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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 computationTue, 18 Dec 2012 14:44:38 -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/2012/Dec/18/t1355860170eu3wmycrayuzdn9.htm/, Retrieved Thu, 28 Mar 2024 23:56:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201617, Retrieved Thu, 28 Mar 2024 23:56:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Variance Reduction Matrix] [] [2012-12-17 14:08:52] [b98453cac15ba1066b407e146608df68]
- RMP   [Exponential Smoothing] [] [2012-12-17 14:23:39] [b98453cac15ba1066b407e146608df68]
- RMP       [Decomposition by Loess] [loes decompostion...] [2012-12-18 19:44:38] [73ad0fd0d8c29b008c87cc4238612a7c] [Current]
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Dataseries X:
164
96
73
49
39
59
169
169
210
278
298
245
200
188
90
79
78
91
167
169
289
247
275
203
223
104
107
85
75
99
135
211
335
488
326
346
261
224
141
148
145
223
272
445
560
612
467
404
518
404
300
210
196
186
247
343
464
680
711
610
513
292
273
322
189
257
324
404
677
858
895
664
628
308
324
248
272




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'George Udny Yule' @ yule.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 & 4 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201617&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201617&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201617&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 time4 seconds
R Server'George Udny Yule' @ yule.wessa.net







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal771078
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 771 & 0 & 78 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201617&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]771[/C][C]0[/C][C]78[/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=201617&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201617&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
Seasonal771078
Trend1912
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
116493.250993450506466.2362151447041168.51279140479-70.7490065494936
29689.8063946415785-64.997068489488167.190673847909-6.19360535842151
37393.0761933430596-112.944749634089165.86855629102920.0761933430596
44975.1741684699339-141.828595871847164.65442740191326.1741684699339
53982.4150235874948-167.855322100292163.44029851279743.4150235874948
65990.5953315459057-135.651391916167163.05606037026131.5953315459057
7169250.281439644-74.9532618717251162.67182222772681.2814396439995
8169182.115629611223-7.17267703545523163.05704742423213.1156296112233
9210134.783198934382121.77452844488163.442272620738-75.216801065618
10278168.202183886435224.109488829227163.688327284338-109.797816113565
11298242.121220727154189.944397324907163.934381947938-55.8787792728455
12245218.762600208621103.338476486087167.898923305292-26.237399791379
13200161.90032019265166.2362151447041171.863464662645-38.0996798073492
14188264.101405854708-64.997068489488176.8956626347876.1014058547079
1590111.016889027174-112.944749634089181.92786060691521.0168890271739
1679116.508327854745-141.828595871847183.32026801710237.5083278547448
1778139.142646673002-167.855322100292184.71267542728961.1426466730023
1891137.171519740696-135.651391916167180.47987217547146.1715197406963
19167232.706192948073-74.9532618717251176.24706892365265.7061929480731
20169174.038776197404-7.17267703545523171.1339008380515.03877619740388
21289290.20473880267121.77452844488166.0207327524511.20473880266957
22247105.932655530458224.109488829227163.957855640315-141.067344469542
23275198.160624146914189.944397324907161.894978528179-76.8393758530863
24203138.998494318379103.338476486087163.663029195533-64.0015056816209
25223214.33270499240866.2362151447041165.431079862888-8.66729500759212
