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R Software Module

rwasp_decomposeloess.wasp

Title produced by software

Decomposition by Loess

Date of computation

Mon, 05 Dec 2011 08:37:39 -0500

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/2011/Dec/05/t13230922973b8p0ii0ou2upoi.htm/, Retrieved Tue, 10 Feb 2026 09:40:26 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=150905, Retrieved Tue, 10 Feb 2026 09:40:26 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

392

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

-     [Classical Decomposition] [] [2011-11-30 10:03:25] [8b1cc7b14f6109a921afd5b897efe79f]
- RMP   [Decomposition by Loess] [] [2011-11-30 10:57:05] [8b1cc7b14f6109a921afd5b897efe79f]
- RMP     [Structural Time Series Models] [] [2011-11-30 11:09:05] [8b1cc7b14f6109a921afd5b897efe79f]
- RMP       [Exponential Smoothing] [] [2011-11-30 11:21:16] [8b1cc7b14f6109a921afd5b897efe79f]
- RMP         [Multiple Regression] [] [2011-11-30 11:39:26] [8b1cc7b14f6109a921afd5b897efe79f]
- RMPD            [Decomposition by Loess] [Paper, Decomposit...] [2011-12-05 13:37:39] [242bbde8f74d68805b56d9ecebfdbe63] [Current]
- RMP               [ARIMA Backward Selection] [paper, ARMA backw...] [2011-12-21 10:09:53] [75512e061a94450f738c2449abbaac12] 
- RMP               [ARIMA Forecasting] [Paper, ARIMA fore...] [2011-12-21 10:12:06] [75512e061a94450f738c2449abbaac12] 

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Dataseries X:

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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	1 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

\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 & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=150905&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=150905&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=150905&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	1 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	751	0	76
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 & 751 & 0 & 76 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=150905&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]751[/C][C]0[/C][C]76[/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=150905&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=150905&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	751	0	76
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	9700	9665.38145341098	216.659724953349	9517.95882163567	-34.618546589023
2	9081	9165.77198053844	-524.32627858629	9520.55429804785	84.771980538444
3	9084	8819.30488240033	-174.454656860353	9523.14977446002	-264.695117599666
4	9743	9815.13667288808	148.134258167628	9522.72906894429	72.1366728880803
5	8587	8390.3144979431	-738.622861371663	9522.30836342857	-196.685502056904
6	9731	9725.13807465835	217.654813785795	9519.20711155585	-5.86192534164547
7	9563	9776.79463081457	-166.900490497697	9516.10585968313	213.794630814566
8	9998	10477.3915983949	4.89909322631054	9513.70930837883	479.391598394863
9	9437	9409.98788403842	-47.3006411129348	9511.31275707452	-27.0121159615846
10	10038	10051.5200681786	519.336152915613	9505.14377890576	13.52006817863
11	9918	9947.88590851771	389.139290745293	9498.97480073699	29.8859085177137
12	9252	8874.86319978722	155.781667904092	9473.35513230869	-377.136800212782
13	9737	9809.60481116626	216.659724953349	9447.73546388039	72.6048111662622
14	9035	9183.97766975103	-524.32627858629	9410.34860883526	148.977669751028
15	9133	9067.49290307022	-174.454656860353	9372.96175379014	-65.5070969297849
16	9487	9479.52410990384	148.134258167628	9346.34163192853	-7.47589009615876
17	8700	8818.90135130474	-738.622861371663	9319.72151006693	118.901351304736
18	9627	9732.83020877481	217.654813785795	9303.51497743939	105.830208774814
19	8947	8773.59204568585	-166.900490497697	9287.30844481185	-173.407954314154
