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Author

*The author of this computation has been verified*

R Software Module

rwasp_decomposeloess.wasp

Title produced by software

Decomposition by Loess

Date of computation

Mon, 27 Dec 2010 10:08:53 +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/27/t1293444477poyxw4z5klufoxf.htm/, Retrieved Mon, 06 May 2024 16:57:55 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115879, Retrieved Mon, 06 May 2024 16:57:55 +0000

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Original text written by user:

Prijsverandering in Nederland

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

203

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

F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [Cross Correlation Function] [Q7 - zonder trans...] [2008-12-01 20:04:13] [299afd6311e4c20059ea2f05c8dd029d]
F RM D    [Variance Reduction Matrix] [Q8] [2008-12-01 20:20:44] [299afd6311e4c20059ea2f05c8dd029d]
F    D      [Variance Reduction Matrix] [Q8 - 2] [2008-12-01 20:25:07] [299afd6311e4c20059ea2f05c8dd029d]
F RM D        [Standard Deviation-Mean Plot] [Deel 2: Step 1] [2008-12-08 20:09:35] [299afd6311e4c20059ea2f05c8dd029d]
-    D          [Standard Deviation-Mean Plot] [Totale Uitvoer - SMP] [2008-12-17 15:57:12] [299afd6311e4c20059ea2f05c8dd029d]
- RMPD            [ARIMA Forecasting] [ARIMA Forecasting] [2010-12-24 14:15:31] [9f313cc7203314d73bf17d2b325aee79]
- RMPD                [Decomposition by Loess] [Decomposition by ...] [2010-12-27 10:08:53] [fba9c6aa004af59d8497d682e70ddad5] [Current]

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Dataseries X:

