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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Wed, 14 Dec 2011 08:05:13 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/14/t1323868885mgvwcn6s5j78gfx.htm/, Retrieved Wed, 01 May 2024 18:41:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154934, Retrieved Wed, 01 May 2024 18:41:33 +0000

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No (this computation is public)

User-defined keywords

KDGP2W92

Estimated Impact

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

-       [Classical Decomposition] [compositief model] [2011-12-14 13:05:13] [659094c92b72720b61457cd096818e91] [Current]

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Dataseries X:

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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	'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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154934&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154934&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154934&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	'George Udny Yule' @ yule.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	31.5	NA	NA	1.00085584254161	NA
2	31.29	NA	NA	1.00023495359466	NA
3	31.3	NA	NA	0.999108704850942	NA
4	31.06	NA	NA	0.999131991143805	NA
5	31.09	NA	NA	0.998604647200005	NA
6	31.11	NA	NA	0.999574504122475	NA
7	31.13	31.0958470847665	31.0541666666667	1.00134218440144	1.00109831113912
8	31.1	31.0742158738009	31.0020833333333	1.00232669977988	1.000829759512
9	31.03	31.0033036564114	30.9545833333333	1.00157392921602	1.00086108060884
10	30.74	30.8284602745458	30.91	0.997362027646257	0.997130564622494
11	30.83	30.8656033020489	30.8741666666667	0.999722636574772	0.99884650555181
12	30.82	30.846242548492	30.84125	1.00016187892813	0.999149246510307
13	30.8	30.8326152992974	30.80625	1.00085584254161	0.998942181875238
14	30.74	30.7797301094917	30.7725	1.00023495359466	0.998709211895283
15	30.71	30.7146830635864	30.7420833333333	0.999108704850942	0.999847530134799
16	30.58	30.7024934778565	30.7291666666667	0.999131991143805	0.996010308480487
17	30.71	30.6883690642651	30.73125	0.998604647200005	1.00070485778145
18	30.7	30.7177574904371	30.7308333333333	0.999574504122475	0.999421914492208
19	30.7	30.7712453266564	30.73	1.00134218440144	0.997684678474983
20	30.72	30.8073463899845	30.7358333333333	1.00232669977988	0.997164754507616
21	30.68	30.7967290335106	30.7483333333333	1.00157392921602	0.996209693783272
22	30.78	30.6879984556523	30.7691666666667	0.997362027646257	1.00299796496929
23	30.84	30.7793772246444	30.7879166666667	0.999722636574772	1.00196959070722
24	30.8	30.7995683274754	30.7945833333333	1.00016187892813	1.00001401553814
25	30.8	30.8251088804783	30.79875	1.00085584254161	0.999185440655678
26	30.88	30.8093203935356	30.8020833333333	1.00023495359466	1.00229409820021
27	30.87	30.7737969952901	30.80125	0.999108704850942	1.00312613372749
28	30.92	30.7616087873325	30.7883333333333	0.999131991143805	1.0051489898907
29	30.82	30.7237363121868	30.7666666666667	0.998604647200005	1.00313320251271
30	30.75	30.7344170655058	30.7475	0.999574504122475	1.0005070190354
