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

Sat, 26 Nov 2016 14:25:27 +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/26/t14801703527jn4n20akj3g0a4.htm/, Retrieved Sat, 04 May 2024 01:30:08 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 01:30:08 +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	95.31	NA	NA	3.71955	NA
2	93.47	NA	NA	3.63355	NA
3	98.92	NA	NA	1.48847	NA
4	101.21	NA	NA	5.5838	NA
5	95.19	NA	NA	2.67305	NA
6	90.95	NA	NA	0.589715	NA
7	93.09	94.7641	93.9775	0.786632	-1.67413
8	90.16	90.1176	94.3567	-4.23903	0.0423681
9	91.86	89.1446	94.3192	-5.17453	2.71537
10	88.82	87.9993	93.7975	-5.7982	0.820701
11	91.58	90.9159	93.4317	-2.51578	0.664118
12	94.9	92.5307	93.2779	-0.747201	2.36928
13	99.85	96.7558	93.0362	3.71955	3.0942
14	98.03	96.2794	92.6458	3.63355	1.75062
15	93.46	93.5218	92.0333	1.48847	-0.0617986
16	94.15	97.05	91.4662	5.5838	-2.90005
17	93.47	93.868	91.195	2.67305	-0.398049
18	88.98	91.4872	90.8975	0.589715	-2.50722
19	89.26	91.1904	90.4037	0.786632	-1.93038
20	84.62	85.8822	90.1212	-4.23903	-1.26222
21	82.7	85.1142	90.2888	-5.17453	-2.41422
22	84.37	84.7918	90.59	-5.7982	-0.421799
23	89.52	88.4517	90.9675	-2.51578	1.06828
24	89.82	90.7953	91.5425	-0.747201	-0.975299
25	93.08	95.8912	92.1717	3.71955	-2.81122
26	98.02	96.4919	92.8583	3.63355	1.52812
27	97.49	95.0372	93.5488	1.48847	2.45278
28	97.35	99.8517	94.2679	5.5838	-2.50172
29	99.33	97.5918	94.9188	2.67305	1.7382
30	96.92	96.0001	95.4104	0.589715	0.919868
31	96.42	96.7587	95.9721	0.786632	-0.338715
32	93.94	92.1014	96.3404	-4.23903	1.83862
33	89.95	91.3571	96.5317	-5.17453	-1.40713
34	94.38	91.2418	97.04	-5.7982	3.1382
35	95.13	95.193	97.7088	-2.51578	-0.0629653
36	96.01	97.6657	98.4129	-0.747201	-1.65572
37	100.37	102.834	99.1142	3.71955	-2.46372
38	99.57	103.224	99.59	3.63355	-3.65355
39	100.53	101.506	100.017	1.48847	-0.975549
40	106.51	105.736	100.152	5.5838	0.774118
41	106.22	102.677	100.004	2.67305	3.54278
42	106.93	100.568	99.9783	0.589715	6.36195
43	103.24	100.87	100.083	0.786632	2.37003
44	98.54	95.896	100.135	-4.23903	2.64403
45	95.6	94.7767	99.9512	-5.17453	0.823285
46	91.97	93.7464	99.5446	-5.7982	-1.77638
47	93.99	96.1625	98.6783	-2.51578	-2.17255
48	96.53	96.629	97.3762	-0.747201	-0.0990486
49	102.37	100.032	96.3125	3.71955	2.33795
50	98.81	98.974	95.3404	3.63355	-0.163965
51	96.88	95.8572	94.3687	1.48847	1.02278
52	100.4	99.2513	93.6675	5.5838	1.1487
53	91.54	95.8543	93.1812	2.67305	-4.3143
54	90.36	93.4439	92.8542	0.589715	-3.08388
