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R Software Module

rwasp_decomposeloess.wasp

Title produced by software

Decomposition by Loess

Date of computation

Wed, 29 Dec 2010 21:07:38 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t1293656745adbzixhb86xjik3.htm/, Retrieved Fri, 03 May 2024 12:06:01 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117120, Retrieved Fri, 03 May 2024 12:06:01 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

158

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Decomposition by Loess] [HPC Retail Sales] [2008-03-06 11:35:25] [74be16979710d4c4e7c6647856088456]
-  M D  [Decomposition by Loess] [Workshop 5 Loess] [2010-12-09 20:44:40] [9856f62fe16b3bb5126cae5dd74e4807]
-    D    [Decomposition by Loess] [loess] [2010-12-29 18:08:58] [f1aa04283d83c25edc8ae3bb0d0fb93e]
-   P         [Decomposition by Loess] [] [2010-12-29 21:07:38] [b90a48a1f8ff99465eedb4ebbc8930ab] [Current]
- R PD          [Decomposition by Loess] [loess] [2011-12-22 07:20:34] [74be16979710d4c4e7c6647856088456] 
-  MP             [Decomposition by Loess] [loess] [2011-12-22 10:25:08] [f1aa04283d83c25edc8ae3bb0d0fb93e] 

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117120&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117120&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117120&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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	601	0	61
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 & 601 & 0 & 61 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117120&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]601[/C][C]0[/C][C]61[/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=117120&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117120&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	601	0	61
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	16	9.22879857055996	7.56960299654242	15.2015984328976	-6.77120142944004
2	17	15.6639420844331	-0.918153609398782	19.2542115249657	-1.33605791556694
3	23	26.499095139531	-3.80591975656485	23.3068246170338	3.49909513953101
4	24	26.5211589442402	-5.79558248048857	27.2744235362484	2.5211589442402
5	27	28.1432166059672	-5.38523906143007	31.2420224554629	1.14321660596719
6	31	27.9933721894237	-1.10790809852643	35.1145359091027	-3.00662781057630
7	40	41.8435289491952	-0.830578311937837	38.9870493627426	1.84352894919524
8	47	49.4682143068662	1.80644776712307	42.7253379260107	2.46821430686619
9	43	41.0928974426089	-1.55652393188780	46.4636264892789	-1.90710255739107
10	60	67.4725343304265	2.96860229427904	49.5588633752945	7.47253433042648
11	64	72.4521627226324	2.89373701605757	52.6541002613101	8.45216272263234
12	65	70.7375551554161	4.16150634286634	55.1009385017176	5.73755515541605
13	65	64.8826202613324	7.56960299654242	57.5477767421251	-0.117379738667559
14	55	51.4446786264436	-0.918153609398782	59.4734749829552	-3.55532137355643
15	57	56.4067465327796	-3.80591975656485	61.3991732237853	-0.593253467220443
16	57	57.1084068166681	-5.79558248048857	62.6871756638205	0.108406816668079
17	57	55.4100609575744	-5.38523906143007	63.9751781038557	-1.58993904242561
