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R Software Module

rwasp_decomposeloess.wasp

Title produced by software

Decomposition by Loess

Date of computation

Wed, 02 Dec 2009 13:09:47 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/02/t1259784665g90pizk28uukjjd.htm/, Retrieved Sun, 28 Apr 2024 11:07:46 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62562, Retrieved Sun, 28 Apr 2024 11:07:46 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

ws 8 Ad hoc forecasting link 2

Estimated Impact

154

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   [Decomposition by Loess] [] [2009-11-27 15:00:29] [b98453cac15ba1066b407e146608df68]
-   PD      [Decomposition by Loess] [ws 8 Ad hoc forec...] [2009-12-02 20:09:47] [88e98f4c87ea17c4967db8279bda8533] [Current]
-   PD        [Decomposition by Loess] [Workshop 9] [2009-12-04 12:08:33] [4fe1472705bb0a32f118ba3ca90ffa8e] 
-   PD          [Decomposition by Loess] [WS9 ] [2009-12-11 12:47:20] [4fe1472705bb0a32f118ba3ca90ffa8e] 
-   PD        [Decomposition by Loess] [ws9: decomposition 2] [2009-12-04 17:18:47] [bd8e774728cf1f2f4e6868fd314defe3] 

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Dataseries X:

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Histogram

Boxplots

8.2
8.0
7.5
6.8
6.5
6.6
7.6
8.0
8.1
7.7
7.5
7.6
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7.0
7.1
7.2
7.1
6.9
7.0
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8.0
8.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62562&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]3 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=62562&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62562&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 Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	681	0	69
Trend	19	1	2
Low-pass	13	1	2

