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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Wed, 04 Jan 2017 12:03:47 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/04/t1483531661i251n6yp6j8bl4r.htm/, Retrieved Mon, 25 May 2026 15:49:32 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Mon, 25 May 2026 15:49:32 +0200

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

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

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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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	75.8	NA	NA	0.803064	NA
2	75.7	NA	NA	0.744073	NA
3	112.3	NA	NA	1.10041	NA
4	110.9	NA	NA	1.09185	NA
5	99.6	NA	NA	1.07552	NA
6	107.5	NA	NA	1.03991	NA
7	90	91.3711	100.221	0.911697	0.984995
8	88.8	89.1093	100.537	0.886329	0.996529
9	129.7	116.996	100.617	1.16279	1.10859
10	120.4	117.586	100.879	1.16562	1.02393
11	93.3	98.7854	101.217	0.975979	0.944472
12	96	105.206	100.892	1.04276	0.912496
13	81.1	80.7481	100.55	0.803064	1.00436
14	78	74.7855	100.508	0.744073	1.04298
15	111.9	109.55	99.5542	1.10041	1.02145
16	117.6	107.279	98.2542	1.09185	1.09621
17	101	105.235	97.8458	1.07552	0.959754
18	98.3	101.972	98.0583	1.03991	0.963988
19	91	89.3691	98.025	0.911697	1.01825
20	86.8	86.3506	97.425	0.886329	1.0052
21	108.8	113.047	97.2208	1.16279	0.962432
22	110.1	112.399	96.4292	1.16562	0.979543
23	93.8	93.3402	95.6375	0.975979	1.00493
24	100.6	99.7749	95.6833	1.04276	1.00827
25	75.7	76.3614	95.0875	0.803064	0.991339
26	69	70.07	94.1708	0.744073	0.98473
27	116	103.241	93.8208	1.10041	1.12358
28	94.5	102.37	93.7583	1.09185	0.92312
29	105.1	100.476	93.4208	1.07552	1.04602
30	95.3	96.7986	93.0833	1.03991	0.984518
31	79.7	84.4194	92.5958	0.911697	0.944096
32	76.1	81.5385	91.9958	0.886329	0.933301
33	111.1	105.402	90.6458	1.16279	1.05406
34	106.3	104.357	89.5292	1.16562	1.01862
35	89.5	86.8987	89.0375	0.975979	1.02993
36	96.8	92.28	88.4958	1.04276	1.04898
37	67.8	71.0411	88.4625	0.803064	0.954377
38	62.5	65.7884	88.4167	0.744073	0.950015
39	90.1	96.5701	87.7583	1.10041	0.933001
40	93.6	94.8092	86.8333	1.09185	0.987246
41	94.2	92.692	86.1833	1.07552	1.01627
42	93.2	88.982	85.5667	1.03991	1.0474
43	81	77.7336	85.2625	0.911697	1.04202
44	73.7	75.5447	85.2333	0.886329	0.975581
45	97.7	99.016	85.1542	1.16279	0.986709
46	97.5	98.9413	84.8833	1.16562	0.985433
47	82.7	82.4621	84.4917	0.975979	1.00288
48	88.8	87.5702	83.9792	1.04276	1.01404
49	68.5	66.9421	83.3583	0.803064	1.02327