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Histogram

Boxplots

13.7
13.7
13.7
1.3
1.3
1.3
-7.4
-7.4
-7.4
-12.9
-12.9
-12.9
-9.6
-9.6
-9.6
-11.1
-11.1
-11.1
-8.3
-8.3
-8.3
-2.7
-2.7
-2.7
5.1
5.1
5.1
4.6
4.6
4.6
5.6
5.6
5.6
5.1
5.1
5.1
0.8
0.8
0.8
6
6
6
9.3
9.3
9.3
8.7
8.7
8.7
11
11
11
8.5
8.5
8.5
4.4
4.4
4.4
2.5
2.5
2.5
0.3
0.3
0.3
-3
-3
-3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115879&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115879&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115879&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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	661	0	67
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 & 661 & 0 & 67 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115879&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]661[/C][C]0[/C][C]67[/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=115879&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115879&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	661	0	67
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	13.7	14.7209227280332	1.12822200965322	11.5508552623136	1.02092272803322
2	13.7	16.6584838997914	1.49448727601464	9.24702882419395	2.95848389979142
3	13.7	18.5960450715496	1.86075254237605	6.94320238607434	4.89604507154961
4	1.3	-1.80151201154961	-0.337917358773868	4.73942937032348	-3.10151201154961
5	1.3	0.100937279273388	-0.0365936338460116	2.53565635457262	-1.19906272072661
6	1.3	1.93536444450873	0.244669119903456	0.419966435587809	0.635364444508735
7	-7.4	-12.1697616104482	-0.934514906154846	-1.69572348339701	-4.76976161044815
8	-7.4	-10.3189972450477	-0.719927902661002	-3.76107485229134	-2.91899724504766
9	-7.4	-8.46823287964717	-0.505340899167158	-5.82642622118568	-1.06823287964717
10	-12.9	-17.5288623704331	-0.885075930956122	-7.38606169861081	-4.62886237043307
11	-12.9	-16.1694900117557	-0.684812812208324	-8.94569717603595	-3.26949001175572
12	-12.9	-15.5620517367793	-0.62394690744708	-9.61400135577358	-2.66205173677934
13	-9.6	-10.045916474142	1.12822200965322	-10.2823055355112	-0.445916474142008
14	-9.6	-10.3677767964421	1.49448727601464	-10.3267104795725	-0.767776796442098
15	-9.6	-10.6896371187422	1.86075254237605	-10.3711154236339	-1.08963711874219
16	-11.1	-12.0115192658895	-0.337917358773868	-9.85056337533665	-0.911519265889481
17	-11.1	-12.8333950391146	-0.0365936338460116	-9.33001132703943	-1.73339503911455
18	-11.1	-14.0693005454885	0.244669119903456	-8.37536857441491	-2.96930054548855
19	-8.3	-8.24475927205477	-0.934514906154846	-7.42072582179039	0.0552407279452307
20	-8.3	-9.69230975802217	-0.719927902661002	-6.18776233931683	-1.39230975802217
21	-8.3	-11.1398602439896	-0.505340899167158	-4.95479885684328	-2.83986024398956
22	-2.7	-0.922082560519593	-0.885075930956122	-3.59284150852429	1.77791743948041
23	-2.7	-2.48430302758638	-0.684812812208324	-2.23088416020529	0.215696972413618
24	-2.7	-3.85601930000428	-0.62394690744708	-0.920033792548643	-1.15601930000428
25	5.1	8.68096141523877	1.12822200965322	0.390816575108008	3.58096141523877
26	5.1	7.20251943678096	1.49448727601464	1.50299328720440	2.10251943678097
27	5.1	5.72407745832316	1.86075254237605	2.61516999930079	0.624077458323158
28	4.6	6.12936859991178	-0.337917358773868	3.40854875886208	1.52936859991178
29	4.6	5.03466611542264	-0.0365936338460116	4.20192751842338	0.434666115422636
30	4.6	4.44217155167073	0.244669119903456	4.51315932842581	-0.15782844832927
31	5.6	7.3101237677266	-0.934514906154846	4.82439113842825	1.71012376772659
32	5.6	7.22918320441601	-0.719927902661002	4.69074469824499	1.62918320441601
33	5.6	7.14824264110543	-0.505340899167158	4.55709825806173	1.54824264110543
34	5.1	6.69621964667233	-0.885075930956122	4.38885628428379	1.59621964667233
35	5.1	6.66419850170247	-0.684812812208324	4.22061431050586	1.56419850170247
36	5.1	6.56777277356656	-0.62394690744708	4.25617413388052	1.46777277356656
37	0.8	-3.81995596690839	1.12822200965322	4.29173395725517	-4.61995596690839
38	0.8	-4.43697049649557	1.49448727601464	4.54248322048093	-5.23697049649557
39	0.8	-5.05398502608274	1.86075254237605	4.79323248370669	-5.85398502608274
40	6	7.10094706268672	-0.337917358773868	5.23697029608715	1.10094706268672
41	6	6.3558855253784	-0.0365936338460116	5.68070810846761	0.355885525378399
42	6	5.42493727549877	0.244669119903456	6.33039360459777	-0.575062724501228
43	9.3	12.5544358054269	-0.934514906154846	6.98007910072793	3.25443580542692
44	9.3	11.6482243382954	-0.719927902661002	7.67170356436562	2.34822433829538
45	9.3	10.7420128711638	-0.505340899167158	8.36332802800332	1.44201287116384
46	8.7	9.54013651823849	-0.885075930956122	8.74493941271763	0.840136518238486
47	8.7	8.95826201477637	-0.684812812208324	9.12655079743196	0.258262014776369
48	8.7	8.91709433064987	-0.62394690744708	9.1068525767972	0.217094330649875
49	11	11.7846236341843	1.12822200965322	9.08715435616246	0.784623634184324
50	11	11.7410016461631	1.49448727601464	8.76451107782227	0.741001646163097
51	11	11.6973796581419	1.86075254237605	8.44186779948208	0.69737965814187
52	8.5	9.3929269772858	-0.337917358773868	7.94499038148806	0.892926977285808
53	8.5	9.58848067035197	-0.0365936338460116	7.44811296349404	1.08848067035197
54	8.5	9.97427596925832	0.244669119903456	6.78105491083822	1.47427596925832
55	4.4	3.62051804797245	-0.934514906154846	6.1139968581824	-0.77948195202755
56	4.4	4.26337309093207	-0.719927902661002	5.25655481172893	-0.13662690906793
57	4.4	4.90622813389169	-0.505340899167158	4.39911276527547	0.506228133891689
58	2.5	2.44222238117224	-0.885075930956122	3.44285354978388	-0.0577776188277621
59	2.5	3.19821847791603	-0.684812812208324	2.4865943342923	0.698218477916027
60	2.5	4.06918125853217	-0.62394690744708	1.55476564891491	1.56918125853217
61	0.3	-1.15115897319074	1.12822200965322	0.622936963537516	-1.45115897319074
62	0.3	-0.57930741820112	1.49448727601464	-0.315179857813516	-0.87930741820112
63	0.3	-0.00745586321150454	1.86075254237605	-1.25329667916455	-0.307455863211505
64	-3	-3.46478992474197	-0.337917358773868	-2.19729271648416	-0.464789924741968
65	-3	-2.82211761235021	-0.0365936338460116	-3.14128875380378	0.177882387649792
66	-3	-2.15977256774678	0.244669119903456	-4.08489655215668	0.840227432253221

