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, 12 May 2014 06:39:50 -0400

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/May/12/t1399891202ilfpoo83xk4h3z0.htm/, Retrieved Wed, 15 May 2024 08:55:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=234806, Retrieved Wed, 15 May 2024 08:55:41 +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

131

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

-       [Classical Decomposition] [] [2014-05-12 10:39:50] [a9350bbdd7016e8c1644512486dec5d2] [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	3 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234806&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234806&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234806&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	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	99.7	NA	NA	0.902241	NA
2	107.5	NA	NA	0.909529	NA
3	107.5	NA	NA	0.955615	NA
4	114.5	NA	NA	1.05021	NA
5	118.7	NA	NA	1.05314	NA
6	117.8	NA	NA	1.06456	NA
7	111.7	116.477	108.429	1.07422	0.958988
8	112.3	112.17	107.758	1.04094	1.00116
9	104.9	107.699	106.783	1.00858	0.974008
10	102.4	106.087	105.85	1.00224	0.965244
11	100.3	98.9593	105.017	0.94232	1.01355
12	106.6	103.663	104.038	0.996403	1.02833
13	94.2	93.2466	103.35	0.902241	1.01022
14	96.9	93.6019	102.912	0.909529	1.03523
15	94.7	97.8351	102.379	0.955615	0.967955
16	104.9	107.135	102.012	1.05021	0.979139
17	108.3	106.946	101.55	1.05314	1.01266
18	104.7	107.397	100.883	1.06456	0.974889
19	108.3	107.753	100.308	1.07422	1.00507
20	105.2	103.842	99.7583	1.04094	1.01308
21	99.2	100.127	99.275	1.00858	0.990746
22	99.3	99.2636	99.0417	1.00224	1.00037
23	92.3	93.1483	98.85	0.94232	0.990893
24	98.6	98.4612	98.8167	0.996403	1.00141
25	88.4	89.3707	99.0542	0.902241	0.989138
26	89.5	90.195	99.1667	0.909529	0.992295
27	90.5	94.9045	99.3125	0.955615	0.95359
28	103.5	104.711	99.7042	1.05021	0.988438
29	105.1	105.314	100	1.05314	0.997971
30	107.1	106.496	100.037	1.06456	1.00567
31	111.6	107.601	100.167	1.07422	1.03716
32	104.6	104.441	100.333	1.04094	1.00153
33	103.3	101.597	100.733	1.00858	1.01676
34	104.6	101.615	101.387	1.00224	1.02938
35	94.1	95.7947	101.658	0.94232	0.982309
36	97.7	101.384	101.75	0.996403	0.963663
37	92.4	91.7654	101.708	0.902241	1.00692
38	89.5	92.3741	101.563	0.909529	0.968887
39	100.1	97.0666	101.575	0.955615	1.03125
40	109.6	106.61	101.512	1.05021	1.02805
41	105.5	106.907	101.512	1.05314	0.986843
42	108.9	108.342	101.771	1.06456	1.00515
43	108.8	109.4	101.842	1.07422	0.994511
44	103.9	106.024	101.854	1.04094	0.97997
45	104.3	102.837	101.962	1.00858	1.01422
46	102.1	102.024	101.796	1.00224	1.00075
47	96.6	95.7397	101.6	0.94232	1.00899
48	101.4	101.164	101.529	0.996403	1.00233
49	90.4	91.3632	101.262	0.902241	0.989458
50	91.8	91.8625	101	0.909529	0.99932
51	100.4	96.2663	100.737	0.955615	1.04294
52	105.3	105.288	100.254	1.05021	1.00011
53	105.1	105.283	99.9708	1.05314	0.998262
54	107.6	106.532	100.071	1.06456	1.01003
55	103.7	NA	NA	1.07422	NA
