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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 15 May 2017 09:25:30 +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/May/15/t149483677256ivvp3izj7loj3.htm/, Retrieved Wed, 15 May 2024 21:15:11 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 15 May 2024 21:15:11 +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	'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=&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=&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	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	91.46	NA	NA	0.9988	NA
2	92.17	NA	NA	1.00151	NA
3	91.91	NA	NA	1.00086	NA
4	92.06	NA	NA	0.999309	NA
5	92.33	NA	NA	1.00037	NA
6	92.73	NA	NA	1.00135	NA
7	93.35	93.2838	92.7792	1.00544	1.00071
8	93.28	93.2241	92.9612	1.00283	1.0006
9	93.22	93.0506	93.1504	0.998928	1.00182
10	93.31	93.2248	93.3512	0.998645	1.00091
11	93.21	93.1805	93.5321	0.996241	1.00032
12	93.14	93.2991	93.7004	0.995717	0.998295
13	93.82	93.7498	93.8625	0.9988	1.00075
14	94.18	94.1622	94.02	1.00151	1.00019
15	94.44	94.2496	94.1683	1.00086	1.00202
16	94.35	94.2474	94.3125	0.999309	1.00109
17	94.38	94.5051	94.4704	1.00037	0.998677
18	94.72	94.7758	94.6479	1.00135	0.999411
19	95.25	95.3722	94.8562	1.00544	0.998719
20	95.16	95.3593	95.0904	1.00283	0.99791
21	94.9	95.2191	95.3212	0.998928	0.996649
22	95.09	95.4168	95.5462	0.998645	0.996575
23	95.22	95.4299	95.79	0.996241	0.997801
24	95.39	95.6419	96.0533	0.995717	0.997366
25	96.57	96.204	96.3196	0.9988	1.0038
26	97.05	96.7353	96.5892	1.00151	1.00325
27	97.11	96.9411	96.8575	1.00086	1.00174
28	97.08	97.0517	97.1188	0.999309	1.00029
29	97.5	97.4078	97.3721	1.00037	1.00095
30	97.92	97.7515	97.6196	1.00135	1.00172
31	98.44	98.3521	97.82	1.00544	1.00089
32	98.44	98.2537	97.9767	1.00283	1.0019
33	98.06	98.049	98.1542	0.998928	1.00011
34	98.2	98.1964	98.3296	0.998645	1.00004
35	98.19	98.1289	98.4992	0.996241	1.00062
36	98.36	98.2598	98.6825	0.995717	1.00102
37	98.41	98.7463	98.865	0.9988	0.996594
38	98.97	99.2019	99.0521	1.00151	0.997662
39	99.45	99.3344	99.2488	1.00086	1.00116
40	98.95	99.3851	99.4538	0.999309	0.995622
41	99.7	99.7057	99.6692	1.00037	0.999943
42	100.12	100.026	99.8912	1.00135	1.00094
43	100.62	100.66	100.116	1.00544	0.999599
44	100.75	100.613	100.33	1.00283	1.00136
45	100.47	100.417	100.525	0.998928	1.00053
46	100.71	100.606	100.742	0.998645	1.00103
47	100.85	100.582	100.962	0.996241	1.00266
48	101.03	100.708	101.141	0.995717	1.0032
