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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decomposeloess.wasp
Title produced by softwareDecomposition by Loess
Date of computationFri, 23 Dec 2011 07:55:57 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/23/t13246449791myqwolc817iznq.htm/, Retrieved Thu, 31 Oct 2024 22:49:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160363, Retrieved Thu, 31 Oct 2024 22:49:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Decomposition by Loess] [Unemployment] [2010-11-30 13:30:23] [b98453cac15ba1066b407e146608df68]
- R  D      [Decomposition by Loess] [paper- Loess] [2011-12-23 12:55:57] [fe2dc4bc83c881ccd49ef12feaba2b65] [Current]
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Dataseries X:
539
548
563
581
572
519
521
531
540
548
556
551
549
564
586
604
601
545
537
552
563
575
580
575
558
564
581
597
587
536
524
537
536
533
528
516
502
506
518
534
528
478
469
490
493
508
517
514
510
527
542
565
555
499
511
526
532
549
561
557
566
588
620
626
620
573
573
574
580
590




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160363&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160363&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160363&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal701071
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 701 & 0 & 71 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160363&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]701[/C][C]0[/C][C]71[/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=160363&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160363&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
ComponentWindowDegreeJump
Seasonal701071
Trend1912
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1539544.925738959867-8.53207670211424541.6063377422475.92573895986709
2548550.2818746830913.04741061054521542.6707147063632.28187468309136
3563560.97134368232621.2935646471938543.73509167048-2.02865631767372
4581580.31611452730636.8092971998294544.874588272864-0.683885472693532
5572569.16088829564128.8250268291106546.014084875248-2.83911170435908
6519514.989521223347-24.1957973750738547.206276151727-4.01047877665337
7521521.151491726121-27.5499591543271548.3984674282060.151491726121094
8531528.436404377292-16.0933502158166549.656945838525-2.56359562270848
9540540.554659590428-11.4700838392726550.9154242488440.5546595904284
10548546.107639559453-2.79674555814926552.689105998697-1.89236044054746
11556553.8487249857973.68848726565402554.462787748549-2.15127501420341
12551548.526727535998-3.02577823304361556.499050697046-2.47327246400198
13549547.996763056572-8.53207670211424558.535313645542-1.00323694342751
14564564.4849857567213.04741061054521560.4676036327330.484985756721358
15586588.30654173288121.2935646471938562.3998936199252.30654173288087
16604606.8402798463836.8092971998294564.350422953792.84027984638044
17601606.87402088323428.8250268291106566.3009522876555.87402088323427
18545546.385297601696-24.1957973750738567.8104997733781.38529760169581
19537532.229911895226-27.5499591543271569.320047259101-4.77008810477378
20552550.384301925856-16.0933502158166569.70904828996-1.61569807414367
21563567.372034518453-11.4700838392726570.098049320824.37203451845301
22575583.256201510061-2.79674555814926569.5405440480888.25620151006137
23580587.328473958993.68848726565402568.9830387753567.32847395898966
24575585.160452179023-3.02577823304361567.86532605402110.1604521790227
