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

Sun, 27 Nov 2016 10:58:38 +0000

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/Nov/27/t1480244342esqjf2o56cf6o40.htm/, Retrieved Mon, 29 Apr 2024 19:56:44 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Mon, 29 Apr 2024 19:56:44 +0200

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

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=&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=&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	2 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	95.9	NA	NA	-3.58014	NA
2	89.2	NA	NA	-9.58097	NA
3	100.2	NA	NA	2.74819	NA
4	102.3	NA	NA	0.300694	NA
5	102.2	NA	NA	4.77486	NA
6	100.5	NA	NA	1.28236	NA
7	104.1	100.264	99.9375	0.326528	3.83597
8	94.9	99.3965	99.975	-0.578472	-4.49653
9	97.3	95.9382	100.238	-4.29931	1.36181
10	100.3	100.114	100.629	-0.515139	0.185972
11	98	99.0049	100.837	-1.83264	-1.00486
12	115.1	111.921	100.967	10.954	3.17931
13	94.4	97.449	101.029	-3.58014	-3.04903
14	91.6	91.569	101.15	-9.58097	0.0309722
15	104.1	104.286	101.538	2.74819	-0.185694
16	107.8	102.059	101.758	0.300694	5.74097
17	101.7	106.7	101.925	4.77486	-4.99986
18	104.1	103.366	102.083	1.28236	0.734306
19	102	102.572	102.246	0.326528	-0.572361
20	99.9	102.047	102.625	-0.578472	-2.14653
21	101.6	98.7882	103.088	-4.29931	2.81181
22	101.3	102.527	103.042	-0.515139	-1.22653
23	101	101.247	103.079	-1.83264	-0.246528
24	115.9	114.525	103.571	10.954	1.37514
25	97.5	100.141	103.721	-3.58014	-2.64069
26	97.6	94.4065	103.988	-9.58097	3.19347
27	109.2	107.052	104.304	2.74819	2.14764
28	101.6	104.742	104.442	0.300694	-3.14236
29	108.8	109.562	104.787	4.77486	-0.762361
30	108.8	106.316	105.033	1.28236	2.48431
31	100.9	105.652	105.325	0.326528	-4.75153
32	107.4	104.959	105.537	-0.578472	2.44097
33	101.7	101.397	105.696	-4.29931	0.303472
34	104.5	105.435	105.95	-0.515139	-0.934861
35	106.1	104.405	106.237	-1.83264	1.69514
36	116.7	117.362	106.408	10.954	-0.662361
37	103.7	103.053	106.633	-3.58014	0.646806
38	96.5	97.5065	107.087	-9.58097	-1.00653
39	114.1	109.919	107.171	2.74819	4.18097
40	102.8	107.484	107.183	0.300694	-4.68403
41	114.5	112.083	107.308	4.77486	2.41681
42	107.2	108.566	107.283	1.28236	-1.36569
43	107.9	107.652	107.325	0.326528	0.248472
44	111.3	106.88	107.458	-0.578472	4.42014
45	99.8	102.98	107.279	-4.29931	-3.17986
46	106.7	106.843	107.358	-0.515139	-0.143194
47	106.9	105.901	107.733	-1.83264	0.999306
48	115.3	118.817	107.863	10.954	-3.51653
