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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Mon, 25 Apr 2016 21:04:39 +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/25/t1461614866b7mpl9xkbey9qg5.htm/, Retrieved Sat, 16 May 2026 06:18:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294781, Retrieved Sat, 16 May 2026 06:18:47 +0000

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Estimated Impact

272

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

-       [Classical Decomposition] [additief model pr...] [2016-04-25 20:04:39] [76c30f62b7052b57088120e90a652e05] [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	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294781&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294781&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294781&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	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	92.49	NA	NA	-0.598446	NA
2	92.46	NA	NA	-0.395321	NA
3	92.55	NA	NA	-0.117196	NA
4	92.24	NA	NA	0.262179	NA
5	92.41	NA	NA	0.392283	NA
6	92.83	NA	NA	0.339991	NA
7	92.85	92.8026	92.7325	0.0700955	0.0474045
8	93.04	93.1651	92.7854	0.379679	-0.125095
9	93.04	93.2435	92.8596	0.38395	-0.203533
10	92.83	92.9649	92.9787	-0.0138628	-0.134887
11	92.96	93.1478	93.145	0.00280382	-0.187804
12	92.83	92.6205	93.3267	-0.706155	0.209488
13	93.01	92.9182	93.5167	-0.598446	0.0917795
14	93.21	93.3384	93.7337	-0.395321	-0.128429
15	93.58	93.8766	93.9937	-0.117196	-0.296554
16	94.07	94.5438	94.2817	0.262179	-0.473845
17	94.57	94.9769	94.5846	0.392283	-0.406866
18	95.03	95.2262	94.8862	0.339991	-0.196241
19	95.21	95.2776	95.2075	0.0700955	-0.0675955
20	95.89	95.9576	95.5779	0.379679	-0.0675955
21	96.43	96.3489	95.965	0.38395	0.0810503
22	96.35	96.3307	96.3446	-0.0138628	0.0192795
23	96.71	96.732	96.7292	0.00280382	-0.0219705
24	96.32	96.4147	97.1208	-0.706155	-0.0946788
25	97.23	96.9066	97.505	-0.598446	0.323446
26	97.88	97.5151	97.9104	-0.395321	0.364905
27	98.2	98.2311	98.3483	-0.117196	-0.0311372
28	98.56	99.0647	98.8025	0.262179	-0.504679
29	99.31	99.6685	99.2762	0.392283	-0.358533
30	99.69	100.099	99.7588	0.339991	-0.408741
31	99.77	100.277	100.207	0.0700955	-0.507179
32	101.06	100.997	100.618	0.379679	0.0628212
33	101.77	101.427	101.043	0.38395	0.342717
34	101.91	101.496	101.51	-0.0138628	0.413863
35	102.52	101.983	101.98	0.00280382	0.53678
36	102.09	101.717	102.423	-0.706155	0.373238
37	102.22	102.251	102.85	-0.598446	-0.0311372
38	102.74	102.828	103.223	-0.395321	-0.0880122
39	103.56	103.396	103.513	-0.117196	0.163863
40	104.4	104.014	103.752	0.262179	0.385738
41	104.76	104.351	103.958	0.392283	0.409384
