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Description of Statistical Computation

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, 08 Dec 2015 17:53:15 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/08/t1449597266dk9dbo29qfc83op.htm/, Retrieved Sun, 19 May 2024 03:10:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285549, Retrieved Sun, 19 May 2024 03:10:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact73

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
- R  D  [Classical Decomposition] [classical decompo...] [2015-12-08 14:52:05] [22b6f4a061c8797aa483199554a73d13]
- RMP     [Decomposition by Loess] [Loess totaal mannen] [2015-12-08 15:45:08] [22b6f4a061c8797aa483199554a73d13]
- RMP       [Structural Time Series Models] [structural time s...] [2015-12-08 16:02:26] [22b6f4a061c8797aa483199554a73d13]
- RMPD        [Classical Decomposition] [classical decompo...] [2015-12-08 17:17:05] [22b6f4a061c8797aa483199554a73d13]
- RMP             [Decomposition by Loess] [Loess totaal vrouwen] [2015-12-08 17:53:15] [20fcaaf1d4bc4a12bf87c6c50d624c14] [Current]
- RMP               [Structural Time Series Models] [structural time s...] [2015-12-08 18:02:34] [22b6f4a061c8797aa483199554a73d13]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
276444
268606
267679
269879
265641
262525
258597
253849
256221
286895
294610
280363
269926
264341
263269
271045
267915
262078
257751
253271
257638
287452
298152
284793
274560
268270
267577
271866
268546
264722
262425
258973
262751
296186
304659
295442
285466
279575
279985
286012
281337
276270
271472
265637
268974
299299
305452
295468
285584
278204
276505
279732
276980
271832
263105
256162
260705
285857
291870
280358
270981

Tables (Output of Computation)





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=285549&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=285549&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285549&T=0

As an alternative you can also use a QR Code:  
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
Seasonal611062
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 611 & 0 & 62 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285549&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]611[/C][C]0[/C][C]62[/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=285549&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285549&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal611062
Trend1912
Low-pass1312






Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1276444277711.0902080263541.22150594703271635.6882860271267.09020802588
2268606268977.730539688-3109.04670706575271343.316167378371.730539687618
3267679268205.949452382-3898.89350111089271050.944048729526.949452381697
4269879268115.068973461884.147480509698270758.783546029-1763.93102653872
5265641263474.989986574-2659.61302990276270466.623043329-2166.01001342613
6262525262071.631374581-7186.42497547211270164.793600891-453.368625418807
7258597259261.271068623-11930.2352270758269862.964158453664.271068622882
8253849255086.358492478-16958.9903902126269570.6318977341237.35849247815
9256221256380.446505209-13216.746142225269278.299637016159.446505208907
10286895287911.97266170816752.189045712269125.838292581016.97266170796
