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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationMon, 29 Nov 2010 18:48:39 +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/2010/Nov/29/t1291056438rgrt7l9oys2bxwz.htm/, Retrieved Mon, 29 Apr 2024 09:58:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103031, Retrieved Mon, 29 Apr 2024 09:58:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165

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]
-  M D  [Classical Decomposition] [WS8 - Classical D...] [2010-11-28 09:41:43] [8ef49741e164ec6343c90c7935194465]
-   PD      [Classical Decomposition] [ws 8 classical de...] [2010-11-29 18:48:39] [b47314d83d48c7bf812ec2bcd743b159] [Current]
- R  D        [Classical Decomposition] [paper classical d...] [2010-12-10 11:47:05] [8214fe6d084e5ad7598b249a26cc9f06]
-    D          [Classical Decomposition] [paper classical d...] [2010-12-21 13:31:40] [8214fe6d084e5ad7598b249a26cc9f06]
-    D            [Classical Decomposition] [CD laag geschoolden] [2010-12-21 19:07:58] [8214fe6d084e5ad7598b249a26cc9f06]
-    D              [Classical Decomposition] [cd middengeschoold] [2010-12-21 19:12:54] [8214fe6d084e5ad7598b249a26cc9f06]
-    D                [Classical Decomposition] [cd hoog geschoold] [2010-12-21 19:15:16] [8214fe6d084e5ad7598b249a26cc9f06]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
167,16
179,84
174,44
180,35
193,17
195,16
202,43
189,91
195,98
212,09
205,81
204,31
196,07
199,98
199,10
198,31
195,72
223,04
238,41
259,73
326,54
335,15
321,81
368,62
369,59
425,00
439,72
362,23
328,76
348,55
328,18
329,34
295,55
237,38
226,85
220,14
239,36
224,69
230,98
233,47
256,70
253,41
224,95
210,37
191,09
198,85
211,04
206,25
201,19
194,37
191,08
192,87
181,61
157,67
196,14
246,35
271,90

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103031&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103031&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103031&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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1167.16NANA0.999982204104269NA
2179.84NANA1.03716151365447NA
3174.44NANA1.05682814473984NA
4180.35NANA0.985682997956348NA
5193.17NANA0.983051682033842NA
6195.16NANA1.03398001321637NA
7202.43193.914943701812192.9254166666670.9895270351457431.06037081029508
8189.91195.964984695419194.9691666666670.9958180287521330.978142014360496
9195.98197.842953812578196.8358333333331.007120479244960.988612649212912
10212.09199.571854037366198.6116666666670.9601873706993531.11213996332746
11205.81200.42208125752199.466250.9558312575201361.07948303406991
12204.31201.728995939599200.7341666666670.9948292729325421.02310396663975
13196.07204.394982204104203.3950.9999822041042690.964003487319606
14199.98208.840494846988207.8033333333331.037161513654470.927871128448571
15199.1217.20932814474216.15251.056828144739840.871578736689774
16198.31227.705682997956226.720.9856829979563480.887396100908084
17195.72237.663885015367236.6808333333330.9830516820338420.8411932260439
18223.04249.394396679883248.3604166666671.033980013216370.868536816829029
19238.41263.426193701812262.4366666666670.9895270351457430.91806258970965
20259.73280.038318028752279.04250.9958180287521330.9346989969479
21326.54299.451287145912298.4441666666671.007120479244961.08640527725365
22335.15316.260187370699315.30.9601873706993531.10702967716330
23321.81328.629164590853327.6733333333330.9558312575201361.02748906244151
24368.62339.441079272932338.446250.9948292729325421.09481473118860
