Free Statistics

of Irreproducible Research!

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 computationSat, 26 Nov 2011 10:28:32 -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/Nov/26/t1322321405zo3h5s3sizfoka7.htm/, Retrieved Mon, 30 Jan 2023 02:24:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147419, Retrieved Mon, 30 Jan 2023 02:24:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Decomposition by Loess] [WS8 Loes] [2011-11-26 15:28:32] [3208276753335f0b81f0071d45b8bac9] [Current]
Feedback Forum

Post a new message
Dataseries X:
37
30
47
35
30
43
82
40
47
19
52
136
80
42
54
66
81
63
137
72
107
58
36
52
79
77
54
84
48
96
83
66
61
53
30
74
69
59
42
65
70
100
63
105
82
81
75
102
121
98
76
77
63
37
35
23
40
29
37
51
20
28
13
22
25
13
16
13
16
17
9
17
25
14
8
7
10
7
10
3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147419&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147419&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147419&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'Gertrude Mary Cox' @ cox.wessa.net







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal801081
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 801 & 0 & 81 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147419&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]801[/C][C]0[/C][C]81[/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=147419&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147419&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
Seasonal801081
Trend1912
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
13732.481100976716910.823930242139730.6949687811434-4.51889902328312
23027.2985899953377-1.0331025132440933.7345125179064-2.70141000466228
34765.973235602222-8.7472918568913236.774056254669318.973235602222
43529.94096977101420.21106312363380739.847967105352-5.05903022898576
53020.9087257445871-3.8306037006216742.9218779560346-9.09127425541292
64338.84603635239861.1688093152146845.9851543323867-4.1539636476014
782103.78337872449411.168190566766949.048430708738821.7833787244942
84031.1818742447283-3.1936594055994352.0117851608711-8.81812575527169
94735.89894206558023.1259183214164354.9751396130034-11.1010579344198
1019-7.09430739591033-12.424126159358657.5184335552689-26.0943073959103
115258.9124544564098-14.974181953944260.06172749753446.91245445640979
12136191.18087221714517.705047364427763.114080418427155.1808722171452
138083.009636418540410.823930242139766.16643333931993.00963641854044
144215.4866052460361-1.0331025132440969.546497267208-26.5133947539639
155443.8207306617952-8.7472918568913272.9265611950961-10.1792693382048
166657.28169394307630.21106312363380774.5072429332899-8.71830605692369
178189.742679029138-3.8306037006216776.08792467148378.74267902913799
186349.47409802912791.1688093152146875.3570926556574-13.5259019708721
19137188.20554879340211.168190566766974.626260639831151.205548793402
207272.9393615229569-3.1936594055994374.25429788264250.939361522956901
21107136.991746553133.1259183214164373.88233512545429.9917465531296
225855.0195757740471-12.424126159358673.4045503853115-2.98042422595289
233614.0474163087752-14.974181953944272.926765645169-21.9525836912248
245214.557072562603917.705047364427771.7378800729685-37.4429274373961
257976.627075257092410.823930242139770.5489945007679-2.37292474290764
267785.6879770932907-1.0331025132440969.34512541995348.68797709329071
275448.6060355177525-8.7472918568913268.1412563391388-5.39396448224749
2884100.0980280850920.21106312363380767.690908791274616.0980280850916
294832.5900424572114-3.8306037006216767.2405612434103-15.4099575427886
3096123.9198812783441.1688093152146866.911309406441127.9198812783442
318388.249751863761111.168190566766966.58205756947195.24975186376115
326669.7518148486831-3.1936594055994365.44184455691633.75181484868311
336154.57245013422293.1259183214164364.3016315443607-6.42754986577714
345354.9904373554191-12.424126159358663.43368880393951.9904373554191