26104100.59249748093-64.997068489488172.404571008557-3.40750251906951
27107147.566687479862-112.944749634089179.37806215422740.5666874798621
2885122.392059343359-141.828595871847189.43653652848837.3920593433585
2975118.360311197542-167.855322100292199.4950109027543.3603111975415
3099126.660792890274-135.651391916167206.99059902589327.6607928902741
31135130.46707472269-74.9532618717251214.486187149035-4.53292527731037
32211210.287280298161-7.17267703545523218.885396737294-0.712719701838893
33335324.940865229568121.77452844488223.284606325553-10.0591347704325
34488523.392604421887224.109488829227228.49790674888635.3926044218875
35326228.344395502874189.944397324907233.711207172219-97.655604497126
36346345.189225647382103.338476486087243.472297866531-0.810774352618239
37261202.53039629445366.2362151447041253.233388560843-58.4696037055472
38224244.171497568781-64.997068489488268.82557092070720.1714975687814
39141110.526996353519-112.944749634089284.41775328057-30.4730036464811
40148137.851609895948-141.828595871847299.976985975899-10.1483901040521
41145142.319103429064-167.855322100292315.536218671228-2.68089657093645
42223252.487879824595-135.651391916167329.16351209157229.4878798245946
43272276.162456359809-74.9532618717251342.7908055119174.16245635980857
44445542.312354236877-7.17267703545523354.86032279857897.312354236877
45560631.29563146988121.77452844488366.9298400852471.2956314698803
46612625.878058385567224.109488829227374.01245278520613.8780583855671
47467362.96053718992189.944397324907381.095065485172-104.03946281008
48404324.438074966231103.338476486087380.223448547682-79.5619250337692
49518590.41195324510466.2362151447041379.35183161019172.4119532451044
50404497.082362807239-64.997068489488375.91470568224993.0823628072388
51300340.467169879782-112.944749634089372.47757975430740.4671698797822
52210186.231538788336-141.828595871847375.597057083511-23.7684612116643
53196181.138787687576-167.855322100292378.716534412716-14.8612123124244
54186122.05942572009-135.651391916167385.591966196077-63.9405742799099
55247176.485863892288-74.9532618717251392.467397979438-70.5141361077125
56343297.375043394955-7.17267703545523395.7976336405-45.624956605045
57464407.097602253557121.77452844488399.127869301563-56.9023977464428
58680732.693984004744224.109488829227403.19652716602952.6939840047443
59711824.790417644598189.944397324907407.265185030495113.790417644598
60610704.528398765733103.338476486087412.1331247481894.5283987657331
61513542.76272038943166.2362151447041417.00106446586429.7627203894315
62292226.315942149745-64.997068489488422.681126339743-65.684057850255
63273230.583561420467-112.944749634089428.361188213622-42.4164385795326
64322347.621442454024-141.828595871847438.20715341782325.6214424540236
6518997.8022034782662-167.855322100292448.053118622025-91.1977965217338
66257189.449925177854-135.651391916167460.201466738313-67.5500748221462
67324250.603447017124-74.9532618717251472.349814854601-73.3965529828757
68404337.028808065358-7.17267703545523478.143868970098-66.9711919346424
69677748.287548469526121.77452844488483.93792308559471.2875484695259
708581007.7951990668224.109488829227484.095312103973149.7951990668
718951115.80290155274189.944397324907484.252701122351220.802901552742
72664739.45373625705103.338476486087485.20778725686375.4537362570495
73628703.60091146392166.2362151447041486.16287339137575.6009114639207
74308195.464019111661-64.997068489488485.533049377827-112.535980888339
75324276.04152426981-112.944749634089484.903225364279-47.9584757301897
76248156.221868260313-141.828595871847481.606727611534-91.778131739687
77272233.545092241502-167.855322100292478.310229858789-38.4549077584978

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 164 & 93.2509934505064 & 66.2362151447041 & 168.51279140479 & -70.7490065494936 \tabularnewline