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 681 & 0 & 69 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62562&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]681[/C][C]0[/C][C]69[/C][/ROW]
[ROW][C]Trend[/C][C]19[/C][C]1[/C][C]2[/C][/ROW]
[ROW][C]Low-pass[/C][C]13[/C][C]1[/C][C]2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62562&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62562&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
Component	Window	Degree	Jump
Seasonal	681	0	69
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	8.2	8.76289232300616	0.351821216209758	7.28528646078408	0.562892323006166
2	8	8.27474967145866	0.406846050296754	7.31840427824459	0.274749671458659
3	7.5	7.40327354283408	0.245204361460822	7.3515220957051	-0.0967264571659188
4	6.8	6.21066064797297	0.00580965090081608	7.38352970112621	-0.589339352027029
5	6.5	5.81804797810215	-0.233585284649477	7.41553730654733	-0.681952021897851
6	6.6	6.15406501168457	-0.394824680884551	7.44075966919999	-0.445934988315434
7	7.6	7.6567487931802	0.0772691749671581	7.46598203185264	0.0567487931801995
8	8	8.42057939993176	0.0891585038214113	7.49026209624683	0.420579399931761
9	8.1	8.65963624824254	0.0258215911164464	7.51454216064101	0.559636248242539
10	7.7	8.02450330976678	-0.185373225080392	7.56086991531362	0.324503309766775
11	7.5	7.68937052428445	-0.296568194270666	7.60719766998622	0.189370524284445
12	7.6	7.66933747732517	-0.0915790245976931	7.62224154727252	0.0693374773251749
13	7.8	7.61089335923143	0.351821216209758	7.63728542455882	-0.189106640768573
14	7.8	7.57757495122438	0.406846050296754	7.61557899847887	-0.222425048775625
15	7.8	7.76092306614025	0.245204361460822	7.59387257239892	-0.0390769338597465
16	7.5	7.40888284585702	0.00580965090081608	7.58530750324216	-0.091117154142979
17	7.5	7.65684285056408	-0.233585284649477	7.5767424340854	0.156842850564076
18	7.1	6.99581843010677	-0.394824680884551	7.59900625077778	-0.104181569893234
19	7.5	7.30146075756267	0.0772691749671581	7.62127006747017	-0.198539242437326
20	7.5	7.25330633454978	0.0891585038214113	7.6575351616288	-0.246693665450215
21	7.6	7.48037815309611	0.0258215911164464	7.69380025578744	-0.119621846903885
22	7.7	7.85135044321368	-0.185373225080392	7.7340227818667	0.151350443213684
23	7.7	7.9223228863247	-0.296568194270666	7.77424530794598	0.222322886324691
24	7.9	8.10633820072276	-0.0915790245976931	7.78524082387493	0.206338200722763
25	8.1	8.05194244398636	0.351821216209758	7.79623633980388	-0.0480575560136423
26	8.2	8.24520832981737	0.406846050296754	7.74794561988587	0.0452083298173722
27	8.2	8.45514073857132	0.245204361460822	7.69965489996786	0.255140738571316
28	8.2	8.77583873246106	0.00580965090081608	7.61835161663813	0.575838732461056
29	7.9	8.49653695134108	-0.233585284649477	7.5370483333084	0.596536951341083
30	7.3	7.54443483151936	-0.394824680884551	7.45038984936519	0.24443483151936
31	6.9	6.35899945961085	0.0772691749671581	7.36373136542199	-0.541000540389146
32	6.6	5.84357709485442	0.0891585038214113	7.26726440132417	-0.756422905145584
33	6.7	6.2033809716572	0.0258215911164464	7.17079743722636	-0.496619028342802
34	6.9	6.90172365480175	-0.185373225080392	7.08364957027864	0.00172365480174985
35	7	7.30006649093974	-0.296568194270666	6.99650170333093	0.300066490939737
36	7.1	7.34175318309095	-0.0915790245976931	6.94982584150674	0.241753183090953
37	7.2	7.1450288041077	0.351821216209758	6.90314997968255	-0.0549711958923078
38	7.1	6.91707733474839	0.406846050296754	6.87607661495486	-0.182922665251612
39	6.9	6.70579238831201	0.245204361460822	6.84900325022716	-0.194207611687986
40	7	7.18987751411169	0.00580965090081608	6.8043128349875	0.189877514111689
41	6.8	7.07396286490165	-0.233585284649477	6.75962241974782	0.273962864901653
42	6.4	6.48202229082464	-0.394824680884551	6.71280239005992	0.0820222908246357
43	6.7	6.65674846466083	0.0772691749671581	6.66598236037201	-0.0432515353391656
44	6.6	6.4886838618706	0.0891585038214113	6.62215763430798	-0.111316138129394
45	6.4	6.1958455006396	0.0258215911164464	6.57833290824396	-0.204154499360403
46	6.3	6.26060306130305	-0.185373225080392	6.52477016377734	-0.0393969386969530
47	6.2	6.22536077495993	-0.296568194270666	6.47120741931073	0.0253607749599345
48	6.5	6.64404891132415	-0.0915790245976931	6.44753011327354	0.144048911324153
49	6.8	6.82432597655389	0.351821216209758	6.42385280723635	0.024325976553893
50	6.8	6.74875064045759	0.406846050296754	6.44440330924566	-0.0512493595424104
51	6.4	6.08984182728422	0.245204361460822	6.46495381125496	-0.310158172715783
52	6.1	5.71482628607599	0.00580965090081608	6.4793640630232	-0.385173713924013
53	5.8	5.33981096985805	-0.233585284649477	6.49377431479143	-0.460189030141954
54	6.1	6.09002592496776	-0.394824680884551	6.50479875591679	-0.0099740750322388
55	7.2	7.8069076279907	0.0772691749671581	6.51582319704215	0.606907627990693
56	7.3	7.93492590156	0.0891585038214113	6.57591559461858	0.634925901560006
57	6.9	7.13817041668854	0.0258215911164464	6.63600799219501	0.23817041668854
58	6.1	5.64676565466351	-0.185373225080392	6.73860757041688	-0.453234345336485
59	5.8	5.05536104563193	-0.296568194270666	6.84120714863874	-0.744638954368073
60	6.2	5.52390786580087	-0.0915790245976931	6.96767115879683	-0.676092134199133
61	7.1	6.75404361483533	0.351821216209758	7.09413516895491	-0.345956385164672
62	7.7	7.77392349071539	0.406846050296754	7.21923045898786	0.0739234907153863
63	7.9	8.21046988951837	0.245204361460822	7.3443257490208	0.310469889518373
64	7.7	7.91633628897512	0.00580965090081608	7.47785406012407	0.216336288975115
65	7.4	7.42220291342215	-0.233585284649477	7.61138237122733	0.0222029134221451
66	7.5	7.64195819165105	-0.394824680884551	7.7528664892335	0.141958191651046
67	8	8.02838021779316	0.0772691749671581	7.89435060723968	0.0283802177931634
68	8.1	8.07076867723918	0.0891585038214113	8.04007281893941	-0.0292313227608236