353012.408435890426-14.974181953944262.5657460635183-17.591564109574
367467.560575786728317.705047364427762.7343768488441-6.43942421327175
376964.273062123690410.823930242139762.9030076341699-4.72693787630963
385954.4760980645201-1.0331025132440964.557004448724-4.52390193547992
394226.5362905936132-8.7472918568913266.2110012632781-15.4637094063868
406560.72003184796740.21106312363380769.0689050283988-4.27996815203261
417071.9037949071022-3.8306037006216771.92680879351951.90379490710215
42100123.4496013076161.1688093152146875.38158937716923.4496013076163
436335.995439472414611.168190566766978.8363699608185-27.0045605275854
44105131.333607920214-3.1936594055994381.86005148538526.3336079202144
458275.99034866863213.1259183214164384.8837330099515-6.00965133136791
468188.5642966485719-12.424126159358685.85982951078677.56429664857191
477578.1382559423223-14.974181953944286.83592601162193.13825594232235
48102101.87112579640617.705047364427784.4238268391667-0.128874203594307
49121149.16434209114910.823930242139782.011727666711428.1643420911489
5098119.633854322911-1.0331025132440977.399248190332921.6338543229112
517687.9605231429369-8.7472918568913272.786768713954411.9605231429369
527786.21030696679720.21106312363380767.5786299095699.21030696679722
536367.4601125954381-3.8306037006216762.37049110518364.4601125954381
543716.11052220533331.1688093152146856.720668479452-20.8894777946667
55357.7609635795125611.168190566766951.0708458537205-27.2390364204874
56233.69281484509672-3.1936594055994345.5008445605027-19.3071851549033
574036.94323841129873.1259183214164339.9308432672849-3.05676158870133
582934.3050316747586-12.424126159358636.11909448459995.30503167475864
593756.6668362520292-14.974181953944232.30734570191519.6668362520292
605154.202942307529217.705047364427730.09201032804313.20294230752922
61201.2993948036890510.823930242139727.8766749541713-18.7006051963109
622830.9885179585308-1.0331025132440926.04458455471332.9885179585308
631310.534797701636-8.7472918568913224.2124941552553-2.46520229836402
642221.37629810152460.21106312363380722.4126387748416-0.623701898475399
652533.2178203061938-3.8306037006216720.61278339442788.21782030619383
66135.558673270346311.1688093152146819.272517414439-7.44132672965369
67162.8995579987829111.168190566766917.9322514344502-13.1004420012171
681312.1124425198698-3.1936594055994317.0812168857296-0.887557480130155
691612.64389934157463.1259183214164316.230182337009-3.35610065842544
701730.9585327255352-12.424126159358615.465593433823413.9585327255352
71918.2731774233064-14.974181953944214.70100453063789.27317742330637
72172.5662050227736817.705047364427713.7287476127987-14.4337949772263
732526.419579062900810.823930242139712.75649069495951.41957906290083
741417.3245134745749-1.0331025132440911.70858903866923.32451347457485
75814.0866044745123-8.7472918568913210.6606873823796.08660447451231
7674.205452331066730.2110631236338079.58348454529946-2.79454766893327
771015.3243219924018-3.830603700621678.506281708219915.32432199240176
7875.457199842586571.168809315214687.37399084219875-1.54280015741343
79102.5901094570554911.16819056676696.24169997617758-7.40989054294451
8034.13708965813065-3.193659405599435.056569747468781.13708965813065

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 37 & 32.4811009767169 & 10.8239302421397 & 30.6949687811434 & -4.51889902328312 \tabularnewline
2 & 30 & 27.2985899953377 & -1.03310251324409 & 33.7345125179064 & -2.70141000466228 \tabularnewline
3 & 47 & 65.973235602222 & -8.74729185689132 & 36.7740562546693 & 18.973235602222 \tabularnewline
4 & 35 & 29.9409697710142 & 0.211063123633807 & 39.847967105352 & -5.05903022898576 \tabularnewline
5 & 30 & 20.9087257445871 & -3.83060370062167 & 42.9218779560346 & -9.09127425541292 \tabularnewline
6 & 43 & 38.8460363523986 & 1.16880931521468 & 45.9851543323867 & -4.1539636476014 \tabularnewline
7 & 82 & 103.783378724494 & 11.1681905667669 & 49.0484307087388 & 21.7833787244942 \tabularnewline
