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R Software Module

rwasp_decomposeloess.wasp

Title produced by software

Decomposition by Loess

Date of computation

Sun, 26 Dec 2010 18:24:41 +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/26/t1293387981qhu75xv0o0chr45.htm/, Retrieved Mon, 06 May 2024 12:24:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115763, Retrieved Mon, 06 May 2024 12:24:29 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

135

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

-     [Decomposition by Loess] [Paper statistiek ...] [2010-12-20 13:18:44] [e7fc384c3b263e46f871dfcba42cc90e]
-    D    [Decomposition by Loess] [Paper statistiek:...] [2010-12-26 18:24:41] [5876f3b3a8c6f0cebdbe74121f58174b] [Current]
-    D      [Decomposition by Loess] [Paper: faillissem...] [2010-12-26 18:30:15] [8e42c8cdf50f15ce85eb45a67cf771d0] 
-    D      [Decomposition by Loess] [Paper: Inflatie -...] [2010-12-26 18:32:45] [8e42c8cdf50f15ce85eb45a67cf771d0] 
-    D      [Decomposition by Loess] [Paper: Nijverheid] [2010-12-26 18:35:04] [8e42c8cdf50f15ce85eb45a67cf771d0] 
-    D        [Decomposition by Loess] [Paper: Beurswaard...] [2010-12-26 18:39:24] [8e42c8cdf50f15ce85eb45a67cf771d0] 

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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	3 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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115763&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115763&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115763&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	601	0	61
Trend	19	1	2
Low-pass	13	1	2

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 601 & 0 & 61 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115763&T=1

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Parameters[/C][/ROW]
[ROW][C]Component[/C][C]Window[/C][C]Degree[/C][C]Jump[/C][/ROW]
[ROW][C]Seasonal[/C][C]601[/C][C]0[/C][C]61[/C][/ROW]
[ROW][C]Trend[/C][C]19[/C][C]1[/C][C]2[/C][/ROW]
[ROW][C]Low-pass[/C][C]13[/C][C]1[/C][C]2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115763&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115763&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	601	0	61
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	493	480.818230902225	9.00162999748834	496.180139100287	-12.1817690977751
2	514	504.680140347835	29.7643269514014	493.555532700764	-9.31985965216535
3	522	525.94205240748	27.1270212912793	490.930926301241	3.94205240747965
4	490	491.662097950526	0.301846850505914	488.036055198968	1.66209795052595
5	484	483.382129116384	-0.523313213078847	485.141184096695	-0.61787088361649
6	506	517.825780203494	12.1543793146487	482.019840481857	11.8257802034944
7	501	501.269423840608	21.8320792923734	478.898496867019	0.269423840608113
8	462	475.876649257514	-27.4751000466452	475.598450789131	13.8766492575138
9	465	474.683868008634	-16.9822727198782	472.298404711244	9.68386800863397
10	454	456.726657374498	-16.1842799895038	467.457622615006	2.72665737449779
11	464	472.56945654299	-7.1862970617575	462.616840518768	8.56945654298977
