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

Mon, 25 Apr 2016 23:05:20 +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/25/t1461621986x88eubf58lhs9er.htm/, Retrieved Tue, 21 Apr 2026 22:53:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294811, Retrieved Tue, 21 Apr 2026 22:53:41 +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

265

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

-       [Classical Decomposition] [] [2016-04-25 22:05:20] [809417a83781bff5791db815734e4daf] [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	'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294811&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294811&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294811&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	90.18	NA	NA	0.0862083	NA
2	90.5	NA	NA	0.113958	NA
3	90.63	NA	NA	0.230792	NA
4	90.75	NA	NA	0.215292	NA
5	90.76	NA	NA	0.0730417	NA
6	90.67	NA	NA	-0.0149583	NA
7	90.5	91.0226	91.2637	-0.241125	-0.522625
8	90.8	91.2958	91.5625	-0.266708	-0.495792
9	91.22	91.6511	91.8929	-0.241792	-0.431125
10	92.19	92.2809	92.2592	0.0217083	-0.090875
11	92.51	92.6876	92.6354	0.0522083	-0.177625
12	92.67	92.9955	93.0242	-0.028625	-0.325542
13	93.75	93.5145	93.4283	0.0862083	0.235458
14	94.1	93.9498	93.8358	0.113958	0.150208
15	94.96	94.4591	94.2283	0.230792	0.500875
16	95.21	94.7878	94.5725	0.215292	0.422208
17	95.33	94.9435	94.8704	0.0730417	0.386542
18	95.43	95.1417	95.1567	-0.0149583	0.288292
19	95.44	95.1605	95.4017	-0.241125	0.279458
20	95.64	95.3308	95.5975	-0.266708	0.309208
21	95.8	95.5145	95.7562	-0.241792	0.285542
22	95.87	95.9075	95.8858	0.0217083	-0.0375417
23	95.98	96.0547	96.0025	0.0522083	-0.0747083
24	96.07	96.0776	96.1062	-0.028625	-0.007625
25	96.23	96.302	96.2158	0.0862083	-0.0720417
26	96.32	96.4465	96.3325	0.113958	-0.126458
27	96.55	96.68	96.4492	0.230792	-0.129958
28	96.73	96.8165	96.6013	0.215292	-0.0865417
29	96.61	96.8705	96.7975	0.0730417	-0.260542
30	96.64	96.9913	97.0062	-0.0149583	-0.351292
31	96.86	96.9768	97.2179	-0.241125	-0.116792
32	97.02	97.1745	97.4412	-0.266708	-0.154542
33	97.22	97.4386	97.6804	-0.241792	-0.218625
34	98.1	97.9446	97.9229	0.0217083	0.155375
35	98.46	98.2235	98.1712	0.0522083	0.236542
36	98.6	98.4085	98.4371	-0.028625	0.191542
37	98.78	98.797	98.7108	0.0862083	-0.0170417
38	99.13	99.0952	98.9812	0.113958	0.0347917
39	99.48	99.482	99.2512	0.230792	-0.00204167
40	99.62	99.7103	99.495	0.215292	-0.0902917
41	99.68	99.7772	99.7042	0.0730417	-0.0972083
42	99.95	99.888	99.9029	-0.0149583	0.0620417
43	100.12	99.8489	100.09	-0.241125	0.271125
44	100.25	99.9954	100.262	-0.266708	0.254625
45	100.47	100.174	100.416	-0.241792	0.295542