8 & 40 & 31.1818742447283 & -3.19365940559943 & 52.0117851608711 & -8.81812575527169 \tabularnewline
9 & 47 & 35.8989420655802 & 3.12591832141643 & 54.9751396130034 & -11.1010579344198 \tabularnewline
10 & 19 & -7.09430739591033 & -12.4241261593586 & 57.5184335552689 & -26.0943073959103 \tabularnewline
11 & 52 & 58.9124544564098 & -14.9741819539442 & 60.0617274975344 & 6.91245445640979 \tabularnewline
12 & 136 & 191.180872217145 & 17.7050473644277 & 63.1140804184271 & 55.1808722171452 \tabularnewline
13 & 80 & 83.0096364185404 & 10.8239302421397 & 66.1664333393199 & 3.00963641854044 \tabularnewline
14 & 42 & 15.4866052460361 & -1.03310251324409 & 69.546497267208 & -26.5133947539639 \tabularnewline
15 & 54 & 43.8207306617952 & -8.74729185689132 & 72.9265611950961 & -10.1792693382048 \tabularnewline
16 & 66 & 57.2816939430763 & 0.211063123633807 & 74.5072429332899 & -8.71830605692369 \tabularnewline
17 & 81 & 89.742679029138 & -3.83060370062167 & 76.0879246714837 & 8.74267902913799 \tabularnewline
18 & 63 & 49.4740980291279 & 1.16880931521468 & 75.3570926556574 & -13.5259019708721 \tabularnewline
19 & 137 & 188.205548793402 & 11.1681905667669 & 74.6262606398311 & 51.205548793402 \tabularnewline
20 & 72 & 72.9393615229569 & -3.19365940559943 & 74.2542978826425 & 0.939361522956901 \tabularnewline
21 & 107 & 136.99174655313 & 3.12591832141643 & 73.882335125454 & 29.9917465531296 \tabularnewline
22 & 58 & 55.0195757740471 & -12.4241261593586 & 73.4045503853115 & -2.98042422595289 \tabularnewline
23 & 36 & 14.0474163087752 & -14.9741819539442 & 72.926765645169 & -21.9525836912248 \tabularnewline
24 & 52 & 14.5570725626039 & 17.7050473644277 & 71.7378800729685 & -37.4429274373961 \tabularnewline
25 & 79 & 76.6270752570924 & 10.8239302421397 & 70.5489945007679 & -2.37292474290764 \tabularnewline
26 & 77 & 85.6879770932907 & -1.03310251324409 & 69.3451254199534 & 8.68797709329071 \tabularnewline
27 & 54 & 48.6060355177525 & -8.74729185689132 & 68.1412563391388 & -5.39396448224749 \tabularnewline
28 & 84 & 100.098028085092 & 0.211063123633807 & 67.6909087912746 & 16.0980280850916 \tabularnewline
29 & 48 & 32.5900424572114 & -3.83060370062167 & 67.2405612434103 & -15.4099575427886 \tabularnewline
30 & 96 & 123.919881278344 & 1.16880931521468 & 66.9113094064411 & 27.9198812783442 \tabularnewline
31 & 83 & 88.2497518637611 & 11.1681905667669 & 66.5820575694719 & 5.24975186376115 \tabularnewline
32 & 66 & 69.7518148486831 & -3.19365940559943 & 65.4418445569163 & 3.75181484868311 \tabularnewline
33 & 61 & 54.5724501342229 & 3.12591832141643 & 64.3016315443607 & -6.42754986577714 \tabularnewline
34 & 53 & 54.9904373554191 & -12.4241261593586 & 63.4336888039395 & 1.9904373554191 \tabularnewline
35 & 30 & 12.408435890426 & -14.9741819539442 & 62.5657460635183 & -17.591564109574 \tabularnewline
36 & 74 & 67.5605757867283 & 17.7050473644277 & 62.7343768488441 & -6.43942421327175 \tabularnewline
37 & 69 & 64.2730621236904 & 10.8239302421397 & 62.9030076341699 & -4.72693787630963 \tabularnewline
38 & 59 & 54.4760980645201 & -1.03310251324409 & 64.557004448724 & -4.52390193547992 \tabularnewline
39 & 42 & 26.5362905936132 & -8.74729185689132 & 66.2110012632781 & -15.4637094063868 \tabularnewline
40 & 65 & 60.7200318479674 & 0.211063123633807 & 69.0689050283988 & -4.27996815203261 \tabularnewline
41 & 70 & 71.9037949071022 & -3.83060370062167 & 71.9268087935195 & 1.90379490710215 \tabularnewline
42 & 100 & 123.449601307616 & 1.16880931521468 & 75.381589377169 & 23.4496013076163 \tabularnewline
43 & 63 & 35.9954394724146 & 11.1681905667669 & 78.8363699608185 & -27.0045605275854 \tabularnewline
44 & 105 & 131.333607920214 & -3.19365940559943 & 81.860051485385 & 26.3336079202144 \tabularnewline
45 & 82 & 75.9903486686321 & 3.12591832141643 & 84.8837330099515 & -6.00965133136791 \tabularnewline
46 & 81 & 88.5642966485719 & -12.4241261593586 & 85.8598295107867 & 7.56429664857191 \tabularnewline