25369.59348.416232204104347.416250.9999822041042691.06384366678920
26425355.094244846988354.0570833333331.037161513654471.15736208221459
27439.72356.72307814474355.666251.056828144739841.16984734235277
28362.23351.286932997956350.301250.9856829979563481.04907240486157
29328.76343.253885015367342.2708333333330.9830516820338420.97708586117988
30348.55333.161480013216332.12751.033980013216371.01495808317140
31328.18321.504110368479320.5145833333330.9895270351457431.03475289920842
32329.34307.737901362085306.7420833333330.9958180287521331.07817965762691
33295.55290.705453812578289.6983333333331.007120479244961.01298622530989
34237.38276.596020704033275.6358333333330.9601873706993530.896917421734397
35226.85268.224164590853267.2683333333330.9558312575201360.88799403709152
36220.14261.296495939599260.3016666666670.9948292729325420.85010673591638
37239.36253.036232204104252.036250.9999822041042690.949721556726096
38224.69243.815078180321242.7779166666671.037161513654470.892335500171742
39230.98234.525161478073233.4683333333331.056828144739840.936142631792353
40233.47228.496099664623227.5104166666670.9856829979563481.04110020506525
41256.7226.229301682034225.246250.9830516820338421.15928962361112
42253.41225.042730013216224.008751.033980013216371.09407381083447
43224.95222.829110368479221.8395833333330.9895270351457431.02475322191683
44210.37219.981651362085218.9858333333330.9958180287521330.964690056260092
45191.09217.067120479245216.061.007120479244960.878177208271354
46198.85213.666020704033212.7058333333330.9601873706993530.973621611937199
47211.04208.841247924187207.8854166666670.9558312575201361.06208561130187
48206.25201.762329272933200.76750.9948292729325421.03264724372992
49201.19NA195.577916666667NANA
50194.37NA195.876666666667NANA
51191.08NA200.742916666667NANA
52192.87NANANANA
53181.61NANANANA
54157.67NANANANA
55196.14NANANANA
56246.35NANANANA
57271.9NANANANA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 167.16 & NA & NA & 0.999982204104269 & NA \tabularnewline
2 & 179.84 & NA & NA & 1.03716151365447 & NA \tabularnewline
3 & 174.44 & NA & NA & 1.05682814473984 & NA \tabularnewline
4 & 180.35 & NA & NA & 0.985682997956348 & NA \tabularnewline
5 & 193.17 & NA & NA & 0.983051682033842 & NA \tabularnewline
6 & 195.16 & NA & NA & 1.03398001321637 & NA \tabularnewline
7 & 202.43 & 193.914943701812 & 192.925416666667 & 0.989527035145743 & 1.06037081029508 \tabularnewline
8 & 189.91 & 195.964984695419 & 194.969166666667 & 0.995818028752133 & 0.978142014360496 \tabularnewline
9 & 195.98 & 197.842953812578 & 196.835833333333 & 1.00712047924496 & 0.988612649212912 \tabularnewline
10 & 212.09 & 199.571854037366 & 198.611666666667 & 0.960187370699353 & 1.11213996332746 \tabularnewline
11 & 205.81 & 200.42208125752 & 199.46625 & 0.955831257520136 & 1.07948303406991 \tabularnewline
12 & 204.31 & 201.728995939599 & 200.734166666667 & 0.994829272932542 & 1.02310396663975 \tabularnewline
13 & 196.07 & 204.394982204104 & 203.395 & 0.999982204104269 & 0.964003487319606 \tabularnewline
14 & 199.98 & 208.840494846988 & 207.803333333333 & 1.03716151365447 & 0.927871128448571 \tabularnewline
15 & 199.1 & 217.20932814474 & 216.1525 & 1.05682814473984 & 0.871578736689774 \tabularnewline
16 & 198.31 & 227.705682997956 & 226.72 & 0.985682997956348 & 0.887396100908084 \tabularnewline
17 & 195.72 & 237.663885015367 & 236.680833333333 & 0.983051682033842 & 0.8411932260439 \tabularnewline
