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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sun, 30 Nov 2014 21:27:22 +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/2014/Nov/30/t1417382900pujxy8r68ku6mmg.htm/, Retrieved Sun, 19 May 2024 13:53:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=261677, Retrieved Sun, 19 May 2024 13:53:40 +0000

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-       [Classical Decomposition] [] [2014-11-30 21:27:22] [d49f5b304cc347c7e802f63d6679cbb3] [Current]

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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	'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=261677&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=261677&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=261677&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	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	103.77	NA	NA	1.00048	NA
2	103.82	NA	NA	1.00085	NA
3	103.86	NA	NA	1.00049	NA
4	103.9	NA	NA	1.00023	NA
5	103.63	NA	NA	0.998529	NA
6	103.65	NA	NA	0.999348	NA
7	103.7	103.855	103.89	0.999661	0.998509
8	103.77	103.934	103.95	0.999854	0.998418
9	103.94	104.047	104.021	1.00024	0.998975
10	104.03	104.15	104.098	1.0005	0.998848
11	104.03	104.091	104.155	0.999386	0.999413
12	104.29	104.237	104.193	1.00042	1.00051
13	104.35	104.288	104.238	1.00048	1.0006
14	104.67	104.374	104.285	1.00085	1.00284
15	104.73	104.379	104.328	1.00049	1.00336
16	104.86	104.387	104.363	1.00023	1.00453
17	104.05	104.241	104.394	0.998529	0.998172
18	104.15	104.355	104.423	0.999348	0.998038
19	104.27	104.411	104.447	0.999661	0.998647
20	104.33	104.441	104.457	0.999854	0.998933
21	104.41	104.474	104.448	1.00024	0.999389
22	104.4	104.479	104.426	1.0005	0.999245
23	104.41	104.376	104.44	0.999386	1.00032
24	104.6	104.543	104.5	1.00042	1.00054
25	104.61	104.608	104.557	1.00048	1.00002
26	104.65	104.698	104.609	1.00085	0.999537
27	104.55	104.708	104.657	1.00049	0.99849
28	104.51	104.727	104.703	1.00023	0.997924
29	104.74	104.604	104.758	0.998529	1.0013
30	104.89	104.75	104.818	0.999348	1.00134
31	104.91	104.842	104.877	0.999661	1.00065
32	104.93	104.923	104.938	0.999854	1.00007
33	104.95	105.031	105.005	1.00024	0.999232
34	104.97	105.128	105.075	1.0005	0.998493
35	105.16	105.085	105.15	0.999386	1.00071
36	105.29	105.275	105.231	1.00042	1.00014
37	105.35	105.361	105.311	1.00048	0.999892
38	105.36	105.482	105.392	1.00085	0.998839
39	105.45	105.529	105.478	1.00049	0.999248
40	105.3	105.594	105.569	1.00023	0.997217
41	105.73	105.472	105.627	0.998529	1.00245
42	105.86	105.571	105.64	0.999348	1.00274
43	105.85	105.61	105.646	0.999661	1.00227
44	105.95	105.633	105.648	0.999854	1.003
45	105.97	105.666	105.64	1.00024	1.00288
46	106.15	105.686	105.632	1.0005	1.00439
47	105.37	105.538	105.603	0.999386	0.998407
48	105.39	105.586	105.542	1.00042	0.998141
49	105.39	105.54	105.489	1.00048	0.998581
50	105.38	105.527	105.437	1.00085	0.99861
51	105.23	105.433	105.382	1.00049	0.99807
52	105.34	105.346	105.321	1.00023	0.999948
53	104.98	105.135	105.29	0.998529	0.998521