50	61.1	61.9844	83.3042	0.744073	0.985732
51	89.6	91.2331	82.9083	1.10041	0.9821
52	87.6	89.6729	82.1292	1.09185	0.976884
53	90.8	87.9776	81.8	1.07552	1.03208
54	84.3	84.7053	81.4542	1.03991	0.995215
55	75	73.9424	81.1042	0.911697	1.0143
56	78.4	71.7261	80.925	0.886329	1.09305
57	83.5	93.6236	80.5167	1.16279	0.891869
58	93	93.3609	80.0958	1.16562	0.996134
59	79.3	77.6351	79.5458	0.975979	1.02145
60	83.9	82.2652	78.8917	1.04276	1.01987
61	65	63.0874	78.5583	0.803064	1.03032
62	60.3	57.9695	77.9083	0.744073	1.0402
63	80.6	85.4238	77.6292	1.10041	0.943531
64	86.5	84.7869	77.6542	1.09185	1.02021
65	78.7	82.8824	77.0625	1.07552	0.949539
66	80.7	79.7007	76.6417	1.03991	1.01254
67	70.6	NA	NA	0.911697	NA
68	67.2	NA	NA	0.886329	NA
69	88	NA	NA	1.16279	NA
70	89.1	NA	NA	1.16562	NA
71	69	NA	NA	0.975979	NA
72	84.1	NA	NA	1.04276	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 75.8 & NA & NA & 0.803064 & NA \tabularnewline
2 & 75.7 & NA & NA & 0.744073 & NA \tabularnewline
3 & 112.3 & NA & NA & 1.10041 & NA \tabularnewline
4 & 110.9 & NA & NA & 1.09185 & NA \tabularnewline
5 & 99.6 & NA & NA & 1.07552 & NA \tabularnewline
6 & 107.5 & NA & NA & 1.03991 & NA \tabularnewline
7 & 90 & 91.3711 & 100.221 & 0.911697 & 0.984995 \tabularnewline
8 & 88.8 & 89.1093 & 100.537 & 0.886329 & 0.996529 \tabularnewline
9 & 129.7 & 116.996 & 100.617 & 1.16279 & 1.10859 \tabularnewline
10 & 120.4 & 117.586 & 100.879 & 1.16562 & 1.02393 \tabularnewline
11 & 93.3 & 98.7854 & 101.217 & 0.975979 & 0.944472 \tabularnewline
12 & 96 & 105.206 & 100.892 & 1.04276 & 0.912496 \tabularnewline
13 & 81.1 & 80.7481 & 100.55 & 0.803064 & 1.00436 \tabularnewline
14 & 78 & 74.7855 & 100.508 & 0.744073 & 1.04298 \tabularnewline
15 & 111.9 & 109.55 & 99.5542 & 1.10041 & 1.02145 \tabularnewline
16 & 117.6 & 107.279 & 98.2542 & 1.09185 & 1.09621 \tabularnewline
17 & 101 & 105.235 & 97.8458 & 1.07552 & 0.959754 \tabularnewline
18 & 98.3 & 101.972 & 98.0583 & 1.03991 & 0.963988 \tabularnewline
19 & 91 & 89.3691 & 98.025 & 0.911697 & 1.01825 \tabularnewline
20 & 86.8 & 86.3506 & 97.425 & 0.886329 & 1.0052 \tabularnewline
21 & 108.8 & 113.047 & 97.2208 & 1.16279 & 0.962432 \tabularnewline
22 & 110.1 & 112.399 & 96.4292 & 1.16562 & 0.979543 \tabularnewline
23 & 93.8 & 93.3402 & 95.6375 & 0.975979 & 1.00493 \tabularnewline
24 & 100.6 & 99.7749 & 95.6833 & 1.04276 & 1.00827 \tabularnewline
25 & 75.7 & 76.3614 & 95.0875 & 0.803064 & 0.991339 \tabularnewline
26 & 69 & 70.07 & 94.1708 & 0.744073 & 0.98473 \tabularnewline