42	104.86	104.48	104.14	0.339991	0.380009
43	104.84	104.397	104.327	0.0700955	0.442821
44	104.96	104.915	104.535	0.379679	0.0453212
45	104.83	105.135	104.751	0.38395	-0.304783
46	104.58	104.963	104.977	-0.0138628	-0.382804
47	104.8	105.212	105.209	0.00280382	-0.411554
48	104.17	104.743	105.449	-0.706155	-0.572595
49	104.63	105.099	105.697	-0.598446	-0.468637
50	105.32	105.553	105.948	-0.395321	-0.233012
51	106.16	106.081	106.198	-0.117196	0.0792795
52	107.22	106.712	106.45	0.262179	0.508238
53	107.51	107.239	106.846	0.392283	0.271467
54	107.87	107.73	107.39	0.339991	0.140425
55	107.79	NA	NA	0.0700955	NA
56	108.04	NA	NA	0.379679	NA
57	107.74	NA	NA	0.38395	NA
58	107.71	NA	NA	-0.0138628	NA
59	111.19	NA	NA	0.00280382	NA
60	110.82	NA	NA	-0.706155	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 92.49 & NA & NA & -0.598446 & NA \tabularnewline
2 & 92.46 & NA & NA & -0.395321 & NA \tabularnewline
3 & 92.55 & NA & NA & -0.117196 & NA \tabularnewline
4 & 92.24 & NA & NA & 0.262179 & NA \tabularnewline
5 & 92.41 & NA & NA & 0.392283 & NA \tabularnewline
6 & 92.83 & NA & NA & 0.339991 & NA \tabularnewline
7 & 92.85 & 92.8026 & 92.7325 & 0.0700955 & 0.0474045 \tabularnewline
8 & 93.04 & 93.1651 & 92.7854 & 0.379679 & -0.125095 \tabularnewline
9 & 93.04 & 93.2435 & 92.8596 & 0.38395 & -0.203533 \tabularnewline
10 & 92.83 & 92.9649 & 92.9787 & -0.0138628 & -0.134887 \tabularnewline
11 & 92.96 & 93.1478 & 93.145 & 0.00280382 & -0.187804 \tabularnewline
12 & 92.83 & 92.6205 & 93.3267 & -0.706155 & 0.209488 \tabularnewline
13 & 93.01 & 92.9182 & 93.5167 & -0.598446 & 0.0917795 \tabularnewline
14 & 93.21 & 93.3384 & 93.7337 & -0.395321 & -0.128429 \tabularnewline
15 & 93.58 & 93.8766 & 93.9937 & -0.117196 & -0.296554 \tabularnewline
16 & 94.07 & 94.5438 & 94.2817 & 0.262179 & -0.473845 \tabularnewline
17 & 94.57 & 94.9769 & 94.5846 & 0.392283 & -0.406866 \tabularnewline
18 & 95.03 & 95.2262 & 94.8862 & 0.339991 & -0.196241 \tabularnewline
19 & 95.21 & 95.2776 & 95.2075 & 0.0700955 & -0.0675955 \tabularnewline
20 & 95.89 & 95.9576 & 95.5779 & 0.379679 & -0.0675955 \tabularnewline
21 & 96.43 & 96.3489 & 95.965 & 0.38395 & 0.0810503 \tabularnewline
22 & 96.35 & 96.3307 & 96.3446 & -0.0138628 & 0.0192795 \tabularnewline
23 & 96.71 & 96.732 & 96.7292 & 0.00280382 & -0.0219705 \tabularnewline
24 & 96.32 & 96.4147 & 97.1208 & -0.706155 & -0.0946788 \tabularnewline
25 & 97.23 & 96.9066 & 97.505 & -0.598446 & 0.323446 \tabularnewline
26 & 97.88 & 97.5151 & 97.9104 & -0.395321 & 0.364905 \tabularnewline
27 & 98.2 & 98.2311 & 98.3483 & -0.117196 & -0.0311372 \tabularnewline
28 & 98.56 & 99.0647 & 98.8025 & 0.262179 & -0.504679 \tabularnewline