49	101.13	101.192	101.314	0.9988	0.999386
50	101.38	101.629	101.476	1.00151	0.997547
51	101.73	101.721	101.634	1.00086	1.00008
52	101.89	101.731	101.802	0.999309	1.00156
53	102.02	101.983	101.945	1.00037	1.00036
54	102.11	102.21	102.072	1.00135	0.999022
55	102.77	102.773	102.217	1.00544	0.999974
56	102.49	102.682	102.392	1.00283	0.99813
57	102.52	102.439	102.549	0.998928	1.00079
58	102.69	102.553	102.692	0.998645	1.00134
59	102.32	102.475	102.861	0.996241	0.998492
60	102.6	102.598	103.04	0.995717	1.00002
61	103.03	103.096	103.22	0.9988	0.999363
62	103.7	103.571	103.415	1.00151	1.00125
63	103.17	103.7	103.611	1.00086	0.994887
64	103.88	103.742	103.813	0.999309	1.00133
65	104.09	104.094	104.055	1.00037	0.999966
66	104.32	104.445	104.304	1.00135	0.998802
67	104.88	NA	NA	1.00544	NA
68	105.06	NA	NA	1.00283	NA
69	104.66	NA	NA	0.998928	NA
70	105.41	NA	NA	0.998645	NA
71	105.41	NA	NA	0.996241	NA
72	105.48	NA	NA	0.995717	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 91.46 & NA & NA & 0.9988 & NA \tabularnewline
2 & 92.17 & NA & NA & 1.00151 & NA \tabularnewline
3 & 91.91 & NA & NA & 1.00086 & NA \tabularnewline
4 & 92.06 & NA & NA & 0.999309 & NA \tabularnewline
5 & 92.33 & NA & NA & 1.00037 & NA \tabularnewline
6 & 92.73 & NA & NA & 1.00135 & NA \tabularnewline
7 & 93.35 & 93.2838 & 92.7792 & 1.00544 & 1.00071 \tabularnewline
8 & 93.28 & 93.2241 & 92.9612 & 1.00283 & 1.0006 \tabularnewline
9 & 93.22 & 93.0506 & 93.1504 & 0.998928 & 1.00182 \tabularnewline
10 & 93.31 & 93.2248 & 93.3512 & 0.998645 & 1.00091 \tabularnewline
11 & 93.21 & 93.1805 & 93.5321 & 0.996241 & 1.00032 \tabularnewline
12 & 93.14 & 93.2991 & 93.7004 & 0.995717 & 0.998295 \tabularnewline
13 & 93.82 & 93.7498 & 93.8625 & 0.9988 & 1.00075 \tabularnewline
14 & 94.18 & 94.1622 & 94.02 & 1.00151 & 1.00019 \tabularnewline
15 & 94.44 & 94.2496 & 94.1683 & 1.00086 & 1.00202 \tabularnewline
16 & 94.35 & 94.2474 & 94.3125 & 0.999309 & 1.00109 \tabularnewline
17 & 94.38 & 94.5051 & 94.4704 & 1.00037 & 0.998677 \tabularnewline
18 & 94.72 & 94.7758 & 94.6479 & 1.00135 & 0.999411 \tabularnewline
19 & 95.25 & 95.3722 & 94.8562 & 1.00544 & 0.998719 \tabularnewline
20 & 95.16 & 95.3593 & 95.0904 & 1.00283 & 0.99791 \tabularnewline
21 & 94.9 & 95.2191 & 95.3212 & 0.998928 & 0.996649 \tabularnewline
22 & 95.09 & 95.4168 & 95.5462 & 0.998645 & 0.996575 \tabularnewline
23 & 95.22 & 95.4299 & 95.79 & 0.996241 & 0.997801 \tabularnewline
24 & 95.39 & 95.6419 & 96.0533 & 0.995717 & 0.997366 \tabularnewline
