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Decomposition by Loess

Date of computation

Thu, 22 Dec 2011 05:25:08 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t13245495223brxg7ma957ijpv.htm/, Retrieved Fri, 03 May 2024 13:13:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159252, Retrieved Fri, 03 May 2024 13:13:37 +0000

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IsPrivate?

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User-defined keywords

Estimated Impact

121

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] [99820e5c3330fe494c612533a1ea567a]
- R PD        [Decomposition by Loess] [loess] [2011-12-22 07:20:34] [74be16979710d4c4e7c6647856088456]
-  MP             [Decomposition by Loess] [loess] [2011-12-22 10:25:08] [cfea828c93f35e07cca4521b1fb38047] [Current]

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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	2 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159252&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159252&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159252&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	'Gwilym Jenkins' @ jenkins.wessa.net

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	601	0	61
Trend	12	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 & 12 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159252&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]12[/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=159252&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159252&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	12	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	31	26.4404439993515	4.25938950683364	31.3001664938149	-4.55955600064852
2	36	40.4716812051603	3.31436923256388	28.2139495622758	4.47168120516032
3	24	23.5029093925507	-0.630642023287429	25.1277326307367	-0.497090607449294
4	22	25.6467758471734	-3.76385889910042	22.117083051927	3.64677584717338
5	17	18.5906378579399	-3.69707133105721	19.1064334731173	1.59063785793986
6	8	2.77641496468405	-2.82427056353417	16.0478555988501	-5.22358503531595
7	12	12.362194554761	-1.35147227934385	12.9892777245829	0.362194554760956
8	5	-0.38799721535611	-0.705398966236302	11.0933961815924	-5.38799721535611
9	6	5.86181415136634	-3.05932878996827	9.19751463860193	-0.138185848633663
10	5	0.229929531086533	0.37025812356621	9.39981234534726	-4.77007046891347
11	8	3.79804347308768	2.59984647481974	9.60211005209258	-4.20195652691232
12	15	12.5792100064012	5.48817891615964	11.9326110774392	-2.42078999359882
13	16	13.4774983903806	4.25938950683364	14.2631121027858	-2.52250160961942
14	17	12.6363196806978	3.31436923256388	18.0493110867383	-4.36368031930218
15	23	24.7951319525966	-0.630642023287429	21.8355100706908	1.79513195259661
16	24	25.3017362087282	-3.76385889910042	26.4621226903722	1.30173620872822
17	27	26.6083360210036	-3.69707133105721	31.0887353100536	-0.391663978996359
18	31	28.836308015386	-2.82427056353417	35.9879625481482	-2.163691984614
19	40	40.4642824931011	-1.35147227934385	40.8871897862428	0.464282493101059
20	47	49.494689065884	-0.705398966236302	45.2107099003523	2.49468906588402
21	43	39.5250987755065	-3.05932878996827	49.5342300144618	-3.47490122449352
22	60	67.3295960750802	0.37025812356621	52.3001458013536	7.32959607508023
23	64	70.3340919369349	2.59984647481974	55.0660615882453	6.33409193693493
24	65	67.9501446362635	5.48817891615964	56.5616764475768	2.95014463626354
25	65	67.683319186258	4.25938950683364	58.0572913069083	2.68331918625805
