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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Wed, 23 Nov 2016 14:22:44 +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/23/t1479910980rfde7vuswgyrqxs.htm/, Retrieved Tue, 07 May 2024 00:33:32 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Tue, 07 May 2024 00:33:32 +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	1 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 & 1 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]1 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

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	1 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	96.07	NA	NA	1.23534	NA
2	95	NA	NA	1.40065	NA
3	93.27	NA	NA	1.78461	NA
4	91.94	NA	NA	0.918047	NA
5	91.62	NA	NA	0.0260677	NA
6	91.01	NA	NA	-1.5631	NA
7	90.62	92.241	95.6646	-3.42362	-1.62096
8	97.72	94.1482	96.16	-2.01185	3.57185
9	99.09	96.7469	96.8617	-0.114766	2.3431
10	99.72	98.3688	97.6654	0.703359	1.35122
11	100.22	99.6626	98.3621	1.30055	0.55737
12	99.15	98.6222	98.8775	-0.255286	0.527786
13	101.16	100.481	99.2458	1.23534	0.678828
14	101.8	100.729	99.3283	1.40065	1.07102
15	103.31	101.084	99.2996	1.78461	2.22581
16	101.19	100.393	99.4754	0.918047	0.796536
17	99.09	99.7644	99.7383	0.0260677	-0.674401
18	95.91	98.3877	99.9508	-1.5631	-2.47773
19	94.56	96.6955	100.119	-3.42362	-2.13555
20	95.76	98.3307	100.342	-2.01185	-2.57065
21	100.36	100.421	100.535	-0.114766	-0.060651
22	102.67	101.44	100.737	0.703359	1.22956
23	103.58	102.371	101.071	1.30055	1.20862
24	100.89	101.225	101.48	-0.255286	-0.33513
25	103.46	103.128	101.892	1.23534	0.332161
26	104.86	103.533	102.132	1.40065	1.32727
27	104.88	103.743	101.958	1.78461	1.13747
28	104.46	102.368	101.45	0.918047	2.09154
29	103.83	100.859	100.832	0.0260677	2.97143
30	101	98.6911	100.254	-1.5631	2.30893
31	99.36	96.2401	99.6637	-3.42362	3.11987
32	96.71	96.9007	98.9125	-2.01185	-0.190651
33	95.23	97.9644	98.0792	-0.114766	-2.7344
34	95.62	97.9567	97.2533	0.703359	-2.33669
35	95.8	97.7439	96.4433	1.30055	-1.94388
36	94.79	95.498	95.7533	-0.255286	-0.708047
37	95.39	96.4712	95.2358	1.23534	-1.08117
38	94.9	96.3019	94.9013	1.40065	-1.4019
39	94.84	96.6867	94.9021	1.78461	-1.84669
40	94.68	96.0355	95.1175	0.918047	-1.35555
41	94.17	95.4132	95.3871	0.0260677	-1.24315
42	94.1	94.1486	95.7117	-1.5631	-0.0485677
43	93.84	92.6514	96.075	-3.42362	1.18862
44	94.2	94.4586	96.4704	-2.01185	-0.258568
45	97.76	96.7561	96.8708	-0.114766	1.00393
46	98.26	97.9521	97.2487	0.703359	0.307891
47	99.63	98.9001	97.5996	1.30055	0.72987
48	98.75	97.6826	97.9379	-0.255286	1.06737
49	100.15	99.5278	98.2925	1.23534	0.622161
50	99.63	100.074	98.6737	1.40065	-0.444401
51	99.72	100.685	98.9	1.78461	-0.964609
52	98.87	99.8505	98.9325	0.918047	-0.980547
53	98.4	98.9019	98.8758	0.0260677	-0.501901
54	97.99	97.2207	98.7837	-1.5631	0.769349
55	98.46	NA	NA	-3.42362	NA
56	98.73	NA	NA	-2.01185	NA
57	98.66	NA	NA	-0.114766	NA
58	98.14	NA	NA	0.703359	NA
