Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Wed, 27 Apr 2016 22:04:59 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Apr/27/t1461791114qq0bnwc7ob9vrvb.htm/, Retrieved Fri, 03 May 2024 07:21:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=295001, Retrieved Fri, 03 May 2024 07:21:04 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

175

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

-       [Classical Decomposition] [] [2016-04-27 21:04:59] [aeeb828bd20a6dc3c22e186b82add773] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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=295001&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=295001&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295001&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	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	1.1216	NA	NA	0.0425579	NA
2	1.0569	NA	NA	0.0644454	NA
3	1.0486	NA	NA	0.0305466	NA
4	1.038	NA	NA	-0.0449606	NA
5	0.9865	NA	NA	-0.0452231	NA
6	0.9332	NA	NA	-0.0168627	NA
7	0.9052	0.928739	0.951475	-0.0227363	-0.0235387
8	0.8683	0.935251	0.935512	-0.000261267	-0.0669512
9	0.9232	0.93515	0.927058	0.00809207	-0.0119504
10	0.8725	0.889809	0.92275	-0.0329413	-0.0173087
11	0.8903	0.9087	0.92475	-0.0160496	-0.0184004
12	0.8959	0.970251	0.936858	0.0333929	-0.0743512
13	0.8766	0.997066	0.954508	0.0425579	-0.120466
14	0.9188	1.04147	0.977021	0.0644454	-0.122666
15	0.9838	1.03453	1.00399	0.0305466	-0.0507341
16	0.9994	0.986469	1.03143	-0.0449606	0.0129315
17	1.0731	1.01386	1.05909	-0.0452231	0.0592356
18	1.1372	1.07279	1.08965	-0.0168627	0.0644127
19	1.1248	1.10177	1.1245	-0.0227363	0.0230321
20	1.189	1.15655	1.15681	-0.000261267	0.0324529
21	1.2497	1.18893	1.18084	0.00809207	0.0607704
22	1.2046	1.16561	1.19855	-0.0329413	0.0389913
23	1.222	1.19576	1.21181	-0.0160496	0.0262413
24	1.2977	1.25563	1.22223	0.0333929	0.0420738
25	1.3113	1.27606	1.2335	0.0425579	0.0352379
26	1.2594	1.30833	1.24389	0.0644454	-0.0489329
27	1.2199	1.28113	1.25058	0.0305466	-0.0612258
28	1.1884	1.21414	1.2591	-0.0449606	-0.0257352
29	1.2023	1.22618	1.2714	-0.0452231	-0.0238769
30	1.2582	1.26715	1.28401	-0.0168627	-0.00894977
31	1.2743	1.27533	1.29806	-0.0227363	-0.00102623
32	1.2887	1.31818	1.31844	-0.000261267	-0.0294804
33	1.3106	1.35103	1.34294	0.00809207	-0.0404296
34	1.3481	1.32728	1.36022	-0.0329413	0.0208246
35	1.3738	1.35376	1.36981	-0.0160496	0.0200413
36	1.4486	1.41177	1.37838	0.0333929	0.0368321
37	1.4976	1.43181	1.38925	0.0425579	0.0657921
38	1.5622	1.46808	1.40363	0.0644454	0.0941213
39	1.505	1.44508	1.41453	0.0305466	0.0599242
40	1.318	1.36936	1.41432	-0.0449606	-0.0513602