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 13.7 & 14.7209227280332 & 1.12822200965322 & 11.5508552623136 & 1.02092272803322 \tabularnewline
2 & 13.7 & 16.6584838997914 & 1.49448727601464 & 9.24702882419395 & 2.95848389979142 \tabularnewline
3 & 13.7 & 18.5960450715496 & 1.86075254237605 & 6.94320238607434 & 4.89604507154961 \tabularnewline
4 & 1.3 & -1.80151201154961 & -0.337917358773868 & 4.73942937032348 & -3.10151201154961 \tabularnewline
5 & 1.3 & 0.100937279273388 & -0.0365936338460116 & 2.53565635457262 & -1.19906272072661 \tabularnewline
6 & 1.3 & 1.93536444450873 & 0.244669119903456 & 0.419966435587809 & 0.635364444508735 \tabularnewline
7 & -7.4 & -12.1697616104482 & -0.934514906154846 & -1.69572348339701 & -4.76976161044815 \tabularnewline
8 & -7.4 & -10.3189972450477 & -0.719927902661002 & -3.76107485229134 & -2.91899724504766 \tabularnewline
9 & -7.4 & -8.46823287964717 & -0.505340899167158 & -5.82642622118568 & -1.06823287964717 \tabularnewline
10 & -12.9 & -17.5288623704331 & -0.885075930956122 & -7.38606169861081 & -4.62886237043307 \tabularnewline
11 & -12.9 & -16.1694900117557 & -0.684812812208324 & -8.94569717603595 & -3.26949001175572 \tabularnewline
12 & -12.9 & -15.5620517367793 & -0.62394690744708 & -9.61400135577358 & -2.66205173677934 \tabularnewline
13 & -9.6 & -10.045916474142 & 1.12822200965322 & -10.2823055355112 & -0.445916474142008 \tabularnewline
14 & -9.6 & -10.3677767964421 & 1.49448727601464 & -10.3267104795725 & -0.767776796442098 \tabularnewline
15 & -9.6 & -10.6896371187422 & 1.86075254237605 & -10.3711154236339 & -1.08963711874219 \tabularnewline
16 & -11.1 & -12.0115192658895 & -0.337917358773868 & -9.85056337533665 & -0.911519265889481 \tabularnewline
17 & -11.1 & -12.8333950391146 & -0.0365936338460116 & -9.33001132703943 & -1.73339503911455 \tabularnewline
18 & -11.1 & -14.0693005454885 & 0.244669119903456 & -8.37536857441491 & -2.96930054548855 \tabularnewline
19 & -8.3 & -8.24475927205477 & -0.934514906154846 & -7.42072582179039 & 0.0552407279452307 \tabularnewline
20 & -8.3 & -9.69230975802217 & -0.719927902661002 & -6.18776233931683 & -1.39230975802217 \tabularnewline
21 & -8.3 & -11.1398602439896 & -0.505340899167158 & -4.95479885684328 & -2.83986024398956 \tabularnewline
22 & -2.7 & -0.922082560519593 & -0.885075930956122 & -3.59284150852429 & 1.77791743948041 \tabularnewline
23 & -2.7 & -2.48430302758638 & -0.684812812208324 & -2.23088416020529 & 0.215696972413618 \tabularnewline
24 & -2.7 & -3.85601930000428 & -0.62394690744708 & -0.920033792548643 & -1.15601930000428 \tabularnewline
25 & 5.1 & 8.68096141523877 & 1.12822200965322 & 0.390816575108008 & 3.58096141523877 \tabularnewline
26 & 5.1 & 7.20251943678096 & 1.49448727601464 & 1.50299328720440 & 2.10251943678097 \tabularnewline
27 & 5.1 & 5.72407745832316 & 1.86075254237605 & 2.61516999930079 & 0.624077458323158 \tabularnewline
28 & 4.6 & 6.12936859991178 & -0.337917358773868 & 3.40854875886208 & 1.52936859991178 \tabularnewline
29 & 4.6 & 5.03466611542264 & -0.0365936338460116 & 4.20192751842338 & 0.434666115422636 \tabularnewline
30 & 4.6 & 4.44217155167073 & 0.244669119903456 & 4.51315932842581 & -0.15782844832927 \tabularnewline
31 & 5.6 & 7.3101237677266 & -0.934514906154846 & 4.82439113842825 & 1.71012376772659 \tabularnewline
32 & 5.6 & 7.22918320441601 & -0.719927902661002 & 4.69074469824499 & 1.62918320441601 \tabularnewline
33 & 5.6 & 7.14824264110543 & -0.505340899167158 & 4.55709825806173 & 1.54824264110543 \tabularnewline