12	427	430.176734517691	-31.8300260244715	455.653291506781	3.17673451769053
13	460	462.308627507718	9.00162999748834	448.689742494794	2.30862750771752
14	473	475.182720107169	29.7643269514014	441.052952941429	2.18272010716925
15	465	469.456815320656	27.1270212912793	433.416163388064	4.45681532065618
16	422	417.478822971285	0.301846850505914	426.219330178209	-4.52117702871493
17	415	411.500816244725	-0.523313213078847	419.022496968353	-3.49918375527466
18	413	401.218936839048	12.1543793146487	412.626683846303	-11.7810631609519
19	420	411.937049983374	21.8320792923734	406.230870724253	-8.06295001662636
20	363	352.952128596696	-27.4751000466452	400.52297144995	-10.0478714033044
21	376	374.167200544232	-16.9822727198782	394.815072175646	-1.83279945576811
22	380	386.745184002376	-16.1842799895038	389.439095987128	6.7451840023756
23	384	391.123177263147	-7.1862970617575	384.06311979861	7.1231772631474
24	346	344.949862429613	-31.8300260244715	378.880163594859	-1.05013757038745
25	389	395.301162611404	9.00162999748834	373.697207391108	6.30116261140398
26	407	415.966416741832	29.7643269514014	368.269256306766	8.96641674183223
27	393	396.031673486296	27.1270212912793	362.841305222425	3.03167348629563
28	346	334.609198693265	0.301846850505914	357.088954456229	-11.390801306735
29	348	345.186709523046	-0.523313213078847	351.336603690033	-2.81329047695425
30	353	348.11330853846	12.1543793146487	345.732312146891	-4.88669146153978
31	364	366.039900103878	21.8320792923734	340.128020603749	2.03990010387753
32	305	302.498470097238	-27.4751000466452	334.976629949407	-2.50152990276194
33	307	301.157033424813	-16.9822727198782	329.825239295065	-5.84296657518695
34	312	314.790843368745	-16.1842799895038	325.393436620759	2.79084336874524
35	312	310.224663115306	-7.1862970617575	320.961633946452	-1.77533688469447
36	286	286.723896936261	-31.8300260244715	317.10612908821	0.723896936261156
37	324	325.747745772543	9.00162999748834	313.250624229969	1.74774577254306
38	336	332.307479169094	29.7643269514014	309.928193879505	-3.69252083090595
39	327	320.26721517968	27.1270212912793	306.605763529040	-6.73278482031981
40	302	299.570618966688	0.301846850505914	304.127534182806	-2.42938103331238
41	299	296.874008376506	-0.523313213078847	301.649304836572	-2.12599162349363
42	311	309.134005159677	12.1543793146487	300.711615525674	-1.86599484032263
43	315	308.393994492851	21.8320792923734	299.773926214775	-6.60600550714878
44	264	254.433114810935	-27.4751000466452	301.04198523571	-9.56688518906486
45	278	270.672228463234	-16.9822727198782	302.310044256645	-7.32777153676648
46	278	265.902654006528	-16.1842799895038	306.281625982976	-12.0973459934719
47	287	270.933089352451	-7.1862970617575	310.253207709307	-16.0669106475493
48	279	272.916242463392	-31.8300260244715	316.913783561079	-6.083757536608
49	324	315.424010589660	9.00162999748834	323.574359412852	-8.57598941034047
50	354	345.765151955643	29.7643269514014	332.470521092956	-8.23484804435725
51	354	339.506295935661	27.1270212912793	341.366682773060	-14.4937040643389
52	360	367.892328276689	0.301846850505914	351.805824872805	7.89232827668877
53	363	364.278346240528	-0.523313213078847	362.244966972551	1.27834624052775
