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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Fri, 22 Apr 2016 07:14:51 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Apr/22/t1461305860d3dk1fc5j5va608.htm/, Retrieved Mon, 06 May 2024 09:25:39 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294582, Retrieved Mon, 06 May 2024 09:25:39 +0000

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Estimated Impact

166

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

-       [Classical Decomposition] [] [2016-04-22 06:14:51] [3fd8a2781b4a66a294f894c9e659cf7e] [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	'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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294582&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294582&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294582&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	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	97.78	NA	NA	-0.324175	NA
2	97.73	NA	NA	-0.347717	NA
3	97.61	NA	NA	-0.380634	NA
4	97.69	NA	NA	-0.463446	NA
5	97.68	NA	NA	-0.541675	NA
6	97.67	NA	NA	-0.533759	NA
7	97.67	98.0346	98.3254	-0.290842	-0.364575
8	97.96	98.2556	98.4937	-0.238134	-0.295616
9	98.27	98.8606	98.6792	0.18145	-0.590616
10	99.52	99.8079	98.885	0.922908	-0.287908
11	99.59	100.081	99.0917	0.989679	-0.491345
12	99.75	100.318	99.2921	1.02635	-0.568429
13	99.75	99.1687	99.4929	-0.324175	0.581259
14	99.8	99.3331	99.6808	-0.347717	0.466884
15	99.99	99.5014	99.8821	-0.380634	0.48855
16	100.25	99.6278	100.091	-0.463446	0.622196
17	100.08	99.7379	100.28	-0.541675	0.342092
18	100.08	99.9308	100.465	-0.533759	0.149175
19	100.08	100.358	100.649	-0.290842	-0.277908
20	100.06	100.591	100.83	-0.238134	-0.53145
21	101	101.185	101.003	0.18145	-0.184783
22	101.81	102.089	101.166	0.922908	-0.278741
23	101.82	102.316	101.326	0.989679	-0.495929
24	101.96	102.53	101.503	1.02635	-0.569679
25	101.96	101.367	101.692	-0.324175	0.592509
26	101.93	101.533	101.88	-0.347717	0.3973
27	102.03	101.668	102.049	-0.380634	0.361884
28	102.11	101.712	102.175	-0.463446	0.39803
29	102.07	101.752	102.293	-0.541675	0.318342
30	102.34	101.886	102.42	-0.533759	0.453759
31	102.34	102.072	102.363	-0.290842	0.268342
32	102.33	101.889	102.128	-0.238134	0.440634
33	102.77	102.076	101.894	0.18145	0.694384
34	103.08	102.57	101.648	0.922908	0.509592
35	103.38	102.383	101.394	0.989679	0.996571
36	103.44	102.161	101.135	1.02635	1.27907
37	99.1	100.583	100.907	-0.324175	-1.48291
38	99.15	100.382	100.73	-0.347717	-1.23228
39	99.21	100.173	100.553	-0.380634	-0.9627
40	99.01	99.9266	100.39	-0.463446	-0.916554
41	99.08	99.6987	100.24	-0.541675	-0.618741
42	99.11	99.5471	100.081	-0.533759	-0.437075
43	100.11	99.7754	100.066	-0.290842	0.334592
44	100.31	99.9631	100.201	-0.238134	0.346884
45	100.55	100.509	100.327	0.18145	0.041467
46	101.38	101.362	100.44	0.922908	0.0175087
47	101.49	101.539	100.549	0.989679	-0.0488455
48	101.5	101.681	100.654	1.02635	-0.180512
49	100.69	100.42	100.745	-0.324175	0.269592
50	100.8	100.471	100.819	-0.347717	0.32855
51	100.58	100.507	100.888	-0.380634	0.072717
52	100.34	100.483	100.947	-0.463446	-0.14322
