Free Statistics

of Irreproducible Research!

Author's title

Decompositiemodel-consumptieprijsindex kleding&schoeisel-Charlotte Tilborgh...

Author

*Unverified author*

R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Fri, 28 Apr 2017 13:03:14 +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/2017/Apr/28/t1493381379g3xu434z5nd3r60.htm/, Retrieved Fri, 10 May 2024 10:54:51 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Fri, 10 May 2024 10:54:51 +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	3 seconds
R Server	'George Udny Yule' @ yule.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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.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	3 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	100.57	NA	NA	-0.0382812	NA
2	100.27	NA	NA	-0.33342	NA
3	100.27	NA	NA	-0.271337	NA
4	100.18	NA	NA	-0.166753	NA
5	100.16	NA	NA	-0.0586285	NA
6	100.18	NA	NA	-0.041684	NA
7	100.18	100.274	100.563	-0.289045	-0.0938715
8	100.59	100.535	100.61	-0.0747396	0.0547396
9	100.69	100.721	100.66	0.0606771	-0.0310937
10	101.06	101.056	100.728	0.328108	0.00397569
11	101.15	101.26	100.815	0.445816	-0.110399
12	101.16	101.349	100.91	0.439288	-0.189288
13	101.16	100.969	101.007	-0.0382812	0.191198
14	100.81	100.769	101.102	-0.33342	0.0413368
15	100.94	100.934	101.205	-0.271337	0.00633681
16	101.13	101.154	101.321	-0.166753	-0.0240799
17	101.29	101.384	101.443	-0.0586285	-0.0942882
18	101.34	101.529	101.571	-0.041684	-0.189149
19	101.35	101.412	101.701	-0.289045	-0.0622049
20	101.7	101.749	101.824	-0.0747396	-0.0490104
21	102.05	101.995	101.934	0.0606771	0.0551562
22	102.48	102.367	102.039	0.328108	0.112726
23	102.66	102.588	102.142	0.445816	0.071684
24	102.72	102.686	102.247	0.439288	0.0336285
25	102.73	102.315	102.353	-0.0382812	0.415365
26	102.18	102.127	102.46	-0.33342	0.0534201
27	102.22	102.289	102.561	-0.271337	-0.0694965
28	102.37	102.484	102.651	-0.166753	-0.11408
29	102.53	102.678	102.737	-0.0586285	-0.148455
30	102.61	102.78	102.822	-0.041684	-0.170399
31	102.62	102.443	102.732	-0.289045	0.176545
32	103	102.397	102.472	-0.0747396	0.602656
33	103.17	102.276	102.215	0.0606771	0.894323
34	103.52	102.289	101.961	0.328108	1.23064
35	103.69	102.16	101.714	0.445816	1.53043
36	103.73	101.908	101.468	0.439288	1.82238
37	99.57	101.189	101.227	-0.0382812	-1.61922
38	99.09	100.658	100.991	-0.33342	-1.56783
39	99.14	100.488	100.759	-0.271337	-1.34783
40	99.36	100.369	100.536	-0.166753	-1.0095
41	99.6	100.261	100.32	-0.0586285	-0.661372
42	99.65	100.065	100.107	-0.041684	-0.414983
43	99.8	99.7793	100.068	-0.289045	0.0207118
44	100.15	100.146	100.22	-0.0747396	0.00432292
45	100.45	100.449	100.389	0.0606771	0.000572917