11294610295594.69993972124651.9231121351268973.376948144984.699939720798
12280363278652.67739293813130.466902468268942.855704594-1710.32260706217
13269926267398.4440330093541.22150594703268912.334461044-2527.55596699117
14264341262858.69159228-3109.04670706575268932.355114786-1482.30840772041
15263269261484.517732583-3898.89350111089268952.375768528-1784.4822674173
16271045272061.111349035884.147480509698269144.7411704551016.11134903505
17267915269152.50645752-2659.61302990276269337.1065723821237.50645752047
18262078261661.470829478-7186.42497547211269680.954145994-416.529170521826
19257751257407.43350747-11930.2352270758270024.801719606-343.566492529702
20253271253152.406843214-16958.9903902126270348.583546999-118.593156786053
21257638257820.380767833-13216.746142225270672.365374392182.380767833209
22287452287283.3023450916752.189045712270868.508609198-168.697654910211
23298152300587.4250438624651.9231121351271064.6518440052435.42504386016
24284793285179.35310043313130.466902468271276.179997099386.353100433131
25274560274091.070343863541.22150594703271487.708150193-468.929656140041
26268270267802.439052854-3109.04670706575271846.607654212-467.5609471465
27267577266847.386342879-3898.89350111089272205.507158232-729.613657120673
28271866270072.310967322884.147480509698272775.541552168-1793.68903267791
29268546266406.037083798-2659.61302990276273345.575946105-2139.96291620203
30264722262460.921907167-7186.42497547211274169.503068305-2261.07809283287
31262425261786.805036571-11930.2352270758274993.430190505-638.194963429356
32258973258865.186576476-16958.9903902126276039.803813736-107.813423523621
33262751261632.568705258-13216.746142225277086.177436967-1118.43129474233
34296186297421.63038389316752.189045712278198.1805703951235.63038389338
35304659305355.89318404324651.9231121351279310.183703822696.893184042885
36295442297493.96123766813130.466902468280259.5718598642051.96123766794
37285466286181.8184781473541.22150594703281208.960015906715.818478146975
38279575280440.438985665-3109.04670706575281818.607721401865.438985665096
39279985281440.638074216-3898.89350111089282428.2554268951455.63807421556
40286012288452.426548781884.147480509698282687.4259707092440.42654878111
41281337282387.01651538-2659.61302990276282946.5965145231050.01651537971
42276270276783.499315821-7186.42497547211282942.925659651513.499315821042
43271472271934.980422297-11930.2352270758282939.254804779462.980422296678
44265637265488.155168966-16958.9903902126282744.835221247-148.844831034134
45268974268614.330504511-13216.746142225282550.415637714-359.66949548939
46299299299643.82564997616752.189045712282201.985304312344.825649976323
47305452304398.52191695624651.9231121351281853.554970909-1053.47808304417
48295468296432.82424273613130.466902468281372.708854796964.824242736446
49285584286734.9157553713541.22150594703280891.8627386821150.91575537098
50278204279313.885880334-3109.04670706575280203.1608267311109.88588033442
51276505277394.43458633-3898.89350111089279514.458914781889.434586330142
52279732280170.488672198884.147480509698278409.363847293438.488672197505
53276980279315.344250098-2659.61302990276277304.2687798052335.34425009793
54271832274829.862020507-7186.42497547211276020.5629549662997.86202050658