26	55	47.5669255931272	3.31436923256388	59.118705174309	-7.43307440687285
27	57	54.4505229815778	-0.630642023287429	60.1801190417096	-2.54947701842221
28	57	55.9593981378301	-3.76385889910042	61.8044607612703	-1.04060186216991
29	57	54.2682688502262	-3.69707133105721	63.428802480831	-2.73173114977379
30	65	67.380410433361	-2.82427056353417	65.4438601301732	2.38041043336101
31	69	71.8925544998285	-1.35147227934385	67.4589177795153	2.89255449982852
32	70	72.6099525527656	-0.705398966236302	68.0954464134707	2.60995255276556
33	71	76.3273537425421	-3.05932878996827	68.7319750474261	5.32735374254214
34	71	74.7673133381483	0.37025812356621	66.8624285382855	3.76731333814828
35	73	78.4072714960354	2.59984647481974	64.9928820291449	5.40727149603539
36	68	70.62289993614	5.48817891615964	59.8889211477004	2.62289993613998
37	65	70.9556502269105	4.25938950683364	54.7849602662559	5.95565022691046
38	57	62.7921357136989	3.31436923256388	47.8934950537373	5.79213571369885
39	41	41.6286121820688	-0.630642023287429	41.0020298412186	0.628612182068785
40	21	11.7477504526949	-3.76385889910042	34.0161084464055	-9.25224954730505
41	21	18.6668842794649	-3.69707133105721	27.0301870515923	-2.33311572053509
42	17	15.8091629718793	-2.82427056353417	21.0151075916549	-1.19083702812074
43	9	4.35144414762633	-1.35147227934385	15.0000281317175	-4.64855585237367
44	11	11.7180945981954	-0.705398966236302	10.9873043680409	0.718094598195373
45	6	8.08474818560392	-3.05932878996827	6.97458060436434	2.08474818560393
46	-2	-9.25719049815405	0.37025812356621	4.88693237458784	-7.25719049815405
47	0	-5.39913061963107	2.59984647481974	2.79928414481133	-5.39913061963107
48	5	1.69272284090144	5.48817891615964	2.81909824293891	-3.30727715909856
49	3	-1.09830184790014	4.25938950683364	2.8389123410665	-4.09830184790014
50	7	5.98713426867344	3.31436923256388	4.69849649876268	-1.01286573132656
51	4	2.07256136682856	-0.630642023287429	6.55808065645887	-1.92743863317144
52	8	11.2972000708257	-3.76385889910042	8.46665882827475	3.29720007082567
53	9	11.3218343309666	-3.69707133105721	10.3752370000906	2.32183433096658
54	14	19.5747322630786	-2.82427056353417	11.2495383004556	5.57473226307862
55	12	13.2276326785234	-1.35147227934385	12.1238396008205	1.22763267852337
56	12	12.2491005281204	-0.705398966236302	12.4562984381159	0.24910052812039
57	7	4.27057151455692	-3.05932878996827	12.7887572754113	-2.72942848544308
58	15	16.653211597701	0.37025812356621	12.9765302787328	1.653211597701
59	14	12.235850243126	2.59984647481974	13.1643032820542	-1.76414975687398
60	19	19.2357512324519	5.48817891615964	13.2760698513884	0.235751232451937

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 31 & 26.4404439993515 & 4.25938950683364 & 31.3001664938149 & -4.55955600064852 \tabularnewline
2 & 36 & 40.4716812051603 & 3.31436923256388 & 28.2139495622758 & 4.47168120516032 \tabularnewline
3 & 24 & 23.5029093925507 & -0.630642023287429 & 25.1277326307367 & -0.497090607449294 \tabularnewline
4 & 22 & 25.6467758471734 & -3.76385889910042 & 22.117083051927 & 3.64677584717338 \tabularnewline
5 & 17 & 18.5906378579399 & -3.69707133105721 & 19.1064334731173 & 1.59063785793986 \tabularnewline
6 & 8 & 2.77641496468405 & -2.82427056353417 & 16.0478555988501 & -5.22358503531595 \tabularnewline
7 & 12 & 12.362194554761 & -1.35147227934385 & 12.9892777245829 & 0.362194554760956 \tabularnewline
8 & 5 & -0.38799721535611 & -0.705398966236302 & 11.0933961815924 & -5.38799721535611 \tabularnewline
9 & 6 & 5.86181415136634 & -3.05932878996827 & 9.19751463860193 & -0.138185848633663 \tabularnewline