25 & 96.57 & 96.204 & 96.3196 & 0.9988 & 1.0038 \tabularnewline
26 & 97.05 & 96.7353 & 96.5892 & 1.00151 & 1.00325 \tabularnewline
27 & 97.11 & 96.9411 & 96.8575 & 1.00086 & 1.00174 \tabularnewline
28 & 97.08 & 97.0517 & 97.1188 & 0.999309 & 1.00029 \tabularnewline
29 & 97.5 & 97.4078 & 97.3721 & 1.00037 & 1.00095 \tabularnewline
30 & 97.92 & 97.7515 & 97.6196 & 1.00135 & 1.00172 \tabularnewline
31 & 98.44 & 98.3521 & 97.82 & 1.00544 & 1.00089 \tabularnewline
32 & 98.44 & 98.2537 & 97.9767 & 1.00283 & 1.0019 \tabularnewline
33 & 98.06 & 98.049 & 98.1542 & 0.998928 & 1.00011 \tabularnewline
34 & 98.2 & 98.1964 & 98.3296 & 0.998645 & 1.00004 \tabularnewline
35 & 98.19 & 98.1289 & 98.4992 & 0.996241 & 1.00062 \tabularnewline
36 & 98.36 & 98.2598 & 98.6825 & 0.995717 & 1.00102 \tabularnewline
37 & 98.41 & 98.7463 & 98.865 & 0.9988 & 0.996594 \tabularnewline
38 & 98.97 & 99.2019 & 99.0521 & 1.00151 & 0.997662 \tabularnewline
39 & 99.45 & 99.3344 & 99.2488 & 1.00086 & 1.00116 \tabularnewline
40 & 98.95 & 99.3851 & 99.4538 & 0.999309 & 0.995622 \tabularnewline
41 & 99.7 & 99.7057 & 99.6692 & 1.00037 & 0.999943 \tabularnewline
42 & 100.12 & 100.026 & 99.8912 & 1.00135 & 1.00094 \tabularnewline
43 & 100.62 & 100.66 & 100.116 & 1.00544 & 0.999599 \tabularnewline
44 & 100.75 & 100.613 & 100.33 & 1.00283 & 1.00136 \tabularnewline
45 & 100.47 & 100.417 & 100.525 & 0.998928 & 1.00053 \tabularnewline
46 & 100.71 & 100.606 & 100.742 & 0.998645 & 1.00103 \tabularnewline
47 & 100.85 & 100.582 & 100.962 & 0.996241 & 1.00266 \tabularnewline
48 & 101.03 & 100.708 & 101.141 & 0.995717 & 1.0032 \tabularnewline
49 & 101.13 & 101.192 & 101.314 & 0.9988 & 0.999386 \tabularnewline
50 & 101.38 & 101.629 & 101.476 & 1.00151 & 0.997547 \tabularnewline
51 & 101.73 & 101.721 & 101.634 & 1.00086 & 1.00008 \tabularnewline
52 & 101.89 & 101.731 & 101.802 & 0.999309 & 1.00156 \tabularnewline
53 & 102.02 & 101.983 & 101.945 & 1.00037 & 1.00036 \tabularnewline
54 & 102.11 & 102.21 & 102.072 & 1.00135 & 0.999022 \tabularnewline
55 & 102.77 & 102.773 & 102.217 & 1.00544 & 0.999974 \tabularnewline
56 & 102.49 & 102.682 & 102.392 & 1.00283 & 0.99813 \tabularnewline
57 & 102.52 & 102.439 & 102.549 & 0.998928 & 1.00079 \tabularnewline
58 & 102.69 & 102.553 & 102.692 & 0.998645 & 1.00134 \tabularnewline
59 & 102.32 & 102.475 & 102.861 & 0.996241 & 0.998492 \tabularnewline
60 & 102.6 & 102.598 & 103.04 & 0.995717 & 1.00002 \tabularnewline
61 & 103.03 & 103.096 & 103.22 & 0.9988 & 0.999363 \tabularnewline