31	30.75	30.7720797784767	30.7308333333333	1.00134218440144	0.999282473637281
32	30.75	30.7835411308648	30.7120833333333	1.00232669977988	0.998910419996122
33	30.63	30.7370519202282	30.68875	1.00157392921602	0.996517170205328
34	30.52	30.5820287402149	30.6629166666667	0.997362027646257	0.997971725788966
35	30.58	30.6294188291582	30.6379166666667	0.999722636574772	0.998386556746839
36	30.6	30.624539998663	30.6195833333333	1.00016187892813	0.999198681885049
37	30.6	30.6332781773245	30.6070833333333	1.00085584254161	0.998913659284785
38	30.63	30.6030219343567	30.5958333333333	1.00023495359466	1.00088154907385
39	30.56	30.5648167578588	30.5920833333333	0.999108704850942	0.999842408416941
40	30.61	30.5592845591259	30.5858333333333	0.999131991143805	1.00165957553018
41	30.53	30.5248475532861	30.5675	0.998604647200005	1.00016879516613
42	30.6	30.5311702496676	30.5441666666667	0.999574504122475	1.00225440917494
43	30.6	30.5601290161118	30.5191666666667	1.00134218440144	1.00130467328417
44	30.63	30.554676534415	30.48375	1.00232669977988	1.00246520251982
45	30.66	30.4837371806306	30.4358333333333	1.00157392921602	1.00578219193811
46	30.34	30.3015206699394	30.3816666666667	0.997362027646257	1.0012698811548
47	30.32	30.3149227497357	30.3233333333333	0.999722636574772	1.00016748352969
48	30.3	30.2694825316435	30.2645833333333	1.00016187892813	1.00100819260206
49	30.3	30.2295996541661	30.20375	1.00085584254161	1.00232885471985
50	30.08	30.155833557187	30.14875	1.00023495359466	0.99748527736621
51	29.96	30.0635972242585	30.0904166666667	0.999108704850942	0.996554064256324
52	29.91	30.0135087089636	30.0395833333333	0.999131991143805	0.996551262634193
53	29.83	29.9689576330115	30.0108333333333	0.998604647200005	0.995363281075268
54	29.89	29.974323952996	29.9870833333333	0.999574504122475	0.997186793833007
55	29.85	NA	NA	1.00134218440144	NA
56	30.06	NA	NA	1.00232669977988	NA
57	29.83	NA	NA	1.00157392921602	NA
58	29.95	NA	NA	0.997362027646257	NA
59	30.02	NA	NA	0.999722636574772	NA
60	30.03	NA	NA	1.00016187892813	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 31.5 & NA & NA & 1.00085584254161 & NA \tabularnewline
2 & 31.29 & NA & NA & 1.00023495359466 & NA \tabularnewline
3 & 31.3 & NA & NA & 0.999108704850942 & NA \tabularnewline
4 & 31.06 & NA & NA & 0.999131991143805 & NA \tabularnewline
5 & 31.09 & NA & NA & 0.998604647200005 & NA \tabularnewline
6 & 31.11 & NA & NA & 0.999574504122475 & NA \tabularnewline
7 & 31.13 & 31.0958470847665 & 31.0541666666667 & 1.00134218440144 & 1.00109831113912 \tabularnewline
8 & 31.1 & 31.0742158738009 & 31.0020833333333 & 1.00232669977988 & 1.000829759512 \tabularnewline
9 & 31.03 & 31.0033036564114 & 30.9545833333333 & 1.00157392921602 & 1.00086108060884 \tabularnewline
10 & 30.74 & 30.8284602745458 & 30.91 & 0.997362027646257 & 0.997130564622494 \tabularnewline
11 & 30.83 & 30.8656033020489 & 30.8741666666667 & 0.999722636574772 & 0.99884650555181 \tabularnewline
12 & 30.82 & 30.846242548492 & 30.84125 & 1.00016187892813 & 0.999149246510307 \tabularnewline
13 & 30.8 & 30.8326152992974 & 30.80625 & 1.00085584254161 & 0.998942181875238 \tabularnewline
14 & 30.74 & 30.7797301094917 & 30.7725 & 1.00023495359466 & 0.998709211895283 \tabularnewline