29 & 99.31 & 99.6685 & 99.2762 & 0.392283 & -0.358533 \tabularnewline
30 & 99.69 & 100.099 & 99.7588 & 0.339991 & -0.408741 \tabularnewline
31 & 99.77 & 100.277 & 100.207 & 0.0700955 & -0.507179 \tabularnewline
32 & 101.06 & 100.997 & 100.618 & 0.379679 & 0.0628212 \tabularnewline
33 & 101.77 & 101.427 & 101.043 & 0.38395 & 0.342717 \tabularnewline
34 & 101.91 & 101.496 & 101.51 & -0.0138628 & 0.413863 \tabularnewline
35 & 102.52 & 101.983 & 101.98 & 0.00280382 & 0.53678 \tabularnewline
36 & 102.09 & 101.717 & 102.423 & -0.706155 & 0.373238 \tabularnewline
37 & 102.22 & 102.251 & 102.85 & -0.598446 & -0.0311372 \tabularnewline
38 & 102.74 & 102.828 & 103.223 & -0.395321 & -0.0880122 \tabularnewline
39 & 103.56 & 103.396 & 103.513 & -0.117196 & 0.163863 \tabularnewline
40 & 104.4 & 104.014 & 103.752 & 0.262179 & 0.385738 \tabularnewline
41 & 104.76 & 104.351 & 103.958 & 0.392283 & 0.409384 \tabularnewline
42 & 104.86 & 104.48 & 104.14 & 0.339991 & 0.380009 \tabularnewline
43 & 104.84 & 104.397 & 104.327 & 0.0700955 & 0.442821 \tabularnewline
44 & 104.96 & 104.915 & 104.535 & 0.379679 & 0.0453212 \tabularnewline
45 & 104.83 & 105.135 & 104.751 & 0.38395 & -0.304783 \tabularnewline
46 & 104.58 & 104.963 & 104.977 & -0.0138628 & -0.382804 \tabularnewline
47 & 104.8 & 105.212 & 105.209 & 0.00280382 & -0.411554 \tabularnewline
48 & 104.17 & 104.743 & 105.449 & -0.706155 & -0.572595 \tabularnewline
49 & 104.63 & 105.099 & 105.697 & -0.598446 & -0.468637 \tabularnewline
50 & 105.32 & 105.553 & 105.948 & -0.395321 & -0.233012 \tabularnewline
51 & 106.16 & 106.081 & 106.198 & -0.117196 & 0.0792795 \tabularnewline
52 & 107.22 & 106.712 & 106.45 & 0.262179 & 0.508238 \tabularnewline
53 & 107.51 & 107.239 & 106.846 & 0.392283 & 0.271467 \tabularnewline
54 & 107.87 & 107.73 & 107.39 & 0.339991 & 0.140425 \tabularnewline
55 & 107.79 & NA & NA & 0.0700955 & NA \tabularnewline
56 & 108.04 & NA & NA & 0.379679 & NA \tabularnewline
57 & 107.74 & NA & NA & 0.38395 & NA \tabularnewline
58 & 107.71 & NA & NA & -0.0138628 & NA \tabularnewline
59 & 111.19 & NA & NA & 0.00280382 & NA \tabularnewline
60 & 110.82 & NA & NA & -0.706155 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294781&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]92.49[/C][C]NA[/C][C]NA[/C][C]-0.598446[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]92.46[/C][C]NA[/C][C]NA[/C][C]-0.395321[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]92.55[/C][C]NA[/C][C]NA[/C][C]-0.117196[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]92.24[/C][C]NA[/C][C]NA[/C][C]0.262179[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]92.41[/C][C]NA[/C][C]NA[/C][C]0.392283[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]92.83[/C][C]NA[/C][C]NA[/C][C]0.339991[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]92.85[/C][C]92.8026[/C][C]92.7325[/C][C]0.0700955[/C][C]0.0474045[/C][/ROW]
