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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sun, 30 Nov 2014 17:10:42 +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/t1417367459gv2b6c6uh7e6hiw.htm/, Retrieved Sun, 19 May 2024 17:05:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=261548, Retrieved Sun, 19 May 2024 17:05:42 +0000

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-       [Classical Decomposition] [] [2014-11-30 17:10:42] [9458cab04bab2efa06ad058ca673aa95] [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	'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=261548&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=261548&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=261548&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	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	18.3	NA	NA	-1.04014	NA
2	18.6	NA	NA	-0.365972	NA
3	18.7	NA	NA	1.83153	NA
4	20.1	NA	NA	0.101528	NA
5	18.9	NA	NA	0.261528	NA
6	20.1	NA	NA	0.841528	NA
7	19.8	18.2815	18.1125	0.169028	1.51847
8	15.9	15.7149	17.7208	-2.00597	0.185139
9	19.9	18.2557	17.3708	0.884861	1.64431
10	19.6	17.9015	16.9667	0.934861	1.69847
11	15.6	16.364	16.4917	-0.127639	-0.764028
12	14.2	14.5815	16.0667	-1.48514	-0.381528
13	13.6	14.614	15.6542	-1.04014	-1.01403
14	13.9	14.934	15.3	-0.365972	-1.03403
15	15	16.8315	15	1.83153	-1.83153
16	14.1	14.789	14.6875	0.101528	-0.689028
17	13.5	14.8074	14.5458	0.261528	-1.30736
18	15.3	15.4624	14.6208	0.841528	-0.162361
19	14.7	14.8982	14.7292	0.169028	-0.198194
20	12.5	12.8274	14.8333	-2.00597	-0.327361
21	16.1	15.9182	15.0333	0.884861	0.181806
22	15.9	16.2224	15.2875	0.934861	-0.322361
23	15.9	15.3974	15.525	-0.127639	0.502639
24	15.7	14.3315	15.8167	-1.48514	1.36847
25	14.7	15.0432	16.0833	-1.04014	-0.343194
26	15.3	15.9507	16.3167	-0.365972	-0.650694
27	18.4	18.4065	16.575	1.83153	-0.00652778
28	16.8	16.914	16.8125	0.101528	-0.114028
29	16.5	17.2907	17.0292	0.261528	-0.790694
30	19.3	18.0332	17.1917	0.841528	1.26681
31	17.1	17.569	17.4	0.169028	-0.469028
32	15.7	15.7149	17.7208	-2.00597	-0.0148611
33	19.1	18.939	18.0542	0.884861	0.160972
34	18.6	19.2599	18.325	0.934861	-0.659861
35	18.4	18.5057	18.6333	-0.127639	-0.105694
36	17.1	17.3899	18.875	-1.48514	-0.289861
37	18.3	17.9682	19.0083	-1.04014	0.331806
38	19.4	18.809	19.175	-0.365972	0.590972
39	22.3	21.1149	19.2833	1.83153	1.18514
40	19.4	19.4599	19.3583	0.101528	-0.0598611
41	21.3	19.7157	19.4542	0.261528	1.58431
42	20.3	20.3915	19.55	0.841528	-0.0915278
43	19.3	19.794	19.625	0.169028	-0.494028
44	17.5	17.7065	19.7125	-2.00597	-0.206528