27 & 116 & 103.241 & 93.8208 & 1.10041 & 1.12358 \tabularnewline
28 & 94.5 & 102.37 & 93.7583 & 1.09185 & 0.92312 \tabularnewline
29 & 105.1 & 100.476 & 93.4208 & 1.07552 & 1.04602 \tabularnewline
30 & 95.3 & 96.7986 & 93.0833 & 1.03991 & 0.984518 \tabularnewline
31 & 79.7 & 84.4194 & 92.5958 & 0.911697 & 0.944096 \tabularnewline
32 & 76.1 & 81.5385 & 91.9958 & 0.886329 & 0.933301 \tabularnewline
33 & 111.1 & 105.402 & 90.6458 & 1.16279 & 1.05406 \tabularnewline
34 & 106.3 & 104.357 & 89.5292 & 1.16562 & 1.01862 \tabularnewline
35 & 89.5 & 86.8987 & 89.0375 & 0.975979 & 1.02993 \tabularnewline
36 & 96.8 & 92.28 & 88.4958 & 1.04276 & 1.04898 \tabularnewline
37 & 67.8 & 71.0411 & 88.4625 & 0.803064 & 0.954377 \tabularnewline
38 & 62.5 & 65.7884 & 88.4167 & 0.744073 & 0.950015 \tabularnewline
39 & 90.1 & 96.5701 & 87.7583 & 1.10041 & 0.933001 \tabularnewline
40 & 93.6 & 94.8092 & 86.8333 & 1.09185 & 0.987246 \tabularnewline
41 & 94.2 & 92.692 & 86.1833 & 1.07552 & 1.01627 \tabularnewline
42 & 93.2 & 88.982 & 85.5667 & 1.03991 & 1.0474 \tabularnewline
43 & 81 & 77.7336 & 85.2625 & 0.911697 & 1.04202 \tabularnewline
44 & 73.7 & 75.5447 & 85.2333 & 0.886329 & 0.975581 \tabularnewline
45 & 97.7 & 99.016 & 85.1542 & 1.16279 & 0.986709 \tabularnewline
46 & 97.5 & 98.9413 & 84.8833 & 1.16562 & 0.985433 \tabularnewline
47 & 82.7 & 82.4621 & 84.4917 & 0.975979 & 1.00288 \tabularnewline
48 & 88.8 & 87.5702 & 83.9792 & 1.04276 & 1.01404 \tabularnewline
49 & 68.5 & 66.9421 & 83.3583 & 0.803064 & 1.02327 \tabularnewline
50 & 61.1 & 61.9844 & 83.3042 & 0.744073 & 0.985732 \tabularnewline
51 & 89.6 & 91.2331 & 82.9083 & 1.10041 & 0.9821 \tabularnewline
52 & 87.6 & 89.6729 & 82.1292 & 1.09185 & 0.976884 \tabularnewline
53 & 90.8 & 87.9776 & 81.8 & 1.07552 & 1.03208 \tabularnewline
54 & 84.3 & 84.7053 & 81.4542 & 1.03991 & 0.995215 \tabularnewline
55 & 75 & 73.9424 & 81.1042 & 0.911697 & 1.0143 \tabularnewline
56 & 78.4 & 71.7261 & 80.925 & 0.886329 & 1.09305 \tabularnewline
57 & 83.5 & 93.6236 & 80.5167 & 1.16279 & 0.891869 \tabularnewline
58 & 93 & 93.3609 & 80.0958 & 1.16562 & 0.996134 \tabularnewline
59 & 79.3 & 77.6351 & 79.5458 & 0.975979 & 1.02145 \tabularnewline
60 & 83.9 & 82.2652 & 78.8917 & 1.04276 & 1.01987 \tabularnewline
61 & 65 & 63.0874 & 78.5583 & 0.803064 & 1.03032 \tabularnewline
62 & 60.3 & 57.9695 & 77.9083 & 0.744073 & 1.0402 \tabularnewline
63 & 80.6 & 85.4238 & 77.6292 & 1.10041 & 0.943531 \tabularnewline