18	65	66.5281392357107	-1.10790809852643	64.5797688628158	1.52813923571065
19	69	73.646218690162	-0.830578311937837	65.1843596217759	4.64621869016196
20	70	73.4099411342622	1.80644776712307	64.7836110986147	3.4099411342622
21	71	79.1736613564342	-1.55652393188780	64.3828625754536	8.17366135643422
22	71	76.8603505373465	2.96860229427904	62.1710471683745	5.86035053734646
23	73	83.147031222647	2.89373701605757	59.9592317612954	10.147031222647
24	68	75.9580605110555	4.16150634286634	55.8804331460781	7.95806051105556
25	65	70.6287624725968	7.56960299654242	51.8016345308608	5.6287624725968
26	57	68.5128612552755	-0.918153609398782	46.4052923541233	11.5128612552755
27	41	44.796969579179	-3.80591975656485	41.0089501773859	3.79696957917898
28	21	12.7857986727298	-5.79558248048857	35.0097838077587	-8.21420132727016
29	21	18.3746216232985	-5.38523906143007	29.0106174381316	-2.62537837670151
30	17	11.6870666610641	-1.10790809852643	23.4208414374623	-5.31293333893589
31	9	0.999512875144775	-0.830578311937837	17.8310654367931	-8.00048712485522
32	11	6.3537603858581	1.80644776712307	13.8397918470188	-4.64623961414189
33	6	3.70800567464321	-1.55652393188780	9.84851825724458	-2.29199432535679
34	-2	-14.9963065330395	2.96860229427904	8.02770423876046	-12.9963065330395
35	0	-9.1006272363339	2.89373701605757	6.20689022027634	-9.1006272363339
36	5	-0.159007143869911	4.16150634286634	5.99750080100357	-5.15900714386991
37	3	-7.35771437827324	7.56960299654242	5.78811138173081	-10.3577143782732
38	7	8.5195560071401	-0.918153609398782	6.39859760225868	1.51955600714010
39	4	4.7968359337783	-3.80591975656485	7.00908382278655	0.7968359337783
40	8	13.7298036139462	-5.79558248048857	8.0657788665424	5.72980361394617
41	9	14.2627651511318	-5.38523906143007	9.12247391029823	5.26276515113184
42	14	18.6752090893764	-1.10790809852643	10.4326990091501	4.67520908937636
43	12	13.0876542039359	-0.830578311937837	11.7429241080019	1.08765420393592
44	12	9.4356598932701	1.80644776712307	12.7578923396068	-2.5643401067299
45	7	1.78366336067606	-1.55652393188780	13.7728605712117	-5.21633663932394
46	15	12.5128721653975	2.96860229427904	14.5185255403235	-2.48712783460250
47	14	9.84207247450725	2.89373701605757	15.2641905094352	-4.15792752549275
48	19	17.6152793429999	4.16150634286634	16.2232143141338	-1.38472065700010
49	39	53.2481588846252	7.56960299654242	17.1822381188323	14.2481588846252
50	12	6.44415179136552	-0.918153609398782	18.4740018180333	-5.55584820863448
51	11	6.04015423933067	-3.80591975656485	19.7657655172342	-4.95984576066933
52	17	19.1941171911601	-5.79558248048857	20.6014652893285	2.19411719116008
53	16	15.9480740000073	-5.38523906143007	21.4371650614228	-0.0519259999927186
54	25	29.0114424867354	-1.10790809852643	22.0964656117911	4.01144248673537
55	24	26.0748121497785	-0.830578311937837	22.7557661621593	2.0748121497785
56	28	30.8024378569234	1.80644776712307	23.3911143759535	2.80243785692343
57	25	27.5300613421401	-1.55652393188780	24.0264625897476	2.53006134214015
58	31	34.410790408189	2.96860229427904	24.6206072975320	3.41079040818900
59	24	19.8915109786262	2.89373701605757	25.2147520053163	-4.10848902137382