47 & 75 & 78.1382559423223 & -14.9741819539442 & 86.8359260116219 & 3.13825594232235 \tabularnewline
48 & 102 & 101.871125796406 & 17.7050473644277 & 84.4238268391667 & -0.128874203594307 \tabularnewline
49 & 121 & 149.164342091149 & 10.8239302421397 & 82.0117276667114 & 28.1643420911489 \tabularnewline
50 & 98 & 119.633854322911 & -1.03310251324409 & 77.3992481903329 & 21.6338543229112 \tabularnewline
51 & 76 & 87.9605231429369 & -8.74729185689132 & 72.7867687139544 & 11.9605231429369 \tabularnewline
52 & 77 & 86.2103069667972 & 0.211063123633807 & 67.578629909569 & 9.21030696679722 \tabularnewline
53 & 63 & 67.4601125954381 & -3.83060370062167 & 62.3704911051836 & 4.4601125954381 \tabularnewline
54 & 37 & 16.1105222053333 & 1.16880931521468 & 56.720668479452 & -20.8894777946667 \tabularnewline
55 & 35 & 7.76096357951256 & 11.1681905667669 & 51.0708458537205 & -27.2390364204874 \tabularnewline
56 & 23 & 3.69281484509672 & -3.19365940559943 & 45.5008445605027 & -19.3071851549033 \tabularnewline
57 & 40 & 36.9432384112987 & 3.12591832141643 & 39.9308432672849 & -3.05676158870133 \tabularnewline
58 & 29 & 34.3050316747586 & -12.4241261593586 & 36.1190944845999 & 5.30503167475864 \tabularnewline
59 & 37 & 56.6668362520292 & -14.9741819539442 & 32.307345701915 & 19.6668362520292 \tabularnewline
60 & 51 & 54.2029423075292 & 17.7050473644277 & 30.0920103280431 & 3.20294230752922 \tabularnewline
61 & 20 & 1.29939480368905 & 10.8239302421397 & 27.8766749541713 & -18.7006051963109 \tabularnewline
62 & 28 & 30.9885179585308 & -1.03310251324409 & 26.0445845547133 & 2.9885179585308 \tabularnewline
63 & 13 & 10.534797701636 & -8.74729185689132 & 24.2124941552553 & -2.46520229836402 \tabularnewline
64 & 22 & 21.3762981015246 & 0.211063123633807 & 22.4126387748416 & -0.623701898475399 \tabularnewline
65 & 25 & 33.2178203061938 & -3.83060370062167 & 20.6127833944278 & 8.21782030619383 \tabularnewline
66 & 13 & 5.55867327034631 & 1.16880931521468 & 19.272517414439 & -7.44132672965369 \tabularnewline
67 & 16 & 2.89955799878291 & 11.1681905667669 & 17.9322514344502 & -13.1004420012171 \tabularnewline
68 & 13 & 12.1124425198698 & -3.19365940559943 & 17.0812168857296 & -0.887557480130155 \tabularnewline
69 & 16 & 12.6438993415746 & 3.12591832141643 & 16.230182337009 & -3.35610065842544 \tabularnewline
70 & 17 & 30.9585327255352 & -12.4241261593586 & 15.4655934338234 & 13.9585327255352 \tabularnewline
71 & 9 & 18.2731774233064 & -14.9741819539442 & 14.7010045306378 & 9.27317742330637 \tabularnewline
72 & 17 & 2.56620502277368 & 17.7050473644277 & 13.7287476127987 & -14.4337949772263 \tabularnewline
73 & 25 & 26.4195790629008 & 10.8239302421397 & 12.7564906949595 & 1.41957906290083 \tabularnewline
74 & 14 & 17.3245134745749 & -1.03310251324409 & 11.7085890386692 & 3.32451347457485 \tabularnewline
75 & 8 & 14.0866044745123 & -8.74729185689132 & 10.660687382379 & 6.08660447451231 \tabularnewline
76 & 7 & 4.20545233106673 & 0.211063123633807 & 9.58348454529946 & -2.79454766893327 \tabularnewline
77 & 10 & 15.3243219924018 & -3.83060370062167 & 8.50628170821991 & 5.32432199240176 \tabularnewline
78 & 7 & 5.45719984258657 & 1.16880931521468 & 7.37399084219875 & -1.54280015741343 \tabularnewline
79 & 10 & 2.59010945705549 & 11.1681905667669 & 6.24169997617758 & -7.40989054294451 \tabularnewline
80 & 3 & 4.13708965813065 & -3.19365940559943 & 5.05656974746878 & 1.13708965813065 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147419&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]37[/C][C]32.4811009767169[/C][C]10.8239302421397[/C][C]30.6949687811434[/C][C]-4.51889902328312[/C][/ROW]
[ROW][C]2[/C][C]30[/C][C]27.2985899953377[/C][C]-1.03310251324409[/C][C]33.7345125179064[/C][C]-2.70141000466228[/C][/ROW]
[ROW][C]3[/C][C]47[/C][C]65.973235602222[/C][C]-8.74729185689132[/C][C]36.7740562546693[/C][C]18.973235602222[/C][/ROW]
