-123.718872965677
297664.18300.568809840741881.261376366175146.36981379309636.468809840739
304283.74173.85702068721-691.9763517142765085.51933102707-109.84297931279
313254.72904.36075576183-1384.314574599344989.35381883751-350.339244238166
325046.64949.93362503956195.0289847436014948.23739021684-96.6663749604431
337470.68199.996306888571881.261376366174859.94231674525729.396306888573
343655.83255.65161730805-691.9763517142764747.92473440623-400.148382691953
352937.32685.14685727422-1384.314574599344573.76771732512-252.153142725777
364923.95240.07515197131195.0289847436014412.69586328508316.175151971314
376344.76589.815581101521881.261376366174218.3230425323245.115581101521
382981.72713.33772852343-691.9763517142763942.03862319085-268.36227147657
392114.71915.56819575655-1384.314574599343698.14637884279-199.131804243451
403919.54077.22613581962195.0289847436013566.74487943678157.726135819619
415380.85378.702283435781881.261376366173501.63634019805-2.0977165642239
4226612582.45666543572-691.9763517142763431.51968627856-78.5433345642818
431935.91823.0582402036-1384.314574599343433.05633439574-112.841759796398
443669.93668.76920754154195.0289847436013476.00180771486-1.13079245846029
455669.75996.864382908011881.261376366173461.27424072581327.164382908012
462508.92270.23663249986-691.9763517142763439.53971921441-238.663367500139
471911.61776.11631230809-1384.314574599343431.39826229126-135.483687691912
483758.13853.30002597039195.0289847436013467.8709892860195.2000259703877
495597.75836.308334377971881.261376366173477.83028925585238.608334377973
502573.42348.82285521857-691.9763517142763489.95349649571-224.577144781435
511916.71702.97816823572-1384.314574599343514.73640636363-213.721831764283
524160.14614.98121049982195.0289847436013510.18980475658454.881210499824
535292.65223.533992175041881.261376366173480.40463145879-69.0660078249648
5425472342.21197918223-691.9763517142763443.76437253205-204.788020817773
551850.41659.22788957959-1384.314574599343425.88668501975-191.17211042041
563855.64095.06520746221195.0289847436013421.10580779419239.465207462213



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 4 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = 4 ; par2 = periodic ; par3 = 1 ; par4 = ; par5 = 1 ; par6 = ; par7 = 1 ; par8 = FALSE ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #seasonal period
if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window
par3 <- as.numeric(par3) #s.degree
if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window
par5 <- as.numeric(par5)#t.degree
if (par6 != '') par6 <- as.numeric(par6)#l.window
par7 <- as.numeric(par7)#l.degree
if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust
nx <- length(x)
x <- ts(x,frequency=par1)
if (par6 != '') {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8)
} else {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8)
}
m$time.series
m$win
m$deg
m$jump
m$inner
m$outer
bitmap(file='test1.png')
plot(m,main=main)
dev.off()
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Component',header=TRUE)
a<-table.element(a,'Window',header=TRUE)
a<-table.element(a,'Degree',header=TRUE)
a<-table.element(a,'Jump',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,m$win['s'])
a<-table.element(a,m$deg['s'])
a<-table.element(a,m$jump['s'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,m$win['t'])
a<-table.element(a,m$deg['t'])
a<-table.element(a,m$jump['t'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Low-pass',header=TRUE)
a<-table.element(a,m$win['l'])
a<-table.element(a,m$deg['l'])
a<-table.element(a,m$jump['l'])
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,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Remainder',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]+m$time.series[i,'remainder'])
a<-table.element(a,m$time.series[i,'seasonal'])
a<-table.element(a,m$time.series[i,'trend'])
a<-table.element(a,m$time.series[i,'remainder'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')