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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Fri, 25 Nov 2016 14:57:54 +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/2016/Nov/25/t1480085894o1fapmynnea2rqq.htm/, Retrieved Sun, 19 May 2024 04:02:20 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sun, 19 May 2024 04:02:20 +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	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.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]'Gwilym Jenkins' @ jenkins.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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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	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	2341	NA	NA	-28.6747	NA
2	2115	NA	NA	1.6015	NA
3	2402	NA	NA	13.5926	NA
4	2180	NA	NA	12.3777	NA
5	2453	NA	NA	13.8676	NA
6	2507	NA	NA	14.7471	NA
7	2679	1725.46	1549.67	175.791	953.543
8	2622	1576.47	1372.98	203.49	1045.53
9	103.1	1053.23	1193.77	-140.547	-950.128
10	95.2	891.929	1011.48	-119.546	-796.729
11	110.2	740.11	826.892	-86.7818	-629.91
12	105.3	569.14	629.058	-59.9181	-463.84
13	107.4	393.104	421.779	-28.6747	-285.704
14	108.1	212.218	210.617	1.6015	-104.118
15	108	120.234	106.642	13.5926	-12.2342
16	98.8	118.928	106.55	12.3777	-20.1277
17	104.2	120.501	106.633	13.8676	-16.3009
18	107.8	121.555	106.808	14.7471	-13.7555
19	103.5	282.566	106.775	175.791	-179.066
20	129.6	310.053	106.562	203.49	-180.453
21	100.1	-34.2675	106.279	-140.547	134.368
22	96	-13.4044	106.142	-119.546	109.404
23	111.4	19.5057	106.288	-86.7818	91.8943
24	108.3	46.311	106.229	-59.9181	61.989
25	103.6	77.4586	106.133	-28.6747	26.1414
26	106.8	107.618	106.017	1.6015	-0.818171
27	102.5	119.513	105.921	13.5926	-17.0134
28	101	118.474	106.096	12.3777	-17.4735
29	105.5	120.188	106.321	13.8676	-14.6884
30	105.1	120.772	106.025	14.7471	-15.6721
31	103.9	281.624	105.833	175.791	-177.724
32	126.4	309.615	106.125	203.49	-183.215
33	101	-34.4217	106.125	-140.547	135.422
34	99.3	-13.3336	106.212	-119.546	112.634
35	113.5	19.514	106.296	-86.7818	93.986
36	99.1	46.3027	106.221	-59.9181	52.7973
37	108.2	77.667	106.342	-28.6747	30.533
38	109.2	107.86	106.258	1.6015	1.34016
39	100.1	119.713	106.121	13.5926	-19.6134
40	105.5	118.336	105.958	12.3777	-12.836
41	103	119.568	105.7	13.8676	-16.5676
42	105.8	120.326	105.579	14.7471	-14.5263
43	106.1	281.382	105.592	175.791	-175.282
44	122.2	308.965	105.475	203.49	-186.765
45	101.9	-35.18	105.367	-140.547	137.08
46	94.5	-14.0753	105.471	-119.546	108.575
47	112.1	18.5223	105.304	-86.7818	93.5777
48	97.6	45.2152	105.133	-59.9181	52.3848
49	110	76.4711	105.146	-28.6747	33.5289
50	104.6	106.689	105.088	1.6015	-2.089
51	102.1	118.759	105.167	13.5926	-16.6592
52	106	118.007	105.629	12.3777	-12.0069
53	98.5	119.08	105.213	13.8676	-20.5801
54	106.2	119.547	104.8	14.7471	-13.3471