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 8.2 & 8.76289232300616 & 0.351821216209758 & 7.28528646078408 & 0.562892323006166 \tabularnewline
2 & 8 & 8.27474967145866 & 0.406846050296754 & 7.31840427824459 & 0.274749671458659 \tabularnewline
3 & 7.5 & 7.40327354283408 & 0.245204361460822 & 7.3515220957051 & -0.0967264571659188 \tabularnewline
4 & 6.8 & 6.21066064797297 & 0.00580965090081608 & 7.38352970112621 & -0.589339352027029 \tabularnewline
5 & 6.5 & 5.81804797810215 & -0.233585284649477 & 7.41553730654733 & -0.681952021897851 \tabularnewline
6 & 6.6 & 6.15406501168457 & -0.394824680884551 & 7.44075966919999 & -0.445934988315434 \tabularnewline
7 & 7.6 & 7.6567487931802 & 0.0772691749671581 & 7.46598203185264 & 0.0567487931801995 \tabularnewline
8 & 8 & 8.42057939993176 & 0.0891585038214113 & 7.49026209624683 & 0.420579399931761 \tabularnewline
9 & 8.1 & 8.65963624824254 & 0.0258215911164464 & 7.51454216064101 & 0.559636248242539 \tabularnewline
10 & 7.7 & 8.02450330976678 & -0.185373225080392 & 7.56086991531362 & 0.324503309766775 \tabularnewline
11 & 7.5 & 7.68937052428445 & -0.296568194270666 & 7.60719766998622 & 0.189370524284445 \tabularnewline
12 & 7.6 & 7.66933747732517 & -0.0915790245976931 & 7.62224154727252 & 0.0693374773251749 \tabularnewline
13 & 7.8 & 7.61089335923143 & 0.351821216209758 & 7.63728542455882 & -0.189106640768573 \tabularnewline
14 & 7.8 & 7.57757495122438 & 0.406846050296754 & 7.61557899847887 & -0.222425048775625 \tabularnewline
15 & 7.8 & 7.76092306614025 & 0.245204361460822 & 7.59387257239892 & -0.0390769338597465 \tabularnewline
16 & 7.5 & 7.40888284585702 & 0.00580965090081608 & 7.58530750324216 & -0.091117154142979 \tabularnewline
17 & 7.5 & 7.65684285056408 & -0.233585284649477 & 7.5767424340854 & 0.156842850564076 \tabularnewline
18 & 7.1 & 6.99581843010677 & -0.394824680884551 & 7.59900625077778 & -0.104181569893234 \tabularnewline
19 & 7.5 & 7.30146075756267 & 0.0772691749671581 & 7.62127006747017 & -0.198539242437326 \tabularnewline
20 & 7.5 & 7.25330633454978 & 0.0891585038214113 & 7.6575351616288 & -0.246693665450215 \tabularnewline
21 & 7.6 & 7.48037815309611 & 0.0258215911164464 & 7.69380025578744 & -0.119621846903885 \tabularnewline
22 & 7.7 & 7.85135044321368 & -0.185373225080392 & 7.7340227818667 & 0.151350443213684 \tabularnewline
23 & 7.7 & 7.9223228863247 & -0.296568194270666 & 7.77424530794598 & 0.222322886324691 \tabularnewline
24 & 7.9 & 8.10633820072276 & -0.0915790245976931 & 7.78524082387493 & 0.206338200722763 \tabularnewline
25 & 8.1 & 8.05194244398636 & 0.351821216209758 & 7.79623633980388 & -0.0480575560136423 \tabularnewline
26 & 8.2 & 8.24520832981737 & 0.406846050296754 & 7.74794561988587 & 0.0452083298173722 \tabularnewline
27 & 8.2 & 8.45514073857132 & 0.245204361460822 & 7.69965489996786 & 0.255140738571316 \tabularnewline
28 & 8.2 & 8.77583873246106 & 0.00580965090081608 & 7.61835161663813 & 0.575838732461056 \tabularnewline
29 & 7.9 & 8.49653695134108 & -0.233585284649477 & 7.5370483333084 & 0.596536951341083 \tabularnewline
30 & 7.3 & 7.54443483151936 & -0.394824680884551 & 7.45038984936519 & 0.24443483151936 \tabularnewline
31 & 6.9 & 6.35899945961085 & 0.0772691749671581 & 7.36373136542199 & -0.541000540389146 \tabularnewline
32 & 6.6 & 5.84357709485442 & 0.0891585038214113 & 7.26726440132417 & -0.756422905145584 \tabularnewline
33 & 6.7 & 6.2033809716572 & 0.0258215911164464 & 7.17079743722636 & -0.496619028342802 \tabularnewline
34 & 6.9 & 6.90172365480175 & -0.185373225080392 & 7.08364957027864 & 0.00172365480174985 \tabularnewline