54	385	385.066107562784	12.1543793146487	372.779513122568	0.0661075627836567
55	412	418.853861435042	21.8320792923734	383.314059272584	6.85386143504235
56	370	373.520776035701	-27.4751000466452	393.954324010944	3.52077603570126
57	389	390.387683970575	-16.9822727198782	404.594588749304	1.38768397057464
58	395	390.921088565861	-16.1842799895038	415.263191423643	-4.07891143413946
59	417	415.254502963775	-7.1862970617575	425.931794097983	-1.74549703622546
60	404	403.243438185595	-31.8300260244715	436.586587838876	-0.756561814404733

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 493 & 480.818230902225 & 9.00162999748834 & 496.180139100287 & -12.1817690977751 \tabularnewline
2 & 514 & 504.680140347835 & 29.7643269514014 & 493.555532700764 & -9.31985965216535 \tabularnewline
3 & 522 & 525.94205240748 & 27.1270212912793 & 490.930926301241 & 3.94205240747965 \tabularnewline
4 & 490 & 491.662097950526 & 0.301846850505914 & 488.036055198968 & 1.66209795052595 \tabularnewline
5 & 484 & 483.382129116384 & -0.523313213078847 & 485.141184096695 & -0.61787088361649 \tabularnewline
6 & 506 & 517.825780203494 & 12.1543793146487 & 482.019840481857 & 11.8257802034944 \tabularnewline
7 & 501 & 501.269423840608 & 21.8320792923734 & 478.898496867019 & 0.269423840608113 \tabularnewline
8 & 462 & 475.876649257514 & -27.4751000466452 & 475.598450789131 & 13.8766492575138 \tabularnewline
9 & 465 & 474.683868008634 & -16.9822727198782 & 472.298404711244 & 9.68386800863397 \tabularnewline
10 & 454 & 456.726657374498 & -16.1842799895038 & 467.457622615006 & 2.72665737449779 \tabularnewline
11 & 464 & 472.56945654299 & -7.1862970617575 & 462.616840518768 & 8.56945654298977 \tabularnewline
12 & 427 & 430.176734517691 & -31.8300260244715 & 455.653291506781 & 3.17673451769053 \tabularnewline
13 & 460 & 462.308627507718 & 9.00162999748834 & 448.689742494794 & 2.30862750771752 \tabularnewline
14 & 473 & 475.182720107169 & 29.7643269514014 & 441.052952941429 & 2.18272010716925 \tabularnewline
15 & 465 & 469.456815320656 & 27.1270212912793 & 433.416163388064 & 4.45681532065618 \tabularnewline
16 & 422 & 417.478822971285 & 0.301846850505914 & 426.219330178209 & -4.52117702871493 \tabularnewline
17 & 415 & 411.500816244725 & -0.523313213078847 & 419.022496968353 & -3.49918375527466 \tabularnewline
18 & 413 & 401.218936839048 & 12.1543793146487 & 412.626683846303 & -11.7810631609519 \tabularnewline
19 & 420 & 411.937049983374 & 21.8320792923734 & 406.230870724253 & -8.06295001662636 \tabularnewline
20 & 363 & 352.952128596696 & -27.4751000466452 & 400.52297144995 & -10.0478714033044 \tabularnewline
21 & 376 & 374.167200544232 & -16.9822727198782 & 394.815072175646 & -1.83279945576811 \tabularnewline
22 & 380 & 386.745184002376 & -16.1842799895038 & 389.439095987128 & 6.7451840023756 \tabularnewline
23 & 384 & 391.123177263147 & -7.1862970617575 & 384.06311979861 & 7.1231772631474 \tabularnewline
24 & 346 & 344.949862429613 & -31.8300260244715 & 378.880163594859 & -1.05013757038745 \tabularnewline
25 & 389 & 395.301162611404 & 9.00162999748834 & 373.697207391108 & 6.30116261140398 \tabularnewline