10 & 5 & 0.229929531086533 & 0.37025812356621 & 9.39981234534726 & -4.77007046891347 \tabularnewline
11 & 8 & 3.79804347308768 & 2.59984647481974 & 9.60211005209258 & -4.20195652691232 \tabularnewline
12 & 15 & 12.5792100064012 & 5.48817891615964 & 11.9326110774392 & -2.42078999359882 \tabularnewline
13 & 16 & 13.4774983903806 & 4.25938950683364 & 14.2631121027858 & -2.52250160961942 \tabularnewline
14 & 17 & 12.6363196806978 & 3.31436923256388 & 18.0493110867383 & -4.36368031930218 \tabularnewline
15 & 23 & 24.7951319525966 & -0.630642023287429 & 21.8355100706908 & 1.79513195259661 \tabularnewline
16 & 24 & 25.3017362087282 & -3.76385889910042 & 26.4621226903722 & 1.30173620872822 \tabularnewline
17 & 27 & 26.6083360210036 & -3.69707133105721 & 31.0887353100536 & -0.391663978996359 \tabularnewline
18 & 31 & 28.836308015386 & -2.82427056353417 & 35.9879625481482 & -2.163691984614 \tabularnewline
19 & 40 & 40.4642824931011 & -1.35147227934385 & 40.8871897862428 & 0.464282493101059 \tabularnewline
20 & 47 & 49.494689065884 & -0.705398966236302 & 45.2107099003523 & 2.49468906588402 \tabularnewline
21 & 43 & 39.5250987755065 & -3.05932878996827 & 49.5342300144618 & -3.47490122449352 \tabularnewline
22 & 60 & 67.3295960750802 & 0.37025812356621 & 52.3001458013536 & 7.32959607508023 \tabularnewline
23 & 64 & 70.3340919369349 & 2.59984647481974 & 55.0660615882453 & 6.33409193693493 \tabularnewline
24 & 65 & 67.9501446362635 & 5.48817891615964 & 56.5616764475768 & 2.95014463626354 \tabularnewline
25 & 65 & 67.683319186258 & 4.25938950683364 & 58.0572913069083 & 2.68331918625805 \tabularnewline
26 & 55 & 47.5669255931272 & 3.31436923256388 & 59.118705174309 & -7.43307440687285 \tabularnewline
27 & 57 & 54.4505229815778 & -0.630642023287429 & 60.1801190417096 & -2.54947701842221 \tabularnewline
28 & 57 & 55.9593981378301 & -3.76385889910042 & 61.8044607612703 & -1.04060186216991 \tabularnewline
29 & 57 & 54.2682688502262 & -3.69707133105721 & 63.428802480831 & -2.73173114977379 \tabularnewline
30 & 65 & 67.380410433361 & -2.82427056353417 & 65.4438601301732 & 2.38041043336101 \tabularnewline
31 & 69 & 71.8925544998285 & -1.35147227934385 & 67.4589177795153 & 2.89255449982852 \tabularnewline
32 & 70 & 72.6099525527656 & -0.705398966236302 & 68.0954464134707 & 2.60995255276556 \tabularnewline
33 & 71 & 76.3273537425421 & -3.05932878996827 & 68.7319750474261 & 5.32735374254214 \tabularnewline
34 & 71 & 74.7673133381483 & 0.37025812356621 & 66.8624285382855 & 3.76731333814828 \tabularnewline
35 & 73 & 78.4072714960354 & 2.59984647481974 & 64.9928820291449 & 5.40727149603539 \tabularnewline
36 & 68 & 70.62289993614 & 5.48817891615964 & 59.8889211477004 & 2.62289993613998 \tabularnewline
37 & 65 & 70.9556502269105 & 4.25938950683364 & 54.7849602662559 & 5.95565022691046 \tabularnewline
38 & 57 & 62.7921357136989 & 3.31436923256388 & 47.8934950537373 & 5.79213571369885 \tabularnewline
39 & 41 & 41.6286121820688 & -0.630642023287429 & 41.0020298412186 & 0.628612182068785 \tabularnewline
40 & 21 & 11.7477504526949 & -3.76385889910042 & 34.0161084464055 & -9.25224954730505 \tabularnewline
41 & 21 & 18.6668842794649 & -3.69707133105721 & 27.0301870515923 & -2.33311572053509 \tabularnewline
42 & 17 & 15.8091629718793 & -2.82427056353417 & 21.0151075916549 & -1.19083702812074 \tabularnewline
43 & 9 & 4.35144414762633 & -1.35147227934385 & 15.0000281317175 & -4.64855585237367 \tabularnewline
44 & 11 & 11.7180945981954 & -0.705398966236302 & 10.9873043680409 & 0.718094598195373 \tabularnewline