53	100.38	100.461	101.003	-0.541675	-0.0812413
54	100.33	100.535	101.069	-0.533759	-0.205408
55	101.06	NA	NA	-0.290842	NA
56	101.15	NA	NA	-0.238134	NA
57	101.36	NA	NA	0.18145	NA
58	101.98	NA	NA	0.922908	NA
59	102.24	NA	NA	0.989679	NA
60	102.34	NA	NA	1.02635	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 97.78 & NA & NA & -0.324175 & NA \tabularnewline
2 & 97.73 & NA & NA & -0.347717 & NA \tabularnewline
3 & 97.61 & NA & NA & -0.380634 & NA \tabularnewline
4 & 97.69 & NA & NA & -0.463446 & NA \tabularnewline
5 & 97.68 & NA & NA & -0.541675 & NA \tabularnewline
6 & 97.67 & NA & NA & -0.533759 & NA \tabularnewline
7 & 97.67 & 98.0346 & 98.3254 & -0.290842 & -0.364575 \tabularnewline
8 & 97.96 & 98.2556 & 98.4937 & -0.238134 & -0.295616 \tabularnewline
9 & 98.27 & 98.8606 & 98.6792 & 0.18145 & -0.590616 \tabularnewline
10 & 99.52 & 99.8079 & 98.885 & 0.922908 & -0.287908 \tabularnewline
11 & 99.59 & 100.081 & 99.0917 & 0.989679 & -0.491345 \tabularnewline
12 & 99.75 & 100.318 & 99.2921 & 1.02635 & -0.568429 \tabularnewline
13 & 99.75 & 99.1687 & 99.4929 & -0.324175 & 0.581259 \tabularnewline
14 & 99.8 & 99.3331 & 99.6808 & -0.347717 & 0.466884 \tabularnewline
15 & 99.99 & 99.5014 & 99.8821 & -0.380634 & 0.48855 \tabularnewline
16 & 100.25 & 99.6278 & 100.091 & -0.463446 & 0.622196 \tabularnewline
17 & 100.08 & 99.7379 & 100.28 & -0.541675 & 0.342092 \tabularnewline
18 & 100.08 & 99.9308 & 100.465 & -0.533759 & 0.149175 \tabularnewline
19 & 100.08 & 100.358 & 100.649 & -0.290842 & -0.277908 \tabularnewline
20 & 100.06 & 100.591 & 100.83 & -0.238134 & -0.53145 \tabularnewline
21 & 101 & 101.185 & 101.003 & 0.18145 & -0.184783 \tabularnewline
22 & 101.81 & 102.089 & 101.166 & 0.922908 & -0.278741 \tabularnewline
23 & 101.82 & 102.316 & 101.326 & 0.989679 & -0.495929 \tabularnewline
24 & 101.96 & 102.53 & 101.503 & 1.02635 & -0.569679 \tabularnewline
25 & 101.96 & 101.367 & 101.692 & -0.324175 & 0.592509 \tabularnewline
26 & 101.93 & 101.533 & 101.88 & -0.347717 & 0.3973 \tabularnewline
27 & 102.03 & 101.668 & 102.049 & -0.380634 & 0.361884 \tabularnewline
28 & 102.11 & 101.712 & 102.175 & -0.463446 & 0.39803 \tabularnewline
29 & 102.07 & 101.752 & 102.293 & -0.541675 & 0.318342 \tabularnewline
30 & 102.34 & 101.886 & 102.42 & -0.533759 & 0.453759 \tabularnewline
31 & 102.34 & 102.072 & 102.363 & -0.290842 & 0.268342 \tabularnewline
32 & 102.33 & 101.889 & 102.128 & -0.238134 & 0.440634 \tabularnewline
33 & 102.77 & 102.076 & 101.894 & 0.18145 & 0.694384 \tabularnewline
34 & 103.08 & 102.57 & 101.648 & 0.922908 & 0.509592 \tabularnewline
35 & 103.38 & 102.383 & 101.394 & 0.989679 & 0.996571 \tabularnewline
36 & 103.44 & 102.161 & 101.135 & 1.02635 & 1.27907 \tabularnewline
37 & 99.1 & 100.583 & 100.907 & -0.324175 & -1.48291 \tabularnewline
38 & 99.15 & 100.382 & 100.73 & -0.347717 & -1.23228 \tabularnewline
39 & 99.21 & 100.173 & 100.553 & -0.380634 & -0.9627 \tabularnewline
40 & 99.01 & 99.9266 & 100.39 & -0.463446 & -0.916554 \tabularnewline
41 & 99.08 & 99.6987 & 100.24 & -0.541675 & -0.618741 \tabularnewline
42 & 99.11 & 99.5471 & 100.081 & -0.533759 & -0.437075 \tabularnewline