20	9283	9285.24500345376	4.89909322631054	9275.85590331993	2.245003453756
21	8829	8440.89727928492	-47.3006411129348	9264.40336182801	-388.102720715078
22	9947	10115.1181643594	519.336152915613	9259.54568272501	168.118164359375
23	9628	9612.1727056327	389.139290745293	9254.68800362201	-15.8272943673019
24	9318	9223.30241590875	155.781667904092	9256.91591618715	-94.6975840912455
25	9605	9734.19644629435	216.659724953349	9259.1438287523	129.196446294349
26	8640	8534.35586053479	-524.32627858629	9269.9704180515	-105.644139465214
27	9214	9321.65764950965	-174.454656860353	9280.7970073507	107.657649509649
28	9567	9693.8923956418	148.134258167628	9291.97334619057	126.892395641802
29	8547	8529.47317634122	-738.622861371663	9303.14968503044	-17.5268236587763
30	9185	8843.67047554606	217.654813785795	9308.67471066814	-341.329524453937
31	9470	9792.70075419185	-166.900490497697	9314.19973630584	322.700754191854
32	9123	8918.07357542625	4.89909322631054	9323.02733134744	-204.92642457375
33	9278	9271.4457147239	-47.3006411129348	9331.85492638903	-6.55428527609911
34	10170	10468.1371071333	519.336152915613	9352.52673995109	298.137107133294
35	9434	9105.66215574156	389.139290745293	9373.19855351315	-328.337844258442
36	9655	9760.51674400655	155.781667904092	9393.70158808936	105.516744006552
37	9429	9227.13565238109	216.659724953349	9414.20462266556	-201.864347618912
38	8739	8573.70419384917	-524.32627858629	9428.62208473712	-165.295806150829
39	9552	9835.41511005168	-174.454656860353	9443.03954680868	283.415110051677
40	9687	9758.56511370315	148.134258167628	9467.30062812922	71.5651137031509
41	9019	9285.06115192189	-738.622861371663	9491.56170944977	266.061151921895
42	9672	9605.97213280895	217.654813785795	9520.37305340526	-66.0278671910528
43	9206	9029.71609313695	-166.900490497697	9549.18439736075	-176.283906863049
44	9069	8558.39427184334	4.89909322631054	9574.70663493034	-510.605728156655
45	9788	10023.071768613	-47.3006411129348	9600.22887249994	235.071768612994
46	10312	10480.8282002468	519.336152915613	9623.8356468376	168.828200246788
47	10105	10173.4182880795	389.139290745293	9647.44242117526	68.4182880794506
48	9863	9890.35914461412	155.781667904092	9679.85918748179	27.3591446141199
49	9656	9383.06432125833	216.659724953349	9712.27595378832	-272.935678741671
50	9295	9374.16734837944	-524.32627858629	9740.15893020685	79.167348379442
51	9946	10298.412750235	-174.454656860353	9768.04190662537	352.412750234978
52	9701	9472.4536957728	148.134258167628	9781.41204605957	-228.5463042272
53	9049	9041.84067587789	-738.622861371663	9794.78218549377	-7.15932412210896
54	10190	10354.4579532181	217.654813785795	9807.88723299606	164.457953218143
55	9706	9757.90820999935	-166.900490497697	9820.99228049835	51.9082099993466
56	9765	9692.77073374114	4.89909322631054	9832.33017303255	-72.2292662588607
57	9893	9989.63257554619	-47.3006411129348	9843.66806556675	96.6325755461876
58	9994	9615.54519313481	519.336152915613	9853.11865394957	-378.454806865186
59	10433	10614.2914669223	389.139290745293	9862.5692423324	181.29146692231
60	10073	10111.7158882826	155.781667904092	9878.50244381331	38.7158882825952
61	10112	10112.9046297524	216.659724953349	9894.43564529423	0.904629752420078
62	9266	9136.50938088752	-524.32627858629	9919.81689769877	-129.490619112479
63	9820	9869.25650675705	-174.454656860353	9945.19815010331	49.2565067570467
64	10097	10071.7159847449	148.134258167628	9974.14975708747	-25.2840152550998
65	9115	8965.52149730002	-738.622861371663	10003.1013640716	-149.478502699976
66	10411	10567.8772587978	217.654813785795	10036.4679274164	156.877258797789
67	9678	9453.06599973651	-166.900490497697	10069.8344907612	-224.934000263493
68	10408	10720.2986443903	4.89909322631054	10090.8022623834	312.298644390328
69	10153	10241.5306071074	-47.3006411129348	10111.7700340055	88.5306071074065
70	10368	10087.1032512669	519.336152915613	10129.5605958175	-280.89674873307
