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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Wed, 02 Dec 2009 09:39:05 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/02/t1259772069na10089qd5771j0.htm/, Retrieved Sat, 27 Apr 2024 16:28:39 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62426, Retrieved Sat, 27 Apr 2024 16:28:39 +0000

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Estimated Impact

156

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

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Classical Decomposition] [] [2009-11-27 14:58:37] [b98453cac15ba1066b407e146608df68]
-   PD      [Classical Decomposition] [WS9 Berekening2 TVD] [2009-12-02 16:39:05] [37de18e38c1490dd77c2b362ed87f3bb] [Current]
-   P         [Classical Decomposition] [BDM 8] [2009-12-02 17:26:53] [f5d341d4bbba73282fc6e80153a6d315] 
-   P         [Classical Decomposition] [TG 8] [2009-12-02 18:03:32] [a21bac9c8d3d56fdec8be4e719e2c7ed] 
-   PD          [Classical Decomposition] [WS9-ClasDec] [2009-12-04 13:43:42] [a94022e7c2399c0f4d62eea578db3411] 
-    D            [Classical Decomposition] [] [2010-12-07 09:46:51] [fb3a7008aea9486db3846dc25434607b] 
-               [Classical Decomposition] [WorkShop9 (SHW)] [2009-12-04 14:49:59] [37daf76adc256428993ec4063536c760] 
F    D          [Classical Decomposition] [WS 9 Classical De...] [2009-12-11 09:02:56] [1d635fe1113b56bab3f378c464a289bc] 
-   PD        [Classical Decomposition] [blog 4] [2009-12-07 20:38:59] [42ad1186d39724f834063794eac7cea3] 

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62426&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62426&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62426&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	101.3	NA	NA	0.942724391828947	NA
2	106.3	NA	NA	0.954412978450807	NA
3	94	NA	NA	1.11947962014922	NA
4	102.8	NA	NA	1.05783668893803	NA
5	102	NA	NA	1.02827152273714	NA
6	105.1	NA	NA	1.14065121022909	NA
7	92.4	89.7675137836764	99.7833333333333	0.89962432387182	1.02932560015717
8	81.4	80.0460353352085	99.225	0.806712374252543	1.01691482481451
9	105.8	103.252653452573	99.35	1.03928186665901	1.02467100323574
10	120.3	109.520568119282	99.7625	1.09781298703703	1.09842381267579
11	100.7	100.756341065575	99.65	1.01110226859584	0.999440818662337
12	88.8	89.7391383046095	99.4791666666667	0.902089767250525	0.989534796941979
13	94.3	93.6989485111988	99.3916666666667	0.942724391828947	1.00641470900529
14	99.9	94.546535677783	99.0625	0.954412978450807	1.05662253284945
15	103.4	110.711869934341	98.8958333333333	1.11947962014922	0.933955862739224
16	103.3	104.479003644113	98.7666666666667	1.05783668893803	0.988715401152478
17	98.8	101.147642119910	98.3666666666667	1.02827152273714	0.976789947143533
18	104.2	112.059475978256	98.2416666666667	1.14065121022909	0.929863352388146
19	91.2	88.5155365996214	98.3916666666667	0.89962432387182	1.03032759562337
20	74.7	79.4813366732318	98.525	0.806712374252543	0.939843278015202
21	108.5	103.243992770351	99.3416666666667	1.03928186665901	1.05090860096180
22	114.5	110.490302924498	100.645833333333	1.09781298703703	1.03629003604273
23	96.9	102.702712932622	101.575	1.01110226859584	0.943499906020702
24	89.6	92.949074393076	103.0375	0.902089767250525	0.96396871711801