34 & 5.1 & 6.69621964667233 & -0.885075930956122 & 4.38885628428379 & 1.59621964667233 \tabularnewline
35 & 5.1 & 6.66419850170247 & -0.684812812208324 & 4.22061431050586 & 1.56419850170247 \tabularnewline
36 & 5.1 & 6.56777277356656 & -0.62394690744708 & 4.25617413388052 & 1.46777277356656 \tabularnewline
37 & 0.8 & -3.81995596690839 & 1.12822200965322 & 4.29173395725517 & -4.61995596690839 \tabularnewline
38 & 0.8 & -4.43697049649557 & 1.49448727601464 & 4.54248322048093 & -5.23697049649557 \tabularnewline
39 & 0.8 & -5.05398502608274 & 1.86075254237605 & 4.79323248370669 & -5.85398502608274 \tabularnewline
40 & 6 & 7.10094706268672 & -0.337917358773868 & 5.23697029608715 & 1.10094706268672 \tabularnewline
41 & 6 & 6.3558855253784 & -0.0365936338460116 & 5.68070810846761 & 0.355885525378399 \tabularnewline
42 & 6 & 5.42493727549877 & 0.244669119903456 & 6.33039360459777 & -0.575062724501228 \tabularnewline
43 & 9.3 & 12.5544358054269 & -0.934514906154846 & 6.98007910072793 & 3.25443580542692 \tabularnewline
44 & 9.3 & 11.6482243382954 & -0.719927902661002 & 7.67170356436562 & 2.34822433829538 \tabularnewline
45 & 9.3 & 10.7420128711638 & -0.505340899167158 & 8.36332802800332 & 1.44201287116384 \tabularnewline
46 & 8.7 & 9.54013651823849 & -0.885075930956122 & 8.74493941271763 & 0.840136518238486 \tabularnewline
47 & 8.7 & 8.95826201477637 & -0.684812812208324 & 9.12655079743196 & 0.258262014776369 \tabularnewline
48 & 8.7 & 8.91709433064987 & -0.62394690744708 & 9.1068525767972 & 0.217094330649875 \tabularnewline
49 & 11 & 11.7846236341843 & 1.12822200965322 & 9.08715435616246 & 0.784623634184324 \tabularnewline
50 & 11 & 11.7410016461631 & 1.49448727601464 & 8.76451107782227 & 0.741001646163097 \tabularnewline
51 & 11 & 11.6973796581419 & 1.86075254237605 & 8.44186779948208 & 0.69737965814187 \tabularnewline
52 & 8.5 & 9.3929269772858 & -0.337917358773868 & 7.94499038148806 & 0.892926977285808 \tabularnewline
53 & 8.5 & 9.58848067035197 & -0.0365936338460116 & 7.44811296349404 & 1.08848067035197 \tabularnewline
54 & 8.5 & 9.97427596925832 & 0.244669119903456 & 6.78105491083822 & 1.47427596925832 \tabularnewline
55 & 4.4 & 3.62051804797245 & -0.934514906154846 & 6.1139968581824 & -0.77948195202755 \tabularnewline
56 & 4.4 & 4.26337309093207 & -0.719927902661002 & 5.25655481172893 & -0.13662690906793 \tabularnewline
57 & 4.4 & 4.90622813389169 & -0.505340899167158 & 4.39911276527547 & 0.506228133891689 \tabularnewline
58 & 2.5 & 2.44222238117224 & -0.885075930956122 & 3.44285354978388 & -0.0577776188277621 \tabularnewline
59 & 2.5 & 3.19821847791603 & -0.684812812208324 & 2.4865943342923 & 0.698218477916027 \tabularnewline
60 & 2.5 & 4.06918125853217 & -0.62394690744708 & 1.55476564891491 & 1.56918125853217 \tabularnewline
61 & 0.3 & -1.15115897319074 & 1.12822200965322 & 0.622936963537516 & -1.45115897319074 \tabularnewline
62 & 0.3 & -0.57930741820112 & 1.49448727601464 & -0.315179857813516 & -0.87930741820112 \tabularnewline
63 & 0.3 & -0.00745586321150454 & 1.86075254237605 & -1.25329667916455 & -0.307455863211505 \tabularnewline
64 & -3 & -3.46478992474197 & -0.337917358773868 & -2.19729271648416 & -0.464789924741968 \tabularnewline
65 & -3 & -2.82211761235021 & -0.0365936338460116 & -3.14128875380378 & 0.177882387649792 \tabularnewline