41	1.3029	1.36243	1.40765	-0.0452231	-0.0595269
42	1.3632	1.38357	1.40044	-0.0168627	-0.0203748
43	1.4303	1.36854	1.39128	-0.0227363	0.0617613
44	1.4779	1.38048	1.38075	-0.000261267	0.0974154
45	1.3829	1.37986	1.37177	0.00809207	0.0030371
46	1.2708	1.33624	1.36918	-0.0329413	-0.0654421
47	1.291	1.35472	1.37077	-0.0160496	-0.0637212
48	1.3583	1.40108	1.36769	0.0333929	-0.0427846
49	1.368	1.39934	1.35678	0.0425579	-0.0313371
50	1.4391	1.40617	1.34172	0.0644454	0.0329296
51	1.4127	1.36213	1.33158	0.0305466	0.0505742
52	1.3482	1.2855	1.33046	-0.0449606	0.0627023
53	1.3108	1.28809	1.33332	-0.0452231	0.0227065
54	1.2814	1.31795	1.33481	-0.0168627	-0.0365498
55	1.2502	1.31226	1.33499	-0.0227363	-0.0620554
56	1.2967	1.33196	1.33222	-0.000261267	-0.0352637
57	1.3206	1.33385	1.32576	0.00809207	-0.0132546
58	1.3062	1.28509	1.31803	-0.0329413	0.0211079
59	1.3242	1.29019	1.30624	-0.0160496	0.0340121
60	1.361	1.3246	1.2912	0.0333929	0.0364029
61	1.3696	1.32065	1.2781	0.0425579	0.0489463
62	1.3711	1.32838	1.26393	0.0644454	0.0427213
63	1.3256	NA	NA	0.0305466	NA
64	1.2498	NA	NA	-0.0449606	NA
65	1.1261	NA	NA	-0.0452231	NA
66	1.1053	NA	NA	-0.0168627	NA
67	1.1117	NA	NA	-0.0227363	NA
68	1.0953	NA	NA	-0.000261267	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 1.1216 & NA & NA & 0.0425579 & NA \tabularnewline
2 & 1.0569 & NA & NA & 0.0644454 & NA \tabularnewline
3 & 1.0486 & NA & NA & 0.0305466 & NA \tabularnewline
4 & 1.038 & NA & NA & -0.0449606 & NA \tabularnewline
5 & 0.9865 & NA & NA & -0.0452231 & NA \tabularnewline
6 & 0.9332 & NA & NA & -0.0168627 & NA \tabularnewline
7 & 0.9052 & 0.928739 & 0.951475 & -0.0227363 & -0.0235387 \tabularnewline
8 & 0.8683 & 0.935251 & 0.935512 & -0.000261267 & -0.0669512 \tabularnewline
9 & 0.9232 & 0.93515 & 0.927058 & 0.00809207 & -0.0119504 \tabularnewline
10 & 0.8725 & 0.889809 & 0.92275 & -0.0329413 & -0.0173087 \tabularnewline
11 & 0.8903 & 0.9087 & 0.92475 & -0.0160496 & -0.0184004 \tabularnewline
12 & 0.8959 & 0.970251 & 0.936858 & 0.0333929 & -0.0743512 \tabularnewline
13 & 0.8766 & 0.997066 & 0.954508 & 0.0425579 & -0.120466 \tabularnewline
14 & 0.9188 & 1.04147 & 0.977021 & 0.0644454 & -0.122666 \tabularnewline
15 & 0.9838 & 1.03453 & 1.00399 & 0.0305466 & -0.0507341 \tabularnewline
16 & 0.9994 & 0.986469 & 1.03143 & -0.0449606 & 0.0129315 \tabularnewline
17 & 1.0731 & 1.01386 & 1.05909 & -0.0452231 & 0.0592356 \tabularnewline
18 & 1.1372 & 1.07279 & 1.08965 & -0.0168627 & 0.0644127 \tabularnewline
19 & 1.1248 & 1.10177 & 1.1245 & -0.0227363 & 0.0230321 \tabularnewline
20 & 1.189 & 1.15655 & 1.15681 & -0.000261267 & 0.0324529 \tabularnewline