25	97.1	98.569691202315	104.558333333333	0.942724391828947	0.985089826452855
26	100.3	101.108124904632	105.9375	0.954412978450807	0.992007319833153
27	122.6	120.493323115395	107.633333333333	1.11947962014922	1.01748376449530
28	115.4	115.339460317210	109.033333333333	1.05783668893803	1.00052488266049
29	109	114.001036154124	110.866666666667	1.02827152273714	0.95613166052839
30	129.1	129.302320106219	113.358333333333	1.14065121022909	0.998435294076295
31	102.8	103.715439238372	115.2875	0.89962432387182	0.99117354903865
32	96.2	94.0088820128963	116.533333333333	0.806712374252543	1.02330756350026
33	127.7	122.206556495767	117.5875	1.03928186665901	1.04495211764210
34	128.9	130.282956236620	118.675	1.09781298703703	0.989384979612316
35	126.5	121.146903482258	119.816666666667	1.01110226859584	1.04418682082556
36	119.8	108.679264709507	120.475	0.902089767250525	1.10232619184734
37	113.2	113.790762112053	120.704166666667	0.942724391828947	0.994808347346584
38	114.1	115.571458248906	121.091666666667	0.954412978450807	0.987267978866057
39	134.1	135.382402063379	120.933333333333	1.11947962014922	0.990527557172616
40	130	127.345906736656	120.383333333333	1.05783668893803	1.02084160638812
41	121.8	124.172355299866	120.758333333333	1.02827152273714	0.980894658121472
42	132.1	138.280195673397	121.229166666667	1.14065121022909	0.9553067187727
43	105.3	109.979073593330	122.25	0.89962432387182	0.957454873545928
44	103	100.173509072809	124.175	0.806712374252543	1.02821595203514
45	117.1	131.906520584584	126.920833333333	1.03928186665901	0.887749896525476
46	126.3	142.843766496635	130.116666666667	1.09781298703703	0.884182790034281
47	138.1	135.239141350812	133.754166666667	1.01110226859584	1.02115407285652
48	119.5	125.465651795094	139.083333333333	0.902089767250525	0.952451912457787
49	138	135.033485074599	143.2375	0.942724391828947	1.02196873556039
50	135.5	139.348271574561	146.004166666667	0.954412978450807	0.972383786816457
51	178.6	167.492809167993	149.616666666667	1.11947962014922	1.06631443395798
52	162.2	162.492530726622	153.608333333333	1.05783668893803	0.998199728164032
53	176.9	161.631429980244	157.1875	1.02827152273714	1.09446535257172
54	204.9	182.185761840465	159.720833333333	1.14065121022909	1.12467625312798
55	132.2	NA	NA	0.89962432387182	NA
56	142.5	NA	NA	0.806712374252543	NA
57	164.3	NA	NA	1.03928186665901	NA
58	174.9	NA	NA	1.09781298703703	NA
59	175.4	NA	NA	1.01110226859584	NA
60	143	NA	NA	0.902089767250525	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 101.3 & NA & NA & 0.942724391828947 & NA \tabularnewline
2 & 106.3 & NA & NA & 0.954412978450807 & NA \tabularnewline
3 & 94 & NA & NA & 1.11947962014922 & NA \tabularnewline
4 & 102.8 & NA & NA & 1.05783668893803 & NA \tabularnewline
5 & 102 & NA & NA & 1.02827152273714 & NA \tabularnewline
6 & 105.1 & NA & NA & 1.14065121022909 & NA \tabularnewline
7 & 92.4 & 89.7675137836764 & 99.7833333333333 & 0.89962432387182 & 1.02932560015717 \tabularnewline
8 & 81.4 & 80.0460353352085 & 99.225 & 0.806712374252543 & 1.01691482481451 \tabularnewline
9 & 105.8 & 103.252653452573 & 99.35 & 1.03928186665901 & 1.02467100323574 \tabularnewline
10 & 120.3 & 109.520568119282 & 99.7625 & 1.09781298703703 & 1.09842381267579 \tabularnewline