59	98.39	NA	NA	1.30055	NA
60	97.78	NA	NA	-0.255286	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 96.07 & NA & NA & 1.23534 & NA \tabularnewline
2 & 95 & NA & NA & 1.40065 & NA \tabularnewline
3 & 93.27 & NA & NA & 1.78461 & NA \tabularnewline
4 & 91.94 & NA & NA & 0.918047 & NA \tabularnewline
5 & 91.62 & NA & NA & 0.0260677 & NA \tabularnewline
6 & 91.01 & NA & NA & -1.5631 & NA \tabularnewline
7 & 90.62 & 92.241 & 95.6646 & -3.42362 & -1.62096 \tabularnewline
8 & 97.72 & 94.1482 & 96.16 & -2.01185 & 3.57185 \tabularnewline
9 & 99.09 & 96.7469 & 96.8617 & -0.114766 & 2.3431 \tabularnewline
10 & 99.72 & 98.3688 & 97.6654 & 0.703359 & 1.35122 \tabularnewline
11 & 100.22 & 99.6626 & 98.3621 & 1.30055 & 0.55737 \tabularnewline
12 & 99.15 & 98.6222 & 98.8775 & -0.255286 & 0.527786 \tabularnewline
13 & 101.16 & 100.481 & 99.2458 & 1.23534 & 0.678828 \tabularnewline
14 & 101.8 & 100.729 & 99.3283 & 1.40065 & 1.07102 \tabularnewline
15 & 103.31 & 101.084 & 99.2996 & 1.78461 & 2.22581 \tabularnewline
16 & 101.19 & 100.393 & 99.4754 & 0.918047 & 0.796536 \tabularnewline
17 & 99.09 & 99.7644 & 99.7383 & 0.0260677 & -0.674401 \tabularnewline
18 & 95.91 & 98.3877 & 99.9508 & -1.5631 & -2.47773 \tabularnewline
19 & 94.56 & 96.6955 & 100.119 & -3.42362 & -2.13555 \tabularnewline
20 & 95.76 & 98.3307 & 100.342 & -2.01185 & -2.57065 \tabularnewline
21 & 100.36 & 100.421 & 100.535 & -0.114766 & -0.060651 \tabularnewline
22 & 102.67 & 101.44 & 100.737 & 0.703359 & 1.22956 \tabularnewline
23 & 103.58 & 102.371 & 101.071 & 1.30055 & 1.20862 \tabularnewline
24 & 100.89 & 101.225 & 101.48 & -0.255286 & -0.33513 \tabularnewline
25 & 103.46 & 103.128 & 101.892 & 1.23534 & 0.332161 \tabularnewline
26 & 104.86 & 103.533 & 102.132 & 1.40065 & 1.32727 \tabularnewline
27 & 104.88 & 103.743 & 101.958 & 1.78461 & 1.13747 \tabularnewline
28 & 104.46 & 102.368 & 101.45 & 0.918047 & 2.09154 \tabularnewline
29 & 103.83 & 100.859 & 100.832 & 0.0260677 & 2.97143 \tabularnewline
30 & 101 & 98.6911 & 100.254 & -1.5631 & 2.30893 \tabularnewline
31 & 99.36 & 96.2401 & 99.6637 & -3.42362 & 3.11987 \tabularnewline
32 & 96.71 & 96.9007 & 98.9125 & -2.01185 & -0.190651 \tabularnewline
33 & 95.23 & 97.9644 & 98.0792 & -0.114766 & -2.7344 \tabularnewline
34 & 95.62 & 97.9567 & 97.2533 & 0.703359 & -2.33669 \tabularnewline
35 & 95.8 & 97.7439 & 96.4433 & 1.30055 & -1.94388 \tabularnewline
36 & 94.79 & 95.498 & 95.7533 & -0.255286 & -0.708047 \tabularnewline
37 & 95.39 & 96.4712 & 95.2358 & 1.23534 & -1.08117 \tabularnewline
38 & 94.9 & 96.3019 & 94.9013 & 1.40065 & -1.4019 \tabularnewline
39 & 94.84 & 96.6867 & 94.9021 & 1.78461 & -1.84669 \tabularnewline
40 & 94.68 & 96.0355 & 95.1175 & 0.918047 & -1.35555 \tabularnewline
41 & 94.17 & 95.4132 & 95.3871 & 0.0260677 & -1.24315 \tabularnewline
42 & 94.1 & 94.1486 & 95.7117 & -1.5631 & -0.0485677 \tabularnewline
43 & 93.84 & 92.6514 & 96.075 & -3.42362 & 1.18862 \tabularnewline
44 & 94.2 & 94.4586 & 96.4704 & -2.01185 & -0.258568 \tabularnewline