[ROW][C]4[/C][C]35[/C][C]29.9409697710142[/C][C]0.211063123633807[/C][C]39.847967105352[/C][C]-5.05903022898576[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]20.9087257445871[/C][C]-3.83060370062167[/C][C]42.9218779560346[/C][C]-9.09127425541292[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]38.8460363523986[/C][C]1.16880931521468[/C][C]45.9851543323867[/C][C]-4.1539636476014[/C][/ROW]
[ROW][C]7[/C][C]82[/C][C]103.783378724494[/C][C]11.1681905667669[/C][C]49.0484307087388[/C][C]21.7833787244942[/C][/ROW]
[ROW][C]8[/C][C]40[/C][C]31.1818742447283[/C][C]-3.19365940559943[/C][C]52.0117851608711[/C][C]-8.81812575527169[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]35.8989420655802[/C][C]3.12591832141643[/C][C]54.9751396130034[/C][C]-11.1010579344198[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]-7.09430739591033[/C][C]-12.4241261593586[/C][C]57.5184335552689[/C][C]-26.0943073959103[/C][/ROW]
[ROW][C]11[/C][C]52[/C][C]58.9124544564098[/C][C]-14.9741819539442[/C][C]60.0617274975344[/C][C]6.91245445640979[/C][/ROW]
[ROW][C]12[/C][C]136[/C][C]191.180872217145[/C][C]17.7050473644277[/C][C]63.1140804184271[/C][C]55.1808722171452[/C][/ROW]
[ROW][C]13[/C][C]80[/C][C]83.0096364185404[/C][C]10.8239302421397[/C][C]66.1664333393199[/C][C]3.00963641854044[/C][/ROW]
[ROW][C]14[/C][C]42[/C][C]15.4866052460361[/C][C]-1.03310251324409[/C][C]69.546497267208[/C][C]-26.5133947539639[/C][/ROW]
[ROW][C]15[/C][C]54[/C][C]43.8207306617952[/C][C]-8.74729185689132[/C][C]72.9265611950961[/C][C]-10.1792693382048[/C][/ROW]
[ROW][C]16[/C][C]66[/C][C]57.2816939430763[/C][C]0.211063123633807[/C][C]74.5072429332899[/C][C]-8.71830605692369[/C][/ROW]
[ROW][C]17[/C][C]81[/C][C]89.742679029138[/C][C]-3.83060370062167[/C][C]76.0879246714837[/C][C]8.74267902913799[/C][/ROW]
[ROW][C]18[/C][C]63[/C][C]49.4740980291279[/C][C]1.16880931521468[/C][C]75.3570926556574[/C][C]-13.5259019708721[/C][/ROW]
[ROW][C]19[/C][C]137[/C][C]188.205548793402[/C][C]11.1681905667669[/C][C]74.6262606398311[/C][C]51.205548793402[/C][/ROW]
[ROW][C]20[/C][C]72[/C][C]72.9393615229569[/C][C]-3.19365940559943[/C][C]74.2542978826425[/C][C]0.939361522956901[/C][/ROW]
[ROW][C]21[/C][C]107[/C][C]136.99174655313[/C][C]3.12591832141643[/C][C]73.882335125454[/C][C]29.9917465531296[/C][/ROW]
[ROW][C]22[/C][C]58[/C][C]55.0195757740471[/C][C]-12.4241261593586[/C][C]73.4045503853115[/C][C]-2.98042422595289[/C][/ROW]
[ROW][C]23[/C][C]36[/C][C]14.0474163087752[/C][C]-14.9741819539442[/C][C]72.926765645169[/C][C]-21.9525836912248[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]14.5570725626039[/C][C]17.7050473644277[/C][C]71.7378800729685[/C][C]-37.4429274373961[/C][/ROW]
[ROW][C]25[/C][C]79[/C][C]76.6270752570924[/C][C]10.8239302421397[/C][C]70.5489945007679[/C][C]-2.37292474290764[/C][/ROW]
[ROW][C]26[/C][C]77[/C][C]85.6879770932907[/C][C]-1.03310251324409[/C][C]69.3451254199534[/C][C]8.68797709329071[/C][/ROW]
[ROW][C]27[/C][C]54[/C][C]48.6060355177525[/C][C]-8.74729185689132[/C][C]68.1412563391388[/C][C]-5.39396448224749[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]100.098028085092[/C][C]0.211063123633807[/C][C]67.6909087912746[/C][C]16.0980280850916[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]32.5900424572114[/C][C]-3.83060370062167[/C][C]67.2405612434103[/C][C]-15.4099575427886[/C][/ROW]
[ROW][C]30[/C][C]96[/C][C]123.919881278344[/C][C]1.16880931521468[/C][C]66.9113094064411[/C][C]27.9198812783442[/C][/ROW]
[ROW][C]31[/C][C]83[/C][C]88.2497518637611[/C][C]11.1681905667669[/C][C]66.5820575694719[/C][C]5.24975186376115[/C][/ROW]
[ROW][C]32[/C][C]66[/C][C]69.7518148486831[/C][C]-3.19365940559943[/C][C]65.4418445569163[/C][C]3.75181484868311[/C][/ROW]
[ROW][C]33[/C][C]61[/C][C]54.5724501342229[/C][C]3.12591832141643[/C][C]64.3016315443607[/C][C]-6.42754986577714[/C][/ROW]
[ROW][C]34[/C][C]53[/C][C]54.9904373554191[/C][C]-12.4241261593586[/C][C]63.4336888039395[/C][C]1.9904373554191[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]12.408435890426[/C][C]-14.9741819539442[/C][C]62.5657460635183[/C][C]-17.591564109574[/C][/ROW]