62 & 103.7 & 103.571 & 103.415 & 1.00151 & 1.00125 \tabularnewline
63 & 103.17 & 103.7 & 103.611 & 1.00086 & 0.994887 \tabularnewline
64 & 103.88 & 103.742 & 103.813 & 0.999309 & 1.00133 \tabularnewline
65 & 104.09 & 104.094 & 104.055 & 1.00037 & 0.999966 \tabularnewline
66 & 104.32 & 104.445 & 104.304 & 1.00135 & 0.998802 \tabularnewline
67 & 104.88 & NA & NA & 1.00544 & NA \tabularnewline
68 & 105.06 & NA & NA & 1.00283 & NA \tabularnewline
69 & 104.66 & NA & NA & 0.998928 & NA \tabularnewline
70 & 105.41 & NA & NA & 0.998645 & NA \tabularnewline
71 & 105.41 & NA & NA & 0.996241 & NA \tabularnewline
72 & 105.48 & NA & NA & 0.995717 & 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]91.46[/C][C]NA[/C][C]NA[/C][C]0.9988[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]92.17[/C][C]NA[/C][C]NA[/C][C]1.00151[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]91.91[/C][C]NA[/C][C]NA[/C][C]1.00086[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]92.06[/C][C]NA[/C][C]NA[/C][C]0.999309[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]92.33[/C][C]NA[/C][C]NA[/C][C]1.00037[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]92.73[/C][C]NA[/C][C]NA[/C][C]1.00135[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]93.35[/C][C]93.2838[/C][C]92.7792[/C][C]1.00544[/C][C]1.00071[/C][/ROW]
[ROW][C]8[/C][C]93.28[/C][C]93.2241[/C][C]92.9612[/C][C]1.00283[/C][C]1.0006[/C][/ROW]
[ROW][C]9[/C][C]93.22[/C][C]93.0506[/C][C]93.1504[/C][C]0.998928[/C][C]1.00182[/C][/ROW]
[ROW][C]10[/C][C]93.31[/C][C]93.2248[/C][C]93.3512[/C][C]0.998645[/C][C]1.00091[/C][/ROW]
[ROW][C]11[/C][C]93.21[/C][C]93.1805[/C][C]93.5321[/C][C]0.996241[/C][C]1.00032[/C][/ROW]
[ROW][C]12[/C][C]93.14[/C][C]93.2991[/C][C]93.7004[/C][C]0.995717[/C][C]0.998295[/C][/ROW]
[ROW][C]13[/C][C]93.82[/C][C]93.7498[/C][C]93.8625[/C][C]0.9988[/C][C]1.00075[/C][/ROW]
[ROW][C]14[/C][C]94.18[/C][C]94.1622[/C][C]94.02[/C][C]1.00151[/C][C]1.00019[/C][/ROW]
[ROW][C]15[/C][C]94.44[/C][C]94.2496[/C][C]94.1683[/C][C]1.00086[/C][C]1.00202[/C][/ROW]
[ROW][C]16[/C][C]94.35[/C][C]94.2474[/C][C]94.3125[/C][C]0.999309[/C][C]1.00109[/C][/ROW]
[ROW][C]17[/C][C]94.38[/C][C]94.5051[/C][C]94.4704[/C][C]1.00037[/C][C]0.998677[/C][/ROW]
[ROW][C]18[/C][C]94.72[/C][C]94.7758[/C][C]94.6479[/C][C]1.00135[/C][C]0.999411[/C][/ROW]
[ROW][C]19[/C][C]95.25[/C][C]95.3722[/C][C]94.8562[/C][C]1.00544[/C][C]0.998719[/C][/ROW]
[ROW][C]20[/C][C]95.16[/C][C]95.3593[/C][C]95.0904[/C][C]1.00283[/C][C]0.99791[/C][/ROW]
[ROW][C]21[/C][C]94.9[/C][C]95.2191[/C][C]95.3212[/C][C]0.998928[/C][C]0.996649[/C][/ROW]