55	94.28	93.2445	92.4579	0.786632	1.03545
56	84.17	87.9705	92.2096	-4.23903	-3.80055
57	86.65	86.905	92.0796	-5.17453	-0.255049
58	84.09	86.3885	92.1867	-5.7982	-2.29847
59	90.2	90.2346	92.7504	-2.51578	-0.0346319
60	92.47	92.647	93.3942	-0.747201	-0.176965
61	96.92	97.615	93.8954	3.71955	-0.694965
62	98.3	98.299	94.6654	3.63355	0.00103472
63	94.27	97.246	95.7575	1.48847	-2.97597
64	105.58	102.639	97.055	5.5838	2.9412
65	99.89	100.996	98.3233	2.67305	-1.10638
66	97.46	99.6885	99.0988	0.589715	-2.22847
67	99.21	NA	NA	0.786632	NA
68	97.72	NA	NA	-4.23903	NA
69	99.31	NA	NA	-5.17453	NA
70	102.57	NA	NA	-5.7982	NA
71	102.16	NA	NA	-2.51578	NA
72	99.12	NA	NA	-0.747201	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 95.31 & NA & NA & 3.71955 & NA \tabularnewline
2 & 93.47 & NA & NA & 3.63355 & NA \tabularnewline
3 & 98.92 & NA & NA & 1.48847 & NA \tabularnewline
4 & 101.21 & NA & NA & 5.5838 & NA \tabularnewline
5 & 95.19 & NA & NA & 2.67305 & NA \tabularnewline
6 & 90.95 & NA & NA & 0.589715 & NA \tabularnewline
7 & 93.09 & 94.7641 & 93.9775 & 0.786632 & -1.67413 \tabularnewline
8 & 90.16 & 90.1176 & 94.3567 & -4.23903 & 0.0423681 \tabularnewline
9 & 91.86 & 89.1446 & 94.3192 & -5.17453 & 2.71537 \tabularnewline
10 & 88.82 & 87.9993 & 93.7975 & -5.7982 & 0.820701 \tabularnewline
11 & 91.58 & 90.9159 & 93.4317 & -2.51578 & 0.664118 \tabularnewline
12 & 94.9 & 92.5307 & 93.2779 & -0.747201 & 2.36928 \tabularnewline
13 & 99.85 & 96.7558 & 93.0362 & 3.71955 & 3.0942 \tabularnewline
14 & 98.03 & 96.2794 & 92.6458 & 3.63355 & 1.75062 \tabularnewline
15 & 93.46 & 93.5218 & 92.0333 & 1.48847 & -0.0617986 \tabularnewline
16 & 94.15 & 97.05 & 91.4662 & 5.5838 & -2.90005 \tabularnewline
17 & 93.47 & 93.868 & 91.195 & 2.67305 & -0.398049 \tabularnewline
18 & 88.98 & 91.4872 & 90.8975 & 0.589715 & -2.50722 \tabularnewline
19 & 89.26 & 91.1904 & 90.4037 & 0.786632 & -1.93038 \tabularnewline
20 & 84.62 & 85.8822 & 90.1212 & -4.23903 & -1.26222 \tabularnewline
21 & 82.7 & 85.1142 & 90.2888 & -5.17453 & -2.41422 \tabularnewline
22 & 84.37 & 84.7918 & 90.59 & -5.7982 & -0.421799 \tabularnewline
23 & 89.52 & 88.4517 & 90.9675 & -2.51578 & 1.06828 \tabularnewline
24 & 89.82 & 90.7953 & 91.5425 & -0.747201 & -0.975299 \tabularnewline
25 & 93.08 & 95.8912 & 92.1717 & 3.71955 & -2.81122 \tabularnewline
26 & 98.02 & 96.4919 & 92.8583 & 3.63355 & 1.52812 \tabularnewline
27 & 97.49 & 95.0372 & 93.5488 & 1.48847 & 2.45278 \tabularnewline