26 & 407 & 415.966416741832 & 29.7643269514014 & 368.269256306766 & 8.96641674183223 \tabularnewline
27 & 393 & 396.031673486296 & 27.1270212912793 & 362.841305222425 & 3.03167348629563 \tabularnewline
28 & 346 & 334.609198693265 & 0.301846850505914 & 357.088954456229 & -11.390801306735 \tabularnewline
29 & 348 & 345.186709523046 & -0.523313213078847 & 351.336603690033 & -2.81329047695425 \tabularnewline
30 & 353 & 348.11330853846 & 12.1543793146487 & 345.732312146891 & -4.88669146153978 \tabularnewline
31 & 364 & 366.039900103878 & 21.8320792923734 & 340.128020603749 & 2.03990010387753 \tabularnewline
32 & 305 & 302.498470097238 & -27.4751000466452 & 334.976629949407 & -2.50152990276194 \tabularnewline
33 & 307 & 301.157033424813 & -16.9822727198782 & 329.825239295065 & -5.84296657518695 \tabularnewline
34 & 312 & 314.790843368745 & -16.1842799895038 & 325.393436620759 & 2.79084336874524 \tabularnewline
35 & 312 & 310.224663115306 & -7.1862970617575 & 320.961633946452 & -1.77533688469447 \tabularnewline
36 & 286 & 286.723896936261 & -31.8300260244715 & 317.10612908821 & 0.723896936261156 \tabularnewline
37 & 324 & 325.747745772543 & 9.00162999748834 & 313.250624229969 & 1.74774577254306 \tabularnewline
38 & 336 & 332.307479169094 & 29.7643269514014 & 309.928193879505 & -3.69252083090595 \tabularnewline
39 & 327 & 320.26721517968 & 27.1270212912793 & 306.605763529040 & -6.73278482031981 \tabularnewline
40 & 302 & 299.570618966688 & 0.301846850505914 & 304.127534182806 & -2.42938103331238 \tabularnewline
41 & 299 & 296.874008376506 & -0.523313213078847 & 301.649304836572 & -2.12599162349363 \tabularnewline
42 & 311 & 309.134005159677 & 12.1543793146487 & 300.711615525674 & -1.86599484032263 \tabularnewline
43 & 315 & 308.393994492851 & 21.8320792923734 & 299.773926214775 & -6.60600550714878 \tabularnewline
44 & 264 & 254.433114810935 & -27.4751000466452 & 301.04198523571 & -9.56688518906486 \tabularnewline
45 & 278 & 270.672228463234 & -16.9822727198782 & 302.310044256645 & -7.32777153676648 \tabularnewline
46 & 278 & 265.902654006528 & -16.1842799895038 & 306.281625982976 & -12.0973459934719 \tabularnewline
47 & 287 & 270.933089352451 & -7.1862970617575 & 310.253207709307 & -16.0669106475493 \tabularnewline
48 & 279 & 272.916242463392 & -31.8300260244715 & 316.913783561079 & -6.083757536608 \tabularnewline
49 & 324 & 315.424010589660 & 9.00162999748834 & 323.574359412852 & -8.57598941034047 \tabularnewline
50 & 354 & 345.765151955643 & 29.7643269514014 & 332.470521092956 & -8.23484804435725 \tabularnewline
51 & 354 & 339.506295935661 & 27.1270212912793 & 341.366682773060 & -14.4937040643389 \tabularnewline
52 & 360 & 367.892328276689 & 0.301846850505914 & 351.805824872805 & 7.89232827668877 \tabularnewline
53 & 363 & 364.278346240528 & -0.523313213078847 & 362.244966972551 & 1.27834624052775 \tabularnewline
54 & 385 & 385.066107562784 & 12.1543793146487 & 372.779513122568 & 0.0661075627836567 \tabularnewline
55 & 412 & 418.853861435042 & 21.8320792923734 & 383.314059272584 & 6.85386143504235 \tabularnewline
56 & 370 & 373.520776035701 & -27.4751000466452 & 393.954324010944 & 3.52077603570126 \tabularnewline