54	105.16	105.232	105.3	0.999348	0.999318
55	105.27	105.279	105.315	0.999661	0.999912
56	105.27	105.316	105.331	0.999854	0.999565
57	105.33	105.381	105.355	1.00024	0.999515
58	105.33	105.434	105.381	1.0005	0.99901
59	105.46	105.341	105.405	0.999386	1.00113
60	105.54	105.471	105.427	1.00042	1.00065
61	105.59	105.495	105.445	1.00048	1.0009
62	105.57	105.552	105.462	1.00085	1.00017
63	105.62	105.534	105.482	1.00049	1.00082
64	105.57	105.531	105.506	1.00023	1.00037
65	105.33	105.378	105.533	0.998529	0.999544
66	105.34	105.492	105.561	0.999348	0.998555
67	105.5	NA	NA	0.999661	NA
68	105.47	NA	NA	0.999854	NA
69	105.59	NA	NA	1.00024	NA
70	105.65	NA	NA	1.0005	NA
71	105.8	NA	NA	0.999386	NA
72	105.87	NA	NA	1.00042	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 103.77 & NA & NA & 1.00048 & NA \tabularnewline
2 & 103.82 & NA & NA & 1.00085 & NA \tabularnewline
3 & 103.86 & NA & NA & 1.00049 & NA \tabularnewline
4 & 103.9 & NA & NA & 1.00023 & NA \tabularnewline
5 & 103.63 & NA & NA & 0.998529 & NA \tabularnewline
6 & 103.65 & NA & NA & 0.999348 & NA \tabularnewline
7 & 103.7 & 103.855 & 103.89 & 0.999661 & 0.998509 \tabularnewline
8 & 103.77 & 103.934 & 103.95 & 0.999854 & 0.998418 \tabularnewline
9 & 103.94 & 104.047 & 104.021 & 1.00024 & 0.998975 \tabularnewline
10 & 104.03 & 104.15 & 104.098 & 1.0005 & 0.998848 \tabularnewline
11 & 104.03 & 104.091 & 104.155 & 0.999386 & 0.999413 \tabularnewline
12 & 104.29 & 104.237 & 104.193 & 1.00042 & 1.00051 \tabularnewline
13 & 104.35 & 104.288 & 104.238 & 1.00048 & 1.0006 \tabularnewline
14 & 104.67 & 104.374 & 104.285 & 1.00085 & 1.00284 \tabularnewline
15 & 104.73 & 104.379 & 104.328 & 1.00049 & 1.00336 \tabularnewline
16 & 104.86 & 104.387 & 104.363 & 1.00023 & 1.00453 \tabularnewline
17 & 104.05 & 104.241 & 104.394 & 0.998529 & 0.998172 \tabularnewline
18 & 104.15 & 104.355 & 104.423 & 0.999348 & 0.998038 \tabularnewline
19 & 104.27 & 104.411 & 104.447 & 0.999661 & 0.998647 \tabularnewline
20 & 104.33 & 104.441 & 104.457 & 0.999854 & 0.998933 \tabularnewline
21 & 104.41 & 104.474 & 104.448 & 1.00024 & 0.999389 \tabularnewline
22 & 104.4 & 104.479 & 104.426 & 1.0005 & 0.999245 \tabularnewline
23 & 104.41 & 104.376 & 104.44 & 0.999386 & 1.00032 \tabularnewline
24 & 104.6 & 104.543 & 104.5 & 1.00042 & 1.00054 \tabularnewline
25 & 104.61 & 104.608 & 104.557 & 1.00048 & 1.00002 \tabularnewline
26 & 104.65 & 104.698 & 104.609 & 1.00085 & 0.999537 \tabularnewline
27 & 104.55 & 104.708 & 104.657 & 1.00049 & 0.99849 \tabularnewline
28 & 104.51 & 104.727 & 104.703 & 1.00023 & 0.997924 \tabularnewline
29 & 104.74 & 104.604 & 104.758 & 0.998529 & 1.0013 \tabularnewline
30 & 104.89 & 104.75 & 104.818 & 0.999348 & 1.00134 \tabularnewline
31 & 104.91 & 104.842 & 104.877 & 0.999661 & 1.00065 \tabularnewline
32 & 104.93 & 104.923 & 104.938 & 0.999854 & 1.00007 \tabularnewline
33 & 104.95 & 105.031 & 105.005 & 1.00024 & 0.999232 \tabularnewline
34 & 104.97 & 105.128 & 105.075 & 1.0005 & 0.998493 \tabularnewline
35 & 105.16 & 105.085 & 105.15 & 0.999386 & 1.00071 \tabularnewline