25558557.784463369429-8.53207670211424566.747613332686-0.21553663057125
26564560.0763863944693.04741061054521564.876202994986-3.92361360553093
27581577.7016426955221.2935646471938563.004792657286-3.29835730447974
28597597.33970877958636.8092971998294559.8509940205840.339708779586431
29587588.47777778700728.8250268291106556.6971953838831.47777778700663
30536543.729798771997-24.1957973750738552.4659986030777.72979877199691
31524527.315157332056-27.5499591543271548.2348018222713.31515733205606
32537546.858147194001-16.0933502158166543.2352030218169.85814719400071
33536545.234479617912-11.4700838392726538.2356042213619.2344796179118
34533535.993267029374-2.79674555814926532.8034785287762.99326702937367
35528524.9401598981563.68848726565402527.37135283619-3.05984010184443
36516512.827354855858-3.02577823304361522.198423377185-3.1726451441416
37502495.506582783934-8.53207670211424517.02549391818-6.49341721606578
38506495.9200501047673.04741061054521513.032539284688-10.079949895233
39518505.66685070161121.2935646471938509.039584651195-12.3331492983893
40534524.21479339419736.8092971998294506.975909405974-9.78520660580318
41528522.26273901013728.8250268291106504.912234160752-5.73726098986276
42478475.23715969494-24.1957973750738504.958637680134-2.76284030505997
43469460.544917954812-27.5499591543271505.005041199515-8.45508204518825
44490489.504309858667-16.0933502158166506.589040357149-0.49569014133283
45493489.297044324489-11.4700838392726508.173039514783-3.70295567551085
46508508.311941953123-2.79674555814926510.4848036050260.31194195312338
47517517.5149450390783.68848726565402512.7965676952680.514945039077702
48514515.653869791267-3.02577823304361515.3719084417771.65386979126674
49510510.584827513829-8.53207670211424517.9472491882850.584827513828827
50527530.0742356476173.04741061054521520.8783537418373.07423564761746
51542538.89697705741721.2935646471938523.809458295389-3.10302294258304
52565566.06400560251136.8092971998294527.1266971976591.06400560251143
53555550.7310370709628.8250268291106530.443936099929-4.26896292903984
54499487.776124786328-24.1957973750738534.419672588745-11.2238752136716
55511511.154550076765-27.5499591543271538.3954090775620.154550076765418
56526524.597374194686-16.0933502158166543.495976021131-1.4026258053143
57532526.873540874572-11.4700838392726548.5965429647-5.12645912542757
58549546.392643162825-2.79674555814926554.404102395324-2.60735683717462
59561558.0998509083983.68848726565402560.211661825948-2.90014909160163
60557551.126341628078-3.02577823304361565.899436604966-5.87365837192215
61566568.94486531813-8.53207670211424571.5872113839842.9448653181305
62588597.4653945934923.04741061054521575.4871947959639.46539459349162
63620639.31925714486421.2935646471938579.38717820794219.3192571448636
64626632.12819023170436.8092971998294583.0625125684676.12819023170368
65620624.43712624189828.8250268291106586.7378469289924.43712624189789
66573579.912152025754-24.1957973750738590.283645349326.91215202575404
67573579.720515384679-27.5499591543271593.8294437696486.72051538467883
68574566.960248644407-16.0933502158166597.13310157141-7.03975135559324
69580571.033324466101-11.4700838392726600.436759373171-8.96667553389887
70590579.283182738306-2.79674555814926603.513562819843-10.7168172616937

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 539 & 544.925738959867 & -8.53207670211424 & 541.606337742247 & 5.92573895986709 \tabularnewline
2 & 548 & 550.281874683091 & 3.04741061054521 & 542.670714706363 & 2.28187468309136 \tabularnewline