35 & 7 & 7.30006649093974 & -0.296568194270666 & 6.99650170333093 & 0.300066490939737 \tabularnewline
36 & 7.1 & 7.34175318309095 & -0.0915790245976931 & 6.94982584150674 & 0.241753183090953 \tabularnewline
37 & 7.2 & 7.1450288041077 & 0.351821216209758 & 6.90314997968255 & -0.0549711958923078 \tabularnewline
38 & 7.1 & 6.91707733474839 & 0.406846050296754 & 6.87607661495486 & -0.182922665251612 \tabularnewline
39 & 6.9 & 6.70579238831201 & 0.245204361460822 & 6.84900325022716 & -0.194207611687986 \tabularnewline
40 & 7 & 7.18987751411169 & 0.00580965090081608 & 6.8043128349875 & 0.189877514111689 \tabularnewline
41 & 6.8 & 7.07396286490165 & -0.233585284649477 & 6.75962241974782 & 0.273962864901653 \tabularnewline
42 & 6.4 & 6.48202229082464 & -0.394824680884551 & 6.71280239005992 & 0.0820222908246357 \tabularnewline
43 & 6.7 & 6.65674846466083 & 0.0772691749671581 & 6.66598236037201 & -0.0432515353391656 \tabularnewline
44 & 6.6 & 6.4886838618706 & 0.0891585038214113 & 6.62215763430798 & -0.111316138129394 \tabularnewline
45 & 6.4 & 6.1958455006396 & 0.0258215911164464 & 6.57833290824396 & -0.204154499360403 \tabularnewline
46 & 6.3 & 6.26060306130305 & -0.185373225080392 & 6.52477016377734 & -0.0393969386969530 \tabularnewline
47 & 6.2 & 6.22536077495993 & -0.296568194270666 & 6.47120741931073 & 0.0253607749599345 \tabularnewline
48 & 6.5 & 6.64404891132415 & -0.0915790245976931 & 6.44753011327354 & 0.144048911324153 \tabularnewline
49 & 6.8 & 6.82432597655389 & 0.351821216209758 & 6.42385280723635 & 0.024325976553893 \tabularnewline
50 & 6.8 & 6.74875064045759 & 0.406846050296754 & 6.44440330924566 & -0.0512493595424104 \tabularnewline
51 & 6.4 & 6.08984182728422 & 0.245204361460822 & 6.46495381125496 & -0.310158172715783 \tabularnewline
52 & 6.1 & 5.71482628607599 & 0.00580965090081608 & 6.4793640630232 & -0.385173713924013 \tabularnewline
53 & 5.8 & 5.33981096985805 & -0.233585284649477 & 6.49377431479143 & -0.460189030141954 \tabularnewline
54 & 6.1 & 6.09002592496776 & -0.394824680884551 & 6.50479875591679 & -0.0099740750322388 \tabularnewline
55 & 7.2 & 7.8069076279907 & 0.0772691749671581 & 6.51582319704215 & 0.606907627990693 \tabularnewline
56 & 7.3 & 7.93492590156 & 0.0891585038214113 & 6.57591559461858 & 0.634925901560006 \tabularnewline
57 & 6.9 & 7.13817041668854 & 0.0258215911164464 & 6.63600799219501 & 0.23817041668854 \tabularnewline
58 & 6.1 & 5.64676565466351 & -0.185373225080392 & 6.73860757041688 & -0.453234345336485 \tabularnewline
59 & 5.8 & 5.05536104563193 & -0.296568194270666 & 6.84120714863874 & -0.744638954368073 \tabularnewline
60 & 6.2 & 5.52390786580087 & -0.0915790245976931 & 6.96767115879683 & -0.676092134199133 \tabularnewline
61 & 7.1 & 6.75404361483533 & 0.351821216209758 & 7.09413516895491 & -0.345956385164672 \tabularnewline
62 & 7.7 & 7.77392349071539 & 0.406846050296754 & 7.21923045898786 & 0.0739234907153863 \tabularnewline
63 & 7.9 & 8.21046988951837 & 0.245204361460822 & 7.3443257490208 & 0.310469889518373 \tabularnewline
64 & 7.7 & 7.91633628897512 & 0.00580965090081608 & 7.47785406012407 & 0.216336288975115 \tabularnewline
65 & 7.4 & 7.42220291342215 & -0.233585284649477 & 7.61138237122733 & 0.0222029134221451 \tabularnewline
66 & 7.5 & 7.64195819165105 & -0.394824680884551 & 7.7528664892335 & 0.141958191651046 \tabularnewline
67 & 8 & 8.02838021779316 & 0.0772691749671581 & 7.89435060723968 & 0.0283802177931634 \tabularnewline