46	100.7	100.578	100.557	0.0217083	0.121625
47	100.88	100.745	100.693	0.0522083	0.135292
48	100.95	100.787	100.815	-0.028625	0.163208
49	100.92	101.007	100.921	0.0862083	-0.0870417
50	101.12	101.13	101.016	0.113958	-0.0102083
51	101.19	101.334	101.103	0.230792	-0.144125
52	101.28	101.396	101.181	0.215292	-0.116125
53	101.28	101.326	101.253	0.0730417	-0.0459583
54	101.3	101.31	101.325	-0.0149583	-0.0104583
55	101.3	101.167	101.408	-0.241125	0.133208
56	101.36	101.229	101.496	-0.266708	0.130875
57	101.45	101.337	101.579	-0.241792	0.113042
58	101.58	101.684	101.662	0.0217083	-0.104208
59	101.73	101.805	101.753	0.0522083	-0.075125
60	101.84	101.817	101.846	-0.028625	0.0227917
61	102.01	102.025	101.939	0.0862083	-0.0149583
62	102.14	102.144	102.03	0.113958	-0.00395833
63	102.16	102.34	102.11	0.230792	-0.180375
64	102.32	102.405	102.19	0.215292	-0.084875
65	102.41	102.348	102.275	0.0730417	0.0615417
66	102.4	102.344	102.359	-0.0149583	0.0557917
67	102.43	NA	NA	-0.241125	NA
68	102.42	NA	NA	-0.266708	NA
69	102.3	NA	NA	-0.241792	NA
70	102.65	NA	NA	0.0217083	NA
71	102.72	NA	NA	0.0522083	NA
72	102.86	NA	NA	-0.028625	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 90.18 & NA & NA & 0.0862083 & NA \tabularnewline
2 & 90.5 & NA & NA & 0.113958 & NA \tabularnewline
3 & 90.63 & NA & NA & 0.230792 & NA \tabularnewline
4 & 90.75 & NA & NA & 0.215292 & NA \tabularnewline
5 & 90.76 & NA & NA & 0.0730417 & NA \tabularnewline
6 & 90.67 & NA & NA & -0.0149583 & NA \tabularnewline
7 & 90.5 & 91.0226 & 91.2637 & -0.241125 & -0.522625 \tabularnewline
8 & 90.8 & 91.2958 & 91.5625 & -0.266708 & -0.495792 \tabularnewline
9 & 91.22 & 91.6511 & 91.8929 & -0.241792 & -0.431125 \tabularnewline
10 & 92.19 & 92.2809 & 92.2592 & 0.0217083 & -0.090875 \tabularnewline
11 & 92.51 & 92.6876 & 92.6354 & 0.0522083 & -0.177625 \tabularnewline
12 & 92.67 & 92.9955 & 93.0242 & -0.028625 & -0.325542 \tabularnewline
13 & 93.75 & 93.5145 & 93.4283 & 0.0862083 & 0.235458 \tabularnewline
14 & 94.1 & 93.9498 & 93.8358 & 0.113958 & 0.150208 \tabularnewline
15 & 94.96 & 94.4591 & 94.2283 & 0.230792 & 0.500875 \tabularnewline
16 & 95.21 & 94.7878 & 94.5725 & 0.215292 & 0.422208 \tabularnewline
17 & 95.33 & 94.9435 & 94.8704 & 0.0730417 & 0.386542 \tabularnewline
18 & 95.43 & 95.1417 & 95.1567 & -0.0149583 & 0.288292 \tabularnewline
19 & 95.44 & 95.1605 & 95.4017 & -0.241125 & 0.279458 \tabularnewline
20 & 95.64 & 95.3308 & 95.5975 & -0.266708 & 0.309208 \tabularnewline
21 & 95.8 & 95.5145 & 95.7562 & -0.241792 & 0.285542 \tabularnewline
22 & 95.87 & 95.9075 & 95.8858 & 0.0217083 & -0.0375417 \tabularnewline
23 & 95.98 & 96.0547 & 96.0025 & 0.0522083 & -0.0747083 \tabularnewline
