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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Sun, 18 Dec 2011 05:31:05 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/18/t132420430705a8pp0u3y2ktps.htm/, Retrieved Sun, 05 May 2024 18:39:15 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156670, Retrieved Sun, 05 May 2024 18:39:15 +0000

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User-defined keywords

KDGP2W92

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

-       [Classical Decomposition] [Opgave9Opdr2] [2011-12-18 10:31:05] [d41d8cd98f00b204e9800998ecf8427e] [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	'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=156670&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=156670&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156670&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	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	100.32	NA	NA	-0.0968315972222147	NA
2	100.33	NA	NA	0.184210069444442	NA
3	100.38	NA	NA	0.278793402777781	NA
4	100.35	NA	NA	0.263064236111118	NA
5	100.44	NA	NA	0.197230902777784	NA
6	100.47	NA	NA	-0.0850607638888885	NA
7	100.49	100.386918402778	100.450833333333	-0.0639149305555531	0.103081597222229
8	100.52	100.360043402778	100.486666666667	-0.12662326388889	0.159956597222219
9	100.47	100.408897569444	100.534583333333	-0.125685763888898	0.0611024305555503
10	100.48	100.422126736111	100.586666666667	-0.164539930555561	0.0578732638888795
11	100.48	100.512960069444	100.638333333333	-0.125373263888891	-0.0329600694444423
12	100.53	100.545980902778	100.68125	-0.135269097222229	-0.0159809027777555
13	100.62	100.621501736111	100.718333333333	-0.0968315972222147	-0.00150173611109494
14	100.89	100.940043402778	100.755833333333	0.184210069444442	-0.0500434027777743
15	100.97	101.078793402778	100.8	0.278793402777781	-0.108793402777778
16	101.01	101.113897569444	100.850833333333	0.263064236111118	-0.103897569444442
17	101.02	101.100147569444	100.902916666667	0.197230902777784	-0.0801475694444491
18	100.92	100.871605902778	100.956666666667	-0.0850607638888885	0.0483940972222285
19	100.93	100.949001736111	101.012916666667	-0.0639149305555531	-0.0190017361110932
20	100.98	100.941293402778	101.067916666667	-0.12662326388889	0.0387065972222302
21	101.07	100.995147569444	101.120833333333	-0.125685763888898	0.0748524305555662
22	101.1	101.009626736111	101.174166666667	-0.164539930555561	0.0903732638889068
23	101.11	101.102126736111	101.2275	-0.125373263888891	0.00787326388891074
24	101.19	101.145564236111	101.280833333333	-0.135269097222229	0.0444357638888988
25	101.31	101.244835069444	101.341666666667	-0.0968315972222147	0.0651649305555537
26	101.52	101.595460069444	101.41125	0.184210069444442	-0.0754600694444463
27	101.61	101.763376736111	101.484583333333	0.278793402777781	-0.153376736111099
28	101.65	101.825564236111	101.5625	0.263064236111118	-0.175564236111114
29	101.66	101.839730902778	101.6425	0.197230902777784	-0.179730902777777
30	101.56	101.635355902778	101.720416666667	-0.0850607638888885	-0.0753559027777584
31	101.75	101.740668402778	101.804583333333	-0.0639149305555531	0.00933159722224275
32	101.83	101.782126736111	101.90875	-0.12662326388889	0.0478732638888886
33	101.98	101.904730902778	102.030416666667	-0.125685763888898	0.0752690972222325
34	102.06	101.995460069444	102.16	-0.164539930555561	0.0645399305555685
35	102.07	102.165876736111	102.29125	-0.125373263888891	-0.0958767361111228
