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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sat, 29 Apr 2017 13:36:35 +0100

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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/Apr/29/t14934694193920wmwehyeoxqf.htm/, Retrieved Tue, 25 Aug 2026 15:18:53 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Tue, 25 Aug 2026 15:18:53 +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	3 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 & 3 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]3 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	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	70.3	NA	NA	-18.7288	NA
2	90.2	NA	NA	-17.2296	NA
3	107.3	NA	NA	5.07958	NA
4	104.6	NA	NA	4.35625	NA
5	102.7	NA	NA	11.8988	NA
6	124.5	NA	NA	19.4929	NA
7	117.8	109.58	100.671	8.90875	8.22042
8	104.2	102.702	101.388	1.31458	1.49792
9	99.9	104.333	101.783	2.54958	-4.43292
10	91.5	97.3829	102.512	-5.12958	-5.88292
11	95.7	95.6321	104.117	-8.48458	0.0679167
12	91.4	101.168	105.196	-4.02792	-9.76792
13	86.2	85.8046	104.533	-18.7288	0.395417
14	91.5	86.9162	104.146	-17.2296	4.58375
15	115.5	109.942	104.862	5.07958	5.55792
16	113.9	110.173	105.817	4.35625	3.72708
17	131.9	118.47	106.571	11.8988	13.4304
18	121.2	126.96	107.467	19.4929	-5.75958
19	105.2	117.18	108.271	8.90875	-11.9796
20	107.5	110.485	109.171	1.31458	-2.98542
21	113.8	112.416	109.867	2.54958	1.38375
22	100.5	104.654	109.783	-5.12958	-4.15375
23	104.8	100.786	109.271	-8.48458	4.01375
24	103.8	105.651	109.679	-4.02792	-1.85125
25	93.1	91.9462	110.675	-18.7288	1.15375
26	106.2	94.2912	111.521	-17.2296	11.9088
27	117.5	117.084	112.004	5.07958	0.41625
28	109.9	116.552	112.196	4.35625	-6.65208
29	123.6	124.361	112.462	11.8988	-0.76125
30	139.3	132.055	112.562	19.4929	7.24458
31	111	121.571	112.662	8.90875	-10.5712
32	122	112.981	111.667	1.31458	9.01875
33	110.9	112.983	110.433	2.54958	-2.08292
34	108	104.983	110.113	-5.12958	3.01708
35	103.7	101.495	109.979	-8.48458	2.20542
36	107.3	105.11	109.137	-4.02792	2.19042
37	92	90.4504	109.179	-18.7288	1.54958
38	83.4	92.2246	109.454	-17.2296	-8.82458
39	110.7	114.113	109.033	5.07958	-3.41292
40	109	113.54	109.183	4.35625	-4.53958
41	121.3	121.028	109.129	11.8988	0.272083
42	121.4	128.58	109.087	19.4929	-7.18042
43	129.9	118.1	109.192	8.90875	11.7996
44	109.7	110.86	109.546	1.31458	-1.16042
45	113.1	112.575	110.025	2.54958	0.525417
46	109.4	105.454	110.583	-5.12958	3.94625
47	101	102.453	110.937	-8.48458	-1.45292