43 & 100.11 & 99.7754 & 100.066 & -0.290842 & 0.334592 \tabularnewline
44 & 100.31 & 99.9631 & 100.201 & -0.238134 & 0.346884 \tabularnewline
45 & 100.55 & 100.509 & 100.327 & 0.18145 & 0.041467 \tabularnewline
46 & 101.38 & 101.362 & 100.44 & 0.922908 & 0.0175087 \tabularnewline
47 & 101.49 & 101.539 & 100.549 & 0.989679 & -0.0488455 \tabularnewline
48 & 101.5 & 101.681 & 100.654 & 1.02635 & -0.180512 \tabularnewline
49 & 100.69 & 100.42 & 100.745 & -0.324175 & 0.269592 \tabularnewline
50 & 100.8 & 100.471 & 100.819 & -0.347717 & 0.32855 \tabularnewline
51 & 100.58 & 100.507 & 100.888 & -0.380634 & 0.072717 \tabularnewline
52 & 100.34 & 100.483 & 100.947 & -0.463446 & -0.14322 \tabularnewline
53 & 100.38 & 100.461 & 101.003 & -0.541675 & -0.0812413 \tabularnewline
54 & 100.33 & 100.535 & 101.069 & -0.533759 & -0.205408 \tabularnewline
55 & 101.06 & NA & NA & -0.290842 & NA \tabularnewline
56 & 101.15 & NA & NA & -0.238134 & NA \tabularnewline
57 & 101.36 & NA & NA & 0.18145 & NA \tabularnewline
58 & 101.98 & NA & NA & 0.922908 & NA \tabularnewline
59 & 102.24 & NA & NA & 0.989679 & NA \tabularnewline
60 & 102.34 & NA & NA & 1.02635 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294582&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]97.78[/C][C]NA[/C][C]NA[/C][C]-0.324175[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]97.73[/C][C]NA[/C][C]NA[/C][C]-0.347717[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]97.61[/C][C]NA[/C][C]NA[/C][C]-0.380634[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]97.69[/C][C]NA[/C][C]NA[/C][C]-0.463446[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]97.68[/C][C]NA[/C][C]NA[/C][C]-0.541675[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]97.67[/C][C]NA[/C][C]NA[/C][C]-0.533759[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]97.67[/C][C]98.0346[/C][C]98.3254[/C][C]-0.290842[/C][C]-0.364575[/C][/ROW]
[ROW][C]8[/C][C]97.96[/C][C]98.2556[/C][C]98.4937[/C][C]-0.238134[/C][C]-0.295616[/C][/ROW]
[ROW][C]9[/C][C]98.27[/C][C]98.8606[/C][C]98.6792[/C][C]0.18145[/C][C]-0.590616[/C][/ROW]
[ROW][C]10[/C][C]99.52[/C][C]99.8079[/C][C]98.885[/C][C]0.922908[/C][C]-0.287908[/C][/ROW]
[ROW][C]11[/C][C]99.59[/C][C]100.081[/C][C]99.0917[/C][C]0.989679[/C][C]-0.491345[/C][/ROW]
[ROW][C]12[/C][C]99.75[/C][C]100.318[/C][C]99.2921[/C][C]1.02635[/C][C]-0.568429[/C][/ROW]
[ROW][C]13[/C][C]99.75[/C][C]99.1687[/C][C]99.4929[/C][C]-0.324175[/C][C]0.581259[/C][/ROW]
[ROW][C]14[/C][C]99.8[/C][C]99.3331[/C][C]99.6808[/C][C]-0.347717[/C][C]0.466884[/C][/ROW]
[ROW][C]15[/C][C]99.99[/C][C]99.5014[/C][C]99.8821[/C][C]-0.380634[/C][C]0.48855[/C][/ROW]
[ROW][C]16[/C][C]100.25[/C][C]99.6278[/C][C]100.091[/C][C]-0.463446[/C][C]0.622196[/C][/ROW]
[ROW][C]17[/C][C]100.08[/C][C]99.7379[/C][C]100.28[/C][C]-0.541675[/C][C]0.342092[/C][/ROW]
[ROW][C]18[/C][C]100.08[/C][C]99.9308[/C][C]100.465[/C][C]-0.533759[/C][C]0.149175[/C][/ROW]
[ROW][C]19[/C][C]100.08[/C][C]100.358[/C][C]100.649[/C][C]-0.290842[/C][C]-0.277908[/C][/ROW]
[ROW][C]20[/C][C]100.06[/C][C]100.591[/C][C]100.83[/C][C]-0.238134[/C][C]-0.53145[/C][/ROW]