49	106.1	104.449	108.029	-3.58014	1.65097
50	97.3	98.4607	108.042	-9.58097	-1.16069
51	109	110.857	108.108	2.74819	-1.85653
52	109.8	108.776	108.475	0.300694	1.02431
53	116.5	113.425	108.65	4.77486	3.07514
54	108.3	110.132	108.85	1.28236	-1.83236
55	110.8	109.556	109.229	0.326528	1.24431
56	108.7	108.913	109.492	-0.578472	-0.213194
57	104	105.292	109.592	-4.29931	-1.29236
58	111.3	109.177	109.692	-0.515139	2.12347
59	106.5	107.938	109.771	-1.83264	-1.43819
60	120.5	120.871	109.917	10.954	-0.370694
61	110	106.603	110.183	-3.58014	3.39681
62	99.7	100.752	110.333	-9.58097	-1.05236
63	109	113.282	110.533	2.74819	-4.28153
64	112.2	111.134	110.833	0.300694	1.06597
65	116	115.725	110.95	4.77486	0.275139
66	112.3	112.316	111.033	1.28236	-0.0156944
67	113.2	NA	NA	0.326528	NA
68	109.9	NA	NA	-0.578472	NA
69	107.6	NA	NA	-4.29931	NA
70	114.9	NA	NA	-0.515139	NA
71	105.7	NA	NA	-1.83264	NA
72	123.3	NA	NA	10.954	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 95.9 & NA & NA & -3.58014 & NA \tabularnewline
2 & 89.2 & NA & NA & -9.58097 & NA \tabularnewline
3 & 100.2 & NA & NA & 2.74819 & NA \tabularnewline
4 & 102.3 & NA & NA & 0.300694 & NA \tabularnewline
5 & 102.2 & NA & NA & 4.77486 & NA \tabularnewline
6 & 100.5 & NA & NA & 1.28236 & NA \tabularnewline
7 & 104.1 & 100.264 & 99.9375 & 0.326528 & 3.83597 \tabularnewline
8 & 94.9 & 99.3965 & 99.975 & -0.578472 & -4.49653 \tabularnewline
9 & 97.3 & 95.9382 & 100.238 & -4.29931 & 1.36181 \tabularnewline
10 & 100.3 & 100.114 & 100.629 & -0.515139 & 0.185972 \tabularnewline
11 & 98 & 99.0049 & 100.837 & -1.83264 & -1.00486 \tabularnewline
12 & 115.1 & 111.921 & 100.967 & 10.954 & 3.17931 \tabularnewline
13 & 94.4 & 97.449 & 101.029 & -3.58014 & -3.04903 \tabularnewline
14 & 91.6 & 91.569 & 101.15 & -9.58097 & 0.0309722 \tabularnewline
15 & 104.1 & 104.286 & 101.538 & 2.74819 & -0.185694 \tabularnewline
16 & 107.8 & 102.059 & 101.758 & 0.300694 & 5.74097 \tabularnewline
17 & 101.7 & 106.7 & 101.925 & 4.77486 & -4.99986 \tabularnewline
18 & 104.1 & 103.366 & 102.083 & 1.28236 & 0.734306 \tabularnewline
19 & 102 & 102.572 & 102.246 & 0.326528 & -0.572361 \tabularnewline
20 & 99.9 & 102.047 & 102.625 & -0.578472 & -2.14653 \tabularnewline
21 & 101.6 & 98.7882 & 103.088 & -4.29931 & 2.81181 \tabularnewline
22 & 101.3 & 102.527 & 103.042 & -0.515139 & -1.22653 \tabularnewline
23 & 101 & 101.247 & 103.079 & -1.83264 & -0.246528 \tabularnewline
24 & 115.9 & 114.525 & 103.571 & 10.954 & 1.37514 \tabularnewline
25 & 97.5 & 100.141 & 103.721 & -3.58014 & -2.64069 \tabularnewline
