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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Tue, 08 Dec 2015 17:17:05 +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/2015/Dec/08/t14495950673w55qbv8vvajn36.htm/, Retrieved Thu, 16 May 2024 15:02:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285547, Retrieved Thu, 16 May 2024 15:02:21 +0000

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Estimated Impact

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

-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
- R  D  [Classical Decomposition] [classical decompo...] [2015-12-08 14:52:05] [22b6f4a061c8797aa483199554a73d13]
- RMP     [Decomposition by Loess] [Loess totaal mannen] [2015-12-08 15:45:08] [22b6f4a061c8797aa483199554a73d13]
- RMP       [Structural Time Series Models] [structural time s...] [2015-12-08 16:02:26] [22b6f4a061c8797aa483199554a73d13]
- RMPD          [Classical Decomposition] [classical decompo...] [2015-12-08 17:17:05] [20fcaaf1d4bc4a12bf87c6c50d624c14] [Current]
- RMP             [Decomposition by Loess] [Loess totaal vrouwen] [2015-12-08 17:53:15] [22b6f4a061c8797aa483199554a73d13] 
- RMP               [Structural Time Series Models] [structural time s...] [2015-12-08 18:02:34] [22b6f4a061c8797aa483199554a73d13] 

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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	'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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285547&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285547&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285547&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	3 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	276444	NA	NA	3022.63	NA
2	268606	NA	NA	-3334.92	NA
3	267679	NA	NA	-4169.22	NA
4	269879	NA	NA	1124.63	NA
5	265641	NA	NA	-2305.26	NA
6	262525	NA	NA	-7245.67	NA
7	258597	257708	269838	-12129.7	889.204
8	253849	252426	269388	-16962.7	1423.45
9	256221	255336	269027	-13691.1	885.325
10	286895	286068	268892	17176.4	827.065
11	294610	294251	269035	25215.9	359.231
12	280363	282410	269111	13299	-2047.08
13	269926	272080	269057	3022.63	-2153.8
14	264341	265663	268998	-3334.92	-1321.91
15	263269	264864	269033	-4169.22	-1594.57
16	271045	270240	269115	1124.63	805.325
17	267915	266981	269286	-2305.26	934.429
18	262078	262372	269618	-7245.67	-294.331
19	257751	257866	269996	-12129.7	-114.962
20	253271	253390	270352	-16962.7	-118.8
21	257638	257005	270696	-13691.1	633.409
22	287452	288086	270909	17176.4	-633.727
23	298152	296186	270970	25215.9	1966.27
24	284793	284405	271106	13299	387.627
25	274560	274434	271411	3022.63	126.117
26	268270	268509	271844	-3334.92	-238.664
27	267577	268125	272294	-4169.22	-547.987
28	271866	273996	272871	1124.63	-2129.8
29	268546	271201	273506	-2305.26	-2654.95
30	264722	266975	274221	-7245.67	-2253.37
31	262425	262989	275119	-12129.7	-564.462
32	258973	259082	276045	-16962.7	-108.966
33	262751	263342	277033	-13691.1	-590.591
34	296186	295315	278139	17176.4	870.565
35	304659	304477	279261	25215.9	181.69
36	295442	293575	280276	13299	1867.38
37	285466	284156	281134	3022.63	1309.66
38	279575	278453	281788	-3334.92	1121.59