15 & 30.71 & 30.7146830635864 & 30.7420833333333 & 0.999108704850942 & 0.999847530134799 \tabularnewline
16 & 30.58 & 30.7024934778565 & 30.7291666666667 & 0.999131991143805 & 0.996010308480487 \tabularnewline
17 & 30.71 & 30.6883690642651 & 30.73125 & 0.998604647200005 & 1.00070485778145 \tabularnewline
18 & 30.7 & 30.7177574904371 & 30.7308333333333 & 0.999574504122475 & 0.999421914492208 \tabularnewline
19 & 30.7 & 30.7712453266564 & 30.73 & 1.00134218440144 & 0.997684678474983 \tabularnewline
20 & 30.72 & 30.8073463899845 & 30.7358333333333 & 1.00232669977988 & 0.997164754507616 \tabularnewline
21 & 30.68 & 30.7967290335106 & 30.7483333333333 & 1.00157392921602 & 0.996209693783272 \tabularnewline
22 & 30.78 & 30.6879984556523 & 30.7691666666667 & 0.997362027646257 & 1.00299796496929 \tabularnewline
23 & 30.84 & 30.7793772246444 & 30.7879166666667 & 0.999722636574772 & 1.00196959070722 \tabularnewline
24 & 30.8 & 30.7995683274754 & 30.7945833333333 & 1.00016187892813 & 1.00001401553814 \tabularnewline
25 & 30.8 & 30.8251088804783 & 30.79875 & 1.00085584254161 & 0.999185440655678 \tabularnewline
26 & 30.88 & 30.8093203935356 & 30.8020833333333 & 1.00023495359466 & 1.00229409820021 \tabularnewline
27 & 30.87 & 30.7737969952901 & 30.80125 & 0.999108704850942 & 1.00312613372749 \tabularnewline
28 & 30.92 & 30.7616087873325 & 30.7883333333333 & 0.999131991143805 & 1.0051489898907 \tabularnewline
29 & 30.82 & 30.7237363121868 & 30.7666666666667 & 0.998604647200005 & 1.00313320251271 \tabularnewline
30 & 30.75 & 30.7344170655058 & 30.7475 & 0.999574504122475 & 1.0005070190354 \tabularnewline
31 & 30.75 & 30.7720797784767 & 30.7308333333333 & 1.00134218440144 & 0.999282473637281 \tabularnewline
32 & 30.75 & 30.7835411308648 & 30.7120833333333 & 1.00232669977988 & 0.998910419996122 \tabularnewline
33 & 30.63 & 30.7370519202282 & 30.68875 & 1.00157392921602 & 0.996517170205328 \tabularnewline
34 & 30.52 & 30.5820287402149 & 30.6629166666667 & 0.997362027646257 & 0.997971725788966 \tabularnewline
35 & 30.58 & 30.6294188291582 & 30.6379166666667 & 0.999722636574772 & 0.998386556746839 \tabularnewline
36 & 30.6 & 30.624539998663 & 30.6195833333333 & 1.00016187892813 & 0.999198681885049 \tabularnewline
37 & 30.6 & 30.6332781773245 & 30.6070833333333 & 1.00085584254161 & 0.998913659284785 \tabularnewline
38 & 30.63 & 30.6030219343567 & 30.5958333333333 & 1.00023495359466 & 1.00088154907385 \tabularnewline
39 & 30.56 & 30.5648167578588 & 30.5920833333333 & 0.999108704850942 & 0.999842408416941 \tabularnewline
40 & 30.61 & 30.5592845591259 & 30.5858333333333 & 0.999131991143805 & 1.00165957553018 \tabularnewline
41 & 30.53 & 30.5248475532861 & 30.5675 & 0.998604647200005 & 1.00016879516613 \tabularnewline
42 & 30.6 & 30.5311702496676 & 30.5441666666667 & 0.999574504122475 & 1.00225440917494 \tabularnewline
43 & 30.6 & 30.5601290161118 & 30.5191666666667 & 1.00134218440144 & 1.00130467328417 \tabularnewline
44 & 30.63 & 30.554676534415 & 30.48375 & 1.00232669977988 & 1.00246520251982 \tabularnewline
45 & 30.66 & 30.4837371806306 & 30.4358333333333 & 1.00157392921602 & 1.00578219193811 \tabularnewline