28 & 97.35 & 99.8517 & 94.2679 & 5.5838 & -2.50172 \tabularnewline
29 & 99.33 & 97.5918 & 94.9188 & 2.67305 & 1.7382 \tabularnewline
30 & 96.92 & 96.0001 & 95.4104 & 0.589715 & 0.919868 \tabularnewline
31 & 96.42 & 96.7587 & 95.9721 & 0.786632 & -0.338715 \tabularnewline
32 & 93.94 & 92.1014 & 96.3404 & -4.23903 & 1.83862 \tabularnewline
33 & 89.95 & 91.3571 & 96.5317 & -5.17453 & -1.40713 \tabularnewline
34 & 94.38 & 91.2418 & 97.04 & -5.7982 & 3.1382 \tabularnewline
35 & 95.13 & 95.193 & 97.7088 & -2.51578 & -0.0629653 \tabularnewline
36 & 96.01 & 97.6657 & 98.4129 & -0.747201 & -1.65572 \tabularnewline
37 & 100.37 & 102.834 & 99.1142 & 3.71955 & -2.46372 \tabularnewline
38 & 99.57 & 103.224 & 99.59 & 3.63355 & -3.65355 \tabularnewline
39 & 100.53 & 101.506 & 100.017 & 1.48847 & -0.975549 \tabularnewline
40 & 106.51 & 105.736 & 100.152 & 5.5838 & 0.774118 \tabularnewline
41 & 106.22 & 102.677 & 100.004 & 2.67305 & 3.54278 \tabularnewline
42 & 106.93 & 100.568 & 99.9783 & 0.589715 & 6.36195 \tabularnewline
43 & 103.24 & 100.87 & 100.083 & 0.786632 & 2.37003 \tabularnewline
44 & 98.54 & 95.896 & 100.135 & -4.23903 & 2.64403 \tabularnewline
45 & 95.6 & 94.7767 & 99.9512 & -5.17453 & 0.823285 \tabularnewline
46 & 91.97 & 93.7464 & 99.5446 & -5.7982 & -1.77638 \tabularnewline
47 & 93.99 & 96.1625 & 98.6783 & -2.51578 & -2.17255 \tabularnewline
48 & 96.53 & 96.629 & 97.3762 & -0.747201 & -0.0990486 \tabularnewline
49 & 102.37 & 100.032 & 96.3125 & 3.71955 & 2.33795 \tabularnewline
50 & 98.81 & 98.974 & 95.3404 & 3.63355 & -0.163965 \tabularnewline
51 & 96.88 & 95.8572 & 94.3687 & 1.48847 & 1.02278 \tabularnewline
52 & 100.4 & 99.2513 & 93.6675 & 5.5838 & 1.1487 \tabularnewline
53 & 91.54 & 95.8543 & 93.1812 & 2.67305 & -4.3143 \tabularnewline
54 & 90.36 & 93.4439 & 92.8542 & 0.589715 & -3.08388 \tabularnewline
55 & 94.28 & 93.2445 & 92.4579 & 0.786632 & 1.03545 \tabularnewline
56 & 84.17 & 87.9705 & 92.2096 & -4.23903 & -3.80055 \tabularnewline
57 & 86.65 & 86.905 & 92.0796 & -5.17453 & -0.255049 \tabularnewline
58 & 84.09 & 86.3885 & 92.1867 & -5.7982 & -2.29847 \tabularnewline
59 & 90.2 & 90.2346 & 92.7504 & -2.51578 & -0.0346319 \tabularnewline
60 & 92.47 & 92.647 & 93.3942 & -0.747201 & -0.176965 \tabularnewline
61 & 96.92 & 97.615 & 93.8954 & 3.71955 & -0.694965 \tabularnewline
62 & 98.3 & 98.299 & 94.6654 & 3.63355 & 0.00103472 \tabularnewline
63 & 94.27 & 97.246 & 95.7575 & 1.48847 & -2.97597 \tabularnewline
64 & 105.58 & 102.639 & 97.055 & 5.5838 & 2.9412 \tabularnewline