18 & 223.04 & 249.394396679883 & 248.360416666667 & 1.03398001321637 & 0.868536816829029 \tabularnewline
19 & 238.41 & 263.426193701812 & 262.436666666667 & 0.989527035145743 & 0.91806258970965 \tabularnewline
20 & 259.73 & 280.038318028752 & 279.0425 & 0.995818028752133 & 0.9346989969479 \tabularnewline
21 & 326.54 & 299.451287145912 & 298.444166666667 & 1.00712047924496 & 1.08640527725365 \tabularnewline
22 & 335.15 & 316.260187370699 & 315.3 & 0.960187370699353 & 1.10702967716330 \tabularnewline
23 & 321.81 & 328.629164590853 & 327.673333333333 & 0.955831257520136 & 1.02748906244151 \tabularnewline
24 & 368.62 & 339.441079272932 & 338.44625 & 0.994829272932542 & 1.09481473118860 \tabularnewline
25 & 369.59 & 348.416232204104 & 347.41625 & 0.999982204104269 & 1.06384366678920 \tabularnewline
26 & 425 & 355.094244846988 & 354.057083333333 & 1.03716151365447 & 1.15736208221459 \tabularnewline
27 & 439.72 & 356.72307814474 & 355.66625 & 1.05682814473984 & 1.16984734235277 \tabularnewline
28 & 362.23 & 351.286932997956 & 350.30125 & 0.985682997956348 & 1.04907240486157 \tabularnewline
29 & 328.76 & 343.253885015367 & 342.270833333333 & 0.983051682033842 & 0.97708586117988 \tabularnewline
30 & 348.55 & 333.161480013216 & 332.1275 & 1.03398001321637 & 1.01495808317140 \tabularnewline
31 & 328.18 & 321.504110368479 & 320.514583333333 & 0.989527035145743 & 1.03475289920842 \tabularnewline
32 & 329.34 & 307.737901362085 & 306.742083333333 & 0.995818028752133 & 1.07817965762691 \tabularnewline
33 & 295.55 & 290.705453812578 & 289.698333333333 & 1.00712047924496 & 1.01298622530989 \tabularnewline
34 & 237.38 & 276.596020704033 & 275.635833333333 & 0.960187370699353 & 0.896917421734397 \tabularnewline
35 & 226.85 & 268.224164590853 & 267.268333333333 & 0.955831257520136 & 0.88799403709152 \tabularnewline
36 & 220.14 & 261.296495939599 & 260.301666666667 & 0.994829272932542 & 0.85010673591638 \tabularnewline
37 & 239.36 & 253.036232204104 & 252.03625 & 0.999982204104269 & 0.949721556726096 \tabularnewline
38 & 224.69 & 243.815078180321 & 242.777916666667 & 1.03716151365447 & 0.892335500171742 \tabularnewline
39 & 230.98 & 234.525161478073 & 233.468333333333 & 1.05682814473984 & 0.936142631792353 \tabularnewline
40 & 233.47 & 228.496099664623 & 227.510416666667 & 0.985682997956348 & 1.04110020506525 \tabularnewline
41 & 256.7 & 226.229301682034 & 225.24625 & 0.983051682033842 & 1.15928962361112 \tabularnewline
42 & 253.41 & 225.042730013216 & 224.00875 & 1.03398001321637 & 1.09407381083447 \tabularnewline
43 & 224.95 & 222.829110368479 & 221.839583333333 & 0.989527035145743 & 1.02475322191683 \tabularnewline
44 & 210.37 & 219.981651362085 & 218.985833333333 & 0.995818028752133 & 0.964690056260092 \tabularnewline
45 & 191.09 & 217.067120479245 & 216.06 & 1.00712047924496 & 0.878177208271354 \tabularnewline
46 & 198.85 & 213.666020704033 & 212.705833333333 & 0.960187370699353 & 0.973621611937199 \tabularnewline
47 & 211.04 & 208.841247924187 & 207.885416666667 & 0.955831257520136 & 1.06208561130187 \tabularnewline
48 & 206.25 & 201.762329272933 & 200.7675 & 0.994829272932542 & 1.03264724372992 \tabularnewline
49 & 201.19 & NA & 195.577916666667 & NA & NA \tabularnewline
50 & 194.37 & NA & 195.876666666667 & NA & NA \tabularnewline
51 & 191.08 & NA & 200.742916666667 & NA & NA \tabularnewline
52 & 192.87 & NA & NA & NA & NA \tabularnewline
53 & 181.61 & NA & NA & NA & NA \tabularnewline
54 & 157.67 & NA & NA & NA & NA \tabularnewline
55 & 196.14 & NA & NA & NA & NA \tabularnewline