36	102.1	102.277647569444	102.412916666667	-0.135269097222229	-0.177647569444431
37	102.42	102.429001736111	102.525833333333	-0.0968315972222147	-0.00900173611108812
38	102.91	102.821293402778	102.637083333333	0.184210069444442	0.0887065972222274
39	103.14	103.023793402778	102.745	0.278793402777781	0.116206597222245
40	103.23	103.113064236111	102.85	0.263064236111118	0.116935763888904
41	103.23	103.150980902778	102.95375	0.197230902777784	0.0790190972222291
42	102.91	102.976605902778	103.061666666667	-0.0850607638888885	-0.0666059027777806
43	103.11	103.100251736111	103.164166666667	-0.0639149305555531	0.00974826388888062
44	103.14	103.128793402778	103.255416666667	-0.12662326388889	0.0112065972222268
45	103.26	103.220147569444	103.345833333333	-0.125685763888898	0.0398524305555554
46	103.3	103.271293402778	103.435833333333	-0.164539930555561	0.0287065972222251
47	103.32	103.398793402778	103.524166666667	-0.125373263888891	-0.0787934027777766
48	103.44	103.477647569444	103.612916666667	-0.135269097222229	-0.0376475694444309
49	103.54	103.596918402778	103.69375	-0.0968315972222147	-0.0569184027777681
50	103.98	103.945460069444	103.76125	0.184210069444442	0.0345399305555532
51	104.24	104.096293402778	103.8175	0.278793402777781	0.14370659722222
52	104.29	104.129730902778	103.866666666667	0.263064236111118	0.160269097222226
53	104.29	104.111397569444	103.914166666667	0.197230902777784	0.178602430555557
54	103.98	103.888689236111	103.97375	-0.0850607638888885	0.0913107638888988
55	103.98	103.982335069444	104.04625	-0.0639149305555531	-0.00233506944444173
56	103.89	103.990043402778	104.116666666667	-0.12662326388889	-0.100043402777771
57	103.86	104.052230902778	104.177916666667	-0.125685763888898	-0.19223090277778
58	103.88	104.065876736111	104.230416666667	-0.164539930555561	-0.185876736111112
59	103.88	NA	NA	-0.125373263888891	NA
60	104.31	NA	NA	-0.135269097222229	NA
61	104.41	NA	NA	NA	NA
62	104.8	NA	NA	NA	NA
63	104.89	NA	NA	NA	NA
64	104.9	NA	NA	NA	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 100.32 & NA & NA & -0.0968315972222147 & NA \tabularnewline
2 & 100.33 & NA & NA & 0.184210069444442 & NA \tabularnewline
3 & 100.38 & NA & NA & 0.278793402777781 & NA \tabularnewline
4 & 100.35 & NA & NA & 0.263064236111118 & NA \tabularnewline
5 & 100.44 & NA & NA & 0.197230902777784 & NA \tabularnewline
6 & 100.47 & NA & NA & -0.0850607638888885 & NA \tabularnewline
7 & 100.49 & 100.386918402778 & 100.450833333333 & -0.0639149305555531 & 0.103081597222229 \tabularnewline
8 & 100.52 & 100.360043402778 & 100.486666666667 & -0.12662326388889 & 0.159956597222219 \tabularnewline
9 & 100.47 & 100.408897569444 & 100.534583333333 & -0.125685763888898 & 0.0611024305555503 \tabularnewline
10 & 100.48 & 100.422126736111 & 100.586666666667 & -0.164539930555561 & 0.0578732638888795 \tabularnewline
11 & 100.48 & 100.512960069444 & 100.638333333333 & -0.125373263888891 & -0.0329600694444423 \tabularnewline
12 & 100.53 & 100.545980902778 & 100.68125 & -0.135269097222229 & -0.0159809027777555 \tabularnewline
13 & 100.62 & 100.621501736111 & 100.718333333333 & -0.0968315972222147 & -0.00150173611109494 \tabularnewline
14 & 100.89 & 100.940043402778 & 100.755833333333 & 0.184210069444442 & -0.0500434027777743 \tabularnewline
15 & 100.97 & 101.078793402778 & 100.8 & 0.278793402777781 & -0.108793402777778 \tabularnewline
16 & 101.01 & 101.113897569444 & 100.850833333333 & 0.263064236111118 & -0.103897569444442 \tabularnewline