3 & 563 & 560.971343682326 & 21.2935646471938 & 543.73509167048 & -2.02865631767372 \tabularnewline
4 & 581 & 580.316114527306 & 36.8092971998294 & 544.874588272864 & -0.683885472693532 \tabularnewline
5 & 572 & 569.160888295641 & 28.8250268291106 & 546.014084875248 & -2.83911170435908 \tabularnewline
6 & 519 & 514.989521223347 & -24.1957973750738 & 547.206276151727 & -4.01047877665337 \tabularnewline
7 & 521 & 521.151491726121 & -27.5499591543271 & 548.398467428206 & 0.151491726121094 \tabularnewline
8 & 531 & 528.436404377292 & -16.0933502158166 & 549.656945838525 & -2.56359562270848 \tabularnewline
9 & 540 & 540.554659590428 & -11.4700838392726 & 550.915424248844 & 0.5546595904284 \tabularnewline
10 & 548 & 546.107639559453 & -2.79674555814926 & 552.689105998697 & -1.89236044054746 \tabularnewline
11 & 556 & 553.848724985797 & 3.68848726565402 & 554.462787748549 & -2.15127501420341 \tabularnewline
12 & 551 & 548.526727535998 & -3.02577823304361 & 556.499050697046 & -2.47327246400198 \tabularnewline
13 & 549 & 547.996763056572 & -8.53207670211424 & 558.535313645542 & -1.00323694342751 \tabularnewline
14 & 564 & 564.484985756721 & 3.04741061054521 & 560.467603632733 & 0.484985756721358 \tabularnewline
15 & 586 & 588.306541732881 & 21.2935646471938 & 562.399893619925 & 2.30654173288087 \tabularnewline
16 & 604 & 606.84027984638 & 36.8092971998294 & 564.35042295379 & 2.84027984638044 \tabularnewline
17 & 601 & 606.874020883234 & 28.8250268291106 & 566.300952287655 & 5.87402088323427 \tabularnewline
18 & 545 & 546.385297601696 & -24.1957973750738 & 567.810499773378 & 1.38529760169581 \tabularnewline
19 & 537 & 532.229911895226 & -27.5499591543271 & 569.320047259101 & -4.77008810477378 \tabularnewline
20 & 552 & 550.384301925856 & -16.0933502158166 & 569.70904828996 & -1.61569807414367 \tabularnewline
21 & 563 & 567.372034518453 & -11.4700838392726 & 570.09804932082 & 4.37203451845301 \tabularnewline
22 & 575 & 583.256201510061 & -2.79674555814926 & 569.540544048088 & 8.25620151006137 \tabularnewline
23 & 580 & 587.32847395899 & 3.68848726565402 & 568.983038775356 & 7.32847395898966 \tabularnewline
24 & 575 & 585.160452179023 & -3.02577823304361 & 567.865326054021 & 10.1604521790227 \tabularnewline
25 & 558 & 557.784463369429 & -8.53207670211424 & 566.747613332686 & -0.21553663057125 \tabularnewline
26 & 564 & 560.076386394469 & 3.04741061054521 & 564.876202994986 & -3.92361360553093 \tabularnewline
27 & 581 & 577.70164269552 & 21.2935646471938 & 563.004792657286 & -3.29835730447974 \tabularnewline
28 & 597 & 597.339708779586 & 36.8092971998294 & 559.850994020584 & 0.339708779586431 \tabularnewline
29 & 587 & 588.477777787007 & 28.8250268291106 & 556.697195383883 & 1.47777778700663 \tabularnewline
30 & 536 & 543.729798771997 & -24.1957973750738 & 552.465998603077 & 7.72979877199691 \tabularnewline
31 & 524 & 527.315157332056 & -27.5499591543271 & 548.234801822271 & 3.31515733205606 \tabularnewline
32 & 537 & 546.858147194001 & -16.0933502158166 & 543.235203021816 & 9.85814719400071 \tabularnewline
33 & 536 & 545.234479617912 & -11.4700838392726 & 538.235604221361 & 9.2344796179118 \tabularnewline
34 & 533 & 535.993267029374 & -2.79674555814926 & 532.803478528776 & 2.99326702937367 \tabularnewline
35 & 528 & 524.940159898156 & 3.68848726565402 & 527.37135283619 & -3.05984010184443 \tabularnewline
36 & 516 & 512.827354855858 & -3.02577823304361 & 522.198423377185 & -3.1726451441416 \tabularnewline