45	19.9	20.6599	19.775	0.884861	-0.759861
46	19.6	20.7432	19.8083	0.934861	-1.14319
47	19.7	19.6599	19.7875	-0.127639	0.0401389
48	18.1	18.2607	19.7458	-1.48514	-0.160694
49	19.1	18.7224	19.7625	-1.04014	0.377639
50	20.7	19.434	19.8	-0.365972	1.26597
51	22.5	21.6315	19.8	1.83153	0.868472
52	20	19.9349	19.8333	0.101528	0.0651389
53	20.2	20.1615	19.9	0.261528	0.0384722
54	20.4	20.7332	19.8917	0.841528	-0.333194
55	19.6	20.0565	19.8875	0.169028	-0.456528
56	18.1	17.8357	19.8417	-2.00597	0.264306
57	19.3	20.6265	19.7417	0.884861	-1.32653
58	21	20.6724	19.7375	0.934861	0.327639
59	19.9	19.6724	19.8	-0.127639	0.227639
60	17.7	18.3357	19.8208	-1.48514	-0.635694
61	19.4	18.8515	19.8917	-1.04014	0.548472
62	19.3	19.5715	19.9375	-0.365972	-0.271528
63	21.5	21.8149	19.9833	1.83153	-0.314861
64	20.9	20.2015	20.1	0.101528	0.698472
65	20.8	20.424	20.1625	0.261528	0.375972
66	20.3	21.079	20.2375	0.841528	-0.779028
67	21.4	NA	NA	0.169028	NA
68	17.4	NA	NA	-2.00597	NA
69	21.1	NA	NA	0.884861	NA
70	22	NA	NA	0.934861	NA
71	20.4	NA	NA	-0.127639	NA
72	19	NA	NA	-1.48514	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 18.3 & NA & NA & -1.04014 & NA \tabularnewline
2 & 18.6 & NA & NA & -0.365972 & NA \tabularnewline
3 & 18.7 & NA & NA & 1.83153 & NA \tabularnewline
4 & 20.1 & NA & NA & 0.101528 & NA \tabularnewline
5 & 18.9 & NA & NA & 0.261528 & NA \tabularnewline
6 & 20.1 & NA & NA & 0.841528 & NA \tabularnewline
7 & 19.8 & 18.2815 & 18.1125 & 0.169028 & 1.51847 \tabularnewline
8 & 15.9 & 15.7149 & 17.7208 & -2.00597 & 0.185139 \tabularnewline
9 & 19.9 & 18.2557 & 17.3708 & 0.884861 & 1.64431 \tabularnewline
10 & 19.6 & 17.9015 & 16.9667 & 0.934861 & 1.69847 \tabularnewline
11 & 15.6 & 16.364 & 16.4917 & -0.127639 & -0.764028 \tabularnewline
12 & 14.2 & 14.5815 & 16.0667 & -1.48514 & -0.381528 \tabularnewline
13 & 13.6 & 14.614 & 15.6542 & -1.04014 & -1.01403 \tabularnewline
14 & 13.9 & 14.934 & 15.3 & -0.365972 & -1.03403 \tabularnewline
15 & 15 & 16.8315 & 15 & 1.83153 & -1.83153 \tabularnewline
16 & 14.1 & 14.789 & 14.6875 & 0.101528 & -0.689028 \tabularnewline
17 & 13.5 & 14.8074 & 14.5458 & 0.261528 & -1.30736 \tabularnewline
18 & 15.3 & 15.4624 & 14.6208 & 0.841528 & -0.162361 \tabularnewline
19 & 14.7 & 14.8982 & 14.7292 & 0.169028 & -0.198194 \tabularnewline
20 & 12.5 & 12.8274 & 14.8333 & -2.00597 & -0.327361 \tabularnewline
21 & 16.1 & 15.9182 & 15.0333 & 0.884861 & 0.181806 \tabularnewline
22 & 15.9 & 16.2224 & 15.2875 & 0.934861 & -0.322361 \tabularnewline
23 & 15.9 & 15.3974 & 15.525 & -0.127639 & 0.502639 \tabularnewline