64 & 86.5 & 84.7869 & 77.6542 & 1.09185 & 1.02021 \tabularnewline
65 & 78.7 & 82.8824 & 77.0625 & 1.07552 & 0.949539 \tabularnewline
66 & 80.7 & 79.7007 & 76.6417 & 1.03991 & 1.01254 \tabularnewline
67 & 70.6 & NA & NA & 0.911697 & NA \tabularnewline
68 & 67.2 & NA & NA & 0.886329 & NA \tabularnewline
69 & 88 & NA & NA & 1.16279 & NA \tabularnewline
70 & 89.1 & NA & NA & 1.16562 & NA \tabularnewline
71 & 69 & NA & NA & 0.975979 & NA \tabularnewline
72 & 84.1 & NA & NA & 1.04276 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]75.8[/C][C]NA[/C][C]NA[/C][C]0.803064[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]75.7[/C][C]NA[/C][C]NA[/C][C]0.744073[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]112.3[/C][C]NA[/C][C]NA[/C][C]1.10041[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]110.9[/C][C]NA[/C][C]NA[/C][C]1.09185[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]99.6[/C][C]NA[/C][C]NA[/C][C]1.07552[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]107.5[/C][C]NA[/C][C]NA[/C][C]1.03991[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]90[/C][C]91.3711[/C][C]100.221[/C][C]0.911697[/C][C]0.984995[/C][/ROW]
[ROW][C]8[/C][C]88.8[/C][C]89.1093[/C][C]100.537[/C][C]0.886329[/C][C]0.996529[/C][/ROW]
[ROW][C]9[/C][C]129.7[/C][C]116.996[/C][C]100.617[/C][C]1.16279[/C][C]1.10859[/C][/ROW]
[ROW][C]10[/C][C]120.4[/C][C]117.586[/C][C]100.879[/C][C]1.16562[/C][C]1.02393[/C][/ROW]
[ROW][C]11[/C][C]93.3[/C][C]98.7854[/C][C]101.217[/C][C]0.975979[/C][C]0.944472[/C][/ROW]
[ROW][C]12[/C][C]96[/C][C]105.206[/C][C]100.892[/C][C]1.04276[/C][C]0.912496[/C][/ROW]
[ROW][C]13[/C][C]81.1[/C][C]80.7481[/C][C]100.55[/C][C]0.803064[/C][C]1.00436[/C][/ROW]
[ROW][C]14[/C][C]78[/C][C]74.7855[/C][C]100.508[/C][C]0.744073[/C][C]1.04298[/C][/ROW]
[ROW][C]15[/C][C]111.9[/C][C]109.55[/C][C]99.5542[/C][C]1.10041[/C][C]1.02145[/C][/ROW]
[ROW][C]16[/C][C]117.6[/C][C]107.279[/C][C]98.2542[/C][C]1.09185[/C][C]1.09621[/C][/ROW]
[ROW][C]17[/C][C]101[/C][C]105.235[/C][C]97.8458[/C][C]1.07552[/C][C]0.959754[/C][/ROW]
[ROW][C]18[/C][C]98.3[/C][C]101.972[/C][C]98.0583[/C][C]1.03991[/C][C]0.963988[/C][/ROW]
[ROW][C]19[/C][C]91[/C][C]89.3691[/C][C]98.025[/C][C]0.911697[/C][C]1.01825[/C][/ROW]
[ROW][C]20[/C][C]86.8[/C][C]86.3506[/C][C]97.425[/C][C]0.886329[/C][C]1.0052[/C][/ROW]
[ROW][C]21[/C][C]108.8[/C][C]113.047[/C][C]97.2208[/C][C]1.16279[/C][C]0.962432[/C][/ROW]
[ROW][C]22[/C][C]110.1[/C][C]112.399[/C][C]96.4292[/C][C]1.16562[/C][C]0.979543[/C][/ROW]