37 & 502 & 495.506582783934 & -8.53207670211424 & 517.02549391818 & -6.49341721606578 \tabularnewline
38 & 506 & 495.920050104767 & 3.04741061054521 & 513.032539284688 & -10.079949895233 \tabularnewline
39 & 518 & 505.666850701611 & 21.2935646471938 & 509.039584651195 & -12.3331492983893 \tabularnewline
40 & 534 & 524.214793394197 & 36.8092971998294 & 506.975909405974 & -9.78520660580318 \tabularnewline
41 & 528 & 522.262739010137 & 28.8250268291106 & 504.912234160752 & -5.73726098986276 \tabularnewline
42 & 478 & 475.23715969494 & -24.1957973750738 & 504.958637680134 & -2.76284030505997 \tabularnewline
43 & 469 & 460.544917954812 & -27.5499591543271 & 505.005041199515 & -8.45508204518825 \tabularnewline
44 & 490 & 489.504309858667 & -16.0933502158166 & 506.589040357149 & -0.49569014133283 \tabularnewline
45 & 493 & 489.297044324489 & -11.4700838392726 & 508.173039514783 & -3.70295567551085 \tabularnewline
46 & 508 & 508.311941953123 & -2.79674555814926 & 510.484803605026 & 0.31194195312338 \tabularnewline
47 & 517 & 517.514945039078 & 3.68848726565402 & 512.796567695268 & 0.514945039077702 \tabularnewline
48 & 514 & 515.653869791267 & -3.02577823304361 & 515.371908441777 & 1.65386979126674 \tabularnewline
49 & 510 & 510.584827513829 & -8.53207670211424 & 517.947249188285 & 0.584827513828827 \tabularnewline
50 & 527 & 530.074235647617 & 3.04741061054521 & 520.878353741837 & 3.07423564761746 \tabularnewline
51 & 542 & 538.896977057417 & 21.2935646471938 & 523.809458295389 & -3.10302294258304 \tabularnewline
52 & 565 & 566.064005602511 & 36.8092971998294 & 527.126697197659 & 1.06400560251143 \tabularnewline
53 & 555 & 550.73103707096 & 28.8250268291106 & 530.443936099929 & -4.26896292903984 \tabularnewline
54 & 499 & 487.776124786328 & -24.1957973750738 & 534.419672588745 & -11.2238752136716 \tabularnewline
55 & 511 & 511.154550076765 & -27.5499591543271 & 538.395409077562 & 0.154550076765418 \tabularnewline
56 & 526 & 524.597374194686 & -16.0933502158166 & 543.495976021131 & -1.4026258053143 \tabularnewline
57 & 532 & 526.873540874572 & -11.4700838392726 & 548.5965429647 & -5.12645912542757 \tabularnewline
58 & 549 & 546.392643162825 & -2.79674555814926 & 554.404102395324 & -2.60735683717462 \tabularnewline
59 & 561 & 558.099850908398 & 3.68848726565402 & 560.211661825948 & -2.90014909160163 \tabularnewline
60 & 557 & 551.126341628078 & -3.02577823304361 & 565.899436604966 & -5.87365837192215 \tabularnewline
61 & 566 & 568.94486531813 & -8.53207670211424 & 571.587211383984 & 2.9448653181305 \tabularnewline
62 & 588 & 597.465394593492 & 3.04741061054521 & 575.487194795963 & 9.46539459349162 \tabularnewline
63 & 620 & 639.319257144864 & 21.2935646471938 & 579.387178207942 & 19.3192571448636 \tabularnewline
64 & 626 & 632.128190231704 & 36.8092971998294 & 583.062512568467 & 6.12819023170368 \tabularnewline
65 & 620 & 624.437126241898 & 28.8250268291106 & 586.737846928992 & 4.43712624189789 \tabularnewline
66 & 573 & 579.912152025754 & -24.1957973750738 & 590.28364534932 & 6.91215202575404 \tabularnewline
67 & 573 & 579.720515384679 & -27.5499591543271 & 593.829443769648 & 6.72051538467883 \tabularnewline
68 & 574 & 566.960248644407 & -16.0933502158166 & 597.13310157141 & -7.03975135559324 \tabularnewline
69 & 580 & 571.033324466101 & -11.4700838392726 & 600.436759373171 & -8.96667553389887 \tabularnewline