56 & 246.35 & NA & NA & NA & NA \tabularnewline
57 & 271.9 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103031&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]167.16[/C][C]NA[/C][C]NA[/C][C]0.999982204104269[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]179.84[/C][C]NA[/C][C]NA[/C][C]1.03716151365447[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]174.44[/C][C]NA[/C][C]NA[/C][C]1.05682814473984[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]180.35[/C][C]NA[/C][C]NA[/C][C]0.985682997956348[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]193.17[/C][C]NA[/C][C]NA[/C][C]0.983051682033842[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]195.16[/C][C]NA[/C][C]NA[/C][C]1.03398001321637[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]202.43[/C][C]193.914943701812[/C][C]192.925416666667[/C][C]0.989527035145743[/C][C]1.06037081029508[/C][/ROW]
[ROW][C]8[/C][C]189.91[/C][C]195.964984695419[/C][C]194.969166666667[/C][C]0.995818028752133[/C][C]0.978142014360496[/C][/ROW]
[ROW][C]9[/C][C]195.98[/C][C]197.842953812578[/C][C]196.835833333333[/C][C]1.00712047924496[/C][C]0.988612649212912[/C][/ROW]
[ROW][C]10[/C][C]212.09[/C][C]199.571854037366[/C][C]198.611666666667[/C][C]0.960187370699353[/C][C]1.11213996332746[/C][/ROW]
[ROW][C]11[/C][C]205.81[/C][C]200.42208125752[/C][C]199.46625[/C][C]0.955831257520136[/C][C]1.07948303406991[/C][/ROW]
[ROW][C]12[/C][C]204.31[/C][C]201.728995939599[/C][C]200.734166666667[/C][C]0.994829272932542[/C][C]1.02310396663975[/C][/ROW]
[ROW][C]13[/C][C]196.07[/C][C]204.394982204104[/C][C]203.395[/C][C]0.999982204104269[/C][C]0.964003487319606[/C][/ROW]
[ROW][C]14[/C][C]199.98[/C][C]208.840494846988[/C][C]207.803333333333[/C][C]1.03716151365447[/C][C]0.927871128448571[/C][/ROW]
[ROW][C]15[/C][C]199.1[/C][C]217.20932814474[/C][C]216.1525[/C][C]1.05682814473984[/C][C]0.871578736689774[/C][/ROW]
[ROW][C]16[/C][C]198.31[/C][C]227.705682997956[/C][C]226.72[/C][C]0.985682997956348[/C][C]0.887396100908084[/C][/ROW]
[ROW][C]17[/C][C]195.72[/C][C]237.663885015367[/C][C]236.680833333333[/C][C]0.983051682033842[/C][C]0.8411932260439[/C][/ROW]
[ROW][C]18[/C][C]223.04[/C][C]249.394396679883[/C][C]248.360416666667[/C][C]1.03398001321637[/C][C]0.868536816829029[/C][/ROW]
[ROW][C]19[/C][C]238.41[/C][C]263.426193701812[/C][C]262.436666666667[/C][C]0.989527035145743[/C][C]0.91806258970965[/C][/ROW]
[ROW][C]20[/C][C]259.73[/C][C]280.038318028752[/C][C]279.0425[/C][C]0.995818028752133[/C][C]0.9346989969479[/C][/ROW]
[ROW][C]21[/C][C]326.54[/C][C]299.451287145912[/C][C]298.444166666667[/C][C]1.00712047924496[/C][C]1.08640527725365[/C][/ROW]
[ROW][C]22[/C][C]335.15[/C][C]316.260187370699[/C][C]315.3[/C][C]0.960187370699353[/C][C]1.10702967716330[/C][/ROW]
[ROW][C]23[/C][C]321.81[/C][C]328.629164590853[/C][C]327.673333333333[/C][C]0.955831257520136[/C][C]1.02748906244151[/C][/ROW]
[ROW][C]24[/C][C]368.62[/C][C]339.441079272932[/C][C]338.44625[/C][C]0.994829272932542[/C][C]1.09481473118860[/C][/ROW]
[ROW][C]25[/C][C]369.59[/C][C]348.416232204104[/C][C]347.41625[/C][C]0.999982204104269[/C][C]1.06384366678920[/C][/ROW]
[ROW][C]26[/C][C]425[/C][C]355.094244846988[/C][C]354.057083333333[/C][C]1.03716151365447[/C][C]1.15736208221459[/C][/ROW]
[ROW][C]27[/C][C]439.72[/C][C]356.72307814474[/C][C]355.66625[/C][C]1.05682814473984[/C][C]1.16984734235277[/C][/ROW]
[ROW][C]28[/C][C]362.23[/C][C]351.286932997956[/C][C]350.30125[/C][C]0.985682997956348[/C][C]1.04907240486157[/C][/ROW]