45 & 6 & 8.08474818560392 & -3.05932878996827 & 6.97458060436434 & 2.08474818560393 \tabularnewline
46 & -2 & -9.25719049815405 & 0.37025812356621 & 4.88693237458784 & -7.25719049815405 \tabularnewline
47 & 0 & -5.39913061963107 & 2.59984647481974 & 2.79928414481133 & -5.39913061963107 \tabularnewline
48 & 5 & 1.69272284090144 & 5.48817891615964 & 2.81909824293891 & -3.30727715909856 \tabularnewline
49 & 3 & -1.09830184790014 & 4.25938950683364 & 2.8389123410665 & -4.09830184790014 \tabularnewline
50 & 7 & 5.98713426867344 & 3.31436923256388 & 4.69849649876268 & -1.01286573132656 \tabularnewline
51 & 4 & 2.07256136682856 & -0.630642023287429 & 6.55808065645887 & -1.92743863317144 \tabularnewline
52 & 8 & 11.2972000708257 & -3.76385889910042 & 8.46665882827475 & 3.29720007082567 \tabularnewline
53 & 9 & 11.3218343309666 & -3.69707133105721 & 10.3752370000906 & 2.32183433096658 \tabularnewline
54 & 14 & 19.5747322630786 & -2.82427056353417 & 11.2495383004556 & 5.57473226307862 \tabularnewline
55 & 12 & 13.2276326785234 & -1.35147227934385 & 12.1238396008205 & 1.22763267852337 \tabularnewline
56 & 12 & 12.2491005281204 & -0.705398966236302 & 12.4562984381159 & 0.24910052812039 \tabularnewline
57 & 7 & 4.27057151455692 & -3.05932878996827 & 12.7887572754113 & -2.72942848544308 \tabularnewline
58 & 15 & 16.653211597701 & 0.37025812356621 & 12.9765302787328 & 1.653211597701 \tabularnewline
59 & 14 & 12.235850243126 & 2.59984647481974 & 13.1643032820542 & -1.76414975687398 \tabularnewline
60 & 19 & 19.2357512324519 & 5.48817891615964 & 13.2760698513884 & 0.235751232451937 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159252&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]31[/C][C]26.4404439993515[/C][C]4.25938950683364[/C][C]31.3001664938149[/C][C]-4.55955600064852[/C][/ROW]
[ROW][C]2[/C][C]36[/C][C]40.4716812051603[/C][C]3.31436923256388[/C][C]28.2139495622758[/C][C]4.47168120516032[/C][/ROW]
[ROW][C]3[/C][C]24[/C][C]23.5029093925507[/C][C]-0.630642023287429[/C][C]25.1277326307367[/C][C]-0.497090607449294[/C][/ROW]
[ROW][C]4[/C][C]22[/C][C]25.6467758471734[/C][C]-3.76385889910042[/C][C]22.117083051927[/C][C]3.64677584717338[/C][/ROW]
[ROW][C]5[/C][C]17[/C][C]18.5906378579399[/C][C]-3.69707133105721[/C][C]19.1064334731173[/C][C]1.59063785793986[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]2.77641496468405[/C][C]-2.82427056353417[/C][C]16.0478555988501[/C][C]-5.22358503531595[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.362194554761[/C][C]-1.35147227934385[/C][C]12.9892777245829[/C][C]0.362194554760956[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]-0.38799721535611[/C][C]-0.705398966236302[/C][C]11.0933961815924[/C][C]-5.38799721535611[/C][/ROW]
[ROW][C]9[/C][C]6[/C][C]5.86181415136634[/C][C]-3.05932878996827[/C][C]9.19751463860193[/C][C]-0.138185848633663[/C][/ROW]
[ROW][C]10[/C][C]5[/C][C]0.229929531086533[/C][C]0.37025812356621[/C][C]9.39981234534726[/C][C]-4.77007046891347[/C][/ROW]
[ROW][C]11[/C][C]8[/C][C]3.79804347308768[/C][C]2.59984647481974[/C][C]9.60211005209258[/C][C]-4.20195652691232[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]12.5792100064012[/C][C]5.48817891615964[/C][C]11.9326110774392[/C][C]-2.42078999359882[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]13.4774983903806[/C][C]4.25938950683364[/C][C]14.2631121027858[/C][C]-2.52250160961942[/C][/ROW]
[ROW][C]14[/C][C]17[/C][C]12.6363196806978[/C][C]3.31436923256388[/C][C]18.0493110867383[/C][C]-4.36368031930218[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]24.7951319525966[/C][C]-0.630642023287429[/C][C]21.8355100706908[/C][C]1.79513195259661[/C][/ROW]