70 & 590 & 579.283182738306 & -2.79674555814926 & 603.513562819843 & -10.7168172616937 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160363&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]539[/C][C]544.925738959867[/C][C]-8.53207670211424[/C][C]541.606337742247[/C][C]5.92573895986709[/C][/ROW]
[ROW][C]2[/C][C]548[/C][C]550.281874683091[/C][C]3.04741061054521[/C][C]542.670714706363[/C][C]2.28187468309136[/C][/ROW]
[ROW][C]3[/C][C]563[/C][C]560.971343682326[/C][C]21.2935646471938[/C][C]543.73509167048[/C][C]-2.02865631767372[/C][/ROW]
[ROW][C]4[/C][C]581[/C][C]580.316114527306[/C][C]36.8092971998294[/C][C]544.874588272864[/C][C]-0.683885472693532[/C][/ROW]
[ROW][C]5[/C][C]572[/C][C]569.160888295641[/C][C]28.8250268291106[/C][C]546.014084875248[/C][C]-2.83911170435908[/C][/ROW]
[ROW][C]6[/C][C]519[/C][C]514.989521223347[/C][C]-24.1957973750738[/C][C]547.206276151727[/C][C]-4.01047877665337[/C][/ROW]
[ROW][C]7[/C][C]521[/C][C]521.151491726121[/C][C]-27.5499591543271[/C][C]548.398467428206[/C][C]0.151491726121094[/C][/ROW]
[ROW][C]8[/C][C]531[/C][C]528.436404377292[/C][C]-16.0933502158166[/C][C]549.656945838525[/C][C]-2.56359562270848[/C][/ROW]
[ROW][C]9[/C][C]540[/C][C]540.554659590428[/C][C]-11.4700838392726[/C][C]550.915424248844[/C][C]0.5546595904284[/C][/ROW]
[ROW][C]10[/C][C]548[/C][C]546.107639559453[/C][C]-2.79674555814926[/C][C]552.689105998697[/C][C]-1.89236044054746[/C][/ROW]
[ROW][C]11[/C][C]556[/C][C]553.848724985797[/C][C]3.68848726565402[/C][C]554.462787748549[/C][C]-2.15127501420341[/C][/ROW]
[ROW][C]12[/C][C]551[/C][C]548.526727535998[/C][C]-3.02577823304361[/C][C]556.499050697046[/C][C]-2.47327246400198[/C][/ROW]
[ROW][C]13[/C][C]549[/C][C]547.996763056572[/C][C]-8.53207670211424[/C][C]558.535313645542[/C][C]-1.00323694342751[/C][/ROW]
[ROW][C]14[/C][C]564[/C][C]564.484985756721[/C][C]3.04741061054521[/C][C]560.467603632733[/C][C]0.484985756721358[/C][/ROW]
[ROW][C]15[/C][C]586[/C][C]588.306541732881[/C][C]21.2935646471938[/C][C]562.399893619925[/C][C]2.30654173288087[/C][/ROW]
[ROW][C]16[/C][C]604[/C][C]606.84027984638[/C][C]36.8092971998294[/C][C]564.35042295379[/C][C]2.84027984638044[/C][/ROW]
[ROW][C]17[/C][C]601[/C][C]606.874020883234[/C][C]28.8250268291106[/C][C]566.300952287655[/C][C]5.87402088323427[/C][/ROW]
[ROW][C]18[/C][C]545[/C][C]546.385297601696[/C][C]-24.1957973750738[/C][C]567.810499773378[/C][C]1.38529760169581[/C][/ROW]
[ROW][C]19[/C][C]537[/C][C]532.229911895226[/C][C]-27.5499591543271[/C][C]569.320047259101[/C][C]-4.77008810477378[/C][/ROW]
[ROW][C]20[/C][C]552[/C][C]550.384301925856[/C][C]-16.0933502158166[/C][C]569.70904828996[/C][C]-1.61569807414367[/C][/ROW]
[ROW][C]21[/C][C]563[/C][C]567.372034518453[/C][C]-11.4700838392726[/C][C]570.09804932082[/C][C]4.37203451845301[/C][/ROW]
[ROW][C]22[/C][C]575[/C][C]583.256201510061[/C][C]-2.79674555814926[/C][C]569.540544048088[/C][C]8.25620151006137[/C][/ROW]
[ROW][C]23[/C][C]580[/C][C]587.32847395899[/C][C]3.68848726565402[/C][C]568.983038775356[/C][C]7.32847395898966[/C][/ROW]
[ROW][C]24[/C][C]575[/C][C]585.160452179023[/C][C]-3.02577823304361[/C][C]567.865326054021[/C][C]10.1604521790227[/C][/ROW]
[ROW][C]25[/C][C]558[/C][C]557.784463369429[/C][C]-8.53207670211424[/C][C]566.747613332686[/C][C]-0.21553663057125[/C][/ROW]
[ROW][C]26[/C][C]564[/C][C]560.076386394469[/C][C]3.04741061054521[/C][C]564.876202994986[/C][C]-3.92361360553093[/C][/ROW]
[ROW][C]27[/C][C]581[/C][C]577.70164269552[/C][C]21.2935646471938[/C][C]563.004792657286[/C][C]-3.29835730447974[/C][/ROW]
[ROW][C]28[/C][C]597[/C][C]597.339708779586[/C][C]36.8092971998294[/C][C]559.850994020584[/C][C]0.339708779586431[/C][/ROW]
