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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationWed, 09 Dec 2009 09:07:46 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/09/t1260374939iq4kxnwfk5otmtw.htm/, Retrieved Mon, 29 Apr 2024 16:15:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65016, Retrieved Mon, 29 Apr 2024 16:15:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact145
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] [workshop 9 bereke...] [2009-12-03 17:49:36] [eaf42bcf5162b5692bb3c7f9d4636222]
-   PD        [Classical Decomposition] [] [2009-12-09 16:07:46] [17416e80e7873ecccac25c455c5f767e] [Current]
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Dataseries X:
153.4
145
137.7
148.3
152.2
169.4
168.6
161.1
174.1
179
190.6
190
181.6
174.8
180.5
196.8
193.8
197
216.3
221.4
217.9
229.7
227.4
204.2
196.6
198.8
207.5
190.7
201.6
210.5
223.5
223.8
231.2
244
234.7
250.2
265.7
287.6
283.3
295.4
312.3
333.8
347.7
383.2
407.1
413.6
362.7
321.9
239.4
191
159.7
163.4
157.6
166.2
176.7
198.3
226.2
216.2
235.9
226.9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65016&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65016&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65016&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1153.4NANA0.95714564467741NA
2145NANA0.91854708876129NA
3137.7NANA0.893643458283059NA
4148.3NANA0.903290503909825NA
5152.2NANA0.915789291918695NA
6169.4NANA0.95501459237464NA
7168.6172.597249064462165.2916666666671.044198128950610.97684059806209
8161.1177.381286982606167.7083333333331.057677239150940.908213051897618
9174.1186.722617678812170.7333333333331.093650630684180.93239909639375
10179197.288756282014174.53751.130351679621940.90729955104045
11190.6194.381536205865178.2916666666671.090244652708750.980545805534431
12190188.503001342085181.1751.040447088958661.00794151099588
13181.6176.413906634605184.31250.957145644677411.02939730469286
14174.8173.433172196741188.81250.918547088761291.00788100561125
15180.5172.607233967373193.150.8936434582830591.04572673955322
16196.8178.027267189328197.08750.9032905039098251.10544863776799
17193.8183.829437197813200.7333333333330.9157892919186951.05423811851993
18197193.732668518132202.8583333333330.955014592374641.01686515499353
19216.3213.094733165596204.0751.044198128950611.01504151128838
20221.4217.564208093349205.71.057677239150941.01763062012942
21217.9227.287942321939207.8251.093650630684180.958695818942116
22229.7235.899685738433208.6958333333331.130351679621940.973718974151973
23227.4227.606741997164208.7666666666671.090244652708750.99909167015287
24204.2218.134067396386209.6541666666671.040447088958660.936121544137047
25196.6201.495110632006210.5166666666670.957145644677410.975706057498606
26198.8193.736890137902210.9166666666670.918547088761291.02613394825577
27207.5189.068891171829211.5708333333330.8936434582830591.09748356122436
28190.7192.148708733784212.7208333333330.9032905039098250.992460481554463
29201.6195.631671697415213.6208333333330.9157892919186951.03050798600656
30210.5206.131941309130215.8416666666670.955014592374641.02119059599948
31223.5230.389264676341220.63751.044198128950610.970097284324342
32223.8240.32189668908227.2166666666671.057677239150940.931250972480233
33231.2255.996271377399234.0751.093650630684180.903138154145834
34244273.088255997995241.5958333333331.130351679621940.89348404642414
35234.7273.183511166443250.5708333333331.090244652708750.859129451107333
36250.2270.850053236958260.3208333333331.040447088958660.923758356366678
37265.7259.035516304530270.6333333333330.957145644677411.02572806922598