56	102.7	NA	NA	1.04094	NA
57	99.2	NA	NA	1.00858	NA
58	95.6	NA	NA	1.00224	NA
59	96.3	NA	NA	0.94232	NA
60	104.1	NA	NA	0.996403	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 99.7 & NA & NA & 0.902241 & NA \tabularnewline
2 & 107.5 & NA & NA & 0.909529 & NA \tabularnewline
3 & 107.5 & NA & NA & 0.955615 & NA \tabularnewline
4 & 114.5 & NA & NA & 1.05021 & NA \tabularnewline
5 & 118.7 & NA & NA & 1.05314 & NA \tabularnewline
6 & 117.8 & NA & NA & 1.06456 & NA \tabularnewline
7 & 111.7 & 116.477 & 108.429 & 1.07422 & 0.958988 \tabularnewline
8 & 112.3 & 112.17 & 107.758 & 1.04094 & 1.00116 \tabularnewline
9 & 104.9 & 107.699 & 106.783 & 1.00858 & 0.974008 \tabularnewline
10 & 102.4 & 106.087 & 105.85 & 1.00224 & 0.965244 \tabularnewline
11 & 100.3 & 98.9593 & 105.017 & 0.94232 & 1.01355 \tabularnewline
12 & 106.6 & 103.663 & 104.038 & 0.996403 & 1.02833 \tabularnewline
13 & 94.2 & 93.2466 & 103.35 & 0.902241 & 1.01022 \tabularnewline
14 & 96.9 & 93.6019 & 102.912 & 0.909529 & 1.03523 \tabularnewline
15 & 94.7 & 97.8351 & 102.379 & 0.955615 & 0.967955 \tabularnewline
16 & 104.9 & 107.135 & 102.012 & 1.05021 & 0.979139 \tabularnewline
17 & 108.3 & 106.946 & 101.55 & 1.05314 & 1.01266 \tabularnewline
18 & 104.7 & 107.397 & 100.883 & 1.06456 & 0.974889 \tabularnewline
19 & 108.3 & 107.753 & 100.308 & 1.07422 & 1.00507 \tabularnewline
20 & 105.2 & 103.842 & 99.7583 & 1.04094 & 1.01308 \tabularnewline
21 & 99.2 & 100.127 & 99.275 & 1.00858 & 0.990746 \tabularnewline
22 & 99.3 & 99.2636 & 99.0417 & 1.00224 & 1.00037 \tabularnewline
23 & 92.3 & 93.1483 & 98.85 & 0.94232 & 0.990893 \tabularnewline
24 & 98.6 & 98.4612 & 98.8167 & 0.996403 & 1.00141 \tabularnewline
25 & 88.4 & 89.3707 & 99.0542 & 0.902241 & 0.989138 \tabularnewline
26 & 89.5 & 90.195 & 99.1667 & 0.909529 & 0.992295 \tabularnewline
27 & 90.5 & 94.9045 & 99.3125 & 0.955615 & 0.95359 \tabularnewline
28 & 103.5 & 104.711 & 99.7042 & 1.05021 & 0.988438 \tabularnewline
29 & 105.1 & 105.314 & 100 & 1.05314 & 0.997971 \tabularnewline
30 & 107.1 & 106.496 & 100.037 & 1.06456 & 1.00567 \tabularnewline
31 & 111.6 & 107.601 & 100.167 & 1.07422 & 1.03716 \tabularnewline
32 & 104.6 & 104.441 & 100.333 & 1.04094 & 1.00153 \tabularnewline
33 & 103.3 & 101.597 & 100.733 & 1.00858 & 1.01676 \tabularnewline
34 & 104.6 & 101.615 & 101.387 & 1.00224 & 1.02938 \tabularnewline
35 & 94.1 & 95.7947 & 101.658 & 0.94232 & 0.982309 \tabularnewline
36 & 97.7 & 101.384 & 101.75 & 0.996403 & 0.963663 \tabularnewline
37 & 92.4 & 91.7654 & 101.708 & 0.902241 & 1.00692 \tabularnewline
38 & 89.5 & 92.3741 & 101.563 & 0.909529 & 0.968887 \tabularnewline
39 & 100.1 & 97.0666 & 101.575 & 0.955615 & 1.03125 \tabularnewline
40 & 109.6 & 106.61 & 101.512 & 1.05021 & 1.02805 \tabularnewline
41 & 105.5 & 106.907 & 101.512 & 1.05314 & 0.986843 \tabularnewline
42 & 108.9 & 108.342 & 101.771 & 1.06456 & 1.00515 \tabularnewline
43 & 108.8 & 109.4 & 101.842 & 1.07422 & 0.994511 \tabularnewline
44 & 103.9 & 106.024 & 101.854 & 1.04094 & 0.97997 \tabularnewline