71	10581	10625.5095516253	389.139290745293	10147.3511576294	44.5095516253241
72	10597	10873.4076819219	155.781667904092	10164.810650174	276.407681921864
73	10680	10961.0701323279	216.659724953349	10182.2701427187	281.070132327946
74	9738	9801.49489133259	-524.32627858629	10198.8313872537	63.4948913325898
75	9556	9071.06202507166	-174.454656860353	10215.3926317887	-484.937974928343

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 9700 & 9665.38145341098 & 216.659724953349 & 9517.95882163567 & -34.618546589023 \tabularnewline
2 & 9081 & 9165.77198053844 & -524.32627858629 & 9520.55429804785 & 84.771980538444 \tabularnewline
3 & 9084 & 8819.30488240033 & -174.454656860353 & 9523.14977446002 & -264.695117599666 \tabularnewline
4 & 9743 & 9815.13667288808 & 148.134258167628 & 9522.72906894429 & 72.1366728880803 \tabularnewline
5 & 8587 & 8390.3144979431 & -738.622861371663 & 9522.30836342857 & -196.685502056904 \tabularnewline
6 & 9731 & 9725.13807465835 & 217.654813785795 & 9519.20711155585 & -5.86192534164547 \tabularnewline
7 & 9563 & 9776.79463081457 & -166.900490497697 & 9516.10585968313 & 213.794630814566 \tabularnewline
8 & 9998 & 10477.3915983949 & 4.89909322631054 & 9513.70930837883 & 479.391598394863 \tabularnewline
9 & 9437 & 9409.98788403842 & -47.3006411129348 & 9511.31275707452 & -27.0121159615846 \tabularnewline
10 & 10038 & 10051.5200681786 & 519.336152915613 & 9505.14377890576 & 13.52006817863 \tabularnewline
11 & 9918 & 9947.88590851771 & 389.139290745293 & 9498.97480073699 & 29.8859085177137 \tabularnewline
12 & 9252 & 8874.86319978722 & 155.781667904092 & 9473.35513230869 & -377.136800212782 \tabularnewline
13 & 9737 & 9809.60481116626 & 216.659724953349 & 9447.73546388039 & 72.6048111662622 \tabularnewline
14 & 9035 & 9183.97766975103 & -524.32627858629 & 9410.34860883526 & 148.977669751028 \tabularnewline
15 & 9133 & 9067.49290307022 & -174.454656860353 & 9372.96175379014 & -65.5070969297849 \tabularnewline
16 & 9487 & 9479.52410990384 & 148.134258167628 & 9346.34163192853 & -7.47589009615876 \tabularnewline
17 & 8700 & 8818.90135130474 & -738.622861371663 & 9319.72151006693 & 118.901351304736 \tabularnewline
18 & 9627 & 9732.83020877481 & 217.654813785795 & 9303.51497743939 & 105.830208774814 \tabularnewline
19 & 8947 & 8773.59204568585 & -166.900490497697 & 9287.30844481185 & -173.407954314154 \tabularnewline
20 & 9283 & 9285.24500345376 & 4.89909322631054 & 9275.85590331993 & 2.245003453756 \tabularnewline
21 & 8829 & 8440.89727928492 & -47.3006411129348 & 9264.40336182801 & -388.102720715078 \tabularnewline
22 & 9947 & 10115.1181643594 & 519.336152915613 & 9259.54568272501 & 168.118164359375 \tabularnewline
23 & 9628 & 9612.1727056327 & 389.139290745293 & 9254.68800362201 & -15.8272943673019 \tabularnewline
24 & 9318 & 9223.30241590875 & 155.781667904092 & 9256.91591618715 & -94.6975840912455 \tabularnewline
25 & 9605 & 9734.19644629435 & 216.659724953349 & 9259.1438287523 & 129.196446294349 \tabularnewline
26 & 8640 & 8534.35586053479 & -524.32627858629 & 9269.9704180515 & -105.644139465214 \tabularnewline
27 & 9214 & 9321.65764950965 & -174.454656860353 & 9280.7970073507 & 107.657649509649 \tabularnewline
28 & 9567 & 9693.8923956418 & 148.134258167628 & 9291.97334619057 & 126.892395641802 \tabularnewline
29 & 8547 & 8529.47317634122 & -738.622861371663 & 9303.14968503044 & -17.5268236587763 \tabularnewline
30 & 9185 & 8843.67047554606 & 217.654813785795 & 9308.67471066814 & -341.329524453937 \tabularnewline
31 & 9470 & 9792.70075419185 & -166.900490497697 & 9314.19973630584 & 322.700754191854 \tabularnewline
32 & 9123 & 8918.07357542625 & 4.89909322631054 & 9323.02733134744 & -204.92642457375 \tabularnewline
33 & 9278 & 9271.4457147239 & -47.3006411129348 & 9331.85492638903 & -6.55428527609911 \tabularnewline
34 & 10170 & 10468.1371071333 & 519.336152915613 & 9352.52673995109 & 298.137107133294 \tabularnewline