[ROW][C]23[/C][C]93.8[/C][C]93.3402[/C][C]95.6375[/C][C]0.975979[/C][C]1.00493[/C][/ROW]
[ROW][C]24[/C][C]100.6[/C][C]99.7749[/C][C]95.6833[/C][C]1.04276[/C][C]1.00827[/C][/ROW]
[ROW][C]25[/C][C]75.7[/C][C]76.3614[/C][C]95.0875[/C][C]0.803064[/C][C]0.991339[/C][/ROW]
[ROW][C]26[/C][C]69[/C][C]70.07[/C][C]94.1708[/C][C]0.744073[/C][C]0.98473[/C][/ROW]
[ROW][C]27[/C][C]116[/C][C]103.241[/C][C]93.8208[/C][C]1.10041[/C][C]1.12358[/C][/ROW]
[ROW][C]28[/C][C]94.5[/C][C]102.37[/C][C]93.7583[/C][C]1.09185[/C][C]0.92312[/C][/ROW]
[ROW][C]29[/C][C]105.1[/C][C]100.476[/C][C]93.4208[/C][C]1.07552[/C][C]1.04602[/C][/ROW]
[ROW][C]30[/C][C]95.3[/C][C]96.7986[/C][C]93.0833[/C][C]1.03991[/C][C]0.984518[/C][/ROW]
[ROW][C]31[/C][C]79.7[/C][C]84.4194[/C][C]92.5958[/C][C]0.911697[/C][C]0.944096[/C][/ROW]
[ROW][C]32[/C][C]76.1[/C][C]81.5385[/C][C]91.9958[/C][C]0.886329[/C][C]0.933301[/C][/ROW]
[ROW][C]33[/C][C]111.1[/C][C]105.402[/C][C]90.6458[/C][C]1.16279[/C][C]1.05406[/C][/ROW]
[ROW][C]34[/C][C]106.3[/C][C]104.357[/C][C]89.5292[/C][C]1.16562[/C][C]1.01862[/C][/ROW]
[ROW][C]35[/C][C]89.5[/C][C]86.8987[/C][C]89.0375[/C][C]0.975979[/C][C]1.02993[/C][/ROW]
[ROW][C]36[/C][C]96.8[/C][C]92.28[/C][C]88.4958[/C][C]1.04276[/C][C]1.04898[/C][/ROW]
[ROW][C]37[/C][C]67.8[/C][C]71.0411[/C][C]88.4625[/C][C]0.803064[/C][C]0.954377[/C][/ROW]
[ROW][C]38[/C][C]62.5[/C][C]65.7884[/C][C]88.4167[/C][C]0.744073[/C][C]0.950015[/C][/ROW]
[ROW][C]39[/C][C]90.1[/C][C]96.5701[/C][C]87.7583[/C][C]1.10041[/C][C]0.933001[/C][/ROW]
[ROW][C]40[/C][C]93.6[/C][C]94.8092[/C][C]86.8333[/C][C]1.09185[/C][C]0.987246[/C][/ROW]
[ROW][C]41[/C][C]94.2[/C][C]92.692[/C][C]86.1833[/C][C]1.07552[/C][C]1.01627[/C][/ROW]
[ROW][C]42[/C][C]93.2[/C][C]88.982[/C][C]85.5667[/C][C]1.03991[/C][C]1.0474[/C][/ROW]
[ROW][C]43[/C][C]81[/C][C]77.7336[/C][C]85.2625[/C][C]0.911697[/C][C]1.04202[/C][/ROW]
[ROW][C]44[/C][C]73.7[/C][C]75.5447[/C][C]85.2333[/C][C]0.886329[/C][C]0.975581[/C][/ROW]
[ROW][C]45[/C][C]97.7[/C][C]99.016[/C][C]85.1542[/C][C]1.16279[/C][C]0.986709[/C][/ROW]
[ROW][C]46[/C][C]97.5[/C][C]98.9413[/C][C]84.8833[/C][C]1.16562[/C][C]0.985433[/C][/ROW]
[ROW][C]47[/C][C]82.7[/C][C]82.4621[/C][C]84.4917[/C][C]0.975979[/C][C]1.00288[/C][/ROW]
[ROW][C]48[/C][C]88.8[/C][C]87.5702[/C][C]83.9792[/C][C]1.04276[/C][C]1.01404[/C][/ROW]
[ROW][C]49[/C][C]68.5[/C][C]66.9421[/C][C]83.3583[/C][C]0.803064[/C][C]1.02327[/C][/ROW]
[ROW][C]50[/C][C]61.1[/C][C]61.9844[/C][C]83.3042[/C][C]0.744073[/C][C]0.985732[/C][/ROW]