24 & 96.07 & 96.0776 & 96.1062 & -0.028625 & -0.007625 \tabularnewline
25 & 96.23 & 96.302 & 96.2158 & 0.0862083 & -0.0720417 \tabularnewline
26 & 96.32 & 96.4465 & 96.3325 & 0.113958 & -0.126458 \tabularnewline
27 & 96.55 & 96.68 & 96.4492 & 0.230792 & -0.129958 \tabularnewline
28 & 96.73 & 96.8165 & 96.6013 & 0.215292 & -0.0865417 \tabularnewline
29 & 96.61 & 96.8705 & 96.7975 & 0.0730417 & -0.260542 \tabularnewline
30 & 96.64 & 96.9913 & 97.0062 & -0.0149583 & -0.351292 \tabularnewline
31 & 96.86 & 96.9768 & 97.2179 & -0.241125 & -0.116792 \tabularnewline
32 & 97.02 & 97.1745 & 97.4412 & -0.266708 & -0.154542 \tabularnewline
33 & 97.22 & 97.4386 & 97.6804 & -0.241792 & -0.218625 \tabularnewline
34 & 98.1 & 97.9446 & 97.9229 & 0.0217083 & 0.155375 \tabularnewline
35 & 98.46 & 98.2235 & 98.1712 & 0.0522083 & 0.236542 \tabularnewline
36 & 98.6 & 98.4085 & 98.4371 & -0.028625 & 0.191542 \tabularnewline
37 & 98.78 & 98.797 & 98.7108 & 0.0862083 & -0.0170417 \tabularnewline
38 & 99.13 & 99.0952 & 98.9812 & 0.113958 & 0.0347917 \tabularnewline
39 & 99.48 & 99.482 & 99.2512 & 0.230792 & -0.00204167 \tabularnewline
40 & 99.62 & 99.7103 & 99.495 & 0.215292 & -0.0902917 \tabularnewline
41 & 99.68 & 99.7772 & 99.7042 & 0.0730417 & -0.0972083 \tabularnewline
42 & 99.95 & 99.888 & 99.9029 & -0.0149583 & 0.0620417 \tabularnewline
43 & 100.12 & 99.8489 & 100.09 & -0.241125 & 0.271125 \tabularnewline
44 & 100.25 & 99.9954 & 100.262 & -0.266708 & 0.254625 \tabularnewline
45 & 100.47 & 100.174 & 100.416 & -0.241792 & 0.295542 \tabularnewline
46 & 100.7 & 100.578 & 100.557 & 0.0217083 & 0.121625 \tabularnewline
47 & 100.88 & 100.745 & 100.693 & 0.0522083 & 0.135292 \tabularnewline
48 & 100.95 & 100.787 & 100.815 & -0.028625 & 0.163208 \tabularnewline
49 & 100.92 & 101.007 & 100.921 & 0.0862083 & -0.0870417 \tabularnewline
50 & 101.12 & 101.13 & 101.016 & 0.113958 & -0.0102083 \tabularnewline
51 & 101.19 & 101.334 & 101.103 & 0.230792 & -0.144125 \tabularnewline
52 & 101.28 & 101.396 & 101.181 & 0.215292 & -0.116125 \tabularnewline
53 & 101.28 & 101.326 & 101.253 & 0.0730417 & -0.0459583 \tabularnewline
54 & 101.3 & 101.31 & 101.325 & -0.0149583 & -0.0104583 \tabularnewline
55 & 101.3 & 101.167 & 101.408 & -0.241125 & 0.133208 \tabularnewline
56 & 101.36 & 101.229 & 101.496 & -0.266708 & 0.130875 \tabularnewline
57 & 101.45 & 101.337 & 101.579 & -0.241792 & 0.113042 \tabularnewline
58 & 101.58 & 101.684 & 101.662 & 0.0217083 & -0.104208 \tabularnewline
59 & 101.73 & 101.805 & 101.753 & 0.0522083 & -0.075125 \tabularnewline
60 & 101.84 & 101.817 & 101.846 & -0.028625 & 0.0227917 \tabularnewline