2 & 96 & 89.8063946415785 & -64.997068489488 & 167.190673847909 & -6.19360535842151 \tabularnewline
3 & 73 & 93.0761933430596 & -112.944749634089 & 165.868556291029 & 20.0761933430596 \tabularnewline
4 & 49 & 75.1741684699339 & -141.828595871847 & 164.654427401913 & 26.1741684699339 \tabularnewline
5 & 39 & 82.4150235874948 & -167.855322100292 & 163.440298512797 & 43.4150235874948 \tabularnewline
6 & 59 & 90.5953315459057 & -135.651391916167 & 163.056060370261 & 31.5953315459057 \tabularnewline
7 & 169 & 250.281439644 & -74.9532618717251 & 162.671822227726 & 81.2814396439995 \tabularnewline
8 & 169 & 182.115629611223 & -7.17267703545523 & 163.057047424232 & 13.1156296112233 \tabularnewline
9 & 210 & 134.783198934382 & 121.77452844488 & 163.442272620738 & -75.216801065618 \tabularnewline
10 & 278 & 168.202183886435 & 224.109488829227 & 163.688327284338 & -109.797816113565 \tabularnewline
11 & 298 & 242.121220727154 & 189.944397324907 & 163.934381947938 & -55.8787792728455 \tabularnewline
12 & 245 & 218.762600208621 & 103.338476486087 & 167.898923305292 & -26.237399791379 \tabularnewline
13 & 200 & 161.900320192651 & 66.2362151447041 & 171.863464662645 & -38.0996798073492 \tabularnewline
14 & 188 & 264.101405854708 & -64.997068489488 & 176.89566263478 & 76.1014058547079 \tabularnewline
15 & 90 & 111.016889027174 & -112.944749634089 & 181.927860606915 & 21.0168890271739 \tabularnewline
16 & 79 & 116.508327854745 & -141.828595871847 & 183.320268017102 & 37.5083278547448 \tabularnewline
17 & 78 & 139.142646673002 & -167.855322100292 & 184.712675427289 & 61.1426466730023 \tabularnewline
18 & 91 & 137.171519740696 & -135.651391916167 & 180.479872175471 & 46.1715197406963 \tabularnewline
19 & 167 & 232.706192948073 & -74.9532618717251 & 176.247068923652 & 65.7061929480731 \tabularnewline
20 & 169 & 174.038776197404 & -7.17267703545523 & 171.133900838051 & 5.03877619740388 \tabularnewline
21 & 289 & 290.20473880267 & 121.77452844488 & 166.020732752451 & 1.20473880266957 \tabularnewline
22 & 247 & 105.932655530458 & 224.109488829227 & 163.957855640315 & -141.067344469542 \tabularnewline
23 & 275 & 198.160624146914 & 189.944397324907 & 161.894978528179 & -76.8393758530863 \tabularnewline
24 & 203 & 138.998494318379 & 103.338476486087 & 163.663029195533 & -64.0015056816209 \tabularnewline
25 & 223 & 214.332704992408 & 66.2362151447041 & 165.431079862888 & -8.66729500759212 \tabularnewline
26 & 104 & 100.59249748093 & -64.997068489488 & 172.404571008557 & -3.40750251906951 \tabularnewline
27 & 107 & 147.566687479862 & -112.944749634089 & 179.378062154227 & 40.5666874798621 \tabularnewline
28 & 85 & 122.392059343359 & -141.828595871847 & 189.436536528488 & 37.3920593433585 \tabularnewline
29 & 75 & 118.360311197542 & -167.855322100292 & 199.49501090275 & 43.3603111975415 \tabularnewline
30 & 99 & 126.660792890274 & -135.651391916167 & 206.990599025893 & 27.6607928902741 \tabularnewline
31 & 135 & 130.46707472269 & -74.9532618717251 & 214.486187149035 & -4.53292527731037 \tabularnewline
32 & 211 & 210.287280298161 & -7.17267703545523 & 218.885396737294 & -0.712719701838893 \tabularnewline
33 & 335 & 324.940865229568 & 121.77452844488 & 223.284606325553 & -10.0591347704325 \tabularnewline
34 & 488 & 523.392604421887 & 224.109488829227 & 228.497906748886 & 35.3926044218875 \tabularnewline
35 & 326 & 228.344395502874 & 189.944397324907 & 233.711207172219 & -97.655604497126 \tabularnewline
36 & 346 & 345.189225647382 & 103.338476486087 & 243.472297866531 & -0.810774352618239 \tabularnewline
37 & 261 & 202.530396294453 & 66.2362151447041 & 253.233388560843 & -58.4696037055472 \tabularnewline
38 & 224 & 244.171497568781 & -64.997068489488 & 268.825570920707 & 20.1714975687814 \tabularnewline
39 & 141 & 110.526996353519 & -112.944749634089 & 284.41775328057 & -30.4730036464811 \tabularnewline