48	109	107.468	111.496	-4.02792	1.53208
49	92.8	93.0754	111.804	-18.7288	-0.275417
50	91.1	94.1246	111.354	-17.2296	-3.02458
51	114.5	116.488	111.408	5.07958	-1.98792
52	118.6	115.952	111.596	4.35625	2.64792
53	120.2	123.32	111.421	11.8988	-3.11958
54	135.9	130.964	111.471	19.4929	4.93625
55	122.8	120.396	111.488	8.90875	2.40375
56	106	112.498	111.183	1.31458	-6.49792
57	118.1	113.62	111.071	2.54958	4.47958
58	108.9	105.954	111.083	-5.12958	2.94625
59	97.3	102.261	110.746	-8.48458	-4.96125
60	113.9	106.13	110.158	-4.02792	7.76958
61	88.3	91.2504	109.979	-18.7288	-2.95042
62	88.3	93.0704	110.3	-17.2296	-4.77042
63	114.6	115.3	110.221	5.07958	-0.700417
64	118.8	114.11	109.754	4.35625	4.68958
65	111.9	121.849	109.95	11.8988	-9.94875
66	130.1	129.468	109.975	19.4929	0.632083
67	124.3	NA	NA	8.90875	NA
68	112.2	NA	NA	1.31458	NA
69	110	NA	NA	2.54958	NA
70	105.8	NA	NA	-5.12958	NA
71	105.1	NA	NA	-8.48458	NA
72	106.7	NA	NA	-4.02792	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 70.3 & NA & NA & -18.7288 & NA \tabularnewline
2 & 90.2 & NA & NA & -17.2296 & NA \tabularnewline
3 & 107.3 & NA & NA & 5.07958 & NA \tabularnewline
4 & 104.6 & NA & NA & 4.35625 & NA \tabularnewline
5 & 102.7 & NA & NA & 11.8988 & NA \tabularnewline
6 & 124.5 & NA & NA & 19.4929 & NA \tabularnewline
7 & 117.8 & 109.58 & 100.671 & 8.90875 & 8.22042 \tabularnewline
8 & 104.2 & 102.702 & 101.388 & 1.31458 & 1.49792 \tabularnewline
9 & 99.9 & 104.333 & 101.783 & 2.54958 & -4.43292 \tabularnewline
10 & 91.5 & 97.3829 & 102.512 & -5.12958 & -5.88292 \tabularnewline
11 & 95.7 & 95.6321 & 104.117 & -8.48458 & 0.0679167 \tabularnewline
12 & 91.4 & 101.168 & 105.196 & -4.02792 & -9.76792 \tabularnewline
13 & 86.2 & 85.8046 & 104.533 & -18.7288 & 0.395417 \tabularnewline
14 & 91.5 & 86.9162 & 104.146 & -17.2296 & 4.58375 \tabularnewline
15 & 115.5 & 109.942 & 104.862 & 5.07958 & 5.55792 \tabularnewline
16 & 113.9 & 110.173 & 105.817 & 4.35625 & 3.72708 \tabularnewline
17 & 131.9 & 118.47 & 106.571 & 11.8988 & 13.4304 \tabularnewline
18 & 121.2 & 126.96 & 107.467 & 19.4929 & -5.75958 \tabularnewline
19 & 105.2 & 117.18 & 108.271 & 8.90875 & -11.9796 \tabularnewline
20 & 107.5 & 110.485 & 109.171 & 1.31458 & -2.98542 \tabularnewline
21 & 113.8 & 112.416 & 109.867 & 2.54958 & 1.38375 \tabularnewline
22 & 100.5 & 104.654 & 109.783 & -5.12958 & -4.15375 \tabularnewline
23 & 104.8 & 100.786 & 109.271 & -8.48458 & 4.01375 \tabularnewline
24 & 103.8 & 105.651 & 109.679 & -4.02792 & -1.85125 \tabularnewline