57 & 389 & 390.387683970575 & -16.9822727198782 & 404.594588749304 & 1.38768397057464 \tabularnewline
58 & 395 & 390.921088565861 & -16.1842799895038 & 415.263191423643 & -4.07891143413946 \tabularnewline
59 & 417 & 415.254502963775 & -7.1862970617575 & 425.931794097983 & -1.74549703622546 \tabularnewline
60 & 404 & 403.243438185595 & -31.8300260244715 & 436.586587838876 & -0.756561814404733 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115763&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]493[/C][C]480.818230902225[/C][C]9.00162999748834[/C][C]496.180139100287[/C][C]-12.1817690977751[/C][/ROW]
[ROW][C]2[/C][C]514[/C][C]504.680140347835[/C][C]29.7643269514014[/C][C]493.555532700764[/C][C]-9.31985965216535[/C][/ROW]
[ROW][C]3[/C][C]522[/C][C]525.94205240748[/C][C]27.1270212912793[/C][C]490.930926301241[/C][C]3.94205240747965[/C][/ROW]
[ROW][C]4[/C][C]490[/C][C]491.662097950526[/C][C]0.301846850505914[/C][C]488.036055198968[/C][C]1.66209795052595[/C][/ROW]
[ROW][C]5[/C][C]484[/C][C]483.382129116384[/C][C]-0.523313213078847[/C][C]485.141184096695[/C][C]-0.61787088361649[/C][/ROW]
[ROW][C]6[/C][C]506[/C][C]517.825780203494[/C][C]12.1543793146487[/C][C]482.019840481857[/C][C]11.8257802034944[/C][/ROW]
[ROW][C]7[/C][C]501[/C][C]501.269423840608[/C][C]21.8320792923734[/C][C]478.898496867019[/C][C]0.269423840608113[/C][/ROW]
[ROW][C]8[/C][C]462[/C][C]475.876649257514[/C][C]-27.4751000466452[/C][C]475.598450789131[/C][C]13.8766492575138[/C][/ROW]
[ROW][C]9[/C][C]465[/C][C]474.683868008634[/C][C]-16.9822727198782[/C][C]472.298404711244[/C][C]9.68386800863397[/C][/ROW]
[ROW][C]10[/C][C]454[/C][C]456.726657374498[/C][C]-16.1842799895038[/C][C]467.457622615006[/C][C]2.72665737449779[/C][/ROW]
[ROW][C]11[/C][C]464[/C][C]472.56945654299[/C][C]-7.1862970617575[/C][C]462.616840518768[/C][C]8.56945654298977[/C][/ROW]
[ROW][C]12[/C][C]427[/C][C]430.176734517691[/C][C]-31.8300260244715[/C][C]455.653291506781[/C][C]3.17673451769053[/C][/ROW]
[ROW][C]13[/C][C]460[/C][C]462.308627507718[/C][C]9.00162999748834[/C][C]448.689742494794[/C][C]2.30862750771752[/C][/ROW]
[ROW][C]14[/C][C]473[/C][C]475.182720107169[/C][C]29.7643269514014[/C][C]441.052952941429[/C][C]2.18272010716925[/C][/ROW]
[ROW][C]15[/C][C]465[/C][C]469.456815320656[/C][C]27.1270212912793[/C][C]433.416163388064[/C][C]4.45681532065618[/C][/ROW]
[ROW][C]16[/C][C]422[/C][C]417.478822971285[/C][C]0.301846850505914[/C][C]426.219330178209[/C][C]-4.52117702871493[/C][/ROW]
[ROW][C]17[/C][C]415[/C][C]411.500816244725[/C][C]-0.523313213078847[/C][C]419.022496968353[/C][C]-3.49918375527466[/C][/ROW]
[ROW][C]18[/C][C]413[/C][C]401.218936839048[/C][C]12.1543793146487[/C][C]412.626683846303[/C][C]-11.7810631609519[/C][/ROW]
[ROW][C]19[/C][C]420[/C][C]411.937049983374[/C][C]21.8320792923734[/C][C]406.230870724253[/C][C]-8.06295001662636[/C][/ROW]
[ROW][C]20[/C][C]363[/C][C]352.952128596696[/C][C]-27.4751000466452[/C][C]400.52297144995[/C][C]-10.0478714033044[/C][/ROW]
[ROW][C]21[/C][C]376[/C][C]374.167200544232[/C][C]-16.9822727198782[/C][C]394.815072175646[/C][C]-1.83279945576811[/C][/ROW]
[ROW][C]22[/C][C]380[/C][C]386.745184002376[/C][C]-16.1842799895038[/C][C]389.439095987128[/C][C]6.7451840023756[/C][/ROW]