35 & 9434 & 9105.66215574156 & 389.139290745293 & 9373.19855351315 & -328.337844258442 \tabularnewline
36 & 9655 & 9760.51674400655 & 155.781667904092 & 9393.70158808936 & 105.516744006552 \tabularnewline
37 & 9429 & 9227.13565238109 & 216.659724953349 & 9414.20462266556 & -201.864347618912 \tabularnewline
38 & 8739 & 8573.70419384917 & -524.32627858629 & 9428.62208473712 & -165.295806150829 \tabularnewline
39 & 9552 & 9835.41511005168 & -174.454656860353 & 9443.03954680868 & 283.415110051677 \tabularnewline
40 & 9687 & 9758.56511370315 & 148.134258167628 & 9467.30062812922 & 71.5651137031509 \tabularnewline
41 & 9019 & 9285.06115192189 & -738.622861371663 & 9491.56170944977 & 266.061151921895 \tabularnewline
42 & 9672 & 9605.97213280895 & 217.654813785795 & 9520.37305340526 & -66.0278671910528 \tabularnewline
43 & 9206 & 9029.71609313695 & -166.900490497697 & 9549.18439736075 & -176.283906863049 \tabularnewline
44 & 9069 & 8558.39427184334 & 4.89909322631054 & 9574.70663493034 & -510.605728156655 \tabularnewline
45 & 9788 & 10023.071768613 & -47.3006411129348 & 9600.22887249994 & 235.071768612994 \tabularnewline
46 & 10312 & 10480.8282002468 & 519.336152915613 & 9623.8356468376 & 168.828200246788 \tabularnewline
47 & 10105 & 10173.4182880795 & 389.139290745293 & 9647.44242117526 & 68.4182880794506 \tabularnewline
48 & 9863 & 9890.35914461412 & 155.781667904092 & 9679.85918748179 & 27.3591446141199 \tabularnewline
49 & 9656 & 9383.06432125833 & 216.659724953349 & 9712.27595378832 & -272.935678741671 \tabularnewline
50 & 9295 & 9374.16734837944 & -524.32627858629 & 9740.15893020685 & 79.167348379442 \tabularnewline
51 & 9946 & 10298.412750235 & -174.454656860353 & 9768.04190662537 & 352.412750234978 \tabularnewline
52 & 9701 & 9472.4536957728 & 148.134258167628 & 9781.41204605957 & -228.5463042272 \tabularnewline
53 & 9049 & 9041.84067587789 & -738.622861371663 & 9794.78218549377 & -7.15932412210896 \tabularnewline
54 & 10190 & 10354.4579532181 & 217.654813785795 & 9807.88723299606 & 164.457953218143 \tabularnewline
55 & 9706 & 9757.90820999935 & -166.900490497697 & 9820.99228049835 & 51.9082099993466 \tabularnewline
56 & 9765 & 9692.77073374114 & 4.89909322631054 & 9832.33017303255 & -72.2292662588607 \tabularnewline
57 & 9893 & 9989.63257554619 & -47.3006411129348 & 9843.66806556675 & 96.6325755461876 \tabularnewline
58 & 9994 & 9615.54519313481 & 519.336152915613 & 9853.11865394957 & -378.454806865186 \tabularnewline
59 & 10433 & 10614.2914669223 & 389.139290745293 & 9862.5692423324 & 181.29146692231 \tabularnewline
60 & 10073 & 10111.7158882826 & 155.781667904092 & 9878.50244381331 & 38.7158882825952 \tabularnewline
61 & 10112 & 10112.9046297524 & 216.659724953349 & 9894.43564529423 & 0.904629752420078 \tabularnewline
62 & 9266 & 9136.50938088752 & -524.32627858629 & 9919.81689769877 & -129.490619112479 \tabularnewline
63 & 9820 & 9869.25650675705 & -174.454656860353 & 9945.19815010331 & 49.2565067570467 \tabularnewline
64 & 10097 & 10071.7159847449 & 148.134258167628 & 9974.14975708747 & -25.2840152550998 \tabularnewline
65 & 9115 & 8965.52149730002 & -738.622861371663 & 10003.1013640716 & -149.478502699976 \tabularnewline
66 & 10411 & 10567.8772587978 & 217.654813785795 & 10036.4679274164 & 156.877258797789 \tabularnewline
67 & 9678 & 9453.06599973651 & -166.900490497697 & 10069.8344907612 & -224.934000263493 \tabularnewline
68 & 10408 & 10720.2986443903 & 4.89909322631054 & 10090.8022623834 & 312.298644390328 \tabularnewline
69 & 10153 & 10241.5306071074 & -47.3006411129348 & 10111.7700340055 & 88.5306071074065 \tabularnewline
70 & 10368 & 10087.1032512669 & 519.336152915613 & 10129.5605958175 & -280.89674873307 \tabularnewline
71 & 10581 & 10625.5095516253 & 389.139290745293 & 10147.3511576294 & 44.5095516253241 \tabularnewline
72 & 10597 & 10873.4076819219 & 155.781667904092 & 10164.810650174 & 276.407681921864 \tabularnewline
