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R Software Module

rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sat, 23 Apr 2016 21:46:59 +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/2016/Apr/23/t1461444486wttflgubfclvket.htm/, Retrieved Mon, 13 May 2024 14:10:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294616, Retrieved Mon, 13 May 2024 14:10:58 +0000

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No (this computation is public)

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Estimated Impact

143

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Classical Decomposition] [Classical Deompos...] [2016-04-23 20:46:59] [b2b9e3f51b35fbbda207a2f484be6b24] [Current]

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Dataseries X:

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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	'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294616&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294616&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294616&T=0

As an alternative you can also use a QR Code:

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	'Herman Ole Andreas Wold' @ wold.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	90.75	NA	NA	-2.31582	NA
2	92.82	NA	NA	-1.08874	NA
3	97.78	NA	NA	0.56553	NA
4	99.32	NA	NA	-0.1927	NA
5	98.33	NA	NA	-0.363012	NA
6	98.66	NA	NA	-0.787283	NA
7	98.13	98.3363	98.7533	-0.417075	-0.206259
8	97.8	100.561	99.7871	0.773759	-2.76084
9	99.36	103.084	100.625	2.45907	-3.72365
10	100.37	102.595	101.277	1.31709	-2.22459
11	103.22	102.693	101.774	0.918759	0.527491
12	101.68	101.183	102.052	-0.869575	0.497491
13	104.39	99.9229	102.239	-2.31582	4.46707
14	103.99	101.613	102.701	-1.08874	2.37749
15	106.71	103.969	103.404	0.56553	2.74072
16	106.06	103.862	104.055	-0.1927	2.1977
17	103.5	104.049	104.412	-0.363012	-0.549488
18	100.17	103.738	104.525	-0.787283	-3.56772
19	101.1	104.053	104.47	-0.417075	-2.95334
20	105.93	105.108	104.334	0.773759	0.822075
21	108.09	106.561	104.102	2.45907	1.52885
22	107.27	104.979	103.662	1.31709	2.29082
23	104.9	104.086	103.167	0.918759	0.813741
24	102.7	101.934	102.804	-0.869575	0.765825
25	102.06	100.312	102.627	-2.31582	1.74832
26	103.05	101.214	102.303	-1.08874	1.83582
27	102.08	102.379	101.814	0.56553	-0.29928
28	100.13	101.049	101.241	-0.1927	-0.91855
29	97.56	100.237	100.6	-0.363012	-2.67657
30	97.38	99.3648	100.152	-0.787283	-1.9848
31	99.66	99.4	99.8171	-0.417075	0.259991
32	99.58	100.197	99.4233	0.773759	-0.617092
33	102.7	101.468	99.0092	2.45907	1.23176
34	98.92	99.9813	98.6642	1.31709	-1.06126