55263105263403.37809695-11930.2352270758274736.857130126298.378096949542
56256162255844.236406876-16958.9903902126273438.753983336-317.76359312376
57260705262486.095305678-13216.746142225272140.6508365461781.09530567849
58285857284162.51422292416752.189045712270799.296731364-1694.48577707581
59291870289630.13426168424651.9231121351269457.942626181-2239.86573831638
60280358279512.57967842513130.466902468268072.953419107-845.420321574784
61270981271732.8142820213541.22150594703266687.964212032751.814282020787

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 276444 & 277711.090208026 & 3541.22150594703 & 271635.688286027 & 1267.09020802588 \tabularnewline
2 & 268606 & 268977.730539688 & -3109.04670706575 & 271343.316167378 & 371.730539687618 \tabularnewline
3 & 267679 & 268205.949452382 & -3898.89350111089 & 271050.944048729 & 526.949452381697 \tabularnewline
4 & 269879 & 268115.068973461 & 884.147480509698 & 270758.783546029 & -1763.93102653872 \tabularnewline
5 & 265641 & 263474.989986574 & -2659.61302990276 & 270466.623043329 & -2166.01001342613 \tabularnewline
6 & 262525 & 262071.631374581 & -7186.42497547211 & 270164.793600891 & -453.368625418807 \tabularnewline
7 & 258597 & 259261.271068623 & -11930.2352270758 & 269862.964158453 & 664.271068622882 \tabularnewline
8 & 253849 & 255086.358492478 & -16958.9903902126 & 269570.631897734 & 1237.35849247815 \tabularnewline
9 & 256221 & 256380.446505209 & -13216.746142225 & 269278.299637016 & 159.446505208907 \tabularnewline
10 & 286895 & 287911.972661708 & 16752.189045712 & 269125.83829258 & 1016.97266170796 \tabularnewline
11 & 294610 & 295594.699939721 & 24651.9231121351 & 268973.376948144 & 984.699939720798 \tabularnewline
12 & 280363 & 278652.677392938 & 13130.466902468 & 268942.855704594 & -1710.32260706217 \tabularnewline
13 & 269926 & 267398.444033009 & 3541.22150594703 & 268912.334461044 & -2527.55596699117 \tabularnewline
14 & 264341 & 262858.69159228 & -3109.04670706575 & 268932.355114786 & -1482.30840772041 \tabularnewline
15 & 263269 & 261484.517732583 & -3898.89350111089 & 268952.375768528 & -1784.4822674173 \tabularnewline
16 & 271045 & 272061.111349035 & 884.147480509698 & 269144.741170455 & 1016.11134903505 \tabularnewline
17 & 267915 & 269152.50645752 & -2659.61302990276 & 269337.106572382 & 1237.50645752047 \tabularnewline
18 & 262078 & 261661.470829478 & -7186.42497547211 & 269680.954145994 & -416.529170521826 \tabularnewline
19 & 257751 & 257407.43350747 & -11930.2352270758 & 270024.801719606 & -343.566492529702 \tabularnewline
20 & 253271 & 253152.406843214 & -16958.9903902126 & 270348.583546999 & -118.593156786053 \tabularnewline
21 & 257638 & 257820.380767833 & -13216.746142225 & 270672.365374392 & 182.380767833209 \tabularnewline
22 & 287452 & 287283.30234509 & 16752.189045712 & 270868.508609198 & -168.697654910211 \tabularnewline
23 & 298152 & 300587.42504386 & 24651.9231121351 & 271064.651844005 & 2435.42504386016 \tabularnewline
24 & 284793 & 285179.353100433 & 13130.466902468 & 271276.179997099 & 386.353100433131 \tabularnewline
25 & 274560 & 274091.07034386 & 3541.22150594703 & 271487.708150193 & -468.929656140041 \tabularnewline
26 & 268270 & 267802.439052854 & -3109.04670706575 & 271846.607654212 & -467.5609471465 \tabularnewline