73 & 10680 & 10961.0701323279 & 216.659724953349 & 10182.2701427187 & 281.070132327946 \tabularnewline
74 & 9738 & 9801.49489133259 & -524.32627858629 & 10198.8313872537 & 63.4948913325898 \tabularnewline
75 & 9556 & 9071.06202507166 & -174.454656860353 & 10215.3926317887 & -484.937974928343 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=150905&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]9700[/C][C]9665.38145341098[/C][C]216.659724953349[/C][C]9517.95882163567[/C][C]-34.618546589023[/C][/ROW]
[ROW][C]2[/C][C]9081[/C][C]9165.77198053844[/C][C]-524.32627858629[/C][C]9520.55429804785[/C][C]84.771980538444[/C][/ROW]
[ROW][C]3[/C][C]9084[/C][C]8819.30488240033[/C][C]-174.454656860353[/C][C]9523.14977446002[/C][C]-264.695117599666[/C][/ROW]
[ROW][C]4[/C][C]9743[/C][C]9815.13667288808[/C][C]148.134258167628[/C][C]9522.72906894429[/C][C]72.1366728880803[/C][/ROW]
[ROW][C]5[/C][C]8587[/C][C]8390.3144979431[/C][C]-738.622861371663[/C][C]9522.30836342857[/C][C]-196.685502056904[/C][/ROW]
[ROW][C]6[/C][C]9731[/C][C]9725.13807465835[/C][C]217.654813785795[/C][C]9519.20711155585[/C][C]-5.86192534164547[/C][/ROW]
[ROW][C]7[/C][C]9563[/C][C]9776.79463081457[/C][C]-166.900490497697[/C][C]9516.10585968313[/C][C]213.794630814566[/C][/ROW]
[ROW][C]8[/C][C]9998[/C][C]10477.3915983949[/C][C]4.89909322631054[/C][C]9513.70930837883[/C][C]479.391598394863[/C][/ROW]
[ROW][C]9[/C][C]9437[/C][C]9409.98788403842[/C][C]-47.3006411129348[/C][C]9511.31275707452[/C][C]-27.0121159615846[/C][/ROW]
[ROW][C]10[/C][C]10038[/C][C]10051.5200681786[/C][C]519.336152915613[/C][C]9505.14377890576[/C][C]13.52006817863[/C][/ROW]
[ROW][C]11[/C][C]9918[/C][C]9947.88590851771[/C][C]389.139290745293[/C][C]9498.97480073699[/C][C]29.8859085177137[/C][/ROW]
[ROW][C]12[/C][C]9252[/C][C]8874.86319978722[/C][C]155.781667904092[/C][C]9473.35513230869[/C][C]-377.136800212782[/C][/ROW]
[ROW][C]13[/C][C]9737[/C][C]9809.60481116626[/C][C]216.659724953349[/C][C]9447.73546388039[/C][C]72.6048111662622[/C][/ROW]
[ROW][C]14[/C][C]9035[/C][C]9183.97766975103[/C][C]-524.32627858629[/C][C]9410.34860883526[/C][C]148.977669751028[/C][/ROW]
[ROW][C]15[/C][C]9133[/C][C]9067.49290307022[/C][C]-174.454656860353[/C][C]9372.96175379014[/C][C]-65.5070969297849[/C][/ROW]
[ROW][C]16[/C][C]9487[/C][C]9479.52410990384[/C][C]148.134258167628[/C][C]9346.34163192853[/C][C]-7.47589009615876[/C][/ROW]
[ROW][C]17[/C][C]8700[/C][C]8818.90135130474[/C][C]-738.622861371663[/C][C]9319.72151006693[/C][C]118.901351304736[/C][/ROW]
[ROW][C]18[/C][C]9627[/C][C]9732.83020877481[/C][C]217.654813785795[/C][C]9303.51497743939[/C][C]105.830208774814[/C][/ROW]
[ROW][C]19[/C][C]8947[/C][C]8773.59204568585[/C][C]-166.900490497697[/C][C]9287.30844481185[/C][C]-173.407954314154[/C][/ROW]
[ROW][C]20[/C][C]9283[/C][C]9285.24500345376[/C][C]4.89909322631054[/C][C]9275.85590331993[/C][C]2.245003453756[/C][/ROW]
[ROW][C]21[/C][C]8829[/C][C]8440.89727928492[/C][C]-47.3006411129348[/C][C]9264.40336182801[/C][C]-388.102720715078[/C][/ROW]
[ROW][C]22[/C][C]9947[/C][C]10115.1181643594[/C][C]519.336152915613[/C][C]9259.54568272501[/C][C]168.118164359375[/C][/ROW]
[ROW][C]23[/C][C]9628[/C][C]9612.1727056327[/C][C]389.139290745293[/C][C]9254.68800362201[/C][C]-15.8272943673019[/C][/ROW]
[ROW][C]24[/C][C]9318[/C][C]9223.30241590875[/C][C]155.781667904092[/C][C]9256.91591618715[/C][C]-94.6975840912455[/C][/ROW]
[ROW][C]25[/C][C]9605[/C][C]9734.19644629435[/C][C]216.659724953349[/C][C]9259.1438287523[/C][C]129.196446294349[/C][/ROW]
[ROW][C]26[/C][C]8640[/C][C]8534.35586053479[/C][C]-524.32627858629[/C][C]9269.9704180515[/C][C]-105.644139465214[/C][/ROW]
[ROW][C]27[/C][C]9214[/C][C]9321.65764950965[/C][C]-174.454656860353[/C][C]9280.7970073507[/C][C]107.657649509649[/C][/ROW]
[ROW][C]28[/C][C]9567[/C][C]9693.8923956418[/C][C]148.134258167628[/C][C]9291.97334619057[/C][C]126.892395641802[/C][/ROW]
[ROW][C]29[/C][C]8547[/C][C]8529.47317634122[/C][C]-738.622861371663[/C][C]9303.14968503044[/C][C]-17.5268236587763[/C][/ROW]