55	106	280.628	104.837	175.791	-174.628
56	120.9	308.315	104.825	203.49	-187.415
57	105.1	-35.3009	105.246	-140.547	140.401
58	102.4	-14.2211	105.325	-119.546	116.621
59	94.2	18.6973	105.479	-86.7818	75.5027
60	105.6	45.6027	105.521	-59.9181	59.9973
61	102.9	76.8128	105.487	-28.6747	26.0872
62	111.4	106.518	104.917	1.6015	4.88183
63	105.4	118.643	105.05	13.5926	-13.2426
64	104.6	118.415	106.037	12.3777	-13.8152
65	103.6	120.551	106.683	13.8676	-16.9509
66	102.1	121.539	106.792	14.7471	-19.4388
67	109.3	282.299	106.508	175.791	-172.999
68	103.9	309.828	106.337	203.49	-205.928
69	125.3	-34.255	106.292	-140.547	159.555
70	105.9	-12.8294	106.717	-119.546	118.729
71	106.2	20.3223	107.104	-86.7818	85.8777
72	96.2	47.5735	107.492	-59.9181	48.6265
73	105.5	78.7711	107.446	-28.6747	26.7289
74	104.7	108.81	107.208	1.6015	-4.10984
75	111	119.943	106.35	13.5926	-8.94258
76	109.2	118.14	105.762	12.3777	-8.9402
77	108.3	119.776	105.908	13.8676	-11.4759
78	106.7	121.901	107.154	14.7471	-15.2013
79	103.6	284.707	108.917	175.791	-181.107
80	103.9	312.919	109.429	203.49	-209.019
81	104.7	-31.3384	109.208	-140.547	136.038
82	112.4	-11.1003	108.446	-119.546	123.5
83	103.2	21.3932	108.175	-86.7818	81.8068
84	129.1	48.3194	108.237	-59.9181	80.7806
85	114.9	79.4795	108.154	-28.6747	35.4205
86	107.6	109.952	108.35	1.6015	-2.3515
87	102.8	122.359	108.767	13.5926	-19.5592
88	99.1	121.165	108.788	12.3777	-22.0652
89	111.9	122.601	108.733	13.8676	-10.7009
90	104.6	NA	NA	14.7471	NA
91	103.7	NA	NA	175.791	NA
92	108.5	NA	NA	203.49	NA
93	110.1	NA	NA	-140.547	NA
94	107.5	NA	NA	-119.546	NA
95	106.8	NA	NA	-86.7818	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 2341 & NA & NA & -28.6747 & NA \tabularnewline
2 & 2115 & NA & NA & 1.6015 & NA \tabularnewline
3 & 2402 & NA & NA & 13.5926 & NA \tabularnewline
4 & 2180 & NA & NA & 12.3777 & NA \tabularnewline
5 & 2453 & NA & NA & 13.8676 & NA \tabularnewline
6 & 2507 & NA & NA & 14.7471 & NA \tabularnewline
7 & 2679 & 1725.46 & 1549.67 & 175.791 & 953.543 \tabularnewline
8 & 2622 & 1576.47 & 1372.98 & 203.49 & 1045.53 \tabularnewline
9 & 103.1 & 1053.23 & 1193.77 & -140.547 & -950.128 \tabularnewline
10 & 95.2 & 891.929 & 1011.48 & -119.546 & -796.729 \tabularnewline
11 & 110.2 & 740.11 & 826.892 & -86.7818 & -629.91 \tabularnewline
12 & 105.3 & 569.14 & 629.058 & -59.9181 & -463.84 \tabularnewline
13 & 107.4 & 393.104 & 421.779 & -28.6747 & -285.704 \tabularnewline
14 & 108.1 & 212.218 & 210.617 & 1.6015 & -104.118 \tabularnewline
15 & 108 & 120.234 & 106.642 & 13.5926 & -12.2342 \tabularnewline
16 & 98.8 & 118.928 & 106.55 & 12.3777 & -20.1277 \tabularnewline
17 & 104.2 & 120.501 & 106.633 & 13.8676 & -16.3009 \tabularnewline
18 & 107.8 & 121.555 & 106.808 & 14.7471 & -13.7555 \tabularnewline
19 & 103.5 & 282.566 & 106.775 & 175.791 & -179.066 \tabularnewline
20 & 129.6 & 310.053 & 106.562 & 203.49 & -180.453 \tabularnewline