27 & 267577 & 266847.386342879 & -3898.89350111089 & 272205.507158232 & -729.613657120673 \tabularnewline
28 & 271866 & 270072.310967322 & 884.147480509698 & 272775.541552168 & -1793.68903267791 \tabularnewline
29 & 268546 & 266406.037083798 & -2659.61302990276 & 273345.575946105 & -2139.96291620203 \tabularnewline
30 & 264722 & 262460.921907167 & -7186.42497547211 & 274169.503068305 & -2261.07809283287 \tabularnewline
31 & 262425 & 261786.805036571 & -11930.2352270758 & 274993.430190505 & -638.194963429356 \tabularnewline
32 & 258973 & 258865.186576476 & -16958.9903902126 & 276039.803813736 & -107.813423523621 \tabularnewline
33 & 262751 & 261632.568705258 & -13216.746142225 & 277086.177436967 & -1118.43129474233 \tabularnewline
34 & 296186 & 297421.630383893 & 16752.189045712 & 278198.180570395 & 1235.63038389338 \tabularnewline
35 & 304659 & 305355.893184043 & 24651.9231121351 & 279310.183703822 & 696.893184042885 \tabularnewline
36 & 295442 & 297493.961237668 & 13130.466902468 & 280259.571859864 & 2051.96123766794 \tabularnewline
37 & 285466 & 286181.818478147 & 3541.22150594703 & 281208.960015906 & 715.818478146975 \tabularnewline
38 & 279575 & 280440.438985665 & -3109.04670706575 & 281818.607721401 & 865.438985665096 \tabularnewline
39 & 279985 & 281440.638074216 & -3898.89350111089 & 282428.255426895 & 1455.63807421556 \tabularnewline
40 & 286012 & 288452.426548781 & 884.147480509698 & 282687.425970709 & 2440.42654878111 \tabularnewline
41 & 281337 & 282387.01651538 & -2659.61302990276 & 282946.596514523 & 1050.01651537971 \tabularnewline
42 & 276270 & 276783.499315821 & -7186.42497547211 & 282942.925659651 & 513.499315821042 \tabularnewline
43 & 271472 & 271934.980422297 & -11930.2352270758 & 282939.254804779 & 462.980422296678 \tabularnewline
44 & 265637 & 265488.155168966 & -16958.9903902126 & 282744.835221247 & -148.844831034134 \tabularnewline
45 & 268974 & 268614.330504511 & -13216.746142225 & 282550.415637714 & -359.66949548939 \tabularnewline
46 & 299299 & 299643.825649976 & 16752.189045712 & 282201.985304312 & 344.825649976323 \tabularnewline
47 & 305452 & 304398.521916956 & 24651.9231121351 & 281853.554970909 & -1053.47808304417 \tabularnewline
48 & 295468 & 296432.824242736 & 13130.466902468 & 281372.708854796 & 964.824242736446 \tabularnewline
49 & 285584 & 286734.915755371 & 3541.22150594703 & 280891.862738682 & 1150.91575537098 \tabularnewline
50 & 278204 & 279313.885880334 & -3109.04670706575 & 280203.160826731 & 1109.88588033442 \tabularnewline
51 & 276505 & 277394.43458633 & -3898.89350111089 & 279514.458914781 & 889.434586330142 \tabularnewline
52 & 279732 & 280170.488672198 & 884.147480509698 & 278409.363847293 & 438.488672197505 \tabularnewline
53 & 276980 & 279315.344250098 & -2659.61302990276 & 277304.268779805 & 2335.34425009793 \tabularnewline
54 & 271832 & 274829.862020507 & -7186.42497547211 & 276020.562954966 & 2997.86202050658 \tabularnewline
55 & 263105 & 263403.37809695 & -11930.2352270758 & 274736.857130126 & 298.378096949542 \tabularnewline
56 & 256162 & 255844.236406876 & -16958.9903902126 & 273438.753983336 & -317.76359312376 \tabularnewline
57 & 260705 & 262486.095305678 & -13216.746142225 & 272140.650836546 & 1781.09530567849 \tabularnewline