46	100.89	100.874	100.546	0.328108	0.0156424
47	101.13	101.132	100.686	0.445816	-0.00164931
48	101.17	101.254	100.815	0.439288	-0.0842882
49	101.21	100.886	100.924	-0.0382812	0.324115
50	101.1	100.677	101.01	-0.33342	0.423003
51	101.17	100.811	101.082	-0.271337	0.359253
52	101.11	100.962	101.129	-0.166753	0.147587
53	101.2	101.096	101.155	-0.0586285	0.103628
54	101.15	101.135	101.177	-0.041684	0.0150174
55	100.92	100.929	101.218	-0.289045	-0.00928819
56	101.1	101.209	101.283	-0.0747396	-0.108594
57	101.22	101.415	101.354	0.0606771	-0.194844
58	101.25	101.759	101.431	0.328108	-0.508941
59	101.39	101.954	101.508	0.445816	-0.564149
60	101.43	102.027	101.588	0.439288	-0.597205
61	101.95	101.621	101.66	-0.0382812	0.328698
62	101.92	101.369	101.703	-0.33342	0.550503
63	102.05	101.453	101.724	-0.271337	0.59717
64	102.07	101.579	101.746	-0.166753	0.490503
65	102.1	101.716	101.775	-0.0586285	0.384045
66	102.16	101.759	101.801	-0.041684	0.400851
67	101.63	101.554	101.843	-0.289045	0.0757118
68	101.43	101.827	101.901	-0.0747396	-0.39651
69	101.4	102.017	101.956	0.0606771	-0.61651
70	101.6	102.346	102.018	0.328108	-0.746441
71	101.72	102.538	102.092	0.445816	-0.818316
72	101.73	102.608	102.168	0.439288	-0.877622
73	102.67	102.203	102.241	-0.0382812	0.467448
74	102.59	101.983	102.316	-0.33342	0.60717
75	102.69	102.128	102.399	-0.271337	0.56217
76	102.93	102.313	102.48	-0.166753	0.61717
77	103.02	102.496	102.555	-0.0586285	0.524045
78	103.06	102.594	102.635	-0.041684	0.466267
79	102.47	NA	NA	-0.289045	NA
80	102.4	NA	NA	-0.0747396	NA
81	102.42	NA	NA	0.0606771	NA
82	102.51	NA	NA	0.328108	NA
83	102.61	NA	NA	0.445816	NA
84	102.78	NA	NA	0.439288	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 100.57 & NA & NA & -0.0382812 & NA \tabularnewline
2 & 100.27 & NA & NA & -0.33342 & NA \tabularnewline
3 & 100.27 & NA & NA & -0.271337 & NA \tabularnewline
4 & 100.18 & NA & NA & -0.166753 & NA \tabularnewline
5 & 100.16 & NA & NA & -0.0586285 & NA \tabularnewline
6 & 100.18 & NA & NA & -0.041684 & NA \tabularnewline
7 & 100.18 & 100.274 & 100.563 & -0.289045 & -0.0938715 \tabularnewline
8 & 100.59 & 100.535 & 100.61 & -0.0747396 & 0.0547396 \tabularnewline
9 & 100.69 & 100.721 & 100.66 & 0.0606771 & -0.0310937 \tabularnewline
10 & 101.06 & 101.056 & 100.728 & 0.328108 & 0.00397569 \tabularnewline
11 & 101.15 & 101.26 & 100.815 & 0.445816 & -0.110399 \tabularnewline
12 & 101.16 & 101.349 & 100.91 & 0.439288 & -0.189288 \tabularnewline
13 & 101.16 & 100.969 & 101.007 & -0.0382812 & 0.191198 \tabularnewline
14 & 100.81 & 100.769 & 101.102 & -0.33342 & 0.0413368 \tabularnewline
15 & 100.94 & 100.934 & 101.205 & -0.271337 & 0.00633681 \tabularnewline
16 & 101.13 & 101.154 & 101.321 & -0.166753 & -0.0240799 \tabularnewline