11 & 100.7 & 100.756341065575 & 99.65 & 1.01110226859584 & 0.999440818662337 \tabularnewline
12 & 88.8 & 89.7391383046095 & 99.4791666666667 & 0.902089767250525 & 0.989534796941979 \tabularnewline
13 & 94.3 & 93.6989485111988 & 99.3916666666667 & 0.942724391828947 & 1.00641470900529 \tabularnewline
14 & 99.9 & 94.546535677783 & 99.0625 & 0.954412978450807 & 1.05662253284945 \tabularnewline
15 & 103.4 & 110.711869934341 & 98.8958333333333 & 1.11947962014922 & 0.933955862739224 \tabularnewline
16 & 103.3 & 104.479003644113 & 98.7666666666667 & 1.05783668893803 & 0.988715401152478 \tabularnewline
17 & 98.8 & 101.147642119910 & 98.3666666666667 & 1.02827152273714 & 0.976789947143533 \tabularnewline
18 & 104.2 & 112.059475978256 & 98.2416666666667 & 1.14065121022909 & 0.929863352388146 \tabularnewline
19 & 91.2 & 88.5155365996214 & 98.3916666666667 & 0.89962432387182 & 1.03032759562337 \tabularnewline
20 & 74.7 & 79.4813366732318 & 98.525 & 0.806712374252543 & 0.939843278015202 \tabularnewline
21 & 108.5 & 103.243992770351 & 99.3416666666667 & 1.03928186665901 & 1.05090860096180 \tabularnewline
22 & 114.5 & 110.490302924498 & 100.645833333333 & 1.09781298703703 & 1.03629003604273 \tabularnewline
23 & 96.9 & 102.702712932622 & 101.575 & 1.01110226859584 & 0.943499906020702 \tabularnewline
24 & 89.6 & 92.949074393076 & 103.0375 & 0.902089767250525 & 0.96396871711801 \tabularnewline
25 & 97.1 & 98.569691202315 & 104.558333333333 & 0.942724391828947 & 0.985089826452855 \tabularnewline
26 & 100.3 & 101.108124904632 & 105.9375 & 0.954412978450807 & 0.992007319833153 \tabularnewline
27 & 122.6 & 120.493323115395 & 107.633333333333 & 1.11947962014922 & 1.01748376449530 \tabularnewline
28 & 115.4 & 115.339460317210 & 109.033333333333 & 1.05783668893803 & 1.00052488266049 \tabularnewline
29 & 109 & 114.001036154124 & 110.866666666667 & 1.02827152273714 & 0.95613166052839 \tabularnewline
30 & 129.1 & 129.302320106219 & 113.358333333333 & 1.14065121022909 & 0.998435294076295 \tabularnewline
31 & 102.8 & 103.715439238372 & 115.2875 & 0.89962432387182 & 0.99117354903865 \tabularnewline
32 & 96.2 & 94.0088820128963 & 116.533333333333 & 0.806712374252543 & 1.02330756350026 \tabularnewline
33 & 127.7 & 122.206556495767 & 117.5875 & 1.03928186665901 & 1.04495211764210 \tabularnewline
34 & 128.9 & 130.282956236620 & 118.675 & 1.09781298703703 & 0.989384979612316 \tabularnewline
35 & 126.5 & 121.146903482258 & 119.816666666667 & 1.01110226859584 & 1.04418682082556 \tabularnewline
36 & 119.8 & 108.679264709507 & 120.475 & 0.902089767250525 & 1.10232619184734 \tabularnewline
37 & 113.2 & 113.790762112053 & 120.704166666667 & 0.942724391828947 & 0.994808347346584 \tabularnewline
38 & 114.1 & 115.571458248906 & 121.091666666667 & 0.954412978450807 & 0.987267978866057 \tabularnewline
39 & 134.1 & 135.382402063379 & 120.933333333333 & 1.11947962014922 & 0.990527557172616 \tabularnewline
40 & 130 & 127.345906736656 & 120.383333333333 & 1.05783668893803 & 1.02084160638812 \tabularnewline
41 & 121.8 & 124.172355299866 & 120.758333333333 & 1.02827152273714 & 0.980894658121472 \tabularnewline
42 & 132.1 & 138.280195673397 & 121.229166666667 & 1.14065121022909 & 0.9553067187727 \tabularnewline
43 & 105.3 & 109.979073593330 & 122.25 & 0.89962432387182 & 0.957454873545928 \tabularnewline