60	24	18.085099237513	4.16150634286634	25.7533944196206	-5.91490076248698

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 16 & 9.22879857055996 & 7.56960299654242 & 15.2015984328976 & -6.77120142944004 \tabularnewline
2 & 17 & 15.6639420844331 & -0.918153609398782 & 19.2542115249657 & -1.33605791556694 \tabularnewline
3 & 23 & 26.499095139531 & -3.80591975656485 & 23.3068246170338 & 3.49909513953101 \tabularnewline
4 & 24 & 26.5211589442402 & -5.79558248048857 & 27.2744235362484 & 2.5211589442402 \tabularnewline
5 & 27 & 28.1432166059672 & -5.38523906143007 & 31.2420224554629 & 1.14321660596719 \tabularnewline
6 & 31 & 27.9933721894237 & -1.10790809852643 & 35.1145359091027 & -3.00662781057630 \tabularnewline
7 & 40 & 41.8435289491952 & -0.830578311937837 & 38.9870493627426 & 1.84352894919524 \tabularnewline
8 & 47 & 49.4682143068662 & 1.80644776712307 & 42.7253379260107 & 2.46821430686619 \tabularnewline
9 & 43 & 41.0928974426089 & -1.55652393188780 & 46.4636264892789 & -1.90710255739107 \tabularnewline
10 & 60 & 67.4725343304265 & 2.96860229427904 & 49.5588633752945 & 7.47253433042648 \tabularnewline
11 & 64 & 72.4521627226324 & 2.89373701605757 & 52.6541002613101 & 8.45216272263234 \tabularnewline
12 & 65 & 70.7375551554161 & 4.16150634286634 & 55.1009385017176 & 5.73755515541605 \tabularnewline
13 & 65 & 64.8826202613324 & 7.56960299654242 & 57.5477767421251 & -0.117379738667559 \tabularnewline
14 & 55 & 51.4446786264436 & -0.918153609398782 & 59.4734749829552 & -3.55532137355643 \tabularnewline
15 & 57 & 56.4067465327796 & -3.80591975656485 & 61.3991732237853 & -0.593253467220443 \tabularnewline
16 & 57 & 57.1084068166681 & -5.79558248048857 & 62.6871756638205 & 0.108406816668079 \tabularnewline
17 & 57 & 55.4100609575744 & -5.38523906143007 & 63.9751781038557 & -1.58993904242561 \tabularnewline
18 & 65 & 66.5281392357107 & -1.10790809852643 & 64.5797688628158 & 1.52813923571065 \tabularnewline
19 & 69 & 73.646218690162 & -0.830578311937837 & 65.1843596217759 & 4.64621869016196 \tabularnewline
20 & 70 & 73.4099411342622 & 1.80644776712307 & 64.7836110986147 & 3.4099411342622 \tabularnewline
21 & 71 & 79.1736613564342 & -1.55652393188780 & 64.3828625754536 & 8.17366135643422 \tabularnewline
22 & 71 & 76.8603505373465 & 2.96860229427904 & 62.1710471683745 & 5.86035053734646 \tabularnewline
23 & 73 & 83.147031222647 & 2.89373701605757 & 59.9592317612954 & 10.147031222647 \tabularnewline
24 & 68 & 75.9580605110555 & 4.16150634286634 & 55.8804331460781 & 7.95806051105556 \tabularnewline
25 & 65 & 70.6287624725968 & 7.56960299654242 & 51.8016345308608 & 5.6287624725968 \tabularnewline
26 & 57 & 68.5128612552755 & -0.918153609398782 & 46.4052923541233 & 11.5128612552755 \tabularnewline
27 & 41 & 44.796969579179 & -3.80591975656485 & 41.0089501773859 & 3.79696957917898 \tabularnewline
28 & 21 & 12.7857986727298 & -5.79558248048857 & 35.0097838077587 & -8.21420132727016 \tabularnewline
29 & 21 & 18.3746216232985 & -5.38523906143007 & 29.0106174381316 & -2.62537837670151 \tabularnewline
30 & 17 & 11.6870666610641 & -1.10790809852643 & 23.4208414374623 & -5.31293333893589 \tabularnewline
