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

Thu, 27 Nov 2014 14:36:25 +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/2014/Nov/27/t1417099033csh6yebt6sy4io0.htm/, Retrieved Sun, 19 May 2024 18:19:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=260298, Retrieved Sun, 19 May 2024 18:19:29 +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

Alessio De Looze

Estimated Impact

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

-       [Classical Decomposition] [] [2014-11-27 14:36:25] [072d4f39c76834f6beee313555a90f83] [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=260298&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=260298&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=260298&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	164.88	NA	NA	1.54193	NA
2	164.88	NA	NA	-0.432649	NA
3	164.57	NA	NA	-0.245462	NA
4	164.53	NA	NA	-0.369003	NA
5	165.03	NA	NA	-0.697337	NA
6	165.92	NA	NA	-0.58692	NA
7	165.92	164.863	165.49	-0.62617	1.05659
8	165.92	165.233	165.55	-0.317649	0.687233
9	165.92	165.253	165.638	-0.385253	0.667337
10	166.12	165.69	165.751	-0.0608785	0.429628
11	166.34	166.772	165.857	0.914642	-0.432142
12	165.48	167.189	165.924	1.26475	-1.70891
13	165.61	167.504	165.962	1.54193	-1.89402
14	165.61	165.572	166.005	-0.432649	0.038066
15	165.94	165.792	166.038	-0.245462	0.147962
16	165.88	165.684	166.053	-0.369003	0.196087
17	166.23	165.429	166.127	-0.697337	0.80067
18	166.32	165.743	166.33	-0.58692	0.57692
19	166.43	165.964	166.59	-0.62617	0.46617
20	166.43	166.526	166.843	-0.317649	-0.095684
21	166.2	166.702	167.087	-0.385253	-0.50183
22	166.21	167.265	167.326	-0.0608785	-1.05495
23	168.02	168.46	167.545	0.914642	-0.440059
24	168.68	169.019	167.754	1.26475	-0.338913
25	168.65	169.514	167.972	1.54193	-0.864017
26	168.65	167.754	168.186	-0.432649	0.896399
27	168.75	168.162	168.408	-0.245462	0.587962
28	168.8	168.309	168.678	-0.369003	0.491087
29	168.58	168.286	168.983	-0.697337	0.29442
30	168.98	168.7	169.287	-0.58692	0.279837
31	169	168.983	169.609	-0.62617	0.0170035
32	169	169.625	169.942	-0.317649	-0.624851
33	168.94	169.886	170.272	-0.385253	-0.946413
34	169.96	170.522	170.583	-0.0608785	-0.562455
35	171.59	171.788	170.873	0.914642	-0.197559
36	172.41	172.402	171.137	1.26475	0.00817014
37	172.65	172.922	171.38	1.54193	-0.272351
38	172.65	171.187	171.62	-0.432649	1.46307
39	172.65	171.617	171.863	-0.245462	1.03255
40	172.38	171.702	172.071	-0.369003	0.677753
41	171.95	171.482	172.179	-0.697337	0.46817
42	171.95	171.629	172.216	-0.58692	0.321087
43	171.87	171.626	172.252	-0.62617	0.244087
44	171.87	171.637	171.955	-0.317649	0.232649