36 & 105.29 & 105.275 & 105.231 & 1.00042 & 1.00014 \tabularnewline
37 & 105.35 & 105.361 & 105.311 & 1.00048 & 0.999892 \tabularnewline
38 & 105.36 & 105.482 & 105.392 & 1.00085 & 0.998839 \tabularnewline
39 & 105.45 & 105.529 & 105.478 & 1.00049 & 0.999248 \tabularnewline
40 & 105.3 & 105.594 & 105.569 & 1.00023 & 0.997217 \tabularnewline
41 & 105.73 & 105.472 & 105.627 & 0.998529 & 1.00245 \tabularnewline
42 & 105.86 & 105.571 & 105.64 & 0.999348 & 1.00274 \tabularnewline
43 & 105.85 & 105.61 & 105.646 & 0.999661 & 1.00227 \tabularnewline
44 & 105.95 & 105.633 & 105.648 & 0.999854 & 1.003 \tabularnewline
45 & 105.97 & 105.666 & 105.64 & 1.00024 & 1.00288 \tabularnewline
46 & 106.15 & 105.686 & 105.632 & 1.0005 & 1.00439 \tabularnewline
47 & 105.37 & 105.538 & 105.603 & 0.999386 & 0.998407 \tabularnewline
48 & 105.39 & 105.586 & 105.542 & 1.00042 & 0.998141 \tabularnewline
49 & 105.39 & 105.54 & 105.489 & 1.00048 & 0.998581 \tabularnewline
50 & 105.38 & 105.527 & 105.437 & 1.00085 & 0.99861 \tabularnewline
51 & 105.23 & 105.433 & 105.382 & 1.00049 & 0.99807 \tabularnewline
52 & 105.34 & 105.346 & 105.321 & 1.00023 & 0.999948 \tabularnewline
53 & 104.98 & 105.135 & 105.29 & 0.998529 & 0.998521 \tabularnewline
54 & 105.16 & 105.232 & 105.3 & 0.999348 & 0.999318 \tabularnewline
55 & 105.27 & 105.279 & 105.315 & 0.999661 & 0.999912 \tabularnewline
56 & 105.27 & 105.316 & 105.331 & 0.999854 & 0.999565 \tabularnewline
57 & 105.33 & 105.381 & 105.355 & 1.00024 & 0.999515 \tabularnewline
58 & 105.33 & 105.434 & 105.381 & 1.0005 & 0.99901 \tabularnewline
59 & 105.46 & 105.341 & 105.405 & 0.999386 & 1.00113 \tabularnewline
60 & 105.54 & 105.471 & 105.427 & 1.00042 & 1.00065 \tabularnewline
61 & 105.59 & 105.495 & 105.445 & 1.00048 & 1.0009 \tabularnewline
62 & 105.57 & 105.552 & 105.462 & 1.00085 & 1.00017 \tabularnewline
63 & 105.62 & 105.534 & 105.482 & 1.00049 & 1.00082 \tabularnewline
64 & 105.57 & 105.531 & 105.506 & 1.00023 & 1.00037 \tabularnewline
65 & 105.33 & 105.378 & 105.533 & 0.998529 & 0.999544 \tabularnewline
66 & 105.34 & 105.492 & 105.561 & 0.999348 & 0.998555 \tabularnewline
67 & 105.5 & NA & NA & 0.999661 & NA \tabularnewline
68 & 105.47 & NA & NA & 0.999854 & NA \tabularnewline
69 & 105.59 & NA & NA & 1.00024 & NA \tabularnewline
70 & 105.65 & NA & NA & 1.0005 & NA \tabularnewline
71 & 105.8 & NA & NA & 0.999386 & NA \tabularnewline
72 & 105.87 & NA & NA & 1.00042 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=261677&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]103.77[/C][C]NA[/C][C]NA[/C][C]1.00048[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]103.82[/C][C]NA[/C][C]NA[/C][C]1.00085[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]103.86[/C][C]NA[/C][C]NA[/C][C]1.00049[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]103.9[/C][C]NA[/C][C]NA[/C][C]1.00023[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]103.63[/C][C]NA[/C][C]NA[/C][C]0.998529[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]103.65[/C][C]NA[/C][C]NA[/C][C]0.999348[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]103.7[/C][C]103.855[/C][C]103.89[/C][C]0.999661[/C][C]0.998509[/C][/ROW]