17 & 101.02 & 101.100147569444 & 100.902916666667 & 0.197230902777784 & -0.0801475694444491 \tabularnewline
18 & 100.92 & 100.871605902778 & 100.956666666667 & -0.0850607638888885 & 0.0483940972222285 \tabularnewline
19 & 100.93 & 100.949001736111 & 101.012916666667 & -0.0639149305555531 & -0.0190017361110932 \tabularnewline
20 & 100.98 & 100.941293402778 & 101.067916666667 & -0.12662326388889 & 0.0387065972222302 \tabularnewline
21 & 101.07 & 100.995147569444 & 101.120833333333 & -0.125685763888898 & 0.0748524305555662 \tabularnewline
22 & 101.1 & 101.009626736111 & 101.174166666667 & -0.164539930555561 & 0.0903732638889068 \tabularnewline
23 & 101.11 & 101.102126736111 & 101.2275 & -0.125373263888891 & 0.00787326388891074 \tabularnewline
24 & 101.19 & 101.145564236111 & 101.280833333333 & -0.135269097222229 & 0.0444357638888988 \tabularnewline
25 & 101.31 & 101.244835069444 & 101.341666666667 & -0.0968315972222147 & 0.0651649305555537 \tabularnewline
26 & 101.52 & 101.595460069444 & 101.41125 & 0.184210069444442 & -0.0754600694444463 \tabularnewline
27 & 101.61 & 101.763376736111 & 101.484583333333 & 0.278793402777781 & -0.153376736111099 \tabularnewline
28 & 101.65 & 101.825564236111 & 101.5625 & 0.263064236111118 & -0.175564236111114 \tabularnewline
29 & 101.66 & 101.839730902778 & 101.6425 & 0.197230902777784 & -0.179730902777777 \tabularnewline
30 & 101.56 & 101.635355902778 & 101.720416666667 & -0.0850607638888885 & -0.0753559027777584 \tabularnewline
31 & 101.75 & 101.740668402778 & 101.804583333333 & -0.0639149305555531 & 0.00933159722224275 \tabularnewline
32 & 101.83 & 101.782126736111 & 101.90875 & -0.12662326388889 & 0.0478732638888886 \tabularnewline
33 & 101.98 & 101.904730902778 & 102.030416666667 & -0.125685763888898 & 0.0752690972222325 \tabularnewline
34 & 102.06 & 101.995460069444 & 102.16 & -0.164539930555561 & 0.0645399305555685 \tabularnewline
35 & 102.07 & 102.165876736111 & 102.29125 & -0.125373263888891 & -0.0958767361111228 \tabularnewline
36 & 102.1 & 102.277647569444 & 102.412916666667 & -0.135269097222229 & -0.177647569444431 \tabularnewline
37 & 102.42 & 102.429001736111 & 102.525833333333 & -0.0968315972222147 & -0.00900173611108812 \tabularnewline
38 & 102.91 & 102.821293402778 & 102.637083333333 & 0.184210069444442 & 0.0887065972222274 \tabularnewline
39 & 103.14 & 103.023793402778 & 102.745 & 0.278793402777781 & 0.116206597222245 \tabularnewline
40 & 103.23 & 103.113064236111 & 102.85 & 0.263064236111118 & 0.116935763888904 \tabularnewline
41 & 103.23 & 103.150980902778 & 102.95375 & 0.197230902777784 & 0.0790190972222291 \tabularnewline
42 & 102.91 & 102.976605902778 & 103.061666666667 & -0.0850607638888885 & -0.0666059027777806 \tabularnewline
43 & 103.11 & 103.100251736111 & 103.164166666667 & -0.0639149305555531 & 0.00974826388888062 \tabularnewline
44 & 103.14 & 103.128793402778 & 103.255416666667 & -0.12662326388889 & 0.0112065972222268 \tabularnewline
45 & 103.26 & 103.220147569444 & 103.345833333333 & -0.125685763888898 & 0.0398524305555554 \tabularnewline
46 & 103.3 & 103.271293402778 & 103.435833333333 & -0.164539930555561 & 0.0287065972222251 \tabularnewline
47 & 103.32 & 103.398793402778 & 103.524166666667 & -0.125373263888891 & -0.0787934027777766 \tabularnewline
48 & 103.44 & 103.477647569444 & 103.612916666667 & -0.135269097222229 & -0.0376475694444309 \tabularnewline
49 & 103.54 & 103.596918402778 & 103.69375 & -0.0968315972222147 & -0.0569184027777681 \tabularnewline