65 & 99.89 & 100.996 & 98.3233 & 2.67305 & -1.10638 \tabularnewline
66 & 97.46 & 99.6885 & 99.0988 & 0.589715 & -2.22847 \tabularnewline
67 & 99.21 & NA & NA & 0.786632 & NA \tabularnewline
68 & 97.72 & NA & NA & -4.23903 & NA \tabularnewline
69 & 99.31 & NA & NA & -5.17453 & NA \tabularnewline
70 & 102.57 & NA & NA & -5.7982 & NA \tabularnewline
71 & 102.16 & NA & NA & -2.51578 & NA \tabularnewline
72 & 99.12 & NA & NA & -0.747201 & 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]95.31[/C][C]NA[/C][C]NA[/C][C]3.71955[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]93.47[/C][C]NA[/C][C]NA[/C][C]3.63355[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]98.92[/C][C]NA[/C][C]NA[/C][C]1.48847[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]101.21[/C][C]NA[/C][C]NA[/C][C]5.5838[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]95.19[/C][C]NA[/C][C]NA[/C][C]2.67305[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]90.95[/C][C]NA[/C][C]NA[/C][C]0.589715[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]93.09[/C][C]94.7641[/C][C]93.9775[/C][C]0.786632[/C][C]-1.67413[/C][/ROW]
[ROW][C]8[/C][C]90.16[/C][C]90.1176[/C][C]94.3567[/C][C]-4.23903[/C][C]0.0423681[/C][/ROW]
[ROW][C]9[/C][C]91.86[/C][C]89.1446[/C][C]94.3192[/C][C]-5.17453[/C][C]2.71537[/C][/ROW]
[ROW][C]10[/C][C]88.82[/C][C]87.9993[/C][C]93.7975[/C][C]-5.7982[/C][C]0.820701[/C][/ROW]
[ROW][C]11[/C][C]91.58[/C][C]90.9159[/C][C]93.4317[/C][C]-2.51578[/C][C]0.664118[/C][/ROW]
[ROW][C]12[/C][C]94.9[/C][C]92.5307[/C][C]93.2779[/C][C]-0.747201[/C][C]2.36928[/C][/ROW]
[ROW][C]13[/C][C]99.85[/C][C]96.7558[/C][C]93.0362[/C][C]3.71955[/C][C]3.0942[/C][/ROW]
[ROW][C]14[/C][C]98.03[/C][C]96.2794[/C][C]92.6458[/C][C]3.63355[/C][C]1.75062[/C][/ROW]
[ROW][C]15[/C][C]93.46[/C][C]93.5218[/C][C]92.0333[/C][C]1.48847[/C][C]-0.0617986[/C][/ROW]
[ROW][C]16[/C][C]94.15[/C][C]97.05[/C][C]91.4662[/C][C]5.5838[/C][C]-2.90005[/C][/ROW]
[ROW][C]17[/C][C]93.47[/C][C]93.868[/C][C]91.195[/C][C]2.67305[/C][C]-0.398049[/C][/ROW]
[ROW][C]18[/C][C]88.98[/C][C]91.4872[/C][C]90.8975[/C][C]0.589715[/C][C]-2.50722[/C][/ROW]
[ROW][C]19[/C][C]89.26[/C][C]91.1904[/C][C]90.4037[/C][C]0.786632[/C][C]-1.93038[/C][/ROW]
[ROW][C]20[/C][C]84.62[/C][C]85.8822[/C][C]90.1212[/C][C]-4.23903[/C][C]-1.26222[/C][/ROW]
[ROW][C]21[/C][C]82.7[/C][C]85.1142[/C][C]90.2888[/C][C]-5.17453[/C][C]-2.41422[/C][/ROW]
[ROW][C]22[/C][C]84.37[/C][C]84.7918[/C][C]90.59[/C][C]-5.7982[/C][C]-0.421799[/C][/ROW]