26 & 97.6 & 94.4065 & 103.988 & -9.58097 & 3.19347 \tabularnewline
27 & 109.2 & 107.052 & 104.304 & 2.74819 & 2.14764 \tabularnewline
28 & 101.6 & 104.742 & 104.442 & 0.300694 & -3.14236 \tabularnewline
29 & 108.8 & 109.562 & 104.787 & 4.77486 & -0.762361 \tabularnewline
30 & 108.8 & 106.316 & 105.033 & 1.28236 & 2.48431 \tabularnewline
31 & 100.9 & 105.652 & 105.325 & 0.326528 & -4.75153 \tabularnewline
32 & 107.4 & 104.959 & 105.537 & -0.578472 & 2.44097 \tabularnewline
33 & 101.7 & 101.397 & 105.696 & -4.29931 & 0.303472 \tabularnewline
34 & 104.5 & 105.435 & 105.95 & -0.515139 & -0.934861 \tabularnewline
35 & 106.1 & 104.405 & 106.237 & -1.83264 & 1.69514 \tabularnewline
36 & 116.7 & 117.362 & 106.408 & 10.954 & -0.662361 \tabularnewline
37 & 103.7 & 103.053 & 106.633 & -3.58014 & 0.646806 \tabularnewline
38 & 96.5 & 97.5065 & 107.087 & -9.58097 & -1.00653 \tabularnewline
39 & 114.1 & 109.919 & 107.171 & 2.74819 & 4.18097 \tabularnewline
40 & 102.8 & 107.484 & 107.183 & 0.300694 & -4.68403 \tabularnewline
41 & 114.5 & 112.083 & 107.308 & 4.77486 & 2.41681 \tabularnewline
42 & 107.2 & 108.566 & 107.283 & 1.28236 & -1.36569 \tabularnewline
43 & 107.9 & 107.652 & 107.325 & 0.326528 & 0.248472 \tabularnewline
44 & 111.3 & 106.88 & 107.458 & -0.578472 & 4.42014 \tabularnewline
45 & 99.8 & 102.98 & 107.279 & -4.29931 & -3.17986 \tabularnewline
46 & 106.7 & 106.843 & 107.358 & -0.515139 & -0.143194 \tabularnewline
47 & 106.9 & 105.901 & 107.733 & -1.83264 & 0.999306 \tabularnewline
48 & 115.3 & 118.817 & 107.863 & 10.954 & -3.51653 \tabularnewline
49 & 106.1 & 104.449 & 108.029 & -3.58014 & 1.65097 \tabularnewline
50 & 97.3 & 98.4607 & 108.042 & -9.58097 & -1.16069 \tabularnewline
51 & 109 & 110.857 & 108.108 & 2.74819 & -1.85653 \tabularnewline
52 & 109.8 & 108.776 & 108.475 & 0.300694 & 1.02431 \tabularnewline
53 & 116.5 & 113.425 & 108.65 & 4.77486 & 3.07514 \tabularnewline
54 & 108.3 & 110.132 & 108.85 & 1.28236 & -1.83236 \tabularnewline
55 & 110.8 & 109.556 & 109.229 & 0.326528 & 1.24431 \tabularnewline
56 & 108.7 & 108.913 & 109.492 & -0.578472 & -0.213194 \tabularnewline
57 & 104 & 105.292 & 109.592 & -4.29931 & -1.29236 \tabularnewline
58 & 111.3 & 109.177 & 109.692 & -0.515139 & 2.12347 \tabularnewline
59 & 106.5 & 107.938 & 109.771 & -1.83264 & -1.43819 \tabularnewline
60 & 120.5 & 120.871 & 109.917 & 10.954 & -0.370694 \tabularnewline
61 & 110 & 106.603 & 110.183 & -3.58014 & 3.39681 \tabularnewline