45 & 97.76 & 96.7561 & 96.8708 & -0.114766 & 1.00393 \tabularnewline
46 & 98.26 & 97.9521 & 97.2487 & 0.703359 & 0.307891 \tabularnewline
47 & 99.63 & 98.9001 & 97.5996 & 1.30055 & 0.72987 \tabularnewline
48 & 98.75 & 97.6826 & 97.9379 & -0.255286 & 1.06737 \tabularnewline
49 & 100.15 & 99.5278 & 98.2925 & 1.23534 & 0.622161 \tabularnewline
50 & 99.63 & 100.074 & 98.6737 & 1.40065 & -0.444401 \tabularnewline
51 & 99.72 & 100.685 & 98.9 & 1.78461 & -0.964609 \tabularnewline
52 & 98.87 & 99.8505 & 98.9325 & 0.918047 & -0.980547 \tabularnewline
53 & 98.4 & 98.9019 & 98.8758 & 0.0260677 & -0.501901 \tabularnewline
54 & 97.99 & 97.2207 & 98.7837 & -1.5631 & 0.769349 \tabularnewline
55 & 98.46 & NA & NA & -3.42362 & NA \tabularnewline
56 & 98.73 & NA & NA & -2.01185 & NA \tabularnewline
57 & 98.66 & NA & NA & -0.114766 & NA \tabularnewline
58 & 98.14 & NA & NA & 0.703359 & NA \tabularnewline
59 & 98.39 & NA & NA & 1.30055 & NA \tabularnewline
60 & 97.78 & NA & NA & -0.255286 & 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]96.07[/C][C]NA[/C][C]NA[/C][C]1.23534[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]95[/C][C]NA[/C][C]NA[/C][C]1.40065[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]93.27[/C][C]NA[/C][C]NA[/C][C]1.78461[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]91.94[/C][C]NA[/C][C]NA[/C][C]0.918047[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]91.62[/C][C]NA[/C][C]NA[/C][C]0.0260677[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]91.01[/C][C]NA[/C][C]NA[/C][C]-1.5631[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]90.62[/C][C]92.241[/C][C]95.6646[/C][C]-3.42362[/C][C]-1.62096[/C][/ROW]
[ROW][C]8[/C][C]97.72[/C][C]94.1482[/C][C]96.16[/C][C]-2.01185[/C][C]3.57185[/C][/ROW]
[ROW][C]9[/C][C]99.09[/C][C]96.7469[/C][C]96.8617[/C][C]-0.114766[/C][C]2.3431[/C][/ROW]
[ROW][C]10[/C][C]99.72[/C][C]98.3688[/C][C]97.6654[/C][C]0.703359[/C][C]1.35122[/C][/ROW]
[ROW][C]11[/C][C]100.22[/C][C]99.6626[/C][C]98.3621[/C][C]1.30055[/C][C]0.55737[/C][/ROW]
[ROW][C]12[/C][C]99.15[/C][C]98.6222[/C][C]98.8775[/C][C]-0.255286[/C][C]0.527786[/C][/ROW]
[ROW][C]13[/C][C]101.16[/C][C]100.481[/C][C]99.2458[/C][C]1.23534[/C][C]0.678828[/C][/ROW]
[ROW][C]14[/C][C]101.8[/C][C]100.729[/C][C]99.3283[/C][C]1.40065[/C][C]1.07102[/C][/ROW]
[ROW][C]15[/C][C]103.31[/C][C]101.084[/C][C]99.2996[/C][C]1.78461[/C][C]2.22581[/C][/ROW]
[ROW][C]16[/C][C]101.19[/C][C]100.393[/C][C]99.4754[/C][C]0.918047[/C][C]0.796536[/C][/ROW]
[ROW][C]17[/C][C]99.09[/C][C]99.7644[/C][C]99.7383[/C][C]0.0260677[/C][C]-0.674401[/C][/ROW]
[ROW][C]18[/C][C]95.91[/C][C]98.3877[/C][C]99.9508[/C][C]-1.5631[/C][C]-2.47773[/C][/ROW]
[ROW][C]19[/C][C]94.56[/C][C]96.6955[/C][C]100.119[/C][C]-3.42362[/C][C]-2.13555[/C][/ROW]
[ROW][C]20[/C][C]95.76[/C][C]98.3307[/C][C]100.342[/C][C]-2.01185[/C][C]-2.57065[/C][/ROW]
[ROW][C]21[/C][C]100.36[/C][C]100.421[/C][C]100.535[/C][C]-0.114766[/C][C]-0.060651[/C][/ROW]