31 & 9 & 0.999512875144775 & -0.830578311937837 & 17.8310654367931 & -8.00048712485522 \tabularnewline
32 & 11 & 6.3537603858581 & 1.80644776712307 & 13.8397918470188 & -4.64623961414189 \tabularnewline
33 & 6 & 3.70800567464321 & -1.55652393188780 & 9.84851825724458 & -2.29199432535679 \tabularnewline
34 & -2 & -14.9963065330395 & 2.96860229427904 & 8.02770423876046 & -12.9963065330395 \tabularnewline
35 & 0 & -9.1006272363339 & 2.89373701605757 & 6.20689022027634 & -9.1006272363339 \tabularnewline
36 & 5 & -0.159007143869911 & 4.16150634286634 & 5.99750080100357 & -5.15900714386991 \tabularnewline
37 & 3 & -7.35771437827324 & 7.56960299654242 & 5.78811138173081 & -10.3577143782732 \tabularnewline
38 & 7 & 8.5195560071401 & -0.918153609398782 & 6.39859760225868 & 1.51955600714010 \tabularnewline
39 & 4 & 4.7968359337783 & -3.80591975656485 & 7.00908382278655 & 0.7968359337783 \tabularnewline
40 & 8 & 13.7298036139462 & -5.79558248048857 & 8.0657788665424 & 5.72980361394617 \tabularnewline
41 & 9 & 14.2627651511318 & -5.38523906143007 & 9.12247391029823 & 5.26276515113184 \tabularnewline
42 & 14 & 18.6752090893764 & -1.10790809852643 & 10.4326990091501 & 4.67520908937636 \tabularnewline
43 & 12 & 13.0876542039359 & -0.830578311937837 & 11.7429241080019 & 1.08765420393592 \tabularnewline
44 & 12 & 9.4356598932701 & 1.80644776712307 & 12.7578923396068 & -2.5643401067299 \tabularnewline
45 & 7 & 1.78366336067606 & -1.55652393188780 & 13.7728605712117 & -5.21633663932394 \tabularnewline
46 & 15 & 12.5128721653975 & 2.96860229427904 & 14.5185255403235 & -2.48712783460250 \tabularnewline
47 & 14 & 9.84207247450725 & 2.89373701605757 & 15.2641905094352 & -4.15792752549275 \tabularnewline
48 & 19 & 17.6152793429999 & 4.16150634286634 & 16.2232143141338 & -1.38472065700010 \tabularnewline
49 & 39 & 53.2481588846252 & 7.56960299654242 & 17.1822381188323 & 14.2481588846252 \tabularnewline
50 & 12 & 6.44415179136552 & -0.918153609398782 & 18.4740018180333 & -5.55584820863448 \tabularnewline
51 & 11 & 6.04015423933067 & -3.80591975656485 & 19.7657655172342 & -4.95984576066933 \tabularnewline
52 & 17 & 19.1941171911601 & -5.79558248048857 & 20.6014652893285 & 2.19411719116008 \tabularnewline
53 & 16 & 15.9480740000073 & -5.38523906143007 & 21.4371650614228 & -0.0519259999927186 \tabularnewline
54 & 25 & 29.0114424867354 & -1.10790809852643 & 22.0964656117911 & 4.01144248673537 \tabularnewline
55 & 24 & 26.0748121497785 & -0.830578311937837 & 22.7557661621593 & 2.0748121497785 \tabularnewline
56 & 28 & 30.8024378569234 & 1.80644776712307 & 23.3911143759535 & 2.80243785692343 \tabularnewline
57 & 25 & 27.5300613421401 & -1.55652393188780 & 24.0264625897476 & 2.53006134214015 \tabularnewline
58 & 31 & 34.410790408189 & 2.96860229427904 & 24.6206072975320 & 3.41079040818900 \tabularnewline
59 & 24 & 19.8915109786262 & 2.89373701605757 & 25.2147520053163 & -4.10848902137382 \tabularnewline
60 & 24 & 18.085099237513 & 4.16150634286634 & 25.7533944196206 & -5.91490076248698 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117120&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]16[/C][C]9.22879857055996[/C][C]7.56960299654242[/C][C]15.2015984328976[/C][C]-6.77120142944004[/C][/ROW]