46 & 30.34 & 30.3015206699394 & 30.3816666666667 & 0.997362027646257 & 1.0012698811548 \tabularnewline
47 & 30.32 & 30.3149227497357 & 30.3233333333333 & 0.999722636574772 & 1.00016748352969 \tabularnewline
48 & 30.3 & 30.2694825316435 & 30.2645833333333 & 1.00016187892813 & 1.00100819260206 \tabularnewline
49 & 30.3 & 30.2295996541661 & 30.20375 & 1.00085584254161 & 1.00232885471985 \tabularnewline
50 & 30.08 & 30.155833557187 & 30.14875 & 1.00023495359466 & 0.99748527736621 \tabularnewline
51 & 29.96 & 30.0635972242585 & 30.0904166666667 & 0.999108704850942 & 0.996554064256324 \tabularnewline
52 & 29.91 & 30.0135087089636 & 30.0395833333333 & 0.999131991143805 & 0.996551262634193 \tabularnewline
53 & 29.83 & 29.9689576330115 & 30.0108333333333 & 0.998604647200005 & 0.995363281075268 \tabularnewline
54 & 29.89 & 29.974323952996 & 29.9870833333333 & 0.999574504122475 & 0.997186793833007 \tabularnewline
55 & 29.85 & NA & NA & 1.00134218440144 & NA \tabularnewline
56 & 30.06 & NA & NA & 1.00232669977988 & NA \tabularnewline
57 & 29.83 & NA & NA & 1.00157392921602 & NA \tabularnewline
58 & 29.95 & NA & NA & 0.997362027646257 & NA \tabularnewline
59 & 30.02 & NA & NA & 0.999722636574772 & NA \tabularnewline
60 & 30.03 & NA & NA & 1.00016187892813 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154934&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]31.5[/C][C]NA[/C][C]NA[/C][C]1.00085584254161[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]31.29[/C][C]NA[/C][C]NA[/C][C]1.00023495359466[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]31.3[/C][C]NA[/C][C]NA[/C][C]0.999108704850942[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]31.06[/C][C]NA[/C][C]NA[/C][C]0.999131991143805[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]31.09[/C][C]NA[/C][C]NA[/C][C]0.998604647200005[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]31.11[/C][C]NA[/C][C]NA[/C][C]0.999574504122475[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]31.13[/C][C]31.0958470847665[/C][C]31.0541666666667[/C][C]1.00134218440144[/C][C]1.00109831113912[/C][/ROW]
[ROW][C]8[/C][C]31.1[/C][C]31.0742158738009[/C][C]31.0020833333333[/C][C]1.00232669977988[/C][C]1.000829759512[/C][/ROW]
[ROW][C]9[/C][C]31.03[/C][C]31.0033036564114[/C][C]30.9545833333333[/C][C]1.00157392921602[/C][C]1.00086108060884[/C][/ROW]
[ROW][C]10[/C][C]30.74[/C][C]30.8284602745458[/C][C]30.91[/C][C]0.997362027646257[/C][C]0.997130564622494[/C][/ROW]
[ROW][C]11[/C][C]30.83[/C][C]30.8656033020489[/C][C]30.8741666666667[/C][C]0.999722636574772[/C][C]0.99884650555181[/C][/ROW]
[ROW][C]12[/C][C]30.82[/C][C]30.846242548492[/C][C]30.84125[/C][C]1.00016187892813[/C][C]0.999149246510307[/C][/ROW]
[ROW][C]13[/C][C]30.8[/C][C]30.8326152992974[/C][C]30.80625[/C][C]1.00085584254161[/C][C]0.998942181875238[/C][/ROW]
[ROW][C]14[/C][C]30.74[/C][C]30.7797301094917[/C][C]30.7725[/C][C]1.00023495359466[/C][C]0.998709211895283[/C][/ROW]
[ROW][C]15[/C][C]30.71[/C][C]30.7146830635864[/C][C]30.7420833333333[/C][C]0.999108704850942[/C][C]0.999847530134799[/C][/ROW]
[ROW][C]16[/C][C]30.58[/C][C]30.7024934778565[/C][C]30.7291666666667[/C][C]0.999131991143805[/C][C]0.996010308480487[/C][/ROW]