40 & 148 & 137.851609895948 & -141.828595871847 & 299.976985975899 & -10.1483901040521 \tabularnewline
41 & 145 & 142.319103429064 & -167.855322100292 & 315.536218671228 & -2.68089657093645 \tabularnewline
42 & 223 & 252.487879824595 & -135.651391916167 & 329.163512091572 & 29.4878798245946 \tabularnewline
43 & 272 & 276.162456359809 & -74.9532618717251 & 342.790805511917 & 4.16245635980857 \tabularnewline
44 & 445 & 542.312354236877 & -7.17267703545523 & 354.860322798578 & 97.312354236877 \tabularnewline
45 & 560 & 631.29563146988 & 121.77452844488 & 366.92984008524 & 71.2956314698803 \tabularnewline
46 & 612 & 625.878058385567 & 224.109488829227 & 374.012452785206 & 13.8780583855671 \tabularnewline
47 & 467 & 362.96053718992 & 189.944397324907 & 381.095065485172 & -104.03946281008 \tabularnewline
48 & 404 & 324.438074966231 & 103.338476486087 & 380.223448547682 & -79.5619250337692 \tabularnewline
49 & 518 & 590.411953245104 & 66.2362151447041 & 379.351831610191 & 72.4119532451044 \tabularnewline
50 & 404 & 497.082362807239 & -64.997068489488 & 375.914705682249 & 93.0823628072388 \tabularnewline
51 & 300 & 340.467169879782 & -112.944749634089 & 372.477579754307 & 40.4671698797822 \tabularnewline
52 & 210 & 186.231538788336 & -141.828595871847 & 375.597057083511 & -23.7684612116643 \tabularnewline
53 & 196 & 181.138787687576 & -167.855322100292 & 378.716534412716 & -14.8612123124244 \tabularnewline
54 & 186 & 122.05942572009 & -135.651391916167 & 385.591966196077 & -63.9405742799099 \tabularnewline
55 & 247 & 176.485863892288 & -74.9532618717251 & 392.467397979438 & -70.5141361077125 \tabularnewline
56 & 343 & 297.375043394955 & -7.17267703545523 & 395.7976336405 & -45.624956605045 \tabularnewline
57 & 464 & 407.097602253557 & 121.77452844488 & 399.127869301563 & -56.9023977464428 \tabularnewline
58 & 680 & 732.693984004744 & 224.109488829227 & 403.196527166029 & 52.6939840047443 \tabularnewline
59 & 711 & 824.790417644598 & 189.944397324907 & 407.265185030495 & 113.790417644598 \tabularnewline
60 & 610 & 704.528398765733 & 103.338476486087 & 412.13312474818 & 94.5283987657331 \tabularnewline
61 & 513 & 542.762720389431 & 66.2362151447041 & 417.001064465864 & 29.7627203894315 \tabularnewline
62 & 292 & 226.315942149745 & -64.997068489488 & 422.681126339743 & -65.684057850255 \tabularnewline
63 & 273 & 230.583561420467 & -112.944749634089 & 428.361188213622 & -42.4164385795326 \tabularnewline
64 & 322 & 347.621442454024 & -141.828595871847 & 438.207153417823 & 25.6214424540236 \tabularnewline
65 & 189 & 97.8022034782662 & -167.855322100292 & 448.053118622025 & -91.1977965217338 \tabularnewline
66 & 257 & 189.449925177854 & -135.651391916167 & 460.201466738313 & -67.5500748221462 \tabularnewline
67 & 324 & 250.603447017124 & -74.9532618717251 & 472.349814854601 & -73.3965529828757 \tabularnewline
68 & 404 & 337.028808065358 & -7.17267703545523 & 478.143868970098 & -66.9711919346424 \tabularnewline
69 & 677 & 748.287548469526 & 121.77452844488 & 483.937923085594 & 71.2875484695259 \tabularnewline
70 & 858 & 1007.7951990668 & 224.109488829227 & 484.095312103973 & 149.7951990668 \tabularnewline
71 & 895 & 1115.80290155274 & 189.944397324907 & 484.252701122351 & 220.802901552742 \tabularnewline
72 & 664 & 739.45373625705 & 103.338476486087 & 485.207787256863 & 75.4537362570495 \tabularnewline
73 & 628 & 703.600911463921 & 66.2362151447041 & 486.162873391375 & 75.6009114639207 \tabularnewline
74 & 308 & 195.464019111661 & -64.997068489488 & 485.533049377827 & -112.535980888339 \tabularnewline
75 & 324 & 276.04152426981 & -112.944749634089 & 484.903225364279 & -47.9584757301897 \tabularnewline
76 & 248 & 156.221868260313 & -141.828595871847 & 481.606727611534 & -91.778131739687 \tabularnewline