[ROW][C]30[/C][C]9185[/C][C]8843.67047554606[/C][C]217.654813785795[/C][C]9308.67471066814[/C][C]-341.329524453937[/C][/ROW]
[ROW][C]31[/C][C]9470[/C][C]9792.70075419185[/C][C]-166.900490497697[/C][C]9314.19973630584[/C][C]322.700754191854[/C][/ROW]
[ROW][C]32[/C][C]9123[/C][C]8918.07357542625[/C][C]4.89909322631054[/C][C]9323.02733134744[/C][C]-204.92642457375[/C][/ROW]
[ROW][C]33[/C][C]9278[/C][C]9271.4457147239[/C][C]-47.3006411129348[/C][C]9331.85492638903[/C][C]-6.55428527609911[/C][/ROW]
[ROW][C]34[/C][C]10170[/C][C]10468.1371071333[/C][C]519.336152915613[/C][C]9352.52673995109[/C][C]298.137107133294[/C][/ROW]
[ROW][C]35[/C][C]9434[/C][C]9105.66215574156[/C][C]389.139290745293[/C][C]9373.19855351315[/C][C]-328.337844258442[/C][/ROW]
[ROW][C]36[/C][C]9655[/C][C]9760.51674400655[/C][C]155.781667904092[/C][C]9393.70158808936[/C][C]105.516744006552[/C][/ROW]
[ROW][C]37[/C][C]9429[/C][C]9227.13565238109[/C][C]216.659724953349[/C][C]9414.20462266556[/C][C]-201.864347618912[/C][/ROW]
[ROW][C]38[/C][C]8739[/C][C]8573.70419384917[/C][C]-524.32627858629[/C][C]9428.62208473712[/C][C]-165.295806150829[/C][/ROW]
[ROW][C]39[/C][C]9552[/C][C]9835.41511005168[/C][C]-174.454656860353[/C][C]9443.03954680868[/C][C]283.415110051677[/C][/ROW]
[ROW][C]40[/C][C]9687[/C][C]9758.56511370315[/C][C]148.134258167628[/C][C]9467.30062812922[/C][C]71.5651137031509[/C][/ROW]
[ROW][C]41[/C][C]9019[/C][C]9285.06115192189[/C][C]-738.622861371663[/C][C]9491.56170944977[/C][C]266.061151921895[/C][/ROW]
[ROW][C]42[/C][C]9672[/C][C]9605.97213280895[/C][C]217.654813785795[/C][C]9520.37305340526[/C][C]-66.0278671910528[/C][/ROW]
[ROW][C]43[/C][C]9206[/C][C]9029.71609313695[/C][C]-166.900490497697[/C][C]9549.18439736075[/C][C]-176.283906863049[/C][/ROW]
[ROW][C]44[/C][C]9069[/C][C]8558.39427184334[/C][C]4.89909322631054[/C][C]9574.70663493034[/C][C]-510.605728156655[/C][/ROW]
[ROW][C]45[/C][C]9788[/C][C]10023.071768613[/C][C]-47.3006411129348[/C][C]9600.22887249994[/C][C]235.071768612994[/C][/ROW]
[ROW][C]46[/C][C]10312[/C][C]10480.8282002468[/C][C]519.336152915613[/C][C]9623.8356468376[/C][C]168.828200246788[/C][/ROW]
[ROW][C]47[/C][C]10105[/C][C]10173.4182880795[/C][C]389.139290745293[/C][C]9647.44242117526[/C][C]68.4182880794506[/C][/ROW]
[ROW][C]48[/C][C]9863[/C][C]9890.35914461412[/C][C]155.781667904092[/C][C]9679.85918748179[/C][C]27.3591446141199[/C][/ROW]
[ROW][C]49[/C][C]9656[/C][C]9383.06432125833[/C][C]216.659724953349[/C][C]9712.27595378832[/C][C]-272.935678741671[/C][/ROW]
[ROW][C]50[/C][C]9295[/C][C]9374.16734837944[/C][C]-524.32627858629[/C][C]9740.15893020685[/C][C]79.167348379442[/C][/ROW]
[ROW][C]51[/C][C]9946[/C][C]10298.412750235[/C][C]-174.454656860353[/C][C]9768.04190662537[/C][C]352.412750234978[/C][/ROW]
[ROW][C]52[/C][C]9701[/C][C]9472.4536957728[/C][C]148.134258167628[/C][C]9781.41204605957[/C][C]-228.5463042272[/C][/ROW]
[ROW][C]53[/C][C]9049[/C][C]9041.84067587789[/C][C]-738.622861371663[/C][C]9794.78218549377[/C][C]-7.15932412210896[/C][/ROW]
[ROW][C]54[/C][C]10190[/C][C]10354.4579532181[/C][C]217.654813785795[/C][C]9807.88723299606[/C][C]164.457953218143[/C][/ROW]
[ROW][C]55[/C][C]9706[/C][C]9757.90820999935[/C][C]-166.900490497697[/C][C]9820.99228049835[/C][C]51.9082099993466[/C][/ROW]
[ROW][C]56[/C][C]9765[/C][C]9692.77073374114[/C][C]4.89909322631054[/C][C]9832.33017303255[/C][C]-72.2292662588607[/C][/ROW]
[ROW][C]57[/C][C]9893[/C][C]9989.63257554619[/C][C]-47.3006411129348[/C][C]9843.66806556675[/C][C]96.6325755461876[/C][/ROW]
[ROW][C]58[/C][C]9994[/C][C]9615.54519313481[/C][C]519.336152915613[/C][C]9853.11865394957[/C][C]-378.454806865186[/C][/ROW]
[ROW][C]59[/C][C]10433[/C][C]10614.2914669223[/C][C]389.139290745293[/C][C]9862.5692423324[/C][C]181.29146692231[/C][/ROW]
[ROW][C]60[/C][C]10073[/C][C]10111.7158882826[/C][C]155.781667904092[/C][C]9878.50244381331[/C][C]38.7158882825952[/C][/ROW]