66 & -3 & -2.15977256774678 & 0.244669119903456 & -4.08489655215668 & 0.840227432253221 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115879&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]13.7[/C][C]14.7209227280332[/C][C]1.12822200965322[/C][C]11.5508552623136[/C][C]1.02092272803322[/C][/ROW]
[ROW][C]2[/C][C]13.7[/C][C]16.6584838997914[/C][C]1.49448727601464[/C][C]9.24702882419395[/C][C]2.95848389979142[/C][/ROW]
[ROW][C]3[/C][C]13.7[/C][C]18.5960450715496[/C][C]1.86075254237605[/C][C]6.94320238607434[/C][C]4.89604507154961[/C][/ROW]
[ROW][C]4[/C][C]1.3[/C][C]-1.80151201154961[/C][C]-0.337917358773868[/C][C]4.73942937032348[/C][C]-3.10151201154961[/C][/ROW]
[ROW][C]5[/C][C]1.3[/C][C]0.100937279273388[/C][C]-0.0365936338460116[/C][C]2.53565635457262[/C][C]-1.19906272072661[/C][/ROW]
[ROW][C]6[/C][C]1.3[/C][C]1.93536444450873[/C][C]0.244669119903456[/C][C]0.419966435587809[/C][C]0.635364444508735[/C][/ROW]
[ROW][C]7[/C][C]-7.4[/C][C]-12.1697616104482[/C][C]-0.934514906154846[/C][C]-1.69572348339701[/C][C]-4.76976161044815[/C][/ROW]
[ROW][C]8[/C][C]-7.4[/C][C]-10.3189972450477[/C][C]-0.719927902661002[/C][C]-3.76107485229134[/C][C]-2.91899724504766[/C][/ROW]
[ROW][C]9[/C][C]-7.4[/C][C]-8.46823287964717[/C][C]-0.505340899167158[/C][C]-5.82642622118568[/C][C]-1.06823287964717[/C][/ROW]
[ROW][C]10[/C][C]-12.9[/C][C]-17.5288623704331[/C][C]-0.885075930956122[/C][C]-7.38606169861081[/C][C]-4.62886237043307[/C][/ROW]
[ROW][C]11[/C][C]-12.9[/C][C]-16.1694900117557[/C][C]-0.684812812208324[/C][C]-8.94569717603595[/C][C]-3.26949001175572[/C][/ROW]
[ROW][C]12[/C][C]-12.9[/C][C]-15.5620517367793[/C][C]-0.62394690744708[/C][C]-9.61400135577358[/C][C]-2.66205173677934[/C][/ROW]
[ROW][C]13[/C][C]-9.6[/C][C]-10.045916474142[/C][C]1.12822200965322[/C][C]-10.2823055355112[/C][C]-0.445916474142008[/C][/ROW]
[ROW][C]14[/C][C]-9.6[/C][C]-10.3677767964421[/C][C]1.49448727601464[/C][C]-10.3267104795725[/C][C]-0.767776796442098[/C][/ROW]
[ROW][C]15[/C][C]-9.6[/C][C]-10.6896371187422[/C][C]1.86075254237605[/C][C]-10.3711154236339[/C][C]-1.08963711874219[/C][/ROW]
[ROW][C]16[/C][C]-11.1[/C][C]-12.0115192658895[/C][C]-0.337917358773868[/C][C]-9.85056337533665[/C][C]-0.911519265889481[/C][/ROW]
[ROW][C]17[/C][C]-11.1[/C][C]-12.8333950391146[/C][C]-0.0365936338460116[/C][C]-9.33001132703943[/C][C]-1.73339503911455[/C][/ROW]
[ROW][C]18[/C][C]-11.1[/C][C]-14.0693005454885[/C][C]0.244669119903456[/C][C]-8.37536857441491[/C][C]-2.96930054548855[/C][/ROW]
[ROW][C]19[/C][C]-8.3[/C][C]-8.24475927205477[/C][C]-0.934514906154846[/C][C]-7.42072582179039[/C][C]0.0552407279452307[/C][/ROW]
[ROW][C]20[/C][C]-8.3[/C][C]-9.69230975802217[/C][C]-0.719927902661002[/C][C]-6.18776233931683[/C][C]-1.39230975802217[/C][/ROW]
[ROW][C]21[/C][C]-8.3[/C][C]-11.1398602439896[/C][C]-0.505340899167158[/C][C]-4.95479885684328[/C][C]-2.83986024398956[/C][/ROW]
[ROW][C]22[/C][C]-2.7[/C][C]-0.922082560519593[/C][C]-0.885075930956122[/C][C]-3.59284150852429[/C][C]1.77791743948041[/C][/ROW]
[ROW][C]23[/C][C]-2.7[/C][C]-2.48430302758638[/C][C]-0.684812812208324[/C][C]-2.23088416020529[/C][C]0.215696972413618[/C][/ROW]
[ROW][C]24[/C][C]-2.7[/C][C]-3.85601930000428[/C][C]-0.62394690744708[/C][C]-0.920033792548643[/C][C]-1.15601930000428[/C][/ROW]
[ROW][C]25[/C][C]5.1[/C][C]8.68096141523877[/C][C]1.12822200965322[/C][C]0.390816575108008[/C][C]3.58096141523877[/C][/ROW]
[ROW][C]26[/C][C]5.1[/C][C]7.20251943678096[/C][C]1.49448727601464[/C][C]1.50299328720440[/C][C]2.10251943678097[/C][/ROW]
[ROW][C]27[/C][C]5.1[/C][C]5.72407745832316[/C][C]1.86075254237605[/C][C]2.61516999930079[/C][C]0.624077458323158[/C][/ROW]