17 & 101.29 & 101.384 & 101.443 & -0.0586285 & -0.0942882 \tabularnewline
18 & 101.34 & 101.529 & 101.571 & -0.041684 & -0.189149 \tabularnewline
19 & 101.35 & 101.412 & 101.701 & -0.289045 & -0.0622049 \tabularnewline
20 & 101.7 & 101.749 & 101.824 & -0.0747396 & -0.0490104 \tabularnewline
21 & 102.05 & 101.995 & 101.934 & 0.0606771 & 0.0551562 \tabularnewline
22 & 102.48 & 102.367 & 102.039 & 0.328108 & 0.112726 \tabularnewline
23 & 102.66 & 102.588 & 102.142 & 0.445816 & 0.071684 \tabularnewline
24 & 102.72 & 102.686 & 102.247 & 0.439288 & 0.0336285 \tabularnewline
25 & 102.73 & 102.315 & 102.353 & -0.0382812 & 0.415365 \tabularnewline
26 & 102.18 & 102.127 & 102.46 & -0.33342 & 0.0534201 \tabularnewline
27 & 102.22 & 102.289 & 102.561 & -0.271337 & -0.0694965 \tabularnewline
28 & 102.37 & 102.484 & 102.651 & -0.166753 & -0.11408 \tabularnewline
29 & 102.53 & 102.678 & 102.737 & -0.0586285 & -0.148455 \tabularnewline
30 & 102.61 & 102.78 & 102.822 & -0.041684 & -0.170399 \tabularnewline
31 & 102.62 & 102.443 & 102.732 & -0.289045 & 0.176545 \tabularnewline
32 & 103 & 102.397 & 102.472 & -0.0747396 & 0.602656 \tabularnewline
33 & 103.17 & 102.276 & 102.215 & 0.0606771 & 0.894323 \tabularnewline
34 & 103.52 & 102.289 & 101.961 & 0.328108 & 1.23064 \tabularnewline
35 & 103.69 & 102.16 & 101.714 & 0.445816 & 1.53043 \tabularnewline
36 & 103.73 & 101.908 & 101.468 & 0.439288 & 1.82238 \tabularnewline
37 & 99.57 & 101.189 & 101.227 & -0.0382812 & -1.61922 \tabularnewline
38 & 99.09 & 100.658 & 100.991 & -0.33342 & -1.56783 \tabularnewline
39 & 99.14 & 100.488 & 100.759 & -0.271337 & -1.34783 \tabularnewline
40 & 99.36 & 100.369 & 100.536 & -0.166753 & -1.0095 \tabularnewline
41 & 99.6 & 100.261 & 100.32 & -0.0586285 & -0.661372 \tabularnewline
42 & 99.65 & 100.065 & 100.107 & -0.041684 & -0.414983 \tabularnewline
43 & 99.8 & 99.7793 & 100.068 & -0.289045 & 0.0207118 \tabularnewline
44 & 100.15 & 100.146 & 100.22 & -0.0747396 & 0.00432292 \tabularnewline
45 & 100.45 & 100.449 & 100.389 & 0.0606771 & 0.000572917 \tabularnewline
46 & 100.89 & 100.874 & 100.546 & 0.328108 & 0.0156424 \tabularnewline
47 & 101.13 & 101.132 & 100.686 & 0.445816 & -0.00164931 \tabularnewline
48 & 101.17 & 101.254 & 100.815 & 0.439288 & -0.0842882 \tabularnewline
49 & 101.21 & 100.886 & 100.924 & -0.0382812 & 0.324115 \tabularnewline
50 & 101.1 & 100.677 & 101.01 & -0.33342 & 0.423003 \tabularnewline
51 & 101.17 & 100.811 & 101.082 & -0.271337 & 0.359253 \tabularnewline
52 & 101.11 & 100.962 & 101.129 & -0.166753 & 0.147587 \tabularnewline
53 & 101.2 & 101.096 & 101.155 & -0.0586285 & 0.103628 \tabularnewline
54 & 101.15 & 101.135 & 101.177 & -0.041684 & 0.0150174 \tabularnewline