[ROW][C]23[/C][C]384[/C][C]391.123177263147[/C][C]-7.1862970617575[/C][C]384.06311979861[/C][C]7.1231772631474[/C][/ROW]
[ROW][C]24[/C][C]346[/C][C]344.949862429613[/C][C]-31.8300260244715[/C][C]378.880163594859[/C][C]-1.05013757038745[/C][/ROW]
[ROW][C]25[/C][C]389[/C][C]395.301162611404[/C][C]9.00162999748834[/C][C]373.697207391108[/C][C]6.30116261140398[/C][/ROW]
[ROW][C]26[/C][C]407[/C][C]415.966416741832[/C][C]29.7643269514014[/C][C]368.269256306766[/C][C]8.96641674183223[/C][/ROW]
[ROW][C]27[/C][C]393[/C][C]396.031673486296[/C][C]27.1270212912793[/C][C]362.841305222425[/C][C]3.03167348629563[/C][/ROW]
[ROW][C]28[/C][C]346[/C][C]334.609198693265[/C][C]0.301846850505914[/C][C]357.088954456229[/C][C]-11.390801306735[/C][/ROW]
[ROW][C]29[/C][C]348[/C][C]345.186709523046[/C][C]-0.523313213078847[/C][C]351.336603690033[/C][C]-2.81329047695425[/C][/ROW]
[ROW][C]30[/C][C]353[/C][C]348.11330853846[/C][C]12.1543793146487[/C][C]345.732312146891[/C][C]-4.88669146153978[/C][/ROW]
[ROW][C]31[/C][C]364[/C][C]366.039900103878[/C][C]21.8320792923734[/C][C]340.128020603749[/C][C]2.03990010387753[/C][/ROW]
[ROW][C]32[/C][C]305[/C][C]302.498470097238[/C][C]-27.4751000466452[/C][C]334.976629949407[/C][C]-2.50152990276194[/C][/ROW]
[ROW][C]33[/C][C]307[/C][C]301.157033424813[/C][C]-16.9822727198782[/C][C]329.825239295065[/C][C]-5.84296657518695[/C][/ROW]
[ROW][C]34[/C][C]312[/C][C]314.790843368745[/C][C]-16.1842799895038[/C][C]325.393436620759[/C][C]2.79084336874524[/C][/ROW]
[ROW][C]35[/C][C]312[/C][C]310.224663115306[/C][C]-7.1862970617575[/C][C]320.961633946452[/C][C]-1.77533688469447[/C][/ROW]
[ROW][C]36[/C][C]286[/C][C]286.723896936261[/C][C]-31.8300260244715[/C][C]317.10612908821[/C][C]0.723896936261156[/C][/ROW]
[ROW][C]37[/C][C]324[/C][C]325.747745772543[/C][C]9.00162999748834[/C][C]313.250624229969[/C][C]1.74774577254306[/C][/ROW]
[ROW][C]38[/C][C]336[/C][C]332.307479169094[/C][C]29.7643269514014[/C][C]309.928193879505[/C][C]-3.69252083090595[/C][/ROW]
[ROW][C]39[/C][C]327[/C][C]320.26721517968[/C][C]27.1270212912793[/C][C]306.605763529040[/C][C]-6.73278482031981[/C][/ROW]
[ROW][C]40[/C][C]302[/C][C]299.570618966688[/C][C]0.301846850505914[/C][C]304.127534182806[/C][C]-2.42938103331238[/C][/ROW]
[ROW][C]41[/C][C]299[/C][C]296.874008376506[/C][C]-0.523313213078847[/C][C]301.649304836572[/C][C]-2.12599162349363[/C][/ROW]
[ROW][C]42[/C][C]311[/C][C]309.134005159677[/C][C]12.1543793146487[/C][C]300.711615525674[/C][C]-1.86599484032263[/C][/ROW]
[ROW][C]43[/C][C]315[/C][C]308.393994492851[/C][C]21.8320792923734[/C][C]299.773926214775[/C][C]-6.60600550714878[/C][/ROW]
[ROW][C]44[/C][C]264[/C][C]254.433114810935[/C][C]-27.4751000466452[/C][C]301.04198523571[/C][C]-9.56688518906486[/C][/ROW]
[ROW][C]45[/C][C]278[/C][C]270.672228463234[/C][C]-16.9822727198782[/C][C]302.310044256645[/C][C]-7.32777153676648[/C][/ROW]
[ROW][C]46[/C][C]278[/C][C]265.902654006528[/C][C]-16.1842799895038[/C][C]306.281625982976[/C][C]-12.0973459934719[/C][/ROW]
[ROW][C]47[/C][C]287[/C][C]270.933089352451[/C][C]-7.1862970617575[/C][C]310.253207709307[/C][C]-16.0669106475493[/C][/ROW]