[ROW][C]28[/C][C]4.6[/C][C]6.12936859991178[/C][C]-0.337917358773868[/C][C]3.40854875886208[/C][C]1.52936859991178[/C][/ROW]
[ROW][C]29[/C][C]4.6[/C][C]5.03466611542264[/C][C]-0.0365936338460116[/C][C]4.20192751842338[/C][C]0.434666115422636[/C][/ROW]
[ROW][C]30[/C][C]4.6[/C][C]4.44217155167073[/C][C]0.244669119903456[/C][C]4.51315932842581[/C][C]-0.15782844832927[/C][/ROW]
[ROW][C]31[/C][C]5.6[/C][C]7.3101237677266[/C][C]-0.934514906154846[/C][C]4.82439113842825[/C][C]1.71012376772659[/C][/ROW]
[ROW][C]32[/C][C]5.6[/C][C]7.22918320441601[/C][C]-0.719927902661002[/C][C]4.69074469824499[/C][C]1.62918320441601[/C][/ROW]
[ROW][C]33[/C][C]5.6[/C][C]7.14824264110543[/C][C]-0.505340899167158[/C][C]4.55709825806173[/C][C]1.54824264110543[/C][/ROW]
[ROW][C]34[/C][C]5.1[/C][C]6.69621964667233[/C][C]-0.885075930956122[/C][C]4.38885628428379[/C][C]1.59621964667233[/C][/ROW]
[ROW][C]35[/C][C]5.1[/C][C]6.66419850170247[/C][C]-0.684812812208324[/C][C]4.22061431050586[/C][C]1.56419850170247[/C][/ROW]
[ROW][C]36[/C][C]5.1[/C][C]6.56777277356656[/C][C]-0.62394690744708[/C][C]4.25617413388052[/C][C]1.46777277356656[/C][/ROW]
[ROW][C]37[/C][C]0.8[/C][C]-3.81995596690839[/C][C]1.12822200965322[/C][C]4.29173395725517[/C][C]-4.61995596690839[/C][/ROW]
[ROW][C]38[/C][C]0.8[/C][C]-4.43697049649557[/C][C]1.49448727601464[/C][C]4.54248322048093[/C][C]-5.23697049649557[/C][/ROW]
[ROW][C]39[/C][C]0.8[/C][C]-5.05398502608274[/C][C]1.86075254237605[/C][C]4.79323248370669[/C][C]-5.85398502608274[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]7.10094706268672[/C][C]-0.337917358773868[/C][C]5.23697029608715[/C][C]1.10094706268672[/C][/ROW]
[ROW][C]41[/C][C]6[/C][C]6.3558855253784[/C][C]-0.0365936338460116[/C][C]5.68070810846761[/C][C]0.355885525378399[/C][/ROW]
[ROW][C]42[/C][C]6[/C][C]5.42493727549877[/C][C]0.244669119903456[/C][C]6.33039360459777[/C][C]-0.575062724501228[/C][/ROW]
[ROW][C]43[/C][C]9.3[/C][C]12.5544358054269[/C][C]-0.934514906154846[/C][C]6.98007910072793[/C][C]3.25443580542692[/C][/ROW]
[ROW][C]44[/C][C]9.3[/C][C]11.6482243382954[/C][C]-0.719927902661002[/C][C]7.67170356436562[/C][C]2.34822433829538[/C][/ROW]
[ROW][C]45[/C][C]9.3[/C][C]10.7420128711638[/C][C]-0.505340899167158[/C][C]8.36332802800332[/C][C]1.44201287116384[/C][/ROW]
[ROW][C]46[/C][C]8.7[/C][C]9.54013651823849[/C][C]-0.885075930956122[/C][C]8.74493941271763[/C][C]0.840136518238486[/C][/ROW]
[ROW][C]47[/C][C]8.7[/C][C]8.95826201477637[/C][C]-0.684812812208324[/C][C]9.12655079743196[/C][C]0.258262014776369[/C][/ROW]
[ROW][C]48[/C][C]8.7[/C][C]8.91709433064987[/C][C]-0.62394690744708[/C][C]9.1068525767972[/C][C]0.217094330649875[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.7846236341843[/C][C]1.12822200965322[/C][C]9.08715435616246[/C][C]0.784623634184324[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]11.7410016461631[/C][C]1.49448727601464[/C][C]8.76451107782227[/C][C]0.741001646163097[/C][/ROW]
[ROW][C]51[/C][C]11[/C][C]11.6973796581419[/C][C]1.86075254237605[/C][C]8.44186779948208[/C][C]0.69737965814187[/C][/ROW]
[ROW][C]52[/C][C]8.5[/C][C]9.3929269772858[/C][C]-0.337917358773868[/C][C]7.94499038148806[/C][C]0.892926977285808[/C][/ROW]
[ROW][C]53[/C][C]8.5[/C][C]9.58848067035197[/C][C]-0.0365936338460116[/C][C]7.44811296349404[/C][C]1.08848067035197[/C][/ROW]
[ROW][C]54[/C][C]8.5[/C][C]9.97427596925832[/C][C]0.244669119903456[/C][C]6.78105491083822[/C][C]1.47427596925832[/C][/ROW]