44 & 103 & 100.173509072809 & 124.175 & 0.806712374252543 & 1.02821595203514 \tabularnewline
45 & 117.1 & 131.906520584584 & 126.920833333333 & 1.03928186665901 & 0.887749896525476 \tabularnewline
46 & 126.3 & 142.843766496635 & 130.116666666667 & 1.09781298703703 & 0.884182790034281 \tabularnewline
47 & 138.1 & 135.239141350812 & 133.754166666667 & 1.01110226859584 & 1.02115407285652 \tabularnewline
48 & 119.5 & 125.465651795094 & 139.083333333333 & 0.902089767250525 & 0.952451912457787 \tabularnewline
49 & 138 & 135.033485074599 & 143.2375 & 0.942724391828947 & 1.02196873556039 \tabularnewline
50 & 135.5 & 139.348271574561 & 146.004166666667 & 0.954412978450807 & 0.972383786816457 \tabularnewline
51 & 178.6 & 167.492809167993 & 149.616666666667 & 1.11947962014922 & 1.06631443395798 \tabularnewline
52 & 162.2 & 162.492530726622 & 153.608333333333 & 1.05783668893803 & 0.998199728164032 \tabularnewline
53 & 176.9 & 161.631429980244 & 157.1875 & 1.02827152273714 & 1.09446535257172 \tabularnewline
54 & 204.9 & 182.185761840465 & 159.720833333333 & 1.14065121022909 & 1.12467625312798 \tabularnewline
55 & 132.2 & NA & NA & 0.89962432387182 & NA \tabularnewline
56 & 142.5 & NA & NA & 0.806712374252543 & NA \tabularnewline
57 & 164.3 & NA & NA & 1.03928186665901 & NA \tabularnewline
58 & 174.9 & NA & NA & 1.09781298703703 & NA \tabularnewline
59 & 175.4 & NA & NA & 1.01110226859584 & NA \tabularnewline
60 & 143 & NA & NA & 0.902089767250525 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62426&T=1

[TABLE]
[ROW][C]Classical Decomposition by Moving Averages[/C][/ROW]
[ROW][C]t[/C][C]Observations[/C][C]Fit[/C][C]Trend[/C][C]Seasonal[/C][C]Random[/C][/ROW]
[ROW][C]1[/C][C]101.3[/C][C]NA[/C][C]NA[/C][C]0.942724391828947[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]106.3[/C][C]NA[/C][C]NA[/C][C]0.954412978450807[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]94[/C][C]NA[/C][C]NA[/C][C]1.11947962014922[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]102.8[/C][C]NA[/C][C]NA[/C][C]1.05783668893803[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]102[/C][C]NA[/C][C]NA[/C][C]1.02827152273714[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]105.1[/C][C]NA[/C][C]NA[/C][C]1.14065121022909[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]92.4[/C][C]89.7675137836764[/C][C]99.7833333333333[/C][C]0.89962432387182[/C][C]1.02932560015717[/C][/ROW]
[ROW][C]8[/C][C]81.4[/C][C]80.0460353352085[/C][C]99.225[/C][C]0.806712374252543[/C][C]1.01691482481451[/C][/ROW]
[ROW][C]9[/C][C]105.8[/C][C]103.252653452573[/C][C]99.35[/C][C]1.03928186665901[/C][C]1.02467100323574[/C][/ROW]
[ROW][C]10[/C][C]120.3[/C][C]109.520568119282[/C][C]99.7625[/C][C]1.09781298703703[/C][C]1.09842381267579[/C][/ROW]
[ROW][C]11[/C][C]100.7[/C][C]100.756341065575[/C][C]99.65[/C][C]1.01110226859584[/C][C]0.999440818662337[/C][/ROW]
[ROW][C]12[/C][C]88.8[/C][C]89.7391383046095[/C][C]99.4791666666667[/C][C]0.902089767250525[/C][C]0.989534796941979[/C][/ROW]
[ROW][C]13[/C][C]94.3[/C][C]93.6989485111988[/C][C]99.3916666666667[/C][C]0.942724391828947[/C][C]1.00641470900529[/C][/ROW]
[ROW][C]14[/C][C]99.9[/C][C]94.546535677783[/C][C]99.0625[/C][C]0.954412978450807[/C][C]1.05662253284945[/C][/ROW]