38287.6259.443625220626282.450.918547088761291.10852598423041
39283.3264.894538607146296.4208333333330.8936434582830591.06948222296176
40295.4280.757743456905310.8166666666670.9032905039098251.05215263651434
41312.3295.998362302988323.2166666666670.9157892919186951.05507340503569
42333.8316.623150419407331.53750.955014592374641.05425013792529
43347.7348.166111970895333.4291666666671.044198128950610.998661236820963
44383.2347.244251590248328.3083333333331.057677239150941.10354598598850
45407.1349.020371272344319.1333333333331.093650630684181.16640756101407
46413.6348.694653968707308.4833333333331.130351679621941.18613805887921
47362.7323.298423702622296.53751.090244652708751.12187370370114
48321.9294.559241276604283.1083333333331.040447088958661.09281921899616
49239.4257.4721784182232690.957145644677410.929809199078326
50191233.467879006364254.1708333333330.918547088761290.818099692398342
51159.7213.517486784689238.9291666666670.8936434582830590.747948106756432
52163.4201.584330789209223.1666666666670.9032905039098250.810578874658976
53157.6192.002856628187209.6583333333330.9157892919186950.82082112093359
54166.2191.400841221751200.4166666666670.955014592374640.868334741577473
55176.7NANA1.04419812895061NA
56198.3NANA1.05767723915094NA
57226.2NANA1.09365063068418NA
58216.2NANA1.13035167962194NA
59235.9NANA1.09024465270875NA
60226.9NANA1.04044708895866NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 153.4 & NA & NA & 0.95714564467741 & NA \tabularnewline
2 & 145 & NA & NA & 0.91854708876129 & NA \tabularnewline
3 & 137.7 & NA & NA & 0.893643458283059 & NA \tabularnewline
4 & 148.3 & NA & NA & 0.903290503909825 & NA \tabularnewline
5 & 152.2 & NA & NA & 0.915789291918695 & NA \tabularnewline
6 & 169.4 & NA & NA & 0.95501459237464 & NA \tabularnewline
7 & 168.6 & 172.597249064462 & 165.291666666667 & 1.04419812895061 & 0.97684059806209 \tabularnewline
8 & 161.1 & 177.381286982606 & 167.708333333333 & 1.05767723915094 & 0.908213051897618 \tabularnewline
9 & 174.1 & 186.722617678812 & 170.733333333333 & 1.09365063068418 & 0.93239909639375 \tabularnewline
10 & 179 & 197.288756282014 & 174.5375 & 1.13035167962194 & 0.90729955104045 \tabularnewline
11 & 190.6 & 194.381536205865 & 178.291666666667 & 1.09024465270875 & 0.980545805534431 \tabularnewline
12 & 190 & 188.503001342085 & 181.175 & 1.04044708895866 & 1.00794151099588 \tabularnewline
13 & 181.6 & 176.413906634605 & 184.3125 & 0.95714564467741 & 1.02939730469286 \tabularnewline
14 & 174.8 & 173.433172196741 & 188.8125 & 0.91854708876129 & 1.00788100561125 \tabularnewline
15 & 180.5 & 172.607233967373 & 193.15 & 0.893643458283059 & 1.04572673955322 \tabularnewline
16 & 196.8 & 178.027267189328 & 197.0875 & 0.903290503909825 & 1.10544863776799 \tabularnewline
17 & 193.8 & 183.829437197813 & 200.733333333333 & 0.915789291918695 & 1.05423811851993 \tabularnewline
18 & 197 & 193.732668518132 & 202.858333333333 & 0.95501459237464 & 1.01686515499353 \tabularnewline
19 & 216.3 & 213.094733165596 & 204.075 & 1.04419812895061 & 1.01504151128838 \tabularnewline
20 & 221.4 & 217.564208093349 & 205.7 & 1.05767723915094 & 1.01763062012942 \tabularnewline
21 & 217.9 & 227.287942321939 & 207.825 & 1.09365063068418 & 0.958695818942116 \tabularnewline
22 & 229.7 & 235.899685738433 & 208.695833333333 & 1.13035167962194 & 0.973718974151973 \tabularnewline
23 & 227.4 & 227.606741997164 & 208.766666666667 & 1.09024465270875 & 0.99909167015287 \tabularnewline
24 & 204.2 & 218.134067396386 & 209.654166666667 & 1.04044708895866 & 0.936121544137047 \tabularnewline
25 & 196.6 & 201.495110632006 & 210.516666666667 & 0.95714564467741 & 0.975706057498606 \tabularnewline