[ROW][C]36[/C][C]74[/C][C]67.5605757867283[/C][C]17.7050473644277[/C][C]62.7343768488441[/C][C]-6.43942421327175[/C][/ROW]
[ROW][C]37[/C][C]69[/C][C]64.2730621236904[/C][C]10.8239302421397[/C][C]62.9030076341699[/C][C]-4.72693787630963[/C][/ROW]
[ROW][C]38[/C][C]59[/C][C]54.4760980645201[/C][C]-1.03310251324409[/C][C]64.557004448724[/C][C]-4.52390193547992[/C][/ROW]
[ROW][C]39[/C][C]42[/C][C]26.5362905936132[/C][C]-8.74729185689132[/C][C]66.2110012632781[/C][C]-15.4637094063868[/C][/ROW]
[ROW][C]40[/C][C]65[/C][C]60.7200318479674[/C][C]0.211063123633807[/C][C]69.0689050283988[/C][C]-4.27996815203261[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]71.9037949071022[/C][C]-3.83060370062167[/C][C]71.9268087935195[/C][C]1.90379490710215[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]123.449601307616[/C][C]1.16880931521468[/C][C]75.381589377169[/C][C]23.4496013076163[/C][/ROW]
[ROW][C]43[/C][C]63[/C][C]35.9954394724146[/C][C]11.1681905667669[/C][C]78.8363699608185[/C][C]-27.0045605275854[/C][/ROW]
[ROW][C]44[/C][C]105[/C][C]131.333607920214[/C][C]-3.19365940559943[/C][C]81.860051485385[/C][C]26.3336079202144[/C][/ROW]
[ROW][C]45[/C][C]82[/C][C]75.9903486686321[/C][C]3.12591832141643[/C][C]84.8837330099515[/C][C]-6.00965133136791[/C][/ROW]
[ROW][C]46[/C][C]81[/C][C]88.5642966485719[/C][C]-12.4241261593586[/C][C]85.8598295107867[/C][C]7.56429664857191[/C][/ROW]
[ROW][C]47[/C][C]75[/C][C]78.1382559423223[/C][C]-14.9741819539442[/C][C]86.8359260116219[/C][C]3.13825594232235[/C][/ROW]
[ROW][C]48[/C][C]102[/C][C]101.871125796406[/C][C]17.7050473644277[/C][C]84.4238268391667[/C][C]-0.128874203594307[/C][/ROW]
[ROW][C]49[/C][C]121[/C][C]149.164342091149[/C][C]10.8239302421397[/C][C]82.0117276667114[/C][C]28.1643420911489[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]119.633854322911[/C][C]-1.03310251324409[/C][C]77.3992481903329[/C][C]21.6338543229112[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]87.9605231429369[/C][C]-8.74729185689132[/C][C]72.7867687139544[/C][C]11.9605231429369[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]86.2103069667972[/C][C]0.211063123633807[/C][C]67.578629909569[/C][C]9.21030696679722[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]67.4601125954381[/C][C]-3.83060370062167[/C][C]62.3704911051836[/C][C]4.4601125954381[/C][/ROW]
[ROW][C]54[/C][C]37[/C][C]16.1105222053333[/C][C]1.16880931521468[/C][C]56.720668479452[/C][C]-20.8894777946667[/C][/ROW]
[ROW][C]55[/C][C]35[/C][C]7.76096357951256[/C][C]11.1681905667669[/C][C]51.0708458537205[/C][C]-27.2390364204874[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]3.69281484509672[/C][C]-3.19365940559943[/C][C]45.5008445605027[/C][C]-19.3071851549033[/C][/ROW]
[ROW][C]57[/C][C]40[/C][C]36.9432384112987[/C][C]3.12591832141643[/C][C]39.9308432672849[/C][C]-3.05676158870133[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.3050316747586[/C][C]-12.4241261593586[/C][C]36.1190944845999[/C][C]5.30503167475864[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]56.6668362520292[/C][C]-14.9741819539442[/C][C]32.307345701915[/C][C]19.6668362520292[/C][/ROW]
[ROW][C]60[/C][C]51[/C][C]54.2029423075292[/C][C]17.7050473644277[/C][C]30.0920103280431[/C][C]3.20294230752922[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]1.29939480368905[/C][C]10.8239302421397[/C][C]27.8766749541713[/C][C]-18.7006051963109[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]30.9885179585308[/C][C]-1.03310251324409[/C][C]26.0445845547133[/C][C]2.9885179585308[/C][/ROW]
[ROW][C]63[/C][C]13[/C][C]10.534797701636[/C][C]-8.74729185689132[/C][C]24.2124941552553[/C][C]-2.46520229836402[/C][/ROW]
[ROW][C]64[/C][C]22[/C][C]21.3762981015246[/C][C]0.211063123633807[/C][C]22.4126387748416[/C][C]-0.623701898475399[/C][/ROW]