35	97.85	99.3917	98.4729	0.918759	-1.54168
36	99.01	97.5392	98.4088	-0.869575	1.47082
37	97.71	96.0225	98.3383	-2.31582	1.68749
38	97.95	97.1213	98.21	-1.08874	0.828741
39	97.24	98.4551	97.8896	0.56553	-1.21511
40	96.69	97.2761	97.4687	-0.1927	-0.58605
41	96.41	96.7153	97.0783	-0.363012	-0.305321
42	96.99	95.5811	96.3683	-0.787283	1.40895
43	98.36	94.7	95.1171	-0.417075	3.65999
44	97.8	94.4838	93.71	0.773759	3.31624
45	96.79	95.0666	92.6075	2.45907	1.72343
46	94.73	92.9746	91.6575	1.31709	1.75541
47	92.67	91.7092	90.7904	0.918759	0.960825
48	87.15	89.1238	89.9933	-0.869575	-1.97376
49	79.54	86.6825	88.9983	-2.31582	-7.14251
50	82.35	86.6317	87.7204	-1.08874	-4.28168
51	86.38	86.8459	86.2804	0.56553	-0.465946
52	84.75	84.6827	84.8754	-0.1927	0.067283
53	87.54	83.2482	83.6112	-0.363012	4.29176
54	86.73	81.8261	82.6133	-0.787283	4.90395
55	84.74	NA	NA	-0.417075	NA
56	80.75	NA	NA	0.773759	NA
57	79.28	NA	NA	2.45907	NA
58	78.52	NA	NA	1.31709	NA
59	78.54	NA	NA	0.918759	NA
60	77.33	NA	NA	-0.869575	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 90.75 & NA & NA & -2.31582 & NA \tabularnewline
2 & 92.82 & NA & NA & -1.08874 & NA \tabularnewline
3 & 97.78 & NA & NA & 0.56553 & NA \tabularnewline
4 & 99.32 & NA & NA & -0.1927 & NA \tabularnewline
5 & 98.33 & NA & NA & -0.363012 & NA \tabularnewline
6 & 98.66 & NA & NA & -0.787283 & NA \tabularnewline
7 & 98.13 & 98.3363 & 98.7533 & -0.417075 & -0.206259 \tabularnewline
8 & 97.8 & 100.561 & 99.7871 & 0.773759 & -2.76084 \tabularnewline
9 & 99.36 & 103.084 & 100.625 & 2.45907 & -3.72365 \tabularnewline
10 & 100.37 & 102.595 & 101.277 & 1.31709 & -2.22459 \tabularnewline
11 & 103.22 & 102.693 & 101.774 & 0.918759 & 0.527491 \tabularnewline
12 & 101.68 & 101.183 & 102.052 & -0.869575 & 0.497491 \tabularnewline
13 & 104.39 & 99.9229 & 102.239 & -2.31582 & 4.46707 \tabularnewline
14 & 103.99 & 101.613 & 102.701 & -1.08874 & 2.37749 \tabularnewline
15 & 106.71 & 103.969 & 103.404 & 0.56553 & 2.74072 \tabularnewline
16 & 106.06 & 103.862 & 104.055 & -0.1927 & 2.1977 \tabularnewline
17 & 103.5 & 104.049 & 104.412 & -0.363012 & -0.549488 \tabularnewline
18 & 100.17 & 103.738 & 104.525 & -0.787283 & -3.56772 \tabularnewline
19 & 101.1 & 104.053 & 104.47 & -0.417075 & -2.95334 \tabularnewline
20 & 105.93 & 105.108 & 104.334 & 0.773759 & 0.822075 \tabularnewline
21 & 108.09 & 106.561 & 104.102 & 2.45907 & 1.52885 \tabularnewline
22 & 107.27 & 104.979 & 103.662 & 1.31709 & 2.29082 \tabularnewline
23 & 104.9 & 104.086 & 103.167 & 0.918759 & 0.813741 \tabularnewline
24 & 102.7 & 101.934 & 102.804 & -0.869575 & 0.765825 \tabularnewline