68 & 8.1 & 8.07076867723918 & 0.0891585038214113 & 8.04007281893941 & -0.0292313227608236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62562&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]8.2[/C][C]8.76289232300616[/C][C]0.351821216209758[/C][C]7.28528646078408[/C][C]0.562892323006166[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]8.27474967145866[/C][C]0.406846050296754[/C][C]7.31840427824459[/C][C]0.274749671458659[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]7.40327354283408[/C][C]0.245204361460822[/C][C]7.3515220957051[/C][C]-0.0967264571659188[/C][/ROW]
[ROW][C]4[/C][C]6.8[/C][C]6.21066064797297[/C][C]0.00580965090081608[/C][C]7.38352970112621[/C][C]-0.589339352027029[/C][/ROW]
[ROW][C]5[/C][C]6.5[/C][C]5.81804797810215[/C][C]-0.233585284649477[/C][C]7.41553730654733[/C][C]-0.681952021897851[/C][/ROW]
[ROW][C]6[/C][C]6.6[/C][C]6.15406501168457[/C][C]-0.394824680884551[/C][C]7.44075966919999[/C][C]-0.445934988315434[/C][/ROW]
[ROW][C]7[/C][C]7.6[/C][C]7.6567487931802[/C][C]0.0772691749671581[/C][C]7.46598203185264[/C][C]0.0567487931801995[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]8.42057939993176[/C][C]0.0891585038214113[/C][C]7.49026209624683[/C][C]0.420579399931761[/C][/ROW]
[ROW][C]9[/C][C]8.1[/C][C]8.65963624824254[/C][C]0.0258215911164464[/C][C]7.51454216064101[/C][C]0.559636248242539[/C][/ROW]
[ROW][C]10[/C][C]7.7[/C][C]8.02450330976678[/C][C]-0.185373225080392[/C][C]7.56086991531362[/C][C]0.324503309766775[/C][/ROW]
[ROW][C]11[/C][C]7.5[/C][C]7.68937052428445[/C][C]-0.296568194270666[/C][C]7.60719766998622[/C][C]0.189370524284445[/C][/ROW]
[ROW][C]12[/C][C]7.6[/C][C]7.66933747732517[/C][C]-0.0915790245976931[/C][C]7.62224154727252[/C][C]0.0693374773251749[/C][/ROW]
[ROW][C]13[/C][C]7.8[/C][C]7.61089335923143[/C][C]0.351821216209758[/C][C]7.63728542455882[/C][C]-0.189106640768573[/C][/ROW]
[ROW][C]14[/C][C]7.8[/C][C]7.57757495122438[/C][C]0.406846050296754[/C][C]7.61557899847887[/C][C]-0.222425048775625[/C][/ROW]
[ROW][C]15[/C][C]7.8[/C][C]7.76092306614025[/C][C]0.245204361460822[/C][C]7.59387257239892[/C][C]-0.0390769338597465[/C][/ROW]
[ROW][C]16[/C][C]7.5[/C][C]7.40888284585702[/C][C]0.00580965090081608[/C][C]7.58530750324216[/C][C]-0.091117154142979[/C][/ROW]
[ROW][C]17[/C][C]7.5[/C][C]7.65684285056408[/C][C]-0.233585284649477[/C][C]7.5767424340854[/C][C]0.156842850564076[/C][/ROW]
[ROW][C]18[/C][C]7.1[/C][C]6.99581843010677[/C][C]-0.394824680884551[/C][C]7.59900625077778[/C][C]-0.104181569893234[/C][/ROW]
[ROW][C]19[/C][C]7.5[/C][C]7.30146075756267[/C][C]0.0772691749671581[/C][C]7.62127006747017[/C][C]-0.198539242437326[/C][/ROW]
[ROW][C]20[/C][C]7.5[/C][C]7.25330633454978[/C][C]0.0891585038214113[/C][C]7.6575351616288[/C][C]-0.246693665450215[/C][/ROW]
[ROW][C]21[/C][C]7.6[/C][C]7.48037815309611[/C][C]0.0258215911164464[/C][C]7.69380025578744[/C][C]-0.119621846903885[/C][/ROW]
[ROW][C]22[/C][C]7.7[/C][C]7.85135044321368[/C][C]-0.185373225080392[/C][C]7.7340227818667[/C][C]0.151350443213684[/C][/ROW]
[ROW][C]23[/C][C]7.7[/C][C]7.9223228863247[/C][C]-0.296568194270666[/C][C]7.77424530794598[/C][C]0.222322886324691[/C][/ROW]
[ROW][C]24[/C][C]7.9[/C][C]8.10633820072276[/C][C]-0.0915790245976931[/C][C]7.78524082387493[/C][C]0.206338200722763[/C][/ROW]
[ROW][C]25[/C][C]8.1[/C][C]8.05194244398636[/C][C]0.351821216209758[/C][C]7.79623633980388[/C][C]-0.0480575560136423[/C][/ROW]
[ROW][C]26[/C][C]8.2[/C][C]8.24520832981737[/C][C]0.406846050296754[/C][C]7.74794561988587[/C][C]0.0452083298173722[/C][/ROW]
[ROW][C]27[/C][C]8.2[/C][C]8.45514073857132[/C][C]0.245204361460822[/C][C]7.69965489996786[/C][C]0.255140738571316[/C][/ROW]
[ROW][C]28[/C][C]8.2[/C][C]8.77583873246106[/C][C]0.00580965090081608[/C][C]7.61835161663813[/C][C]0.575838732461056[/C][/ROW]