45 & 104.3 & 102.837 & 101.962 & 1.00858 & 1.01422 \tabularnewline
46 & 102.1 & 102.024 & 101.796 & 1.00224 & 1.00075 \tabularnewline
47 & 96.6 & 95.7397 & 101.6 & 0.94232 & 1.00899 \tabularnewline
48 & 101.4 & 101.164 & 101.529 & 0.996403 & 1.00233 \tabularnewline
49 & 90.4 & 91.3632 & 101.262 & 0.902241 & 0.989458 \tabularnewline
50 & 91.8 & 91.8625 & 101 & 0.909529 & 0.99932 \tabularnewline
51 & 100.4 & 96.2663 & 100.737 & 0.955615 & 1.04294 \tabularnewline
52 & 105.3 & 105.288 & 100.254 & 1.05021 & 1.00011 \tabularnewline
53 & 105.1 & 105.283 & 99.9708 & 1.05314 & 0.998262 \tabularnewline
54 & 107.6 & 106.532 & 100.071 & 1.06456 & 1.01003 \tabularnewline
55 & 103.7 & NA & NA & 1.07422 & NA \tabularnewline
56 & 102.7 & NA & NA & 1.04094 & NA \tabularnewline
57 & 99.2 & NA & NA & 1.00858 & NA \tabularnewline
58 & 95.6 & NA & NA & 1.00224 & NA \tabularnewline
59 & 96.3 & NA & NA & 0.94232 & NA \tabularnewline
60 & 104.1 & NA & NA & 0.996403 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234806&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]99.7[/C][C]NA[/C][C]NA[/C][C]0.902241[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]107.5[/C][C]NA[/C][C]NA[/C][C]0.909529[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]107.5[/C][C]NA[/C][C]NA[/C][C]0.955615[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]114.5[/C][C]NA[/C][C]NA[/C][C]1.05021[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]118.7[/C][C]NA[/C][C]NA[/C][C]1.05314[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]117.8[/C][C]NA[/C][C]NA[/C][C]1.06456[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]111.7[/C][C]116.477[/C][C]108.429[/C][C]1.07422[/C][C]0.958988[/C][/ROW]
[ROW][C]8[/C][C]112.3[/C][C]112.17[/C][C]107.758[/C][C]1.04094[/C][C]1.00116[/C][/ROW]
[ROW][C]9[/C][C]104.9[/C][C]107.699[/C][C]106.783[/C][C]1.00858[/C][C]0.974008[/C][/ROW]
[ROW][C]10[/C][C]102.4[/C][C]106.087[/C][C]105.85[/C][C]1.00224[/C][C]0.965244[/C][/ROW]
[ROW][C]11[/C][C]100.3[/C][C]98.9593[/C][C]105.017[/C][C]0.94232[/C][C]1.01355[/C][/ROW]
[ROW][C]12[/C][C]106.6[/C][C]103.663[/C][C]104.038[/C][C]0.996403[/C][C]1.02833[/C][/ROW]
[ROW][C]13[/C][C]94.2[/C][C]93.2466[/C][C]103.35[/C][C]0.902241[/C][C]1.01022[/C][/ROW]
[ROW][C]14[/C][C]96.9[/C][C]93.6019[/C][C]102.912[/C][C]0.909529[/C][C]1.03523[/C][/ROW]
[ROW][C]15[/C][C]94.7[/C][C]97.8351[/C][C]102.379[/C][C]0.955615[/C][C]0.967955[/C][/ROW]
[ROW][C]16[/C][C]104.9[/C][C]107.135[/C][C]102.012[/C][C]1.05021[/C][C]0.979139[/C][/ROW]
[ROW][C]17[/C][C]108.3[/C][C]106.946[/C][C]101.55[/C][C]1.05314[/C][C]1.01266[/C][/ROW]
[ROW][C]18[/C][C]104.7[/C][C]107.397[/C][C]100.883[/C][C]1.06456[/C][C]0.974889[/C][/ROW]
[ROW][C]19[/C][C]108.3[/C][C]107.753[/C][C]100.308[/C][C]1.07422[/C][C]1.00507[/C][/ROW]
[ROW][C]20[/C][C]105.2[/C][C]103.842[/C][C]99.7583[/C][C]1.04094[/C][C]1.01308[/C][/ROW]
[ROW][C]21[/C][C]99.2[/C][C]100.127[/C][C]99.275[/C][C]1.00858[/C][C]0.990746[/C][/ROW]