[ROW][C]61[/C][C]10112[/C][C]10112.9046297524[/C][C]216.659724953349[/C][C]9894.43564529423[/C][C]0.904629752420078[/C][/ROW]
[ROW][C]62[/C][C]9266[/C][C]9136.50938088752[/C][C]-524.32627858629[/C][C]9919.81689769877[/C][C]-129.490619112479[/C][/ROW]
[ROW][C]63[/C][C]9820[/C][C]9869.25650675705[/C][C]-174.454656860353[/C][C]9945.19815010331[/C][C]49.2565067570467[/C][/ROW]
[ROW][C]64[/C][C]10097[/C][C]10071.7159847449[/C][C]148.134258167628[/C][C]9974.14975708747[/C][C]-25.2840152550998[/C][/ROW]
[ROW][C]65[/C][C]9115[/C][C]8965.52149730002[/C][C]-738.622861371663[/C][C]10003.1013640716[/C][C]-149.478502699976[/C][/ROW]
[ROW][C]66[/C][C]10411[/C][C]10567.8772587978[/C][C]217.654813785795[/C][C]10036.4679274164[/C][C]156.877258797789[/C][/ROW]
[ROW][C]67[/C][C]9678[/C][C]9453.06599973651[/C][C]-166.900490497697[/C][C]10069.8344907612[/C][C]-224.934000263493[/C][/ROW]
[ROW][C]68[/C][C]10408[/C][C]10720.2986443903[/C][C]4.89909322631054[/C][C]10090.8022623834[/C][C]312.298644390328[/C][/ROW]
[ROW][C]69[/C][C]10153[/C][C]10241.5306071074[/C][C]-47.3006411129348[/C][C]10111.7700340055[/C][C]88.5306071074065[/C][/ROW]
[ROW][C]70[/C][C]10368[/C][C]10087.1032512669[/C][C]519.336152915613[/C][C]10129.5605958175[/C][C]-280.89674873307[/C][/ROW]
[ROW][C]71[/C][C]10581[/C][C]10625.5095516253[/C][C]389.139290745293[/C][C]10147.3511576294[/C][C]44.5095516253241[/C][/ROW]
[ROW][C]72[/C][C]10597[/C][C]10873.4076819219[/C][C]155.781667904092[/C][C]10164.810650174[/C][C]276.407681921864[/C][/ROW]
[ROW][C]73[/C][C]10680[/C][C]10961.0701323279[/C][C]216.659724953349[/C][C]10182.2701427187[/C][C]281.070132327946[/C][/ROW]
[ROW][C]74[/C][C]9738[/C][C]9801.49489133259[/C][C]-524.32627858629[/C][C]10198.8313872537[/C][C]63.4948913325898[/C][/ROW]
[ROW][C]75[/C][C]9556[/C][C]9071.06202507166[/C][C]-174.454656860353[/C][C]10215.3926317887[/C][C]-484.937974928343[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=150905&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=150905&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	9700	9665.38145341098	216.659724953349	9517.95882163567	-34.618546589023
2	9081	9165.77198053844	-524.32627858629	9520.55429804785	84.771980538444
3	9084	8819.30488240033	-174.454656860353	9523.14977446002	-264.695117599666
4	9743	9815.13667288808	148.134258167628	9522.72906894429	72.1366728880803
5	8587	8390.3144979431	-738.622861371663	9522.30836342857	-196.685502056904
6	9731	9725.13807465835	217.654813785795	9519.20711155585	-5.86192534164547
7	9563	9776.79463081457	-166.900490497697	9516.10585968313	213.794630814566
8	9998	10477.3915983949	4.89909322631054	9513.70930837883	479.391598394863
9	9437	9409.98788403842	-47.3006411129348	9511.31275707452	-27.0121159615846
10	10038	10051.5200681786	519.336152915613	9505.14377890576	13.52006817863
11	9918	9947.88590851771	389.139290745293	9498.97480073699	29.8859085177137
12	9252	8874.86319978722	155.781667904092	9473.35513230869	-377.136800212782
13	9737	9809.60481116626	216.659724953349	9447.73546388039	72.6048111662622
14	9035	9183.97766975103	-524.32627858629	9410.34860883526	148.977669751028
15	9133	9067.49290307022	-174.454656860353	9372.96175379014	-65.5070969297849
16	9487	9479.52410990384	148.134258167628	9346.34163192853	-7.47589009615876
17	8700	8818.90135130474	-738.622861371663	9319.72151006693	118.901351304736
18	9627	9732.83020877481	217.654813785795	9303.51497743939	105.830208774814
19	8947	8773.59204568585	-166.900490497697	9287.30844481185	-173.407954314154
20	9283	9285.24500345376	4.89909322631054	9275.85590331993	2.245003453756
21	8829	8440.89727928492	-47.3006411129348	9264.40336182801	-388.102720715078
22	9947	10115.1181643594	519.336152915613	9259.54568272501	168.118164359375
23	9628	9612.1727056327	389.139290745293	9254.68800362201	-15.8272943673019
24	9318	9223.30241590875	155.781667904092	9256.91591618715	-94.6975840912455
25	9605	9734.19644629435	216.659724953349	9259.1438287523	129.196446294349