[ROW][C]8[/C][C]103.77[/C][C]103.934[/C][C]103.95[/C][C]0.999854[/C][C]0.998418[/C][/ROW]
[ROW][C]9[/C][C]103.94[/C][C]104.047[/C][C]104.021[/C][C]1.00024[/C][C]0.998975[/C][/ROW]
[ROW][C]10[/C][C]104.03[/C][C]104.15[/C][C]104.098[/C][C]1.0005[/C][C]0.998848[/C][/ROW]
[ROW][C]11[/C][C]104.03[/C][C]104.091[/C][C]104.155[/C][C]0.999386[/C][C]0.999413[/C][/ROW]
[ROW][C]12[/C][C]104.29[/C][C]104.237[/C][C]104.193[/C][C]1.00042[/C][C]1.00051[/C][/ROW]
[ROW][C]13[/C][C]104.35[/C][C]104.288[/C][C]104.238[/C][C]1.00048[/C][C]1.0006[/C][/ROW]
[ROW][C]14[/C][C]104.67[/C][C]104.374[/C][C]104.285[/C][C]1.00085[/C][C]1.00284[/C][/ROW]
[ROW][C]15[/C][C]104.73[/C][C]104.379[/C][C]104.328[/C][C]1.00049[/C][C]1.00336[/C][/ROW]
[ROW][C]16[/C][C]104.86[/C][C]104.387[/C][C]104.363[/C][C]1.00023[/C][C]1.00453[/C][/ROW]
[ROW][C]17[/C][C]104.05[/C][C]104.241[/C][C]104.394[/C][C]0.998529[/C][C]0.998172[/C][/ROW]
[ROW][C]18[/C][C]104.15[/C][C]104.355[/C][C]104.423[/C][C]0.999348[/C][C]0.998038[/C][/ROW]
[ROW][C]19[/C][C]104.27[/C][C]104.411[/C][C]104.447[/C][C]0.999661[/C][C]0.998647[/C][/ROW]
[ROW][C]20[/C][C]104.33[/C][C]104.441[/C][C]104.457[/C][C]0.999854[/C][C]0.998933[/C][/ROW]
[ROW][C]21[/C][C]104.41[/C][C]104.474[/C][C]104.448[/C][C]1.00024[/C][C]0.999389[/C][/ROW]
[ROW][C]22[/C][C]104.4[/C][C]104.479[/C][C]104.426[/C][C]1.0005[/C][C]0.999245[/C][/ROW]
[ROW][C]23[/C][C]104.41[/C][C]104.376[/C][C]104.44[/C][C]0.999386[/C][C]1.00032[/C][/ROW]
[ROW][C]24[/C][C]104.6[/C][C]104.543[/C][C]104.5[/C][C]1.00042[/C][C]1.00054[/C][/ROW]
[ROW][C]25[/C][C]104.61[/C][C]104.608[/C][C]104.557[/C][C]1.00048[/C][C]1.00002[/C][/ROW]
[ROW][C]26[/C][C]104.65[/C][C]104.698[/C][C]104.609[/C][C]1.00085[/C][C]0.999537[/C][/ROW]
[ROW][C]27[/C][C]104.55[/C][C]104.708[/C][C]104.657[/C][C]1.00049[/C][C]0.99849[/C][/ROW]
[ROW][C]28[/C][C]104.51[/C][C]104.727[/C][C]104.703[/C][C]1.00023[/C][C]0.997924[/C][/ROW]
[ROW][C]29[/C][C]104.74[/C][C]104.604[/C][C]104.758[/C][C]0.998529[/C][C]1.0013[/C][/ROW]
[ROW][C]30[/C][C]104.89[/C][C]104.75[/C][C]104.818[/C][C]0.999348[/C][C]1.00134[/C][/ROW]
[ROW][C]31[/C][C]104.91[/C][C]104.842[/C][C]104.877[/C][C]0.999661[/C][C]1.00065[/C][/ROW]
[ROW][C]32[/C][C]104.93[/C][C]104.923[/C][C]104.938[/C][C]0.999854[/C][C]1.00007[/C][/ROW]
[ROW][C]33[/C][C]104.95[/C][C]105.031[/C][C]105.005[/C][C]1.00024[/C][C]0.999232[/C][/ROW]
[ROW][C]34[/C][C]104.97[/C][C]105.128[/C][C]105.075[/C][C]1.0005[/C][C]0.998493[/C][/ROW]
[ROW][C]35[/C][C]105.16[/C][C]105.085[/C][C]105.15[/C][C]0.999386[/C][C]1.00071[/C][/ROW]
[ROW][C]36[/C][C]105.29[/C][C]105.275[/C][C]105.231[/C][C]1.00042[/C][C]1.00014[/C][/ROW]
[ROW][C]37[/C][C]105.35[/C][C]105.361[/C][C]105.311[/C][C]1.00048[/C][C]0.999892[/C][/ROW]
[ROW][C]38[/C][C]105.36[/C][C]105.482[/C][C]105.392[/C][C]1.00085[/C][C]0.998839[/C][/ROW]
[ROW][C]39[/C][C]105.45[/C][C]105.529[/C][C]105.478[/C][C]1.00049[/C][C]0.999248[/C][/ROW]