25 & 102.06 & 100.312 & 102.627 & -2.31582 & 1.74832 \tabularnewline
26 & 103.05 & 101.214 & 102.303 & -1.08874 & 1.83582 \tabularnewline
27 & 102.08 & 102.379 & 101.814 & 0.56553 & -0.29928 \tabularnewline
28 & 100.13 & 101.049 & 101.241 & -0.1927 & -0.91855 \tabularnewline
29 & 97.56 & 100.237 & 100.6 & -0.363012 & -2.67657 \tabularnewline
30 & 97.38 & 99.3648 & 100.152 & -0.787283 & -1.9848 \tabularnewline
31 & 99.66 & 99.4 & 99.8171 & -0.417075 & 0.259991 \tabularnewline
32 & 99.58 & 100.197 & 99.4233 & 0.773759 & -0.617092 \tabularnewline
33 & 102.7 & 101.468 & 99.0092 & 2.45907 & 1.23176 \tabularnewline
34 & 98.92 & 99.9813 & 98.6642 & 1.31709 & -1.06126 \tabularnewline
35 & 97.85 & 99.3917 & 98.4729 & 0.918759 & -1.54168 \tabularnewline
36 & 99.01 & 97.5392 & 98.4088 & -0.869575 & 1.47082 \tabularnewline
37 & 97.71 & 96.0225 & 98.3383 & -2.31582 & 1.68749 \tabularnewline
38 & 97.95 & 97.1213 & 98.21 & -1.08874 & 0.828741 \tabularnewline
39 & 97.24 & 98.4551 & 97.8896 & 0.56553 & -1.21511 \tabularnewline
40 & 96.69 & 97.2761 & 97.4687 & -0.1927 & -0.58605 \tabularnewline
41 & 96.41 & 96.7153 & 97.0783 & -0.363012 & -0.305321 \tabularnewline
42 & 96.99 & 95.5811 & 96.3683 & -0.787283 & 1.40895 \tabularnewline
43 & 98.36 & 94.7 & 95.1171 & -0.417075 & 3.65999 \tabularnewline
44 & 97.8 & 94.4838 & 93.71 & 0.773759 & 3.31624 \tabularnewline
45 & 96.79 & 95.0666 & 92.6075 & 2.45907 & 1.72343 \tabularnewline
46 & 94.73 & 92.9746 & 91.6575 & 1.31709 & 1.75541 \tabularnewline
47 & 92.67 & 91.7092 & 90.7904 & 0.918759 & 0.960825 \tabularnewline
48 & 87.15 & 89.1238 & 89.9933 & -0.869575 & -1.97376 \tabularnewline
49 & 79.54 & 86.6825 & 88.9983 & -2.31582 & -7.14251 \tabularnewline
50 & 82.35 & 86.6317 & 87.7204 & -1.08874 & -4.28168 \tabularnewline
51 & 86.38 & 86.8459 & 86.2804 & 0.56553 & -0.465946 \tabularnewline
52 & 84.75 & 84.6827 & 84.8754 & -0.1927 & 0.067283 \tabularnewline
53 & 87.54 & 83.2482 & 83.6112 & -0.363012 & 4.29176 \tabularnewline
54 & 86.73 & 81.8261 & 82.6133 & -0.787283 & 4.90395 \tabularnewline
55 & 84.74 & NA & NA & -0.417075 & NA \tabularnewline
56 & 80.75 & NA & NA & 0.773759 & NA \tabularnewline
57 & 79.28 & NA & NA & 2.45907 & NA \tabularnewline
58 & 78.52 & NA & NA & 1.31709 & NA \tabularnewline
59 & 78.54 & NA & NA & 0.918759 & NA \tabularnewline