26 & 198.8 & 193.736890137902 & 210.916666666667 & 0.91854708876129 & 1.02613394825577 \tabularnewline
27 & 207.5 & 189.068891171829 & 211.570833333333 & 0.893643458283059 & 1.09748356122436 \tabularnewline
28 & 190.7 & 192.148708733784 & 212.720833333333 & 0.903290503909825 & 0.992460481554463 \tabularnewline
29 & 201.6 & 195.631671697415 & 213.620833333333 & 0.915789291918695 & 1.03050798600656 \tabularnewline
30 & 210.5 & 206.131941309130 & 215.841666666667 & 0.95501459237464 & 1.02119059599948 \tabularnewline
31 & 223.5 & 230.389264676341 & 220.6375 & 1.04419812895061 & 0.970097284324342 \tabularnewline
32 & 223.8 & 240.32189668908 & 227.216666666667 & 1.05767723915094 & 0.931250972480233 \tabularnewline
33 & 231.2 & 255.996271377399 & 234.075 & 1.09365063068418 & 0.903138154145834 \tabularnewline
34 & 244 & 273.088255997995 & 241.595833333333 & 1.13035167962194 & 0.89348404642414 \tabularnewline
35 & 234.7 & 273.183511166443 & 250.570833333333 & 1.09024465270875 & 0.859129451107333 \tabularnewline
36 & 250.2 & 270.850053236958 & 260.320833333333 & 1.04044708895866 & 0.923758356366678 \tabularnewline
37 & 265.7 & 259.035516304530 & 270.633333333333 & 0.95714564467741 & 1.02572806922598 \tabularnewline
38 & 287.6 & 259.443625220626 & 282.45 & 0.91854708876129 & 1.10852598423041 \tabularnewline
39 & 283.3 & 264.894538607146 & 296.420833333333 & 0.893643458283059 & 1.06948222296176 \tabularnewline
40 & 295.4 & 280.757743456905 & 310.816666666667 & 0.903290503909825 & 1.05215263651434 \tabularnewline
41 & 312.3 & 295.998362302988 & 323.216666666667 & 0.915789291918695 & 1.05507340503569 \tabularnewline
42 & 333.8 & 316.623150419407 & 331.5375 & 0.95501459237464 & 1.05425013792529 \tabularnewline
43 & 347.7 & 348.166111970895 & 333.429166666667 & 1.04419812895061 & 0.998661236820963 \tabularnewline
44 & 383.2 & 347.244251590248 & 328.308333333333 & 1.05767723915094 & 1.10354598598850 \tabularnewline
45 & 407.1 & 349.020371272344 & 319.133333333333 & 1.09365063068418 & 1.16640756101407 \tabularnewline
46 & 413.6 & 348.694653968707 & 308.483333333333 & 1.13035167962194 & 1.18613805887921 \tabularnewline
47 & 362.7 & 323.298423702622 & 296.5375 & 1.09024465270875 & 1.12187370370114 \tabularnewline
48 & 321.9 & 294.559241276604 & 283.108333333333 & 1.04044708895866 & 1.09281921899616 \tabularnewline
49 & 239.4 & 257.472178418223 & 269 & 0.95714564467741 & 0.929809199078326 \tabularnewline
50 & 191 & 233.467879006364 & 254.170833333333 & 0.91854708876129 & 0.818099692398342 \tabularnewline
51 & 159.7 & 213.517486784689 & 238.929166666667 & 0.893643458283059 & 0.747948106756432 \tabularnewline
52 & 163.4 & 201.584330789209 & 223.166666666667 & 0.903290503909825 & 0.810578874658976 \tabularnewline
53 & 157.6 & 192.002856628187 & 209.658333333333 & 0.915789291918695 & 0.82082112093359 \tabularnewline
54 & 166.2 & 191.400841221751 & 200.416666666667 & 0.95501459237464 & 0.868334741577473 \tabularnewline
55 & 176.7 & NA & NA & 1.04419812895061 & NA \tabularnewline
56 & 198.3 & NA & NA & 1.05767723915094 & NA \tabularnewline
57 & 226.2 & NA & NA & 1.09365063068418 & NA \tabularnewline
58 & 216.2 & NA & NA & 1.13035167962194 & NA \tabularnewline
59 & 235.9 & NA & NA & 1.09024465270875 & NA \tabularnewline