[ROW][C]17[/C][C]30.71[/C][C]30.6883690642651[/C][C]30.73125[/C][C]0.998604647200005[/C][C]1.00070485778145[/C][/ROW]
[ROW][C]18[/C][C]30.7[/C][C]30.7177574904371[/C][C]30.7308333333333[/C][C]0.999574504122475[/C][C]0.999421914492208[/C][/ROW]
[ROW][C]19[/C][C]30.7[/C][C]30.7712453266564[/C][C]30.73[/C][C]1.00134218440144[/C][C]0.997684678474983[/C][/ROW]
[ROW][C]20[/C][C]30.72[/C][C]30.8073463899845[/C][C]30.7358333333333[/C][C]1.00232669977988[/C][C]0.997164754507616[/C][/ROW]
[ROW][C]21[/C][C]30.68[/C][C]30.7967290335106[/C][C]30.7483333333333[/C][C]1.00157392921602[/C][C]0.996209693783272[/C][/ROW]
[ROW][C]22[/C][C]30.78[/C][C]30.6879984556523[/C][C]30.7691666666667[/C][C]0.997362027646257[/C][C]1.00299796496929[/C][/ROW]
[ROW][C]23[/C][C]30.84[/C][C]30.7793772246444[/C][C]30.7879166666667[/C][C]0.999722636574772[/C][C]1.00196959070722[/C][/ROW]
[ROW][C]24[/C][C]30.8[/C][C]30.7995683274754[/C][C]30.7945833333333[/C][C]1.00016187892813[/C][C]1.00001401553814[/C][/ROW]
[ROW][C]25[/C][C]30.8[/C][C]30.8251088804783[/C][C]30.79875[/C][C]1.00085584254161[/C][C]0.999185440655678[/C][/ROW]
[ROW][C]26[/C][C]30.88[/C][C]30.8093203935356[/C][C]30.8020833333333[/C][C]1.00023495359466[/C][C]1.00229409820021[/C][/ROW]
[ROW][C]27[/C][C]30.87[/C][C]30.7737969952901[/C][C]30.80125[/C][C]0.999108704850942[/C][C]1.00312613372749[/C][/ROW]
[ROW][C]28[/C][C]30.92[/C][C]30.7616087873325[/C][C]30.7883333333333[/C][C]0.999131991143805[/C][C]1.0051489898907[/C][/ROW]
[ROW][C]29[/C][C]30.82[/C][C]30.7237363121868[/C][C]30.7666666666667[/C][C]0.998604647200005[/C][C]1.00313320251271[/C][/ROW]
[ROW][C]30[/C][C]30.75[/C][C]30.7344170655058[/C][C]30.7475[/C][C]0.999574504122475[/C][C]1.0005070190354[/C][/ROW]
[ROW][C]31[/C][C]30.75[/C][C]30.7720797784767[/C][C]30.7308333333333[/C][C]1.00134218440144[/C][C]0.999282473637281[/C][/ROW]
[ROW][C]32[/C][C]30.75[/C][C]30.7835411308648[/C][C]30.7120833333333[/C][C]1.00232669977988[/C][C]0.998910419996122[/C][/ROW]
[ROW][C]33[/C][C]30.63[/C][C]30.7370519202282[/C][C]30.68875[/C][C]1.00157392921602[/C][C]0.996517170205328[/C][/ROW]
[ROW][C]34[/C][C]30.52[/C][C]30.5820287402149[/C][C]30.6629166666667[/C][C]0.997362027646257[/C][C]0.997971725788966[/C][/ROW]
[ROW][C]35[/C][C]30.58[/C][C]30.6294188291582[/C][C]30.6379166666667[/C][C]0.999722636574772[/C][C]0.998386556746839[/C][/ROW]
[ROW][C]36[/C][C]30.6[/C][C]30.624539998663[/C][C]30.6195833333333[/C][C]1.00016187892813[/C][C]0.999198681885049[/C][/ROW]
[ROW][C]37[/C][C]30.6[/C][C]30.6332781773245[/C][C]30.6070833333333[/C][C]1.00085584254161[/C][C]0.998913659284785[/C][/ROW]
[ROW][C]38[/C][C]30.63[/C][C]30.6030219343567[/C][C]30.5958333333333[/C][C]1.00023495359466[/C][C]1.00088154907385[/C][/ROW]
[ROW][C]39[/C][C]30.56[/C][C]30.5648167578588[/C][C]30.5920833333333[/C][C]0.999108704850942[/C][C]0.999842408416941[/C][/ROW]
[ROW][C]40[/C][C]30.61[/C][C]30.5592845591259[/C][C]30.5858333333333[/C][C]0.999131991143805[/C][C]1.00165957553018[/C][/ROW]
[ROW][C]41[/C][C]30.53[/C][C]30.5248475532861[/C][C]30.5675[/C][C]0.998604647200005[/C][C]1.00016879516613[/C][/ROW]
[ROW][C]42[/C][C]30.6[/C][C]30.5311702496676[/C][C]30.5441666666667[/C][C]0.999574504122475[/C][C]1.00225440917494[/C][/ROW]