77 & 272 & 233.545092241502 & -167.855322100292 & 478.310229858789 & -38.4549077584978 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201617&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]164[/C][C]93.2509934505064[/C][C]66.2362151447041[/C][C]168.51279140479[/C][C]-70.7490065494936[/C][/ROW]
[ROW][C]2[/C][C]96[/C][C]89.8063946415785[/C][C]-64.997068489488[/C][C]167.190673847909[/C][C]-6.19360535842151[/C][/ROW]
[ROW][C]3[/C][C]73[/C][C]93.0761933430596[/C][C]-112.944749634089[/C][C]165.868556291029[/C][C]20.0761933430596[/C][/ROW]
[ROW][C]4[/C][C]49[/C][C]75.1741684699339[/C][C]-141.828595871847[/C][C]164.654427401913[/C][C]26.1741684699339[/C][/ROW]
[ROW][C]5[/C][C]39[/C][C]82.4150235874948[/C][C]-167.855322100292[/C][C]163.440298512797[/C][C]43.4150235874948[/C][/ROW]
[ROW][C]6[/C][C]59[/C][C]90.5953315459057[/C][C]-135.651391916167[/C][C]163.056060370261[/C][C]31.5953315459057[/C][/ROW]
[ROW][C]7[/C][C]169[/C][C]250.281439644[/C][C]-74.9532618717251[/C][C]162.671822227726[/C][C]81.2814396439995[/C][/ROW]
[ROW][C]8[/C][C]169[/C][C]182.115629611223[/C][C]-7.17267703545523[/C][C]163.057047424232[/C][C]13.1156296112233[/C][/ROW]
[ROW][C]9[/C][C]210[/C][C]134.783198934382[/C][C]121.77452844488[/C][C]163.442272620738[/C][C]-75.216801065618[/C][/ROW]
[ROW][C]10[/C][C]278[/C][C]168.202183886435[/C][C]224.109488829227[/C][C]163.688327284338[/C][C]-109.797816113565[/C][/ROW]
[ROW][C]11[/C][C]298[/C][C]242.121220727154[/C][C]189.944397324907[/C][C]163.934381947938[/C][C]-55.8787792728455[/C][/ROW]
[ROW][C]12[/C][C]245[/C][C]218.762600208621[/C][C]103.338476486087[/C][C]167.898923305292[/C][C]-26.237399791379[/C][/ROW]
[ROW][C]13[/C][C]200[/C][C]161.900320192651[/C][C]66.2362151447041[/C][C]171.863464662645[/C][C]-38.0996798073492[/C][/ROW]
[ROW][C]14[/C][C]188[/C][C]264.101405854708[/C][C]-64.997068489488[/C][C]176.89566263478[/C][C]76.1014058547079[/C][/ROW]
[ROW][C]15[/C][C]90[/C][C]111.016889027174[/C][C]-112.944749634089[/C][C]181.927860606915[/C][C]21.0168890271739[/C][/ROW]
[ROW][C]16[/C][C]79[/C][C]116.508327854745[/C][C]-141.828595871847[/C][C]183.320268017102[/C][C]37.5083278547448[/C][/ROW]
[ROW][C]17[/C][C]78[/C][C]139.142646673002[/C][C]-167.855322100292[/C][C]184.712675427289[/C][C]61.1426466730023[/C][/ROW]
[ROW][C]18[/C][C]91[/C][C]137.171519740696[/C][C]-135.651391916167[/C][C]180.479872175471[/C][C]46.1715197406963[/C][/ROW]
[ROW][C]19[/C][C]167[/C][C]232.706192948073[/C][C]-74.9532618717251[/C][C]176.247068923652[/C][C]65.7061929480731[/C][/ROW]
[ROW][C]20[/C][C]169[/C][C]174.038776197404[/C][C]-7.17267703545523[/C][C]171.133900838051[/C][C]5.03877619740388[/C][/ROW]
[ROW][C]21[/C][C]289[/C][C]290.20473880267[/C][C]121.77452844488[/C][C]166.020732752451[/C][C]1.20473880266957[/C][/ROW]
[ROW][C]22[/C][C]247[/C][C]105.932655530458[/C][C]224.109488829227[/C][C]163.957855640315[/C][C]-141.067344469542[/C][/ROW]
[ROW][C]23[/C][C]275[/C][C]198.160624146914[/C][C]189.944397324907[/C][C]161.894978528179[/C][C]-76.8393758530863[/C][/ROW]
[ROW][C]24[/C][C]203[/C][C]138.998494318379[/C][C]103.338476486087[/C][C]163.663029195533[/C][C]-64.0015056816209[/C][/ROW]
[ROW][C]25[/C][C]223[/C][C]214.332704992408[/C][C]66.2362151447041[/C][C]165.431079862888[/C][C]-8.66729500759212[/C][/ROW]
[ROW][C]26[/C][C]104[/C][C]100.59249748093[/C][C]-64.997068489488[/C][C]172.404571008557[/C][C]-3.40750251906951[/C][/ROW]
[ROW][C]27[/C][C]107[/C][C]147.566687479862[/C][C]-112.944749634089[/C][C]179.378062154227[/C][C]40.5666874798621[/C][/ROW]
[ROW][C]28[/C][C]85[/C][C]122.392059343359[/C][C]-141.828595871847[/C][C]189.436536528488[/C][C]37.3920593433585[/C][/ROW]
[ROW][C]29[/C][C]75[/C][C]118.360311197542[/C][C]-167.855322100292[/C][C]199.49501090275[/C][C]43.3603111975415[/C][/ROW]
[ROW][C]30[/C][C]99[/C][C]126.660792890274[/C][C]-135.651391916167[/C][C]206.990599025893[/C][C]27.6607928902741[/C][/ROW]