[ROW][C]22[/C][C]95.09[/C][C]95.4168[/C][C]95.5462[/C][C]0.998645[/C][C]0.996575[/C][/ROW]
[ROW][C]23[/C][C]95.22[/C][C]95.4299[/C][C]95.79[/C][C]0.996241[/C][C]0.997801[/C][/ROW]
[ROW][C]24[/C][C]95.39[/C][C]95.6419[/C][C]96.0533[/C][C]0.995717[/C][C]0.997366[/C][/ROW]
[ROW][C]25[/C][C]96.57[/C][C]96.204[/C][C]96.3196[/C][C]0.9988[/C][C]1.0038[/C][/ROW]
[ROW][C]26[/C][C]97.05[/C][C]96.7353[/C][C]96.5892[/C][C]1.00151[/C][C]1.00325[/C][/ROW]
[ROW][C]27[/C][C]97.11[/C][C]96.9411[/C][C]96.8575[/C][C]1.00086[/C][C]1.00174[/C][/ROW]
[ROW][C]28[/C][C]97.08[/C][C]97.0517[/C][C]97.1188[/C][C]0.999309[/C][C]1.00029[/C][/ROW]
[ROW][C]29[/C][C]97.5[/C][C]97.4078[/C][C]97.3721[/C][C]1.00037[/C][C]1.00095[/C][/ROW]
[ROW][C]30[/C][C]97.92[/C][C]97.7515[/C][C]97.6196[/C][C]1.00135[/C][C]1.00172[/C][/ROW]
[ROW][C]31[/C][C]98.44[/C][C]98.3521[/C][C]97.82[/C][C]1.00544[/C][C]1.00089[/C][/ROW]
[ROW][C]32[/C][C]98.44[/C][C]98.2537[/C][C]97.9767[/C][C]1.00283[/C][C]1.0019[/C][/ROW]
[ROW][C]33[/C][C]98.06[/C][C]98.049[/C][C]98.1542[/C][C]0.998928[/C][C]1.00011[/C][/ROW]
[ROW][C]34[/C][C]98.2[/C][C]98.1964[/C][C]98.3296[/C][C]0.998645[/C][C]1.00004[/C][/ROW]
[ROW][C]35[/C][C]98.19[/C][C]98.1289[/C][C]98.4992[/C][C]0.996241[/C][C]1.00062[/C][/ROW]
[ROW][C]36[/C][C]98.36[/C][C]98.2598[/C][C]98.6825[/C][C]0.995717[/C][C]1.00102[/C][/ROW]
[ROW][C]37[/C][C]98.41[/C][C]98.7463[/C][C]98.865[/C][C]0.9988[/C][C]0.996594[/C][/ROW]
[ROW][C]38[/C][C]98.97[/C][C]99.2019[/C][C]99.0521[/C][C]1.00151[/C][C]0.997662[/C][/ROW]
[ROW][C]39[/C][C]99.45[/C][C]99.3344[/C][C]99.2488[/C][C]1.00086[/C][C]1.00116[/C][/ROW]
[ROW][C]40[/C][C]98.95[/C][C]99.3851[/C][C]99.4538[/C][C]0.999309[/C][C]0.995622[/C][/ROW]
[ROW][C]41[/C][C]99.7[/C][C]99.7057[/C][C]99.6692[/C][C]1.00037[/C][C]0.999943[/C][/ROW]
[ROW][C]42[/C][C]100.12[/C][C]100.026[/C][C]99.8912[/C][C]1.00135[/C][C]1.00094[/C][/ROW]
[ROW][C]43[/C][C]100.62[/C][C]100.66[/C][C]100.116[/C][C]1.00544[/C][C]0.999599[/C][/ROW]
[ROW][C]44[/C][C]100.75[/C][C]100.613[/C][C]100.33[/C][C]1.00283[/C][C]1.00136[/C][/ROW]
[ROW][C]45[/C][C]100.47[/C][C]100.417[/C][C]100.525[/C][C]0.998928[/C][C]1.00053[/C][/ROW]
[ROW][C]46[/C][C]100.71[/C][C]100.606[/C][C]100.742[/C][C]0.998645[/C][C]1.00103[/C][/ROW]
[ROW][C]47[/C][C]100.85[/C][C]100.582[/C][C]100.962[/C][C]0.996241[/C][C]1.00266[/C][/ROW]
[ROW][C]48[/C][C]101.03[/C][C]100.708[/C][C]101.141[/C][C]0.995717[/C][C]1.0032[/C][/ROW]
[ROW][C]49[/C][C]101.13[/C][C]101.192[/C][C]101.314[/C][C]0.9988[/C][C]0.999386[/C][/ROW]