[ROW][C]21[/C][C]101[/C][C]101.185[/C][C]101.003[/C][C]0.18145[/C][C]-0.184783[/C][/ROW]
[ROW][C]22[/C][C]101.81[/C][C]102.089[/C][C]101.166[/C][C]0.922908[/C][C]-0.278741[/C][/ROW]
[ROW][C]23[/C][C]101.82[/C][C]102.316[/C][C]101.326[/C][C]0.989679[/C][C]-0.495929[/C][/ROW]
[ROW][C]24[/C][C]101.96[/C][C]102.53[/C][C]101.503[/C][C]1.02635[/C][C]-0.569679[/C][/ROW]
[ROW][C]25[/C][C]101.96[/C][C]101.367[/C][C]101.692[/C][C]-0.324175[/C][C]0.592509[/C][/ROW]
[ROW][C]26[/C][C]101.93[/C][C]101.533[/C][C]101.88[/C][C]-0.347717[/C][C]0.3973[/C][/ROW]
[ROW][C]27[/C][C]102.03[/C][C]101.668[/C][C]102.049[/C][C]-0.380634[/C][C]0.361884[/C][/ROW]
[ROW][C]28[/C][C]102.11[/C][C]101.712[/C][C]102.175[/C][C]-0.463446[/C][C]0.39803[/C][/ROW]
[ROW][C]29[/C][C]102.07[/C][C]101.752[/C][C]102.293[/C][C]-0.541675[/C][C]0.318342[/C][/ROW]
[ROW][C]30[/C][C]102.34[/C][C]101.886[/C][C]102.42[/C][C]-0.533759[/C][C]0.453759[/C][/ROW]
[ROW][C]31[/C][C]102.34[/C][C]102.072[/C][C]102.363[/C][C]-0.290842[/C][C]0.268342[/C][/ROW]
[ROW][C]32[/C][C]102.33[/C][C]101.889[/C][C]102.128[/C][C]-0.238134[/C][C]0.440634[/C][/ROW]
[ROW][C]33[/C][C]102.77[/C][C]102.076[/C][C]101.894[/C][C]0.18145[/C][C]0.694384[/C][/ROW]
[ROW][C]34[/C][C]103.08[/C][C]102.57[/C][C]101.648[/C][C]0.922908[/C][C]0.509592[/C][/ROW]
[ROW][C]35[/C][C]103.38[/C][C]102.383[/C][C]101.394[/C][C]0.989679[/C][C]0.996571[/C][/ROW]
[ROW][C]36[/C][C]103.44[/C][C]102.161[/C][C]101.135[/C][C]1.02635[/C][C]1.27907[/C][/ROW]
[ROW][C]37[/C][C]99.1[/C][C]100.583[/C][C]100.907[/C][C]-0.324175[/C][C]-1.48291[/C][/ROW]
[ROW][C]38[/C][C]99.15[/C][C]100.382[/C][C]100.73[/C][C]-0.347717[/C][C]-1.23228[/C][/ROW]
[ROW][C]39[/C][C]99.21[/C][C]100.173[/C][C]100.553[/C][C]-0.380634[/C][C]-0.9627[/C][/ROW]
[ROW][C]40[/C][C]99.01[/C][C]99.9266[/C][C]100.39[/C][C]-0.463446[/C][C]-0.916554[/C][/ROW]
[ROW][C]41[/C][C]99.08[/C][C]99.6987[/C][C]100.24[/C][C]-0.541675[/C][C]-0.618741[/C][/ROW]
[ROW][C]42[/C][C]99.11[/C][C]99.5471[/C][C]100.081[/C][C]-0.533759[/C][C]-0.437075[/C][/ROW]
[ROW][C]43[/C][C]100.11[/C][C]99.7754[/C][C]100.066[/C][C]-0.290842[/C][C]0.334592[/C][/ROW]
[ROW][C]44[/C][C]100.31[/C][C]99.9631[/C][C]100.201[/C][C]-0.238134[/C][C]0.346884[/C][/ROW]
[ROW][C]45[/C][C]100.55[/C][C]100.509[/C][C]100.327[/C][C]0.18145[/C][C]0.041467[/C][/ROW]
[ROW][C]46[/C][C]101.38[/C][C]101.362[/C][C]100.44[/C][C]0.922908[/C][C]0.0175087[/C][/ROW]
[ROW][C]47[/C][C]101.49[/C][C]101.539[/C][C]100.549[/C][C]0.989679[/C][C]-0.0488455[/C][/ROW]
[ROW][C]48[/C][C]101.5[/C][C]101.681[/C][C]100.654[/C][C]1.02635[/C][C]-0.180512[/C][/ROW]
[ROW][C]49[/C][C]100.69[/C][C]100.42[/C][C]100.745[/C][C]-0.324175[/C][C]0.269592[/C][/ROW]
[ROW][C]50[/C][C]100.8[/C][C]100.471[/C][C]100.819[/C][C]-0.347717[/C][C]0.32855[/C][/ROW]
[ROW][C]51[/C][C]100.58[/C][C]100.507[/C][C]100.888[/C][C]-0.380634[/C][C]0.072717[/C][/ROW]
[ROW][C]52[/C][C]100.34[/C][C]100.483[/C][C]100.947[/C][C]-0.463446[/C][C]-0.14322[/C][/ROW]
[ROW][C]53[/C][C]100.38[/C][C]100.461[/C][C]101.003[/C][C]-0.541675[/C][C]-0.0812413[/C][/ROW]
[ROW][C]54[/C][C]100.33[/C][C]100.535[/C][C]101.069[/C][C]-0.533759[/C][C]-0.205408[/C][/ROW]