55 & 100.92 & 100.929 & 101.218 & -0.289045 & -0.00928819 \tabularnewline
56 & 101.1 & 101.209 & 101.283 & -0.0747396 & -0.108594 \tabularnewline
57 & 101.22 & 101.415 & 101.354 & 0.0606771 & -0.194844 \tabularnewline
58 & 101.25 & 101.759 & 101.431 & 0.328108 & -0.508941 \tabularnewline
59 & 101.39 & 101.954 & 101.508 & 0.445816 & -0.564149 \tabularnewline
60 & 101.43 & 102.027 & 101.588 & 0.439288 & -0.597205 \tabularnewline
61 & 101.95 & 101.621 & 101.66 & -0.0382812 & 0.328698 \tabularnewline
62 & 101.92 & 101.369 & 101.703 & -0.33342 & 0.550503 \tabularnewline
63 & 102.05 & 101.453 & 101.724 & -0.271337 & 0.59717 \tabularnewline
64 & 102.07 & 101.579 & 101.746 & -0.166753 & 0.490503 \tabularnewline
65 & 102.1 & 101.716 & 101.775 & -0.0586285 & 0.384045 \tabularnewline
66 & 102.16 & 101.759 & 101.801 & -0.041684 & 0.400851 \tabularnewline
67 & 101.63 & 101.554 & 101.843 & -0.289045 & 0.0757118 \tabularnewline
68 & 101.43 & 101.827 & 101.901 & -0.0747396 & -0.39651 \tabularnewline
69 & 101.4 & 102.017 & 101.956 & 0.0606771 & -0.61651 \tabularnewline
70 & 101.6 & 102.346 & 102.018 & 0.328108 & -0.746441 \tabularnewline
71 & 101.72 & 102.538 & 102.092 & 0.445816 & -0.818316 \tabularnewline
72 & 101.73 & 102.608 & 102.168 & 0.439288 & -0.877622 \tabularnewline
73 & 102.67 & 102.203 & 102.241 & -0.0382812 & 0.467448 \tabularnewline
74 & 102.59 & 101.983 & 102.316 & -0.33342 & 0.60717 \tabularnewline
75 & 102.69 & 102.128 & 102.399 & -0.271337 & 0.56217 \tabularnewline
76 & 102.93 & 102.313 & 102.48 & -0.166753 & 0.61717 \tabularnewline
77 & 103.02 & 102.496 & 102.555 & -0.0586285 & 0.524045 \tabularnewline
78 & 103.06 & 102.594 & 102.635 & -0.041684 & 0.466267 \tabularnewline
79 & 102.47 & NA & NA & -0.289045 & NA \tabularnewline
80 & 102.4 & NA & NA & -0.0747396 & NA \tabularnewline
81 & 102.42 & NA & NA & 0.0606771 & NA \tabularnewline
82 & 102.51 & NA & NA & 0.328108 & NA \tabularnewline
83 & 102.61 & NA & NA & 0.445816 & NA \tabularnewline
84 & 102.78 & NA & NA & 0.439288 & 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]100.57[/C][C]NA[/C][C]NA[/C][C]-0.0382812[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]100.27[/C][C]NA[/C][C]NA[/C][C]-0.33342[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]100.27[/C][C]NA[/C][C]NA[/C][C]-0.271337[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]100.18[/C][C]NA[/C][C]NA[/C][C]-0.166753[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]100.16[/C][C]NA[/C][C]NA[/C][C]-0.0586285[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]100.18[/C][C]NA[/C][C]NA[/C][C]-0.041684[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]100.18[/C][C]100.274[/C][C]100.563[/C][C]-0.289045[/C][C]-0.0938715[/C][/ROW]
[ROW][C]8[/C][C]100.59[/C][C]100.535[/C][C]100.61[/C][C]-0.0747396[/C][C]0.0547396[/C][/ROW]