60 & 226.9 & NA & NA & 1.04044708895866 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65016&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]153.4[/C][C]NA[/C][C]NA[/C][C]0.95714564467741[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]145[/C][C]NA[/C][C]NA[/C][C]0.91854708876129[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]137.7[/C][C]NA[/C][C]NA[/C][C]0.893643458283059[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]148.3[/C][C]NA[/C][C]NA[/C][C]0.903290503909825[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]152.2[/C][C]NA[/C][C]NA[/C][C]0.915789291918695[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]169.4[/C][C]NA[/C][C]NA[/C][C]0.95501459237464[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]168.6[/C][C]172.597249064462[/C][C]165.291666666667[/C][C]1.04419812895061[/C][C]0.97684059806209[/C][/ROW]
[ROW][C]8[/C][C]161.1[/C][C]177.381286982606[/C][C]167.708333333333[/C][C]1.05767723915094[/C][C]0.908213051897618[/C][/ROW]
[ROW][C]9[/C][C]174.1[/C][C]186.722617678812[/C][C]170.733333333333[/C][C]1.09365063068418[/C][C]0.93239909639375[/C][/ROW]
[ROW][C]10[/C][C]179[/C][C]197.288756282014[/C][C]174.5375[/C][C]1.13035167962194[/C][C]0.90729955104045[/C][/ROW]
[ROW][C]11[/C][C]190.6[/C][C]194.381536205865[/C][C]178.291666666667[/C][C]1.09024465270875[/C][C]0.980545805534431[/C][/ROW]
[ROW][C]12[/C][C]190[/C][C]188.503001342085[/C][C]181.175[/C][C]1.04044708895866[/C][C]1.00794151099588[/C][/ROW]
[ROW][C]13[/C][C]181.6[/C][C]176.413906634605[/C][C]184.3125[/C][C]0.95714564467741[/C][C]1.02939730469286[/C][/ROW]
[ROW][C]14[/C][C]174.8[/C][C]173.433172196741[/C][C]188.8125[/C][C]0.91854708876129[/C][C]1.00788100561125[/C][/ROW]
[ROW][C]15[/C][C]180.5[/C][C]172.607233967373[/C][C]193.15[/C][C]0.893643458283059[/C][C]1.04572673955322[/C][/ROW]
[ROW][C]16[/C][C]196.8[/C][C]178.027267189328[/C][C]197.0875[/C][C]0.903290503909825[/C][C]1.10544863776799[/C][/ROW]
[ROW][C]17[/C][C]193.8[/C][C]183.829437197813[/C][C]200.733333333333[/C][C]0.915789291918695[/C][C]1.05423811851993[/C][/ROW]
[ROW][C]18[/C][C]197[/C][C]193.732668518132[/C][C]202.858333333333[/C][C]0.95501459237464[/C][C]1.01686515499353[/C][/ROW]
[ROW][C]19[/C][C]216.3[/C][C]213.094733165596[/C][C]204.075[/C][C]1.04419812895061[/C][C]1.01504151128838[/C][/ROW]
[ROW][C]20[/C][C]221.4[/C][C]217.564208093349[/C][C]205.7[/C][C]1.05767723915094[/C][C]1.01763062012942[/C][/ROW]
[ROW][C]21[/C][C]217.9[/C][C]227.287942321939[/C][C]207.825[/C][C]1.09365063068418[/C][C]0.958695818942116[/C][/ROW]
[ROW][C]22[/C][C]229.7[/C][C]235.899685738433[/C][C]208.695833333333[/C][C]1.13035167962194[/C][C]0.973718974151973[/C][/ROW]
[ROW][C]23[/C][C]227.4[/C][C]227.606741997164[/C][C]208.766666666667[/C][C]1.09024465270875[/C][C]0.99909167015287[/C][/ROW]
[ROW][C]24[/C][C]204.2[/C][C]218.134067396386[/C][C]209.654166666667[/C][C]1.04044708895866[/C][C]0.936121544137047[/C][/ROW]
[ROW][C]25[/C][C]196.6[/C][C]201.495110632006[/C][C]210.516666666667[/C][C]0.95714564467741[/C][C]0.975706057498606[/C][/ROW]
[ROW][C]26[/C][C]198.8[/C][C]193.736890137902[/C][C]210.916666666667[/C][C]0.91854708876129[/C][C]1.02613394825577[/C][/ROW]
[ROW][C]27[/C][C]207.5[/C][C]189.068891171829[/C][C]211.570833333333[/C][C]0.893643458283059[/C][C]1.09748356122436[/C][/ROW]
[ROW][C]28[/C][C]190.7[/C][C]192.148708733784[/C][C]212.720833333333[/C][C]0.903290503909825[/C][C]0.992460481554463[/C][/ROW]
[ROW][C]29[/C][C]201.6[/C][C]195.631671697415[/C][C]213.620833333333[/C][C]0.915789291918695[/C][C]1.03050798600656[/C][/ROW]
[ROW][C]30[/C][C]210.5[/C][C]206.131941309130[/C][C]215.841666666667[/C][C]0.95501459237464[/C][C]1.02119059599948[/C][/ROW]
[ROW][C]31[/C][C]223.5[/C][C]230.389264676341[/C][C]220.6375[/C][C]1.04419812895061[/C][C]0.970097284324342[/C][/ROW]
[ROW][C]32[/C][C]223.8[/C][C]240.32189668908[/C][C]227.216666666667[/C][C]1.05767723915094[/C][C]0.931250972480233[/C][/ROW]
[ROW][C]33[/C][C]231.2[/C][C]255.996271377399[/C][C]234.075[/C][C]1.09365063068418[/C][C]0.903138154145834[/C][/ROW]