21 & 1.2497 & 1.18893 & 1.18084 & 0.00809207 & 0.0607704 \tabularnewline
22 & 1.2046 & 1.16561 & 1.19855 & -0.0329413 & 0.0389913 \tabularnewline
23 & 1.222 & 1.19576 & 1.21181 & -0.0160496 & 0.0262413 \tabularnewline
24 & 1.2977 & 1.25563 & 1.22223 & 0.0333929 & 0.0420738 \tabularnewline
25 & 1.3113 & 1.27606 & 1.2335 & 0.0425579 & 0.0352379 \tabularnewline
26 & 1.2594 & 1.30833 & 1.24389 & 0.0644454 & -0.0489329 \tabularnewline
27 & 1.2199 & 1.28113 & 1.25058 & 0.0305466 & -0.0612258 \tabularnewline
28 & 1.1884 & 1.21414 & 1.2591 & -0.0449606 & -0.0257352 \tabularnewline
29 & 1.2023 & 1.22618 & 1.2714 & -0.0452231 & -0.0238769 \tabularnewline
30 & 1.2582 & 1.26715 & 1.28401 & -0.0168627 & -0.00894977 \tabularnewline
31 & 1.2743 & 1.27533 & 1.29806 & -0.0227363 & -0.00102623 \tabularnewline
32 & 1.2887 & 1.31818 & 1.31844 & -0.000261267 & -0.0294804 \tabularnewline
33 & 1.3106 & 1.35103 & 1.34294 & 0.00809207 & -0.0404296 \tabularnewline
34 & 1.3481 & 1.32728 & 1.36022 & -0.0329413 & 0.0208246 \tabularnewline
35 & 1.3738 & 1.35376 & 1.36981 & -0.0160496 & 0.0200413 \tabularnewline
36 & 1.4486 & 1.41177 & 1.37838 & 0.0333929 & 0.0368321 \tabularnewline
37 & 1.4976 & 1.43181 & 1.38925 & 0.0425579 & 0.0657921 \tabularnewline
38 & 1.5622 & 1.46808 & 1.40363 & 0.0644454 & 0.0941213 \tabularnewline
39 & 1.505 & 1.44508 & 1.41453 & 0.0305466 & 0.0599242 \tabularnewline
40 & 1.318 & 1.36936 & 1.41432 & -0.0449606 & -0.0513602 \tabularnewline
41 & 1.3029 & 1.36243 & 1.40765 & -0.0452231 & -0.0595269 \tabularnewline
42 & 1.3632 & 1.38357 & 1.40044 & -0.0168627 & -0.0203748 \tabularnewline
43 & 1.4303 & 1.36854 & 1.39128 & -0.0227363 & 0.0617613 \tabularnewline
44 & 1.4779 & 1.38048 & 1.38075 & -0.000261267 & 0.0974154 \tabularnewline
45 & 1.3829 & 1.37986 & 1.37177 & 0.00809207 & 0.0030371 \tabularnewline
46 & 1.2708 & 1.33624 & 1.36918 & -0.0329413 & -0.0654421 \tabularnewline
47 & 1.291 & 1.35472 & 1.37077 & -0.0160496 & -0.0637212 \tabularnewline
48 & 1.3583 & 1.40108 & 1.36769 & 0.0333929 & -0.0427846 \tabularnewline
49 & 1.368 & 1.39934 & 1.35678 & 0.0425579 & -0.0313371 \tabularnewline
50 & 1.4391 & 1.40617 & 1.34172 & 0.0644454 & 0.0329296 \tabularnewline
51 & 1.4127 & 1.36213 & 1.33158 & 0.0305466 & 0.0505742 \tabularnewline
52 & 1.3482 & 1.2855 & 1.33046 & -0.0449606 & 0.0627023 \tabularnewline
53 & 1.3108 & 1.28809 & 1.33332 & -0.0452231 & 0.0227065 \tabularnewline
54 & 1.2814 & 1.31795 & 1.33481 & -0.0168627 & -0.0365498 \tabularnewline
55 & 1.2502 & 1.31226 & 1.33499 & -0.0227363 & -0.0620554 \tabularnewline
56 & 1.2967 & 1.33196 & 1.33222 & -0.000261267 & -0.0352637 \tabularnewline