[ROW][C]29[/C][C]7.9[/C][C]8.49653695134108[/C][C]-0.233585284649477[/C][C]7.5370483333084[/C][C]0.596536951341083[/C][/ROW]
[ROW][C]30[/C][C]7.3[/C][C]7.54443483151936[/C][C]-0.394824680884551[/C][C]7.45038984936519[/C][C]0.24443483151936[/C][/ROW]
[ROW][C]31[/C][C]6.9[/C][C]6.35899945961085[/C][C]0.0772691749671581[/C][C]7.36373136542199[/C][C]-0.541000540389146[/C][/ROW]
[ROW][C]32[/C][C]6.6[/C][C]5.84357709485442[/C][C]0.0891585038214113[/C][C]7.26726440132417[/C][C]-0.756422905145584[/C][/ROW]
[ROW][C]33[/C][C]6.7[/C][C]6.2033809716572[/C][C]0.0258215911164464[/C][C]7.17079743722636[/C][C]-0.496619028342802[/C][/ROW]
[ROW][C]34[/C][C]6.9[/C][C]6.90172365480175[/C][C]-0.185373225080392[/C][C]7.08364957027864[/C][C]0.00172365480174985[/C][/ROW]
[ROW][C]35[/C][C]7[/C][C]7.30006649093974[/C][C]-0.296568194270666[/C][C]6.99650170333093[/C][C]0.300066490939737[/C][/ROW]
[ROW][C]36[/C][C]7.1[/C][C]7.34175318309095[/C][C]-0.0915790245976931[/C][C]6.94982584150674[/C][C]0.241753183090953[/C][/ROW]
[ROW][C]37[/C][C]7.2[/C][C]7.1450288041077[/C][C]0.351821216209758[/C][C]6.90314997968255[/C][C]-0.0549711958923078[/C][/ROW]
[ROW][C]38[/C][C]7.1[/C][C]6.91707733474839[/C][C]0.406846050296754[/C][C]6.87607661495486[/C][C]-0.182922665251612[/C][/ROW]
[ROW][C]39[/C][C]6.9[/C][C]6.70579238831201[/C][C]0.245204361460822[/C][C]6.84900325022716[/C][C]-0.194207611687986[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]7.18987751411169[/C][C]0.00580965090081608[/C][C]6.8043128349875[/C][C]0.189877514111689[/C][/ROW]
[ROW][C]41[/C][C]6.8[/C][C]7.07396286490165[/C][C]-0.233585284649477[/C][C]6.75962241974782[/C][C]0.273962864901653[/C][/ROW]
[ROW][C]42[/C][C]6.4[/C][C]6.48202229082464[/C][C]-0.394824680884551[/C][C]6.71280239005992[/C][C]0.0820222908246357[/C][/ROW]
[ROW][C]43[/C][C]6.7[/C][C]6.65674846466083[/C][C]0.0772691749671581[/C][C]6.66598236037201[/C][C]-0.0432515353391656[/C][/ROW]
[ROW][C]44[/C][C]6.6[/C][C]6.4886838618706[/C][C]0.0891585038214113[/C][C]6.62215763430798[/C][C]-0.111316138129394[/C][/ROW]
[ROW][C]45[/C][C]6.4[/C][C]6.1958455006396[/C][C]0.0258215911164464[/C][C]6.57833290824396[/C][C]-0.204154499360403[/C][/ROW]
[ROW][C]46[/C][C]6.3[/C][C]6.26060306130305[/C][C]-0.185373225080392[/C][C]6.52477016377734[/C][C]-0.0393969386969530[/C][/ROW]
[ROW][C]47[/C][C]6.2[/C][C]6.22536077495993[/C][C]-0.296568194270666[/C][C]6.47120741931073[/C][C]0.0253607749599345[/C][/ROW]
[ROW][C]48[/C][C]6.5[/C][C]6.64404891132415[/C][C]-0.0915790245976931[/C][C]6.44753011327354[/C][C]0.144048911324153[/C][/ROW]
[ROW][C]49[/C][C]6.8[/C][C]6.82432597655389[/C][C]0.351821216209758[/C][C]6.42385280723635[/C][C]0.024325976553893[/C][/ROW]
[ROW][C]50[/C][C]6.8[/C][C]6.74875064045759[/C][C]0.406846050296754[/C][C]6.44440330924566[/C][C]-0.0512493595424104[/C][/ROW]
[ROW][C]51[/C][C]6.4[/C][C]6.08984182728422[/C][C]0.245204361460822[/C][C]6.46495381125496[/C][C]-0.310158172715783[/C][/ROW]
[ROW][C]52[/C][C]6.1[/C][C]5.71482628607599[/C][C]0.00580965090081608[/C][C]6.4793640630232[/C][C]-0.385173713924013[/C][/ROW]
[ROW][C]53[/C][C]5.8[/C][C]5.33981096985805[/C][C]-0.233585284649477[/C][C]6.49377431479143[/C][C]-0.460189030141954[/C][/ROW]
[ROW][C]54[/C][C]6.1[/C][C]6.09002592496776[/C][C]-0.394824680884551[/C][C]6.50479875591679[/C][C]-0.0099740750322388[/C][/ROW]
[ROW][C]55[/C][C]7.2[/C][C]7.8069076279907[/C][C]0.0772691749671581[/C][C]6.51582319704215[/C][C]0.606907627990693[/C][/ROW]
[ROW][C]56[/C][C]7.3[/C][C]7.93492590156[/C][C]0.0891585038214113[/C][C]6.57591559461858[/C][C]0.634925901560006[/C][/ROW]