[ROW][C]65[/C][C]25[/C][C]33.2178203061938[/C][C]-3.83060370062167[/C][C]20.6127833944278[/C][C]8.21782030619383[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]5.55867327034631[/C][C]1.16880931521468[/C][C]19.272517414439[/C][C]-7.44132672965369[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]2.89955799878291[/C][C]11.1681905667669[/C][C]17.9322514344502[/C][C]-13.1004420012171[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]12.1124425198698[/C][C]-3.19365940559943[/C][C]17.0812168857296[/C][C]-0.887557480130155[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]12.6438993415746[/C][C]3.12591832141643[/C][C]16.230182337009[/C][C]-3.35610065842544[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]30.9585327255352[/C][C]-12.4241261593586[/C][C]15.4655934338234[/C][C]13.9585327255352[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]18.2731774233064[/C][C]-14.9741819539442[/C][C]14.7010045306378[/C][C]9.27317742330637[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]2.56620502277368[/C][C]17.7050473644277[/C][C]13.7287476127987[/C][C]-14.4337949772263[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]26.4195790629008[/C][C]10.8239302421397[/C][C]12.7564906949595[/C][C]1.41957906290083[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]17.3245134745749[/C][C]-1.03310251324409[/C][C]11.7085890386692[/C][C]3.32451347457485[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]14.0866044745123[/C][C]-8.74729185689132[/C][C]10.660687382379[/C][C]6.08660447451231[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]4.20545233106673[/C][C]0.211063123633807[/C][C]9.58348454529946[/C][C]-2.79454766893327[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]15.3243219924018[/C][C]-3.83060370062167[/C][C]8.50628170821991[/C][C]5.32432199240176[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]5.45719984258657[/C][C]1.16880931521468[/C][C]7.37399084219875[/C][C]-1.54280015741343[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]2.59010945705549[/C][C]11.1681905667669[/C][C]6.24169997617758[/C][C]-7.40989054294451[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]4.13708965813065[/C][C]-3.19365940559943[/C][C]5.05656974746878[/C][C]1.13708965813065[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147419&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147419&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
13732.481100976716910.823930242139730.6949687811434-4.51889902328312
23027.2985899953377-1.0331025132440933.7345125179064-2.70141000466228
34765.973235602222-8.7472918568913236.774056254669318.973235602222
43529.94096977101420.21106312363380739.847967105352-5.05903022898576
53020.9087257445871-3.8306037006216742.9218779560346-9.09127425541292
64338.84603635239861.1688093152146845.9851543323867-4.1539636476014
782103.78337872449411.168190566766949.048430708738821.7833787244942
84031.1818742447283-3.1936594055994352.0117851608711-8.81812575527169
94735.89894206558023.1259183214164354.9751396130034-11.1010579344198
1019-7.09430739591033-12.424126159358657.5184335552689-26.0943073959103
115258.9124544564098-14.974181953944260.06172749753446.91245445640979
12136191.18087221714517.705047364427763.114080418427155.1808722171452
138083.009636418540410.823930242139766.16643333931993.00963641854044
144215.4866052460361-1.0331025132440969.546497267208-26.5133947539639
155443.8207306617952-8.7472918568913272.9265611950961-10.1792693382048
166657.28169394307630.21106312363380774.5072429332899-8.71830605692369
178189.742679029138-3.8306037006216776.08792467148378.74267902913799
186349.47409802912791.1688093152146875.3570926556574-13.5259019708721
19137188.20554879340211.168190566766974.626260639831151.205548793402
207272.9393615229569-3.1936594055994374.25429788264250.939361522956901
21107136.991746553133.1259183214164373.88233512545429.9917465531296
225855.0195757740471-12.424126159358673.4045503853115-2.98042422595289
233614.0474163087752-14.974181953944272.926765645169-21.9525836912248
245214.557072562603917.705047364427771.7378800729685-37.4429274373961