57 & 1.3206 & 1.33385 & 1.32576 & 0.00809207 & -0.0132546 \tabularnewline
58 & 1.3062 & 1.28509 & 1.31803 & -0.0329413 & 0.0211079 \tabularnewline
59 & 1.3242 & 1.29019 & 1.30624 & -0.0160496 & 0.0340121 \tabularnewline
60 & 1.361 & 1.3246 & 1.2912 & 0.0333929 & 0.0364029 \tabularnewline
61 & 1.3696 & 1.32065 & 1.2781 & 0.0425579 & 0.0489463 \tabularnewline
62 & 1.3711 & 1.32838 & 1.26393 & 0.0644454 & 0.0427213 \tabularnewline
63 & 1.3256 & NA & NA & 0.0305466 & NA \tabularnewline
64 & 1.2498 & NA & NA & -0.0449606 & NA \tabularnewline
65 & 1.1261 & NA & NA & -0.0452231 & NA \tabularnewline
66 & 1.1053 & NA & NA & -0.0168627 & NA \tabularnewline
67 & 1.1117 & NA & NA & -0.0227363 & NA \tabularnewline
68 & 1.0953 & NA & NA & -0.000261267 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=295001&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]1.1216[/C][C]NA[/C][C]NA[/C][C]0.0425579[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]1.0569[/C][C]NA[/C][C]NA[/C][C]0.0644454[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]1.0486[/C][C]NA[/C][C]NA[/C][C]0.0305466[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]1.038[/C][C]NA[/C][C]NA[/C][C]-0.0449606[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]0.9865[/C][C]NA[/C][C]NA[/C][C]-0.0452231[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]0.9332[/C][C]NA[/C][C]NA[/C][C]-0.0168627[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]0.9052[/C][C]0.928739[/C][C]0.951475[/C][C]-0.0227363[/C][C]-0.0235387[/C][/ROW]
[ROW][C]8[/C][C]0.8683[/C][C]0.935251[/C][C]0.935512[/C][C]-0.000261267[/C][C]-0.0669512[/C][/ROW]
[ROW][C]9[/C][C]0.9232[/C][C]0.93515[/C][C]0.927058[/C][C]0.00809207[/C][C]-0.0119504[/C][/ROW]
[ROW][C]10[/C][C]0.8725[/C][C]0.889809[/C][C]0.92275[/C][C]-0.0329413[/C][C]-0.0173087[/C][/ROW]
[ROW][C]11[/C][C]0.8903[/C][C]0.9087[/C][C]0.92475[/C][C]-0.0160496[/C][C]-0.0184004[/C][/ROW]
[ROW][C]12[/C][C]0.8959[/C][C]0.970251[/C][C]0.936858[/C][C]0.0333929[/C][C]-0.0743512[/C][/ROW]
[ROW][C]13[/C][C]0.8766[/C][C]0.997066[/C][C]0.954508[/C][C]0.0425579[/C][C]-0.120466[/C][/ROW]
[ROW][C]14[/C][C]0.9188[/C][C]1.04147[/C][C]0.977021[/C][C]0.0644454[/C][C]-0.122666[/C][/ROW]
[ROW][C]15[/C][C]0.9838[/C][C]1.03453[/C][C]1.00399[/C][C]0.0305466[/C][C]-0.0507341[/C][/ROW]
[ROW][C]16[/C][C]0.9994[/C][C]0.986469[/C][C]1.03143[/C][C]-0.0449606[/C][C]0.0129315[/C][/ROW]
[ROW][C]17[/C][C]1.0731[/C][C]1.01386[/C][C]1.05909[/C][C]-0.0452231[/C][C]0.0592356[/C][/ROW]
[ROW][C]18[/C][C]1.1372[/C][C]1.07279[/C][C]1.08965[/C][C]-0.0168627[/C][C]0.0644127[/C][/ROW]