24 & 15.7 & 14.3315 & 15.8167 & -1.48514 & 1.36847 \tabularnewline
25 & 14.7 & 15.0432 & 16.0833 & -1.04014 & -0.343194 \tabularnewline
26 & 15.3 & 15.9507 & 16.3167 & -0.365972 & -0.650694 \tabularnewline
27 & 18.4 & 18.4065 & 16.575 & 1.83153 & -0.00652778 \tabularnewline
28 & 16.8 & 16.914 & 16.8125 & 0.101528 & -0.114028 \tabularnewline
29 & 16.5 & 17.2907 & 17.0292 & 0.261528 & -0.790694 \tabularnewline
30 & 19.3 & 18.0332 & 17.1917 & 0.841528 & 1.26681 \tabularnewline
31 & 17.1 & 17.569 & 17.4 & 0.169028 & -0.469028 \tabularnewline
32 & 15.7 & 15.7149 & 17.7208 & -2.00597 & -0.0148611 \tabularnewline
33 & 19.1 & 18.939 & 18.0542 & 0.884861 & 0.160972 \tabularnewline
34 & 18.6 & 19.2599 & 18.325 & 0.934861 & -0.659861 \tabularnewline
35 & 18.4 & 18.5057 & 18.6333 & -0.127639 & -0.105694 \tabularnewline
36 & 17.1 & 17.3899 & 18.875 & -1.48514 & -0.289861 \tabularnewline
37 & 18.3 & 17.9682 & 19.0083 & -1.04014 & 0.331806 \tabularnewline
38 & 19.4 & 18.809 & 19.175 & -0.365972 & 0.590972 \tabularnewline
39 & 22.3 & 21.1149 & 19.2833 & 1.83153 & 1.18514 \tabularnewline
40 & 19.4 & 19.4599 & 19.3583 & 0.101528 & -0.0598611 \tabularnewline
41 & 21.3 & 19.7157 & 19.4542 & 0.261528 & 1.58431 \tabularnewline
42 & 20.3 & 20.3915 & 19.55 & 0.841528 & -0.0915278 \tabularnewline
43 & 19.3 & 19.794 & 19.625 & 0.169028 & -0.494028 \tabularnewline
44 & 17.5 & 17.7065 & 19.7125 & -2.00597 & -0.206528 \tabularnewline
45 & 19.9 & 20.6599 & 19.775 & 0.884861 & -0.759861 \tabularnewline
46 & 19.6 & 20.7432 & 19.8083 & 0.934861 & -1.14319 \tabularnewline
47 & 19.7 & 19.6599 & 19.7875 & -0.127639 & 0.0401389 \tabularnewline
48 & 18.1 & 18.2607 & 19.7458 & -1.48514 & -0.160694 \tabularnewline
49 & 19.1 & 18.7224 & 19.7625 & -1.04014 & 0.377639 \tabularnewline
50 & 20.7 & 19.434 & 19.8 & -0.365972 & 1.26597 \tabularnewline
51 & 22.5 & 21.6315 & 19.8 & 1.83153 & 0.868472 \tabularnewline
52 & 20 & 19.9349 & 19.8333 & 0.101528 & 0.0651389 \tabularnewline
53 & 20.2 & 20.1615 & 19.9 & 0.261528 & 0.0384722 \tabularnewline
54 & 20.4 & 20.7332 & 19.8917 & 0.841528 & -0.333194 \tabularnewline
55 & 19.6 & 20.0565 & 19.8875 & 0.169028 & -0.456528 \tabularnewline
56 & 18.1 & 17.8357 & 19.8417 & -2.00597 & 0.264306 \tabularnewline
57 & 19.3 & 20.6265 & 19.7417 & 0.884861 & -1.32653 \tabularnewline
58 & 21 & 20.6724 & 19.7375 & 0.934861 & 0.327639 \tabularnewline
59 & 19.9 & 19.6724 & 19.8 & -0.127639 & 0.227639 \tabularnewline
60 & 17.7 & 18.3357 & 19.8208 & -1.48514 & -0.635694 \tabularnewline
61 & 19.4 & 18.8515 & 19.8917 & -1.04014 & 0.548472 \tabularnewline