[ROW][C]2[/C][C]17[/C][C]15.6639420844331[/C][C]-0.918153609398782[/C][C]19.2542115249657[/C][C]-1.33605791556694[/C][/ROW]
[ROW][C]3[/C][C]23[/C][C]26.499095139531[/C][C]-3.80591975656485[/C][C]23.3068246170338[/C][C]3.49909513953101[/C][/ROW]
[ROW][C]4[/C][C]24[/C][C]26.5211589442402[/C][C]-5.79558248048857[/C][C]27.2744235362484[/C][C]2.5211589442402[/C][/ROW]
[ROW][C]5[/C][C]27[/C][C]28.1432166059672[/C][C]-5.38523906143007[/C][C]31.2420224554629[/C][C]1.14321660596719[/C][/ROW]
[ROW][C]6[/C][C]31[/C][C]27.9933721894237[/C][C]-1.10790809852643[/C][C]35.1145359091027[/C][C]-3.00662781057630[/C][/ROW]
[ROW][C]7[/C][C]40[/C][C]41.8435289491952[/C][C]-0.830578311937837[/C][C]38.9870493627426[/C][C]1.84352894919524[/C][/ROW]
[ROW][C]8[/C][C]47[/C][C]49.4682143068662[/C][C]1.80644776712307[/C][C]42.7253379260107[/C][C]2.46821430686619[/C][/ROW]
[ROW][C]9[/C][C]43[/C][C]41.0928974426089[/C][C]-1.55652393188780[/C][C]46.4636264892789[/C][C]-1.90710255739107[/C][/ROW]
[ROW][C]10[/C][C]60[/C][C]67.4725343304265[/C][C]2.96860229427904[/C][C]49.5588633752945[/C][C]7.47253433042648[/C][/ROW]
[ROW][C]11[/C][C]64[/C][C]72.4521627226324[/C][C]2.89373701605757[/C][C]52.6541002613101[/C][C]8.45216272263234[/C][/ROW]
[ROW][C]12[/C][C]65[/C][C]70.7375551554161[/C][C]4.16150634286634[/C][C]55.1009385017176[/C][C]5.73755515541605[/C][/ROW]
[ROW][C]13[/C][C]65[/C][C]64.8826202613324[/C][C]7.56960299654242[/C][C]57.5477767421251[/C][C]-0.117379738667559[/C][/ROW]
[ROW][C]14[/C][C]55[/C][C]51.4446786264436[/C][C]-0.918153609398782[/C][C]59.4734749829552[/C][C]-3.55532137355643[/C][/ROW]
[ROW][C]15[/C][C]57[/C][C]56.4067465327796[/C][C]-3.80591975656485[/C][C]61.3991732237853[/C][C]-0.593253467220443[/C][/ROW]
[ROW][C]16[/C][C]57[/C][C]57.1084068166681[/C][C]-5.79558248048857[/C][C]62.6871756638205[/C][C]0.108406816668079[/C][/ROW]
[ROW][C]17[/C][C]57[/C][C]55.4100609575744[/C][C]-5.38523906143007[/C][C]63.9751781038557[/C][C]-1.58993904242561[/C][/ROW]
[ROW][C]18[/C][C]65[/C][C]66.5281392357107[/C][C]-1.10790809852643[/C][C]64.5797688628158[/C][C]1.52813923571065[/C][/ROW]
[ROW][C]19[/C][C]69[/C][C]73.646218690162[/C][C]-0.830578311937837[/C][C]65.1843596217759[/C][C]4.64621869016196[/C][/ROW]
[ROW][C]20[/C][C]70[/C][C]73.4099411342622[/C][C]1.80644776712307[/C][C]64.7836110986147[/C][C]3.4099411342622[/C][/ROW]
[ROW][C]21[/C][C]71[/C][C]79.1736613564342[/C][C]-1.55652393188780[/C][C]64.3828625754536[/C][C]8.17366135643422[/C][/ROW]
[ROW][C]22[/C][C]71[/C][C]76.8603505373465[/C][C]2.96860229427904[/C][C]62.1710471683745[/C][C]5.86035053734646[/C][/ROW]
[ROW][C]23[/C][C]73[/C][C]83.147031222647[/C][C]2.89373701605757[/C][C]59.9592317612954[/C][C]10.147031222647[/C][/ROW]
[ROW][C]24[/C][C]68[/C][C]75.9580605110555[/C][C]4.16150634286634[/C][C]55.8804331460781[/C][C]7.95806051105556[/C][/ROW]
[ROW][C]25[/C][C]65[/C][C]70.6287624725968[/C][C]7.56960299654242[/C][C]51.8016345308608[/C][C]5.6287624725968[/C][/ROW]
[ROW][C]26[/C][C]57[/C][C]68.5128612552755[/C][C]-0.918153609398782[/C][C]46.4052923541233[/C][C]11.5128612552755[/C][/ROW]