[ROW][C]23[/C][C]89.52[/C][C]88.4517[/C][C]90.9675[/C][C]-2.51578[/C][C]1.06828[/C][/ROW]
[ROW][C]24[/C][C]89.82[/C][C]90.7953[/C][C]91.5425[/C][C]-0.747201[/C][C]-0.975299[/C][/ROW]
[ROW][C]25[/C][C]93.08[/C][C]95.8912[/C][C]92.1717[/C][C]3.71955[/C][C]-2.81122[/C][/ROW]
[ROW][C]26[/C][C]98.02[/C][C]96.4919[/C][C]92.8583[/C][C]3.63355[/C][C]1.52812[/C][/ROW]
[ROW][C]27[/C][C]97.49[/C][C]95.0372[/C][C]93.5488[/C][C]1.48847[/C][C]2.45278[/C][/ROW]
[ROW][C]28[/C][C]97.35[/C][C]99.8517[/C][C]94.2679[/C][C]5.5838[/C][C]-2.50172[/C][/ROW]
[ROW][C]29[/C][C]99.33[/C][C]97.5918[/C][C]94.9188[/C][C]2.67305[/C][C]1.7382[/C][/ROW]
[ROW][C]30[/C][C]96.92[/C][C]96.0001[/C][C]95.4104[/C][C]0.589715[/C][C]0.919868[/C][/ROW]
[ROW][C]31[/C][C]96.42[/C][C]96.7587[/C][C]95.9721[/C][C]0.786632[/C][C]-0.338715[/C][/ROW]
[ROW][C]32[/C][C]93.94[/C][C]92.1014[/C][C]96.3404[/C][C]-4.23903[/C][C]1.83862[/C][/ROW]
[ROW][C]33[/C][C]89.95[/C][C]91.3571[/C][C]96.5317[/C][C]-5.17453[/C][C]-1.40713[/C][/ROW]
[ROW][C]34[/C][C]94.38[/C][C]91.2418[/C][C]97.04[/C][C]-5.7982[/C][C]3.1382[/C][/ROW]
[ROW][C]35[/C][C]95.13[/C][C]95.193[/C][C]97.7088[/C][C]-2.51578[/C][C]-0.0629653[/C][/ROW]
[ROW][C]36[/C][C]96.01[/C][C]97.6657[/C][C]98.4129[/C][C]-0.747201[/C][C]-1.65572[/C][/ROW]
[ROW][C]37[/C][C]100.37[/C][C]102.834[/C][C]99.1142[/C][C]3.71955[/C][C]-2.46372[/C][/ROW]
[ROW][C]38[/C][C]99.57[/C][C]103.224[/C][C]99.59[/C][C]3.63355[/C][C]-3.65355[/C][/ROW]
[ROW][C]39[/C][C]100.53[/C][C]101.506[/C][C]100.017[/C][C]1.48847[/C][C]-0.975549[/C][/ROW]
[ROW][C]40[/C][C]106.51[/C][C]105.736[/C][C]100.152[/C][C]5.5838[/C][C]0.774118[/C][/ROW]
[ROW][C]41[/C][C]106.22[/C][C]102.677[/C][C]100.004[/C][C]2.67305[/C][C]3.54278[/C][/ROW]
[ROW][C]42[/C][C]106.93[/C][C]100.568[/C][C]99.9783[/C][C]0.589715[/C][C]6.36195[/C][/ROW]
[ROW][C]43[/C][C]103.24[/C][C]100.87[/C][C]100.083[/C][C]0.786632[/C][C]2.37003[/C][/ROW]
[ROW][C]44[/C][C]98.54[/C][C]95.896[/C][C]100.135[/C][C]-4.23903[/C][C]2.64403[/C][/ROW]
[ROW][C]45[/C][C]95.6[/C][C]94.7767[/C][C]99.9512[/C][C]-5.17453[/C][C]0.823285[/C][/ROW]
[ROW][C]46[/C][C]91.97[/C][C]93.7464[/C][C]99.5446[/C][C]-5.7982[/C][C]-1.77638[/C][/ROW]
[ROW][C]47[/C][C]93.99[/C][C]96.1625[/C][C]98.6783[/C][C]-2.51578[/C][C]-2.17255[/C][/ROW]
[ROW][C]48[/C][C]96.53[/C][C]96.629[/C][C]97.3762[/C][C]-0.747201[/C][C]-0.0990486[/C][/ROW]
[ROW][C]49[/C][C]102.37[/C][C]100.032[/C][C]96.3125[/C][C]3.71955[/C][C]2.33795[/C][/ROW]