[ROW][C]19[/C][C]1.1248[/C][C]1.10177[/C][C]1.1245[/C][C]-0.0227363[/C][C]0.0230321[/C][/ROW]
[ROW][C]20[/C][C]1.189[/C][C]1.15655[/C][C]1.15681[/C][C]-0.000261267[/C][C]0.0324529[/C][/ROW]
[ROW][C]21[/C][C]1.2497[/C][C]1.18893[/C][C]1.18084[/C][C]0.00809207[/C][C]0.0607704[/C][/ROW]
[ROW][C]22[/C][C]1.2046[/C][C]1.16561[/C][C]1.19855[/C][C]-0.0329413[/C][C]0.0389913[/C][/ROW]
[ROW][C]23[/C][C]1.222[/C][C]1.19576[/C][C]1.21181[/C][C]-0.0160496[/C][C]0.0262413[/C][/ROW]
[ROW][C]24[/C][C]1.2977[/C][C]1.25563[/C][C]1.22223[/C][C]0.0333929[/C][C]0.0420738[/C][/ROW]
[ROW][C]25[/C][C]1.3113[/C][C]1.27606[/C][C]1.2335[/C][C]0.0425579[/C][C]0.0352379[/C][/ROW]
[ROW][C]26[/C][C]1.2594[/C][C]1.30833[/C][C]1.24389[/C][C]0.0644454[/C][C]-0.0489329[/C][/ROW]
[ROW][C]27[/C][C]1.2199[/C][C]1.28113[/C][C]1.25058[/C][C]0.0305466[/C][C]-0.0612258[/C][/ROW]
[ROW][C]28[/C][C]1.1884[/C][C]1.21414[/C][C]1.2591[/C][C]-0.0449606[/C][C]-0.0257352[/C][/ROW]
[ROW][C]29[/C][C]1.2023[/C][C]1.22618[/C][C]1.2714[/C][C]-0.0452231[/C][C]-0.0238769[/C][/ROW]
[ROW][C]30[/C][C]1.2582[/C][C]1.26715[/C][C]1.28401[/C][C]-0.0168627[/C][C]-0.00894977[/C][/ROW]
[ROW][C]31[/C][C]1.2743[/C][C]1.27533[/C][C]1.29806[/C][C]-0.0227363[/C][C]-0.00102623[/C][/ROW]
[ROW][C]32[/C][C]1.2887[/C][C]1.31818[/C][C]1.31844[/C][C]-0.000261267[/C][C]-0.0294804[/C][/ROW]
[ROW][C]33[/C][C]1.3106[/C][C]1.35103[/C][C]1.34294[/C][C]0.00809207[/C][C]-0.0404296[/C][/ROW]
[ROW][C]34[/C][C]1.3481[/C][C]1.32728[/C][C]1.36022[/C][C]-0.0329413[/C][C]0.0208246[/C][/ROW]
[ROW][C]35[/C][C]1.3738[/C][C]1.35376[/C][C]1.36981[/C][C]-0.0160496[/C][C]0.0200413[/C][/ROW]
[ROW][C]36[/C][C]1.4486[/C][C]1.41177[/C][C]1.37838[/C][C]0.0333929[/C][C]0.0368321[/C][/ROW]
[ROW][C]37[/C][C]1.4976[/C][C]1.43181[/C][C]1.38925[/C][C]0.0425579[/C][C]0.0657921[/C][/ROW]
[ROW][C]38[/C][C]1.5622[/C][C]1.46808[/C][C]1.40363[/C][C]0.0644454[/C][C]0.0941213[/C][/ROW]
[ROW][C]39[/C][C]1.505[/C][C]1.44508[/C][C]1.41453[/C][C]0.0305466[/C][C]0.0599242[/C][/ROW]
[ROW][C]40[/C][C]1.318[/C][C]1.36936[/C][C]1.41432[/C][C]-0.0449606[/C][C]-0.0513602[/C][/ROW]
[ROW][C]41[/C][C]1.3029[/C][C]1.36243[/C][C]1.40765[/C][C]-0.0452231[/C][C]-0.0595269[/C][/ROW]
[ROW][C]42[/C][C]1.3632[/C][C]1.38357[/C][C]1.40044[/C][C]-0.0168627[/C][C]-0.0203748[/C][/ROW]
[ROW][C]43[/C][C]1.4303[/C][C]1.36854[/C][C]1.39128[/C][C]-0.0227363[/C][C]0.0617613[/C][/ROW]
[ROW][C]44[/C][C]1.4779[/C][C]1.38048[/C][C]1.38075[/C][C]-0.000261267[/C][C]0.0974154[/C][/ROW]
[ROW][C]45[/C][C]1.3829[/C][C]1.37986[/C][C]1.37177[/C][C]0.00809207[/C][C]0.0030371[/C][/ROW]
[ROW][C]46[/C][C]1.2708[/C][C]1.33624[/C][C]1.36918[/C][C]-0.0329413[/C][C]-0.0654421[/C][/ROW]