[ROW][C]29[/C][C]328.76[/C][C]343.253885015367[/C][C]342.270833333333[/C][C]0.983051682033842[/C][C]0.97708586117988[/C][/ROW]
[ROW][C]30[/C][C]348.55[/C][C]333.161480013216[/C][C]332.1275[/C][C]1.03398001321637[/C][C]1.01495808317140[/C][/ROW]
[ROW][C]31[/C][C]328.18[/C][C]321.504110368479[/C][C]320.514583333333[/C][C]0.989527035145743[/C][C]1.03475289920842[/C][/ROW]
[ROW][C]32[/C][C]329.34[/C][C]307.737901362085[/C][C]306.742083333333[/C][C]0.995818028752133[/C][C]1.07817965762691[/C][/ROW]
[ROW][C]33[/C][C]295.55[/C][C]290.705453812578[/C][C]289.698333333333[/C][C]1.00712047924496[/C][C]1.01298622530989[/C][/ROW]
[ROW][C]34[/C][C]237.38[/C][C]276.596020704033[/C][C]275.635833333333[/C][C]0.960187370699353[/C][C]0.896917421734397[/C][/ROW]
[ROW][C]35[/C][C]226.85[/C][C]268.224164590853[/C][C]267.268333333333[/C][C]0.955831257520136[/C][C]0.88799403709152[/C][/ROW]
[ROW][C]36[/C][C]220.14[/C][C]261.296495939599[/C][C]260.301666666667[/C][C]0.994829272932542[/C][C]0.85010673591638[/C][/ROW]
[ROW][C]37[/C][C]239.36[/C][C]253.036232204104[/C][C]252.03625[/C][C]0.999982204104269[/C][C]0.949721556726096[/C][/ROW]
[ROW][C]38[/C][C]224.69[/C][C]243.815078180321[/C][C]242.777916666667[/C][C]1.03716151365447[/C][C]0.892335500171742[/C][/ROW]
[ROW][C]39[/C][C]230.98[/C][C]234.525161478073[/C][C]233.468333333333[/C][C]1.05682814473984[/C][C]0.936142631792353[/C][/ROW]
[ROW][C]40[/C][C]233.47[/C][C]228.496099664623[/C][C]227.510416666667[/C][C]0.985682997956348[/C][C]1.04110020506525[/C][/ROW]
[ROW][C]41[/C][C]256.7[/C][C]226.229301682034[/C][C]225.24625[/C][C]0.983051682033842[/C][C]1.15928962361112[/C][/ROW]
[ROW][C]42[/C][C]253.41[/C][C]225.042730013216[/C][C]224.00875[/C][C]1.03398001321637[/C][C]1.09407381083447[/C][/ROW]
[ROW][C]43[/C][C]224.95[/C][C]222.829110368479[/C][C]221.839583333333[/C][C]0.989527035145743[/C][C]1.02475322191683[/C][/ROW]
[ROW][C]44[/C][C]210.37[/C][C]219.981651362085[/C][C]218.985833333333[/C][C]0.995818028752133[/C][C]0.964690056260092[/C][/ROW]
[ROW][C]45[/C][C]191.09[/C][C]217.067120479245[/C][C]216.06[/C][C]1.00712047924496[/C][C]0.878177208271354[/C][/ROW]
[ROW][C]46[/C][C]198.85[/C][C]213.666020704033[/C][C]212.705833333333[/C][C]0.960187370699353[/C][C]0.973621611937199[/C][/ROW]
[ROW][C]47[/C][C]211.04[/C][C]208.841247924187[/C][C]207.885416666667[/C][C]0.955831257520136[/C][C]1.06208561130187[/C][/ROW]
[ROW][C]48[/C][C]206.25[/C][C]201.762329272933[/C][C]200.7675[/C][C]0.994829272932542[/C][C]1.03264724372992[/C][/ROW]
[ROW][C]49[/C][C]201.19[/C][C]NA[/C][C]195.577916666667[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]50[/C][C]194.37[/C][C]NA[/C][C]195.876666666667[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]51[/C][C]191.08[/C][C]NA[/C][C]200.742916666667[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]52[/C][C]192.87[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]53[/C][C]181.61[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]54[/C][C]157.67[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]55[/C][C]196.14[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]246.35[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]271.9[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103031&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103031&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:

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1167.16NANA0.999982204104269NA
2179.84NANA1.03716151365447NA
3174.44NANA1.05682814473984NA