[ROW][C]51[/C][C]89.6[/C][C]91.2331[/C][C]82.9083[/C][C]1.10041[/C][C]0.9821[/C][/ROW]
[ROW][C]52[/C][C]87.6[/C][C]89.6729[/C][C]82.1292[/C][C]1.09185[/C][C]0.976884[/C][/ROW]
[ROW][C]53[/C][C]90.8[/C][C]87.9776[/C][C]81.8[/C][C]1.07552[/C][C]1.03208[/C][/ROW]
[ROW][C]54[/C][C]84.3[/C][C]84.7053[/C][C]81.4542[/C][C]1.03991[/C][C]0.995215[/C][/ROW]
[ROW][C]55[/C][C]75[/C][C]73.9424[/C][C]81.1042[/C][C]0.911697[/C][C]1.0143[/C][/ROW]
[ROW][C]56[/C][C]78.4[/C][C]71.7261[/C][C]80.925[/C][C]0.886329[/C][C]1.09305[/C][/ROW]
[ROW][C]57[/C][C]83.5[/C][C]93.6236[/C][C]80.5167[/C][C]1.16279[/C][C]0.891869[/C][/ROW]
[ROW][C]58[/C][C]93[/C][C]93.3609[/C][C]80.0958[/C][C]1.16562[/C][C]0.996134[/C][/ROW]
[ROW][C]59[/C][C]79.3[/C][C]77.6351[/C][C]79.5458[/C][C]0.975979[/C][C]1.02145[/C][/ROW]
[ROW][C]60[/C][C]83.9[/C][C]82.2652[/C][C]78.8917[/C][C]1.04276[/C][C]1.01987[/C][/ROW]
[ROW][C]61[/C][C]65[/C][C]63.0874[/C][C]78.5583[/C][C]0.803064[/C][C]1.03032[/C][/ROW]
[ROW][C]62[/C][C]60.3[/C][C]57.9695[/C][C]77.9083[/C][C]0.744073[/C][C]1.0402[/C][/ROW]
[ROW][C]63[/C][C]80.6[/C][C]85.4238[/C][C]77.6292[/C][C]1.10041[/C][C]0.943531[/C][/ROW]
[ROW][C]64[/C][C]86.5[/C][C]84.7869[/C][C]77.6542[/C][C]1.09185[/C][C]1.02021[/C][/ROW]
[ROW][C]65[/C][C]78.7[/C][C]82.8824[/C][C]77.0625[/C][C]1.07552[/C][C]0.949539[/C][/ROW]
[ROW][C]66[/C][C]80.7[/C][C]79.7007[/C][C]76.6417[/C][C]1.03991[/C][C]1.01254[/C][/ROW]
[ROW][C]67[/C][C]70.6[/C][C]NA[/C][C]NA[/C][C]0.911697[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]67.2[/C][C]NA[/C][C]NA[/C][C]0.886329[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]88[/C][C]NA[/C][C]NA[/C][C]1.16279[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]89.1[/C][C]NA[/C][C]NA[/C][C]1.16562[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]69[/C][C]NA[/C][C]NA[/C][C]0.975979[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]84.1[/C][C]NA[/C][C]NA[/C][C]1.04276[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	75.8	NA	NA	0.803064	NA
2	75.7	NA	NA	0.744073	NA
3	112.3	NA	NA	1.10041	NA
4	110.9	NA	NA	1.09185	NA
5	99.6	NA	NA	1.07552	NA
6	107.5	NA	NA	1.03991	NA
7	90	91.3711	100.221	0.911697	0.984995
8	88.8	89.1093	100.537	0.886329	0.996529
9	129.7	116.996	100.617	1.16279	1.10859
10	120.4	117.586	100.879	1.16562	1.02393
11	93.3	98.7854	101.217	0.975979	0.944472
12	96	105.206	100.892	1.04276	0.912496
13	81.1	80.7481	100.55	0.803064	1.00436
14	78	74.7855	100.508	0.744073	1.04298
15	111.9	109.55	99.5542	1.10041	1.02145
16	117.6	107.279	98.2542	1.09185	1.09621