[ROW][C]16[/C][C]24[/C][C]25.3017362087282[/C][C]-3.76385889910042[/C][C]26.4621226903722[/C][C]1.30173620872822[/C][/ROW]
[ROW][C]17[/C][C]27[/C][C]26.6083360210036[/C][C]-3.69707133105721[/C][C]31.0887353100536[/C][C]-0.391663978996359[/C][/ROW]
[ROW][C]18[/C][C]31[/C][C]28.836308015386[/C][C]-2.82427056353417[/C][C]35.9879625481482[/C][C]-2.163691984614[/C][/ROW]
[ROW][C]19[/C][C]40[/C][C]40.4642824931011[/C][C]-1.35147227934385[/C][C]40.8871897862428[/C][C]0.464282493101059[/C][/ROW]
[ROW][C]20[/C][C]47[/C][C]49.494689065884[/C][C]-0.705398966236302[/C][C]45.2107099003523[/C][C]2.49468906588402[/C][/ROW]
[ROW][C]21[/C][C]43[/C][C]39.5250987755065[/C][C]-3.05932878996827[/C][C]49.5342300144618[/C][C]-3.47490122449352[/C][/ROW]
[ROW][C]22[/C][C]60[/C][C]67.3295960750802[/C][C]0.37025812356621[/C][C]52.3001458013536[/C][C]7.32959607508023[/C][/ROW]
[ROW][C]23[/C][C]64[/C][C]70.3340919369349[/C][C]2.59984647481974[/C][C]55.0660615882453[/C][C]6.33409193693493[/C][/ROW]
[ROW][C]24[/C][C]65[/C][C]67.9501446362635[/C][C]5.48817891615964[/C][C]56.5616764475768[/C][C]2.95014463626354[/C][/ROW]
[ROW][C]25[/C][C]65[/C][C]67.683319186258[/C][C]4.25938950683364[/C][C]58.0572913069083[/C][C]2.68331918625805[/C][/ROW]
[ROW][C]26[/C][C]55[/C][C]47.5669255931272[/C][C]3.31436923256388[/C][C]59.118705174309[/C][C]-7.43307440687285[/C][/ROW]
[ROW][C]27[/C][C]57[/C][C]54.4505229815778[/C][C]-0.630642023287429[/C][C]60.1801190417096[/C][C]-2.54947701842221[/C][/ROW]
[ROW][C]28[/C][C]57[/C][C]55.9593981378301[/C][C]-3.76385889910042[/C][C]61.8044607612703[/C][C]-1.04060186216991[/C][/ROW]
[ROW][C]29[/C][C]57[/C][C]54.2682688502262[/C][C]-3.69707133105721[/C][C]63.428802480831[/C][C]-2.73173114977379[/C][/ROW]
[ROW][C]30[/C][C]65[/C][C]67.380410433361[/C][C]-2.82427056353417[/C][C]65.4438601301732[/C][C]2.38041043336101[/C][/ROW]
[ROW][C]31[/C][C]69[/C][C]71.8925544998285[/C][C]-1.35147227934385[/C][C]67.4589177795153[/C][C]2.89255449982852[/C][/ROW]
[ROW][C]32[/C][C]70[/C][C]72.6099525527656[/C][C]-0.705398966236302[/C][C]68.0954464134707[/C][C]2.60995255276556[/C][/ROW]
[ROW][C]33[/C][C]71[/C][C]76.3273537425421[/C][C]-3.05932878996827[/C][C]68.7319750474261[/C][C]5.32735374254214[/C][/ROW]
[ROW][C]34[/C][C]71[/C][C]74.7673133381483[/C][C]0.37025812356621[/C][C]66.8624285382855[/C][C]3.76731333814828[/C][/ROW]
[ROW][C]35[/C][C]73[/C][C]78.4072714960354[/C][C]2.59984647481974[/C][C]64.9928820291449[/C][C]5.40727149603539[/C][/ROW]
[ROW][C]36[/C][C]68[/C][C]70.62289993614[/C][C]5.48817891615964[/C][C]59.8889211477004[/C][C]2.62289993613998[/C][/ROW]
[ROW][C]37[/C][C]65[/C][C]70.9556502269105[/C][C]4.25938950683364[/C][C]54.7849602662559[/C][C]5.95565022691046[/C][/ROW]
[ROW][C]38[/C][C]57[/C][C]62.7921357136989[/C][C]3.31436923256388[/C][C]47.8934950537373[/C][C]5.79213571369885[/C][/ROW]
[ROW][C]39[/C][C]41[/C][C]41.6286121820688[/C][C]-0.630642023287429[/C][C]41.0020298412186[/C][C]0.628612182068785[/C][/ROW]
[ROW][C]40[/C][C]21[/C][C]11.7477504526949[/C][C]-3.76385889910042[/C][C]34.0161084464055[/C][C]-9.25224954730505[/C][/ROW]
[ROW][C]41[/C][C]21[/C][C]18.6668842794649[/C][C]-3.69707133105721[/C][C]27.0301870515923[/C][C]-2.33311572053509[/C][/ROW]
[ROW][C]42[/C][C]17[/C][C]15.8091629718793[/C][C]-2.82427056353417[/C][C]21.0151075916549[/C][C]-1.19083702812074[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]4.35144414762633[/C][C]-1.35147227934385[/C][C]15.0000281317175[/C][C]-4.64855585237367[/C][/ROW]