[ROW][C]22[/C][C]102.67[/C][C]101.44[/C][C]100.737[/C][C]0.703359[/C][C]1.22956[/C][/ROW]
[ROW][C]23[/C][C]103.58[/C][C]102.371[/C][C]101.071[/C][C]1.30055[/C][C]1.20862[/C][/ROW]
[ROW][C]24[/C][C]100.89[/C][C]101.225[/C][C]101.48[/C][C]-0.255286[/C][C]-0.33513[/C][/ROW]
[ROW][C]25[/C][C]103.46[/C][C]103.128[/C][C]101.892[/C][C]1.23534[/C][C]0.332161[/C][/ROW]
[ROW][C]26[/C][C]104.86[/C][C]103.533[/C][C]102.132[/C][C]1.40065[/C][C]1.32727[/C][/ROW]
[ROW][C]27[/C][C]104.88[/C][C]103.743[/C][C]101.958[/C][C]1.78461[/C][C]1.13747[/C][/ROW]
[ROW][C]28[/C][C]104.46[/C][C]102.368[/C][C]101.45[/C][C]0.918047[/C][C]2.09154[/C][/ROW]
[ROW][C]29[/C][C]103.83[/C][C]100.859[/C][C]100.832[/C][C]0.0260677[/C][C]2.97143[/C][/ROW]
[ROW][C]30[/C][C]101[/C][C]98.6911[/C][C]100.254[/C][C]-1.5631[/C][C]2.30893[/C][/ROW]
[ROW][C]31[/C][C]99.36[/C][C]96.2401[/C][C]99.6637[/C][C]-3.42362[/C][C]3.11987[/C][/ROW]
[ROW][C]32[/C][C]96.71[/C][C]96.9007[/C][C]98.9125[/C][C]-2.01185[/C][C]-0.190651[/C][/ROW]
[ROW][C]33[/C][C]95.23[/C][C]97.9644[/C][C]98.0792[/C][C]-0.114766[/C][C]-2.7344[/C][/ROW]
[ROW][C]34[/C][C]95.62[/C][C]97.9567[/C][C]97.2533[/C][C]0.703359[/C][C]-2.33669[/C][/ROW]
[ROW][C]35[/C][C]95.8[/C][C]97.7439[/C][C]96.4433[/C][C]1.30055[/C][C]-1.94388[/C][/ROW]
[ROW][C]36[/C][C]94.79[/C][C]95.498[/C][C]95.7533[/C][C]-0.255286[/C][C]-0.708047[/C][/ROW]
[ROW][C]37[/C][C]95.39[/C][C]96.4712[/C][C]95.2358[/C][C]1.23534[/C][C]-1.08117[/C][/ROW]
[ROW][C]38[/C][C]94.9[/C][C]96.3019[/C][C]94.9013[/C][C]1.40065[/C][C]-1.4019[/C][/ROW]
[ROW][C]39[/C][C]94.84[/C][C]96.6867[/C][C]94.9021[/C][C]1.78461[/C][C]-1.84669[/C][/ROW]
[ROW][C]40[/C][C]94.68[/C][C]96.0355[/C][C]95.1175[/C][C]0.918047[/C][C]-1.35555[/C][/ROW]
[ROW][C]41[/C][C]94.17[/C][C]95.4132[/C][C]95.3871[/C][C]0.0260677[/C][C]-1.24315[/C][/ROW]
[ROW][C]42[/C][C]94.1[/C][C]94.1486[/C][C]95.7117[/C][C]-1.5631[/C][C]-0.0485677[/C][/ROW]
[ROW][C]43[/C][C]93.84[/C][C]92.6514[/C][C]96.075[/C][C]-3.42362[/C][C]1.18862[/C][/ROW]
[ROW][C]44[/C][C]94.2[/C][C]94.4586[/C][C]96.4704[/C][C]-2.01185[/C][C]-0.258568[/C][/ROW]
[ROW][C]45[/C][C]97.76[/C][C]96.7561[/C][C]96.8708[/C][C]-0.114766[/C][C]1.00393[/C][/ROW]
[ROW][C]46[/C][C]98.26[/C][C]97.9521[/C][C]97.2487[/C][C]0.703359[/C][C]0.307891[/C][/ROW]
[ROW][C]47[/C][C]99.63[/C][C]98.9001[/C][C]97.5996[/C][C]1.30055[/C][C]0.72987[/C][/ROW]
[ROW][C]48[/C][C]98.75[/C][C]97.6826[/C][C]97.9379[/C][C]-0.255286[/C][C]1.06737[/C][/ROW]
[ROW][C]49[/C][C]100.15[/C][C]99.5278[/C][C]98.2925[/C][C]1.23534[/C][C]0.622161[/C][/ROW]
[ROW][C]50[/C][C]99.63[/C][C]100.074[/C][C]98.6737[/C][C]1.40065[/C][C]-0.444401[/C][/ROW]
[ROW][C]51[/C][C]99.72[/C][C]100.685[/C][C]98.9[/C][C]1.78461[/C][C]-0.964609[/C][/ROW]
[ROW][C]52[/C][C]98.87[/C][C]99.8505[/C][C]98.9325[/C][C]0.918047[/C][C]-0.980547[/C][/ROW]
[ROW][C]53[/C][C]98.4[/C][C]98.9019[/C][C]98.8758[/C][C]0.0260677[/C][C]-0.501901[/C][/ROW]
[ROW][C]54[/C][C]97.99[/C][C]97.2207[/C][C]98.7837[/C][C]-1.5631[/C][C]0.769349[/C][/ROW]