[ROW][C]31[/C][C]135[/C][C]130.46707472269[/C][C]-74.9532618717251[/C][C]214.486187149035[/C][C]-4.53292527731037[/C][/ROW]
[ROW][C]32[/C][C]211[/C][C]210.287280298161[/C][C]-7.17267703545523[/C][C]218.885396737294[/C][C]-0.712719701838893[/C][/ROW]
[ROW][C]33[/C][C]335[/C][C]324.940865229568[/C][C]121.77452844488[/C][C]223.284606325553[/C][C]-10.0591347704325[/C][/ROW]
[ROW][C]34[/C][C]488[/C][C]523.392604421887[/C][C]224.109488829227[/C][C]228.497906748886[/C][C]35.3926044218875[/C][/ROW]
[ROW][C]35[/C][C]326[/C][C]228.344395502874[/C][C]189.944397324907[/C][C]233.711207172219[/C][C]-97.655604497126[/C][/ROW]
[ROW][C]36[/C][C]346[/C][C]345.189225647382[/C][C]103.338476486087[/C][C]243.472297866531[/C][C]-0.810774352618239[/C][/ROW]
[ROW][C]37[/C][C]261[/C][C]202.530396294453[/C][C]66.2362151447041[/C][C]253.233388560843[/C][C]-58.4696037055472[/C][/ROW]
[ROW][C]38[/C][C]224[/C][C]244.171497568781[/C][C]-64.997068489488[/C][C]268.825570920707[/C][C]20.1714975687814[/C][/ROW]
[ROW][C]39[/C][C]141[/C][C]110.526996353519[/C][C]-112.944749634089[/C][C]284.41775328057[/C][C]-30.4730036464811[/C][/ROW]
[ROW][C]40[/C][C]148[/C][C]137.851609895948[/C][C]-141.828595871847[/C][C]299.976985975899[/C][C]-10.1483901040521[/C][/ROW]
[ROW][C]41[/C][C]145[/C][C]142.319103429064[/C][C]-167.855322100292[/C][C]315.536218671228[/C][C]-2.68089657093645[/C][/ROW]
[ROW][C]42[/C][C]223[/C][C]252.487879824595[/C][C]-135.651391916167[/C][C]329.163512091572[/C][C]29.4878798245946[/C][/ROW]
[ROW][C]43[/C][C]272[/C][C]276.162456359809[/C][C]-74.9532618717251[/C][C]342.790805511917[/C][C]4.16245635980857[/C][/ROW]
[ROW][C]44[/C][C]445[/C][C]542.312354236877[/C][C]-7.17267703545523[/C][C]354.860322798578[/C][C]97.312354236877[/C][/ROW]
[ROW][C]45[/C][C]560[/C][C]631.29563146988[/C][C]121.77452844488[/C][C]366.92984008524[/C][C]71.2956314698803[/C][/ROW]
[ROW][C]46[/C][C]612[/C][C]625.878058385567[/C][C]224.109488829227[/C][C]374.012452785206[/C][C]13.8780583855671[/C][/ROW]
[ROW][C]47[/C][C]467[/C][C]362.96053718992[/C][C]189.944397324907[/C][C]381.095065485172[/C][C]-104.03946281008[/C][/ROW]
[ROW][C]48[/C][C]404[/C][C]324.438074966231[/C][C]103.338476486087[/C][C]380.223448547682[/C][C]-79.5619250337692[/C][/ROW]
[ROW][C]49[/C][C]518[/C][C]590.411953245104[/C][C]66.2362151447041[/C][C]379.351831610191[/C][C]72.4119532451044[/C][/ROW]
[ROW][C]50[/C][C]404[/C][C]497.082362807239[/C][C]-64.997068489488[/C][C]375.914705682249[/C][C]93.0823628072388[/C][/ROW]
[ROW][C]51[/C][C]300[/C][C]340.467169879782[/C][C]-112.944749634089[/C][C]372.477579754307[/C][C]40.4671698797822[/C][/ROW]
[ROW][C]52[/C][C]210[/C][C]186.231538788336[/C][C]-141.828595871847[/C][C]375.597057083511[/C][C]-23.7684612116643[/C][/ROW]
[ROW][C]53[/C][C]196[/C][C]181.138787687576[/C][C]-167.855322100292[/C][C]378.716534412716[/C][C]-14.8612123124244[/C][/ROW]
[ROW][C]54[/C][C]186[/C][C]122.05942572009[/C][C]-135.651391916167[/C][C]385.591966196077[/C][C]-63.9405742799099[/C][/ROW]
[ROW][C]55[/C][C]247[/C][C]176.485863892288[/C][C]-74.9532618717251[/C][C]392.467397979438[/C][C]-70.5141361077125[/C][/ROW]
[ROW][C]56[/C][C]343[/C][C]297.375043394955[/C][C]-7.17267703545523[/C][C]395.7976336405[/C][C]-45.624956605045[/C][/ROW]
[ROW][C]57[/C][C]464[/C][C]407.097602253557[/C][C]121.77452844488[/C][C]399.127869301563[/C][C]-56.9023977464428[/C][/ROW]
[ROW][C]58[/C][C]680[/C][C]732.693984004744[/C][C]224.109488829227[/C][C]403.196527166029[/C][C]52.6939840047443[/C][/ROW]
[ROW][C]59[/C][C]711[/C][C]824.790417644598[/C][C]189.944397324907[/C][C]407.265185030495[/C][C]113.790417644598[/C][/ROW]
[ROW][C]60[/C][C]610[/C][C]704.528398765733[/C][C]103.338476486087[/C][C]412.13312474818[/C][C]94.5283987657331[/C][/ROW]
[ROW][C]61[/C][C]513[/C][C]542.762720389431[/C][C]66.2362151447041[/C][C]417.001064465864[/C][C]29.7627203894315[/C][/ROW]