39	279985	278156	282325	-4169.22	1828.93
40	286012	283839	282714	1124.63	2173.08
41	281337	280572	282877	-2305.26	765.221
42	276270	275665	282911	-7245.67	604.502
43	271472	270787	282917	-12129.7	684.538
44	265637	265902	282865	-16962.7	-265.3
45	268974	268972	282663	-13691.1	2.24184
46	299299	299433	282256	17176.4	-133.519
47	305452	307029	281813	25215.9	-1576.81
48	295468	294746	281446	13299	722.461
49	285584	283936	280913	3022.63	1648.41
50	278204	276835	280170	-3334.92	1369.38
51	276505	275261	279430	-4169.22	1244.01
52	279732	279650	278526	1124.63	81.7835
53	276980	275094	277400	-2305.26	1885.68
54	271832	268958	276204	-7245.67	2873.59
55	263105	262836	274966	-12129.7	268.663
56	256162	NA	NA	-16962.7	NA
57	260705	NA	NA	-13691.1	NA
58	285857	NA	NA	17176.4	NA
59	291870	NA	NA	25215.9	NA
60	280358	NA	NA	13299	NA
61	270981	NA	NA	3022.63	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 276444 & NA & NA & 3022.63 & NA \tabularnewline
2 & 268606 & NA & NA & -3334.92 & NA \tabularnewline
3 & 267679 & NA & NA & -4169.22 & NA \tabularnewline
4 & 269879 & NA & NA & 1124.63 & NA \tabularnewline
5 & 265641 & NA & NA & -2305.26 & NA \tabularnewline
6 & 262525 & NA & NA & -7245.67 & NA \tabularnewline
7 & 258597 & 257708 & 269838 & -12129.7 & 889.204 \tabularnewline
8 & 253849 & 252426 & 269388 & -16962.7 & 1423.45 \tabularnewline
9 & 256221 & 255336 & 269027 & -13691.1 & 885.325 \tabularnewline
10 & 286895 & 286068 & 268892 & 17176.4 & 827.065 \tabularnewline
11 & 294610 & 294251 & 269035 & 25215.9 & 359.231 \tabularnewline
12 & 280363 & 282410 & 269111 & 13299 & -2047.08 \tabularnewline
13 & 269926 & 272080 & 269057 & 3022.63 & -2153.8 \tabularnewline
14 & 264341 & 265663 & 268998 & -3334.92 & -1321.91 \tabularnewline
15 & 263269 & 264864 & 269033 & -4169.22 & -1594.57 \tabularnewline
16 & 271045 & 270240 & 269115 & 1124.63 & 805.325 \tabularnewline
17 & 267915 & 266981 & 269286 & -2305.26 & 934.429 \tabularnewline
18 & 262078 & 262372 & 269618 & -7245.67 & -294.331 \tabularnewline
19 & 257751 & 257866 & 269996 & -12129.7 & -114.962 \tabularnewline
20 & 253271 & 253390 & 270352 & -16962.7 & -118.8 \tabularnewline
21 & 257638 & 257005 & 270696 & -13691.1 & 633.409 \tabularnewline
22 & 287452 & 288086 & 270909 & 17176.4 & -633.727 \tabularnewline
23 & 298152 & 296186 & 270970 & 25215.9 & 1966.27 \tabularnewline
24 & 284793 & 284405 & 271106 & 13299 & 387.627 \tabularnewline
25 & 274560 & 274434 & 271411 & 3022.63 & 126.117 \tabularnewline
26 & 268270 & 268509 & 271844 & -3334.92 & -238.664 \tabularnewline
27 & 267577 & 268125 & 272294 & -4169.22 & -547.987 \tabularnewline
28 & 271866 & 273996 & 272871 & 1124.63 & -2129.8 \tabularnewline
29 & 268546 & 271201 & 273506 & -2305.26 & -2654.95 \tabularnewline
30 & 264722 & 266975 & 274221 & -7245.67 & -2253.37 \tabularnewline
31 & 262425 & 262989 & 275119 & -12129.7 & -564.462 \tabularnewline
32 & 258973 & 259082 & 276045 & -16962.7 & -108.966 \tabularnewline
33 & 262751 & 263342 & 277033 & -13691.1 & -590.591 \tabularnewline
34 & 296186 & 295315 & 278139 & 17176.4 & 870.565 \tabularnewline
35 & 304659 & 304477 & 279261 & 25215.9 & 181.69 \tabularnewline
36 & 295442 & 293575 & 280276 & 13299 & 1867.38 \tabularnewline