[ROW][C]50[/C][C]98.81[/C][C]98.974[/C][C]95.3404[/C][C]3.63355[/C][C]-0.163965[/C][/ROW]
[ROW][C]51[/C][C]96.88[/C][C]95.8572[/C][C]94.3687[/C][C]1.48847[/C][C]1.02278[/C][/ROW]
[ROW][C]52[/C][C]100.4[/C][C]99.2513[/C][C]93.6675[/C][C]5.5838[/C][C]1.1487[/C][/ROW]
[ROW][C]53[/C][C]91.54[/C][C]95.8543[/C][C]93.1812[/C][C]2.67305[/C][C]-4.3143[/C][/ROW]
[ROW][C]54[/C][C]90.36[/C][C]93.4439[/C][C]92.8542[/C][C]0.589715[/C][C]-3.08388[/C][/ROW]
[ROW][C]55[/C][C]94.28[/C][C]93.2445[/C][C]92.4579[/C][C]0.786632[/C][C]1.03545[/C][/ROW]
[ROW][C]56[/C][C]84.17[/C][C]87.9705[/C][C]92.2096[/C][C]-4.23903[/C][C]-3.80055[/C][/ROW]
[ROW][C]57[/C][C]86.65[/C][C]86.905[/C][C]92.0796[/C][C]-5.17453[/C][C]-0.255049[/C][/ROW]
[ROW][C]58[/C][C]84.09[/C][C]86.3885[/C][C]92.1867[/C][C]-5.7982[/C][C]-2.29847[/C][/ROW]
[ROW][C]59[/C][C]90.2[/C][C]90.2346[/C][C]92.7504[/C][C]-2.51578[/C][C]-0.0346319[/C][/ROW]
[ROW][C]60[/C][C]92.47[/C][C]92.647[/C][C]93.3942[/C][C]-0.747201[/C][C]-0.176965[/C][/ROW]
[ROW][C]61[/C][C]96.92[/C][C]97.615[/C][C]93.8954[/C][C]3.71955[/C][C]-0.694965[/C][/ROW]
[ROW][C]62[/C][C]98.3[/C][C]98.299[/C][C]94.6654[/C][C]3.63355[/C][C]0.00103472[/C][/ROW]
[ROW][C]63[/C][C]94.27[/C][C]97.246[/C][C]95.7575[/C][C]1.48847[/C][C]-2.97597[/C][/ROW]
[ROW][C]64[/C][C]105.58[/C][C]102.639[/C][C]97.055[/C][C]5.5838[/C][C]2.9412[/C][/ROW]
[ROW][C]65[/C][C]99.89[/C][C]100.996[/C][C]98.3233[/C][C]2.67305[/C][C]-1.10638[/C][/ROW]
[ROW][C]66[/C][C]97.46[/C][C]99.6885[/C][C]99.0988[/C][C]0.589715[/C][C]-2.22847[/C][/ROW]
[ROW][C]67[/C][C]99.21[/C][C]NA[/C][C]NA[/C][C]0.786632[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]97.72[/C][C]NA[/C][C]NA[/C][C]-4.23903[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]99.31[/C][C]NA[/C][C]NA[/C][C]-5.17453[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]102.57[/C][C]NA[/C][C]NA[/C][C]-5.7982[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]102.16[/C][C]NA[/C][C]NA[/C][C]-2.51578[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]99.12[/C][C]NA[/C][C]NA[/C][C]-0.747201[/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	95.31	NA	NA	3.71955	NA
2	93.47	NA	NA	3.63355	NA
3	98.92	NA	NA	1.48847	NA
4	101.21	NA	NA	5.5838	NA
5	95.19	NA	NA	2.67305	NA
6	90.95	NA	NA	0.589715	NA
7	93.09	94.7641	93.9775	0.786632	-1.67413
8	90.16	90.1176	94.3567	-4.23903	0.0423681
9	91.86	89.1446	94.3192	-5.17453	2.71537
10	88.82	87.9993	93.7975	-5.7982	0.820701
11	91.58	90.9159	93.4317	-2.51578	0.664118
12	94.9	92.5307	93.2779	-0.747201	2.36928