62 & 19.3 & 19.5715 & 19.9375 & -0.365972 & -0.271528 \tabularnewline
63 & 21.5 & 21.8149 & 19.9833 & 1.83153 & -0.314861 \tabularnewline
64 & 20.9 & 20.2015 & 20.1 & 0.101528 & 0.698472 \tabularnewline
65 & 20.8 & 20.424 & 20.1625 & 0.261528 & 0.375972 \tabularnewline
66 & 20.3 & 21.079 & 20.2375 & 0.841528 & -0.779028 \tabularnewline
67 & 21.4 & NA & NA & 0.169028 & NA \tabularnewline
68 & 17.4 & NA & NA & -2.00597 & NA \tabularnewline
69 & 21.1 & NA & NA & 0.884861 & NA \tabularnewline
70 & 22 & NA & NA & 0.934861 & NA \tabularnewline
71 & 20.4 & NA & NA & -0.127639 & NA \tabularnewline
72 & 19 & NA & NA & -1.48514 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=261548&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]18.3[/C][C]NA[/C][C]NA[/C][C]-1.04014[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]18.6[/C][C]NA[/C][C]NA[/C][C]-0.365972[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]18.7[/C][C]NA[/C][C]NA[/C][C]1.83153[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]20.1[/C][C]NA[/C][C]NA[/C][C]0.101528[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]18.9[/C][C]NA[/C][C]NA[/C][C]0.261528[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]20.1[/C][C]NA[/C][C]NA[/C][C]0.841528[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]19.8[/C][C]18.2815[/C][C]18.1125[/C][C]0.169028[/C][C]1.51847[/C][/ROW]
[ROW][C]8[/C][C]15.9[/C][C]15.7149[/C][C]17.7208[/C][C]-2.00597[/C][C]0.185139[/C][/ROW]
[ROW][C]9[/C][C]19.9[/C][C]18.2557[/C][C]17.3708[/C][C]0.884861[/C][C]1.64431[/C][/ROW]
[ROW][C]10[/C][C]19.6[/C][C]17.9015[/C][C]16.9667[/C][C]0.934861[/C][C]1.69847[/C][/ROW]
[ROW][C]11[/C][C]15.6[/C][C]16.364[/C][C]16.4917[/C][C]-0.127639[/C][C]-0.764028[/C][/ROW]
[ROW][C]12[/C][C]14.2[/C][C]14.5815[/C][C]16.0667[/C][C]-1.48514[/C][C]-0.381528[/C][/ROW]
[ROW][C]13[/C][C]13.6[/C][C]14.614[/C][C]15.6542[/C][C]-1.04014[/C][C]-1.01403[/C][/ROW]
[ROW][C]14[/C][C]13.9[/C][C]14.934[/C][C]15.3[/C][C]-0.365972[/C][C]-1.03403[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]16.8315[/C][C]15[/C][C]1.83153[/C][C]-1.83153[/C][/ROW]
[ROW][C]16[/C][C]14.1[/C][C]14.789[/C][C]14.6875[/C][C]0.101528[/C][C]-0.689028[/C][/ROW]
[ROW][C]17[/C][C]13.5[/C][C]14.8074[/C][C]14.5458[/C][C]0.261528[/C][C]-1.30736[/C][/ROW]
[ROW][C]18[/C][C]15.3[/C][C]15.4624[/C][C]14.6208[/C][C]0.841528[/C][C]-0.162361[/C][/ROW]
[ROW][C]19[/C][C]14.7[/C][C]14.8982[/C][C]14.7292[/C][C]0.169028[/C][C]-0.198194[/C][/ROW]
[ROW][C]20[/C][C]12.5[/C][C]12.8274[/C][C]14.8333[/C][C]-2.00597[/C][C]-0.327361[/C][/ROW]