58 & 285857 & 284162.514222924 & 16752.189045712 & 270799.296731364 & -1694.48577707581 \tabularnewline
59 & 291870 & 289630.134261684 & 24651.9231121351 & 269457.942626181 & -2239.86573831638 \tabularnewline
60 & 280358 & 279512.579678425 & 13130.466902468 & 268072.953419107 & -845.420321574784 \tabularnewline
61 & 270981 & 271732.814282021 & 3541.22150594703 & 266687.964212032 & 751.814282020787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285549&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]276444[/C][C]277711.090208026[/C][C]3541.22150594703[/C][C]271635.688286027[/C][C]1267.09020802588[/C][/ROW]
[ROW][C]2[/C][C]268606[/C][C]268977.730539688[/C][C]-3109.04670706575[/C][C]271343.316167378[/C][C]371.730539687618[/C][/ROW]
[ROW][C]3[/C][C]267679[/C][C]268205.949452382[/C][C]-3898.89350111089[/C][C]271050.944048729[/C][C]526.949452381697[/C][/ROW]
[ROW][C]4[/C][C]269879[/C][C]268115.068973461[/C][C]884.147480509698[/C][C]270758.783546029[/C][C]-1763.93102653872[/C][/ROW]
[ROW][C]5[/C][C]265641[/C][C]263474.989986574[/C][C]-2659.61302990276[/C][C]270466.623043329[/C][C]-2166.01001342613[/C][/ROW]
[ROW][C]6[/C][C]262525[/C][C]262071.631374581[/C][C]-7186.42497547211[/C][C]270164.793600891[/C][C]-453.368625418807[/C][/ROW]
[ROW][C]7[/C][C]258597[/C][C]259261.271068623[/C][C]-11930.2352270758[/C][C]269862.964158453[/C][C]664.271068622882[/C][/ROW]
[ROW][C]8[/C][C]253849[/C][C]255086.358492478[/C][C]-16958.9903902126[/C][C]269570.631897734[/C][C]1237.35849247815[/C][/ROW]
[ROW][C]9[/C][C]256221[/C][C]256380.446505209[/C][C]-13216.746142225[/C][C]269278.299637016[/C][C]159.446505208907[/C][/ROW]
[ROW][C]10[/C][C]286895[/C][C]287911.972661708[/C][C]16752.189045712[/C][C]269125.83829258[/C][C]1016.97266170796[/C][/ROW]
[ROW][C]11[/C][C]294610[/C][C]295594.699939721[/C][C]24651.9231121351[/C][C]268973.376948144[/C][C]984.699939720798[/C][/ROW]
[ROW][C]12[/C][C]280363[/C][C]278652.677392938[/C][C]13130.466902468[/C][C]268942.855704594[/C][C]-1710.32260706217[/C][/ROW]
[ROW][C]13[/C][C]269926[/C][C]267398.444033009[/C][C]3541.22150594703[/C][C]268912.334461044[/C][C]-2527.55596699117[/C][/ROW]
[ROW][C]14[/C][C]264341[/C][C]262858.69159228[/C][C]-3109.04670706575[/C][C]268932.355114786[/C][C]-1482.30840772041[/C][/ROW]
[ROW][C]15[/C][C]263269[/C][C]261484.517732583[/C][C]-3898.89350111089[/C][C]268952.375768528[/C][C]-1784.4822674173[/C][/ROW]
[ROW][C]16[/C][C]271045[/C][C]272061.111349035[/C][C]884.147480509698[/C][C]269144.741170455[/C][C]1016.11134903505[/C][/ROW]
[ROW][C]17[/C][C]267915[/C][C]269152.50645752[/C][C]-2659.61302990276[/C][C]269337.106572382[/C][C]1237.50645752047[/C][/ROW]
[ROW][C]18[/C][C]262078[/C][C]261661.470829478[/C][C]-7186.42497547211[/C][C]269680.954145994[/C][C]-416.529170521826[/C][/ROW]
[ROW][C]19[/C][C]257751[/C][C]257407.43350747[/C][C]-11930.2352270758[/C][C]270024.801719606[/C][C]-343.566492529702[/C][/ROW]
[ROW][C]20[/C][C]253271[/C][C]253152.406843214[/C][C]-16958.9903902126[/C][C]270348.583546999[/C][C]-118.593156786053[/C][/ROW]
[ROW][C]21[/C][C]257638[/C][C]257820.380767833[/C][C]-13216.746142225[/C][C]270672.365374392[/C][C]182.380767833209[/C][/ROW]
[ROW][C]22[/C][C]287452[/C][C]287283.30234509[/C][C]16752.189045712[/C][C]270868.508609198[/C][C]-168.697654910211[/C][/ROW]