[ROW][C]55[/C][C]101.06[/C][C]NA[/C][C]NA[/C][C]-0.290842[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]101.15[/C][C]NA[/C][C]NA[/C][C]-0.238134[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]101.36[/C][C]NA[/C][C]NA[/C][C]0.18145[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]101.98[/C][C]NA[/C][C]NA[/C][C]0.922908[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]102.24[/C][C]NA[/C][C]NA[/C][C]0.989679[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]102.34[/C][C]NA[/C][C]NA[/C][C]1.02635[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294582&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294582&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	97.78	NA	NA	-0.324175	NA
2	97.73	NA	NA	-0.347717	NA
3	97.61	NA	NA	-0.380634	NA
4	97.69	NA	NA	-0.463446	NA
5	97.68	NA	NA	-0.541675	NA
6	97.67	NA	NA	-0.533759	NA
7	97.67	98.0346	98.3254	-0.290842	-0.364575
8	97.96	98.2556	98.4937	-0.238134	-0.295616
9	98.27	98.8606	98.6792	0.18145	-0.590616
10	99.52	99.8079	98.885	0.922908	-0.287908
11	99.59	100.081	99.0917	0.989679	-0.491345
12	99.75	100.318	99.2921	1.02635	-0.568429
13	99.75	99.1687	99.4929	-0.324175	0.581259
14	99.8	99.3331	99.6808	-0.347717	0.466884
15	99.99	99.5014	99.8821	-0.380634	0.48855
16	100.25	99.6278	100.091	-0.463446	0.622196
17	100.08	99.7379	100.28	-0.541675	0.342092
18	100.08	99.9308	100.465	-0.533759	0.149175
19	100.08	100.358	100.649	-0.290842	-0.277908
20	100.06	100.591	100.83	-0.238134	-0.53145
21	101	101.185	101.003	0.18145	-0.184783
22	101.81	102.089	101.166	0.922908	-0.278741
23	101.82	102.316	101.326	0.989679	-0.495929
24	101.96	102.53	101.503	1.02635	-0.569679
25	101.96	101.367	101.692	-0.324175	0.592509
26	101.93	101.533	101.88	-0.347717	0.3973
27	102.03	101.668	102.049	-0.380634	0.361884
28	102.11	101.712	102.175	-0.463446	0.39803
29	102.07	101.752	102.293	-0.541675	0.318342
30	102.34	101.886	102.42	-0.533759	0.453759
31	102.34	102.072	102.363	-0.290842	0.268342
32	102.33	101.889	102.128	-0.238134	0.440634
33	102.77	102.076	101.894	0.18145	0.694384
34	103.08	102.57	101.648	0.922908	0.509592
35	103.38	102.383	101.394	0.989679	0.996571
36	103.44	102.161	101.135	1.02635	1.27907
37	99.1	100.583	100.907	-0.324175	-1.48291
38	99.15	100.382	100.73	-0.347717	-1.23228
39	99.21	100.173	100.553	-0.380634	-0.9627
40	99.01	99.9266	100.39	-0.463446	-0.916554
41	99.08	99.6987	100.24	-0.541675	-0.618741
42	99.11	99.5471	100.081	-0.533759	-0.437075
43	100.11	99.7754	100.066	-0.290842	0.334592
44	100.31	99.9631	100.201	-0.238134	0.346884
45	100.55	100.509	100.327	0.18145	0.041467
46	101.38	101.362	100.44	0.922908	0.0175087
47	101.49	101.539	100.549	0.989679	-0.0488455
48	101.5	101.681	100.654	1.02635	-0.180512
49	100.69	100.42	100.745	-0.324175	0.269592
50	100.8	100.471	100.819	-0.347717	0.32855
51	100.58	100.507	100.888	-0.380634	0.072717
52	100.34	100.483	100.947	-0.463446	-0.14322
53	100.38	100.461	101.003	-0.541675	-0.0812413
54	100.33	100.535	101.069	-0.533759	-0.205408
55	101.06	NA	NA	-0.290842	NA
56	101.15	NA	NA	-0.238134	NA
57	101.36	NA	NA	0.18145	NA
58	101.98	NA	NA	0.922908	NA
59	102.24	NA	NA	0.989679	NA
60	102.34	NA	NA	1.02635	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