[ROW][C]21[/C][C]16.1[/C][C]15.9182[/C][C]15.0333[/C][C]0.884861[/C][C]0.181806[/C][/ROW]
[ROW][C]22[/C][C]15.9[/C][C]16.2224[/C][C]15.2875[/C][C]0.934861[/C][C]-0.322361[/C][/ROW]
[ROW][C]23[/C][C]15.9[/C][C]15.3974[/C][C]15.525[/C][C]-0.127639[/C][C]0.502639[/C][/ROW]
[ROW][C]24[/C][C]15.7[/C][C]14.3315[/C][C]15.8167[/C][C]-1.48514[/C][C]1.36847[/C][/ROW]
[ROW][C]25[/C][C]14.7[/C][C]15.0432[/C][C]16.0833[/C][C]-1.04014[/C][C]-0.343194[/C][/ROW]
[ROW][C]26[/C][C]15.3[/C][C]15.9507[/C][C]16.3167[/C][C]-0.365972[/C][C]-0.650694[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]18.4065[/C][C]16.575[/C][C]1.83153[/C][C]-0.00652778[/C][/ROW]
[ROW][C]28[/C][C]16.8[/C][C]16.914[/C][C]16.8125[/C][C]0.101528[/C][C]-0.114028[/C][/ROW]
[ROW][C]29[/C][C]16.5[/C][C]17.2907[/C][C]17.0292[/C][C]0.261528[/C][C]-0.790694[/C][/ROW]
[ROW][C]30[/C][C]19.3[/C][C]18.0332[/C][C]17.1917[/C][C]0.841528[/C][C]1.26681[/C][/ROW]
[ROW][C]31[/C][C]17.1[/C][C]17.569[/C][C]17.4[/C][C]0.169028[/C][C]-0.469028[/C][/ROW]
[ROW][C]32[/C][C]15.7[/C][C]15.7149[/C][C]17.7208[/C][C]-2.00597[/C][C]-0.0148611[/C][/ROW]
[ROW][C]33[/C][C]19.1[/C][C]18.939[/C][C]18.0542[/C][C]0.884861[/C][C]0.160972[/C][/ROW]
[ROW][C]34[/C][C]18.6[/C][C]19.2599[/C][C]18.325[/C][C]0.934861[/C][C]-0.659861[/C][/ROW]
[ROW][C]35[/C][C]18.4[/C][C]18.5057[/C][C]18.6333[/C][C]-0.127639[/C][C]-0.105694[/C][/ROW]
[ROW][C]36[/C][C]17.1[/C][C]17.3899[/C][C]18.875[/C][C]-1.48514[/C][C]-0.289861[/C][/ROW]
[ROW][C]37[/C][C]18.3[/C][C]17.9682[/C][C]19.0083[/C][C]-1.04014[/C][C]0.331806[/C][/ROW]
[ROW][C]38[/C][C]19.4[/C][C]18.809[/C][C]19.175[/C][C]-0.365972[/C][C]0.590972[/C][/ROW]
[ROW][C]39[/C][C]22.3[/C][C]21.1149[/C][C]19.2833[/C][C]1.83153[/C][C]1.18514[/C][/ROW]
[ROW][C]40[/C][C]19.4[/C][C]19.4599[/C][C]19.3583[/C][C]0.101528[/C][C]-0.0598611[/C][/ROW]
[ROW][C]41[/C][C]21.3[/C][C]19.7157[/C][C]19.4542[/C][C]0.261528[/C][C]1.58431[/C][/ROW]
[ROW][C]42[/C][C]20.3[/C][C]20.3915[/C][C]19.55[/C][C]0.841528[/C][C]-0.0915278[/C][/ROW]
[ROW][C]43[/C][C]19.3[/C][C]19.794[/C][C]19.625[/C][C]0.169028[/C][C]-0.494028[/C][/ROW]
[ROW][C]44[/C][C]17.5[/C][C]17.7065[/C][C]19.7125[/C][C]-2.00597[/C][C]-0.206528[/C][/ROW]
[ROW][C]45[/C][C]19.9[/C][C]20.6599[/C][C]19.775[/C][C]0.884861[/C][C]-0.759861[/C][/ROW]
[ROW][C]46[/C][C]19.6[/C][C]20.7432[/C][C]19.8083[/C][C]0.934861[/C][C]-1.14319[/C][/ROW]
[ROW][C]47[/C][C]19.7[/C][C]19.6599[/C][C]19.7875[/C][C]-0.127639[/C][C]0.0401389[/C][/ROW]
[ROW][C]48[/C][C]18.1[/C][C]18.2607[/C][C]19.7458[/C][C]-1.48514[/C][C]-0.160694[/C][/ROW]
[ROW][C]49[/C][C]19.1[/C][C]18.7224[/C][C]19.7625[/C][C]-1.04014[/C][C]0.377639[/C][/ROW]