[ROW][C]43[/C][C]30.6[/C][C]30.5601290161118[/C][C]30.5191666666667[/C][C]1.00134218440144[/C][C]1.00130467328417[/C][/ROW]
[ROW][C]44[/C][C]30.63[/C][C]30.554676534415[/C][C]30.48375[/C][C]1.00232669977988[/C][C]1.00246520251982[/C][/ROW]
[ROW][C]45[/C][C]30.66[/C][C]30.4837371806306[/C][C]30.4358333333333[/C][C]1.00157392921602[/C][C]1.00578219193811[/C][/ROW]
[ROW][C]46[/C][C]30.34[/C][C]30.3015206699394[/C][C]30.3816666666667[/C][C]0.997362027646257[/C][C]1.0012698811548[/C][/ROW]
[ROW][C]47[/C][C]30.32[/C][C]30.3149227497357[/C][C]30.3233333333333[/C][C]0.999722636574772[/C][C]1.00016748352969[/C][/ROW]
[ROW][C]48[/C][C]30.3[/C][C]30.2694825316435[/C][C]30.2645833333333[/C][C]1.00016187892813[/C][C]1.00100819260206[/C][/ROW]
[ROW][C]49[/C][C]30.3[/C][C]30.2295996541661[/C][C]30.20375[/C][C]1.00085584254161[/C][C]1.00232885471985[/C][/ROW]
[ROW][C]50[/C][C]30.08[/C][C]30.155833557187[/C][C]30.14875[/C][C]1.00023495359466[/C][C]0.99748527736621[/C][/ROW]
[ROW][C]51[/C][C]29.96[/C][C]30.0635972242585[/C][C]30.0904166666667[/C][C]0.999108704850942[/C][C]0.996554064256324[/C][/ROW]
[ROW][C]52[/C][C]29.91[/C][C]30.0135087089636[/C][C]30.0395833333333[/C][C]0.999131991143805[/C][C]0.996551262634193[/C][/ROW]
[ROW][C]53[/C][C]29.83[/C][C]29.9689576330115[/C][C]30.0108333333333[/C][C]0.998604647200005[/C][C]0.995363281075268[/C][/ROW]
[ROW][C]54[/C][C]29.89[/C][C]29.974323952996[/C][C]29.9870833333333[/C][C]0.999574504122475[/C][C]0.997186793833007[/C][/ROW]
[ROW][C]55[/C][C]29.85[/C][C]NA[/C][C]NA[/C][C]1.00134218440144[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]30.06[/C][C]NA[/C][C]NA[/C][C]1.00232669977988[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]29.83[/C][C]NA[/C][C]NA[/C][C]1.00157392921602[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]29.95[/C][C]NA[/C][C]NA[/C][C]0.997362027646257[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]30.02[/C][C]NA[/C][C]NA[/C][C]0.999722636574772[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]30.03[/C][C]NA[/C][C]NA[/C][C]1.00016187892813[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154934&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154934&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	31.5	NA	NA	1.00085584254161	NA
2	31.29	NA	NA	1.00023495359466	NA
3	31.3	NA	NA	0.999108704850942	NA
4	31.06	NA	NA	0.999131991143805	NA
5	31.09	NA	NA	0.998604647200005	NA
6	31.11	NA	NA	0.999574504122475	NA
7	31.13	31.0958470847665	31.0541666666667	1.00134218440144	1.00109831113912
8	31.1	31.0742158738009	31.0020833333333	1.00232669977988	1.000829759512
9	31.03	31.0033036564114	30.9545833333333	1.00157392921602	1.00086108060884
10	30.74	30.8284602745458	30.91	0.997362027646257	0.997130564622494
11	30.83	30.8656033020489	30.8741666666667	0.999722636574772	0.99884650555181
12	30.82	30.846242548492	30.84125	1.00016187892813	0.999149246510307
13	30.8	30.8326152992974	30.80625	1.00085584254161	0.998942181875238
14	30.74	30.7797301094917	30.7725	1.00023495359466	0.998709211895283
15	30.71	30.7146830635864	30.7420833333333	0.999108704850942	0.999847530134799
16	30.58	30.7024934778565	30.7291666666667	0.999131991143805	0.996010308480487
17	30.71	30.6883690642651	30.73125	0.998604647200005	1.00070485778145