[ROW][C]9[/C][C]100.69[/C][C]100.721[/C][C]100.66[/C][C]0.0606771[/C][C]-0.0310937[/C][/ROW]
[ROW][C]10[/C][C]101.06[/C][C]101.056[/C][C]100.728[/C][C]0.328108[/C][C]0.00397569[/C][/ROW]
[ROW][C]11[/C][C]101.15[/C][C]101.26[/C][C]100.815[/C][C]0.445816[/C][C]-0.110399[/C][/ROW]
[ROW][C]12[/C][C]101.16[/C][C]101.349[/C][C]100.91[/C][C]0.439288[/C][C]-0.189288[/C][/ROW]
[ROW][C]13[/C][C]101.16[/C][C]100.969[/C][C]101.007[/C][C]-0.0382812[/C][C]0.191198[/C][/ROW]
[ROW][C]14[/C][C]100.81[/C][C]100.769[/C][C]101.102[/C][C]-0.33342[/C][C]0.0413368[/C][/ROW]
[ROW][C]15[/C][C]100.94[/C][C]100.934[/C][C]101.205[/C][C]-0.271337[/C][C]0.00633681[/C][/ROW]
[ROW][C]16[/C][C]101.13[/C][C]101.154[/C][C]101.321[/C][C]-0.166753[/C][C]-0.0240799[/C][/ROW]
[ROW][C]17[/C][C]101.29[/C][C]101.384[/C][C]101.443[/C][C]-0.0586285[/C][C]-0.0942882[/C][/ROW]
[ROW][C]18[/C][C]101.34[/C][C]101.529[/C][C]101.571[/C][C]-0.041684[/C][C]-0.189149[/C][/ROW]
[ROW][C]19[/C][C]101.35[/C][C]101.412[/C][C]101.701[/C][C]-0.289045[/C][C]-0.0622049[/C][/ROW]
[ROW][C]20[/C][C]101.7[/C][C]101.749[/C][C]101.824[/C][C]-0.0747396[/C][C]-0.0490104[/C][/ROW]
[ROW][C]21[/C][C]102.05[/C][C]101.995[/C][C]101.934[/C][C]0.0606771[/C][C]0.0551562[/C][/ROW]
[ROW][C]22[/C][C]102.48[/C][C]102.367[/C][C]102.039[/C][C]0.328108[/C][C]0.112726[/C][/ROW]
[ROW][C]23[/C][C]102.66[/C][C]102.588[/C][C]102.142[/C][C]0.445816[/C][C]0.071684[/C][/ROW]
[ROW][C]24[/C][C]102.72[/C][C]102.686[/C][C]102.247[/C][C]0.439288[/C][C]0.0336285[/C][/ROW]
[ROW][C]25[/C][C]102.73[/C][C]102.315[/C][C]102.353[/C][C]-0.0382812[/C][C]0.415365[/C][/ROW]
[ROW][C]26[/C][C]102.18[/C][C]102.127[/C][C]102.46[/C][C]-0.33342[/C][C]0.0534201[/C][/ROW]
[ROW][C]27[/C][C]102.22[/C][C]102.289[/C][C]102.561[/C][C]-0.271337[/C][C]-0.0694965[/C][/ROW]
[ROW][C]28[/C][C]102.37[/C][C]102.484[/C][C]102.651[/C][C]-0.166753[/C][C]-0.11408[/C][/ROW]
[ROW][C]29[/C][C]102.53[/C][C]102.678[/C][C]102.737[/C][C]-0.0586285[/C][C]-0.148455[/C][/ROW]
[ROW][C]30[/C][C]102.61[/C][C]102.78[/C][C]102.822[/C][C]-0.041684[/C][C]-0.170399[/C][/ROW]
[ROW][C]31[/C][C]102.62[/C][C]102.443[/C][C]102.732[/C][C]-0.289045[/C][C]0.176545[/C][/ROW]
[ROW][C]32[/C][C]103[/C][C]102.397[/C][C]102.472[/C][C]-0.0747396[/C][C]0.602656[/C][/ROW]
[ROW][C]33[/C][C]103.17[/C][C]102.276[/C][C]102.215[/C][C]0.0606771[/C][C]0.894323[/C][/ROW]
[ROW][C]34[/C][C]103.52[/C][C]102.289[/C][C]101.961[/C][C]0.328108[/C][C]1.23064[/C][/ROW]
[ROW][C]35[/C][C]103.69[/C][C]102.16[/C][C]101.714[/C][C]0.445816[/C][C]1.53043[/C][/ROW]
[ROW][C]36[/C][C]103.73[/C][C]101.908[/C][C]101.468[/C][C]0.439288[/C][C]1.82238[/C][/ROW]
[ROW][C]37[/C][C]99.57[/C][C]101.189[/C][C]101.227[/C][C]-0.0382812[/C][C]-1.61922[/C][/ROW]