[ROW][C]15[/C][C]103.4[/C][C]110.711869934341[/C][C]98.8958333333333[/C][C]1.11947962014922[/C][C]0.933955862739224[/C][/ROW]
[ROW][C]16[/C][C]103.3[/C][C]104.479003644113[/C][C]98.7666666666667[/C][C]1.05783668893803[/C][C]0.988715401152478[/C][/ROW]
[ROW][C]17[/C][C]98.8[/C][C]101.147642119910[/C][C]98.3666666666667[/C][C]1.02827152273714[/C][C]0.976789947143533[/C][/ROW]
[ROW][C]18[/C][C]104.2[/C][C]112.059475978256[/C][C]98.2416666666667[/C][C]1.14065121022909[/C][C]0.929863352388146[/C][/ROW]
[ROW][C]19[/C][C]91.2[/C][C]88.5155365996214[/C][C]98.3916666666667[/C][C]0.89962432387182[/C][C]1.03032759562337[/C][/ROW]
[ROW][C]20[/C][C]74.7[/C][C]79.4813366732318[/C][C]98.525[/C][C]0.806712374252543[/C][C]0.939843278015202[/C][/ROW]
[ROW][C]21[/C][C]108.5[/C][C]103.243992770351[/C][C]99.3416666666667[/C][C]1.03928186665901[/C][C]1.05090860096180[/C][/ROW]
[ROW][C]22[/C][C]114.5[/C][C]110.490302924498[/C][C]100.645833333333[/C][C]1.09781298703703[/C][C]1.03629003604273[/C][/ROW]
[ROW][C]23[/C][C]96.9[/C][C]102.702712932622[/C][C]101.575[/C][C]1.01110226859584[/C][C]0.943499906020702[/C][/ROW]
[ROW][C]24[/C][C]89.6[/C][C]92.949074393076[/C][C]103.0375[/C][C]0.902089767250525[/C][C]0.96396871711801[/C][/ROW]
[ROW][C]25[/C][C]97.1[/C][C]98.569691202315[/C][C]104.558333333333[/C][C]0.942724391828947[/C][C]0.985089826452855[/C][/ROW]
[ROW][C]26[/C][C]100.3[/C][C]101.108124904632[/C][C]105.9375[/C][C]0.954412978450807[/C][C]0.992007319833153[/C][/ROW]
[ROW][C]27[/C][C]122.6[/C][C]120.493323115395[/C][C]107.633333333333[/C][C]1.11947962014922[/C][C]1.01748376449530[/C][/ROW]
[ROW][C]28[/C][C]115.4[/C][C]115.339460317210[/C][C]109.033333333333[/C][C]1.05783668893803[/C][C]1.00052488266049[/C][/ROW]
[ROW][C]29[/C][C]109[/C][C]114.001036154124[/C][C]110.866666666667[/C][C]1.02827152273714[/C][C]0.95613166052839[/C][/ROW]
[ROW][C]30[/C][C]129.1[/C][C]129.302320106219[/C][C]113.358333333333[/C][C]1.14065121022909[/C][C]0.998435294076295[/C][/ROW]
[ROW][C]31[/C][C]102.8[/C][C]103.715439238372[/C][C]115.2875[/C][C]0.89962432387182[/C][C]0.99117354903865[/C][/ROW]
[ROW][C]32[/C][C]96.2[/C][C]94.0088820128963[/C][C]116.533333333333[/C][C]0.806712374252543[/C][C]1.02330756350026[/C][/ROW]
[ROW][C]33[/C][C]127.7[/C][C]122.206556495767[/C][C]117.5875[/C][C]1.03928186665901[/C][C]1.04495211764210[/C][/ROW]
[ROW][C]34[/C][C]128.9[/C][C]130.282956236620[/C][C]118.675[/C][C]1.09781298703703[/C][C]0.989384979612316[/C][/ROW]
[ROW][C]35[/C][C]126.5[/C][C]121.146903482258[/C][C]119.816666666667[/C][C]1.01110226859584[/C][C]1.04418682082556[/C][/ROW]
[ROW][C]36[/C][C]119.8[/C][C]108.679264709507[/C][C]120.475[/C][C]0.902089767250525[/C][C]1.10232619184734[/C][/ROW]
[ROW][C]37[/C][C]113.2[/C][C]113.790762112053[/C][C]120.704166666667[/C][C]0.942724391828947[/C][C]0.994808347346584[/C][/ROW]
[ROW][C]38[/C][C]114.1[/C][C]115.571458248906[/C][C]121.091666666667[/C][C]0.954412978450807[/C][C]0.987267978866057[/C][/ROW]
[ROW][C]39[/C][C]134.1[/C][C]135.382402063379[/C][C]120.933333333333[/C][C]1.11947962014922[/C][C]0.990527557172616[/C][/ROW]
[ROW][C]40[/C][C]130[/C][C]127.345906736656[/C][C]120.383333333333[/C][C]1.05783668893803[/C][C]1.02084160638812[/C][/ROW]
[ROW][C]41[/C][C]121.8[/C][C]124.172355299866[/C][C]120.758333333333[/C][C]1.02827152273714[/C][C]0.980894658121472[/C][/ROW]