60 & 77.33 & NA & NA & -0.869575 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294616&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]90.75[/C][C]NA[/C][C]NA[/C][C]-2.31582[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]92.82[/C][C]NA[/C][C]NA[/C][C]-1.08874[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]97.78[/C][C]NA[/C][C]NA[/C][C]0.56553[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]99.32[/C][C]NA[/C][C]NA[/C][C]-0.1927[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]98.33[/C][C]NA[/C][C]NA[/C][C]-0.363012[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]98.66[/C][C]NA[/C][C]NA[/C][C]-0.787283[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]98.13[/C][C]98.3363[/C][C]98.7533[/C][C]-0.417075[/C][C]-0.206259[/C][/ROW]
[ROW][C]8[/C][C]97.8[/C][C]100.561[/C][C]99.7871[/C][C]0.773759[/C][C]-2.76084[/C][/ROW]
[ROW][C]9[/C][C]99.36[/C][C]103.084[/C][C]100.625[/C][C]2.45907[/C][C]-3.72365[/C][/ROW]
[ROW][C]10[/C][C]100.37[/C][C]102.595[/C][C]101.277[/C][C]1.31709[/C][C]-2.22459[/C][/ROW]
[ROW][C]11[/C][C]103.22[/C][C]102.693[/C][C]101.774[/C][C]0.918759[/C][C]0.527491[/C][/ROW]
[ROW][C]12[/C][C]101.68[/C][C]101.183[/C][C]102.052[/C][C]-0.869575[/C][C]0.497491[/C][/ROW]
[ROW][C]13[/C][C]104.39[/C][C]99.9229[/C][C]102.239[/C][C]-2.31582[/C][C]4.46707[/C][/ROW]
[ROW][C]14[/C][C]103.99[/C][C]101.613[/C][C]102.701[/C][C]-1.08874[/C][C]2.37749[/C][/ROW]
[ROW][C]15[/C][C]106.71[/C][C]103.969[/C][C]103.404[/C][C]0.56553[/C][C]2.74072[/C][/ROW]
[ROW][C]16[/C][C]106.06[/C][C]103.862[/C][C]104.055[/C][C]-0.1927[/C][C]2.1977[/C][/ROW]
[ROW][C]17[/C][C]103.5[/C][C]104.049[/C][C]104.412[/C][C]-0.363012[/C][C]-0.549488[/C][/ROW]
[ROW][C]18[/C][C]100.17[/C][C]103.738[/C][C]104.525[/C][C]-0.787283[/C][C]-3.56772[/C][/ROW]
[ROW][C]19[/C][C]101.1[/C][C]104.053[/C][C]104.47[/C][C]-0.417075[/C][C]-2.95334[/C][/ROW]
[ROW][C]20[/C][C]105.93[/C][C]105.108[/C][C]104.334[/C][C]0.773759[/C][C]0.822075[/C][/ROW]
[ROW][C]21[/C][C]108.09[/C][C]106.561[/C][C]104.102[/C][C]2.45907[/C][C]1.52885[/C][/ROW]
[ROW][C]22[/C][C]107.27[/C][C]104.979[/C][C]103.662[/C][C]1.31709[/C][C]2.29082[/C][/ROW]
[ROW][C]23[/C][C]104.9[/C][C]104.086[/C][C]103.167[/C][C]0.918759[/C][C]0.813741[/C][/ROW]
[ROW][C]24[/C][C]102.7[/C][C]101.934[/C][C]102.804[/C][C]-0.869575[/C][C]0.765825[/C][/ROW]
[ROW][C]25[/C][C]102.06[/C][C]100.312[/C][C]102.627[/C][C]-2.31582[/C][C]1.74832[/C][/ROW]
[ROW][C]26[/C][C]103.05[/C][C]101.214[/C][C]102.303[/C][C]-1.08874[/C][C]1.83582[/C][/ROW]
[ROW][C]27[/C][C]102.08[/C][C]102.379[/C][C]101.814[/C][C]0.56553[/C][C]-0.29928[/C][/ROW]
[ROW][C]28[/C][C]100.13[/C][C]101.049[/C][C]101.241[/C][C]-0.1927[/C][C]-0.91855[/C][/ROW]
[ROW][C]29[/C][C]97.56[/C][C]100.237[/C][C]100.6[/C][C]-0.363012[/C][C]-2.67657[/C][/ROW]
[ROW][C]30[/C][C]97.38[/C][C]99.3648[/C][C]100.152[/C][C]-0.787283[/C][C]-1.9848[/C][/ROW]