45	171.91	170.93	171.315	-0.385253	0.980253
46	171.99	170.603	170.664	-0.0608785	1.38713
47	172.15	170.881	169.966	0.914642	1.26911
48	172.73	170.491	169.226	1.26475	2.239
49	173.2	169.97	168.428	1.54193	3.22973
50	164.97	167.168	167.601	-0.432649	-2.19818
51	164.97	166.539	166.785	-0.245462	-1.56912
52	164.43	165.596	165.965	-0.369003	-1.16558
53	163.16	164.524	165.221	-0.697337	-1.36391
54	162.98	163.958	164.545	-0.58692	-0.978497
55	161.69	163.225	163.851	-0.62617	-1.53466
56	162.19	NA	NA	-0.317649	NA
57	162	NA	NA	-0.385253	NA
58	162.22	NA	NA	-0.0608785	NA
59	164.08	NA	NA	0.914642	NA
60	164.58	NA	NA	1.26475	NA
61	164.68	NA	NA	1.54193	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 164.88 & NA & NA & 1.54193 & NA \tabularnewline
2 & 164.88 & NA & NA & -0.432649 & NA \tabularnewline
3 & 164.57 & NA & NA & -0.245462 & NA \tabularnewline
4 & 164.53 & NA & NA & -0.369003 & NA \tabularnewline
5 & 165.03 & NA & NA & -0.697337 & NA \tabularnewline
6 & 165.92 & NA & NA & -0.58692 & NA \tabularnewline
7 & 165.92 & 164.863 & 165.49 & -0.62617 & 1.05659 \tabularnewline
8 & 165.92 & 165.233 & 165.55 & -0.317649 & 0.687233 \tabularnewline
9 & 165.92 & 165.253 & 165.638 & -0.385253 & 0.667337 \tabularnewline
10 & 166.12 & 165.69 & 165.751 & -0.0608785 & 0.429628 \tabularnewline
11 & 166.34 & 166.772 & 165.857 & 0.914642 & -0.432142 \tabularnewline
12 & 165.48 & 167.189 & 165.924 & 1.26475 & -1.70891 \tabularnewline
13 & 165.61 & 167.504 & 165.962 & 1.54193 & -1.89402 \tabularnewline
14 & 165.61 & 165.572 & 166.005 & -0.432649 & 0.038066 \tabularnewline
15 & 165.94 & 165.792 & 166.038 & -0.245462 & 0.147962 \tabularnewline
16 & 165.88 & 165.684 & 166.053 & -0.369003 & 0.196087 \tabularnewline
17 & 166.23 & 165.429 & 166.127 & -0.697337 & 0.80067 \tabularnewline
18 & 166.32 & 165.743 & 166.33 & -0.58692 & 0.57692 \tabularnewline
19 & 166.43 & 165.964 & 166.59 & -0.62617 & 0.46617 \tabularnewline
20 & 166.43 & 166.526 & 166.843 & -0.317649 & -0.095684 \tabularnewline
21 & 166.2 & 166.702 & 167.087 & -0.385253 & -0.50183 \tabularnewline
22 & 166.21 & 167.265 & 167.326 & -0.0608785 & -1.05495 \tabularnewline
23 & 168.02 & 168.46 & 167.545 & 0.914642 & -0.440059 \tabularnewline
24 & 168.68 & 169.019 & 167.754 & 1.26475 & -0.338913 \tabularnewline
25 & 168.65 & 169.514 & 167.972 & 1.54193 & -0.864017 \tabularnewline
26 & 168.65 & 167.754 & 168.186 & -0.432649 & 0.896399 \tabularnewline
27 & 168.75 & 168.162 & 168.408 & -0.245462 & 0.587962 \tabularnewline
28 & 168.8 & 168.309 & 168.678 & -0.369003 & 0.491087 \tabularnewline
29 & 168.58 & 168.286 & 168.983 & -0.697337 & 0.29442 \tabularnewline