[ROW][C]48[/C][C]279[/C][C]272.916242463392[/C][C]-31.8300260244715[/C][C]316.913783561079[/C][C]-6.083757536608[/C][/ROW]
[ROW][C]49[/C][C]324[/C][C]315.424010589660[/C][C]9.00162999748834[/C][C]323.574359412852[/C][C]-8.57598941034047[/C][/ROW]
[ROW][C]50[/C][C]354[/C][C]345.765151955643[/C][C]29.7643269514014[/C][C]332.470521092956[/C][C]-8.23484804435725[/C][/ROW]
[ROW][C]51[/C][C]354[/C][C]339.506295935661[/C][C]27.1270212912793[/C][C]341.366682773060[/C][C]-14.4937040643389[/C][/ROW]
[ROW][C]52[/C][C]360[/C][C]367.892328276689[/C][C]0.301846850505914[/C][C]351.805824872805[/C][C]7.89232827668877[/C][/ROW]
[ROW][C]53[/C][C]363[/C][C]364.278346240528[/C][C]-0.523313213078847[/C][C]362.244966972551[/C][C]1.27834624052775[/C][/ROW]
[ROW][C]54[/C][C]385[/C][C]385.066107562784[/C][C]12.1543793146487[/C][C]372.779513122568[/C][C]0.0661075627836567[/C][/ROW]
[ROW][C]55[/C][C]412[/C][C]418.853861435042[/C][C]21.8320792923734[/C][C]383.314059272584[/C][C]6.85386143504235[/C][/ROW]
[ROW][C]56[/C][C]370[/C][C]373.520776035701[/C][C]-27.4751000466452[/C][C]393.954324010944[/C][C]3.52077603570126[/C][/ROW]
[ROW][C]57[/C][C]389[/C][C]390.387683970575[/C][C]-16.9822727198782[/C][C]404.594588749304[/C][C]1.38768397057464[/C][/ROW]
[ROW][C]58[/C][C]395[/C][C]390.921088565861[/C][C]-16.1842799895038[/C][C]415.263191423643[/C][C]-4.07891143413946[/C][/ROW]
[ROW][C]59[/C][C]417[/C][C]415.254502963775[/C][C]-7.1862970617575[/C][C]425.931794097983[/C][C]-1.74549703622546[/C][/ROW]
[ROW][C]60[/C][C]404[/C][C]403.243438185595[/C][C]-31.8300260244715[/C][C]436.586587838876[/C][C]-0.756561814404733[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115763&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115763&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	493	480.818230902225	9.00162999748834	496.180139100287	-12.1817690977751
2	514	504.680140347835	29.7643269514014	493.555532700764	-9.31985965216535
3	522	525.94205240748	27.1270212912793	490.930926301241	3.94205240747965
4	490	491.662097950526	0.301846850505914	488.036055198968	1.66209795052595
5	484	483.382129116384	-0.523313213078847	485.141184096695	-0.61787088361649
6	506	517.825780203494	12.1543793146487	482.019840481857	11.8257802034944
7	501	501.269423840608	21.8320792923734	478.898496867019	0.269423840608113
8	462	475.876649257514	-27.4751000466452	475.598450789131	13.8766492575138
9	465	474.683868008634	-16.9822727198782	472.298404711244	9.68386800863397
10	454	456.726657374498	-16.1842799895038	467.457622615006	2.72665737449779
11	464	472.56945654299	-7.1862970617575	462.616840518768	8.56945654298977
12	427	430.176734517691	-31.8300260244715	455.653291506781	3.17673451769053
13	460	462.308627507718	9.00162999748834	448.689742494794	2.30862750771752
14	473	475.182720107169	29.7643269514014	441.052952941429	2.18272010716925
15	465	469.456815320656	27.1270212912793	433.416163388064	4.45681532065618
16	422	417.478822971285	0.301846850505914	426.219330178209	-4.52117702871493
17	415	411.500816244725	-0.523313213078847	419.022496968353	-3.49918375527466
18	413	401.218936839048	12.1543793146487	412.626683846303	-11.7810631609519
19	420	411.937049983374	21.8320792923734	406.230870724253	-8.06295001662636