4180.35NANA0.985682997956348NA
5193.17NANA0.983051682033842NA
6195.16NANA1.03398001321637NA
7202.43193.914943701812192.9254166666670.9895270351457431.06037081029508
8189.91195.964984695419194.9691666666670.9958180287521330.978142014360496
9195.98197.842953812578196.8358333333331.007120479244960.988612649212912
10212.09199.571854037366198.6116666666670.9601873706993531.11213996332746
11205.81200.42208125752199.466250.9558312575201361.07948303406991
12204.31201.728995939599200.7341666666670.9948292729325421.02310396663975
13196.07204.394982204104203.3950.9999822041042690.964003487319606
14199.98208.840494846988207.8033333333331.037161513654470.927871128448571
15199.1217.20932814474216.15251.056828144739840.871578736689774
16198.31227.705682997956226.720.9856829979563480.887396100908084
17195.72237.663885015367236.6808333333330.9830516820338420.8411932260439
18223.04249.394396679883248.3604166666671.033980013216370.868536816829029
19238.41263.426193701812262.4366666666670.9895270351457430.91806258970965
20259.73280.038318028752279.04250.9958180287521330.9346989969479
21326.54299.451287145912298.4441666666671.007120479244961.08640527725365
22335.15316.260187370699315.30.9601873706993531.10702967716330
23321.81328.629164590853327.6733333333330.9558312575201361.02748906244151
24368.62339.441079272932338.446250.9948292729325421.09481473118860
25369.59348.416232204104347.416250.9999822041042691.06384366678920
26425355.094244846988354.0570833333331.037161513654471.15736208221459
27439.72356.72307814474355.666251.056828144739841.16984734235277
28362.23351.286932997956350.301250.9856829979563481.04907240486157
29328.76343.253885015367342.2708333333330.9830516820338420.97708586117988
30348.55333.161480013216332.12751.033980013216371.01495808317140
31328.18321.504110368479320.5145833333330.9895270351457431.03475289920842
32329.34307.737901362085306.7420833333330.9958180287521331.07817965762691
33295.55290.705453812578289.6983333333331.007120479244961.01298622530989
34237.38276.596020704033275.6358333333330.9601873706993530.896917421734397
35226.85268.224164590853267.2683333333330.9558312575201360.88799403709152
36220.14261.296495939599260.3016666666670.9948292729325420.85010673591638
37239.36253.036232204104252.036250.9999822041042690.949721556726096
38224.69243.815078180321242.7779166666671.037161513654470.892335500171742
39230.98234.525161478073233.4683333333331.056828144739840.936142631792353
40233.47228.496099664623227.5104166666670.9856829979563481.04110020506525
41256.7226.229301682034225.246250.9830516820338421.15928962361112
42253.41225.042730013216224.008751.033980013216371.09407381083447
43224.95222.829110368479221.8395833333330.9895270351457431.02475322191683
44210.37219.981651362085218.9858333333330.9958180287521330.964690056260092
45191.09217.067120479245216.061.007120479244960.878177208271354
46198.85213.666020704033212.7058333333330.9601873706993530.973621611937199
47211.04208.841247924187207.8854166666670.9558312575201361.06208561130187
48206.25201.762329272933200.76750.9948292729325421.03264724372992
49201.19NA195.577916666667NANA
50194.37NA195.876666666667NANA
51191.08NA200.742916666667NANA
52192.87NANANANA
53181.61NANANANA
54157.67NANANANA
55196.14NANANANA
56246.35NANANANA
57271.9NANANANA


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = multiplicative ; par2 = 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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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')