[ROW][C]27[/C][C]41[/C][C]44.796969579179[/C][C]-3.80591975656485[/C][C]41.0089501773859[/C][C]3.79696957917898[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]12.7857986727298[/C][C]-5.79558248048857[/C][C]35.0097838077587[/C][C]-8.21420132727016[/C][/ROW]
[ROW][C]29[/C][C]21[/C][C]18.3746216232985[/C][C]-5.38523906143007[/C][C]29.0106174381316[/C][C]-2.62537837670151[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]11.6870666610641[/C][C]-1.10790809852643[/C][C]23.4208414374623[/C][C]-5.31293333893589[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]0.999512875144775[/C][C]-0.830578311937837[/C][C]17.8310654367931[/C][C]-8.00048712485522[/C][/ROW]
[ROW][C]32[/C][C]11[/C][C]6.3537603858581[/C][C]1.80644776712307[/C][C]13.8397918470188[/C][C]-4.64623961414189[/C][/ROW]
[ROW][C]33[/C][C]6[/C][C]3.70800567464321[/C][C]-1.55652393188780[/C][C]9.84851825724458[/C][C]-2.29199432535679[/C][/ROW]
[ROW][C]34[/C][C]-2[/C][C]-14.9963065330395[/C][C]2.96860229427904[/C][C]8.02770423876046[/C][C]-12.9963065330395[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-9.1006272363339[/C][C]2.89373701605757[/C][C]6.20689022027634[/C][C]-9.1006272363339[/C][/ROW]
[ROW][C]36[/C][C]5[/C][C]-0.159007143869911[/C][C]4.16150634286634[/C][C]5.99750080100357[/C][C]-5.15900714386991[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]-7.35771437827324[/C][C]7.56960299654242[/C][C]5.78811138173081[/C][C]-10.3577143782732[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]8.5195560071401[/C][C]-0.918153609398782[/C][C]6.39859760225868[/C][C]1.51955600714010[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]4.7968359337783[/C][C]-3.80591975656485[/C][C]7.00908382278655[/C][C]0.7968359337783[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]13.7298036139462[/C][C]-5.79558248048857[/C][C]8.0657788665424[/C][C]5.72980361394617[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]14.2627651511318[/C][C]-5.38523906143007[/C][C]9.12247391029823[/C][C]5.26276515113184[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]18.6752090893764[/C][C]-1.10790809852643[/C][C]10.4326990091501[/C][C]4.67520908937636[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]13.0876542039359[/C][C]-0.830578311937837[/C][C]11.7429241080019[/C][C]1.08765420393592[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]9.4356598932701[/C][C]1.80644776712307[/C][C]12.7578923396068[/C][C]-2.5643401067299[/C][/ROW]
[ROW][C]45[/C][C]7[/C][C]1.78366336067606[/C][C]-1.55652393188780[/C][C]13.7728605712117[/C][C]-5.21633663932394[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]12.5128721653975[/C][C]2.96860229427904[/C][C]14.5185255403235[/C][C]-2.48712783460250[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]9.84207247450725[/C][C]2.89373701605757[/C][C]15.2641905094352[/C][C]-4.15792752549275[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]17.6152793429999[/C][C]4.16150634286634[/C][C]16.2232143141338[/C][C]-1.38472065700010[/C][/ROW]
[ROW][C]49[/C][C]39[/C][C]53.2481588846252[/C][C]7.56960299654242[/C][C]17.1822381188323[/C][C]14.2481588846252[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]6.44415179136552[/C][C]-0.918153609398782[/C][C]18.4740018180333[/C][C]-5.55584820863448[/C][/ROW]