[ROW][C]29[/C][C]587[/C][C]588.477777787007[/C][C]28.8250268291106[/C][C]556.697195383883[/C][C]1.47777778700663[/C][/ROW]
[ROW][C]30[/C][C]536[/C][C]543.729798771997[/C][C]-24.1957973750738[/C][C]552.465998603077[/C][C]7.72979877199691[/C][/ROW]
[ROW][C]31[/C][C]524[/C][C]527.315157332056[/C][C]-27.5499591543271[/C][C]548.234801822271[/C][C]3.31515733205606[/C][/ROW]
[ROW][C]32[/C][C]537[/C][C]546.858147194001[/C][C]-16.0933502158166[/C][C]543.235203021816[/C][C]9.85814719400071[/C][/ROW]
[ROW][C]33[/C][C]536[/C][C]545.234479617912[/C][C]-11.4700838392726[/C][C]538.235604221361[/C][C]9.2344796179118[/C][/ROW]
[ROW][C]34[/C][C]533[/C][C]535.993267029374[/C][C]-2.79674555814926[/C][C]532.803478528776[/C][C]2.99326702937367[/C][/ROW]
[ROW][C]35[/C][C]528[/C][C]524.940159898156[/C][C]3.68848726565402[/C][C]527.37135283619[/C][C]-3.05984010184443[/C][/ROW]
[ROW][C]36[/C][C]516[/C][C]512.827354855858[/C][C]-3.02577823304361[/C][C]522.198423377185[/C][C]-3.1726451441416[/C][/ROW]
[ROW][C]37[/C][C]502[/C][C]495.506582783934[/C][C]-8.53207670211424[/C][C]517.02549391818[/C][C]-6.49341721606578[/C][/ROW]
[ROW][C]38[/C][C]506[/C][C]495.920050104767[/C][C]3.04741061054521[/C][C]513.032539284688[/C][C]-10.079949895233[/C][/ROW]
[ROW][C]39[/C][C]518[/C][C]505.666850701611[/C][C]21.2935646471938[/C][C]509.039584651195[/C][C]-12.3331492983893[/C][/ROW]
[ROW][C]40[/C][C]534[/C][C]524.214793394197[/C][C]36.8092971998294[/C][C]506.975909405974[/C][C]-9.78520660580318[/C][/ROW]
[ROW][C]41[/C][C]528[/C][C]522.262739010137[/C][C]28.8250268291106[/C][C]504.912234160752[/C][C]-5.73726098986276[/C][/ROW]
[ROW][C]42[/C][C]478[/C][C]475.23715969494[/C][C]-24.1957973750738[/C][C]504.958637680134[/C][C]-2.76284030505997[/C][/ROW]
[ROW][C]43[/C][C]469[/C][C]460.544917954812[/C][C]-27.5499591543271[/C][C]505.005041199515[/C][C]-8.45508204518825[/C][/ROW]
[ROW][C]44[/C][C]490[/C][C]489.504309858667[/C][C]-16.0933502158166[/C][C]506.589040357149[/C][C]-0.49569014133283[/C][/ROW]
[ROW][C]45[/C][C]493[/C][C]489.297044324489[/C][C]-11.4700838392726[/C][C]508.173039514783[/C][C]-3.70295567551085[/C][/ROW]
[ROW][C]46[/C][C]508[/C][C]508.311941953123[/C][C]-2.79674555814926[/C][C]510.484803605026[/C][C]0.31194195312338[/C][/ROW]
[ROW][C]47[/C][C]517[/C][C]517.514945039078[/C][C]3.68848726565402[/C][C]512.796567695268[/C][C]0.514945039077702[/C][/ROW]
[ROW][C]48[/C][C]514[/C][C]515.653869791267[/C][C]-3.02577823304361[/C][C]515.371908441777[/C][C]1.65386979126674[/C][/ROW]
[ROW][C]49[/C][C]510[/C][C]510.584827513829[/C][C]-8.53207670211424[/C][C]517.947249188285[/C][C]0.584827513828827[/C][/ROW]
[ROW][C]50[/C][C]527[/C][C]530.074235647617[/C][C]3.04741061054521[/C][C]520.878353741837[/C][C]3.07423564761746[/C][/ROW]
[ROW][C]51[/C][C]542[/C][C]538.896977057417[/C][C]21.2935646471938[/C][C]523.809458295389[/C][C]-3.10302294258304[/C][/ROW]
[ROW][C]52[/C][C]565[/C][C]566.064005602511[/C][C]36.8092971998294[/C][C]527.126697197659[/C][C]1.06400560251143[/C][/ROW]
[ROW][C]53[/C][C]555[/C][C]550.73103707096[/C][C]28.8250268291106[/C][C]530.443936099929[/C][C]-4.26896292903984[/C][/ROW]
[ROW][C]54[/C][C]499[/C][C]487.776124786328[/C][C]-24.1957973750738[/C][C]534.419672588745[/C][C]-11.2238752136716[/C][/ROW]
[ROW][C]55[/C][C]511[/C][C]511.154550076765[/C][C]-27.5499591543271[/C][C]538.395409077562[/C][C]0.154550076765418[/C][/ROW]
[ROW][C]56[/C][C]526[/C][C]524.597374194686[/C][C]-16.0933502158166[/C][C]543.495976021131[/C][C]-1.4026258053143[/C][/ROW]