[ROW][C]38[/C][C]99.09[/C][C]100.658[/C][C]100.991[/C][C]-0.33342[/C][C]-1.56783[/C][/ROW]
[ROW][C]39[/C][C]99.14[/C][C]100.488[/C][C]100.759[/C][C]-0.271337[/C][C]-1.34783[/C][/ROW]
[ROW][C]40[/C][C]99.36[/C][C]100.369[/C][C]100.536[/C][C]-0.166753[/C][C]-1.0095[/C][/ROW]
[ROW][C]41[/C][C]99.6[/C][C]100.261[/C][C]100.32[/C][C]-0.0586285[/C][C]-0.661372[/C][/ROW]
[ROW][C]42[/C][C]99.65[/C][C]100.065[/C][C]100.107[/C][C]-0.041684[/C][C]-0.414983[/C][/ROW]
[ROW][C]43[/C][C]99.8[/C][C]99.7793[/C][C]100.068[/C][C]-0.289045[/C][C]0.0207118[/C][/ROW]
[ROW][C]44[/C][C]100.15[/C][C]100.146[/C][C]100.22[/C][C]-0.0747396[/C][C]0.00432292[/C][/ROW]
[ROW][C]45[/C][C]100.45[/C][C]100.449[/C][C]100.389[/C][C]0.0606771[/C][C]0.000572917[/C][/ROW]
[ROW][C]46[/C][C]100.89[/C][C]100.874[/C][C]100.546[/C][C]0.328108[/C][C]0.0156424[/C][/ROW]
[ROW][C]47[/C][C]101.13[/C][C]101.132[/C][C]100.686[/C][C]0.445816[/C][C]-0.00164931[/C][/ROW]
[ROW][C]48[/C][C]101.17[/C][C]101.254[/C][C]100.815[/C][C]0.439288[/C][C]-0.0842882[/C][/ROW]
[ROW][C]49[/C][C]101.21[/C][C]100.886[/C][C]100.924[/C][C]-0.0382812[/C][C]0.324115[/C][/ROW]
[ROW][C]50[/C][C]101.1[/C][C]100.677[/C][C]101.01[/C][C]-0.33342[/C][C]0.423003[/C][/ROW]
[ROW][C]51[/C][C]101.17[/C][C]100.811[/C][C]101.082[/C][C]-0.271337[/C][C]0.359253[/C][/ROW]
[ROW][C]52[/C][C]101.11[/C][C]100.962[/C][C]101.129[/C][C]-0.166753[/C][C]0.147587[/C][/ROW]
[ROW][C]53[/C][C]101.2[/C][C]101.096[/C][C]101.155[/C][C]-0.0586285[/C][C]0.103628[/C][/ROW]
[ROW][C]54[/C][C]101.15[/C][C]101.135[/C][C]101.177[/C][C]-0.041684[/C][C]0.0150174[/C][/ROW]
[ROW][C]55[/C][C]100.92[/C][C]100.929[/C][C]101.218[/C][C]-0.289045[/C][C]-0.00928819[/C][/ROW]
[ROW][C]56[/C][C]101.1[/C][C]101.209[/C][C]101.283[/C][C]-0.0747396[/C][C]-0.108594[/C][/ROW]
[ROW][C]57[/C][C]101.22[/C][C]101.415[/C][C]101.354[/C][C]0.0606771[/C][C]-0.194844[/C][/ROW]
[ROW][C]58[/C][C]101.25[/C][C]101.759[/C][C]101.431[/C][C]0.328108[/C][C]-0.508941[/C][/ROW]
[ROW][C]59[/C][C]101.39[/C][C]101.954[/C][C]101.508[/C][C]0.445816[/C][C]-0.564149[/C][/ROW]
[ROW][C]60[/C][C]101.43[/C][C]102.027[/C][C]101.588[/C][C]0.439288[/C][C]-0.597205[/C][/ROW]
[ROW][C]61[/C][C]101.95[/C][C]101.621[/C][C]101.66[/C][C]-0.0382812[/C][C]0.328698[/C][/ROW]
[ROW][C]62[/C][C]101.92[/C][C]101.369[/C][C]101.703[/C][C]-0.33342[/C][C]0.550503[/C][/ROW]
[ROW][C]63[/C][C]102.05[/C][C]101.453[/C][C]101.724[/C][C]-0.271337[/C][C]0.59717[/C][/ROW]
[ROW][C]64[/C][C]102.07[/C][C]101.579[/C][C]101.746[/C][C]-0.166753[/C][C]0.490503[/C][/ROW]
[ROW][C]65[/C][C]102.1[/C][C]101.716[/C][C]101.775[/C][C]-0.0586285[/C][C]0.384045[/C][/ROW]
[ROW][C]66[/C][C]102.16[/C][C]101.759[/C][C]101.801[/C][C]-0.041684[/C][C]0.400851[/C][/ROW]