61 & 102.01 & 102.025 & 101.939 & 0.0862083 & -0.0149583 \tabularnewline
62 & 102.14 & 102.144 & 102.03 & 0.113958 & -0.00395833 \tabularnewline
63 & 102.16 & 102.34 & 102.11 & 0.230792 & -0.180375 \tabularnewline
64 & 102.32 & 102.405 & 102.19 & 0.215292 & -0.084875 \tabularnewline
65 & 102.41 & 102.348 & 102.275 & 0.0730417 & 0.0615417 \tabularnewline
66 & 102.4 & 102.344 & 102.359 & -0.0149583 & 0.0557917 \tabularnewline
67 & 102.43 & NA & NA & -0.241125 & NA \tabularnewline
68 & 102.42 & NA & NA & -0.266708 & NA \tabularnewline
69 & 102.3 & NA & NA & -0.241792 & NA \tabularnewline
70 & 102.65 & NA & NA & 0.0217083 & NA \tabularnewline
71 & 102.72 & NA & NA & 0.0522083 & NA \tabularnewline
72 & 102.86 & NA & NA & -0.028625 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294811&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]90.18[/C][C]NA[/C][C]NA[/C][C]0.0862083[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]90.5[/C][C]NA[/C][C]NA[/C][C]0.113958[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]90.63[/C][C]NA[/C][C]NA[/C][C]0.230792[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]90.75[/C][C]NA[/C][C]NA[/C][C]0.215292[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]90.76[/C][C]NA[/C][C]NA[/C][C]0.0730417[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]90.67[/C][C]NA[/C][C]NA[/C][C]-0.0149583[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]90.5[/C][C]91.0226[/C][C]91.2637[/C][C]-0.241125[/C][C]-0.522625[/C][/ROW]
[ROW][C]8[/C][C]90.8[/C][C]91.2958[/C][C]91.5625[/C][C]-0.266708[/C][C]-0.495792[/C][/ROW]
[ROW][C]9[/C][C]91.22[/C][C]91.6511[/C][C]91.8929[/C][C]-0.241792[/C][C]-0.431125[/C][/ROW]
[ROW][C]10[/C][C]92.19[/C][C]92.2809[/C][C]92.2592[/C][C]0.0217083[/C][C]-0.090875[/C][/ROW]
[ROW][C]11[/C][C]92.51[/C][C]92.6876[/C][C]92.6354[/C][C]0.0522083[/C][C]-0.177625[/C][/ROW]
[ROW][C]12[/C][C]92.67[/C][C]92.9955[/C][C]93.0242[/C][C]-0.028625[/C][C]-0.325542[/C][/ROW]
[ROW][C]13[/C][C]93.75[/C][C]93.5145[/C][C]93.4283[/C][C]0.0862083[/C][C]0.235458[/C][/ROW]
[ROW][C]14[/C][C]94.1[/C][C]93.9498[/C][C]93.8358[/C][C]0.113958[/C][C]0.150208[/C][/ROW]
[ROW][C]15[/C][C]94.96[/C][C]94.4591[/C][C]94.2283[/C][C]0.230792[/C][C]0.500875[/C][/ROW]
[ROW][C]16[/C][C]95.21[/C][C]94.7878[/C][C]94.5725[/C][C]0.215292[/C][C]0.422208[/C][/ROW]
[ROW][C]17[/C][C]95.33[/C][C]94.9435[/C][C]94.8704[/C][C]0.0730417[/C][C]0.386542[/C][/ROW]
[ROW][C]18[/C][C]95.43[/C][C]95.1417[/C][C]95.1567[/C][C]-0.0149583[/C][C]0.288292[/C][/ROW]
[ROW][C]19[/C][C]95.44[/C][C]95.1605[/C][C]95.4017[/C][C]-0.241125[/C][C]0.279458[/C][/ROW]
[ROW][C]20[/C][C]95.64[/C][C]95.3308[/C][C]95.5975[/C][C]-0.266708[/C][C]0.309208[/C][/ROW]