25 & 93.1 & 91.9462 & 110.675 & -18.7288 & 1.15375 \tabularnewline
26 & 106.2 & 94.2912 & 111.521 & -17.2296 & 11.9088 \tabularnewline
27 & 117.5 & 117.084 & 112.004 & 5.07958 & 0.41625 \tabularnewline
28 & 109.9 & 116.552 & 112.196 & 4.35625 & -6.65208 \tabularnewline
29 & 123.6 & 124.361 & 112.462 & 11.8988 & -0.76125 \tabularnewline
30 & 139.3 & 132.055 & 112.562 & 19.4929 & 7.24458 \tabularnewline
31 & 111 & 121.571 & 112.662 & 8.90875 & -10.5712 \tabularnewline
32 & 122 & 112.981 & 111.667 & 1.31458 & 9.01875 \tabularnewline
33 & 110.9 & 112.983 & 110.433 & 2.54958 & -2.08292 \tabularnewline
34 & 108 & 104.983 & 110.113 & -5.12958 & 3.01708 \tabularnewline
35 & 103.7 & 101.495 & 109.979 & -8.48458 & 2.20542 \tabularnewline
36 & 107.3 & 105.11 & 109.137 & -4.02792 & 2.19042 \tabularnewline
37 & 92 & 90.4504 & 109.179 & -18.7288 & 1.54958 \tabularnewline
38 & 83.4 & 92.2246 & 109.454 & -17.2296 & -8.82458 \tabularnewline
39 & 110.7 & 114.113 & 109.033 & 5.07958 & -3.41292 \tabularnewline
40 & 109 & 113.54 & 109.183 & 4.35625 & -4.53958 \tabularnewline
41 & 121.3 & 121.028 & 109.129 & 11.8988 & 0.272083 \tabularnewline
42 & 121.4 & 128.58 & 109.087 & 19.4929 & -7.18042 \tabularnewline
43 & 129.9 & 118.1 & 109.192 & 8.90875 & 11.7996 \tabularnewline
44 & 109.7 & 110.86 & 109.546 & 1.31458 & -1.16042 \tabularnewline
45 & 113.1 & 112.575 & 110.025 & 2.54958 & 0.525417 \tabularnewline
46 & 109.4 & 105.454 & 110.583 & -5.12958 & 3.94625 \tabularnewline
47 & 101 & 102.453 & 110.937 & -8.48458 & -1.45292 \tabularnewline
48 & 109 & 107.468 & 111.496 & -4.02792 & 1.53208 \tabularnewline
49 & 92.8 & 93.0754 & 111.804 & -18.7288 & -0.275417 \tabularnewline
50 & 91.1 & 94.1246 & 111.354 & -17.2296 & -3.02458 \tabularnewline
51 & 114.5 & 116.488 & 111.408 & 5.07958 & -1.98792 \tabularnewline
52 & 118.6 & 115.952 & 111.596 & 4.35625 & 2.64792 \tabularnewline
53 & 120.2 & 123.32 & 111.421 & 11.8988 & -3.11958 \tabularnewline
54 & 135.9 & 130.964 & 111.471 & 19.4929 & 4.93625 \tabularnewline
55 & 122.8 & 120.396 & 111.488 & 8.90875 & 2.40375 \tabularnewline
56 & 106 & 112.498 & 111.183 & 1.31458 & -6.49792 \tabularnewline
57 & 118.1 & 113.62 & 111.071 & 2.54958 & 4.47958 \tabularnewline
58 & 108.9 & 105.954 & 111.083 & -5.12958 & 2.94625 \tabularnewline
59 & 97.3 & 102.261 & 110.746 & -8.48458 & -4.96125 \tabularnewline
60 & 113.9 & 106.13 & 110.158 & -4.02792 & 7.76958 \tabularnewline
61 & 88.3 & 91.2504 & 109.979 & -18.7288 & -2.95042 \tabularnewline