30 & 168.98 & 168.7 & 169.287 & -0.58692 & 0.279837 \tabularnewline
31 & 169 & 168.983 & 169.609 & -0.62617 & 0.0170035 \tabularnewline
32 & 169 & 169.625 & 169.942 & -0.317649 & -0.624851 \tabularnewline
33 & 168.94 & 169.886 & 170.272 & -0.385253 & -0.946413 \tabularnewline
34 & 169.96 & 170.522 & 170.583 & -0.0608785 & -0.562455 \tabularnewline
35 & 171.59 & 171.788 & 170.873 & 0.914642 & -0.197559 \tabularnewline
36 & 172.41 & 172.402 & 171.137 & 1.26475 & 0.00817014 \tabularnewline
37 & 172.65 & 172.922 & 171.38 & 1.54193 & -0.272351 \tabularnewline
38 & 172.65 & 171.187 & 171.62 & -0.432649 & 1.46307 \tabularnewline
39 & 172.65 & 171.617 & 171.863 & -0.245462 & 1.03255 \tabularnewline
40 & 172.38 & 171.702 & 172.071 & -0.369003 & 0.677753 \tabularnewline
41 & 171.95 & 171.482 & 172.179 & -0.697337 & 0.46817 \tabularnewline
42 & 171.95 & 171.629 & 172.216 & -0.58692 & 0.321087 \tabularnewline
43 & 171.87 & 171.626 & 172.252 & -0.62617 & 0.244087 \tabularnewline
44 & 171.87 & 171.637 & 171.955 & -0.317649 & 0.232649 \tabularnewline
45 & 171.91 & 170.93 & 171.315 & -0.385253 & 0.980253 \tabularnewline
46 & 171.99 & 170.603 & 170.664 & -0.0608785 & 1.38713 \tabularnewline
47 & 172.15 & 170.881 & 169.966 & 0.914642 & 1.26911 \tabularnewline
48 & 172.73 & 170.491 & 169.226 & 1.26475 & 2.239 \tabularnewline
49 & 173.2 & 169.97 & 168.428 & 1.54193 & 3.22973 \tabularnewline
50 & 164.97 & 167.168 & 167.601 & -0.432649 & -2.19818 \tabularnewline
51 & 164.97 & 166.539 & 166.785 & -0.245462 & -1.56912 \tabularnewline
52 & 164.43 & 165.596 & 165.965 & -0.369003 & -1.16558 \tabularnewline
53 & 163.16 & 164.524 & 165.221 & -0.697337 & -1.36391 \tabularnewline
54 & 162.98 & 163.958 & 164.545 & -0.58692 & -0.978497 \tabularnewline
55 & 161.69 & 163.225 & 163.851 & -0.62617 & -1.53466 \tabularnewline
56 & 162.19 & NA & NA & -0.317649 & NA \tabularnewline
57 & 162 & NA & NA & -0.385253 & NA \tabularnewline
58 & 162.22 & NA & NA & -0.0608785 & NA \tabularnewline
59 & 164.08 & NA & NA & 0.914642 & NA \tabularnewline
60 & 164.58 & NA & NA & 1.26475 & NA \tabularnewline
61 & 164.68 & NA & NA & 1.54193 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=260298&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]164.88[/C][C]NA[/C][C]NA[/C][C]1.54193[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]164.88[/C][C]NA[/C][C]NA[/C][C]-0.432649[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]164.57[/C][C]NA[/C][C]NA[/C][C]-0.245462[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]164.53[/C][C]NA[/C][C]NA[/C][C]-0.369003[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]165.03[/C][C]NA[/C][C]NA[/C][C]-0.697337[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]165.92[/C][C]NA[/C][C]NA[/C][C]-0.58692[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]165.92[/C][C]164.863[/C][C]165.49[/C][C]-0.62617[/C][C]1.05659[/C][/ROW]