62 & 99.7 & 100.752 & 110.333 & -9.58097 & -1.05236 \tabularnewline
63 & 109 & 113.282 & 110.533 & 2.74819 & -4.28153 \tabularnewline
64 & 112.2 & 111.134 & 110.833 & 0.300694 & 1.06597 \tabularnewline
65 & 116 & 115.725 & 110.95 & 4.77486 & 0.275139 \tabularnewline
66 & 112.3 & 112.316 & 111.033 & 1.28236 & -0.0156944 \tabularnewline
67 & 113.2 & NA & NA & 0.326528 & NA \tabularnewline
68 & 109.9 & NA & NA & -0.578472 & NA \tabularnewline
69 & 107.6 & NA & NA & -4.29931 & NA \tabularnewline
70 & 114.9 & NA & NA & -0.515139 & NA \tabularnewline
71 & 105.7 & NA & NA & -1.83264 & NA \tabularnewline
72 & 123.3 & NA & NA & 10.954 & 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]95.9[/C][C]NA[/C][C]NA[/C][C]-3.58014[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]89.2[/C][C]NA[/C][C]NA[/C][C]-9.58097[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]100.2[/C][C]NA[/C][C]NA[/C][C]2.74819[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]102.3[/C][C]NA[/C][C]NA[/C][C]0.300694[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]102.2[/C][C]NA[/C][C]NA[/C][C]4.77486[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]100.5[/C][C]NA[/C][C]NA[/C][C]1.28236[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]104.1[/C][C]100.264[/C][C]99.9375[/C][C]0.326528[/C][C]3.83597[/C][/ROW]
[ROW][C]8[/C][C]94.9[/C][C]99.3965[/C][C]99.975[/C][C]-0.578472[/C][C]-4.49653[/C][/ROW]
[ROW][C]9[/C][C]97.3[/C][C]95.9382[/C][C]100.238[/C][C]-4.29931[/C][C]1.36181[/C][/ROW]
[ROW][C]10[/C][C]100.3[/C][C]100.114[/C][C]100.629[/C][C]-0.515139[/C][C]0.185972[/C][/ROW]
[ROW][C]11[/C][C]98[/C][C]99.0049[/C][C]100.837[/C][C]-1.83264[/C][C]-1.00486[/C][/ROW]
[ROW][C]12[/C][C]115.1[/C][C]111.921[/C][C]100.967[/C][C]10.954[/C][C]3.17931[/C][/ROW]
[ROW][C]13[/C][C]94.4[/C][C]97.449[/C][C]101.029[/C][C]-3.58014[/C][C]-3.04903[/C][/ROW]
[ROW][C]14[/C][C]91.6[/C][C]91.569[/C][C]101.15[/C][C]-9.58097[/C][C]0.0309722[/C][/ROW]
[ROW][C]15[/C][C]104.1[/C][C]104.286[/C][C]101.538[/C][C]2.74819[/C][C]-0.185694[/C][/ROW]
[ROW][C]16[/C][C]107.8[/C][C]102.059[/C][C]101.758[/C][C]0.300694[/C][C]5.74097[/C][/ROW]
[ROW][C]17[/C][C]101.7[/C][C]106.7[/C][C]101.925[/C][C]4.77486[/C][C]-4.99986[/C][/ROW]
[ROW][C]18[/C][C]104.1[/C][C]103.366[/C][C]102.083[/C][C]1.28236[/C][C]0.734306[/C][/ROW]
[ROW][C]19[/C][C]102[/C][C]102.572[/C][C]102.246[/C][C]0.326528[/C][C]-0.572361[/C][/ROW]
[ROW][C]20[/C][C]99.9[/C][C]102.047[/C][C]102.625[/C][C]-0.578472[/C][C]-2.14653[/C][/ROW]
[ROW][C]21[/C][C]101.6[/C][C]98.7882[/C][C]103.088[/C][C]-4.29931[/C][C]2.81181[/C][/ROW]