[ROW][C]34[/C][C]244[/C][C]273.088255997995[/C][C]241.595833333333[/C][C]1.13035167962194[/C][C]0.89348404642414[/C][/ROW]
[ROW][C]35[/C][C]234.7[/C][C]273.183511166443[/C][C]250.570833333333[/C][C]1.09024465270875[/C][C]0.859129451107333[/C][/ROW]
[ROW][C]36[/C][C]250.2[/C][C]270.850053236958[/C][C]260.320833333333[/C][C]1.04044708895866[/C][C]0.923758356366678[/C][/ROW]
[ROW][C]37[/C][C]265.7[/C][C]259.035516304530[/C][C]270.633333333333[/C][C]0.95714564467741[/C][C]1.02572806922598[/C][/ROW]
[ROW][C]38[/C][C]287.6[/C][C]259.443625220626[/C][C]282.45[/C][C]0.91854708876129[/C][C]1.10852598423041[/C][/ROW]
[ROW][C]39[/C][C]283.3[/C][C]264.894538607146[/C][C]296.420833333333[/C][C]0.893643458283059[/C][C]1.06948222296176[/C][/ROW]
[ROW][C]40[/C][C]295.4[/C][C]280.757743456905[/C][C]310.816666666667[/C][C]0.903290503909825[/C][C]1.05215263651434[/C][/ROW]
[ROW][C]41[/C][C]312.3[/C][C]295.998362302988[/C][C]323.216666666667[/C][C]0.915789291918695[/C][C]1.05507340503569[/C][/ROW]
[ROW][C]42[/C][C]333.8[/C][C]316.623150419407[/C][C]331.5375[/C][C]0.95501459237464[/C][C]1.05425013792529[/C][/ROW]
[ROW][C]43[/C][C]347.7[/C][C]348.166111970895[/C][C]333.429166666667[/C][C]1.04419812895061[/C][C]0.998661236820963[/C][/ROW]
[ROW][C]44[/C][C]383.2[/C][C]347.244251590248[/C][C]328.308333333333[/C][C]1.05767723915094[/C][C]1.10354598598850[/C][/ROW]
[ROW][C]45[/C][C]407.1[/C][C]349.020371272344[/C][C]319.133333333333[/C][C]1.09365063068418[/C][C]1.16640756101407[/C][/ROW]
[ROW][C]46[/C][C]413.6[/C][C]348.694653968707[/C][C]308.483333333333[/C][C]1.13035167962194[/C][C]1.18613805887921[/C][/ROW]
[ROW][C]47[/C][C]362.7[/C][C]323.298423702622[/C][C]296.5375[/C][C]1.09024465270875[/C][C]1.12187370370114[/C][/ROW]
[ROW][C]48[/C][C]321.9[/C][C]294.559241276604[/C][C]283.108333333333[/C][C]1.04044708895866[/C][C]1.09281921899616[/C][/ROW]
[ROW][C]49[/C][C]239.4[/C][C]257.472178418223[/C][C]269[/C][C]0.95714564467741[/C][C]0.929809199078326[/C][/ROW]
[ROW][C]50[/C][C]191[/C][C]233.467879006364[/C][C]254.170833333333[/C][C]0.91854708876129[/C][C]0.818099692398342[/C][/ROW]
[ROW][C]51[/C][C]159.7[/C][C]213.517486784689[/C][C]238.929166666667[/C][C]0.893643458283059[/C][C]0.747948106756432[/C][/ROW]
[ROW][C]52[/C][C]163.4[/C][C]201.584330789209[/C][C]223.166666666667[/C][C]0.903290503909825[/C][C]0.810578874658976[/C][/ROW]
[ROW][C]53[/C][C]157.6[/C][C]192.002856628187[/C][C]209.658333333333[/C][C]0.915789291918695[/C][C]0.82082112093359[/C][/ROW]
[ROW][C]54[/C][C]166.2[/C][C]191.400841221751[/C][C]200.416666666667[/C][C]0.95501459237464[/C][C]0.868334741577473[/C][/ROW]
[ROW][C]55[/C][C]176.7[/C][C]NA[/C][C]NA[/C][C]1.04419812895061[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]198.3[/C][C]NA[/C][C]NA[/C][C]1.05767723915094[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]226.2[/C][C]NA[/C][C]NA[/C][C]1.09365063068418[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]216.2[/C][C]NA[/C][C]NA[/C][C]1.13035167962194[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]235.9[/C][C]NA[/C][C]NA[/C][C]1.09024465270875[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]226.9[/C][C]NA[/C][C]NA[/C][C]1.04044708895866[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65016&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65016&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
tObservationsFitTrendSeasonalRandom
1153.4NANA0.95714564467741NA
2145NANA0.91854708876129NA
3137.7NANA0.893643458283059NA
4148.3NANA0.903290503909825NA
5152.2NANA0.915789291918695NA
6169.4NANA0.95501459237464NA
7168.6172.597249064462165.2916666666671.044198128950610.97684059806209