[ROW][C]31[/C][C]99.66[/C][C]99.4[/C][C]99.8171[/C][C]-0.417075[/C][C]0.259991[/C][/ROW]
[ROW][C]32[/C][C]99.58[/C][C]100.197[/C][C]99.4233[/C][C]0.773759[/C][C]-0.617092[/C][/ROW]
[ROW][C]33[/C][C]102.7[/C][C]101.468[/C][C]99.0092[/C][C]2.45907[/C][C]1.23176[/C][/ROW]
[ROW][C]34[/C][C]98.92[/C][C]99.9813[/C][C]98.6642[/C][C]1.31709[/C][C]-1.06126[/C][/ROW]
[ROW][C]35[/C][C]97.85[/C][C]99.3917[/C][C]98.4729[/C][C]0.918759[/C][C]-1.54168[/C][/ROW]
[ROW][C]36[/C][C]99.01[/C][C]97.5392[/C][C]98.4088[/C][C]-0.869575[/C][C]1.47082[/C][/ROW]
[ROW][C]37[/C][C]97.71[/C][C]96.0225[/C][C]98.3383[/C][C]-2.31582[/C][C]1.68749[/C][/ROW]
[ROW][C]38[/C][C]97.95[/C][C]97.1213[/C][C]98.21[/C][C]-1.08874[/C][C]0.828741[/C][/ROW]
[ROW][C]39[/C][C]97.24[/C][C]98.4551[/C][C]97.8896[/C][C]0.56553[/C][C]-1.21511[/C][/ROW]
[ROW][C]40[/C][C]96.69[/C][C]97.2761[/C][C]97.4687[/C][C]-0.1927[/C][C]-0.58605[/C][/ROW]
[ROW][C]41[/C][C]96.41[/C][C]96.7153[/C][C]97.0783[/C][C]-0.363012[/C][C]-0.305321[/C][/ROW]
[ROW][C]42[/C][C]96.99[/C][C]95.5811[/C][C]96.3683[/C][C]-0.787283[/C][C]1.40895[/C][/ROW]
[ROW][C]43[/C][C]98.36[/C][C]94.7[/C][C]95.1171[/C][C]-0.417075[/C][C]3.65999[/C][/ROW]
[ROW][C]44[/C][C]97.8[/C][C]94.4838[/C][C]93.71[/C][C]0.773759[/C][C]3.31624[/C][/ROW]
[ROW][C]45[/C][C]96.79[/C][C]95.0666[/C][C]92.6075[/C][C]2.45907[/C][C]1.72343[/C][/ROW]
[ROW][C]46[/C][C]94.73[/C][C]92.9746[/C][C]91.6575[/C][C]1.31709[/C][C]1.75541[/C][/ROW]
[ROW][C]47[/C][C]92.67[/C][C]91.7092[/C][C]90.7904[/C][C]0.918759[/C][C]0.960825[/C][/ROW]
[ROW][C]48[/C][C]87.15[/C][C]89.1238[/C][C]89.9933[/C][C]-0.869575[/C][C]-1.97376[/C][/ROW]
[ROW][C]49[/C][C]79.54[/C][C]86.6825[/C][C]88.9983[/C][C]-2.31582[/C][C]-7.14251[/C][/ROW]
[ROW][C]50[/C][C]82.35[/C][C]86.6317[/C][C]87.7204[/C][C]-1.08874[/C][C]-4.28168[/C][/ROW]
[ROW][C]51[/C][C]86.38[/C][C]86.8459[/C][C]86.2804[/C][C]0.56553[/C][C]-0.465946[/C][/ROW]
[ROW][C]52[/C][C]84.75[/C][C]84.6827[/C][C]84.8754[/C][C]-0.1927[/C][C]0.067283[/C][/ROW]
[ROW][C]53[/C][C]87.54[/C][C]83.2482[/C][C]83.6112[/C][C]-0.363012[/C][C]4.29176[/C][/ROW]
[ROW][C]54[/C][C]86.73[/C][C]81.8261[/C][C]82.6133[/C][C]-0.787283[/C][C]4.90395[/C][/ROW]
[ROW][C]55[/C][C]84.74[/C][C]NA[/C][C]NA[/C][C]-0.417075[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]80.75[/C][C]NA[/C][C]NA[/C][C]0.773759[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]79.28[/C][C]NA[/C][C]NA[/C][C]2.45907[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]78.52[/C][C]NA[/C][C]NA[/C][C]1.31709[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]78.54[/C][C]NA[/C][C]NA[/C][C]0.918759[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]77.33[/C][C]NA[/C][C]NA[/C][C]-0.869575[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294616&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294616&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	90.75	NA	NA	-2.31582	NA