50 & 103.98 & 103.945460069444 & 103.76125 & 0.184210069444442 & 0.0345399305555532 \tabularnewline
51 & 104.24 & 104.096293402778 & 103.8175 & 0.278793402777781 & 0.14370659722222 \tabularnewline
52 & 104.29 & 104.129730902778 & 103.866666666667 & 0.263064236111118 & 0.160269097222226 \tabularnewline
53 & 104.29 & 104.111397569444 & 103.914166666667 & 0.197230902777784 & 0.178602430555557 \tabularnewline
54 & 103.98 & 103.888689236111 & 103.97375 & -0.0850607638888885 & 0.0913107638888988 \tabularnewline
55 & 103.98 & 103.982335069444 & 104.04625 & -0.0639149305555531 & -0.00233506944444173 \tabularnewline
56 & 103.89 & 103.990043402778 & 104.116666666667 & -0.12662326388889 & -0.100043402777771 \tabularnewline
57 & 103.86 & 104.052230902778 & 104.177916666667 & -0.125685763888898 & -0.19223090277778 \tabularnewline
58 & 103.88 & 104.065876736111 & 104.230416666667 & -0.164539930555561 & -0.185876736111112 \tabularnewline
59 & 103.88 & NA & NA & -0.125373263888891 & NA \tabularnewline
60 & 104.31 & NA & NA & -0.135269097222229 & NA \tabularnewline
61 & 104.41 & NA & NA & NA & NA \tabularnewline
62 & 104.8 & NA & NA & NA & NA \tabularnewline
63 & 104.89 & NA & NA & NA & NA \tabularnewline
64 & 104.9 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156670&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]100.32[/C][C]NA[/C][C]NA[/C][C]-0.0968315972222147[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]100.33[/C][C]NA[/C][C]NA[/C][C]0.184210069444442[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]100.38[/C][C]NA[/C][C]NA[/C][C]0.278793402777781[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]100.35[/C][C]NA[/C][C]NA[/C][C]0.263064236111118[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]100.44[/C][C]NA[/C][C]NA[/C][C]0.197230902777784[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]100.47[/C][C]NA[/C][C]NA[/C][C]-0.0850607638888885[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]100.49[/C][C]100.386918402778[/C][C]100.450833333333[/C][C]-0.0639149305555531[/C][C]0.103081597222229[/C][/ROW]
[ROW][C]8[/C][C]100.52[/C][C]100.360043402778[/C][C]100.486666666667[/C][C]-0.12662326388889[/C][C]0.159956597222219[/C][/ROW]
[ROW][C]9[/C][C]100.47[/C][C]100.408897569444[/C][C]100.534583333333[/C][C]-0.125685763888898[/C][C]0.0611024305555503[/C][/ROW]
[ROW][C]10[/C][C]100.48[/C][C]100.422126736111[/C][C]100.586666666667[/C][C]-0.164539930555561[/C][C]0.0578732638888795[/C][/ROW]
[ROW][C]11[/C][C]100.48[/C][C]100.512960069444[/C][C]100.638333333333[/C][C]-0.125373263888891[/C][C]-0.0329600694444423[/C][/ROW]
[ROW][C]12[/C][C]100.53[/C][C]100.545980902778[/C][C]100.68125[/C][C]-0.135269097222229[/C][C]-0.0159809027777555[/C][/ROW]
[ROW][C]13[/C][C]100.62[/C][C]100.621501736111[/C][C]100.718333333333[/C][C]-0.0968315972222147[/C][C]-0.00150173611109494[/C][/ROW]
[ROW][C]14[/C][C]100.89[/C][C]100.940043402778[/C][C]100.755833333333[/C][C]0.184210069444442[/C][C]-0.0500434027777743[/C][/ROW]
[ROW][C]15[/C][C]100.97[/C][C]101.078793402778[/C][C]100.8[/C][C]0.278793402777781[/C][C]-0.108793402777778[/C][/ROW]
[ROW][C]16[/C][C]101.01[/C][C]101.113897569444[/C][C]100.850833333333[/C][C]0.263064236111118[/C][C]-0.103897569444442[/C][/ROW]
[ROW][C]17[/C][C]101.02[/C][C]101.100147569444[/C][C]100.902916666667[/C][C]0.197230902777784[/C][C]-0.0801475694444491[/C][/ROW]
[ROW][C]18[/C][C]100.92[/C][C]100.871605902778[/C][C]100.956666666667[/C][C]-0.0850607638888885[/C][C]0.0483940972222285[/C][/ROW]