[ROW][C]22[/C][C]99.3[/C][C]99.2636[/C][C]99.0417[/C][C]1.00224[/C][C]1.00037[/C][/ROW]
[ROW][C]23[/C][C]92.3[/C][C]93.1483[/C][C]98.85[/C][C]0.94232[/C][C]0.990893[/C][/ROW]
[ROW][C]24[/C][C]98.6[/C][C]98.4612[/C][C]98.8167[/C][C]0.996403[/C][C]1.00141[/C][/ROW]
[ROW][C]25[/C][C]88.4[/C][C]89.3707[/C][C]99.0542[/C][C]0.902241[/C][C]0.989138[/C][/ROW]
[ROW][C]26[/C][C]89.5[/C][C]90.195[/C][C]99.1667[/C][C]0.909529[/C][C]0.992295[/C][/ROW]
[ROW][C]27[/C][C]90.5[/C][C]94.9045[/C][C]99.3125[/C][C]0.955615[/C][C]0.95359[/C][/ROW]
[ROW][C]28[/C][C]103.5[/C][C]104.711[/C][C]99.7042[/C][C]1.05021[/C][C]0.988438[/C][/ROW]
[ROW][C]29[/C][C]105.1[/C][C]105.314[/C][C]100[/C][C]1.05314[/C][C]0.997971[/C][/ROW]
[ROW][C]30[/C][C]107.1[/C][C]106.496[/C][C]100.037[/C][C]1.06456[/C][C]1.00567[/C][/ROW]
[ROW][C]31[/C][C]111.6[/C][C]107.601[/C][C]100.167[/C][C]1.07422[/C][C]1.03716[/C][/ROW]
[ROW][C]32[/C][C]104.6[/C][C]104.441[/C][C]100.333[/C][C]1.04094[/C][C]1.00153[/C][/ROW]
[ROW][C]33[/C][C]103.3[/C][C]101.597[/C][C]100.733[/C][C]1.00858[/C][C]1.01676[/C][/ROW]
[ROW][C]34[/C][C]104.6[/C][C]101.615[/C][C]101.387[/C][C]1.00224[/C][C]1.02938[/C][/ROW]
[ROW][C]35[/C][C]94.1[/C][C]95.7947[/C][C]101.658[/C][C]0.94232[/C][C]0.982309[/C][/ROW]
[ROW][C]36[/C][C]97.7[/C][C]101.384[/C][C]101.75[/C][C]0.996403[/C][C]0.963663[/C][/ROW]
[ROW][C]37[/C][C]92.4[/C][C]91.7654[/C][C]101.708[/C][C]0.902241[/C][C]1.00692[/C][/ROW]
[ROW][C]38[/C][C]89.5[/C][C]92.3741[/C][C]101.563[/C][C]0.909529[/C][C]0.968887[/C][/ROW]
[ROW][C]39[/C][C]100.1[/C][C]97.0666[/C][C]101.575[/C][C]0.955615[/C][C]1.03125[/C][/ROW]
[ROW][C]40[/C][C]109.6[/C][C]106.61[/C][C]101.512[/C][C]1.05021[/C][C]1.02805[/C][/ROW]
[ROW][C]41[/C][C]105.5[/C][C]106.907[/C][C]101.512[/C][C]1.05314[/C][C]0.986843[/C][/ROW]
[ROW][C]42[/C][C]108.9[/C][C]108.342[/C][C]101.771[/C][C]1.06456[/C][C]1.00515[/C][/ROW]
[ROW][C]43[/C][C]108.8[/C][C]109.4[/C][C]101.842[/C][C]1.07422[/C][C]0.994511[/C][/ROW]
[ROW][C]44[/C][C]103.9[/C][C]106.024[/C][C]101.854[/C][C]1.04094[/C][C]0.97997[/C][/ROW]
[ROW][C]45[/C][C]104.3[/C][C]102.837[/C][C]101.962[/C][C]1.00858[/C][C]1.01422[/C][/ROW]
[ROW][C]46[/C][C]102.1[/C][C]102.024[/C][C]101.796[/C][C]1.00224[/C][C]1.00075[/C][/ROW]
[ROW][C]47[/C][C]96.6[/C][C]95.7397[/C][C]101.6[/C][C]0.94232[/C][C]1.00899[/C][/ROW]
[ROW][C]48[/C][C]101.4[/C][C]101.164[/C][C]101.529[/C][C]0.996403[/C][C]1.00233[/C][/ROW]
[ROW][C]49[/C][C]90.4[/C][C]91.3632[/C][C]101.262[/C][C]0.902241[/C][C]0.989458[/C][/ROW]
[ROW][C]50[/C][C]91.8[/C][C]91.8625[/C][C]101[/C][C]0.909529[/C][C]0.99932[/C][/ROW]
[ROW][C]51[/C][C]100.4[/C][C]96.2663[/C][C]100.737[/C][C]0.955615[/C][C]1.04294[/C][/ROW]
[ROW][C]52[/C][C]105.3[/C][C]105.288[/C][C]100.254[/C][C]1.05021[/C][C]1.00011[/C][/ROW]
[ROW][C]53[/C][C]105.1[/C][C]105.283[/C][C]99.9708[/C][C]1.05314[/C][C]0.998262[/C][/ROW]
[ROW][C]54[/C][C]107.6[/C][C]106.532[/C][C]100.071[/C][C]1.06456[/C][C]1.01003[/C][/ROW]