[ROW][C]22[/C][C]101.3[/C][C]102.527[/C][C]103.042[/C][C]-0.515139[/C][C]-1.22653[/C][/ROW]
[ROW][C]23[/C][C]101[/C][C]101.247[/C][C]103.079[/C][C]-1.83264[/C][C]-0.246528[/C][/ROW]
[ROW][C]24[/C][C]115.9[/C][C]114.525[/C][C]103.571[/C][C]10.954[/C][C]1.37514[/C][/ROW]
[ROW][C]25[/C][C]97.5[/C][C]100.141[/C][C]103.721[/C][C]-3.58014[/C][C]-2.64069[/C][/ROW]
[ROW][C]26[/C][C]97.6[/C][C]94.4065[/C][C]103.988[/C][C]-9.58097[/C][C]3.19347[/C][/ROW]
[ROW][C]27[/C][C]109.2[/C][C]107.052[/C][C]104.304[/C][C]2.74819[/C][C]2.14764[/C][/ROW]
[ROW][C]28[/C][C]101.6[/C][C]104.742[/C][C]104.442[/C][C]0.300694[/C][C]-3.14236[/C][/ROW]
[ROW][C]29[/C][C]108.8[/C][C]109.562[/C][C]104.787[/C][C]4.77486[/C][C]-0.762361[/C][/ROW]
[ROW][C]30[/C][C]108.8[/C][C]106.316[/C][C]105.033[/C][C]1.28236[/C][C]2.48431[/C][/ROW]
[ROW][C]31[/C][C]100.9[/C][C]105.652[/C][C]105.325[/C][C]0.326528[/C][C]-4.75153[/C][/ROW]
[ROW][C]32[/C][C]107.4[/C][C]104.959[/C][C]105.537[/C][C]-0.578472[/C][C]2.44097[/C][/ROW]
[ROW][C]33[/C][C]101.7[/C][C]101.397[/C][C]105.696[/C][C]-4.29931[/C][C]0.303472[/C][/ROW]
[ROW][C]34[/C][C]104.5[/C][C]105.435[/C][C]105.95[/C][C]-0.515139[/C][C]-0.934861[/C][/ROW]
[ROW][C]35[/C][C]106.1[/C][C]104.405[/C][C]106.237[/C][C]-1.83264[/C][C]1.69514[/C][/ROW]
[ROW][C]36[/C][C]116.7[/C][C]117.362[/C][C]106.408[/C][C]10.954[/C][C]-0.662361[/C][/ROW]
[ROW][C]37[/C][C]103.7[/C][C]103.053[/C][C]106.633[/C][C]-3.58014[/C][C]0.646806[/C][/ROW]
[ROW][C]38[/C][C]96.5[/C][C]97.5065[/C][C]107.087[/C][C]-9.58097[/C][C]-1.00653[/C][/ROW]
[ROW][C]39[/C][C]114.1[/C][C]109.919[/C][C]107.171[/C][C]2.74819[/C][C]4.18097[/C][/ROW]
[ROW][C]40[/C][C]102.8[/C][C]107.484[/C][C]107.183[/C][C]0.300694[/C][C]-4.68403[/C][/ROW]
[ROW][C]41[/C][C]114.5[/C][C]112.083[/C][C]107.308[/C][C]4.77486[/C][C]2.41681[/C][/ROW]
[ROW][C]42[/C][C]107.2[/C][C]108.566[/C][C]107.283[/C][C]1.28236[/C][C]-1.36569[/C][/ROW]
[ROW][C]43[/C][C]107.9[/C][C]107.652[/C][C]107.325[/C][C]0.326528[/C][C]0.248472[/C][/ROW]
[ROW][C]44[/C][C]111.3[/C][C]106.88[/C][C]107.458[/C][C]-0.578472[/C][C]4.42014[/C][/ROW]
[ROW][C]45[/C][C]99.8[/C][C]102.98[/C][C]107.279[/C][C]-4.29931[/C][C]-3.17986[/C][/ROW]
[ROW][C]46[/C][C]106.7[/C][C]106.843[/C][C]107.358[/C][C]-0.515139[/C][C]-0.143194[/C][/ROW]
[ROW][C]47[/C][C]106.9[/C][C]105.901[/C][C]107.733[/C][C]-1.83264[/C][C]0.999306[/C][/ROW]
[ROW][C]48[/C][C]115.3[/C][C]118.817[/C][C]107.863[/C][C]10.954[/C][C]-3.51653[/C][/ROW]
[ROW][C]49[/C][C]106.1[/C][C]104.449[/C][C]108.029[/C][C]-3.58014[/C][C]1.65097[/C][/ROW]