[ROW][C]23[/C][C]298152[/C][C]300587.42504386[/C][C]24651.9231121351[/C][C]271064.651844005[/C][C]2435.42504386016[/C][/ROW]
[ROW][C]24[/C][C]284793[/C][C]285179.353100433[/C][C]13130.466902468[/C][C]271276.179997099[/C][C]386.353100433131[/C][/ROW]
[ROW][C]25[/C][C]274560[/C][C]274091.07034386[/C][C]3541.22150594703[/C][C]271487.708150193[/C][C]-468.929656140041[/C][/ROW]
[ROW][C]26[/C][C]268270[/C][C]267802.439052854[/C][C]-3109.04670706575[/C][C]271846.607654212[/C][C]-467.5609471465[/C][/ROW]
[ROW][C]27[/C][C]267577[/C][C]266847.386342879[/C][C]-3898.89350111089[/C][C]272205.507158232[/C][C]-729.613657120673[/C][/ROW]
[ROW][C]28[/C][C]271866[/C][C]270072.310967322[/C][C]884.147480509698[/C][C]272775.541552168[/C][C]-1793.68903267791[/C][/ROW]
[ROW][C]29[/C][C]268546[/C][C]266406.037083798[/C][C]-2659.61302990276[/C][C]273345.575946105[/C][C]-2139.96291620203[/C][/ROW]
[ROW][C]30[/C][C]264722[/C][C]262460.921907167[/C][C]-7186.42497547211[/C][C]274169.503068305[/C][C]-2261.07809283287[/C][/ROW]
[ROW][C]31[/C][C]262425[/C][C]261786.805036571[/C][C]-11930.2352270758[/C][C]274993.430190505[/C][C]-638.194963429356[/C][/ROW]
[ROW][C]32[/C][C]258973[/C][C]258865.186576476[/C][C]-16958.9903902126[/C][C]276039.803813736[/C][C]-107.813423523621[/C][/ROW]
[ROW][C]33[/C][C]262751[/C][C]261632.568705258[/C][C]-13216.746142225[/C][C]277086.177436967[/C][C]-1118.43129474233[/C][/ROW]
[ROW][C]34[/C][C]296186[/C][C]297421.630383893[/C][C]16752.189045712[/C][C]278198.180570395[/C][C]1235.63038389338[/C][/ROW]
[ROW][C]35[/C][C]304659[/C][C]305355.893184043[/C][C]24651.9231121351[/C][C]279310.183703822[/C][C]696.893184042885[/C][/ROW]
[ROW][C]36[/C][C]295442[/C][C]297493.961237668[/C][C]13130.466902468[/C][C]280259.571859864[/C][C]2051.96123766794[/C][/ROW]
[ROW][C]37[/C][C]285466[/C][C]286181.818478147[/C][C]3541.22150594703[/C][C]281208.960015906[/C][C]715.818478146975[/C][/ROW]
[ROW][C]38[/C][C]279575[/C][C]280440.438985665[/C][C]-3109.04670706575[/C][C]281818.607721401[/C][C]865.438985665096[/C][/ROW]
[ROW][C]39[/C][C]279985[/C][C]281440.638074216[/C][C]-3898.89350111089[/C][C]282428.255426895[/C][C]1455.63807421556[/C][/ROW]
[ROW][C]40[/C][C]286012[/C][C]288452.426548781[/C][C]884.147480509698[/C][C]282687.425970709[/C][C]2440.42654878111[/C][/ROW]
[ROW][C]41[/C][C]281337[/C][C]282387.01651538[/C][C]-2659.61302990276[/C][C]282946.596514523[/C][C]1050.01651537971[/C][/ROW]
[ROW][C]42[/C][C]276270[/C][C]276783.499315821[/C][C]-7186.42497547211[/C][C]282942.925659651[/C][C]513.499315821042[/C][/ROW]
[ROW][C]43[/C][C]271472[/C][C]271934.980422297[/C][C]-11930.2352270758[/C][C]282939.254804779[/C][C]462.980422296678[/C][/ROW]
[ROW][C]44[/C][C]265637[/C][C]265488.155168966[/C][C]-16958.9903902126[/C][C]282744.835221247[/C][C]-148.844831034134[/C][/ROW]
[ROW][C]45[/C][C]268974[/C][C]268614.330504511[/C][C]-13216.746142225[/C][C]282550.415637714[/C][C]-359.66949548939[/C][/ROW]
[ROW][C]46[/C][C]299299[/C][C]299643.825649976[/C][C]16752.189045712[/C][C]282201.985304312[/C][C]344.825649976323[/C][/ROW]
[ROW][C]47[/C][C]305452[/C][C]304398.521916956[/C][C]24651.9231121351[/C][C]281853.554970909[/C][C]-1053.47808304417[/C][/ROW]