[ROW][C]57[/C][C]532[/C][C]526.873540874572[/C][C]-11.4700838392726[/C][C]548.5965429647[/C][C]-5.12645912542757[/C][/ROW]
[ROW][C]58[/C][C]549[/C][C]546.392643162825[/C][C]-2.79674555814926[/C][C]554.404102395324[/C][C]-2.60735683717462[/C][/ROW]
[ROW][C]59[/C][C]561[/C][C]558.099850908398[/C][C]3.68848726565402[/C][C]560.211661825948[/C][C]-2.90014909160163[/C][/ROW]
[ROW][C]60[/C][C]557[/C][C]551.126341628078[/C][C]-3.02577823304361[/C][C]565.899436604966[/C][C]-5.87365837192215[/C][/ROW]
[ROW][C]61[/C][C]566[/C][C]568.94486531813[/C][C]-8.53207670211424[/C][C]571.587211383984[/C][C]2.9448653181305[/C][/ROW]
[ROW][C]62[/C][C]588[/C][C]597.465394593492[/C][C]3.04741061054521[/C][C]575.487194795963[/C][C]9.46539459349162[/C][/ROW]
[ROW][C]63[/C][C]620[/C][C]639.319257144864[/C][C]21.2935646471938[/C][C]579.387178207942[/C][C]19.3192571448636[/C][/ROW]
[ROW][C]64[/C][C]626[/C][C]632.128190231704[/C][C]36.8092971998294[/C][C]583.062512568467[/C][C]6.12819023170368[/C][/ROW]
[ROW][C]65[/C][C]620[/C][C]624.437126241898[/C][C]28.8250268291106[/C][C]586.737846928992[/C][C]4.43712624189789[/C][/ROW]
[ROW][C]66[/C][C]573[/C][C]579.912152025754[/C][C]-24.1957973750738[/C][C]590.28364534932[/C][C]6.91215202575404[/C][/ROW]
[ROW][C]67[/C][C]573[/C][C]579.720515384679[/C][C]-27.5499591543271[/C][C]593.829443769648[/C][C]6.72051538467883[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]566.960248644407[/C][C]-16.0933502158166[/C][C]597.13310157141[/C][C]-7.03975135559324[/C][/ROW]
[ROW][C]69[/C][C]580[/C][C]571.033324466101[/C][C]-11.4700838392726[/C][C]600.436759373171[/C][C]-8.96667553389887[/C][/ROW]
[ROW][C]70[/C][C]590[/C][C]579.283182738306[/C][C]-2.79674555814926[/C][C]603.513562819843[/C][C]-10.7168172616937[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160363&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160363&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
tObservedFittedSeasonalTrendRemainder
1539544.925738959867-8.53207670211424541.6063377422475.92573895986709
2548550.2818746830913.04741061054521542.6707147063632.28187468309136
3563560.97134368232621.2935646471938543.73509167048-2.02865631767372
4581580.31611452730636.8092971998294544.874588272864-0.683885472693532
5572569.16088829564128.8250268291106546.014084875248-2.83911170435908
6519514.989521223347-24.1957973750738547.206276151727-4.01047877665337
7521521.151491726121-27.5499591543271548.3984674282060.151491726121094
8531528.436404377292-16.0933502158166549.656945838525-2.56359562270848
9540540.554659590428-11.4700838392726550.9154242488440.5546595904284
10548546.107639559453-2.79674555814926552.689105998697-1.89236044054746
11556553.8487249857973.68848726565402554.462787748549-2.15127501420341
12551548.526727535998-3.02577823304361556.499050697046-2.47327246400198
13549547.996763056572-8.53207670211424558.535313645542-1.00323694342751
14564564.4849857567213.04741061054521560.4676036327330.484985756721358
15586588.30654173288121.2935646471938562.3998936199252.30654173288087
16604606.8402798463836.8092971998294564.350422953792.84027984638044
17601606.87402088323428.8250268291106566.3009522876555.87402088323427
18545546.385297601696-24.1957973750738567.8104997733781.38529760169581
19537532.229911895226-27.5499591543271569.320047259101-4.77008810477378
20552550.384301925856-16.0933502158166569.70904828996-1.61569807414367
21563567.372034518453-11.4700838392726570.098049320824.37203451845301
22575583.256201510061-2.79674555814926569.5405440480888.25620151006137