[ROW][C]50[/C][C]101.38[/C][C]101.629[/C][C]101.476[/C][C]1.00151[/C][C]0.997547[/C][/ROW]
[ROW][C]51[/C][C]101.73[/C][C]101.721[/C][C]101.634[/C][C]1.00086[/C][C]1.00008[/C][/ROW]
[ROW][C]52[/C][C]101.89[/C][C]101.731[/C][C]101.802[/C][C]0.999309[/C][C]1.00156[/C][/ROW]
[ROW][C]53[/C][C]102.02[/C][C]101.983[/C][C]101.945[/C][C]1.00037[/C][C]1.00036[/C][/ROW]
[ROW][C]54[/C][C]102.11[/C][C]102.21[/C][C]102.072[/C][C]1.00135[/C][C]0.999022[/C][/ROW]
[ROW][C]55[/C][C]102.77[/C][C]102.773[/C][C]102.217[/C][C]1.00544[/C][C]0.999974[/C][/ROW]
[ROW][C]56[/C][C]102.49[/C][C]102.682[/C][C]102.392[/C][C]1.00283[/C][C]0.99813[/C][/ROW]
[ROW][C]57[/C][C]102.52[/C][C]102.439[/C][C]102.549[/C][C]0.998928[/C][C]1.00079[/C][/ROW]
[ROW][C]58[/C][C]102.69[/C][C]102.553[/C][C]102.692[/C][C]0.998645[/C][C]1.00134[/C][/ROW]
[ROW][C]59[/C][C]102.32[/C][C]102.475[/C][C]102.861[/C][C]0.996241[/C][C]0.998492[/C][/ROW]
[ROW][C]60[/C][C]102.6[/C][C]102.598[/C][C]103.04[/C][C]0.995717[/C][C]1.00002[/C][/ROW]
[ROW][C]61[/C][C]103.03[/C][C]103.096[/C][C]103.22[/C][C]0.9988[/C][C]0.999363[/C][/ROW]
[ROW][C]62[/C][C]103.7[/C][C]103.571[/C][C]103.415[/C][C]1.00151[/C][C]1.00125[/C][/ROW]
[ROW][C]63[/C][C]103.17[/C][C]103.7[/C][C]103.611[/C][C]1.00086[/C][C]0.994887[/C][/ROW]
[ROW][C]64[/C][C]103.88[/C][C]103.742[/C][C]103.813[/C][C]0.999309[/C][C]1.00133[/C][/ROW]
[ROW][C]65[/C][C]104.09[/C][C]104.094[/C][C]104.055[/C][C]1.00037[/C][C]0.999966[/C][/ROW]
[ROW][C]66[/C][C]104.32[/C][C]104.445[/C][C]104.304[/C][C]1.00135[/C][C]0.998802[/C][/ROW]
[ROW][C]67[/C][C]104.88[/C][C]NA[/C][C]NA[/C][C]1.00544[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]105.06[/C][C]NA[/C][C]NA[/C][C]1.00283[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]104.66[/C][C]NA[/C][C]NA[/C][C]0.998928[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]105.41[/C][C]NA[/C][C]NA[/C][C]0.998645[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]105.41[/C][C]NA[/C][C]NA[/C][C]0.996241[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]105.48[/C][C]NA[/C][C]NA[/C][C]0.995717[/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	91.46	NA	NA	0.9988	NA
2	92.17	NA	NA	1.00151	NA
3	91.91	NA	NA	1.00086	NA
4	92.06	NA	NA	0.999309	NA
5	92.33	NA	NA	1.00037	NA
6	92.73	NA	NA	1.00135	NA
7	93.35	93.2838	92.7792	1.00544	1.00071
8	93.28	93.2241	92.9612	1.00283	1.0006
9	93.22	93.0506	93.1504	0.998928	1.00182
10	93.31	93.2248	93.3512	0.998645	1.00091
11	93.21	93.1805	93.5321	0.996241	1.00032