21 & 100.1 & -34.2675 & 106.279 & -140.547 & 134.368 \tabularnewline
22 & 96 & -13.4044 & 106.142 & -119.546 & 109.404 \tabularnewline
23 & 111.4 & 19.5057 & 106.288 & -86.7818 & 91.8943 \tabularnewline
24 & 108.3 & 46.311 & 106.229 & -59.9181 & 61.989 \tabularnewline
25 & 103.6 & 77.4586 & 106.133 & -28.6747 & 26.1414 \tabularnewline
26 & 106.8 & 107.618 & 106.017 & 1.6015 & -0.818171 \tabularnewline
27 & 102.5 & 119.513 & 105.921 & 13.5926 & -17.0134 \tabularnewline
28 & 101 & 118.474 & 106.096 & 12.3777 & -17.4735 \tabularnewline
29 & 105.5 & 120.188 & 106.321 & 13.8676 & -14.6884 \tabularnewline
30 & 105.1 & 120.772 & 106.025 & 14.7471 & -15.6721 \tabularnewline
31 & 103.9 & 281.624 & 105.833 & 175.791 & -177.724 \tabularnewline
32 & 126.4 & 309.615 & 106.125 & 203.49 & -183.215 \tabularnewline
33 & 101 & -34.4217 & 106.125 & -140.547 & 135.422 \tabularnewline
34 & 99.3 & -13.3336 & 106.212 & -119.546 & 112.634 \tabularnewline
35 & 113.5 & 19.514 & 106.296 & -86.7818 & 93.986 \tabularnewline
36 & 99.1 & 46.3027 & 106.221 & -59.9181 & 52.7973 \tabularnewline
37 & 108.2 & 77.667 & 106.342 & -28.6747 & 30.533 \tabularnewline
38 & 109.2 & 107.86 & 106.258 & 1.6015 & 1.34016 \tabularnewline
39 & 100.1 & 119.713 & 106.121 & 13.5926 & -19.6134 \tabularnewline
40 & 105.5 & 118.336 & 105.958 & 12.3777 & -12.836 \tabularnewline
41 & 103 & 119.568 & 105.7 & 13.8676 & -16.5676 \tabularnewline
42 & 105.8 & 120.326 & 105.579 & 14.7471 & -14.5263 \tabularnewline
43 & 106.1 & 281.382 & 105.592 & 175.791 & -175.282 \tabularnewline
44 & 122.2 & 308.965 & 105.475 & 203.49 & -186.765 \tabularnewline
45 & 101.9 & -35.18 & 105.367 & -140.547 & 137.08 \tabularnewline
46 & 94.5 & -14.0753 & 105.471 & -119.546 & 108.575 \tabularnewline
47 & 112.1 & 18.5223 & 105.304 & -86.7818 & 93.5777 \tabularnewline
48 & 97.6 & 45.2152 & 105.133 & -59.9181 & 52.3848 \tabularnewline
49 & 110 & 76.4711 & 105.146 & -28.6747 & 33.5289 \tabularnewline
50 & 104.6 & 106.689 & 105.088 & 1.6015 & -2.089 \tabularnewline
51 & 102.1 & 118.759 & 105.167 & 13.5926 & -16.6592 \tabularnewline
52 & 106 & 118.007 & 105.629 & 12.3777 & -12.0069 \tabularnewline
53 & 98.5 & 119.08 & 105.213 & 13.8676 & -20.5801 \tabularnewline
54 & 106.2 & 119.547 & 104.8 & 14.7471 & -13.3471 \tabularnewline
55 & 106 & 280.628 & 104.837 & 175.791 & -174.628 \tabularnewline
56 & 120.9 & 308.315 & 104.825 & 203.49 & -187.415 \tabularnewline
57 & 105.1 & -35.3009 & 105.246 & -140.547 & 140.401 \tabularnewline
58 & 102.4 & -14.2211 & 105.325 & -119.546 & 116.621 \tabularnewline
59 & 94.2 & 18.6973 & 105.479 & -86.7818 & 75.5027 \tabularnewline
60 & 105.6 & 45.6027 & 105.521 & -59.9181 & 59.9973 \tabularnewline
61 & 102.9 & 76.8128 & 105.487 & -28.6747 & 26.0872 \tabularnewline
62 & 111.4 & 106.518 & 104.917 & 1.6015 & 4.88183 \tabularnewline