62 & 88.3 & 93.0704 & 110.3 & -17.2296 & -4.77042 \tabularnewline
63 & 114.6 & 115.3 & 110.221 & 5.07958 & -0.700417 \tabularnewline
64 & 118.8 & 114.11 & 109.754 & 4.35625 & 4.68958 \tabularnewline
65 & 111.9 & 121.849 & 109.95 & 11.8988 & -9.94875 \tabularnewline
66 & 130.1 & 129.468 & 109.975 & 19.4929 & 0.632083 \tabularnewline
67 & 124.3 & NA & NA & 8.90875 & NA \tabularnewline
68 & 112.2 & NA & NA & 1.31458 & NA \tabularnewline
69 & 110 & NA & NA & 2.54958 & NA \tabularnewline
70 & 105.8 & NA & NA & -5.12958 & NA \tabularnewline
71 & 105.1 & NA & NA & -8.48458 & NA \tabularnewline
72 & 106.7 & NA & NA & -4.02792 & 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]70.3[/C][C]NA[/C][C]NA[/C][C]-18.7288[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]90.2[/C][C]NA[/C][C]NA[/C][C]-17.2296[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]107.3[/C][C]NA[/C][C]NA[/C][C]5.07958[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]104.6[/C][C]NA[/C][C]NA[/C][C]4.35625[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]102.7[/C][C]NA[/C][C]NA[/C][C]11.8988[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]124.5[/C][C]NA[/C][C]NA[/C][C]19.4929[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]117.8[/C][C]109.58[/C][C]100.671[/C][C]8.90875[/C][C]8.22042[/C][/ROW]
[ROW][C]8[/C][C]104.2[/C][C]102.702[/C][C]101.388[/C][C]1.31458[/C][C]1.49792[/C][/ROW]
[ROW][C]9[/C][C]99.9[/C][C]104.333[/C][C]101.783[/C][C]2.54958[/C][C]-4.43292[/C][/ROW]
[ROW][C]10[/C][C]91.5[/C][C]97.3829[/C][C]102.512[/C][C]-5.12958[/C][C]-5.88292[/C][/ROW]
[ROW][C]11[/C][C]95.7[/C][C]95.6321[/C][C]104.117[/C][C]-8.48458[/C][C]0.0679167[/C][/ROW]
[ROW][C]12[/C][C]91.4[/C][C]101.168[/C][C]105.196[/C][C]-4.02792[/C][C]-9.76792[/C][/ROW]
[ROW][C]13[/C][C]86.2[/C][C]85.8046[/C][C]104.533[/C][C]-18.7288[/C][C]0.395417[/C][/ROW]
[ROW][C]14[/C][C]91.5[/C][C]86.9162[/C][C]104.146[/C][C]-17.2296[/C][C]4.58375[/C][/ROW]
[ROW][C]15[/C][C]115.5[/C][C]109.942[/C][C]104.862[/C][C]5.07958[/C][C]5.55792[/C][/ROW]
[ROW][C]16[/C][C]113.9[/C][C]110.173[/C][C]105.817[/C][C]4.35625[/C][C]3.72708[/C][/ROW]
[ROW][C]17[/C][C]131.9[/C][C]118.47[/C][C]106.571[/C][C]11.8988[/C][C]13.4304[/C][/ROW]
[ROW][C]18[/C][C]121.2[/C][C]126.96[/C][C]107.467[/C][C]19.4929[/C][C]-5.75958[/C][/ROW]
[ROW][C]19[/C][C]105.2[/C][C]117.18[/C][C]108.271[/C][C]8.90875[/C][C]-11.9796[/C][/ROW]
[ROW][C]20[/C][C]107.5[/C][C]110.485[/C][C]109.171[/C][C]1.31458[/C][C]-2.98542[/C][/ROW]
[ROW][C]21[/C][C]113.8[/C][C]112.416[/C][C]109.867[/C][C]2.54958[/C][C]1.38375[/C][/ROW]