[ROW][C]8[/C][C]165.92[/C][C]165.233[/C][C]165.55[/C][C]-0.317649[/C][C]0.687233[/C][/ROW]
[ROW][C]9[/C][C]165.92[/C][C]165.253[/C][C]165.638[/C][C]-0.385253[/C][C]0.667337[/C][/ROW]
[ROW][C]10[/C][C]166.12[/C][C]165.69[/C][C]165.751[/C][C]-0.0608785[/C][C]0.429628[/C][/ROW]
[ROW][C]11[/C][C]166.34[/C][C]166.772[/C][C]165.857[/C][C]0.914642[/C][C]-0.432142[/C][/ROW]
[ROW][C]12[/C][C]165.48[/C][C]167.189[/C][C]165.924[/C][C]1.26475[/C][C]-1.70891[/C][/ROW]
[ROW][C]13[/C][C]165.61[/C][C]167.504[/C][C]165.962[/C][C]1.54193[/C][C]-1.89402[/C][/ROW]
[ROW][C]14[/C][C]165.61[/C][C]165.572[/C][C]166.005[/C][C]-0.432649[/C][C]0.038066[/C][/ROW]
[ROW][C]15[/C][C]165.94[/C][C]165.792[/C][C]166.038[/C][C]-0.245462[/C][C]0.147962[/C][/ROW]
[ROW][C]16[/C][C]165.88[/C][C]165.684[/C][C]166.053[/C][C]-0.369003[/C][C]0.196087[/C][/ROW]
[ROW][C]17[/C][C]166.23[/C][C]165.429[/C][C]166.127[/C][C]-0.697337[/C][C]0.80067[/C][/ROW]
[ROW][C]18[/C][C]166.32[/C][C]165.743[/C][C]166.33[/C][C]-0.58692[/C][C]0.57692[/C][/ROW]
[ROW][C]19[/C][C]166.43[/C][C]165.964[/C][C]166.59[/C][C]-0.62617[/C][C]0.46617[/C][/ROW]
[ROW][C]20[/C][C]166.43[/C][C]166.526[/C][C]166.843[/C][C]-0.317649[/C][C]-0.095684[/C][/ROW]
[ROW][C]21[/C][C]166.2[/C][C]166.702[/C][C]167.087[/C][C]-0.385253[/C][C]-0.50183[/C][/ROW]
[ROW][C]22[/C][C]166.21[/C][C]167.265[/C][C]167.326[/C][C]-0.0608785[/C][C]-1.05495[/C][/ROW]
[ROW][C]23[/C][C]168.02[/C][C]168.46[/C][C]167.545[/C][C]0.914642[/C][C]-0.440059[/C][/ROW]
[ROW][C]24[/C][C]168.68[/C][C]169.019[/C][C]167.754[/C][C]1.26475[/C][C]-0.338913[/C][/ROW]
[ROW][C]25[/C][C]168.65[/C][C]169.514[/C][C]167.972[/C][C]1.54193[/C][C]-0.864017[/C][/ROW]
[ROW][C]26[/C][C]168.65[/C][C]167.754[/C][C]168.186[/C][C]-0.432649[/C][C]0.896399[/C][/ROW]
[ROW][C]27[/C][C]168.75[/C][C]168.162[/C][C]168.408[/C][C]-0.245462[/C][C]0.587962[/C][/ROW]
[ROW][C]28[/C][C]168.8[/C][C]168.309[/C][C]168.678[/C][C]-0.369003[/C][C]0.491087[/C][/ROW]
[ROW][C]29[/C][C]168.58[/C][C]168.286[/C][C]168.983[/C][C]-0.697337[/C][C]0.29442[/C][/ROW]
[ROW][C]30[/C][C]168.98[/C][C]168.7[/C][C]169.287[/C][C]-0.58692[/C][C]0.279837[/C][/ROW]
[ROW][C]31[/C][C]169[/C][C]168.983[/C][C]169.609[/C][C]-0.62617[/C][C]0.0170035[/C][/ROW]
[ROW][C]32[/C][C]169[/C][C]169.625[/C][C]169.942[/C][C]-0.317649[/C][C]-0.624851[/C][/ROW]
[ROW][C]33[/C][C]168.94[/C][C]169.886[/C][C]170.272[/C][C]-0.385253[/C][C]-0.946413[/C][/ROW]