26	8640	8534.35586053479	-524.32627858629	9269.9704180515	-105.644139465214
27	9214	9321.65764950965	-174.454656860353	9280.7970073507	107.657649509649
28	9567	9693.8923956418	148.134258167628	9291.97334619057	126.892395641802
29	8547	8529.47317634122	-738.622861371663	9303.14968503044	-17.5268236587763
30	9185	8843.67047554606	217.654813785795	9308.67471066814	-341.329524453937
31	9470	9792.70075419185	-166.900490497697	9314.19973630584	322.700754191854
32	9123	8918.07357542625	4.89909322631054	9323.02733134744	-204.92642457375
33	9278	9271.4457147239	-47.3006411129348	9331.85492638903	-6.55428527609911
34	10170	10468.1371071333	519.336152915613	9352.52673995109	298.137107133294
35	9434	9105.66215574156	389.139290745293	9373.19855351315	-328.337844258442
36	9655	9760.51674400655	155.781667904092	9393.70158808936	105.516744006552
37	9429	9227.13565238109	216.659724953349	9414.20462266556	-201.864347618912
38	8739	8573.70419384917	-524.32627858629	9428.62208473712	-165.295806150829
39	9552	9835.41511005168	-174.454656860353	9443.03954680868	283.415110051677
40	9687	9758.56511370315	148.134258167628	9467.30062812922	71.5651137031509
41	9019	9285.06115192189	-738.622861371663	9491.56170944977	266.061151921895
42	9672	9605.97213280895	217.654813785795	9520.37305340526	-66.0278671910528
43	9206	9029.71609313695	-166.900490497697	9549.18439736075	-176.283906863049
44	9069	8558.39427184334	4.89909322631054	9574.70663493034	-510.605728156655
45	9788	10023.071768613	-47.3006411129348	9600.22887249994	235.071768612994
46	10312	10480.8282002468	519.336152915613	9623.8356468376	168.828200246788
47	10105	10173.4182880795	389.139290745293	9647.44242117526	68.4182880794506
48	9863	9890.35914461412	155.781667904092	9679.85918748179	27.3591446141199
49	9656	9383.06432125833	216.659724953349	9712.27595378832	-272.935678741671
50	9295	9374.16734837944	-524.32627858629	9740.15893020685	79.167348379442
51	9946	10298.412750235	-174.454656860353	9768.04190662537	352.412750234978
52	9701	9472.4536957728	148.134258167628	9781.41204605957	-228.5463042272
53	9049	9041.84067587789	-738.622861371663	9794.78218549377	-7.15932412210896
54	10190	10354.4579532181	217.654813785795	9807.88723299606	164.457953218143
55	9706	9757.90820999935	-166.900490497697	9820.99228049835	51.9082099993466
56	9765	9692.77073374114	4.89909322631054	9832.33017303255	-72.2292662588607
57	9893	9989.63257554619	-47.3006411129348	9843.66806556675	96.6325755461876
58	9994	9615.54519313481	519.336152915613	9853.11865394957	-378.454806865186
59	10433	10614.2914669223	389.139290745293	9862.5692423324	181.29146692231
60	10073	10111.7158882826	155.781667904092	9878.50244381331	38.7158882825952
61	10112	10112.9046297524	216.659724953349	9894.43564529423	0.904629752420078
62	9266	9136.50938088752	-524.32627858629	9919.81689769877	-129.490619112479
63	9820	9869.25650675705	-174.454656860353	9945.19815010331	49.2565067570467
64	10097	10071.7159847449	148.134258167628	9974.14975708747	-25.2840152550998
65	9115	8965.52149730002	-738.622861371663	10003.1013640716	-149.478502699976
66	10411	10567.8772587978	217.654813785795	10036.4679274164	156.877258797789
67	9678	9453.06599973651	-166.900490497697	10069.8344907612	-224.934000263493
68	10408	10720.2986443903	4.89909322631054	10090.8022623834	312.298644390328
69	10153	10241.5306071074	-47.3006411129348	10111.7700340055	88.5306071074065
70	10368	10087.1032512669	519.336152915613	10129.5605958175	-280.89674873307
71	10581	10625.5095516253	389.139290745293	10147.3511576294	44.5095516253241
72	10597	10873.4076819219	155.781667904092	10164.810650174	276.407681921864
73	10680	10961.0701323279	216.659724953349	10182.2701427187	281.070132327946
74	9738	9801.49489133259	-524.32627858629	10198.8313872537	63.4948913325898
75	9556	9071.06202507166	-174.454656860353	10215.3926317887	-484.937974928343

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