[ROW][C]21[/C][C]95.8[/C][C]95.5145[/C][C]95.7562[/C][C]-0.241792[/C][C]0.285542[/C][/ROW]
[ROW][C]22[/C][C]95.87[/C][C]95.9075[/C][C]95.8858[/C][C]0.0217083[/C][C]-0.0375417[/C][/ROW]
[ROW][C]23[/C][C]95.98[/C][C]96.0547[/C][C]96.0025[/C][C]0.0522083[/C][C]-0.0747083[/C][/ROW]
[ROW][C]24[/C][C]96.07[/C][C]96.0776[/C][C]96.1062[/C][C]-0.028625[/C][C]-0.007625[/C][/ROW]
[ROW][C]25[/C][C]96.23[/C][C]96.302[/C][C]96.2158[/C][C]0.0862083[/C][C]-0.0720417[/C][/ROW]
[ROW][C]26[/C][C]96.32[/C][C]96.4465[/C][C]96.3325[/C][C]0.113958[/C][C]-0.126458[/C][/ROW]
[ROW][C]27[/C][C]96.55[/C][C]96.68[/C][C]96.4492[/C][C]0.230792[/C][C]-0.129958[/C][/ROW]
[ROW][C]28[/C][C]96.73[/C][C]96.8165[/C][C]96.6013[/C][C]0.215292[/C][C]-0.0865417[/C][/ROW]
[ROW][C]29[/C][C]96.61[/C][C]96.8705[/C][C]96.7975[/C][C]0.0730417[/C][C]-0.260542[/C][/ROW]
[ROW][C]30[/C][C]96.64[/C][C]96.9913[/C][C]97.0062[/C][C]-0.0149583[/C][C]-0.351292[/C][/ROW]
[ROW][C]31[/C][C]96.86[/C][C]96.9768[/C][C]97.2179[/C][C]-0.241125[/C][C]-0.116792[/C][/ROW]
[ROW][C]32[/C][C]97.02[/C][C]97.1745[/C][C]97.4412[/C][C]-0.266708[/C][C]-0.154542[/C][/ROW]
[ROW][C]33[/C][C]97.22[/C][C]97.4386[/C][C]97.6804[/C][C]-0.241792[/C][C]-0.218625[/C][/ROW]
[ROW][C]34[/C][C]98.1[/C][C]97.9446[/C][C]97.9229[/C][C]0.0217083[/C][C]0.155375[/C][/ROW]
[ROW][C]35[/C][C]98.46[/C][C]98.2235[/C][C]98.1712[/C][C]0.0522083[/C][C]0.236542[/C][/ROW]
[ROW][C]36[/C][C]98.6[/C][C]98.4085[/C][C]98.4371[/C][C]-0.028625[/C][C]0.191542[/C][/ROW]
[ROW][C]37[/C][C]98.78[/C][C]98.797[/C][C]98.7108[/C][C]0.0862083[/C][C]-0.0170417[/C][/ROW]
[ROW][C]38[/C][C]99.13[/C][C]99.0952[/C][C]98.9812[/C][C]0.113958[/C][C]0.0347917[/C][/ROW]
[ROW][C]39[/C][C]99.48[/C][C]99.482[/C][C]99.2512[/C][C]0.230792[/C][C]-0.00204167[/C][/ROW]
[ROW][C]40[/C][C]99.62[/C][C]99.7103[/C][C]99.495[/C][C]0.215292[/C][C]-0.0902917[/C][/ROW]
[ROW][C]41[/C][C]99.68[/C][C]99.7772[/C][C]99.7042[/C][C]0.0730417[/C][C]-0.0972083[/C][/ROW]
[ROW][C]42[/C][C]99.95[/C][C]99.888[/C][C]99.9029[/C][C]-0.0149583[/C][C]0.0620417[/C][/ROW]
[ROW][C]43[/C][C]100.12[/C][C]99.8489[/C][C]100.09[/C][C]-0.241125[/C][C]0.271125[/C][/ROW]
[ROW][C]44[/C][C]100.25[/C][C]99.9954[/C][C]100.262[/C][C]-0.266708[/C][C]0.254625[/C][/ROW]
[ROW][C]45[/C][C]100.47[/C][C]100.174[/C][C]100.416[/C][C]-0.241792[/C][C]0.295542[/C][/ROW]
[ROW][C]46[/C][C]100.7[/C][C]100.578[/C][C]100.557[/C][C]0.0217083[/C][C]0.121625[/C][/ROW]
[ROW][C]47[/C][C]100.88[/C][C]100.745[/C][C]100.693[/C][C]0.0522083[/C][C]0.135292[/C][/ROW]
[ROW][C]48[/C][C]100.95[/C][C]100.787[/C][C]100.815[/C][C]-0.028625[/C][C]0.163208[/C][/ROW]
[ROW][C]49[/C][C]100.92[/C][C]101.007[/C][C]100.921[/C][C]0.0862083[/C][C]-0.0870417[/C][/ROW]