[ROW][C]22[/C][C]100.5[/C][C]104.654[/C][C]109.783[/C][C]-5.12958[/C][C]-4.15375[/C][/ROW]
[ROW][C]23[/C][C]104.8[/C][C]100.786[/C][C]109.271[/C][C]-8.48458[/C][C]4.01375[/C][/ROW]
[ROW][C]24[/C][C]103.8[/C][C]105.651[/C][C]109.679[/C][C]-4.02792[/C][C]-1.85125[/C][/ROW]
[ROW][C]25[/C][C]93.1[/C][C]91.9462[/C][C]110.675[/C][C]-18.7288[/C][C]1.15375[/C][/ROW]
[ROW][C]26[/C][C]106.2[/C][C]94.2912[/C][C]111.521[/C][C]-17.2296[/C][C]11.9088[/C][/ROW]
[ROW][C]27[/C][C]117.5[/C][C]117.084[/C][C]112.004[/C][C]5.07958[/C][C]0.41625[/C][/ROW]
[ROW][C]28[/C][C]109.9[/C][C]116.552[/C][C]112.196[/C][C]4.35625[/C][C]-6.65208[/C][/ROW]
[ROW][C]29[/C][C]123.6[/C][C]124.361[/C][C]112.462[/C][C]11.8988[/C][C]-0.76125[/C][/ROW]
[ROW][C]30[/C][C]139.3[/C][C]132.055[/C][C]112.562[/C][C]19.4929[/C][C]7.24458[/C][/ROW]
[ROW][C]31[/C][C]111[/C][C]121.571[/C][C]112.662[/C][C]8.90875[/C][C]-10.5712[/C][/ROW]
[ROW][C]32[/C][C]122[/C][C]112.981[/C][C]111.667[/C][C]1.31458[/C][C]9.01875[/C][/ROW]
[ROW][C]33[/C][C]110.9[/C][C]112.983[/C][C]110.433[/C][C]2.54958[/C][C]-2.08292[/C][/ROW]
[ROW][C]34[/C][C]108[/C][C]104.983[/C][C]110.113[/C][C]-5.12958[/C][C]3.01708[/C][/ROW]
[ROW][C]35[/C][C]103.7[/C][C]101.495[/C][C]109.979[/C][C]-8.48458[/C][C]2.20542[/C][/ROW]
[ROW][C]36[/C][C]107.3[/C][C]105.11[/C][C]109.137[/C][C]-4.02792[/C][C]2.19042[/C][/ROW]
[ROW][C]37[/C][C]92[/C][C]90.4504[/C][C]109.179[/C][C]-18.7288[/C][C]1.54958[/C][/ROW]
[ROW][C]38[/C][C]83.4[/C][C]92.2246[/C][C]109.454[/C][C]-17.2296[/C][C]-8.82458[/C][/ROW]
[ROW][C]39[/C][C]110.7[/C][C]114.113[/C][C]109.033[/C][C]5.07958[/C][C]-3.41292[/C][/ROW]
[ROW][C]40[/C][C]109[/C][C]113.54[/C][C]109.183[/C][C]4.35625[/C][C]-4.53958[/C][/ROW]
[ROW][C]41[/C][C]121.3[/C][C]121.028[/C][C]109.129[/C][C]11.8988[/C][C]0.272083[/C][/ROW]
[ROW][C]42[/C][C]121.4[/C][C]128.58[/C][C]109.087[/C][C]19.4929[/C][C]-7.18042[/C][/ROW]
[ROW][C]43[/C][C]129.9[/C][C]118.1[/C][C]109.192[/C][C]8.90875[/C][C]11.7996[/C][/ROW]
[ROW][C]44[/C][C]109.7[/C][C]110.86[/C][C]109.546[/C][C]1.31458[/C][C]-1.16042[/C][/ROW]
[ROW][C]45[/C][C]113.1[/C][C]112.575[/C][C]110.025[/C][C]2.54958[/C][C]0.525417[/C][/ROW]
[ROW][C]46[/C][C]109.4[/C][C]105.454[/C][C]110.583[/C][C]-5.12958[/C][C]3.94625[/C][/ROW]
[ROW][C]47[/C][C]101[/C][C]102.453[/C][C]110.937[/C][C]-8.48458[/C][C]-1.45292[/C][/ROW]
[ROW][C]48[/C][C]109[/C][C]107.468[/C][C]111.496[/C][C]-4.02792[/C][C]1.53208[/C][/ROW]
[ROW][C]49[/C][C]92.8[/C][C]93.0754[/C][C]111.804[/C][C]-18.7288[/C][C]-0.275417[/C][/ROW]