[ROW][C]55[/C][C]98.46[/C][C]NA[/C][C]NA[/C][C]-3.42362[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]98.73[/C][C]NA[/C][C]NA[/C][C]-2.01185[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]98.66[/C][C]NA[/C][C]NA[/C][C]-0.114766[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]98.14[/C][C]NA[/C][C]NA[/C][C]0.703359[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]98.39[/C][C]NA[/C][C]NA[/C][C]1.30055[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]97.78[/C][C]NA[/C][C]NA[/C][C]-0.255286[/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	96.07	NA	NA	1.23534	NA
2	95	NA	NA	1.40065	NA
3	93.27	NA	NA	1.78461	NA
4	91.94	NA	NA	0.918047	NA
5	91.62	NA	NA	0.0260677	NA
6	91.01	NA	NA	-1.5631	NA
7	90.62	92.241	95.6646	-3.42362	-1.62096
8	97.72	94.1482	96.16	-2.01185	3.57185
9	99.09	96.7469	96.8617	-0.114766	2.3431
10	99.72	98.3688	97.6654	0.703359	1.35122
11	100.22	99.6626	98.3621	1.30055	0.55737
12	99.15	98.6222	98.8775	-0.255286	0.527786
13	101.16	100.481	99.2458	1.23534	0.678828
14	101.8	100.729	99.3283	1.40065	1.07102
15	103.31	101.084	99.2996	1.78461	2.22581
16	101.19	100.393	99.4754	0.918047	0.796536
17	99.09	99.7644	99.7383	0.0260677	-0.674401
18	95.91	98.3877	99.9508	-1.5631	-2.47773
19	94.56	96.6955	100.119	-3.42362	-2.13555
20	95.76	98.3307	100.342	-2.01185	-2.57065
21	100.36	100.421	100.535	-0.114766	-0.060651
22	102.67	101.44	100.737	0.703359	1.22956
23	103.58	102.371	101.071	1.30055	1.20862
24	100.89	101.225	101.48	-0.255286	-0.33513
25	103.46	103.128	101.892	1.23534	0.332161
26	104.86	103.533	102.132	1.40065	1.32727
27	104.88	103.743	101.958	1.78461	1.13747
28	104.46	102.368	101.45	0.918047	2.09154
29	103.83	100.859	100.832	0.0260677	2.97143
30	101	98.6911	100.254	-1.5631	2.30893
31	99.36	96.2401	99.6637	-3.42362	3.11987
32	96.71	96.9007	98.9125	-2.01185	-0.190651
33	95.23	97.9644	98.0792	-0.114766	-2.7344
34	95.62	97.9567	97.2533	0.703359	-2.33669
35	95.8	97.7439	96.4433	1.30055	-1.94388
36	94.79	95.498	95.7533	-0.255286	-0.708047
37	95.39	96.4712	95.2358	1.23534	-1.08117
38	94.9	96.3019	94.9013	1.40065	-1.4019
39	94.84	96.6867	94.9021	1.78461	-1.84669
40	94.68	96.0355	95.1175	0.918047	-1.35555
41	94.17	95.4132	95.3871	0.0260677	-1.24315
42	94.1	94.1486	95.7117	-1.5631	-0.0485677
43	93.84	92.6514	96.075	-3.42362	1.18862
44	94.2	94.4586	96.4704	-2.01185	-0.258568
45	97.76	96.7561	96.8708	-0.114766	1.00393
46	98.26	97.9521	97.2487	0.703359	0.307891
47	99.63	98.9001	97.5996	1.30055	0.72987
48	98.75	97.6826	97.9379	-0.255286	1.06737
49	100.15	99.5278	98.2925	1.23534	0.622161
50	99.63	100.074	98.6737	1.40065	-0.444401
51	99.72	100.685	98.9	1.78461	-0.964609
52	98.87	99.8505	98.9325	0.918047	-0.980547
53	98.4	98.9019	98.8758	0.0260677	-0.501901
54	97.99	97.2207	98.7837	-1.5631	0.769349
55	98.46	NA	NA	-3.42362	NA
56	98.73	NA	NA	-2.01185	NA
57	98.66	NA	NA	-0.114766	NA
58	98.14	NA	NA	0.703359	NA
59	98.39	NA	NA	1.30055	NA
60	97.78	NA	NA	-0.255286	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