[ROW][C]51[/C][C]11[/C][C]6.04015423933067[/C][C]-3.80591975656485[/C][C]19.7657655172342[/C][C]-4.95984576066933[/C][/ROW]
[ROW][C]52[/C][C]17[/C][C]19.1941171911601[/C][C]-5.79558248048857[/C][C]20.6014652893285[/C][C]2.19411719116008[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]15.9480740000073[/C][C]-5.38523906143007[/C][C]21.4371650614228[/C][C]-0.0519259999927186[/C][/ROW]
[ROW][C]54[/C][C]25[/C][C]29.0114424867354[/C][C]-1.10790809852643[/C][C]22.0964656117911[/C][C]4.01144248673537[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]26.0748121497785[/C][C]-0.830578311937837[/C][C]22.7557661621593[/C][C]2.0748121497785[/C][/ROW]
[ROW][C]56[/C][C]28[/C][C]30.8024378569234[/C][C]1.80644776712307[/C][C]23.3911143759535[/C][C]2.80243785692343[/C][/ROW]
[ROW][C]57[/C][C]25[/C][C]27.5300613421401[/C][C]-1.55652393188780[/C][C]24.0264625897476[/C][C]2.53006134214015[/C][/ROW]
[ROW][C]58[/C][C]31[/C][C]34.410790408189[/C][C]2.96860229427904[/C][C]24.6206072975320[/C][C]3.41079040818900[/C][/ROW]
[ROW][C]59[/C][C]24[/C][C]19.8915109786262[/C][C]2.89373701605757[/C][C]25.2147520053163[/C][C]-4.10848902137382[/C][/ROW]
[ROW][C]60[/C][C]24[/C][C]18.085099237513[/C][C]4.16150634286634[/C][C]25.7533944196206[/C][C]-5.91490076248698[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117120&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117120&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	16	9.22879857055996	7.56960299654242	15.2015984328976	-6.77120142944004
2	17	15.6639420844331	-0.918153609398782	19.2542115249657	-1.33605791556694
3	23	26.499095139531	-3.80591975656485	23.3068246170338	3.49909513953101
4	24	26.5211589442402	-5.79558248048857	27.2744235362484	2.5211589442402
5	27	28.1432166059672	-5.38523906143007	31.2420224554629	1.14321660596719
6	31	27.9933721894237	-1.10790809852643	35.1145359091027	-3.00662781057630
7	40	41.8435289491952	-0.830578311937837	38.9870493627426	1.84352894919524
8	47	49.4682143068662	1.80644776712307	42.7253379260107	2.46821430686619
9	43	41.0928974426089	-1.55652393188780	46.4636264892789	-1.90710255739107
10	60	67.4725343304265	2.96860229427904	49.5588633752945	7.47253433042648
11	64	72.4521627226324	2.89373701605757	52.6541002613101	8.45216272263234
12	65	70.7375551554161	4.16150634286634	55.1009385017176	5.73755515541605
13	65	64.8826202613324	7.56960299654242	57.5477767421251	-0.117379738667559
14	55	51.4446786264436	-0.918153609398782	59.4734749829552	-3.55532137355643
15	57	56.4067465327796	-3.80591975656485	61.3991732237853	-0.593253467220443
16	57	57.1084068166681	-5.79558248048857	62.6871756638205	0.108406816668079
17	57	55.4100609575744	-5.38523906143007	63.9751781038557	-1.58993904242561
18	65	66.5281392357107	-1.10790809852643	64.5797688628158	1.52813923571065
19	69	73.646218690162	-0.830578311937837	65.1843596217759	4.64621869016196
20	70	73.4099411342622	1.80644776712307	64.7836110986147	3.4099411342622
21	71	79.1736613564342	-1.55652393188780	64.3828625754536	8.17366135643422
22	71	76.8603505373465	2.96860229427904	62.1710471683745	5.86035053734646