63 & 105.4 & 118.643 & 105.05 & 13.5926 & -13.2426 \tabularnewline
64 & 104.6 & 118.415 & 106.037 & 12.3777 & -13.8152 \tabularnewline
65 & 103.6 & 120.551 & 106.683 & 13.8676 & -16.9509 \tabularnewline
66 & 102.1 & 121.539 & 106.792 & 14.7471 & -19.4388 \tabularnewline
67 & 109.3 & 282.299 & 106.508 & 175.791 & -172.999 \tabularnewline
68 & 103.9 & 309.828 & 106.337 & 203.49 & -205.928 \tabularnewline
69 & 125.3 & -34.255 & 106.292 & -140.547 & 159.555 \tabularnewline
70 & 105.9 & -12.8294 & 106.717 & -119.546 & 118.729 \tabularnewline
71 & 106.2 & 20.3223 & 107.104 & -86.7818 & 85.8777 \tabularnewline
72 & 96.2 & 47.5735 & 107.492 & -59.9181 & 48.6265 \tabularnewline
73 & 105.5 & 78.7711 & 107.446 & -28.6747 & 26.7289 \tabularnewline
74 & 104.7 & 108.81 & 107.208 & 1.6015 & -4.10984 \tabularnewline
75 & 111 & 119.943 & 106.35 & 13.5926 & -8.94258 \tabularnewline
76 & 109.2 & 118.14 & 105.762 & 12.3777 & -8.9402 \tabularnewline
77 & 108.3 & 119.776 & 105.908 & 13.8676 & -11.4759 \tabularnewline
78 & 106.7 & 121.901 & 107.154 & 14.7471 & -15.2013 \tabularnewline
79 & 103.6 & 284.707 & 108.917 & 175.791 & -181.107 \tabularnewline
80 & 103.9 & 312.919 & 109.429 & 203.49 & -209.019 \tabularnewline
81 & 104.7 & -31.3384 & 109.208 & -140.547 & 136.038 \tabularnewline
82 & 112.4 & -11.1003 & 108.446 & -119.546 & 123.5 \tabularnewline
83 & 103.2 & 21.3932 & 108.175 & -86.7818 & 81.8068 \tabularnewline
84 & 129.1 & 48.3194 & 108.237 & -59.9181 & 80.7806 \tabularnewline
85 & 114.9 & 79.4795 & 108.154 & -28.6747 & 35.4205 \tabularnewline
86 & 107.6 & 109.952 & 108.35 & 1.6015 & -2.3515 \tabularnewline
87 & 102.8 & 122.359 & 108.767 & 13.5926 & -19.5592 \tabularnewline
88 & 99.1 & 121.165 & 108.788 & 12.3777 & -22.0652 \tabularnewline
89 & 111.9 & 122.601 & 108.733 & 13.8676 & -10.7009 \tabularnewline
90 & 104.6 & NA & NA & 14.7471 & NA \tabularnewline
91 & 103.7 & NA & NA & 175.791 & NA \tabularnewline
92 & 108.5 & NA & NA & 203.49 & NA \tabularnewline
93 & 110.1 & NA & NA & -140.547 & NA \tabularnewline
94 & 107.5 & NA & NA & -119.546 & NA \tabularnewline
95 & 106.8 & NA & NA & -86.7818 & 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]2341[/C][C]NA[/C][C]NA[/C][C]-28.6747[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]2115[/C][C]NA[/C][C]NA[/C][C]1.6015[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]2402[/C][C]NA[/C][C]NA[/C][C]13.5926[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]2180[/C][C]NA[/C][C]NA[/C][C]12.3777[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]2453[/C][C]NA[/C][C]NA[/C][C]13.8676[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]2507[/C][C]NA[/C][C]NA[/C][C]14.7471[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]2679[/C][C]1725.46[/C][C]1549.67[/C][C]175.791[/C][C]953.543[/C][/ROW]
[ROW][C]8[/C][C]2622[/C][C]1576.47[/C][C]1372.98[/C][C]203.49[/C][C]1045.53[/C][/ROW]