[ROW][C]44[/C][C]11[/C][C]11.7180945981954[/C][C]-0.705398966236302[/C][C]10.9873043680409[/C][C]0.718094598195373[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]8.08474818560392[/C][C]-3.05932878996827[/C][C]6.97458060436434[/C][C]2.08474818560393[/C][/ROW]
[ROW][C]46[/C][C]-2[/C][C]-9.25719049815405[/C][C]0.37025812356621[/C][C]4.88693237458784[/C][C]-7.25719049815405[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-5.39913061963107[/C][C]2.59984647481974[/C][C]2.79928414481133[/C][C]-5.39913061963107[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]1.69272284090144[/C][C]5.48817891615964[/C][C]2.81909824293891[/C][C]-3.30727715909856[/C][/ROW]
[ROW][C]49[/C][C]3[/C][C]-1.09830184790014[/C][C]4.25938950683364[/C][C]2.8389123410665[/C][C]-4.09830184790014[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]5.98713426867344[/C][C]3.31436923256388[/C][C]4.69849649876268[/C][C]-1.01286573132656[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]2.07256136682856[/C][C]-0.630642023287429[/C][C]6.55808065645887[/C][C]-1.92743863317144[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]11.2972000708257[/C][C]-3.76385889910042[/C][C]8.46665882827475[/C][C]3.29720007082567[/C][/ROW]
[ROW][C]53[/C][C]9[/C][C]11.3218343309666[/C][C]-3.69707133105721[/C][C]10.3752370000906[/C][C]2.32183433096658[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]19.5747322630786[/C][C]-2.82427056353417[/C][C]11.2495383004556[/C][C]5.57473226307862[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.2276326785234[/C][C]-1.35147227934385[/C][C]12.1238396008205[/C][C]1.22763267852337[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.2491005281204[/C][C]-0.705398966236302[/C][C]12.4562984381159[/C][C]0.24910052812039[/C][/ROW]
[ROW][C]57[/C][C]7[/C][C]4.27057151455692[/C][C]-3.05932878996827[/C][C]12.7887572754113[/C][C]-2.72942848544308[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]16.653211597701[/C][C]0.37025812356621[/C][C]12.9765302787328[/C][C]1.653211597701[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]12.235850243126[/C][C]2.59984647481974[/C][C]13.1643032820542[/C][C]-1.76414975687398[/C][/ROW]
[ROW][C]60[/C][C]19[/C][C]19.2357512324519[/C][C]5.48817891615964[/C][C]13.2760698513884[/C][C]0.235751232451937[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159252&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159252&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	31	26.4404439993515	4.25938950683364	31.3001664938149	-4.55955600064852
2	36	40.4716812051603	3.31436923256388	28.2139495622758	4.47168120516032
3	24	23.5029093925507	-0.630642023287429	25.1277326307367	-0.497090607449294
4	22	25.6467758471734	-3.76385889910042	22.117083051927	3.64677584717338
5	17	18.5906378579399	-3.69707133105721	19.1064334731173	1.59063785793986
6	8	2.77641496468405	-2.82427056353417	16.0478555988501	-5.22358503531595
7	12	12.362194554761	-1.35147227934385	12.9892777245829	0.362194554760956
8	5	-0.38799721535611	-0.705398966236302	11.0933961815924	-5.38799721535611
9	6	5.86181415136634	-3.05932878996827	9.19751463860193	-0.138185848633663
10	5	0.229929531086533	0.37025812356621	9.39981234534726	-4.77007046891347
11	8	3.79804347308768	2.59984647481974	9.60211005209258	-4.20195652691232
12	15	12.5792100064012	5.48817891615964	11.9326110774392	-2.42078999359882
13	16	13.4774983903806	4.25938950683364	14.2631121027858	-2.52250160961942
14	17	12.6363196806978	3.31436923256388	18.0493110867383	-4.36368031930218
15	23	24.7951319525966	-0.630642023287429	21.8355100706908	1.79513195259661