13	99.85	96.7558	93.0362	3.71955	3.0942
14	98.03	96.2794	92.6458	3.63355	1.75062
15	93.46	93.5218	92.0333	1.48847	-0.0617986
16	94.15	97.05	91.4662	5.5838	-2.90005
17	93.47	93.868	91.195	2.67305	-0.398049
18	88.98	91.4872	90.8975	0.589715	-2.50722
19	89.26	91.1904	90.4037	0.786632	-1.93038
20	84.62	85.8822	90.1212	-4.23903	-1.26222
21	82.7	85.1142	90.2888	-5.17453	-2.41422
22	84.37	84.7918	90.59	-5.7982	-0.421799
23	89.52	88.4517	90.9675	-2.51578	1.06828
24	89.82	90.7953	91.5425	-0.747201	-0.975299
25	93.08	95.8912	92.1717	3.71955	-2.81122
26	98.02	96.4919	92.8583	3.63355	1.52812
27	97.49	95.0372	93.5488	1.48847	2.45278
28	97.35	99.8517	94.2679	5.5838	-2.50172
29	99.33	97.5918	94.9188	2.67305	1.7382
30	96.92	96.0001	95.4104	0.589715	0.919868
31	96.42	96.7587	95.9721	0.786632	-0.338715
32	93.94	92.1014	96.3404	-4.23903	1.83862
33	89.95	91.3571	96.5317	-5.17453	-1.40713
34	94.38	91.2418	97.04	-5.7982	3.1382
35	95.13	95.193	97.7088	-2.51578	-0.0629653
36	96.01	97.6657	98.4129	-0.747201	-1.65572
37	100.37	102.834	99.1142	3.71955	-2.46372
38	99.57	103.224	99.59	3.63355	-3.65355
39	100.53	101.506	100.017	1.48847	-0.975549
40	106.51	105.736	100.152	5.5838	0.774118
41	106.22	102.677	100.004	2.67305	3.54278
42	106.93	100.568	99.9783	0.589715	6.36195
43	103.24	100.87	100.083	0.786632	2.37003
44	98.54	95.896	100.135	-4.23903	2.64403
45	95.6	94.7767	99.9512	-5.17453	0.823285
46	91.97	93.7464	99.5446	-5.7982	-1.77638
47	93.99	96.1625	98.6783	-2.51578	-2.17255
48	96.53	96.629	97.3762	-0.747201	-0.0990486
49	102.37	100.032	96.3125	3.71955	2.33795
50	98.81	98.974	95.3404	3.63355	-0.163965
51	96.88	95.8572	94.3687	1.48847	1.02278
52	100.4	99.2513	93.6675	5.5838	1.1487
53	91.54	95.8543	93.1812	2.67305	-4.3143
54	90.36	93.4439	92.8542	0.589715	-3.08388
55	94.28	93.2445	92.4579	0.786632	1.03545
56	84.17	87.9705	92.2096	-4.23903	-3.80055
57	86.65	86.905	92.0796	-5.17453	-0.255049
58	84.09	86.3885	92.1867	-5.7982	-2.29847
59	90.2	90.2346	92.7504	-2.51578	-0.0346319
60	92.47	92.647	93.3942	-0.747201	-0.176965
61	96.92	97.615	93.8954	3.71955	-0.694965
62	98.3	98.299	94.6654	3.63355	0.00103472
63	94.27	97.246	95.7575	1.48847	-2.97597
64	105.58	102.639	97.055	5.5838	2.9412
65	99.89	100.996	98.3233	2.67305	-1.10638
66	97.46	99.6885	99.0988	0.589715	-2.22847
67	99.21	NA	NA	0.786632	NA
68	97.72	NA	NA	-4.23903	NA
69	99.31	NA	NA	-5.17453	NA
70	102.57	NA	NA	-5.7982	NA
71	102.16	NA	NA	-2.51578	NA
72	99.12	NA	NA	-0.747201	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