[ROW][C]42[/C][C]132.1[/C][C]138.280195673397[/C][C]121.229166666667[/C][C]1.14065121022909[/C][C]0.9553067187727[/C][/ROW]
[ROW][C]43[/C][C]105.3[/C][C]109.979073593330[/C][C]122.25[/C][C]0.89962432387182[/C][C]0.957454873545928[/C][/ROW]
[ROW][C]44[/C][C]103[/C][C]100.173509072809[/C][C]124.175[/C][C]0.806712374252543[/C][C]1.02821595203514[/C][/ROW]
[ROW][C]45[/C][C]117.1[/C][C]131.906520584584[/C][C]126.920833333333[/C][C]1.03928186665901[/C][C]0.887749896525476[/C][/ROW]
[ROW][C]46[/C][C]126.3[/C][C]142.843766496635[/C][C]130.116666666667[/C][C]1.09781298703703[/C][C]0.884182790034281[/C][/ROW]
[ROW][C]47[/C][C]138.1[/C][C]135.239141350812[/C][C]133.754166666667[/C][C]1.01110226859584[/C][C]1.02115407285652[/C][/ROW]
[ROW][C]48[/C][C]119.5[/C][C]125.465651795094[/C][C]139.083333333333[/C][C]0.902089767250525[/C][C]0.952451912457787[/C][/ROW]
[ROW][C]49[/C][C]138[/C][C]135.033485074599[/C][C]143.2375[/C][C]0.942724391828947[/C][C]1.02196873556039[/C][/ROW]
[ROW][C]50[/C][C]135.5[/C][C]139.348271574561[/C][C]146.004166666667[/C][C]0.954412978450807[/C][C]0.972383786816457[/C][/ROW]
[ROW][C]51[/C][C]178.6[/C][C]167.492809167993[/C][C]149.616666666667[/C][C]1.11947962014922[/C][C]1.06631443395798[/C][/ROW]
[ROW][C]52[/C][C]162.2[/C][C]162.492530726622[/C][C]153.608333333333[/C][C]1.05783668893803[/C][C]0.998199728164032[/C][/ROW]
[ROW][C]53[/C][C]176.9[/C][C]161.631429980244[/C][C]157.1875[/C][C]1.02827152273714[/C][C]1.09446535257172[/C][/ROW]
[ROW][C]54[/C][C]204.9[/C][C]182.185761840465[/C][C]159.720833333333[/C][C]1.14065121022909[/C][C]1.12467625312798[/C][/ROW]
[ROW][C]55[/C][C]132.2[/C][C]NA[/C][C]NA[/C][C]0.89962432387182[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]142.5[/C][C]NA[/C][C]NA[/C][C]0.806712374252543[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]164.3[/C][C]NA[/C][C]NA[/C][C]1.03928186665901[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]174.9[/C][C]NA[/C][C]NA[/C][C]1.09781298703703[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]175.4[/C][C]NA[/C][C]NA[/C][C]1.01110226859584[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]143[/C][C]NA[/C][C]NA[/C][C]0.902089767250525[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62426&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62426&T=1

As an alternative you can also use a QR Code:

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

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	101.3	NA	NA	0.942724391828947	NA
2	106.3	NA	NA	0.954412978450807	NA
3	94	NA	NA	1.11947962014922	NA
4	102.8	NA	NA	1.05783668893803	NA
5	102	NA	NA	1.02827152273714	NA
6	105.1	NA	NA	1.14065121022909	NA
7	92.4	89.7675137836764	99.7833333333333	0.89962432387182	1.02932560015717
8	81.4	80.0460353352085	99.225	0.806712374252543	1.01691482481451
9	105.8	103.252653452573	99.35	1.03928186665901	1.02467100323574
10	120.3	109.520568119282	99.7625	1.09781298703703	1.09842381267579
11	100.7	100.756341065575	99.65	1.01110226859584	0.999440818662337
12	88.8	89.7391383046095	99.4791666666667	0.902089767250525	0.989534796941979
13	94.3	93.6989485111988	99.3916666666667	0.942724391828947	1.00641470900529
14	99.9	94.546535677783	99.0625	0.954412978450807	1.05662253284945