[ROW][C]9[/C][C]103.1[/C][C]1053.23[/C][C]1193.77[/C][C]-140.547[/C][C]-950.128[/C][/ROW]
[ROW][C]10[/C][C]95.2[/C][C]891.929[/C][C]1011.48[/C][C]-119.546[/C][C]-796.729[/C][/ROW]
[ROW][C]11[/C][C]110.2[/C][C]740.11[/C][C]826.892[/C][C]-86.7818[/C][C]-629.91[/C][/ROW]
[ROW][C]12[/C][C]105.3[/C][C]569.14[/C][C]629.058[/C][C]-59.9181[/C][C]-463.84[/C][/ROW]
[ROW][C]13[/C][C]107.4[/C][C]393.104[/C][C]421.779[/C][C]-28.6747[/C][C]-285.704[/C][/ROW]
[ROW][C]14[/C][C]108.1[/C][C]212.218[/C][C]210.617[/C][C]1.6015[/C][C]-104.118[/C][/ROW]
[ROW][C]15[/C][C]108[/C][C]120.234[/C][C]106.642[/C][C]13.5926[/C][C]-12.2342[/C][/ROW]
[ROW][C]16[/C][C]98.8[/C][C]118.928[/C][C]106.55[/C][C]12.3777[/C][C]-20.1277[/C][/ROW]
[ROW][C]17[/C][C]104.2[/C][C]120.501[/C][C]106.633[/C][C]13.8676[/C][C]-16.3009[/C][/ROW]
[ROW][C]18[/C][C]107.8[/C][C]121.555[/C][C]106.808[/C][C]14.7471[/C][C]-13.7555[/C][/ROW]
[ROW][C]19[/C][C]103.5[/C][C]282.566[/C][C]106.775[/C][C]175.791[/C][C]-179.066[/C][/ROW]
[ROW][C]20[/C][C]129.6[/C][C]310.053[/C][C]106.562[/C][C]203.49[/C][C]-180.453[/C][/ROW]
[ROW][C]21[/C][C]100.1[/C][C]-34.2675[/C][C]106.279[/C][C]-140.547[/C][C]134.368[/C][/ROW]
[ROW][C]22[/C][C]96[/C][C]-13.4044[/C][C]106.142[/C][C]-119.546[/C][C]109.404[/C][/ROW]
[ROW][C]23[/C][C]111.4[/C][C]19.5057[/C][C]106.288[/C][C]-86.7818[/C][C]91.8943[/C][/ROW]
[ROW][C]24[/C][C]108.3[/C][C]46.311[/C][C]106.229[/C][C]-59.9181[/C][C]61.989[/C][/ROW]
[ROW][C]25[/C][C]103.6[/C][C]77.4586[/C][C]106.133[/C][C]-28.6747[/C][C]26.1414[/C][/ROW]
[ROW][C]26[/C][C]106.8[/C][C]107.618[/C][C]106.017[/C][C]1.6015[/C][C]-0.818171[/C][/ROW]
[ROW][C]27[/C][C]102.5[/C][C]119.513[/C][C]105.921[/C][C]13.5926[/C][C]-17.0134[/C][/ROW]
[ROW][C]28[/C][C]101[/C][C]118.474[/C][C]106.096[/C][C]12.3777[/C][C]-17.4735[/C][/ROW]
[ROW][C]29[/C][C]105.5[/C][C]120.188[/C][C]106.321[/C][C]13.8676[/C][C]-14.6884[/C][/ROW]
[ROW][C]30[/C][C]105.1[/C][C]120.772[/C][C]106.025[/C][C]14.7471[/C][C]-15.6721[/C][/ROW]
[ROW][C]31[/C][C]103.9[/C][C]281.624[/C][C]105.833[/C][C]175.791[/C][C]-177.724[/C][/ROW]
[ROW][C]32[/C][C]126.4[/C][C]309.615[/C][C]106.125[/C][C]203.49[/C][C]-183.215[/C][/ROW]
[ROW][C]33[/C][C]101[/C][C]-34.4217[/C][C]106.125[/C][C]-140.547[/C][C]135.422[/C][/ROW]
[ROW][C]34[/C][C]99.3[/C][C]-13.3336[/C][C]106.212[/C][C]-119.546[/C][C]112.634[/C][/ROW]
[ROW][C]35[/C][C]113.5[/C][C]19.514[/C][C]106.296[/C][C]-86.7818[/C][C]93.986[/C][/ROW]
[ROW][C]36[/C][C]99.1[/C][C]46.3027[/C][C]106.221[/C][C]-59.9181[/C][C]52.7973[/C][/ROW]
[ROW][C]37[/C][C]108.2[/C][C]77.667[/C][C]106.342[/C][C]-28.6747[/C][C]30.533[/C][/ROW]
[ROW][C]38[/C][C]109.2[/C][C]107.86[/C][C]106.258[/C][C]1.6015[/C][C]1.34016[/C][/ROW]
[ROW][C]39[/C][C]100.1[/C][C]119.713[/C][C]106.121[/C][C]13.5926[/C][C]-19.6134[/C][/ROW]
[ROW][C]40[/C][C]105.5[/C][C]118.336[/C][C]105.958[/C][C]12.3777[/C][C]-12.836[/C][/ROW]