[ROW][C]40[/C][C]105.3[/C][C]105.594[/C][C]105.569[/C][C]1.00023[/C][C]0.997217[/C][/ROW]
[ROW][C]41[/C][C]105.73[/C][C]105.472[/C][C]105.627[/C][C]0.998529[/C][C]1.00245[/C][/ROW]
[ROW][C]42[/C][C]105.86[/C][C]105.571[/C][C]105.64[/C][C]0.999348[/C][C]1.00274[/C][/ROW]
[ROW][C]43[/C][C]105.85[/C][C]105.61[/C][C]105.646[/C][C]0.999661[/C][C]1.00227[/C][/ROW]
[ROW][C]44[/C][C]105.95[/C][C]105.633[/C][C]105.648[/C][C]0.999854[/C][C]1.003[/C][/ROW]
[ROW][C]45[/C][C]105.97[/C][C]105.666[/C][C]105.64[/C][C]1.00024[/C][C]1.00288[/C][/ROW]
[ROW][C]46[/C][C]106.15[/C][C]105.686[/C][C]105.632[/C][C]1.0005[/C][C]1.00439[/C][/ROW]
[ROW][C]47[/C][C]105.37[/C][C]105.538[/C][C]105.603[/C][C]0.999386[/C][C]0.998407[/C][/ROW]
[ROW][C]48[/C][C]105.39[/C][C]105.586[/C][C]105.542[/C][C]1.00042[/C][C]0.998141[/C][/ROW]
[ROW][C]49[/C][C]105.39[/C][C]105.54[/C][C]105.489[/C][C]1.00048[/C][C]0.998581[/C][/ROW]
[ROW][C]50[/C][C]105.38[/C][C]105.527[/C][C]105.437[/C][C]1.00085[/C][C]0.99861[/C][/ROW]
[ROW][C]51[/C][C]105.23[/C][C]105.433[/C][C]105.382[/C][C]1.00049[/C][C]0.99807[/C][/ROW]
[ROW][C]52[/C][C]105.34[/C][C]105.346[/C][C]105.321[/C][C]1.00023[/C][C]0.999948[/C][/ROW]
[ROW][C]53[/C][C]104.98[/C][C]105.135[/C][C]105.29[/C][C]0.998529[/C][C]0.998521[/C][/ROW]
[ROW][C]54[/C][C]105.16[/C][C]105.232[/C][C]105.3[/C][C]0.999348[/C][C]0.999318[/C][/ROW]
[ROW][C]55[/C][C]105.27[/C][C]105.279[/C][C]105.315[/C][C]0.999661[/C][C]0.999912[/C][/ROW]
[ROW][C]56[/C][C]105.27[/C][C]105.316[/C][C]105.331[/C][C]0.999854[/C][C]0.999565[/C][/ROW]
[ROW][C]57[/C][C]105.33[/C][C]105.381[/C][C]105.355[/C][C]1.00024[/C][C]0.999515[/C][/ROW]
[ROW][C]58[/C][C]105.33[/C][C]105.434[/C][C]105.381[/C][C]1.0005[/C][C]0.99901[/C][/ROW]
[ROW][C]59[/C][C]105.46[/C][C]105.341[/C][C]105.405[/C][C]0.999386[/C][C]1.00113[/C][/ROW]
[ROW][C]60[/C][C]105.54[/C][C]105.471[/C][C]105.427[/C][C]1.00042[/C][C]1.00065[/C][/ROW]
[ROW][C]61[/C][C]105.59[/C][C]105.495[/C][C]105.445[/C][C]1.00048[/C][C]1.0009[/C][/ROW]
[ROW][C]62[/C][C]105.57[/C][C]105.552[/C][C]105.462[/C][C]1.00085[/C][C]1.00017[/C][/ROW]
[ROW][C]63[/C][C]105.62[/C][C]105.534[/C][C]105.482[/C][C]1.00049[/C][C]1.00082[/C][/ROW]
[ROW][C]64[/C][C]105.57[/C][C]105.531[/C][C]105.506[/C][C]1.00023[/C][C]1.00037[/C][/ROW]
[ROW][C]65[/C][C]105.33[/C][C]105.378[/C][C]105.533[/C][C]0.998529[/C][C]0.999544[/C][/ROW]
[ROW][C]66[/C][C]105.34[/C][C]105.492[/C][C]105.561[/C][C]0.999348[/C][C]0.998555[/C][/ROW]
[ROW][C]67[/C][C]105.5[/C][C]NA[/C][C]NA[/C][C]0.999661[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]105.47[/C][C]NA[/C][C]NA[/C][C]0.999854[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]105.59[/C][C]NA[/C][C]NA[/C][C]1.00024[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]105.65[/C][C]NA[/C][C]NA[/C][C]1.0005[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]105.8[/C][C]NA[/C][C]NA[/C][C]0.999386[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]105.87[/C][C]NA[/C][C]NA[/C][C]1.00042[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=261677&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=261677&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	103.77	NA	NA	1.00048	NA