[ROW][C]8[/C][C]93.04[/C][C]93.1651[/C][C]92.7854[/C][C]0.379679[/C][C]-0.125095[/C][/ROW]
[ROW][C]9[/C][C]93.04[/C][C]93.2435[/C][C]92.8596[/C][C]0.38395[/C][C]-0.203533[/C][/ROW]
[ROW][C]10[/C][C]92.83[/C][C]92.9649[/C][C]92.9787[/C][C]-0.0138628[/C][C]-0.134887[/C][/ROW]
[ROW][C]11[/C][C]92.96[/C][C]93.1478[/C][C]93.145[/C][C]0.00280382[/C][C]-0.187804[/C][/ROW]
[ROW][C]12[/C][C]92.83[/C][C]92.6205[/C][C]93.3267[/C][C]-0.706155[/C][C]0.209488[/C][/ROW]
[ROW][C]13[/C][C]93.01[/C][C]92.9182[/C][C]93.5167[/C][C]-0.598446[/C][C]0.0917795[/C][/ROW]
[ROW][C]14[/C][C]93.21[/C][C]93.3384[/C][C]93.7337[/C][C]-0.395321[/C][C]-0.128429[/C][/ROW]
[ROW][C]15[/C][C]93.58[/C][C]93.8766[/C][C]93.9937[/C][C]-0.117196[/C][C]-0.296554[/C][/ROW]
[ROW][C]16[/C][C]94.07[/C][C]94.5438[/C][C]94.2817[/C][C]0.262179[/C][C]-0.473845[/C][/ROW]
[ROW][C]17[/C][C]94.57[/C][C]94.9769[/C][C]94.5846[/C][C]0.392283[/C][C]-0.406866[/C][/ROW]
[ROW][C]18[/C][C]95.03[/C][C]95.2262[/C][C]94.8862[/C][C]0.339991[/C][C]-0.196241[/C][/ROW]
[ROW][C]19[/C][C]95.21[/C][C]95.2776[/C][C]95.2075[/C][C]0.0700955[/C][C]-0.0675955[/C][/ROW]
[ROW][C]20[/C][C]95.89[/C][C]95.9576[/C][C]95.5779[/C][C]0.379679[/C][C]-0.0675955[/C][/ROW]
[ROW][C]21[/C][C]96.43[/C][C]96.3489[/C][C]95.965[/C][C]0.38395[/C][C]0.0810503[/C][/ROW]
[ROW][C]22[/C][C]96.35[/C][C]96.3307[/C][C]96.3446[/C][C]-0.0138628[/C][C]0.0192795[/C][/ROW]
[ROW][C]23[/C][C]96.71[/C][C]96.732[/C][C]96.7292[/C][C]0.00280382[/C][C]-0.0219705[/C][/ROW]
[ROW][C]24[/C][C]96.32[/C][C]96.4147[/C][C]97.1208[/C][C]-0.706155[/C][C]-0.0946788[/C][/ROW]
[ROW][C]25[/C][C]97.23[/C][C]96.9066[/C][C]97.505[/C][C]-0.598446[/C][C]0.323446[/C][/ROW]
[ROW][C]26[/C][C]97.88[/C][C]97.5151[/C][C]97.9104[/C][C]-0.395321[/C][C]0.364905[/C][/ROW]
[ROW][C]27[/C][C]98.2[/C][C]98.2311[/C][C]98.3483[/C][C]-0.117196[/C][C]-0.0311372[/C][/ROW]
[ROW][C]28[/C][C]98.56[/C][C]99.0647[/C][C]98.8025[/C][C]0.262179[/C][C]-0.504679[/C][/ROW]
[ROW][C]29[/C][C]99.31[/C][C]99.6685[/C][C]99.2762[/C][C]0.392283[/C][C]-0.358533[/C][/ROW]
[ROW][C]30[/C][C]99.69[/C][C]100.099[/C][C]99.7588[/C][C]0.339991[/C][C]-0.408741[/C][/ROW]
[ROW][C]31[/C][C]99.77[/C][C]100.277[/C][C]100.207[/C][C]0.0700955[/C][C]-0.507179[/C][/ROW]
[ROW][C]32[/C][C]101.06[/C][C]100.997[/C][C]100.618[/C][C]0.379679[/C][C]0.0628212[/C][/ROW]
[ROW][C]33[/C][C]101.77[/C][C]101.427[/C][C]101.043[/C][C]0.38395[/C][C]0.342717[/C][/ROW]