[ROW][C]34[/C][C]169.96[/C][C]170.522[/C][C]170.583[/C][C]-0.0608785[/C][C]-0.562455[/C][/ROW]
[ROW][C]35[/C][C]171.59[/C][C]171.788[/C][C]170.873[/C][C]0.914642[/C][C]-0.197559[/C][/ROW]
[ROW][C]36[/C][C]172.41[/C][C]172.402[/C][C]171.137[/C][C]1.26475[/C][C]0.00817014[/C][/ROW]
[ROW][C]37[/C][C]172.65[/C][C]172.922[/C][C]171.38[/C][C]1.54193[/C][C]-0.272351[/C][/ROW]
[ROW][C]38[/C][C]172.65[/C][C]171.187[/C][C]171.62[/C][C]-0.432649[/C][C]1.46307[/C][/ROW]
[ROW][C]39[/C][C]172.65[/C][C]171.617[/C][C]171.863[/C][C]-0.245462[/C][C]1.03255[/C][/ROW]
[ROW][C]40[/C][C]172.38[/C][C]171.702[/C][C]172.071[/C][C]-0.369003[/C][C]0.677753[/C][/ROW]
[ROW][C]41[/C][C]171.95[/C][C]171.482[/C][C]172.179[/C][C]-0.697337[/C][C]0.46817[/C][/ROW]
[ROW][C]42[/C][C]171.95[/C][C]171.629[/C][C]172.216[/C][C]-0.58692[/C][C]0.321087[/C][/ROW]
[ROW][C]43[/C][C]171.87[/C][C]171.626[/C][C]172.252[/C][C]-0.62617[/C][C]0.244087[/C][/ROW]
[ROW][C]44[/C][C]171.87[/C][C]171.637[/C][C]171.955[/C][C]-0.317649[/C][C]0.232649[/C][/ROW]
[ROW][C]45[/C][C]171.91[/C][C]170.93[/C][C]171.315[/C][C]-0.385253[/C][C]0.980253[/C][/ROW]
[ROW][C]46[/C][C]171.99[/C][C]170.603[/C][C]170.664[/C][C]-0.0608785[/C][C]1.38713[/C][/ROW]
[ROW][C]47[/C][C]172.15[/C][C]170.881[/C][C]169.966[/C][C]0.914642[/C][C]1.26911[/C][/ROW]
[ROW][C]48[/C][C]172.73[/C][C]170.491[/C][C]169.226[/C][C]1.26475[/C][C]2.239[/C][/ROW]
[ROW][C]49[/C][C]173.2[/C][C]169.97[/C][C]168.428[/C][C]1.54193[/C][C]3.22973[/C][/ROW]
[ROW][C]50[/C][C]164.97[/C][C]167.168[/C][C]167.601[/C][C]-0.432649[/C][C]-2.19818[/C][/ROW]
[ROW][C]51[/C][C]164.97[/C][C]166.539[/C][C]166.785[/C][C]-0.245462[/C][C]-1.56912[/C][/ROW]
[ROW][C]52[/C][C]164.43[/C][C]165.596[/C][C]165.965[/C][C]-0.369003[/C][C]-1.16558[/C][/ROW]
[ROW][C]53[/C][C]163.16[/C][C]164.524[/C][C]165.221[/C][C]-0.697337[/C][C]-1.36391[/C][/ROW]
[ROW][C]54[/C][C]162.98[/C][C]163.958[/C][C]164.545[/C][C]-0.58692[/C][C]-0.978497[/C][/ROW]
[ROW][C]55[/C][C]161.69[/C][C]163.225[/C][C]163.851[/C][C]-0.62617[/C][C]-1.53466[/C][/ROW]
[ROW][C]56[/C][C]162.19[/C][C]NA[/C][C]NA[/C][C]-0.317649[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]162[/C][C]NA[/C][C]NA[/C][C]-0.385253[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]162.22[/C][C]NA[/C][C]NA[/C][C]-0.0608785[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]164.08[/C][C]NA[/C][C]NA[/C][C]0.914642[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]164.58[/C][C]NA[/C][C]NA[/C][C]1.26475[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]164.68[/C][C]NA[/C][C]NA[/C][C]1.54193[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=260298&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=260298&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	164.88	NA	NA	1.54193	NA