[ROW][C]50[/C][C]91.1[/C][C]94.1246[/C][C]111.354[/C][C]-17.2296[/C][C]-3.02458[/C][/ROW]
[ROW][C]51[/C][C]114.5[/C][C]116.488[/C][C]111.408[/C][C]5.07958[/C][C]-1.98792[/C][/ROW]
[ROW][C]52[/C][C]118.6[/C][C]115.952[/C][C]111.596[/C][C]4.35625[/C][C]2.64792[/C][/ROW]
[ROW][C]53[/C][C]120.2[/C][C]123.32[/C][C]111.421[/C][C]11.8988[/C][C]-3.11958[/C][/ROW]
[ROW][C]54[/C][C]135.9[/C][C]130.964[/C][C]111.471[/C][C]19.4929[/C][C]4.93625[/C][/ROW]
[ROW][C]55[/C][C]122.8[/C][C]120.396[/C][C]111.488[/C][C]8.90875[/C][C]2.40375[/C][/ROW]
[ROW][C]56[/C][C]106[/C][C]112.498[/C][C]111.183[/C][C]1.31458[/C][C]-6.49792[/C][/ROW]
[ROW][C]57[/C][C]118.1[/C][C]113.62[/C][C]111.071[/C][C]2.54958[/C][C]4.47958[/C][/ROW]
[ROW][C]58[/C][C]108.9[/C][C]105.954[/C][C]111.083[/C][C]-5.12958[/C][C]2.94625[/C][/ROW]
[ROW][C]59[/C][C]97.3[/C][C]102.261[/C][C]110.746[/C][C]-8.48458[/C][C]-4.96125[/C][/ROW]
[ROW][C]60[/C][C]113.9[/C][C]106.13[/C][C]110.158[/C][C]-4.02792[/C][C]7.76958[/C][/ROW]
[ROW][C]61[/C][C]88.3[/C][C]91.2504[/C][C]109.979[/C][C]-18.7288[/C][C]-2.95042[/C][/ROW]
[ROW][C]62[/C][C]88.3[/C][C]93.0704[/C][C]110.3[/C][C]-17.2296[/C][C]-4.77042[/C][/ROW]
[ROW][C]63[/C][C]114.6[/C][C]115.3[/C][C]110.221[/C][C]5.07958[/C][C]-0.700417[/C][/ROW]
[ROW][C]64[/C][C]118.8[/C][C]114.11[/C][C]109.754[/C][C]4.35625[/C][C]4.68958[/C][/ROW]
[ROW][C]65[/C][C]111.9[/C][C]121.849[/C][C]109.95[/C][C]11.8988[/C][C]-9.94875[/C][/ROW]
[ROW][C]66[/C][C]130.1[/C][C]129.468[/C][C]109.975[/C][C]19.4929[/C][C]0.632083[/C][/ROW]
[ROW][C]67[/C][C]124.3[/C][C]NA[/C][C]NA[/C][C]8.90875[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]112.2[/C][C]NA[/C][C]NA[/C][C]1.31458[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]110[/C][C]NA[/C][C]NA[/C][C]2.54958[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]105.8[/C][C]NA[/C][C]NA[/C][C]-5.12958[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]105.1[/C][C]NA[/C][C]NA[/C][C]-8.48458[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]106.7[/C][C]NA[/C][C]NA[/C][C]-4.02792[/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	70.3	NA	NA	-18.7288	NA
2	90.2	NA	NA	-17.2296	NA
3	107.3	NA	NA	5.07958	NA
4	104.6	NA	NA	4.35625	NA
5	102.7	NA	NA	11.8988	NA
6	124.5	NA	NA	19.4929	NA
7	117.8	109.58	100.671	8.90875	8.22042
8	104.2	102.702	101.388	1.31458	1.49792
9	99.9	104.333	101.783	2.54958	-4.43292
10	91.5	97.3829	102.512	-5.12958	-5.88292
11	95.7	95.6321	104.117	-8.48458	0.0679167
12	91.4	101.168	105.196	-4.02792	-9.76792
13	86.2	85.8046	104.533	-18.7288	0.395417