[ROW][C]67[/C][C]101.63[/C][C]101.554[/C][C]101.843[/C][C]-0.289045[/C][C]0.0757118[/C][/ROW]
[ROW][C]68[/C][C]101.43[/C][C]101.827[/C][C]101.901[/C][C]-0.0747396[/C][C]-0.39651[/C][/ROW]
[ROW][C]69[/C][C]101.4[/C][C]102.017[/C][C]101.956[/C][C]0.0606771[/C][C]-0.61651[/C][/ROW]
[ROW][C]70[/C][C]101.6[/C][C]102.346[/C][C]102.018[/C][C]0.328108[/C][C]-0.746441[/C][/ROW]
[ROW][C]71[/C][C]101.72[/C][C]102.538[/C][C]102.092[/C][C]0.445816[/C][C]-0.818316[/C][/ROW]
[ROW][C]72[/C][C]101.73[/C][C]102.608[/C][C]102.168[/C][C]0.439288[/C][C]-0.877622[/C][/ROW]
[ROW][C]73[/C][C]102.67[/C][C]102.203[/C][C]102.241[/C][C]-0.0382812[/C][C]0.467448[/C][/ROW]
[ROW][C]74[/C][C]102.59[/C][C]101.983[/C][C]102.316[/C][C]-0.33342[/C][C]0.60717[/C][/ROW]
[ROW][C]75[/C][C]102.69[/C][C]102.128[/C][C]102.399[/C][C]-0.271337[/C][C]0.56217[/C][/ROW]
[ROW][C]76[/C][C]102.93[/C][C]102.313[/C][C]102.48[/C][C]-0.166753[/C][C]0.61717[/C][/ROW]
[ROW][C]77[/C][C]103.02[/C][C]102.496[/C][C]102.555[/C][C]-0.0586285[/C][C]0.524045[/C][/ROW]
[ROW][C]78[/C][C]103.06[/C][C]102.594[/C][C]102.635[/C][C]-0.041684[/C][C]0.466267[/C][/ROW]
[ROW][C]79[/C][C]102.47[/C][C]NA[/C][C]NA[/C][C]-0.289045[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]102.4[/C][C]NA[/C][C]NA[/C][C]-0.0747396[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]102.42[/C][C]NA[/C][C]NA[/C][C]0.0606771[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]102.51[/C][C]NA[/C][C]NA[/C][C]0.328108[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]102.61[/C][C]NA[/C][C]NA[/C][C]0.445816[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]102.78[/C][C]NA[/C][C]NA[/C][C]0.439288[/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	100.57	NA	NA	-0.0382812	NA
2	100.27	NA	NA	-0.33342	NA
3	100.27	NA	NA	-0.271337	NA
4	100.18	NA	NA	-0.166753	NA
5	100.16	NA	NA	-0.0586285	NA
6	100.18	NA	NA	-0.041684	NA
7	100.18	100.274	100.563	-0.289045	-0.0938715
8	100.59	100.535	100.61	-0.0747396	0.0547396
9	100.69	100.721	100.66	0.0606771	-0.0310937
10	101.06	101.056	100.728	0.328108	0.00397569
11	101.15	101.26	100.815	0.445816	-0.110399
12	101.16	101.349	100.91	0.439288	-0.189288
13	101.16	100.969	101.007	-0.0382812	0.191198
14	100.81	100.769	101.102	-0.33342	0.0413368
15	100.94	100.934	101.205	-0.271337	0.00633681
16	101.13	101.154	101.321	-0.166753	-0.0240799
17	101.29	101.384	101.443	-0.0586285	-0.0942882
18	101.34	101.529	101.571	-0.041684	-0.189149
19	101.35	101.412	101.701	-0.289045	-0.0622049
20	101.7	101.749	101.824	-0.0747396	-0.0490104
21	102.05	101.995	101.934	0.0606771	0.0551562
22	102.48	102.367	102.039	0.328108	0.112726
23	102.66	102.588	102.142	0.445816	0.071684
24	102.72	102.686	102.247	0.439288	0.0336285
25	102.73	102.315	102.353	-0.0382812	0.415365