2	103.82	NA	NA	1.00085	NA
3	103.86	NA	NA	1.00049	NA
4	103.9	NA	NA	1.00023	NA
5	103.63	NA	NA	0.998529	NA
6	103.65	NA	NA	0.999348	NA
7	103.7	103.855	103.89	0.999661	0.998509
8	103.77	103.934	103.95	0.999854	0.998418
9	103.94	104.047	104.021	1.00024	0.998975
10	104.03	104.15	104.098	1.0005	0.998848
11	104.03	104.091	104.155	0.999386	0.999413
12	104.29	104.237	104.193	1.00042	1.00051
13	104.35	104.288	104.238	1.00048	1.0006
14	104.67	104.374	104.285	1.00085	1.00284
15	104.73	104.379	104.328	1.00049	1.00336
16	104.86	104.387	104.363	1.00023	1.00453
17	104.05	104.241	104.394	0.998529	0.998172
18	104.15	104.355	104.423	0.999348	0.998038
19	104.27	104.411	104.447	0.999661	0.998647
20	104.33	104.441	104.457	0.999854	0.998933
21	104.41	104.474	104.448	1.00024	0.999389
22	104.4	104.479	104.426	1.0005	0.999245
23	104.41	104.376	104.44	0.999386	1.00032
24	104.6	104.543	104.5	1.00042	1.00054
25	104.61	104.608	104.557	1.00048	1.00002
26	104.65	104.698	104.609	1.00085	0.999537
27	104.55	104.708	104.657	1.00049	0.99849
28	104.51	104.727	104.703	1.00023	0.997924
29	104.74	104.604	104.758	0.998529	1.0013
30	104.89	104.75	104.818	0.999348	1.00134
31	104.91	104.842	104.877	0.999661	1.00065
32	104.93	104.923	104.938	0.999854	1.00007
33	104.95	105.031	105.005	1.00024	0.999232
34	104.97	105.128	105.075	1.0005	0.998493
35	105.16	105.085	105.15	0.999386	1.00071
36	105.29	105.275	105.231	1.00042	1.00014
37	105.35	105.361	105.311	1.00048	0.999892
38	105.36	105.482	105.392	1.00085	0.998839
39	105.45	105.529	105.478	1.00049	0.999248
40	105.3	105.594	105.569	1.00023	0.997217
41	105.73	105.472	105.627	0.998529	1.00245
42	105.86	105.571	105.64	0.999348	1.00274
43	105.85	105.61	105.646	0.999661	1.00227
44	105.95	105.633	105.648	0.999854	1.003
45	105.97	105.666	105.64	1.00024	1.00288
46	106.15	105.686	105.632	1.0005	1.00439
47	105.37	105.538	105.603	0.999386	0.998407
48	105.39	105.586	105.542	1.00042	0.998141
49	105.39	105.54	105.489	1.00048	0.998581
50	105.38	105.527	105.437	1.00085	0.99861
51	105.23	105.433	105.382	1.00049	0.99807
52	105.34	105.346	105.321	1.00023	0.999948
53	104.98	105.135	105.29	0.998529	0.998521
54	105.16	105.232	105.3	0.999348	0.999318
55	105.27	105.279	105.315	0.999661	0.999912
56	105.27	105.316	105.331	0.999854	0.999565
57	105.33	105.381	105.355	1.00024	0.999515
58	105.33	105.434	105.381	1.0005	0.99901
59	105.46	105.341	105.405	0.999386	1.00113
60	105.54	105.471	105.427	1.00042	1.00065
61	105.59	105.495	105.445	1.00048	1.0009
62	105.57	105.552	105.462	1.00085	1.00017
63	105.62	105.534	105.482	1.00049	1.00082
64	105.57	105.531	105.506	1.00023	1.00037
65	105.33	105.378	105.533	0.998529	0.999544
66	105.34	105.492	105.561	0.999348	0.998555
67	105.5	NA	NA	0.999661	NA
68	105.47	NA	NA	0.999854	NA
69	105.59	NA	NA	1.00024	NA
70	105.65	NA	NA	1.0005	NA
71	105.8	NA	NA	0.999386	NA
72	105.87	NA	NA	1.00042	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 <- '12'
par1 <- 'multiplicative'
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