14	91.5	86.9162	104.146	-17.2296	4.58375
15	115.5	109.942	104.862	5.07958	5.55792
16	113.9	110.173	105.817	4.35625	3.72708
17	131.9	118.47	106.571	11.8988	13.4304
18	121.2	126.96	107.467	19.4929	-5.75958
19	105.2	117.18	108.271	8.90875	-11.9796
20	107.5	110.485	109.171	1.31458	-2.98542
21	113.8	112.416	109.867	2.54958	1.38375
22	100.5	104.654	109.783	-5.12958	-4.15375
23	104.8	100.786	109.271	-8.48458	4.01375
24	103.8	105.651	109.679	-4.02792	-1.85125
25	93.1	91.9462	110.675	-18.7288	1.15375
26	106.2	94.2912	111.521	-17.2296	11.9088
27	117.5	117.084	112.004	5.07958	0.41625
28	109.9	116.552	112.196	4.35625	-6.65208
29	123.6	124.361	112.462	11.8988	-0.76125
30	139.3	132.055	112.562	19.4929	7.24458
31	111	121.571	112.662	8.90875	-10.5712
32	122	112.981	111.667	1.31458	9.01875
33	110.9	112.983	110.433	2.54958	-2.08292
34	108	104.983	110.113	-5.12958	3.01708
35	103.7	101.495	109.979	-8.48458	2.20542
36	107.3	105.11	109.137	-4.02792	2.19042
37	92	90.4504	109.179	-18.7288	1.54958
38	83.4	92.2246	109.454	-17.2296	-8.82458
39	110.7	114.113	109.033	5.07958	-3.41292
40	109	113.54	109.183	4.35625	-4.53958
41	121.3	121.028	109.129	11.8988	0.272083
42	121.4	128.58	109.087	19.4929	-7.18042
43	129.9	118.1	109.192	8.90875	11.7996
44	109.7	110.86	109.546	1.31458	-1.16042
45	113.1	112.575	110.025	2.54958	0.525417
46	109.4	105.454	110.583	-5.12958	3.94625
47	101	102.453	110.937	-8.48458	-1.45292
48	109	107.468	111.496	-4.02792	1.53208
49	92.8	93.0754	111.804	-18.7288	-0.275417
50	91.1	94.1246	111.354	-17.2296	-3.02458
51	114.5	116.488	111.408	5.07958	-1.98792
52	118.6	115.952	111.596	4.35625	2.64792
53	120.2	123.32	111.421	11.8988	-3.11958
54	135.9	130.964	111.471	19.4929	4.93625
55	122.8	120.396	111.488	8.90875	2.40375
56	106	112.498	111.183	1.31458	-6.49792
57	118.1	113.62	111.071	2.54958	4.47958
58	108.9	105.954	111.083	-5.12958	2.94625
59	97.3	102.261	110.746	-8.48458	-4.96125
60	113.9	106.13	110.158	-4.02792	7.76958
61	88.3	91.2504	109.979	-18.7288	-2.95042
62	88.3	93.0704	110.3	-17.2296	-4.77042
63	114.6	115.3	110.221	5.07958	-0.700417
64	118.8	114.11	109.754	4.35625	4.68958
65	111.9	121.849	109.95	11.8988	-9.94875
66	130.1	129.468	109.975	19.4929	0.632083
67	124.3	NA	NA	8.90875	NA
68	112.2	NA	NA	1.31458	NA
69	110	NA	NA	2.54958	NA
70	105.8	NA	NA	-5.12958	NA
71	105.1	NA	NA	-8.48458	NA
72	106.7	NA	NA	-4.02792	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 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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