2	92.82	NA	NA	-1.08874	NA
3	97.78	NA	NA	0.56553	NA
4	99.32	NA	NA	-0.1927	NA
5	98.33	NA	NA	-0.363012	NA
6	98.66	NA	NA	-0.787283	NA
7	98.13	98.3363	98.7533	-0.417075	-0.206259
8	97.8	100.561	99.7871	0.773759	-2.76084
9	99.36	103.084	100.625	2.45907	-3.72365
10	100.37	102.595	101.277	1.31709	-2.22459
11	103.22	102.693	101.774	0.918759	0.527491
12	101.68	101.183	102.052	-0.869575	0.497491
13	104.39	99.9229	102.239	-2.31582	4.46707
14	103.99	101.613	102.701	-1.08874	2.37749
15	106.71	103.969	103.404	0.56553	2.74072
16	106.06	103.862	104.055	-0.1927	2.1977
17	103.5	104.049	104.412	-0.363012	-0.549488
18	100.17	103.738	104.525	-0.787283	-3.56772
19	101.1	104.053	104.47	-0.417075	-2.95334
20	105.93	105.108	104.334	0.773759	0.822075
21	108.09	106.561	104.102	2.45907	1.52885
22	107.27	104.979	103.662	1.31709	2.29082
23	104.9	104.086	103.167	0.918759	0.813741
24	102.7	101.934	102.804	-0.869575	0.765825
25	102.06	100.312	102.627	-2.31582	1.74832
26	103.05	101.214	102.303	-1.08874	1.83582
27	102.08	102.379	101.814	0.56553	-0.29928
28	100.13	101.049	101.241	-0.1927	-0.91855
29	97.56	100.237	100.6	-0.363012	-2.67657
30	97.38	99.3648	100.152	-0.787283	-1.9848
31	99.66	99.4	99.8171	-0.417075	0.259991
32	99.58	100.197	99.4233	0.773759	-0.617092
33	102.7	101.468	99.0092	2.45907	1.23176
34	98.92	99.9813	98.6642	1.31709	-1.06126
35	97.85	99.3917	98.4729	0.918759	-1.54168
36	99.01	97.5392	98.4088	-0.869575	1.47082
37	97.71	96.0225	98.3383	-2.31582	1.68749
38	97.95	97.1213	98.21	-1.08874	0.828741
39	97.24	98.4551	97.8896	0.56553	-1.21511
40	96.69	97.2761	97.4687	-0.1927	-0.58605
41	96.41	96.7153	97.0783	-0.363012	-0.305321
42	96.99	95.5811	96.3683	-0.787283	1.40895
43	98.36	94.7	95.1171	-0.417075	3.65999
44	97.8	94.4838	93.71	0.773759	3.31624
45	96.79	95.0666	92.6075	2.45907	1.72343
46	94.73	92.9746	91.6575	1.31709	1.75541
47	92.67	91.7092	90.7904	0.918759	0.960825
48	87.15	89.1238	89.9933	-0.869575	-1.97376
49	79.54	86.6825	88.9983	-2.31582	-7.14251
50	82.35	86.6317	87.7204	-1.08874	-4.28168
51	86.38	86.8459	86.2804	0.56553	-0.465946
52	84.75	84.6827	84.8754	-0.1927	0.067283
53	87.54	83.2482	83.6112	-0.363012	4.29176
54	86.73	81.8261	82.6133	-0.787283	4.90395
55	84.74	NA	NA	-0.417075	NA
56	80.75	NA	NA	0.773759	NA
57	79.28	NA	NA	2.45907	NA
58	78.52	NA	NA	1.31709	NA
59	78.54	NA	NA	0.918759	NA
60	77.33	NA	NA	-0.869575	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