[ROW][C]34[/C][C]101.91[/C][C]101.496[/C][C]101.51[/C][C]-0.0138628[/C][C]0.413863[/C][/ROW]
[ROW][C]35[/C][C]102.52[/C][C]101.983[/C][C]101.98[/C][C]0.00280382[/C][C]0.53678[/C][/ROW]
[ROW][C]36[/C][C]102.09[/C][C]101.717[/C][C]102.423[/C][C]-0.706155[/C][C]0.373238[/C][/ROW]
[ROW][C]37[/C][C]102.22[/C][C]102.251[/C][C]102.85[/C][C]-0.598446[/C][C]-0.0311372[/C][/ROW]
[ROW][C]38[/C][C]102.74[/C][C]102.828[/C][C]103.223[/C][C]-0.395321[/C][C]-0.0880122[/C][/ROW]
[ROW][C]39[/C][C]103.56[/C][C]103.396[/C][C]103.513[/C][C]-0.117196[/C][C]0.163863[/C][/ROW]
[ROW][C]40[/C][C]104.4[/C][C]104.014[/C][C]103.752[/C][C]0.262179[/C][C]0.385738[/C][/ROW]
[ROW][C]41[/C][C]104.76[/C][C]104.351[/C][C]103.958[/C][C]0.392283[/C][C]0.409384[/C][/ROW]
[ROW][C]42[/C][C]104.86[/C][C]104.48[/C][C]104.14[/C][C]0.339991[/C][C]0.380009[/C][/ROW]
[ROW][C]43[/C][C]104.84[/C][C]104.397[/C][C]104.327[/C][C]0.0700955[/C][C]0.442821[/C][/ROW]
[ROW][C]44[/C][C]104.96[/C][C]104.915[/C][C]104.535[/C][C]0.379679[/C][C]0.0453212[/C][/ROW]
[ROW][C]45[/C][C]104.83[/C][C]105.135[/C][C]104.751[/C][C]0.38395[/C][C]-0.304783[/C][/ROW]
[ROW][C]46[/C][C]104.58[/C][C]104.963[/C][C]104.977[/C][C]-0.0138628[/C][C]-0.382804[/C][/ROW]
[ROW][C]47[/C][C]104.8[/C][C]105.212[/C][C]105.209[/C][C]0.00280382[/C][C]-0.411554[/C][/ROW]
[ROW][C]48[/C][C]104.17[/C][C]104.743[/C][C]105.449[/C][C]-0.706155[/C][C]-0.572595[/C][/ROW]
[ROW][C]49[/C][C]104.63[/C][C]105.099[/C][C]105.697[/C][C]-0.598446[/C][C]-0.468637[/C][/ROW]
[ROW][C]50[/C][C]105.32[/C][C]105.553[/C][C]105.948[/C][C]-0.395321[/C][C]-0.233012[/C][/ROW]
[ROW][C]51[/C][C]106.16[/C][C]106.081[/C][C]106.198[/C][C]-0.117196[/C][C]0.0792795[/C][/ROW]
[ROW][C]52[/C][C]107.22[/C][C]106.712[/C][C]106.45[/C][C]0.262179[/C][C]0.508238[/C][/ROW]
[ROW][C]53[/C][C]107.51[/C][C]107.239[/C][C]106.846[/C][C]0.392283[/C][C]0.271467[/C][/ROW]
[ROW][C]54[/C][C]107.87[/C][C]107.73[/C][C]107.39[/C][C]0.339991[/C][C]0.140425[/C][/ROW]
[ROW][C]55[/C][C]107.79[/C][C]NA[/C][C]NA[/C][C]0.0700955[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]108.04[/C][C]NA[/C][C]NA[/C][C]0.379679[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]107.74[/C][C]NA[/C][C]NA[/C][C]0.38395[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]107.71[/C][C]NA[/C][C]NA[/C][C]-0.0138628[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]111.19[/C][C]NA[/C][C]NA[/C][C]0.00280382[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]110.82[/C][C]NA[/C][C]NA[/C][C]-0.706155[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294781&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294781&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	92.49	NA	NA	-0.598446	NA