[ROW][C]50[/C][C]20.7[/C][C]19.434[/C][C]19.8[/C][C]-0.365972[/C][C]1.26597[/C][/ROW]
[ROW][C]51[/C][C]22.5[/C][C]21.6315[/C][C]19.8[/C][C]1.83153[/C][C]0.868472[/C][/ROW]
[ROW][C]52[/C][C]20[/C][C]19.9349[/C][C]19.8333[/C][C]0.101528[/C][C]0.0651389[/C][/ROW]
[ROW][C]53[/C][C]20.2[/C][C]20.1615[/C][C]19.9[/C][C]0.261528[/C][C]0.0384722[/C][/ROW]
[ROW][C]54[/C][C]20.4[/C][C]20.7332[/C][C]19.8917[/C][C]0.841528[/C][C]-0.333194[/C][/ROW]
[ROW][C]55[/C][C]19.6[/C][C]20.0565[/C][C]19.8875[/C][C]0.169028[/C][C]-0.456528[/C][/ROW]
[ROW][C]56[/C][C]18.1[/C][C]17.8357[/C][C]19.8417[/C][C]-2.00597[/C][C]0.264306[/C][/ROW]
[ROW][C]57[/C][C]19.3[/C][C]20.6265[/C][C]19.7417[/C][C]0.884861[/C][C]-1.32653[/C][/ROW]
[ROW][C]58[/C][C]21[/C][C]20.6724[/C][C]19.7375[/C][C]0.934861[/C][C]0.327639[/C][/ROW]
[ROW][C]59[/C][C]19.9[/C][C]19.6724[/C][C]19.8[/C][C]-0.127639[/C][C]0.227639[/C][/ROW]
[ROW][C]60[/C][C]17.7[/C][C]18.3357[/C][C]19.8208[/C][C]-1.48514[/C][C]-0.635694[/C][/ROW]
[ROW][C]61[/C][C]19.4[/C][C]18.8515[/C][C]19.8917[/C][C]-1.04014[/C][C]0.548472[/C][/ROW]
[ROW][C]62[/C][C]19.3[/C][C]19.5715[/C][C]19.9375[/C][C]-0.365972[/C][C]-0.271528[/C][/ROW]
[ROW][C]63[/C][C]21.5[/C][C]21.8149[/C][C]19.9833[/C][C]1.83153[/C][C]-0.314861[/C][/ROW]
[ROW][C]64[/C][C]20.9[/C][C]20.2015[/C][C]20.1[/C][C]0.101528[/C][C]0.698472[/C][/ROW]
[ROW][C]65[/C][C]20.8[/C][C]20.424[/C][C]20.1625[/C][C]0.261528[/C][C]0.375972[/C][/ROW]
[ROW][C]66[/C][C]20.3[/C][C]21.079[/C][C]20.2375[/C][C]0.841528[/C][C]-0.779028[/C][/ROW]
[ROW][C]67[/C][C]21.4[/C][C]NA[/C][C]NA[/C][C]0.169028[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]17.4[/C][C]NA[/C][C]NA[/C][C]-2.00597[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]21.1[/C][C]NA[/C][C]NA[/C][C]0.884861[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]22[/C][C]NA[/C][C]NA[/C][C]0.934861[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]20.4[/C][C]NA[/C][C]NA[/C][C]-0.127639[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]NA[/C][C]NA[/C][C]-1.48514[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=261548&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=261548&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	18.3	NA	NA	-1.04014	NA
2	18.6	NA	NA	-0.365972	NA
3	18.7	NA	NA	1.83153	NA
4	20.1	NA	NA	0.101528	NA
5	18.9	NA	NA	0.261528	NA
6	20.1	NA	NA	0.841528	NA
7	19.8	18.2815	18.1125	0.169028	1.51847
8	15.9	15.7149	17.7208	-2.00597	0.185139
9	19.9	18.2557	17.3708	0.884861	1.64431
10	19.6	17.9015	16.9667	0.934861	1.69847
11	15.6	16.364	16.4917	-0.127639	-0.764028