20	363	352.952128596696	-27.4751000466452	400.52297144995	-10.0478714033044
21	376	374.167200544232	-16.9822727198782	394.815072175646	-1.83279945576811
22	380	386.745184002376	-16.1842799895038	389.439095987128	6.7451840023756
23	384	391.123177263147	-7.1862970617575	384.06311979861	7.1231772631474
24	346	344.949862429613	-31.8300260244715	378.880163594859	-1.05013757038745
25	389	395.301162611404	9.00162999748834	373.697207391108	6.30116261140398
26	407	415.966416741832	29.7643269514014	368.269256306766	8.96641674183223
27	393	396.031673486296	27.1270212912793	362.841305222425	3.03167348629563
28	346	334.609198693265	0.301846850505914	357.088954456229	-11.390801306735
29	348	345.186709523046	-0.523313213078847	351.336603690033	-2.81329047695425
30	353	348.11330853846	12.1543793146487	345.732312146891	-4.88669146153978
31	364	366.039900103878	21.8320792923734	340.128020603749	2.03990010387753
32	305	302.498470097238	-27.4751000466452	334.976629949407	-2.50152990276194
33	307	301.157033424813	-16.9822727198782	329.825239295065	-5.84296657518695
34	312	314.790843368745	-16.1842799895038	325.393436620759	2.79084336874524
35	312	310.224663115306	-7.1862970617575	320.961633946452	-1.77533688469447
36	286	286.723896936261	-31.8300260244715	317.10612908821	0.723896936261156
37	324	325.747745772543	9.00162999748834	313.250624229969	1.74774577254306
38	336	332.307479169094	29.7643269514014	309.928193879505	-3.69252083090595
39	327	320.26721517968	27.1270212912793	306.605763529040	-6.73278482031981
40	302	299.570618966688	0.301846850505914	304.127534182806	-2.42938103331238
41	299	296.874008376506	-0.523313213078847	301.649304836572	-2.12599162349363
42	311	309.134005159677	12.1543793146487	300.711615525674	-1.86599484032263
43	315	308.393994492851	21.8320792923734	299.773926214775	-6.60600550714878
44	264	254.433114810935	-27.4751000466452	301.04198523571	-9.56688518906486
45	278	270.672228463234	-16.9822727198782	302.310044256645	-7.32777153676648
46	278	265.902654006528	-16.1842799895038	306.281625982976	-12.0973459934719
47	287	270.933089352451	-7.1862970617575	310.253207709307	-16.0669106475493
48	279	272.916242463392	-31.8300260244715	316.913783561079	-6.083757536608
49	324	315.424010589660	9.00162999748834	323.574359412852	-8.57598941034047
50	354	345.765151955643	29.7643269514014	332.470521092956	-8.23484804435725
51	354	339.506295935661	27.1270212912793	341.366682773060	-14.4937040643389
52	360	367.892328276689	0.301846850505914	351.805824872805	7.89232827668877
53	363	364.278346240528	-0.523313213078847	362.244966972551	1.27834624052775
54	385	385.066107562784	12.1543793146487	372.779513122568	0.0661075627836567
55	412	418.853861435042	21.8320792923734	383.314059272584	6.85386143504235
56	370	373.520776035701	-27.4751000466452	393.954324010944	3.52077603570126
57	389	390.387683970575	-16.9822727198782	404.594588749304	1.38768397057464
58	395	390.921088565861	-16.1842799895038	415.263191423643	-4.07891143413946
59	417	415.254502963775	-7.1862970617575	425.931794097983	-1.74549703622546
60	404	403.243438185595	-31.8300260244715	436.586587838876	-0.756561814404733

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