12	93.14	93.2991	93.7004	0.995717	0.998295
13	93.82	93.7498	93.8625	0.9988	1.00075
14	94.18	94.1622	94.02	1.00151	1.00019
15	94.44	94.2496	94.1683	1.00086	1.00202
16	94.35	94.2474	94.3125	0.999309	1.00109
17	94.38	94.5051	94.4704	1.00037	0.998677
18	94.72	94.7758	94.6479	1.00135	0.999411
19	95.25	95.3722	94.8562	1.00544	0.998719
20	95.16	95.3593	95.0904	1.00283	0.99791
21	94.9	95.2191	95.3212	0.998928	0.996649
22	95.09	95.4168	95.5462	0.998645	0.996575
23	95.22	95.4299	95.79	0.996241	0.997801
24	95.39	95.6419	96.0533	0.995717	0.997366
25	96.57	96.204	96.3196	0.9988	1.0038
26	97.05	96.7353	96.5892	1.00151	1.00325
27	97.11	96.9411	96.8575	1.00086	1.00174
28	97.08	97.0517	97.1188	0.999309	1.00029
29	97.5	97.4078	97.3721	1.00037	1.00095
30	97.92	97.7515	97.6196	1.00135	1.00172
31	98.44	98.3521	97.82	1.00544	1.00089
32	98.44	98.2537	97.9767	1.00283	1.0019
33	98.06	98.049	98.1542	0.998928	1.00011
34	98.2	98.1964	98.3296	0.998645	1.00004
35	98.19	98.1289	98.4992	0.996241	1.00062
36	98.36	98.2598	98.6825	0.995717	1.00102
37	98.41	98.7463	98.865	0.9988	0.996594
38	98.97	99.2019	99.0521	1.00151	0.997662
39	99.45	99.3344	99.2488	1.00086	1.00116
40	98.95	99.3851	99.4538	0.999309	0.995622
41	99.7	99.7057	99.6692	1.00037	0.999943
42	100.12	100.026	99.8912	1.00135	1.00094
43	100.62	100.66	100.116	1.00544	0.999599
44	100.75	100.613	100.33	1.00283	1.00136
45	100.47	100.417	100.525	0.998928	1.00053
46	100.71	100.606	100.742	0.998645	1.00103
47	100.85	100.582	100.962	0.996241	1.00266
48	101.03	100.708	101.141	0.995717	1.0032
49	101.13	101.192	101.314	0.9988	0.999386
50	101.38	101.629	101.476	1.00151	0.997547
51	101.73	101.721	101.634	1.00086	1.00008
52	101.89	101.731	101.802	0.999309	1.00156
53	102.02	101.983	101.945	1.00037	1.00036
54	102.11	102.21	102.072	1.00135	0.999022
55	102.77	102.773	102.217	1.00544	0.999974
56	102.49	102.682	102.392	1.00283	0.99813
57	102.52	102.439	102.549	0.998928	1.00079
58	102.69	102.553	102.692	0.998645	1.00134
59	102.32	102.475	102.861	0.996241	0.998492
60	102.6	102.598	103.04	0.995717	1.00002
61	103.03	103.096	103.22	0.9988	0.999363
62	103.7	103.571	103.415	1.00151	1.00125
63	103.17	103.7	103.611	1.00086	0.994887
64	103.88	103.742	103.813	0.999309	1.00133
65	104.09	104.094	104.055	1.00037	0.999966
66	104.32	104.445	104.304	1.00135	0.998802
67	104.88	NA	NA	1.00544	NA
68	105.06	NA	NA	1.00283	NA
69	104.66	NA	NA	0.998928	NA
70	105.41	NA	NA	0.998645	NA
71	105.41	NA	NA	0.996241	NA
72	105.48	NA	NA	0.995717	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