23	73	83.147031222647	2.89373701605757	59.9592317612954	10.147031222647
24	68	75.9580605110555	4.16150634286634	55.8804331460781	7.95806051105556
25	65	70.6287624725968	7.56960299654242	51.8016345308608	5.6287624725968
26	57	68.5128612552755	-0.918153609398782	46.4052923541233	11.5128612552755
27	41	44.796969579179	-3.80591975656485	41.0089501773859	3.79696957917898
28	21	12.7857986727298	-5.79558248048857	35.0097838077587	-8.21420132727016
29	21	18.3746216232985	-5.38523906143007	29.0106174381316	-2.62537837670151
30	17	11.6870666610641	-1.10790809852643	23.4208414374623	-5.31293333893589
31	9	0.999512875144775	-0.830578311937837	17.8310654367931	-8.00048712485522
32	11	6.3537603858581	1.80644776712307	13.8397918470188	-4.64623961414189
33	6	3.70800567464321	-1.55652393188780	9.84851825724458	-2.29199432535679
34	-2	-14.9963065330395	2.96860229427904	8.02770423876046	-12.9963065330395
35	0	-9.1006272363339	2.89373701605757	6.20689022027634	-9.1006272363339
36	5	-0.159007143869911	4.16150634286634	5.99750080100357	-5.15900714386991
37	3	-7.35771437827324	7.56960299654242	5.78811138173081	-10.3577143782732
38	7	8.5195560071401	-0.918153609398782	6.39859760225868	1.51955600714010
39	4	4.7968359337783	-3.80591975656485	7.00908382278655	0.7968359337783
40	8	13.7298036139462	-5.79558248048857	8.0657788665424	5.72980361394617
41	9	14.2627651511318	-5.38523906143007	9.12247391029823	5.26276515113184
42	14	18.6752090893764	-1.10790809852643	10.4326990091501	4.67520908937636
43	12	13.0876542039359	-0.830578311937837	11.7429241080019	1.08765420393592
44	12	9.4356598932701	1.80644776712307	12.7578923396068	-2.5643401067299
45	7	1.78366336067606	-1.55652393188780	13.7728605712117	-5.21633663932394
46	15	12.5128721653975	2.96860229427904	14.5185255403235	-2.48712783460250
47	14	9.84207247450725	2.89373701605757	15.2641905094352	-4.15792752549275
48	19	17.6152793429999	4.16150634286634	16.2232143141338	-1.38472065700010
49	39	53.2481588846252	7.56960299654242	17.1822381188323	14.2481588846252
50	12	6.44415179136552	-0.918153609398782	18.4740018180333	-5.55584820863448
51	11	6.04015423933067	-3.80591975656485	19.7657655172342	-4.95984576066933
52	17	19.1941171911601	-5.79558248048857	20.6014652893285	2.19411719116008
53	16	15.9480740000073	-5.38523906143007	21.4371650614228	-0.0519259999927186
54	25	29.0114424867354	-1.10790809852643	22.0964656117911	4.01144248673537
55	24	26.0748121497785	-0.830578311937837	22.7557661621593	2.0748121497785
56	28	30.8024378569234	1.80644776712307	23.3911143759535	2.80243785692343
57	25	27.5300613421401	-1.55652393188780	24.0264625897476	2.53006134214015
58	31	34.410790408189	2.96860229427904	24.6206072975320	3.41079040818900
59	24	19.8915109786262	2.89373701605757	25.2147520053163	-4.10848902137382
60	24	18.085099237513	4.16150634286634	25.7533944196206	-5.91490076248698

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 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par4 = ; par5 = 1 ; par6 = ; par7 = 1 ; par8 = FALSE ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;

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