23580587.328473958993.68848726565402568.9830387753567.32847395898966
24575585.160452179023-3.02577823304361567.86532605402110.1604521790227
25558557.784463369429-8.53207670211424566.747613332686-0.21553663057125
26564560.0763863944693.04741061054521564.876202994986-3.92361360553093
27581577.7016426955221.2935646471938563.004792657286-3.29835730447974
28597597.33970877958636.8092971998294559.8509940205840.339708779586431
29587588.47777778700728.8250268291106556.6971953838831.47777778700663
30536543.729798771997-24.1957973750738552.4659986030777.72979877199691
31524527.315157332056-27.5499591543271548.2348018222713.31515733205606
32537546.858147194001-16.0933502158166543.2352030218169.85814719400071
33536545.234479617912-11.4700838392726538.2356042213619.2344796179118
34533535.993267029374-2.79674555814926532.8034785287762.99326702937367
35528524.9401598981563.68848726565402527.37135283619-3.05984010184443
36516512.827354855858-3.02577823304361522.198423377185-3.1726451441416
37502495.506582783934-8.53207670211424517.02549391818-6.49341721606578
38506495.9200501047673.04741061054521513.032539284688-10.079949895233
39518505.66685070161121.2935646471938509.039584651195-12.3331492983893
40534524.21479339419736.8092971998294506.975909405974-9.78520660580318
41528522.26273901013728.8250268291106504.912234160752-5.73726098986276
42478475.23715969494-24.1957973750738504.958637680134-2.76284030505997
43469460.544917954812-27.5499591543271505.005041199515-8.45508204518825
44490489.504309858667-16.0933502158166506.589040357149-0.49569014133283
45493489.297044324489-11.4700838392726508.173039514783-3.70295567551085
46508508.311941953123-2.79674555814926510.4848036050260.31194195312338
47517517.5149450390783.68848726565402512.7965676952680.514945039077702
48514515.653869791267-3.02577823304361515.3719084417771.65386979126674
49510510.584827513829-8.53207670211424517.9472491882850.584827513828827
50527530.0742356476173.04741061054521520.8783537418373.07423564761746
51542538.89697705741721.2935646471938523.809458295389-3.10302294258304
52565566.06400560251136.8092971998294527.1266971976591.06400560251143
53555550.7310370709628.8250268291106530.443936099929-4.26896292903984
54499487.776124786328-24.1957973750738534.419672588745-11.2238752136716
55511511.154550076765-27.5499591543271538.3954090775620.154550076765418
56526524.597374194686-16.0933502158166543.495976021131-1.4026258053143
57532526.873540874572-11.4700838392726548.5965429647-5.12645912542757
58549546.392643162825-2.79674555814926554.404102395324-2.60735683717462
59561558.0998509083983.68848726565402560.211661825948-2.90014909160163
60557551.126341628078-3.02577823304361565.899436604966-5.87365837192215
61566568.94486531813-8.53207670211424571.5872113839842.9448653181305
62588597.4653945934923.04741061054521575.4871947959639.46539459349162
63620639.31925714486421.2935646471938579.38717820794219.3192571448636
64626632.12819023170436.8092971998294583.0625125684676.12819023170368
65620624.43712624189828.8250268291106586.7378469289924.43712624189789
66573579.912152025754-24.1957973750738590.283645349326.91215202575404
67573579.720515384679-27.5499591543271593.8294437696486.72051538467883
68574566.960248644407-16.0933502158166597.13310157141-7.03975135559324
69580571.033324466101-11.4700838392726600.436759373171-8.96667553389887
70590579.283182738306-2.79674555814926603.513562819843-10.7168172616937



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')