[ROW][C]19[/C][C]100.93[/C][C]100.949001736111[/C][C]101.012916666667[/C][C]-0.0639149305555531[/C][C]-0.0190017361110932[/C][/ROW]
[ROW][C]20[/C][C]100.98[/C][C]100.941293402778[/C][C]101.067916666667[/C][C]-0.12662326388889[/C][C]0.0387065972222302[/C][/ROW]
[ROW][C]21[/C][C]101.07[/C][C]100.995147569444[/C][C]101.120833333333[/C][C]-0.125685763888898[/C][C]0.0748524305555662[/C][/ROW]
[ROW][C]22[/C][C]101.1[/C][C]101.009626736111[/C][C]101.174166666667[/C][C]-0.164539930555561[/C][C]0.0903732638889068[/C][/ROW]
[ROW][C]23[/C][C]101.11[/C][C]101.102126736111[/C][C]101.2275[/C][C]-0.125373263888891[/C][C]0.00787326388891074[/C][/ROW]
[ROW][C]24[/C][C]101.19[/C][C]101.145564236111[/C][C]101.280833333333[/C][C]-0.135269097222229[/C][C]0.0444357638888988[/C][/ROW]
[ROW][C]25[/C][C]101.31[/C][C]101.244835069444[/C][C]101.341666666667[/C][C]-0.0968315972222147[/C][C]0.0651649305555537[/C][/ROW]
[ROW][C]26[/C][C]101.52[/C][C]101.595460069444[/C][C]101.41125[/C][C]0.184210069444442[/C][C]-0.0754600694444463[/C][/ROW]
[ROW][C]27[/C][C]101.61[/C][C]101.763376736111[/C][C]101.484583333333[/C][C]0.278793402777781[/C][C]-0.153376736111099[/C][/ROW]
[ROW][C]28[/C][C]101.65[/C][C]101.825564236111[/C][C]101.5625[/C][C]0.263064236111118[/C][C]-0.175564236111114[/C][/ROW]
[ROW][C]29[/C][C]101.66[/C][C]101.839730902778[/C][C]101.6425[/C][C]0.197230902777784[/C][C]-0.179730902777777[/C][/ROW]
[ROW][C]30[/C][C]101.56[/C][C]101.635355902778[/C][C]101.720416666667[/C][C]-0.0850607638888885[/C][C]-0.0753559027777584[/C][/ROW]
[ROW][C]31[/C][C]101.75[/C][C]101.740668402778[/C][C]101.804583333333[/C][C]-0.0639149305555531[/C][C]0.00933159722224275[/C][/ROW]
[ROW][C]32[/C][C]101.83[/C][C]101.782126736111[/C][C]101.90875[/C][C]-0.12662326388889[/C][C]0.0478732638888886[/C][/ROW]
[ROW][C]33[/C][C]101.98[/C][C]101.904730902778[/C][C]102.030416666667[/C][C]-0.125685763888898[/C][C]0.0752690972222325[/C][/ROW]
[ROW][C]34[/C][C]102.06[/C][C]101.995460069444[/C][C]102.16[/C][C]-0.164539930555561[/C][C]0.0645399305555685[/C][/ROW]
[ROW][C]35[/C][C]102.07[/C][C]102.165876736111[/C][C]102.29125[/C][C]-0.125373263888891[/C][C]-0.0958767361111228[/C][/ROW]
[ROW][C]36[/C][C]102.1[/C][C]102.277647569444[/C][C]102.412916666667[/C][C]-0.135269097222229[/C][C]-0.177647569444431[/C][/ROW]
[ROW][C]37[/C][C]102.42[/C][C]102.429001736111[/C][C]102.525833333333[/C][C]-0.0968315972222147[/C][C]-0.00900173611108812[/C][/ROW]
[ROW][C]38[/C][C]102.91[/C][C]102.821293402778[/C][C]102.637083333333[/C][C]0.184210069444442[/C][C]0.0887065972222274[/C][/ROW]
[ROW][C]39[/C][C]103.14[/C][C]103.023793402778[/C][C]102.745[/C][C]0.278793402777781[/C][C]0.116206597222245[/C][/ROW]
[ROW][C]40[/C][C]103.23[/C][C]103.113064236111[/C][C]102.85[/C][C]0.263064236111118[/C][C]0.116935763888904[/C][/ROW]
[ROW][C]41[/C][C]103.23[/C][C]103.150980902778[/C][C]102.95375[/C][C]0.197230902777784[/C][C]0.0790190972222291[/C][/ROW]
[ROW][C]42[/C][C]102.91[/C][C]102.976605902778[/C][C]103.061666666667[/C][C]-0.0850607638888885[/C][C]-0.0666059027777806[/C][/ROW]
[ROW][C]43[/C][C]103.11[/C][C]103.100251736111[/C][C]103.164166666667[/C][C]-0.0639149305555531[/C][C]0.00974826388888062[/C][/ROW]
[ROW][C]44[/C][C]103.14[/C][C]103.128793402778[/C][C]103.255416666667[/C][C]-0.12662326388889[/C][C]0.0112065972222268[/C][/ROW]
[ROW][C]45[/C][C]103.26[/C][C]103.220147569444[/C][C]103.345833333333[/C][C]-0.125685763888898[/C][C]0.0398524305555554[/C][/ROW]