15	103.4	110.711869934341	98.8958333333333	1.11947962014922	0.933955862739224
16	103.3	104.479003644113	98.7666666666667	1.05783668893803	0.988715401152478
17	98.8	101.147642119910	98.3666666666667	1.02827152273714	0.976789947143533
18	104.2	112.059475978256	98.2416666666667	1.14065121022909	0.929863352388146
19	91.2	88.5155365996214	98.3916666666667	0.89962432387182	1.03032759562337
20	74.7	79.4813366732318	98.525	0.806712374252543	0.939843278015202
21	108.5	103.243992770351	99.3416666666667	1.03928186665901	1.05090860096180
22	114.5	110.490302924498	100.645833333333	1.09781298703703	1.03629003604273
23	96.9	102.702712932622	101.575	1.01110226859584	0.943499906020702
24	89.6	92.949074393076	103.0375	0.902089767250525	0.96396871711801
25	97.1	98.569691202315	104.558333333333	0.942724391828947	0.985089826452855
26	100.3	101.108124904632	105.9375	0.954412978450807	0.992007319833153
27	122.6	120.493323115395	107.633333333333	1.11947962014922	1.01748376449530
28	115.4	115.339460317210	109.033333333333	1.05783668893803	1.00052488266049
29	109	114.001036154124	110.866666666667	1.02827152273714	0.95613166052839
30	129.1	129.302320106219	113.358333333333	1.14065121022909	0.998435294076295
31	102.8	103.715439238372	115.2875	0.89962432387182	0.99117354903865
32	96.2	94.0088820128963	116.533333333333	0.806712374252543	1.02330756350026
33	127.7	122.206556495767	117.5875	1.03928186665901	1.04495211764210
34	128.9	130.282956236620	118.675	1.09781298703703	0.989384979612316
35	126.5	121.146903482258	119.816666666667	1.01110226859584	1.04418682082556
36	119.8	108.679264709507	120.475	0.902089767250525	1.10232619184734
37	113.2	113.790762112053	120.704166666667	0.942724391828947	0.994808347346584
38	114.1	115.571458248906	121.091666666667	0.954412978450807	0.987267978866057
39	134.1	135.382402063379	120.933333333333	1.11947962014922	0.990527557172616
40	130	127.345906736656	120.383333333333	1.05783668893803	1.02084160638812
41	121.8	124.172355299866	120.758333333333	1.02827152273714	0.980894658121472
42	132.1	138.280195673397	121.229166666667	1.14065121022909	0.9553067187727
43	105.3	109.979073593330	122.25	0.89962432387182	0.957454873545928
44	103	100.173509072809	124.175	0.806712374252543	1.02821595203514
45	117.1	131.906520584584	126.920833333333	1.03928186665901	0.887749896525476
46	126.3	142.843766496635	130.116666666667	1.09781298703703	0.884182790034281
47	138.1	135.239141350812	133.754166666667	1.01110226859584	1.02115407285652
48	119.5	125.465651795094	139.083333333333	0.902089767250525	0.952451912457787
49	138	135.033485074599	143.2375	0.942724391828947	1.02196873556039
50	135.5	139.348271574561	146.004166666667	0.954412978450807	0.972383786816457
51	178.6	167.492809167993	149.616666666667	1.11947962014922	1.06631443395798
52	162.2	162.492530726622	153.608333333333	1.05783668893803	0.998199728164032
53	176.9	161.631429980244	157.1875	1.02827152273714	1.09446535257172
54	204.9	182.185761840465	159.720833333333	1.14065121022909	1.12467625312798
55	132.2	NA	NA	0.89962432387182	NA
56	142.5	NA	NA	0.806712374252543	NA
57	164.3	NA	NA	1.03928186665901	NA
58	174.9	NA	NA	1.09781298703703	NA
59	175.4	NA	NA	1.01110226859584	NA
60	143	NA	NA	0.902089767250525	NA

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code