[ROW][C]41[/C][C]103[/C][C]119.568[/C][C]105.7[/C][C]13.8676[/C][C]-16.5676[/C][/ROW]
[ROW][C]42[/C][C]105.8[/C][C]120.326[/C][C]105.579[/C][C]14.7471[/C][C]-14.5263[/C][/ROW]
[ROW][C]43[/C][C]106.1[/C][C]281.382[/C][C]105.592[/C][C]175.791[/C][C]-175.282[/C][/ROW]
[ROW][C]44[/C][C]122.2[/C][C]308.965[/C][C]105.475[/C][C]203.49[/C][C]-186.765[/C][/ROW]
[ROW][C]45[/C][C]101.9[/C][C]-35.18[/C][C]105.367[/C][C]-140.547[/C][C]137.08[/C][/ROW]
[ROW][C]46[/C][C]94.5[/C][C]-14.0753[/C][C]105.471[/C][C]-119.546[/C][C]108.575[/C][/ROW]
[ROW][C]47[/C][C]112.1[/C][C]18.5223[/C][C]105.304[/C][C]-86.7818[/C][C]93.5777[/C][/ROW]
[ROW][C]48[/C][C]97.6[/C][C]45.2152[/C][C]105.133[/C][C]-59.9181[/C][C]52.3848[/C][/ROW]
[ROW][C]49[/C][C]110[/C][C]76.4711[/C][C]105.146[/C][C]-28.6747[/C][C]33.5289[/C][/ROW]
[ROW][C]50[/C][C]104.6[/C][C]106.689[/C][C]105.088[/C][C]1.6015[/C][C]-2.089[/C][/ROW]
[ROW][C]51[/C][C]102.1[/C][C]118.759[/C][C]105.167[/C][C]13.5926[/C][C]-16.6592[/C][/ROW]
[ROW][C]52[/C][C]106[/C][C]118.007[/C][C]105.629[/C][C]12.3777[/C][C]-12.0069[/C][/ROW]
[ROW][C]53[/C][C]98.5[/C][C]119.08[/C][C]105.213[/C][C]13.8676[/C][C]-20.5801[/C][/ROW]
[ROW][C]54[/C][C]106.2[/C][C]119.547[/C][C]104.8[/C][C]14.7471[/C][C]-13.3471[/C][/ROW]
[ROW][C]55[/C][C]106[/C][C]280.628[/C][C]104.837[/C][C]175.791[/C][C]-174.628[/C][/ROW]
[ROW][C]56[/C][C]120.9[/C][C]308.315[/C][C]104.825[/C][C]203.49[/C][C]-187.415[/C][/ROW]
[ROW][C]57[/C][C]105.1[/C][C]-35.3009[/C][C]105.246[/C][C]-140.547[/C][C]140.401[/C][/ROW]
[ROW][C]58[/C][C]102.4[/C][C]-14.2211[/C][C]105.325[/C][C]-119.546[/C][C]116.621[/C][/ROW]
[ROW][C]59[/C][C]94.2[/C][C]18.6973[/C][C]105.479[/C][C]-86.7818[/C][C]75.5027[/C][/ROW]
[ROW][C]60[/C][C]105.6[/C][C]45.6027[/C][C]105.521[/C][C]-59.9181[/C][C]59.9973[/C][/ROW]
[ROW][C]61[/C][C]102.9[/C][C]76.8128[/C][C]105.487[/C][C]-28.6747[/C][C]26.0872[/C][/ROW]
[ROW][C]62[/C][C]111.4[/C][C]106.518[/C][C]104.917[/C][C]1.6015[/C][C]4.88183[/C][/ROW]
[ROW][C]63[/C][C]105.4[/C][C]118.643[/C][C]105.05[/C][C]13.5926[/C][C]-13.2426[/C][/ROW]
[ROW][C]64[/C][C]104.6[/C][C]118.415[/C][C]106.037[/C][C]12.3777[/C][C]-13.8152[/C][/ROW]
[ROW][C]65[/C][C]103.6[/C][C]120.551[/C][C]106.683[/C][C]13.8676[/C][C]-16.9509[/C][/ROW]
[ROW][C]66[/C][C]102.1[/C][C]121.539[/C][C]106.792[/C][C]14.7471[/C][C]-19.4388[/C][/ROW]
[ROW][C]67[/C][C]109.3[/C][C]282.299[/C][C]106.508[/C][C]175.791[/C][C]-172.999[/C][/ROW]
[ROW][C]68[/C][C]103.9[/C][C]309.828[/C][C]106.337[/C][C]203.49[/C][C]-205.928[/C][/ROW]
[ROW][C]69[/C][C]125.3[/C][C]-34.255[/C][C]106.292[/C][C]-140.547[/C][C]159.555[/C][/ROW]
[ROW][C]70[/C][C]105.9[/C][C]-12.8294[/C][C]106.717[/C][C]-119.546[/C][C]118.729[/C][/ROW]
[ROW][C]71[/C][C]106.2[/C][C]20.3223[/C][C]107.104[/C][C]-86.7818[/C][C]85.8777[/C][/ROW]
[ROW][C]72[/C][C]96.2[/C][C]47.5735[/C][C]107.492[/C][C]-59.9181[/C][C]48.6265[/C][/ROW]