[ROW][C]46[/C][C]103.3[/C][C]103.271293402778[/C][C]103.435833333333[/C][C]-0.164539930555561[/C][C]0.0287065972222251[/C][/ROW]
[ROW][C]47[/C][C]103.32[/C][C]103.398793402778[/C][C]103.524166666667[/C][C]-0.125373263888891[/C][C]-0.0787934027777766[/C][/ROW]
[ROW][C]48[/C][C]103.44[/C][C]103.477647569444[/C][C]103.612916666667[/C][C]-0.135269097222229[/C][C]-0.0376475694444309[/C][/ROW]
[ROW][C]49[/C][C]103.54[/C][C]103.596918402778[/C][C]103.69375[/C][C]-0.0968315972222147[/C][C]-0.0569184027777681[/C][/ROW]
[ROW][C]50[/C][C]103.98[/C][C]103.945460069444[/C][C]103.76125[/C][C]0.184210069444442[/C][C]0.0345399305555532[/C][/ROW]
[ROW][C]51[/C][C]104.24[/C][C]104.096293402778[/C][C]103.8175[/C][C]0.278793402777781[/C][C]0.14370659722222[/C][/ROW]
[ROW][C]52[/C][C]104.29[/C][C]104.129730902778[/C][C]103.866666666667[/C][C]0.263064236111118[/C][C]0.160269097222226[/C][/ROW]
[ROW][C]53[/C][C]104.29[/C][C]104.111397569444[/C][C]103.914166666667[/C][C]0.197230902777784[/C][C]0.178602430555557[/C][/ROW]
[ROW][C]54[/C][C]103.98[/C][C]103.888689236111[/C][C]103.97375[/C][C]-0.0850607638888885[/C][C]0.0913107638888988[/C][/ROW]
[ROW][C]55[/C][C]103.98[/C][C]103.982335069444[/C][C]104.04625[/C][C]-0.0639149305555531[/C][C]-0.00233506944444173[/C][/ROW]
[ROW][C]56[/C][C]103.89[/C][C]103.990043402778[/C][C]104.116666666667[/C][C]-0.12662326388889[/C][C]-0.100043402777771[/C][/ROW]
[ROW][C]57[/C][C]103.86[/C][C]104.052230902778[/C][C]104.177916666667[/C][C]-0.125685763888898[/C][C]-0.19223090277778[/C][/ROW]
[ROW][C]58[/C][C]103.88[/C][C]104.065876736111[/C][C]104.230416666667[/C][C]-0.164539930555561[/C][C]-0.185876736111112[/C][/ROW]
[ROW][C]59[/C][C]103.88[/C][C]NA[/C][C]NA[/C][C]-0.125373263888891[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]104.31[/C][C]NA[/C][C]NA[/C][C]-0.135269097222229[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]104.41[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]62[/C][C]104.8[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]63[/C][C]104.89[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]64[/C][C]104.9[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156670&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156670&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	100.32	NA	NA	-0.0968315972222147	NA
2	100.33	NA	NA	0.184210069444442	NA
3	100.38	NA	NA	0.278793402777781	NA
4	100.35	NA	NA	0.263064236111118	NA
5	100.44	NA	NA	0.197230902777784	NA
6	100.47	NA	NA	-0.0850607638888885	NA
7	100.49	100.386918402778	100.450833333333	-0.0639149305555531	0.103081597222229
8	100.52	100.360043402778	100.486666666667	-0.12662326388889	0.159956597222219
9	100.47	100.408897569444	100.534583333333	-0.125685763888898	0.0611024305555503
10	100.48	100.422126736111	100.586666666667	-0.164539930555561	0.0578732638888795
11	100.48	100.512960069444	100.638333333333	-0.125373263888891	-0.0329600694444423
12	100.53	100.545980902778	100.68125	-0.135269097222229	-0.0159809027777555
13	100.62	100.621501736111	100.718333333333	-0.0968315972222147	-0.00150173611109494
14	100.89	100.940043402778	100.755833333333	0.184210069444442	-0.0500434027777743
15	100.97	101.078793402778	100.8	0.278793402777781	-0.108793402777778
16	101.01	101.113897569444	100.850833333333	0.263064236111118	-0.103897569444442
17	101.02	101.100147569444	100.902916666667	0.197230902777784	-0.0801475694444491
18	100.92	100.871605902778	100.956666666667	-0.0850607638888885	0.0483940972222285