[ROW][C]47[/C][C]1.291[/C][C]1.35472[/C][C]1.37077[/C][C]-0.0160496[/C][C]-0.0637212[/C][/ROW]
[ROW][C]48[/C][C]1.3583[/C][C]1.40108[/C][C]1.36769[/C][C]0.0333929[/C][C]-0.0427846[/C][/ROW]
[ROW][C]49[/C][C]1.368[/C][C]1.39934[/C][C]1.35678[/C][C]0.0425579[/C][C]-0.0313371[/C][/ROW]
[ROW][C]50[/C][C]1.4391[/C][C]1.40617[/C][C]1.34172[/C][C]0.0644454[/C][C]0.0329296[/C][/ROW]
[ROW][C]51[/C][C]1.4127[/C][C]1.36213[/C][C]1.33158[/C][C]0.0305466[/C][C]0.0505742[/C][/ROW]
[ROW][C]52[/C][C]1.3482[/C][C]1.2855[/C][C]1.33046[/C][C]-0.0449606[/C][C]0.0627023[/C][/ROW]
[ROW][C]53[/C][C]1.3108[/C][C]1.28809[/C][C]1.33332[/C][C]-0.0452231[/C][C]0.0227065[/C][/ROW]
[ROW][C]54[/C][C]1.2814[/C][C]1.31795[/C][C]1.33481[/C][C]-0.0168627[/C][C]-0.0365498[/C][/ROW]
[ROW][C]55[/C][C]1.2502[/C][C]1.31226[/C][C]1.33499[/C][C]-0.0227363[/C][C]-0.0620554[/C][/ROW]
[ROW][C]56[/C][C]1.2967[/C][C]1.33196[/C][C]1.33222[/C][C]-0.000261267[/C][C]-0.0352637[/C][/ROW]
[ROW][C]57[/C][C]1.3206[/C][C]1.33385[/C][C]1.32576[/C][C]0.00809207[/C][C]-0.0132546[/C][/ROW]
[ROW][C]58[/C][C]1.3062[/C][C]1.28509[/C][C]1.31803[/C][C]-0.0329413[/C][C]0.0211079[/C][/ROW]
[ROW][C]59[/C][C]1.3242[/C][C]1.29019[/C][C]1.30624[/C][C]-0.0160496[/C][C]0.0340121[/C][/ROW]
[ROW][C]60[/C][C]1.361[/C][C]1.3246[/C][C]1.2912[/C][C]0.0333929[/C][C]0.0364029[/C][/ROW]
[ROW][C]61[/C][C]1.3696[/C][C]1.32065[/C][C]1.2781[/C][C]0.0425579[/C][C]0.0489463[/C][/ROW]
[ROW][C]62[/C][C]1.3711[/C][C]1.32838[/C][C]1.26393[/C][C]0.0644454[/C][C]0.0427213[/C][/ROW]
[ROW][C]63[/C][C]1.3256[/C][C]NA[/C][C]NA[/C][C]0.0305466[/C][C]NA[/C][/ROW]
[ROW][C]64[/C][C]1.2498[/C][C]NA[/C][C]NA[/C][C]-0.0449606[/C][C]NA[/C][/ROW]
[ROW][C]65[/C][C]1.1261[/C][C]NA[/C][C]NA[/C][C]-0.0452231[/C][C]NA[/C][/ROW]
[ROW][C]66[/C][C]1.1053[/C][C]NA[/C][C]NA[/C][C]-0.0168627[/C][C]NA[/C][/ROW]
[ROW][C]67[/C][C]1.1117[/C][C]NA[/C][C]NA[/C][C]-0.0227363[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]1.0953[/C][C]NA[/C][C]NA[/C][C]-0.000261267[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=295001&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=295001&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	1.1216	NA	NA	0.0425579	NA
2	1.0569	NA	NA	0.0644454	NA
3	1.0486	NA	NA	0.0305466	NA
4	1.038	NA	NA	-0.0449606	NA
5	0.9865	NA	NA	-0.0452231	NA
6	0.9332	NA	NA	-0.0168627	NA
7	0.9052	0.928739	0.951475	-0.0227363	-0.0235387
8	0.8683	0.935251	0.935512	-0.000261267	-0.0669512
9	0.9232	0.93515	0.927058	0.00809207	-0.0119504
10	0.8725	0.889809	0.92275	-0.0329413	-0.0173087
11	0.8903	0.9087	0.92475	-0.0160496	-0.0184004