8161.1177.381286982606167.7083333333331.057677239150940.908213051897618
9174.1186.722617678812170.7333333333331.093650630684180.93239909639375
10179197.288756282014174.53751.130351679621940.90729955104045
11190.6194.381536205865178.2916666666671.090244652708750.980545805534431
12190188.503001342085181.1751.040447088958661.00794151099588
13181.6176.413906634605184.31250.957145644677411.02939730469286
14174.8173.433172196741188.81250.918547088761291.00788100561125
15180.5172.607233967373193.150.8936434582830591.04572673955322
16196.8178.027267189328197.08750.9032905039098251.10544863776799
17193.8183.829437197813200.7333333333330.9157892919186951.05423811851993
18197193.732668518132202.8583333333330.955014592374641.01686515499353
19216.3213.094733165596204.0751.044198128950611.01504151128838
20221.4217.564208093349205.71.057677239150941.01763062012942
21217.9227.287942321939207.8251.093650630684180.958695818942116
22229.7235.899685738433208.6958333333331.130351679621940.973718974151973
23227.4227.606741997164208.7666666666671.090244652708750.99909167015287
24204.2218.134067396386209.6541666666671.040447088958660.936121544137047
25196.6201.495110632006210.5166666666670.957145644677410.975706057498606
26198.8193.736890137902210.9166666666670.918547088761291.02613394825577
27207.5189.068891171829211.5708333333330.8936434582830591.09748356122436
28190.7192.148708733784212.7208333333330.9032905039098250.992460481554463
29201.6195.631671697415213.6208333333330.9157892919186951.03050798600656
30210.5206.131941309130215.8416666666670.955014592374641.02119059599948
31223.5230.389264676341220.63751.044198128950610.970097284324342
32223.8240.32189668908227.2166666666671.057677239150940.931250972480233
33231.2255.996271377399234.0751.093650630684180.903138154145834
34244273.088255997995241.5958333333331.130351679621940.89348404642414
35234.7273.183511166443250.5708333333331.090244652708750.859129451107333
36250.2270.850053236958260.3208333333331.040447088958660.923758356366678
37265.7259.035516304530270.6333333333330.957145644677411.02572806922598
38287.6259.443625220626282.450.918547088761291.10852598423041
39283.3264.894538607146296.4208333333330.8936434582830591.06948222296176
40295.4280.757743456905310.8166666666670.9032905039098251.05215263651434
41312.3295.998362302988323.2166666666670.9157892919186951.05507340503569
42333.8316.623150419407331.53750.955014592374641.05425013792529
43347.7348.166111970895333.4291666666671.044198128950610.998661236820963
44383.2347.244251590248328.3083333333331.057677239150941.10354598598850
45407.1349.020371272344319.1333333333331.093650630684181.16640756101407
46413.6348.694653968707308.4833333333331.130351679621941.18613805887921
47362.7323.298423702622296.53751.090244652708751.12187370370114
48321.9294.559241276604283.1083333333331.040447088958661.09281921899616
49239.4257.4721784182232690.957145644677410.929809199078326
50191233.467879006364254.1708333333330.918547088761290.818099692398342
51159.7213.517486784689238.9291666666670.8936434582830590.747948106756432
52163.4201.584330789209223.1666666666670.9032905039098250.810578874658976
53157.6192.002856628187209.6583333333330.9157892919186950.82082112093359
54166.2191.400841221751200.4166666666670.955014592374640.868334741577473
55176.7NANA1.04419812895061NA
56198.3NANA1.05767723915094NA
57226.2NANA1.09365063068418NA
58216.2NANA1.13035167962194NA
59235.9NANA1.09024465270875NA
60226.9NANA1.04044708895866NA



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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$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')