19	100.93	100.949001736111	101.012916666667	-0.0639149305555531	-0.0190017361110932
20	100.98	100.941293402778	101.067916666667	-0.12662326388889	0.0387065972222302
21	101.07	100.995147569444	101.120833333333	-0.125685763888898	0.0748524305555662
22	101.1	101.009626736111	101.174166666667	-0.164539930555561	0.0903732638889068
23	101.11	101.102126736111	101.2275	-0.125373263888891	0.00787326388891074
24	101.19	101.145564236111	101.280833333333	-0.135269097222229	0.0444357638888988
25	101.31	101.244835069444	101.341666666667	-0.0968315972222147	0.0651649305555537
26	101.52	101.595460069444	101.41125	0.184210069444442	-0.0754600694444463
27	101.61	101.763376736111	101.484583333333	0.278793402777781	-0.153376736111099
28	101.65	101.825564236111	101.5625	0.263064236111118	-0.175564236111114
29	101.66	101.839730902778	101.6425	0.197230902777784	-0.179730902777777
30	101.56	101.635355902778	101.720416666667	-0.0850607638888885	-0.0753559027777584
31	101.75	101.740668402778	101.804583333333	-0.0639149305555531	0.00933159722224275
32	101.83	101.782126736111	101.90875	-0.12662326388889	0.0478732638888886
33	101.98	101.904730902778	102.030416666667	-0.125685763888898	0.0752690972222325
34	102.06	101.995460069444	102.16	-0.164539930555561	0.0645399305555685
35	102.07	102.165876736111	102.29125	-0.125373263888891	-0.0958767361111228
36	102.1	102.277647569444	102.412916666667	-0.135269097222229	-0.177647569444431
37	102.42	102.429001736111	102.525833333333	-0.0968315972222147	-0.00900173611108812
38	102.91	102.821293402778	102.637083333333	0.184210069444442	0.0887065972222274
39	103.14	103.023793402778	102.745	0.278793402777781	0.116206597222245
40	103.23	103.113064236111	102.85	0.263064236111118	0.116935763888904
41	103.23	103.150980902778	102.95375	0.197230902777784	0.0790190972222291
42	102.91	102.976605902778	103.061666666667	-0.0850607638888885	-0.0666059027777806
43	103.11	103.100251736111	103.164166666667	-0.0639149305555531	0.00974826388888062
44	103.14	103.128793402778	103.255416666667	-0.12662326388889	0.0112065972222268
45	103.26	103.220147569444	103.345833333333	-0.125685763888898	0.0398524305555554
46	103.3	103.271293402778	103.435833333333	-0.164539930555561	0.0287065972222251
47	103.32	103.398793402778	103.524166666667	-0.125373263888891	-0.0787934027777766
48	103.44	103.477647569444	103.612916666667	-0.135269097222229	-0.0376475694444309
49	103.54	103.596918402778	103.69375	-0.0968315972222147	-0.0569184027777681
50	103.98	103.945460069444	103.76125	0.184210069444442	0.0345399305555532
51	104.24	104.096293402778	103.8175	0.278793402777781	0.14370659722222
52	104.29	104.129730902778	103.866666666667	0.263064236111118	0.160269097222226
53	104.29	104.111397569444	103.914166666667	0.197230902777784	0.178602430555557
54	103.98	103.888689236111	103.97375	-0.0850607638888885	0.0913107638888988
55	103.98	103.982335069444	104.04625	-0.0639149305555531	-0.00233506944444173
56	103.89	103.990043402778	104.116666666667	-0.12662326388889	-0.100043402777771
57	103.86	104.052230902778	104.177916666667	-0.125685763888898	-0.19223090277778
58	103.88	104.065876736111	104.230416666667	-0.164539930555561	-0.185876736111112
59	103.88	NA	NA	-0.125373263888891	NA
60	104.31	NA	NA	-0.135269097222229	NA
61	104.41	NA	NA	NA	NA
62	104.8	NA	NA	NA	NA
63	104.89	NA	NA	NA	NA
64	104.9	NA	NA	NA	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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
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