37 & 285466 & 284156 & 281134 & 3022.63 & 1309.66 \tabularnewline
38 & 279575 & 278453 & 281788 & -3334.92 & 1121.59 \tabularnewline
39 & 279985 & 278156 & 282325 & -4169.22 & 1828.93 \tabularnewline
40 & 286012 & 283839 & 282714 & 1124.63 & 2173.08 \tabularnewline
41 & 281337 & 280572 & 282877 & -2305.26 & 765.221 \tabularnewline
42 & 276270 & 275665 & 282911 & -7245.67 & 604.502 \tabularnewline
43 & 271472 & 270787 & 282917 & -12129.7 & 684.538 \tabularnewline
44 & 265637 & 265902 & 282865 & -16962.7 & -265.3 \tabularnewline
45 & 268974 & 268972 & 282663 & -13691.1 & 2.24184 \tabularnewline
46 & 299299 & 299433 & 282256 & 17176.4 & -133.519 \tabularnewline
47 & 305452 & 307029 & 281813 & 25215.9 & -1576.81 \tabularnewline
48 & 295468 & 294746 & 281446 & 13299 & 722.461 \tabularnewline
49 & 285584 & 283936 & 280913 & 3022.63 & 1648.41 \tabularnewline
50 & 278204 & 276835 & 280170 & -3334.92 & 1369.38 \tabularnewline
51 & 276505 & 275261 & 279430 & -4169.22 & 1244.01 \tabularnewline
52 & 279732 & 279650 & 278526 & 1124.63 & 81.7835 \tabularnewline
53 & 276980 & 275094 & 277400 & -2305.26 & 1885.68 \tabularnewline
54 & 271832 & 268958 & 276204 & -7245.67 & 2873.59 \tabularnewline
55 & 263105 & 262836 & 274966 & -12129.7 & 268.663 \tabularnewline
56 & 256162 & NA & NA & -16962.7 & NA \tabularnewline
57 & 260705 & NA & NA & -13691.1 & NA \tabularnewline
58 & 285857 & NA & NA & 17176.4 & NA \tabularnewline
59 & 291870 & NA & NA & 25215.9 & NA \tabularnewline
60 & 280358 & NA & NA & 13299 & NA \tabularnewline
61 & 270981 & NA & NA & 3022.63 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285547&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]276444[/C][C]NA[/C][C]NA[/C][C]3022.63[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]268606[/C][C]NA[/C][C]NA[/C][C]-3334.92[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]267679[/C][C]NA[/C][C]NA[/C][C]-4169.22[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]269879[/C][C]NA[/C][C]NA[/C][C]1124.63[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]265641[/C][C]NA[/C][C]NA[/C][C]-2305.26[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]262525[/C][C]NA[/C][C]NA[/C][C]-7245.67[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]258597[/C][C]257708[/C][C]269838[/C][C]-12129.7[/C][C]889.204[/C][/ROW]
[ROW][C]8[/C][C]253849[/C][C]252426[/C][C]269388[/C][C]-16962.7[/C][C]1423.45[/C][/ROW]
[ROW][C]9[/C][C]256221[/C][C]255336[/C][C]269027[/C][C]-13691.1[/C][C]885.325[/C][/ROW]
[ROW][C]10[/C][C]286895[/C][C]286068[/C][C]268892[/C][C]17176.4[/C][C]827.065[/C][/ROW]
[ROW][C]11[/C][C]294610[/C][C]294251[/C][C]269035[/C][C]25215.9[/C][C]359.231[/C][/ROW]
[ROW][C]12[/C][C]280363[/C][C]282410[/C][C]269111[/C][C]13299[/C][C]-2047.08[/C][/ROW]
[ROW][C]13[/C][C]269926[/C][C]272080[/C][C]269057[/C][C]3022.63[/C][C]-2153.8[/C][/ROW]
[ROW][C]14[/C][C]264341[/C][C]265663[/C][C]268998[/C][C]-3334.92[/C][C]-1321.91[/C][/ROW]
[ROW][C]15[/C][C]263269[/C][C]264864[/C][C]269033[/C][C]-4169.22[/C][C]-1594.57[/C][/ROW]
[ROW][C]16[/C][C]271045[/C][C]270240[/C][C]269115[/C][C]1124.63[/C][C]805.325[/C][/ROW]
[ROW][C]17[/C][C]267915[/C][C]266981[/C][C]269286[/C][C]-2305.26[/C][C]934.429[/C][/ROW]