[ROW][C]55[/C][C]103.7[/C][C]NA[/C][C]NA[/C][C]1.07422[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]102.7[/C][C]NA[/C][C]NA[/C][C]1.04094[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]99.2[/C][C]NA[/C][C]NA[/C][C]1.00858[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]95.6[/C][C]NA[/C][C]NA[/C][C]1.00224[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]96.3[/C][C]NA[/C][C]NA[/C][C]0.94232[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]104.1[/C][C]NA[/C][C]NA[/C][C]0.996403[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234806&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234806&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	99.7	NA	NA	0.902241	NA
2	107.5	NA	NA	0.909529	NA
3	107.5	NA	NA	0.955615	NA
4	114.5	NA	NA	1.05021	NA
5	118.7	NA	NA	1.05314	NA
6	117.8	NA	NA	1.06456	NA
7	111.7	116.477	108.429	1.07422	0.958988
8	112.3	112.17	107.758	1.04094	1.00116
9	104.9	107.699	106.783	1.00858	0.974008
10	102.4	106.087	105.85	1.00224	0.965244
11	100.3	98.9593	105.017	0.94232	1.01355
12	106.6	103.663	104.038	0.996403	1.02833
13	94.2	93.2466	103.35	0.902241	1.01022
14	96.9	93.6019	102.912	0.909529	1.03523
15	94.7	97.8351	102.379	0.955615	0.967955
16	104.9	107.135	102.012	1.05021	0.979139
17	108.3	106.946	101.55	1.05314	1.01266
18	104.7	107.397	100.883	1.06456	0.974889
19	108.3	107.753	100.308	1.07422	1.00507
20	105.2	103.842	99.7583	1.04094	1.01308
21	99.2	100.127	99.275	1.00858	0.990746
22	99.3	99.2636	99.0417	1.00224	1.00037
23	92.3	93.1483	98.85	0.94232	0.990893
24	98.6	98.4612	98.8167	0.996403	1.00141
25	88.4	89.3707	99.0542	0.902241	0.989138
26	89.5	90.195	99.1667	0.909529	0.992295
27	90.5	94.9045	99.3125	0.955615	0.95359
28	103.5	104.711	99.7042	1.05021	0.988438
29	105.1	105.314	100	1.05314	0.997971
30	107.1	106.496	100.037	1.06456	1.00567
31	111.6	107.601	100.167	1.07422	1.03716
32	104.6	104.441	100.333	1.04094	1.00153
33	103.3	101.597	100.733	1.00858	1.01676
34	104.6	101.615	101.387	1.00224	1.02938
35	94.1	95.7947	101.658	0.94232	0.982309
36	97.7	101.384	101.75	0.996403	0.963663
37	92.4	91.7654	101.708	0.902241	1.00692
38	89.5	92.3741	101.563	0.909529	0.968887
39	100.1	97.0666	101.575	0.955615	1.03125
40	109.6	106.61	101.512	1.05021	1.02805
41	105.5	106.907	101.512	1.05314	0.986843
42	108.9	108.342	101.771	1.06456	1.00515
43	108.8	109.4	101.842	1.07422	0.994511
44	103.9	106.024	101.854	1.04094	0.97997
45	104.3	102.837	101.962	1.00858	1.01422
46	102.1	102.024	101.796	1.00224	1.00075
47	96.6	95.7397	101.6	0.94232	1.00899
48	101.4	101.164	101.529	0.996403	1.00233
49	90.4	91.3632	101.262	0.902241	0.989458
50	91.8	91.8625	101	0.909529	0.99932
51	100.4	96.2663	100.737	0.955615	1.04294
52	105.3	105.288	100.254	1.05021	1.00011
53	105.1	105.283	99.9708	1.05314	0.998262
54	107.6	106.532	100.071	1.06456	1.01003
55	103.7	NA	NA	1.07422	NA
56	102.7	NA	NA	1.04094	NA
57	99.2	NA	NA	1.00858	NA
58	95.6	NA	NA	1.00224	NA
59	96.3	NA	NA	0.94232	NA
60	104.1	NA	NA	0.996403	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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$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