16	24	25.3017362087282	-3.76385889910042	26.4621226903722	1.30173620872822
17	27	26.6083360210036	-3.69707133105721	31.0887353100536	-0.391663978996359
18	31	28.836308015386	-2.82427056353417	35.9879625481482	-2.163691984614
19	40	40.4642824931011	-1.35147227934385	40.8871897862428	0.464282493101059
20	47	49.494689065884	-0.705398966236302	45.2107099003523	2.49468906588402
21	43	39.5250987755065	-3.05932878996827	49.5342300144618	-3.47490122449352
22	60	67.3295960750802	0.37025812356621	52.3001458013536	7.32959607508023
23	64	70.3340919369349	2.59984647481974	55.0660615882453	6.33409193693493
24	65	67.9501446362635	5.48817891615964	56.5616764475768	2.95014463626354
25	65	67.683319186258	4.25938950683364	58.0572913069083	2.68331918625805
26	55	47.5669255931272	3.31436923256388	59.118705174309	-7.43307440687285
27	57	54.4505229815778	-0.630642023287429	60.1801190417096	-2.54947701842221
28	57	55.9593981378301	-3.76385889910042	61.8044607612703	-1.04060186216991
29	57	54.2682688502262	-3.69707133105721	63.428802480831	-2.73173114977379
30	65	67.380410433361	-2.82427056353417	65.4438601301732	2.38041043336101
31	69	71.8925544998285	-1.35147227934385	67.4589177795153	2.89255449982852
32	70	72.6099525527656	-0.705398966236302	68.0954464134707	2.60995255276556
33	71	76.3273537425421	-3.05932878996827	68.7319750474261	5.32735374254214
34	71	74.7673133381483	0.37025812356621	66.8624285382855	3.76731333814828
35	73	78.4072714960354	2.59984647481974	64.9928820291449	5.40727149603539
36	68	70.62289993614	5.48817891615964	59.8889211477004	2.62289993613998
37	65	70.9556502269105	4.25938950683364	54.7849602662559	5.95565022691046
38	57	62.7921357136989	3.31436923256388	47.8934950537373	5.79213571369885
39	41	41.6286121820688	-0.630642023287429	41.0020298412186	0.628612182068785
40	21	11.7477504526949	-3.76385889910042	34.0161084464055	-9.25224954730505
41	21	18.6668842794649	-3.69707133105721	27.0301870515923	-2.33311572053509
42	17	15.8091629718793	-2.82427056353417	21.0151075916549	-1.19083702812074
43	9	4.35144414762633	-1.35147227934385	15.0000281317175	-4.64855585237367
44	11	11.7180945981954	-0.705398966236302	10.9873043680409	0.718094598195373
45	6	8.08474818560392	-3.05932878996827	6.97458060436434	2.08474818560393
46	-2	-9.25719049815405	0.37025812356621	4.88693237458784	-7.25719049815405
47	0	-5.39913061963107	2.59984647481974	2.79928414481133	-5.39913061963107
48	5	1.69272284090144	5.48817891615964	2.81909824293891	-3.30727715909856
49	3	-1.09830184790014	4.25938950683364	2.8389123410665	-4.09830184790014
50	7	5.98713426867344	3.31436923256388	4.69849649876268	-1.01286573132656
51	4	2.07256136682856	-0.630642023287429	6.55808065645887	-1.92743863317144
52	8	11.2972000708257	-3.76385889910042	8.46665882827475	3.29720007082567
53	9	11.3218343309666	-3.69707133105721	10.3752370000906	2.32183433096658
54	14	19.5747322630786	-2.82427056353417	11.2495383004556	5.57473226307862
55	12	13.2276326785234	-1.35147227934385	12.1238396008205	1.22763267852337
56	12	12.2491005281204	-0.705398966236302	12.4562984381159	0.24910052812039
57	7	4.27057151455692	-3.05932878996827	12.7887572754113	-2.72942848544308
58	15	16.653211597701	0.37025812356621	12.9765302787328	1.653211597701
59	14	12.235850243126	2.59984647481974	13.1643032820542	-1.76414975687398
60	19	19.2357512324519	5.48817891615964	13.2760698513884	0.235751232451937

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 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par4 = 12 ; 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