12	0.8959	0.970251	0.936858	0.0333929	-0.0743512
13	0.8766	0.997066	0.954508	0.0425579	-0.120466
14	0.9188	1.04147	0.977021	0.0644454	-0.122666
15	0.9838	1.03453	1.00399	0.0305466	-0.0507341
16	0.9994	0.986469	1.03143	-0.0449606	0.0129315
17	1.0731	1.01386	1.05909	-0.0452231	0.0592356
18	1.1372	1.07279	1.08965	-0.0168627	0.0644127
19	1.1248	1.10177	1.1245	-0.0227363	0.0230321
20	1.189	1.15655	1.15681	-0.000261267	0.0324529
21	1.2497	1.18893	1.18084	0.00809207	0.0607704
22	1.2046	1.16561	1.19855	-0.0329413	0.0389913
23	1.222	1.19576	1.21181	-0.0160496	0.0262413
24	1.2977	1.25563	1.22223	0.0333929	0.0420738
25	1.3113	1.27606	1.2335	0.0425579	0.0352379
26	1.2594	1.30833	1.24389	0.0644454	-0.0489329
27	1.2199	1.28113	1.25058	0.0305466	-0.0612258
28	1.1884	1.21414	1.2591	-0.0449606	-0.0257352
29	1.2023	1.22618	1.2714	-0.0452231	-0.0238769
30	1.2582	1.26715	1.28401	-0.0168627	-0.00894977
31	1.2743	1.27533	1.29806	-0.0227363	-0.00102623
32	1.2887	1.31818	1.31844	-0.000261267	-0.0294804
33	1.3106	1.35103	1.34294	0.00809207	-0.0404296
34	1.3481	1.32728	1.36022	-0.0329413	0.0208246
35	1.3738	1.35376	1.36981	-0.0160496	0.0200413
36	1.4486	1.41177	1.37838	0.0333929	0.0368321
37	1.4976	1.43181	1.38925	0.0425579	0.0657921
38	1.5622	1.46808	1.40363	0.0644454	0.0941213
39	1.505	1.44508	1.41453	0.0305466	0.0599242
40	1.318	1.36936	1.41432	-0.0449606	-0.0513602
41	1.3029	1.36243	1.40765	-0.0452231	-0.0595269
42	1.3632	1.38357	1.40044	-0.0168627	-0.0203748
43	1.4303	1.36854	1.39128	-0.0227363	0.0617613
44	1.4779	1.38048	1.38075	-0.000261267	0.0974154
45	1.3829	1.37986	1.37177	0.00809207	0.0030371
46	1.2708	1.33624	1.36918	-0.0329413	-0.0654421
47	1.291	1.35472	1.37077	-0.0160496	-0.0637212
48	1.3583	1.40108	1.36769	0.0333929	-0.0427846
49	1.368	1.39934	1.35678	0.0425579	-0.0313371
50	1.4391	1.40617	1.34172	0.0644454	0.0329296
51	1.4127	1.36213	1.33158	0.0305466	0.0505742
52	1.3482	1.2855	1.33046	-0.0449606	0.0627023
53	1.3108	1.28809	1.33332	-0.0452231	0.0227065
54	1.2814	1.31795	1.33481	-0.0168627	-0.0365498
55	1.2502	1.31226	1.33499	-0.0227363	-0.0620554
56	1.2967	1.33196	1.33222	-0.000261267	-0.0352637
57	1.3206	1.33385	1.32576	0.00809207	-0.0132546
58	1.3062	1.28509	1.31803	-0.0329413	0.0211079
59	1.3242	1.29019	1.30624	-0.0160496	0.0340121
60	1.361	1.3246	1.2912	0.0333929	0.0364029
61	1.3696	1.32065	1.2781	0.0425579	0.0489463
62	1.3711	1.32838	1.26393	0.0644454	0.0427213
63	1.3256	NA	NA	0.0305466	NA
64	1.2498	NA	NA	-0.0449606	NA
65	1.1261	NA	NA	-0.0452231	NA
66	1.1053	NA	NA	-0.0168627	NA
67	1.1117	NA	NA	-0.0227363	NA
68	1.0953	NA	NA	-0.000261267	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