[ROW][C]55[/C][C]4.4[/C][C]3.62051804797245[/C][C]-0.934514906154846[/C][C]6.1139968581824[/C][C]-0.77948195202755[/C][/ROW]
[ROW][C]56[/C][C]4.4[/C][C]4.26337309093207[/C][C]-0.719927902661002[/C][C]5.25655481172893[/C][C]-0.13662690906793[/C][/ROW]
[ROW][C]57[/C][C]4.4[/C][C]4.90622813389169[/C][C]-0.505340899167158[/C][C]4.39911276527547[/C][C]0.506228133891689[/C][/ROW]
[ROW][C]58[/C][C]2.5[/C][C]2.44222238117224[/C][C]-0.885075930956122[/C][C]3.44285354978388[/C][C]-0.0577776188277621[/C][/ROW]
[ROW][C]59[/C][C]2.5[/C][C]3.19821847791603[/C][C]-0.684812812208324[/C][C]2.4865943342923[/C][C]0.698218477916027[/C][/ROW]
[ROW][C]60[/C][C]2.5[/C][C]4.06918125853217[/C][C]-0.62394690744708[/C][C]1.55476564891491[/C][C]1.56918125853217[/C][/ROW]
[ROW][C]61[/C][C]0.3[/C][C]-1.15115897319074[/C][C]1.12822200965322[/C][C]0.622936963537516[/C][C]-1.45115897319074[/C][/ROW]
[ROW][C]62[/C][C]0.3[/C][C]-0.57930741820112[/C][C]1.49448727601464[/C][C]-0.315179857813516[/C][C]-0.87930741820112[/C][/ROW]
[ROW][C]63[/C][C]0.3[/C][C]-0.00745586321150454[/C][C]1.86075254237605[/C][C]-1.25329667916455[/C][C]-0.307455863211505[/C][/ROW]
[ROW][C]64[/C][C]-3[/C][C]-3.46478992474197[/C][C]-0.337917358773868[/C][C]-2.19729271648416[/C][C]-0.464789924741968[/C][/ROW]
[ROW][C]65[/C][C]-3[/C][C]-2.82211761235021[/C][C]-0.0365936338460116[/C][C]-3.14128875380378[/C][C]0.177882387649792[/C][/ROW]
[ROW][C]66[/C][C]-3[/C][C]-2.15977256774678[/C][C]0.244669119903456[/C][C]-4.08489655215668[/C][C]0.840227432253221[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115879&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115879&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	13.7	14.7209227280332	1.12822200965322	11.5508552623136	1.02092272803322
2	13.7	16.6584838997914	1.49448727601464	9.24702882419395	2.95848389979142
3	13.7	18.5960450715496	1.86075254237605	6.94320238607434	4.89604507154961
4	1.3	-1.80151201154961	-0.337917358773868	4.73942937032348	-3.10151201154961
5	1.3	0.100937279273388	-0.0365936338460116	2.53565635457262	-1.19906272072661
6	1.3	1.93536444450873	0.244669119903456	0.419966435587809	0.635364444508735
7	-7.4	-12.1697616104482	-0.934514906154846	-1.69572348339701	-4.76976161044815
8	-7.4	-10.3189972450477	-0.719927902661002	-3.76107485229134	-2.91899724504766
9	-7.4	-8.46823287964717	-0.505340899167158	-5.82642622118568	-1.06823287964717
10	-12.9	-17.5288623704331	-0.885075930956122	-7.38606169861081	-4.62886237043307
11	-12.9	-16.1694900117557	-0.684812812208324	-8.94569717603595	-3.26949001175572
12	-12.9	-15.5620517367793	-0.62394690744708	-9.61400135577358	-2.66205173677934
13	-9.6	-10.045916474142	1.12822200965322	-10.2823055355112	-0.445916474142008
14	-9.6	-10.3677767964421	1.49448727601464	-10.3267104795725	-0.767776796442098
15	-9.6	-10.6896371187422	1.86075254237605	-10.3711154236339	-1.08963711874219
16	-11.1	-12.0115192658895	-0.337917358773868	-9.85056337533665	-0.911519265889481
17	-11.1	-12.8333950391146	-0.0365936338460116	-9.33001132703943	-1.73339503911455
18	-11.1	-14.0693005454885	0.244669119903456	-8.37536857441491	-2.96930054548855
19	-8.3	-8.24475927205477	-0.934514906154846	-7.42072582179039	0.0552407279452307
20	-8.3	-9.69230975802217	-0.719927902661002	-6.18776233931683	-1.39230975802217
21	-8.3	-11.1398602439896	-0.505340899167158	-4.95479885684328	-2.83986024398956
22	-2.7	-0.922082560519593	-0.885075930956122	-3.59284150852429	1.77791743948041
23	-2.7	-2.48430302758638	-0.684812812208324	-2.23088416020529	0.215696972413618