[ROW][C]73[/C][C]105.5[/C][C]78.7711[/C][C]107.446[/C][C]-28.6747[/C][C]26.7289[/C][/ROW]
[ROW][C]74[/C][C]104.7[/C][C]108.81[/C][C]107.208[/C][C]1.6015[/C][C]-4.10984[/C][/ROW]
[ROW][C]75[/C][C]111[/C][C]119.943[/C][C]106.35[/C][C]13.5926[/C][C]-8.94258[/C][/ROW]
[ROW][C]76[/C][C]109.2[/C][C]118.14[/C][C]105.762[/C][C]12.3777[/C][C]-8.9402[/C][/ROW]
[ROW][C]77[/C][C]108.3[/C][C]119.776[/C][C]105.908[/C][C]13.8676[/C][C]-11.4759[/C][/ROW]
[ROW][C]78[/C][C]106.7[/C][C]121.901[/C][C]107.154[/C][C]14.7471[/C][C]-15.2013[/C][/ROW]
[ROW][C]79[/C][C]103.6[/C][C]284.707[/C][C]108.917[/C][C]175.791[/C][C]-181.107[/C][/ROW]
[ROW][C]80[/C][C]103.9[/C][C]312.919[/C][C]109.429[/C][C]203.49[/C][C]-209.019[/C][/ROW]
[ROW][C]81[/C][C]104.7[/C][C]-31.3384[/C][C]109.208[/C][C]-140.547[/C][C]136.038[/C][/ROW]
[ROW][C]82[/C][C]112.4[/C][C]-11.1003[/C][C]108.446[/C][C]-119.546[/C][C]123.5[/C][/ROW]
[ROW][C]83[/C][C]103.2[/C][C]21.3932[/C][C]108.175[/C][C]-86.7818[/C][C]81.8068[/C][/ROW]
[ROW][C]84[/C][C]129.1[/C][C]48.3194[/C][C]108.237[/C][C]-59.9181[/C][C]80.7806[/C][/ROW]
[ROW][C]85[/C][C]114.9[/C][C]79.4795[/C][C]108.154[/C][C]-28.6747[/C][C]35.4205[/C][/ROW]
[ROW][C]86[/C][C]107.6[/C][C]109.952[/C][C]108.35[/C][C]1.6015[/C][C]-2.3515[/C][/ROW]
[ROW][C]87[/C][C]102.8[/C][C]122.359[/C][C]108.767[/C][C]13.5926[/C][C]-19.5592[/C][/ROW]
[ROW][C]88[/C][C]99.1[/C][C]121.165[/C][C]108.788[/C][C]12.3777[/C][C]-22.0652[/C][/ROW]
[ROW][C]89[/C][C]111.9[/C][C]122.601[/C][C]108.733[/C][C]13.8676[/C][C]-10.7009[/C][/ROW]
[ROW][C]90[/C][C]104.6[/C][C]NA[/C][C]NA[/C][C]14.7471[/C][C]NA[/C][/ROW]
[ROW][C]91[/C][C]103.7[/C][C]NA[/C][C]NA[/C][C]175.791[/C][C]NA[/C][/ROW]
[ROW][C]92[/C][C]108.5[/C][C]NA[/C][C]NA[/C][C]203.49[/C][C]NA[/C][/ROW]
[ROW][C]93[/C][C]110.1[/C][C]NA[/C][C]NA[/C][C]-140.547[/C][C]NA[/C][/ROW]
[ROW][C]94[/C][C]107.5[/C][C]NA[/C][C]NA[/C][C]-119.546[/C][C]NA[/C][/ROW]
[ROW][C]95[/C][C]106.8[/C][C]NA[/C][C]NA[/C][C]-86.7818[/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	2341	NA	NA	-28.6747	NA
2	2115	NA	NA	1.6015	NA
3	2402	NA	NA	13.5926	NA
4	2180	NA	NA	12.3777	NA
5	2453	NA	NA	13.8676	NA
6	2507	NA	NA	14.7471	NA
7	2679	1725.46	1549.67	175.791	953.543
8	2622	1576.47	1372.98	203.49	1045.53
9	103.1	1053.23	1193.77	-140.547	-950.128
10	95.2	891.929	1011.48	-119.546	-796.729
11	110.2	740.11	826.892	-86.7818	-629.91
12	105.3	569.14	629.058	-59.9181	-463.84
13	107.4	393.104	421.779	-28.6747	-285.704
14	108.1	212.218	210.617	1.6015	-104.118
15	108	120.234	106.642	13.5926	-12.2342
16	98.8	118.928	106.55	12.3777	-20.1277
17	104.2	120.501	106.633	13.8676	-16.3009
18	107.8	121.555	106.808	14.7471	-13.7555
19	103.5	282.566	106.775	175.791	-179.066
20	129.6	310.053	106.562	203.49	-180.453
21	100.1	-34.2675	106.279	-140.547	134.368
22	96	-13.4044	106.142	-119.546	109.404
23	111.4	19.5057	106.288	-86.7818	91.8943
24	108.3	46.311	106.229	-59.9181	61.989
25	103.6	77.4586	106.133	-28.6747	26.1414