26	102.18	102.127	102.46	-0.33342	0.0534201
27	102.22	102.289	102.561	-0.271337	-0.0694965
28	102.37	102.484	102.651	-0.166753	-0.11408
29	102.53	102.678	102.737	-0.0586285	-0.148455
30	102.61	102.78	102.822	-0.041684	-0.170399
31	102.62	102.443	102.732	-0.289045	0.176545
32	103	102.397	102.472	-0.0747396	0.602656
33	103.17	102.276	102.215	0.0606771	0.894323
34	103.52	102.289	101.961	0.328108	1.23064
35	103.69	102.16	101.714	0.445816	1.53043
36	103.73	101.908	101.468	0.439288	1.82238
37	99.57	101.189	101.227	-0.0382812	-1.61922
38	99.09	100.658	100.991	-0.33342	-1.56783
39	99.14	100.488	100.759	-0.271337	-1.34783
40	99.36	100.369	100.536	-0.166753	-1.0095
41	99.6	100.261	100.32	-0.0586285	-0.661372
42	99.65	100.065	100.107	-0.041684	-0.414983
43	99.8	99.7793	100.068	-0.289045	0.0207118
44	100.15	100.146	100.22	-0.0747396	0.00432292
45	100.45	100.449	100.389	0.0606771	0.000572917
46	100.89	100.874	100.546	0.328108	0.0156424
47	101.13	101.132	100.686	0.445816	-0.00164931
48	101.17	101.254	100.815	0.439288	-0.0842882
49	101.21	100.886	100.924	-0.0382812	0.324115
50	101.1	100.677	101.01	-0.33342	0.423003
51	101.17	100.811	101.082	-0.271337	0.359253
52	101.11	100.962	101.129	-0.166753	0.147587
53	101.2	101.096	101.155	-0.0586285	0.103628
54	101.15	101.135	101.177	-0.041684	0.0150174
55	100.92	100.929	101.218	-0.289045	-0.00928819
56	101.1	101.209	101.283	-0.0747396	-0.108594
57	101.22	101.415	101.354	0.0606771	-0.194844
58	101.25	101.759	101.431	0.328108	-0.508941
59	101.39	101.954	101.508	0.445816	-0.564149
60	101.43	102.027	101.588	0.439288	-0.597205
61	101.95	101.621	101.66	-0.0382812	0.328698
62	101.92	101.369	101.703	-0.33342	0.550503
63	102.05	101.453	101.724	-0.271337	0.59717
64	102.07	101.579	101.746	-0.166753	0.490503
65	102.1	101.716	101.775	-0.0586285	0.384045
66	102.16	101.759	101.801	-0.041684	0.400851
67	101.63	101.554	101.843	-0.289045	0.0757118
68	101.43	101.827	101.901	-0.0747396	-0.39651
69	101.4	102.017	101.956	0.0606771	-0.61651
70	101.6	102.346	102.018	0.328108	-0.746441
71	101.72	102.538	102.092	0.445816	-0.818316
72	101.73	102.608	102.168	0.439288	-0.877622
73	102.67	102.203	102.241	-0.0382812	0.467448
74	102.59	101.983	102.316	-0.33342	0.60717
75	102.69	102.128	102.399	-0.271337	0.56217
76	102.93	102.313	102.48	-0.166753	0.61717
77	103.02	102.496	102.555	-0.0586285	0.524045
78	103.06	102.594	102.635	-0.041684	0.466267
79	102.47	NA	NA	-0.289045	NA
80	102.4	NA	NA	-0.0747396	NA
81	102.42	NA	NA	0.0606771	NA
82	102.51	NA	NA	0.328108	NA
83	102.61	NA	NA	0.445816	NA
84	102.78	NA	NA	0.439288	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

Decompositiemodel-consumptieprijsindex kleding&schoeisel-Charlotte Tilborgh...

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code