257976.627075257092410.823930242139770.5489945007679-2.37292474290764
267785.6879770932907-1.0331025132440969.34512541995348.68797709329071
275448.6060355177525-8.7472918568913268.1412563391388-5.39396448224749
2884100.0980280850920.21106312363380767.690908791274616.0980280850916
294832.5900424572114-3.8306037006216767.2405612434103-15.4099575427886
3096123.9198812783441.1688093152146866.911309406441127.9198812783442
318388.249751863761111.168190566766966.58205756947195.24975186376115
326669.7518148486831-3.1936594055994365.44184455691633.75181484868311
336154.57245013422293.1259183214164364.3016315443607-6.42754986577714
345354.9904373554191-12.424126159358663.43368880393951.9904373554191
353012.408435890426-14.974181953944262.5657460635183-17.591564109574
367467.560575786728317.705047364427762.7343768488441-6.43942421327175
376964.273062123690410.823930242139762.9030076341699-4.72693787630963
385954.4760980645201-1.0331025132440964.557004448724-4.52390193547992
394226.5362905936132-8.7472918568913266.2110012632781-15.4637094063868
406560.72003184796740.21106312363380769.0689050283988-4.27996815203261
417071.9037949071022-3.8306037006216771.92680879351951.90379490710215
42100123.4496013076161.1688093152146875.38158937716923.4496013076163
436335.995439472414611.168190566766978.8363699608185-27.0045605275854
44105131.333607920214-3.1936594055994381.86005148538526.3336079202144
458275.99034866863213.1259183214164384.8837330099515-6.00965133136791
468188.5642966485719-12.424126159358685.85982951078677.56429664857191
477578.1382559423223-14.974181953944286.83592601162193.13825594232235
48102101.87112579640617.705047364427784.4238268391667-0.128874203594307
49121149.16434209114910.823930242139782.011727666711428.1643420911489
5098119.633854322911-1.0331025132440977.399248190332921.6338543229112
517687.9605231429369-8.7472918568913272.786768713954411.9605231429369
527786.21030696679720.21106312363380767.5786299095699.21030696679722
536367.4601125954381-3.8306037006216762.37049110518364.4601125954381
543716.11052220533331.1688093152146856.720668479452-20.8894777946667
55357.7609635795125611.168190566766951.0708458537205-27.2390364204874
56233.69281484509672-3.1936594055994345.5008445605027-19.3071851549033
574036.94323841129873.1259183214164339.9308432672849-3.05676158870133
582934.3050316747586-12.424126159358636.11909448459995.30503167475864
593756.6668362520292-14.974181953944232.30734570191519.6668362520292
605154.202942307529217.705047364427730.09201032804313.20294230752922
61201.2993948036890510.823930242139727.8766749541713-18.7006051963109
622830.9885179585308-1.0331025132440926.04458455471332.9885179585308
631310.534797701636-8.7472918568913224.2124941552553-2.46520229836402
642221.37629810152460.21106312363380722.4126387748416-0.623701898475399
652533.2178203061938-3.8306037006216720.61278339442788.21782030619383
66135.558673270346311.1688093152146819.272517414439-7.44132672965369
67162.8995579987829111.168190566766917.9322514344502-13.1004420012171
681312.1124425198698-3.1936594055994317.0812168857296-0.887557480130155
691612.64389934157463.1259183214164316.230182337009-3.35610065842544
701730.9585327255352-12.424126159358615.465593433823413.9585327255352
71918.2731774233064-14.974181953944214.70100453063789.27317742330637
72172.5662050227736817.705047364427713.7287476127987-14.4337949772263
732526.419579062900810.823930242139712.75649069495951.41957906290083
741417.3245134745749-1.0331025132440911.70858903866923.32451347457485
75814.0866044745123-8.7472918568913210.6606873823796.08660447451231
7674.205452331066730.2110631236338079.58348454529946-2.79454766893327
771015.3243219924018-3.830603700621678.506281708219915.32432199240176
7875.457199842586571.168809315214687.37399084219875-1.54280015741343
79102.5901094570554911.16819056676696.24169997617758-7.40989054294451
8034.13708965813065-3.193659405599435.056569747468781.13708965813065



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')