[ROW][C]62[/C][C]292[/C][C]226.315942149745[/C][C]-64.997068489488[/C][C]422.681126339743[/C][C]-65.684057850255[/C][/ROW]
[ROW][C]63[/C][C]273[/C][C]230.583561420467[/C][C]-112.944749634089[/C][C]428.361188213622[/C][C]-42.4164385795326[/C][/ROW]
[ROW][C]64[/C][C]322[/C][C]347.621442454024[/C][C]-141.828595871847[/C][C]438.207153417823[/C][C]25.6214424540236[/C][/ROW]
[ROW][C]65[/C][C]189[/C][C]97.8022034782662[/C][C]-167.855322100292[/C][C]448.053118622025[/C][C]-91.1977965217338[/C][/ROW]
[ROW][C]66[/C][C]257[/C][C]189.449925177854[/C][C]-135.651391916167[/C][C]460.201466738313[/C][C]-67.5500748221462[/C][/ROW]
[ROW][C]67[/C][C]324[/C][C]250.603447017124[/C][C]-74.9532618717251[/C][C]472.349814854601[/C][C]-73.3965529828757[/C][/ROW]
[ROW][C]68[/C][C]404[/C][C]337.028808065358[/C][C]-7.17267703545523[/C][C]478.143868970098[/C][C]-66.9711919346424[/C][/ROW]
[ROW][C]69[/C][C]677[/C][C]748.287548469526[/C][C]121.77452844488[/C][C]483.937923085594[/C][C]71.2875484695259[/C][/ROW]
[ROW][C]70[/C][C]858[/C][C]1007.7951990668[/C][C]224.109488829227[/C][C]484.095312103973[/C][C]149.7951990668[/C][/ROW]
[ROW][C]71[/C][C]895[/C][C]1115.80290155274[/C][C]189.944397324907[/C][C]484.252701122351[/C][C]220.802901552742[/C][/ROW]
[ROW][C]72[/C][C]664[/C][C]739.45373625705[/C][C]103.338476486087[/C][C]485.207787256863[/C][C]75.4537362570495[/C][/ROW]
[ROW][C]73[/C][C]628[/C][C]703.600911463921[/C][C]66.2362151447041[/C][C]486.162873391375[/C][C]75.6009114639207[/C][/ROW]
[ROW][C]74[/C][C]308[/C][C]195.464019111661[/C][C]-64.997068489488[/C][C]485.533049377827[/C][C]-112.535980888339[/C][/ROW]
[ROW][C]75[/C][C]324[/C][C]276.04152426981[/C][C]-112.944749634089[/C][C]484.903225364279[/C][C]-47.9584757301897[/C][/ROW]
[ROW][C]76[/C][C]248[/C][C]156.221868260313[/C][C]-141.828595871847[/C][C]481.606727611534[/C][C]-91.778131739687[/C][/ROW]
[ROW][C]77[/C][C]272[/C][C]233.545092241502[/C][C]-167.855322100292[/C][C]478.310229858789[/C][C]-38.4549077584978[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201617&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201617&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
116493.250993450506466.2362151447041168.51279140479-70.7490065494936
29689.8063946415785-64.997068489488167.190673847909-6.19360535842151
37393.0761933430596-112.944749634089165.86855629102920.0761933430596
44975.1741684699339-141.828595871847164.65442740191326.1741684699339
53982.4150235874948-167.855322100292163.44029851279743.4150235874948
65990.5953315459057-135.651391916167163.05606037026131.5953315459057
7169250.281439644-74.9532618717251162.67182222772681.2814396439995
8169182.115629611223-7.17267703545523163.05704742423213.1156296112233
9210134.783198934382121.77452844488163.442272620738-75.216801065618
10278168.202183886435224.109488829227163.688327284338-109.797816113565
11298242.121220727154189.944397324907163.934381947938-55.8787792728455
12245218.762600208621103.338476486087167.898923305292-26.237399791379
13200161.90032019265166.2362151447041171.863464662645-38.0996798073492
14188264.101405854708-64.997068489488176.8956626347876.1014058547079
1590111.016889027174-112.944749634089181.92786060691521.0168890271739
1679116.508327854745-141.828595871847183.32026801710237.5083278547448
1778139.142646673002-167.855322100292184.71267542728961.1426466730023
1891137.171519740696-135.651391916167180.47987217547146.1715197406963
19167232.706192948073-74.9532618717251176.24706892365265.7061929480731
20169174.038776197404-7.17267703545523171.1339008380515.03877619740388
21289290.20473880267121.77452844488166.0207327524511.20473880266957
22247105.932655530458224.109488829227163.957855640315-141.067344469542
23275198.160624146914189.944397324907161.894978528179-76.8393758530863
24203138.998494318379103.338476486087163.663029195533-64.0015056816209