18	30.7	30.7177574904371	30.7308333333333	0.999574504122475	0.999421914492208
19	30.7	30.7712453266564	30.73	1.00134218440144	0.997684678474983
20	30.72	30.8073463899845	30.7358333333333	1.00232669977988	0.997164754507616
21	30.68	30.7967290335106	30.7483333333333	1.00157392921602	0.996209693783272
22	30.78	30.6879984556523	30.7691666666667	0.997362027646257	1.00299796496929
23	30.84	30.7793772246444	30.7879166666667	0.999722636574772	1.00196959070722
24	30.8	30.7995683274754	30.7945833333333	1.00016187892813	1.00001401553814
25	30.8	30.8251088804783	30.79875	1.00085584254161	0.999185440655678
26	30.88	30.8093203935356	30.8020833333333	1.00023495359466	1.00229409820021
27	30.87	30.7737969952901	30.80125	0.999108704850942	1.00312613372749
28	30.92	30.7616087873325	30.7883333333333	0.999131991143805	1.0051489898907
29	30.82	30.7237363121868	30.7666666666667	0.998604647200005	1.00313320251271
30	30.75	30.7344170655058	30.7475	0.999574504122475	1.0005070190354
31	30.75	30.7720797784767	30.7308333333333	1.00134218440144	0.999282473637281
32	30.75	30.7835411308648	30.7120833333333	1.00232669977988	0.998910419996122
33	30.63	30.7370519202282	30.68875	1.00157392921602	0.996517170205328
34	30.52	30.5820287402149	30.6629166666667	0.997362027646257	0.997971725788966
35	30.58	30.6294188291582	30.6379166666667	0.999722636574772	0.998386556746839
36	30.6	30.624539998663	30.6195833333333	1.00016187892813	0.999198681885049
37	30.6	30.6332781773245	30.6070833333333	1.00085584254161	0.998913659284785
38	30.63	30.6030219343567	30.5958333333333	1.00023495359466	1.00088154907385
39	30.56	30.5648167578588	30.5920833333333	0.999108704850942	0.999842408416941
40	30.61	30.5592845591259	30.5858333333333	0.999131991143805	1.00165957553018
41	30.53	30.5248475532861	30.5675	0.998604647200005	1.00016879516613
42	30.6	30.5311702496676	30.5441666666667	0.999574504122475	1.00225440917494
43	30.6	30.5601290161118	30.5191666666667	1.00134218440144	1.00130467328417
44	30.63	30.554676534415	30.48375	1.00232669977988	1.00246520251982
45	30.66	30.4837371806306	30.4358333333333	1.00157392921602	1.00578219193811
46	30.34	30.3015206699394	30.3816666666667	0.997362027646257	1.0012698811548
47	30.32	30.3149227497357	30.3233333333333	0.999722636574772	1.00016748352969
48	30.3	30.2694825316435	30.2645833333333	1.00016187892813	1.00100819260206
49	30.3	30.2295996541661	30.20375	1.00085584254161	1.00232885471985
50	30.08	30.155833557187	30.14875	1.00023495359466	0.99748527736621
51	29.96	30.0635972242585	30.0904166666667	0.999108704850942	0.996554064256324
52	29.91	30.0135087089636	30.0395833333333	0.999131991143805	0.996551262634193
53	29.83	29.9689576330115	30.0108333333333	0.998604647200005	0.995363281075268
54	29.89	29.974323952996	29.9870833333333	0.999574504122475	0.997186793833007
55	29.85	NA	NA	1.00134218440144	NA
56	30.06	NA	NA	1.00232669977988	NA
57	29.83	NA	NA	1.00157392921602	NA
58	29.95	NA	NA	0.997362027646257	NA
59	30.02	NA	NA	0.999722636574772	NA
60	30.03	NA	NA	1.00016187892813	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 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
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