[ROW][C]18[/C][C]262078[/C][C]262372[/C][C]269618[/C][C]-7245.67[/C][C]-294.331[/C][/ROW]
[ROW][C]19[/C][C]257751[/C][C]257866[/C][C]269996[/C][C]-12129.7[/C][C]-114.962[/C][/ROW]
[ROW][C]20[/C][C]253271[/C][C]253390[/C][C]270352[/C][C]-16962.7[/C][C]-118.8[/C][/ROW]
[ROW][C]21[/C][C]257638[/C][C]257005[/C][C]270696[/C][C]-13691.1[/C][C]633.409[/C][/ROW]
[ROW][C]22[/C][C]287452[/C][C]288086[/C][C]270909[/C][C]17176.4[/C][C]-633.727[/C][/ROW]
[ROW][C]23[/C][C]298152[/C][C]296186[/C][C]270970[/C][C]25215.9[/C][C]1966.27[/C][/ROW]
[ROW][C]24[/C][C]284793[/C][C]284405[/C][C]271106[/C][C]13299[/C][C]387.627[/C][/ROW]
[ROW][C]25[/C][C]274560[/C][C]274434[/C][C]271411[/C][C]3022.63[/C][C]126.117[/C][/ROW]
[ROW][C]26[/C][C]268270[/C][C]268509[/C][C]271844[/C][C]-3334.92[/C][C]-238.664[/C][/ROW]
[ROW][C]27[/C][C]267577[/C][C]268125[/C][C]272294[/C][C]-4169.22[/C][C]-547.987[/C][/ROW]
[ROW][C]28[/C][C]271866[/C][C]273996[/C][C]272871[/C][C]1124.63[/C][C]-2129.8[/C][/ROW]
[ROW][C]29[/C][C]268546[/C][C]271201[/C][C]273506[/C][C]-2305.26[/C][C]-2654.95[/C][/ROW]
[ROW][C]30[/C][C]264722[/C][C]266975[/C][C]274221[/C][C]-7245.67[/C][C]-2253.37[/C][/ROW]
[ROW][C]31[/C][C]262425[/C][C]262989[/C][C]275119[/C][C]-12129.7[/C][C]-564.462[/C][/ROW]
[ROW][C]32[/C][C]258973[/C][C]259082[/C][C]276045[/C][C]-16962.7[/C][C]-108.966[/C][/ROW]
[ROW][C]33[/C][C]262751[/C][C]263342[/C][C]277033[/C][C]-13691.1[/C][C]-590.591[/C][/ROW]
[ROW][C]34[/C][C]296186[/C][C]295315[/C][C]278139[/C][C]17176.4[/C][C]870.565[/C][/ROW]
[ROW][C]35[/C][C]304659[/C][C]304477[/C][C]279261[/C][C]25215.9[/C][C]181.69[/C][/ROW]
[ROW][C]36[/C][C]295442[/C][C]293575[/C][C]280276[/C][C]13299[/C][C]1867.38[/C][/ROW]
[ROW][C]37[/C][C]285466[/C][C]284156[/C][C]281134[/C][C]3022.63[/C][C]1309.66[/C][/ROW]
[ROW][C]38[/C][C]279575[/C][C]278453[/C][C]281788[/C][C]-3334.92[/C][C]1121.59[/C][/ROW]
[ROW][C]39[/C][C]279985[/C][C]278156[/C][C]282325[/C][C]-4169.22[/C][C]1828.93[/C][/ROW]
[ROW][C]40[/C][C]286012[/C][C]283839[/C][C]282714[/C][C]1124.63[/C][C]2173.08[/C][/ROW]
[ROW][C]41[/C][C]281337[/C][C]280572[/C][C]282877[/C][C]-2305.26[/C][C]765.221[/C][/ROW]
[ROW][C]42[/C][C]276270[/C][C]275665[/C][C]282911[/C][C]-7245.67[/C][C]604.502[/C][/ROW]
[ROW][C]43[/C][C]271472[/C][C]270787[/C][C]282917[/C][C]-12129.7[/C][C]684.538[/C][/ROW]
[ROW][C]44[/C][C]265637[/C][C]265902[/C][C]282865[/C][C]-16962.7[/C][C]-265.3[/C][/ROW]
[ROW][C]45[/C][C]268974[/C][C]268972[/C][C]282663[/C][C]-13691.1[/C][C]2.24184[/C][/ROW]
[ROW][C]46[/C][C]299299[/C][C]299433[/C][C]282256[/C][C]17176.4[/C][C]-133.519[/C][/ROW]
[ROW][C]47[/C][C]305452[/C][C]307029[/C][C]281813[/C][C]25215.9[/C][C]-1576.81[/C][/ROW]
[ROW][C]48[/C][C]295468[/C][C]294746[/C][C]281446[/C][C]13299[/C][C]722.461[/C][/ROW]
[ROW][C]49[/C][C]285584[/C][C]283936[/C][C]280913[/C][C]3022.63[/C][C]1648.41[/C][/ROW]
[ROW][C]50[/C][C]278204[/C][C]276835[/C][C]280170[/C][C]-3334.92[/C][C]1369.38[/C][/ROW]
[ROW][C]51[/C][C]276505[/C][C]275261[/C][C]279430[/C][C]-4169.22[/C][C]1244.01[/C][/ROW]
[ROW][C]52[/C][C]279732[/C][C]279650[/C][C]278526[/C][C]1124.63[/C][C]81.7835[/C][/ROW]
[ROW][C]53[/C][C]276980[/C][C]275094[/C][C]277400[/C][C]-2305.26[/C][C]1885.68[/C][/ROW]