24	-2.7	-3.85601930000428	-0.62394690744708	-0.920033792548643	-1.15601930000428
25	5.1	8.68096141523877	1.12822200965322	0.390816575108008	3.58096141523877
26	5.1	7.20251943678096	1.49448727601464	1.50299328720440	2.10251943678097
27	5.1	5.72407745832316	1.86075254237605	2.61516999930079	0.624077458323158
28	4.6	6.12936859991178	-0.337917358773868	3.40854875886208	1.52936859991178
29	4.6	5.03466611542264	-0.0365936338460116	4.20192751842338	0.434666115422636
30	4.6	4.44217155167073	0.244669119903456	4.51315932842581	-0.15782844832927
31	5.6	7.3101237677266	-0.934514906154846	4.82439113842825	1.71012376772659
32	5.6	7.22918320441601	-0.719927902661002	4.69074469824499	1.62918320441601
33	5.6	7.14824264110543	-0.505340899167158	4.55709825806173	1.54824264110543
34	5.1	6.69621964667233	-0.885075930956122	4.38885628428379	1.59621964667233
35	5.1	6.66419850170247	-0.684812812208324	4.22061431050586	1.56419850170247
36	5.1	6.56777277356656	-0.62394690744708	4.25617413388052	1.46777277356656
37	0.8	-3.81995596690839	1.12822200965322	4.29173395725517	-4.61995596690839
38	0.8	-4.43697049649557	1.49448727601464	4.54248322048093	-5.23697049649557
39	0.8	-5.05398502608274	1.86075254237605	4.79323248370669	-5.85398502608274
40	6	7.10094706268672	-0.337917358773868	5.23697029608715	1.10094706268672
41	6	6.3558855253784	-0.0365936338460116	5.68070810846761	0.355885525378399
42	6	5.42493727549877	0.244669119903456	6.33039360459777	-0.575062724501228
43	9.3	12.5544358054269	-0.934514906154846	6.98007910072793	3.25443580542692
44	9.3	11.6482243382954	-0.719927902661002	7.67170356436562	2.34822433829538
45	9.3	10.7420128711638	-0.505340899167158	8.36332802800332	1.44201287116384
46	8.7	9.54013651823849	-0.885075930956122	8.74493941271763	0.840136518238486
47	8.7	8.95826201477637	-0.684812812208324	9.12655079743196	0.258262014776369
48	8.7	8.91709433064987	-0.62394690744708	9.1068525767972	0.217094330649875
49	11	11.7846236341843	1.12822200965322	9.08715435616246	0.784623634184324
50	11	11.7410016461631	1.49448727601464	8.76451107782227	0.741001646163097
51	11	11.6973796581419	1.86075254237605	8.44186779948208	0.69737965814187
52	8.5	9.3929269772858	-0.337917358773868	7.94499038148806	0.892926977285808
53	8.5	9.58848067035197	-0.0365936338460116	7.44811296349404	1.08848067035197
54	8.5	9.97427596925832	0.244669119903456	6.78105491083822	1.47427596925832
55	4.4	3.62051804797245	-0.934514906154846	6.1139968581824	-0.77948195202755
56	4.4	4.26337309093207	-0.719927902661002	5.25655481172893	-0.13662690906793
57	4.4	4.90622813389169	-0.505340899167158	4.39911276527547	0.506228133891689
58	2.5	2.44222238117224	-0.885075930956122	3.44285354978388	-0.0577776188277621
59	2.5	3.19821847791603	-0.684812812208324	2.4865943342923	0.698218477916027
60	2.5	4.06918125853217	-0.62394690744708	1.55476564891491	1.56918125853217
61	0.3	-1.15115897319074	1.12822200965322	0.622936963537516	-1.45115897319074
62	0.3	-0.57930741820112	1.49448727601464	-0.315179857813516	-0.87930741820112
63	0.3	-0.00745586321150454	1.86075254237605	-1.25329667916455	-0.307455863211505
64	-3	-3.46478992474197	-0.337917358773868	-2.19729271648416	-0.464789924741968
65	-3	-2.82211761235021	-0.0365936338460116	-3.14128875380378	0.177882387649792
66	-3	-2.15977256774678	0.244669119903456	-4.08489655215668	0.840227432253221

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 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

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