2	164.88	NA	NA	-0.432649	NA
3	164.57	NA	NA	-0.245462	NA
4	164.53	NA	NA	-0.369003	NA
5	165.03	NA	NA	-0.697337	NA
6	165.92	NA	NA	-0.58692	NA
7	165.92	164.863	165.49	-0.62617	1.05659
8	165.92	165.233	165.55	-0.317649	0.687233
9	165.92	165.253	165.638	-0.385253	0.667337
10	166.12	165.69	165.751	-0.0608785	0.429628
11	166.34	166.772	165.857	0.914642	-0.432142
12	165.48	167.189	165.924	1.26475	-1.70891
13	165.61	167.504	165.962	1.54193	-1.89402
14	165.61	165.572	166.005	-0.432649	0.038066
15	165.94	165.792	166.038	-0.245462	0.147962
16	165.88	165.684	166.053	-0.369003	0.196087
17	166.23	165.429	166.127	-0.697337	0.80067
18	166.32	165.743	166.33	-0.58692	0.57692
19	166.43	165.964	166.59	-0.62617	0.46617
20	166.43	166.526	166.843	-0.317649	-0.095684
21	166.2	166.702	167.087	-0.385253	-0.50183
22	166.21	167.265	167.326	-0.0608785	-1.05495
23	168.02	168.46	167.545	0.914642	-0.440059
24	168.68	169.019	167.754	1.26475	-0.338913
25	168.65	169.514	167.972	1.54193	-0.864017
26	168.65	167.754	168.186	-0.432649	0.896399
27	168.75	168.162	168.408	-0.245462	0.587962
28	168.8	168.309	168.678	-0.369003	0.491087
29	168.58	168.286	168.983	-0.697337	0.29442
30	168.98	168.7	169.287	-0.58692	0.279837
31	169	168.983	169.609	-0.62617	0.0170035
32	169	169.625	169.942	-0.317649	-0.624851
33	168.94	169.886	170.272	-0.385253	-0.946413
34	169.96	170.522	170.583	-0.0608785	-0.562455
35	171.59	171.788	170.873	0.914642	-0.197559
36	172.41	172.402	171.137	1.26475	0.00817014
37	172.65	172.922	171.38	1.54193	-0.272351
38	172.65	171.187	171.62	-0.432649	1.46307
39	172.65	171.617	171.863	-0.245462	1.03255
40	172.38	171.702	172.071	-0.369003	0.677753
41	171.95	171.482	172.179	-0.697337	0.46817
42	171.95	171.629	172.216	-0.58692	0.321087
43	171.87	171.626	172.252	-0.62617	0.244087
44	171.87	171.637	171.955	-0.317649	0.232649
45	171.91	170.93	171.315	-0.385253	0.980253
46	171.99	170.603	170.664	-0.0608785	1.38713
47	172.15	170.881	169.966	0.914642	1.26911
48	172.73	170.491	169.226	1.26475	2.239
49	173.2	169.97	168.428	1.54193	3.22973
50	164.97	167.168	167.601	-0.432649	-2.19818
51	164.97	166.539	166.785	-0.245462	-1.56912
52	164.43	165.596	165.965	-0.369003	-1.16558
53	163.16	164.524	165.221	-0.697337	-1.36391
54	162.98	163.958	164.545	-0.58692	-0.978497
55	161.69	163.225	163.851	-0.62617	-1.53466
56	162.19	NA	NA	-0.317649	NA
57	162	NA	NA	-0.385253	NA
58	162.22	NA	NA	-0.0608785	NA
59	164.08	NA	NA	0.914642	NA
60	164.58	NA	NA	1.26475	NA
61	164.68	NA	NA	1.54193	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