[ROW][C]50[/C][C]97.3[/C][C]98.4607[/C][C]108.042[/C][C]-9.58097[/C][C]-1.16069[/C][/ROW]
[ROW][C]51[/C][C]109[/C][C]110.857[/C][C]108.108[/C][C]2.74819[/C][C]-1.85653[/C][/ROW]
[ROW][C]52[/C][C]109.8[/C][C]108.776[/C][C]108.475[/C][C]0.300694[/C][C]1.02431[/C][/ROW]
[ROW][C]53[/C][C]116.5[/C][C]113.425[/C][C]108.65[/C][C]4.77486[/C][C]3.07514[/C][/ROW]
[ROW][C]54[/C][C]108.3[/C][C]110.132[/C][C]108.85[/C][C]1.28236[/C][C]-1.83236[/C][/ROW]
[ROW][C]55[/C][C]110.8[/C][C]109.556[/C][C]109.229[/C][C]0.326528[/C][C]1.24431[/C][/ROW]
[ROW][C]56[/C][C]108.7[/C][C]108.913[/C][C]109.492[/C][C]-0.578472[/C][C]-0.213194[/C][/ROW]
[ROW][C]57[/C][C]104[/C][C]105.292[/C][C]109.592[/C][C]-4.29931[/C][C]-1.29236[/C][/ROW]
[ROW][C]58[/C][C]111.3[/C][C]109.177[/C][C]109.692[/C][C]-0.515139[/C][C]2.12347[/C][/ROW]
[ROW][C]59[/C][C]106.5[/C][C]107.938[/C][C]109.771[/C][C]-1.83264[/C][C]-1.43819[/C][/ROW]
[ROW][C]60[/C][C]120.5[/C][C]120.871[/C][C]109.917[/C][C]10.954[/C][C]-0.370694[/C][/ROW]
[ROW][C]61[/C][C]110[/C][C]106.603[/C][C]110.183[/C][C]-3.58014[/C][C]3.39681[/C][/ROW]
[ROW][C]62[/C][C]99.7[/C][C]100.752[/C][C]110.333[/C][C]-9.58097[/C][C]-1.05236[/C][/ROW]
[ROW][C]63[/C][C]109[/C][C]113.282[/C][C]110.533[/C][C]2.74819[/C][C]-4.28153[/C][/ROW]
[ROW][C]64[/C][C]112.2[/C][C]111.134[/C][C]110.833[/C][C]0.300694[/C][C]1.06597[/C][/ROW]
[ROW][C]65[/C][C]116[/C][C]115.725[/C][C]110.95[/C][C]4.77486[/C][C]0.275139[/C][/ROW]
[ROW][C]66[/C][C]112.3[/C][C]112.316[/C][C]111.033[/C][C]1.28236[/C][C]-0.0156944[/C][/ROW]
[ROW][C]67[/C][C]113.2[/C][C]NA[/C][C]NA[/C][C]0.326528[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]109.9[/C][C]NA[/C][C]NA[/C][C]-0.578472[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]107.6[/C][C]NA[/C][C]NA[/C][C]-4.29931[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]114.9[/C][C]NA[/C][C]NA[/C][C]-0.515139[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]105.7[/C][C]NA[/C][C]NA[/C][C]-1.83264[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]123.3[/C][C]NA[/C][C]NA[/C][C]10.954[/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	95.9	NA	NA	-3.58014	NA
2	89.2	NA	NA	-9.58097	NA
3	100.2	NA	NA	2.74819	NA
4	102.3	NA	NA	0.300694	NA
5	102.2	NA	NA	4.77486	NA
6	100.5	NA	NA	1.28236	NA
7	104.1	100.264	99.9375	0.326528	3.83597
8	94.9	99.3965	99.975	-0.578472	-4.49653
9	97.3	95.9382	100.238	-4.29931	1.36181
10	100.3	100.114	100.629	-0.515139	0.185972
11	98	99.0049	100.837	-1.83264	-1.00486