25223214.33270499240866.2362151447041165.431079862888-8.66729500759212
26104100.59249748093-64.997068489488172.404571008557-3.40750251906951
27107147.566687479862-112.944749634089179.37806215422740.5666874798621
2885122.392059343359-141.828595871847189.43653652848837.3920593433585
2975118.360311197542-167.855322100292199.4950109027543.3603111975415
3099126.660792890274-135.651391916167206.99059902589327.6607928902741
31135130.46707472269-74.9532618717251214.486187149035-4.53292527731037
32211210.287280298161-7.17267703545523218.885396737294-0.712719701838893
33335324.940865229568121.77452844488223.284606325553-10.0591347704325
34488523.392604421887224.109488829227228.49790674888635.3926044218875
35326228.344395502874189.944397324907233.711207172219-97.655604497126
36346345.189225647382103.338476486087243.472297866531-0.810774352618239
37261202.53039629445366.2362151447041253.233388560843-58.4696037055472
38224244.171497568781-64.997068489488268.82557092070720.1714975687814
39141110.526996353519-112.944749634089284.41775328057-30.4730036464811
40148137.851609895948-141.828595871847299.976985975899-10.1483901040521
41145142.319103429064-167.855322100292315.536218671228-2.68089657093645
42223252.487879824595-135.651391916167329.16351209157229.4878798245946
43272276.162456359809-74.9532618717251342.7908055119174.16245635980857
44445542.312354236877-7.17267703545523354.86032279857897.312354236877
45560631.29563146988121.77452844488366.9298400852471.2956314698803
46612625.878058385567224.109488829227374.01245278520613.8780583855671
47467362.96053718992189.944397324907381.095065485172-104.03946281008
48404324.438074966231103.338476486087380.223448547682-79.5619250337692
49518590.41195324510466.2362151447041379.35183161019172.4119532451044
50404497.082362807239-64.997068489488375.91470568224993.0823628072388
51300340.467169879782-112.944749634089372.47757975430740.4671698797822
52210186.231538788336-141.828595871847375.597057083511-23.7684612116643
53196181.138787687576-167.855322100292378.716534412716-14.8612123124244
54186122.05942572009-135.651391916167385.591966196077-63.9405742799099
55247176.485863892288-74.9532618717251392.467397979438-70.5141361077125
56343297.375043394955-7.17267703545523395.7976336405-45.624956605045
57464407.097602253557121.77452844488399.127869301563-56.9023977464428
58680732.693984004744224.109488829227403.19652716602952.6939840047443
59711824.790417644598189.944397324907407.265185030495113.790417644598
60610704.528398765733103.338476486087412.1331247481894.5283987657331
61513542.76272038943166.2362151447041417.00106446586429.7627203894315
62292226.315942149745-64.997068489488422.681126339743-65.684057850255
63273230.583561420467-112.944749634089428.361188213622-42.4164385795326
64322347.621442454024-141.828595871847438.20715341782325.6214424540236
6518997.8022034782662-167.855322100292448.053118622025-91.1977965217338
66257189.449925177854-135.651391916167460.201466738313-67.5500748221462
67324250.603447017124-74.9532618717251472.349814854601-73.3965529828757
68404337.028808065358-7.17267703545523478.143868970098-66.9711919346424
69677748.287548469526121.77452844488483.93792308559471.2875484695259
708581007.7951990668224.109488829227484.095312103973149.7951990668
718951115.80290155274189.944397324907484.252701122351220.802901552742
72664739.45373625705103.338476486087485.20778725686375.4537362570495
73628703.60091146392166.2362151447041486.16287339137575.6009114639207
74308195.464019111661-64.997068489488485.533049377827-112.535980888339
75324276.04152426981-112.944749634089484.903225364279-47.9584757301897
76248156.221868260313-141.828595871847481.606727611534-91.778131739687
77272233.545092241502-167.855322100292478.310229858789-38.4549077584978



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')