[ROW][C]48[/C][C]295468[/C][C]296432.824242736[/C][C]13130.466902468[/C][C]281372.708854796[/C][C]964.824242736446[/C][/ROW]
[ROW][C]49[/C][C]285584[/C][C]286734.915755371[/C][C]3541.22150594703[/C][C]280891.862738682[/C][C]1150.91575537098[/C][/ROW]
[ROW][C]50[/C][C]278204[/C][C]279313.885880334[/C][C]-3109.04670706575[/C][C]280203.160826731[/C][C]1109.88588033442[/C][/ROW]
[ROW][C]51[/C][C]276505[/C][C]277394.43458633[/C][C]-3898.89350111089[/C][C]279514.458914781[/C][C]889.434586330142[/C][/ROW]
[ROW][C]52[/C][C]279732[/C][C]280170.488672198[/C][C]884.147480509698[/C][C]278409.363847293[/C][C]438.488672197505[/C][/ROW]
[ROW][C]53[/C][C]276980[/C][C]279315.344250098[/C][C]-2659.61302990276[/C][C]277304.268779805[/C][C]2335.34425009793[/C][/ROW]
[ROW][C]54[/C][C]271832[/C][C]274829.862020507[/C][C]-7186.42497547211[/C][C]276020.562954966[/C][C]2997.86202050658[/C][/ROW]
[ROW][C]55[/C][C]263105[/C][C]263403.37809695[/C][C]-11930.2352270758[/C][C]274736.857130126[/C][C]298.378096949542[/C][/ROW]
[ROW][C]56[/C][C]256162[/C][C]255844.236406876[/C][C]-16958.9903902126[/C][C]273438.753983336[/C][C]-317.76359312376[/C][/ROW]
[ROW][C]57[/C][C]260705[/C][C]262486.095305678[/C][C]-13216.746142225[/C][C]272140.650836546[/C][C]1781.09530567849[/C][/ROW]
[ROW][C]58[/C][C]285857[/C][C]284162.514222924[/C][C]16752.189045712[/C][C]270799.296731364[/C][C]-1694.48577707581[/C][/ROW]
[ROW][C]59[/C][C]291870[/C][C]289630.134261684[/C][C]24651.9231121351[/C][C]269457.942626181[/C][C]-2239.86573831638[/C][/ROW]
[ROW][C]60[/C][C]280358[/C][C]279512.579678425[/C][C]13130.466902468[/C][C]268072.953419107[/C][C]-845.420321574784[/C][/ROW]
[ROW][C]61[/C][C]270981[/C][C]271732.814282021[/C][C]3541.22150594703[/C][C]266687.964212032[/C][C]751.814282020787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285549&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285549&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1276444277711.0902080263541.22150594703271635.6882860271267.09020802588
2268606268977.730539688-3109.04670706575271343.316167378371.730539687618
3267679268205.949452382-3898.89350111089271050.944048729526.949452381697
4269879268115.068973461884.147480509698270758.783546029-1763.93102653872
5265641263474.989986574-2659.61302990276270466.623043329-2166.01001342613
6262525262071.631374581-7186.42497547211270164.793600891-453.368625418807
7258597259261.271068623-11930.2352270758269862.964158453664.271068622882
8253849255086.358492478-16958.9903902126269570.6318977341237.35849247815
9256221256380.446505209-13216.746142225269278.299637016159.446505208907
10286895287911.97266170816752.189045712269125.838292581016.97266170796
11294610295594.69993972124651.9231121351268973.376948144984.699939720798
12280363278652.67739293813130.466902468268942.855704594-1710.32260706217
13269926267398.4440330093541.22150594703268912.334461044-2527.55596699117
14264341262858.69159228-3109.04670706575268932.355114786-1482.30840772041
15263269261484.517732583-3898.89350111089268952.375768528-1784.4822674173
16271045272061.111349035884.147480509698269144.7411704551016.11134903505
17267915269152.50645752-2659.61302990276269337.1065723821237.50645752047
18262078261661.470829478-7186.42497547211269680.954145994-416.529170521826