[ROW][C]57[/C][C]6.9[/C][C]7.13817041668854[/C][C]0.0258215911164464[/C][C]6.63600799219501[/C][C]0.23817041668854[/C][/ROW]
[ROW][C]58[/C][C]6.1[/C][C]5.64676565466351[/C][C]-0.185373225080392[/C][C]6.73860757041688[/C][C]-0.453234345336485[/C][/ROW]
[ROW][C]59[/C][C]5.8[/C][C]5.05536104563193[/C][C]-0.296568194270666[/C][C]6.84120714863874[/C][C]-0.744638954368073[/C][/ROW]
[ROW][C]60[/C][C]6.2[/C][C]5.52390786580087[/C][C]-0.0915790245976931[/C][C]6.96767115879683[/C][C]-0.676092134199133[/C][/ROW]
[ROW][C]61[/C][C]7.1[/C][C]6.75404361483533[/C][C]0.351821216209758[/C][C]7.09413516895491[/C][C]-0.345956385164672[/C][/ROW]
[ROW][C]62[/C][C]7.7[/C][C]7.77392349071539[/C][C]0.406846050296754[/C][C]7.21923045898786[/C][C]0.0739234907153863[/C][/ROW]
[ROW][C]63[/C][C]7.9[/C][C]8.21046988951837[/C][C]0.245204361460822[/C][C]7.3443257490208[/C][C]0.310469889518373[/C][/ROW]
[ROW][C]64[/C][C]7.7[/C][C]7.91633628897512[/C][C]0.00580965090081608[/C][C]7.47785406012407[/C][C]0.216336288975115[/C][/ROW]
[ROW][C]65[/C][C]7.4[/C][C]7.42220291342215[/C][C]-0.233585284649477[/C][C]7.61138237122733[/C][C]0.0222029134221451[/C][/ROW]
[ROW][C]66[/C][C]7.5[/C][C]7.64195819165105[/C][C]-0.394824680884551[/C][C]7.7528664892335[/C][C]0.141958191651046[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]8.02838021779316[/C][C]0.0772691749671581[/C][C]7.89435060723968[/C][C]0.0283802177931634[/C][/ROW]
[ROW][C]68[/C][C]8.1[/C][C]8.07076867723918[/C][C]0.0891585038214113[/C][C]8.04007281893941[/C][C]-0.0292313227608236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62562&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62562&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
t	Observed	Fitted	Seasonal	Trend	Remainder
1	8.2	8.76289232300616	0.351821216209758	7.28528646078408	0.562892323006166
2	8	8.27474967145866	0.406846050296754	7.31840427824459	0.274749671458659
3	7.5	7.40327354283408	0.245204361460822	7.3515220957051	-0.0967264571659188
4	6.8	6.21066064797297	0.00580965090081608	7.38352970112621	-0.589339352027029
5	6.5	5.81804797810215	-0.233585284649477	7.41553730654733	-0.681952021897851
6	6.6	6.15406501168457	-0.394824680884551	7.44075966919999	-0.445934988315434
7	7.6	7.6567487931802	0.0772691749671581	7.46598203185264	0.0567487931801995
8	8	8.42057939993176	0.0891585038214113	7.49026209624683	0.420579399931761
9	8.1	8.65963624824254	0.0258215911164464	7.51454216064101	0.559636248242539
10	7.7	8.02450330976678	-0.185373225080392	7.56086991531362	0.324503309766775
11	7.5	7.68937052428445	-0.296568194270666	7.60719766998622	0.189370524284445
12	7.6	7.66933747732517	-0.0915790245976931	7.62224154727252	0.0693374773251749
13	7.8	7.61089335923143	0.351821216209758	7.63728542455882	-0.189106640768573
14	7.8	7.57757495122438	0.406846050296754	7.61557899847887	-0.222425048775625
15	7.8	7.76092306614025	0.245204361460822	7.59387257239892	-0.0390769338597465
16	7.5	7.40888284585702	0.00580965090081608	7.58530750324216	-0.091117154142979
17	7.5	7.65684285056408	-0.233585284649477	7.5767424340854	0.156842850564076
18	7.1	6.99581843010677	-0.394824680884551	7.59900625077778	-0.104181569893234
19	7.5	7.30146075756267	0.0772691749671581	7.62127006747017	-0.198539242437326
20	7.5	7.25330633454978	0.0891585038214113	7.6575351616288	-0.246693665450215
21	7.6	7.48037815309611	0.0258215911164464	7.69380025578744	-0.119621846903885
22	7.7	7.85135044321368	-0.185373225080392	7.7340227818667	0.151350443213684
23	7.7	7.9223228863247	-0.296568194270666	7.77424530794598	0.222322886324691
24	7.9	8.10633820072276	-0.0915790245976931	7.78524082387493	0.206338200722763