12	115.1	111.921	100.967	10.954	3.17931
13	94.4	97.449	101.029	-3.58014	-3.04903
14	91.6	91.569	101.15	-9.58097	0.0309722
15	104.1	104.286	101.538	2.74819	-0.185694
16	107.8	102.059	101.758	0.300694	5.74097
17	101.7	106.7	101.925	4.77486	-4.99986
18	104.1	103.366	102.083	1.28236	0.734306
19	102	102.572	102.246	0.326528	-0.572361
20	99.9	102.047	102.625	-0.578472	-2.14653
21	101.6	98.7882	103.088	-4.29931	2.81181
22	101.3	102.527	103.042	-0.515139	-1.22653
23	101	101.247	103.079	-1.83264	-0.246528
24	115.9	114.525	103.571	10.954	1.37514
25	97.5	100.141	103.721	-3.58014	-2.64069
26	97.6	94.4065	103.988	-9.58097	3.19347
27	109.2	107.052	104.304	2.74819	2.14764
28	101.6	104.742	104.442	0.300694	-3.14236
29	108.8	109.562	104.787	4.77486	-0.762361
30	108.8	106.316	105.033	1.28236	2.48431
31	100.9	105.652	105.325	0.326528	-4.75153
32	107.4	104.959	105.537	-0.578472	2.44097
33	101.7	101.397	105.696	-4.29931	0.303472
34	104.5	105.435	105.95	-0.515139	-0.934861
35	106.1	104.405	106.237	-1.83264	1.69514
36	116.7	117.362	106.408	10.954	-0.662361
37	103.7	103.053	106.633	-3.58014	0.646806
38	96.5	97.5065	107.087	-9.58097	-1.00653
39	114.1	109.919	107.171	2.74819	4.18097
40	102.8	107.484	107.183	0.300694	-4.68403
41	114.5	112.083	107.308	4.77486	2.41681
42	107.2	108.566	107.283	1.28236	-1.36569
43	107.9	107.652	107.325	0.326528	0.248472
44	111.3	106.88	107.458	-0.578472	4.42014
45	99.8	102.98	107.279	-4.29931	-3.17986
46	106.7	106.843	107.358	-0.515139	-0.143194
47	106.9	105.901	107.733	-1.83264	0.999306
48	115.3	118.817	107.863	10.954	-3.51653
49	106.1	104.449	108.029	-3.58014	1.65097
50	97.3	98.4607	108.042	-9.58097	-1.16069
51	109	110.857	108.108	2.74819	-1.85653
52	109.8	108.776	108.475	0.300694	1.02431
53	116.5	113.425	108.65	4.77486	3.07514
54	108.3	110.132	108.85	1.28236	-1.83236
55	110.8	109.556	109.229	0.326528	1.24431
56	108.7	108.913	109.492	-0.578472	-0.213194
57	104	105.292	109.592	-4.29931	-1.29236
58	111.3	109.177	109.692	-0.515139	2.12347
59	106.5	107.938	109.771	-1.83264	-1.43819
60	120.5	120.871	109.917	10.954	-0.370694
61	110	106.603	110.183	-3.58014	3.39681
62	99.7	100.752	110.333	-9.58097	-1.05236
63	109	113.282	110.533	2.74819	-4.28153
64	112.2	111.134	110.833	0.300694	1.06597
65	116	115.725	110.95	4.77486	0.275139
66	112.3	112.316	111.033	1.28236	-0.0156944
67	113.2	NA	NA	0.326528	NA
68	109.9	NA	NA	-0.578472	NA
69	107.6	NA	NA	-4.29931	NA
70	114.9	NA	NA	-0.515139	NA
71	105.7	NA	NA	-1.83264	NA
72	123.3	NA	NA	10.954	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