[ROW][C]50[/C][C]101.12[/C][C]101.13[/C][C]101.016[/C][C]0.113958[/C][C]-0.0102083[/C][/ROW]
[ROW][C]51[/C][C]101.19[/C][C]101.334[/C][C]101.103[/C][C]0.230792[/C][C]-0.144125[/C][/ROW]
[ROW][C]52[/C][C]101.28[/C][C]101.396[/C][C]101.181[/C][C]0.215292[/C][C]-0.116125[/C][/ROW]
[ROW][C]53[/C][C]101.28[/C][C]101.326[/C][C]101.253[/C][C]0.0730417[/C][C]-0.0459583[/C][/ROW]
[ROW][C]54[/C][C]101.3[/C][C]101.31[/C][C]101.325[/C][C]-0.0149583[/C][C]-0.0104583[/C][/ROW]
[ROW][C]55[/C][C]101.3[/C][C]101.167[/C][C]101.408[/C][C]-0.241125[/C][C]0.133208[/C][/ROW]
[ROW][C]56[/C][C]101.36[/C][C]101.229[/C][C]101.496[/C][C]-0.266708[/C][C]0.130875[/C][/ROW]
[ROW][C]57[/C][C]101.45[/C][C]101.337[/C][C]101.579[/C][C]-0.241792[/C][C]0.113042[/C][/ROW]
[ROW][C]58[/C][C]101.58[/C][C]101.684[/C][C]101.662[/C][C]0.0217083[/C][C]-0.104208[/C][/ROW]
[ROW][C]59[/C][C]101.73[/C][C]101.805[/C][C]101.753[/C][C]0.0522083[/C][C]-0.075125[/C][/ROW]
[ROW][C]60[/C][C]101.84[/C][C]101.817[/C][C]101.846[/C][C]-0.028625[/C][C]0.0227917[/C][/ROW]
[ROW][C]61[/C][C]102.01[/C][C]102.025[/C][C]101.939[/C][C]0.0862083[/C][C]-0.0149583[/C][/ROW]
[ROW][C]62[/C][C]102.14[/C][C]102.144[/C][C]102.03[/C][C]0.113958[/C][C]-0.00395833[/C][/ROW]
[ROW][C]63[/C][C]102.16[/C][C]102.34[/C][C]102.11[/C][C]0.230792[/C][C]-0.180375[/C][/ROW]
[ROW][C]64[/C][C]102.32[/C][C]102.405[/C][C]102.19[/C][C]0.215292[/C][C]-0.084875[/C][/ROW]
[ROW][C]65[/C][C]102.41[/C][C]102.348[/C][C]102.275[/C][C]0.0730417[/C][C]0.0615417[/C][/ROW]
[ROW][C]66[/C][C]102.4[/C][C]102.344[/C][C]102.359[/C][C]-0.0149583[/C][C]0.0557917[/C][/ROW]
[ROW][C]67[/C][C]102.43[/C][C]NA[/C][C]NA[/C][C]-0.241125[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]102.42[/C][C]NA[/C][C]NA[/C][C]-0.266708[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]102.3[/C][C]NA[/C][C]NA[/C][C]-0.241792[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]102.65[/C][C]NA[/C][C]NA[/C][C]0.0217083[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]102.72[/C][C]NA[/C][C]NA[/C][C]0.0522083[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]102.86[/C][C]NA[/C][C]NA[/C][C]-0.028625[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294811&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294811&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	90.18	NA	NA	0.0862083	NA
2	90.5	NA	NA	0.113958	NA
3	90.63	NA	NA	0.230792	NA
4	90.75	NA	NA	0.215292	NA
5	90.76	NA	NA	0.0730417	NA
6	90.67	NA	NA	-0.0149583	NA
7	90.5	91.0226	91.2637	-0.241125	-0.522625
8	90.8	91.2958	91.5625	-0.266708	-0.495792
9	91.22	91.6511	91.8929	-0.241792	-0.431125
10	92.19	92.2809	92.2592	0.0217083	-0.090875
11	92.51	92.6876	92.6354	0.0522083	-0.177625