19257751257407.43350747-11930.2352270758270024.801719606-343.566492529702
20253271253152.406843214-16958.9903902126270348.583546999-118.593156786053
21257638257820.380767833-13216.746142225270672.365374392182.380767833209
22287452287283.3023450916752.189045712270868.508609198-168.697654910211
23298152300587.4250438624651.9231121351271064.6518440052435.42504386016
24284793285179.35310043313130.466902468271276.179997099386.353100433131
25274560274091.070343863541.22150594703271487.708150193-468.929656140041
26268270267802.439052854-3109.04670706575271846.607654212-467.5609471465
27267577266847.386342879-3898.89350111089272205.507158232-729.613657120673
28271866270072.310967322884.147480509698272775.541552168-1793.68903267791
29268546266406.037083798-2659.61302990276273345.575946105-2139.96291620203
30264722262460.921907167-7186.42497547211274169.503068305-2261.07809283287
31262425261786.805036571-11930.2352270758274993.430190505-638.194963429356
32258973258865.186576476-16958.9903902126276039.803813736-107.813423523621
33262751261632.568705258-13216.746142225277086.177436967-1118.43129474233
34296186297421.63038389316752.189045712278198.1805703951235.63038389338
35304659305355.89318404324651.9231121351279310.183703822696.893184042885
36295442297493.96123766813130.466902468280259.5718598642051.96123766794
37285466286181.8184781473541.22150594703281208.960015906715.818478146975
38279575280440.438985665-3109.04670706575281818.607721401865.438985665096
39279985281440.638074216-3898.89350111089282428.2554268951455.63807421556
40286012288452.426548781884.147480509698282687.4259707092440.42654878111
41281337282387.01651538-2659.61302990276282946.5965145231050.01651537971
42276270276783.499315821-7186.42497547211282942.925659651513.499315821042
43271472271934.980422297-11930.2352270758282939.254804779462.980422296678
44265637265488.155168966-16958.9903902126282744.835221247-148.844831034134
45268974268614.330504511-13216.746142225282550.415637714-359.66949548939
46299299299643.82564997616752.189045712282201.985304312344.825649976323
47305452304398.52191695624651.9231121351281853.554970909-1053.47808304417
48295468296432.82424273613130.466902468281372.708854796964.824242736446
49285584286734.9157553713541.22150594703280891.8627386821150.91575537098
50278204279313.885880334-3109.04670706575280203.1608267311109.88588033442
51276505277394.43458633-3898.89350111089279514.458914781889.434586330142
52279732280170.488672198884.147480509698278409.363847293438.488672197505
53276980279315.344250098-2659.61302990276277304.2687798052335.34425009793
54271832274829.862020507-7186.42497547211276020.5629549662997.86202050658
55263105263403.37809695-11930.2352270758274736.857130126298.378096949542
56256162255844.236406876-16958.9903902126273438.753983336-317.76359312376
57260705262486.095305678-13216.746142225272140.6508365461781.09530567849
58285857284162.51422292416752.189045712270799.296731364-1694.48577707581
59291870289630.13426168424651.9231121351269457.942626181-2239.86573831638
60280358279512.57967842513130.466902468268072.953419107-845.420321574784
61270981271732.8142820213541.22150594703266687.964212032751.814282020787


Figures (Output of Computation)

Input Parameters & R Code

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')