12	14.2	14.5815	16.0667	-1.48514	-0.381528
13	13.6	14.614	15.6542	-1.04014	-1.01403
14	13.9	14.934	15.3	-0.365972	-1.03403
15	15	16.8315	15	1.83153	-1.83153
16	14.1	14.789	14.6875	0.101528	-0.689028
17	13.5	14.8074	14.5458	0.261528	-1.30736
18	15.3	15.4624	14.6208	0.841528	-0.162361
19	14.7	14.8982	14.7292	0.169028	-0.198194
20	12.5	12.8274	14.8333	-2.00597	-0.327361
21	16.1	15.9182	15.0333	0.884861	0.181806
22	15.9	16.2224	15.2875	0.934861	-0.322361
23	15.9	15.3974	15.525	-0.127639	0.502639
24	15.7	14.3315	15.8167	-1.48514	1.36847
25	14.7	15.0432	16.0833	-1.04014	-0.343194
26	15.3	15.9507	16.3167	-0.365972	-0.650694
27	18.4	18.4065	16.575	1.83153	-0.00652778
28	16.8	16.914	16.8125	0.101528	-0.114028
29	16.5	17.2907	17.0292	0.261528	-0.790694
30	19.3	18.0332	17.1917	0.841528	1.26681
31	17.1	17.569	17.4	0.169028	-0.469028
32	15.7	15.7149	17.7208	-2.00597	-0.0148611
33	19.1	18.939	18.0542	0.884861	0.160972
34	18.6	19.2599	18.325	0.934861	-0.659861
35	18.4	18.5057	18.6333	-0.127639	-0.105694
36	17.1	17.3899	18.875	-1.48514	-0.289861
37	18.3	17.9682	19.0083	-1.04014	0.331806
38	19.4	18.809	19.175	-0.365972	0.590972
39	22.3	21.1149	19.2833	1.83153	1.18514
40	19.4	19.4599	19.3583	0.101528	-0.0598611
41	21.3	19.7157	19.4542	0.261528	1.58431
42	20.3	20.3915	19.55	0.841528	-0.0915278
43	19.3	19.794	19.625	0.169028	-0.494028
44	17.5	17.7065	19.7125	-2.00597	-0.206528
45	19.9	20.6599	19.775	0.884861	-0.759861
46	19.6	20.7432	19.8083	0.934861	-1.14319
47	19.7	19.6599	19.7875	-0.127639	0.0401389
48	18.1	18.2607	19.7458	-1.48514	-0.160694
49	19.1	18.7224	19.7625	-1.04014	0.377639
50	20.7	19.434	19.8	-0.365972	1.26597
51	22.5	21.6315	19.8	1.83153	0.868472
52	20	19.9349	19.8333	0.101528	0.0651389
53	20.2	20.1615	19.9	0.261528	0.0384722
54	20.4	20.7332	19.8917	0.841528	-0.333194
55	19.6	20.0565	19.8875	0.169028	-0.456528
56	18.1	17.8357	19.8417	-2.00597	0.264306
57	19.3	20.6265	19.7417	0.884861	-1.32653
58	21	20.6724	19.7375	0.934861	0.327639
59	19.9	19.6724	19.8	-0.127639	0.227639
60	17.7	18.3357	19.8208	-1.48514	-0.635694
61	19.4	18.8515	19.8917	-1.04014	0.548472
62	19.3	19.5715	19.9375	-0.365972	-0.271528
63	21.5	21.8149	19.9833	1.83153	-0.314861
64	20.9	20.2015	20.1	0.101528	0.698472
65	20.8	20.424	20.1625	0.261528	0.375972
66	20.3	21.079	20.2375	0.841528	-0.779028
67	21.4	NA	NA	0.169028	NA
68	17.4	NA	NA	-2.00597	NA
69	21.1	NA	NA	0.884861	NA
70	22	NA	NA	0.934861	NA
71	20.4	NA	NA	-0.127639	NA
72	19	NA	NA	-1.48514	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 <- '12'
par1 <- 'additive'
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