2	92.46	NA	NA	-0.395321	NA
3	92.55	NA	NA	-0.117196	NA
4	92.24	NA	NA	0.262179	NA
5	92.41	NA	NA	0.392283	NA
6	92.83	NA	NA	0.339991	NA
7	92.85	92.8026	92.7325	0.0700955	0.0474045
8	93.04	93.1651	92.7854	0.379679	-0.125095
9	93.04	93.2435	92.8596	0.38395	-0.203533
10	92.83	92.9649	92.9787	-0.0138628	-0.134887
11	92.96	93.1478	93.145	0.00280382	-0.187804
12	92.83	92.6205	93.3267	-0.706155	0.209488
13	93.01	92.9182	93.5167	-0.598446	0.0917795
14	93.21	93.3384	93.7337	-0.395321	-0.128429
15	93.58	93.8766	93.9937	-0.117196	-0.296554
16	94.07	94.5438	94.2817	0.262179	-0.473845
17	94.57	94.9769	94.5846	0.392283	-0.406866
18	95.03	95.2262	94.8862	0.339991	-0.196241
19	95.21	95.2776	95.2075	0.0700955	-0.0675955
20	95.89	95.9576	95.5779	0.379679	-0.0675955
21	96.43	96.3489	95.965	0.38395	0.0810503
22	96.35	96.3307	96.3446	-0.0138628	0.0192795
23	96.71	96.732	96.7292	0.00280382	-0.0219705
24	96.32	96.4147	97.1208	-0.706155	-0.0946788
25	97.23	96.9066	97.505	-0.598446	0.323446
26	97.88	97.5151	97.9104	-0.395321	0.364905
27	98.2	98.2311	98.3483	-0.117196	-0.0311372
28	98.56	99.0647	98.8025	0.262179	-0.504679
29	99.31	99.6685	99.2762	0.392283	-0.358533
30	99.69	100.099	99.7588	0.339991	-0.408741
31	99.77	100.277	100.207	0.0700955	-0.507179
32	101.06	100.997	100.618	0.379679	0.0628212
33	101.77	101.427	101.043	0.38395	0.342717
34	101.91	101.496	101.51	-0.0138628	0.413863
35	102.52	101.983	101.98	0.00280382	0.53678
36	102.09	101.717	102.423	-0.706155	0.373238
37	102.22	102.251	102.85	-0.598446	-0.0311372
38	102.74	102.828	103.223	-0.395321	-0.0880122
39	103.56	103.396	103.513	-0.117196	0.163863
40	104.4	104.014	103.752	0.262179	0.385738
41	104.76	104.351	103.958	0.392283	0.409384
42	104.86	104.48	104.14	0.339991	0.380009
43	104.84	104.397	104.327	0.0700955	0.442821
44	104.96	104.915	104.535	0.379679	0.0453212
45	104.83	105.135	104.751	0.38395	-0.304783
46	104.58	104.963	104.977	-0.0138628	-0.382804
47	104.8	105.212	105.209	0.00280382	-0.411554
48	104.17	104.743	105.449	-0.706155	-0.572595
49	104.63	105.099	105.697	-0.598446	-0.468637
50	105.32	105.553	105.948	-0.395321	-0.233012
51	106.16	106.081	106.198	-0.117196	0.0792795
52	107.22	106.712	106.45	0.262179	0.508238
53	107.51	107.239	106.846	0.392283	0.271467
54	107.87	107.73	107.39	0.339991	0.140425
55	107.79	NA	NA	0.0700955	NA
56	108.04	NA	NA	0.379679	NA
57	107.74	NA	NA	0.38395	NA
58	107.71	NA	NA	-0.0138628	NA
59	111.19	NA	NA	0.00280382	NA
60	110.82	NA	NA	-0.706155	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 <- '12'
par1 <- 'multiplicative'
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