25	8.1	8.05194244398636	0.351821216209758	7.79623633980388	-0.0480575560136423
26	8.2	8.24520832981737	0.406846050296754	7.74794561988587	0.0452083298173722
27	8.2	8.45514073857132	0.245204361460822	7.69965489996786	0.255140738571316
28	8.2	8.77583873246106	0.00580965090081608	7.61835161663813	0.575838732461056
29	7.9	8.49653695134108	-0.233585284649477	7.5370483333084	0.596536951341083
30	7.3	7.54443483151936	-0.394824680884551	7.45038984936519	0.24443483151936
31	6.9	6.35899945961085	0.0772691749671581	7.36373136542199	-0.541000540389146
32	6.6	5.84357709485442	0.0891585038214113	7.26726440132417	-0.756422905145584
33	6.7	6.2033809716572	0.0258215911164464	7.17079743722636	-0.496619028342802
34	6.9	6.90172365480175	-0.185373225080392	7.08364957027864	0.00172365480174985
35	7	7.30006649093974	-0.296568194270666	6.99650170333093	0.300066490939737
36	7.1	7.34175318309095	-0.0915790245976931	6.94982584150674	0.241753183090953
37	7.2	7.1450288041077	0.351821216209758	6.90314997968255	-0.0549711958923078
38	7.1	6.91707733474839	0.406846050296754	6.87607661495486	-0.182922665251612
39	6.9	6.70579238831201	0.245204361460822	6.84900325022716	-0.194207611687986
40	7	7.18987751411169	0.00580965090081608	6.8043128349875	0.189877514111689
41	6.8	7.07396286490165	-0.233585284649477	6.75962241974782	0.273962864901653
42	6.4	6.48202229082464	-0.394824680884551	6.71280239005992	0.0820222908246357
43	6.7	6.65674846466083	0.0772691749671581	6.66598236037201	-0.0432515353391656
44	6.6	6.4886838618706	0.0891585038214113	6.62215763430798	-0.111316138129394
45	6.4	6.1958455006396	0.0258215911164464	6.57833290824396	-0.204154499360403
46	6.3	6.26060306130305	-0.185373225080392	6.52477016377734	-0.0393969386969530
47	6.2	6.22536077495993	-0.296568194270666	6.47120741931073	0.0253607749599345
48	6.5	6.64404891132415	-0.0915790245976931	6.44753011327354	0.144048911324153
49	6.8	6.82432597655389	0.351821216209758	6.42385280723635	0.024325976553893
50	6.8	6.74875064045759	0.406846050296754	6.44440330924566	-0.0512493595424104
51	6.4	6.08984182728422	0.245204361460822	6.46495381125496	-0.310158172715783
52	6.1	5.71482628607599	0.00580965090081608	6.4793640630232	-0.385173713924013
53	5.8	5.33981096985805	-0.233585284649477	6.49377431479143	-0.460189030141954
54	6.1	6.09002592496776	-0.394824680884551	6.50479875591679	-0.0099740750322388
55	7.2	7.8069076279907	0.0772691749671581	6.51582319704215	0.606907627990693
56	7.3	7.93492590156	0.0891585038214113	6.57591559461858	0.634925901560006
57	6.9	7.13817041668854	0.0258215911164464	6.63600799219501	0.23817041668854
58	6.1	5.64676565466351	-0.185373225080392	6.73860757041688	-0.453234345336485
59	5.8	5.05536104563193	-0.296568194270666	6.84120714863874	-0.744638954368073
60	6.2	5.52390786580087	-0.0915790245976931	6.96767115879683	-0.676092134199133
61	7.1	6.75404361483533	0.351821216209758	7.09413516895491	-0.345956385164672
62	7.7	7.77392349071539	0.406846050296754	7.21923045898786	0.0739234907153863
63	7.9	8.21046988951837	0.245204361460822	7.3443257490208	0.310469889518373
64	7.7	7.91633628897512	0.00580965090081608	7.47785406012407	0.216336288975115
65	7.4	7.42220291342215	-0.233585284649477	7.61138237122733	0.0222029134221451
66	7.5	7.64195819165105	-0.394824680884551	7.7528664892335	0.141958191651046
67	8	8.02838021779316	0.0772691749671581	7.89435060723968	0.0283802177931634
68	8.1	8.07076867723918	0.0891585038214113	8.04007281893941	-0.0292313227608236

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 0.01 ; par2 = 0.99 ; par3 = 0.005 ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code