17	101	105.235	97.8458	1.07552	0.959754
18	98.3	101.972	98.0583	1.03991	0.963988
19	91	89.3691	98.025	0.911697	1.01825
20	86.8	86.3506	97.425	0.886329	1.0052
21	108.8	113.047	97.2208	1.16279	0.962432
22	110.1	112.399	96.4292	1.16562	0.979543
23	93.8	93.3402	95.6375	0.975979	1.00493
24	100.6	99.7749	95.6833	1.04276	1.00827
25	75.7	76.3614	95.0875	0.803064	0.991339
26	69	70.07	94.1708	0.744073	0.98473
27	116	103.241	93.8208	1.10041	1.12358
28	94.5	102.37	93.7583	1.09185	0.92312
29	105.1	100.476	93.4208	1.07552	1.04602
30	95.3	96.7986	93.0833	1.03991	0.984518
31	79.7	84.4194	92.5958	0.911697	0.944096
32	76.1	81.5385	91.9958	0.886329	0.933301
33	111.1	105.402	90.6458	1.16279	1.05406
34	106.3	104.357	89.5292	1.16562	1.01862
35	89.5	86.8987	89.0375	0.975979	1.02993
36	96.8	92.28	88.4958	1.04276	1.04898
37	67.8	71.0411	88.4625	0.803064	0.954377
38	62.5	65.7884	88.4167	0.744073	0.950015
39	90.1	96.5701	87.7583	1.10041	0.933001
40	93.6	94.8092	86.8333	1.09185	0.987246
41	94.2	92.692	86.1833	1.07552	1.01627
42	93.2	88.982	85.5667	1.03991	1.0474
43	81	77.7336	85.2625	0.911697	1.04202
44	73.7	75.5447	85.2333	0.886329	0.975581
45	97.7	99.016	85.1542	1.16279	0.986709
46	97.5	98.9413	84.8833	1.16562	0.985433
47	82.7	82.4621	84.4917	0.975979	1.00288
48	88.8	87.5702	83.9792	1.04276	1.01404
49	68.5	66.9421	83.3583	0.803064	1.02327
50	61.1	61.9844	83.3042	0.744073	0.985732
51	89.6	91.2331	82.9083	1.10041	0.9821
52	87.6	89.6729	82.1292	1.09185	0.976884
53	90.8	87.9776	81.8	1.07552	1.03208
54	84.3	84.7053	81.4542	1.03991	0.995215
55	75	73.9424	81.1042	0.911697	1.0143
56	78.4	71.7261	80.925	0.886329	1.09305
57	83.5	93.6236	80.5167	1.16279	0.891869
58	93	93.3609	80.0958	1.16562	0.996134
59	79.3	77.6351	79.5458	0.975979	1.02145
60	83.9	82.2652	78.8917	1.04276	1.01987
61	65	63.0874	78.5583	0.803064	1.03032
62	60.3	57.9695	77.9083	0.744073	1.0402
63	80.6	85.4238	77.6292	1.10041	0.943531
64	86.5	84.7869	77.6542	1.09185	1.02021
65	78.7	82.8824	77.0625	1.07552	0.949539
66	80.7	79.7007	76.6417	1.03991	1.01254
67	70.6	NA	NA	0.911697	NA
68	67.2	NA	NA	0.886329	NA
69	88	NA	NA	1.16279	NA
70	89.1	NA	NA	1.16562	NA
71	69	NA	NA	0.975979	NA
72	84.1	NA	NA	1.04276	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 = 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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
a<-table.element(a,signif(m$trend[i],6))
a<-table.element(a,signif(m$seasonal[i],6))
a<-table.element(a,signif(m$random[i],6))
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