[ROW][C]54[/C][C]271832[/C][C]268958[/C][C]276204[/C][C]-7245.67[/C][C]2873.59[/C][/ROW]
[ROW][C]55[/C][C]263105[/C][C]262836[/C][C]274966[/C][C]-12129.7[/C][C]268.663[/C][/ROW]
[ROW][C]56[/C][C]256162[/C][C]NA[/C][C]NA[/C][C]-16962.7[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]260705[/C][C]NA[/C][C]NA[/C][C]-13691.1[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]285857[/C][C]NA[/C][C]NA[/C][C]17176.4[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]291870[/C][C]NA[/C][C]NA[/C][C]25215.9[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]280358[/C][C]NA[/C][C]NA[/C][C]13299[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]270981[/C][C]NA[/C][C]NA[/C][C]3022.63[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285547&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285547&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	276444	NA	NA	3022.63	NA
2	268606	NA	NA	-3334.92	NA
3	267679	NA	NA	-4169.22	NA
4	269879	NA	NA	1124.63	NA
5	265641	NA	NA	-2305.26	NA
6	262525	NA	NA	-7245.67	NA
7	258597	257708	269838	-12129.7	889.204
8	253849	252426	269388	-16962.7	1423.45
9	256221	255336	269027	-13691.1	885.325
10	286895	286068	268892	17176.4	827.065
11	294610	294251	269035	25215.9	359.231
12	280363	282410	269111	13299	-2047.08
13	269926	272080	269057	3022.63	-2153.8
14	264341	265663	268998	-3334.92	-1321.91
15	263269	264864	269033	-4169.22	-1594.57
16	271045	270240	269115	1124.63	805.325
17	267915	266981	269286	-2305.26	934.429
18	262078	262372	269618	-7245.67	-294.331
19	257751	257866	269996	-12129.7	-114.962
20	253271	253390	270352	-16962.7	-118.8
21	257638	257005	270696	-13691.1	633.409
22	287452	288086	270909	17176.4	-633.727
23	298152	296186	270970	25215.9	1966.27
24	284793	284405	271106	13299	387.627
25	274560	274434	271411	3022.63	126.117
26	268270	268509	271844	-3334.92	-238.664
27	267577	268125	272294	-4169.22	-547.987
28	271866	273996	272871	1124.63	-2129.8
29	268546	271201	273506	-2305.26	-2654.95
30	264722	266975	274221	-7245.67	-2253.37
31	262425	262989	275119	-12129.7	-564.462
32	258973	259082	276045	-16962.7	-108.966
33	262751	263342	277033	-13691.1	-590.591
34	296186	295315	278139	17176.4	870.565
35	304659	304477	279261	25215.9	181.69
36	295442	293575	280276	13299	1867.38
37	285466	284156	281134	3022.63	1309.66
38	279575	278453	281788	-3334.92	1121.59
39	279985	278156	282325	-4169.22	1828.93
40	286012	283839	282714	1124.63	2173.08
41	281337	280572	282877	-2305.26	765.221
42	276270	275665	282911	-7245.67	604.502
43	271472	270787	282917	-12129.7	684.538
44	265637	265902	282865	-16962.7	-265.3
45	268974	268972	282663	-13691.1	2.24184
46	299299	299433	282256	17176.4	-133.519
47	305452	307029	281813	25215.9	-1576.81
48	295468	294746	281446	13299	722.461
49	285584	283936	280913	3022.63	1648.41
50	278204	276835	280170	-3334.92	1369.38
51	276505	275261	279430	-4169.22	1244.01
52	279732	279650	278526	1124.63	81.7835
53	276980	275094	277400	-2305.26	1885.68
54	271832	268958	276204	-7245.67	2873.59
55	263105	262836	274966	-12129.7	268.663
56	256162	NA	NA	-16962.7	NA
57	260705	NA	NA	-13691.1	NA
58	285857	NA	NA	17176.4	NA
59	291870	NA	NA	25215.9	NA
60	280358	NA	NA	13299	NA
61	270981	NA	NA	3022.63	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