26	106.8	107.618	106.017	1.6015	-0.818171
27	102.5	119.513	105.921	13.5926	-17.0134
28	101	118.474	106.096	12.3777	-17.4735
29	105.5	120.188	106.321	13.8676	-14.6884
30	105.1	120.772	106.025	14.7471	-15.6721
31	103.9	281.624	105.833	175.791	-177.724
32	126.4	309.615	106.125	203.49	-183.215
33	101	-34.4217	106.125	-140.547	135.422
34	99.3	-13.3336	106.212	-119.546	112.634
35	113.5	19.514	106.296	-86.7818	93.986
36	99.1	46.3027	106.221	-59.9181	52.7973
37	108.2	77.667	106.342	-28.6747	30.533
38	109.2	107.86	106.258	1.6015	1.34016
39	100.1	119.713	106.121	13.5926	-19.6134
40	105.5	118.336	105.958	12.3777	-12.836
41	103	119.568	105.7	13.8676	-16.5676
42	105.8	120.326	105.579	14.7471	-14.5263
43	106.1	281.382	105.592	175.791	-175.282
44	122.2	308.965	105.475	203.49	-186.765
45	101.9	-35.18	105.367	-140.547	137.08
46	94.5	-14.0753	105.471	-119.546	108.575
47	112.1	18.5223	105.304	-86.7818	93.5777
48	97.6	45.2152	105.133	-59.9181	52.3848
49	110	76.4711	105.146	-28.6747	33.5289
50	104.6	106.689	105.088	1.6015	-2.089
51	102.1	118.759	105.167	13.5926	-16.6592
52	106	118.007	105.629	12.3777	-12.0069
53	98.5	119.08	105.213	13.8676	-20.5801
54	106.2	119.547	104.8	14.7471	-13.3471
55	106	280.628	104.837	175.791	-174.628
56	120.9	308.315	104.825	203.49	-187.415
57	105.1	-35.3009	105.246	-140.547	140.401
58	102.4	-14.2211	105.325	-119.546	116.621
59	94.2	18.6973	105.479	-86.7818	75.5027
60	105.6	45.6027	105.521	-59.9181	59.9973
61	102.9	76.8128	105.487	-28.6747	26.0872
62	111.4	106.518	104.917	1.6015	4.88183
63	105.4	118.643	105.05	13.5926	-13.2426
64	104.6	118.415	106.037	12.3777	-13.8152
65	103.6	120.551	106.683	13.8676	-16.9509
66	102.1	121.539	106.792	14.7471	-19.4388
67	109.3	282.299	106.508	175.791	-172.999
68	103.9	309.828	106.337	203.49	-205.928
69	125.3	-34.255	106.292	-140.547	159.555
70	105.9	-12.8294	106.717	-119.546	118.729
71	106.2	20.3223	107.104	-86.7818	85.8777
72	96.2	47.5735	107.492	-59.9181	48.6265
73	105.5	78.7711	107.446	-28.6747	26.7289
74	104.7	108.81	107.208	1.6015	-4.10984
75	111	119.943	106.35	13.5926	-8.94258
76	109.2	118.14	105.762	12.3777	-8.9402
77	108.3	119.776	105.908	13.8676	-11.4759
78	106.7	121.901	107.154	14.7471	-15.2013
79	103.6	284.707	108.917	175.791	-181.107
80	103.9	312.919	109.429	203.49	-209.019
81	104.7	-31.3384	109.208	-140.547	136.038
82	112.4	-11.1003	108.446	-119.546	123.5
83	103.2	21.3932	108.175	-86.7818	81.8068
84	129.1	48.3194	108.237	-59.9181	80.7806
85	114.9	79.4795	108.154	-28.6747	35.4205
86	107.6	109.952	108.35	1.6015	-2.3515
87	102.8	122.359	108.767	13.5926	-19.5592
88	99.1	121.165	108.788	12.3777	-22.0652
89	111.9	122.601	108.733	13.8676	-10.7009
90	104.6	NA	NA	14.7471	NA
91	103.7	NA	NA	175.791	NA
92	108.5	NA	NA	203.49	NA
93	110.1	NA	NA	-140.547	NA
94	107.5	NA	NA	-119.546	NA
95	106.8	NA	NA	-86.7818	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