12	92.67	92.9955	93.0242	-0.028625	-0.325542
13	93.75	93.5145	93.4283	0.0862083	0.235458
14	94.1	93.9498	93.8358	0.113958	0.150208
15	94.96	94.4591	94.2283	0.230792	0.500875
16	95.21	94.7878	94.5725	0.215292	0.422208
17	95.33	94.9435	94.8704	0.0730417	0.386542
18	95.43	95.1417	95.1567	-0.0149583	0.288292
19	95.44	95.1605	95.4017	-0.241125	0.279458
20	95.64	95.3308	95.5975	-0.266708	0.309208
21	95.8	95.5145	95.7562	-0.241792	0.285542
22	95.87	95.9075	95.8858	0.0217083	-0.0375417
23	95.98	96.0547	96.0025	0.0522083	-0.0747083
24	96.07	96.0776	96.1062	-0.028625	-0.007625
25	96.23	96.302	96.2158	0.0862083	-0.0720417
26	96.32	96.4465	96.3325	0.113958	-0.126458
27	96.55	96.68	96.4492	0.230792	-0.129958
28	96.73	96.8165	96.6013	0.215292	-0.0865417
29	96.61	96.8705	96.7975	0.0730417	-0.260542
30	96.64	96.9913	97.0062	-0.0149583	-0.351292
31	96.86	96.9768	97.2179	-0.241125	-0.116792
32	97.02	97.1745	97.4412	-0.266708	-0.154542
33	97.22	97.4386	97.6804	-0.241792	-0.218625
34	98.1	97.9446	97.9229	0.0217083	0.155375
35	98.46	98.2235	98.1712	0.0522083	0.236542
36	98.6	98.4085	98.4371	-0.028625	0.191542
37	98.78	98.797	98.7108	0.0862083	-0.0170417
38	99.13	99.0952	98.9812	0.113958	0.0347917
39	99.48	99.482	99.2512	0.230792	-0.00204167
40	99.62	99.7103	99.495	0.215292	-0.0902917
41	99.68	99.7772	99.7042	0.0730417	-0.0972083
42	99.95	99.888	99.9029	-0.0149583	0.0620417
43	100.12	99.8489	100.09	-0.241125	0.271125
44	100.25	99.9954	100.262	-0.266708	0.254625
45	100.47	100.174	100.416	-0.241792	0.295542
46	100.7	100.578	100.557	0.0217083	0.121625
47	100.88	100.745	100.693	0.0522083	0.135292
48	100.95	100.787	100.815	-0.028625	0.163208
49	100.92	101.007	100.921	0.0862083	-0.0870417
50	101.12	101.13	101.016	0.113958	-0.0102083
51	101.19	101.334	101.103	0.230792	-0.144125
52	101.28	101.396	101.181	0.215292	-0.116125
53	101.28	101.326	101.253	0.0730417	-0.0459583
54	101.3	101.31	101.325	-0.0149583	-0.0104583
55	101.3	101.167	101.408	-0.241125	0.133208
56	101.36	101.229	101.496	-0.266708	0.130875
57	101.45	101.337	101.579	-0.241792	0.113042
58	101.58	101.684	101.662	0.0217083	-0.104208
59	101.73	101.805	101.753	0.0522083	-0.075125
60	101.84	101.817	101.846	-0.028625	0.0227917
61	102.01	102.025	101.939	0.0862083	-0.0149583
62	102.14	102.144	102.03	0.113958	-0.00395833
63	102.16	102.34	102.11	0.230792	-0.180375
64	102.32	102.405	102.19	0.215292	-0.084875
65	102.41	102.348	102.275	0.0730417	0.0615417
66	102.4	102.344	102.359	-0.0149583	0.0557917
67	102.43	NA	NA	-0.241125	NA
68	102.42	NA	NA	-0.266708	NA
69	102.3	NA	NA	-0.241792	NA
70	102.65	NA	NA	0.0217083	NA
71	102.72	NA	NA	0.0522083	NA
72	102.86	NA	NA	-0.028625	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