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R Software Module

rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Tue, 26 Apr 2016 19:10:02 +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/26/t1461694252fqf2fi29jvuyoai.htm/, Retrieved Sat, 04 May 2024 05:05:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294949, Retrieved Sat, 04 May 2024 05:05:52 +0000

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-       [Classical Decomposition] [] [2016-04-26 18:10:02] [66b954879edaa66f79d20403c5a86347] [Current]

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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=294949&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=294949&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294949&T=0

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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	89.8	NA	NA	0.987573	NA
2	89.2	NA	NA	0.999185	NA
3	89.9	NA	NA	0.999122	NA
4	88.9	NA	NA	1.00272	NA
5	84	NA	NA	0.996373	NA
6	86.3	NA	NA	1.01249	NA
7	89.3	91.196	90.1208	1.01193	0.97921
8	90.6	91.5092	90.4167	1.01208	0.990064
9	88.3	89.7344	90.7625	0.988673	0.984015
10	91.6	90.132	91.0833	0.989555	1.01629
11	95.4	91.1507	91.4208	0.997045	1.04662
12	96.8	92.0687	91.7708	1.00325	1.05139
13	92.5	90.8773	92.0208	0.987573	1.01786
14	93.6	92.0625	92.1375	0.999185	1.0167
15	93.8	92.1191	92.2	0.999122	1.01825
16	92.7	92.3509	92.1	1.00272	1.00378
17	88.3	91.3798	91.7125	0.996373	0.966296
18	90.4	92.2801	91.1417	1.01249	0.979627
19	91.2	91.6725	90.5917	1.01193	0.994846
20	91.5	91.2815	90.1917	1.01208	1.00239
21	88.9	88.894	89.9125	0.988673	1.00007
22	88.6	88.8002	89.7375	0.989555	0.997745
23	89.1	89.4972	89.7625	0.997045	0.995562
24	89.4	90.2085	89.9167	1.00325	0.991038
25	86.7	88.9886	90.1083	0.987573	0.974283
26	89.8	90.3305	90.4042	0.999185	0.994127
27	90.9	90.7619	90.8417	0.999122	1.00152
28	91.4	91.6657	91.4167	1.00272	0.997101
29	90.2	91.7203	92.0542	0.996373	0.983425
30	92.2	93.8536	92.6958	1.01249	0.982381
31	94	94.5396	93.425	1.01193	0.994292
32	95.8	95.2792	94.1417	1.01208	1.00547
33	95.1	93.6644	94.7375	0.988673	1.01533
34	96.2	94.383	95.3792	0.989555	1.01925
35	96.8	95.9074	96.1917	0.997045	1.00931
36	97.1	97.4277	97.1125	1.00325	0.996637
37	96.5	96.7945	98.0125	0.987573	0.996957
38	97.2	98.578	98.6583	0.999185	0.986021
39	97.8	98.7757	98.8625	0.999122	0.990122
40	99.9	99.0107	98.7417	1.00272	1.00898
41	101.2	98.1137	98.4708	0.996373	1.03146
42	103.3	99.4519	98.225	1.01249	1.03869
43	104.5	99.321	98.15	1.01193	1.05214
44	100.8	99.5089	98.3208	1.01208	1.01297
45	95	97.4584	98.575	0.988673	0.974775
46	93.4	97.7227	98.7542	0.989555	0.955766
47	93.1	98.7157	99.0083	0.997045	0.943112
48	94.9	99.6348	99.3125	1.00325	0.952479
49	96.9	98.2512	99.4875	0.987573	0.986248
50	100.9	99.7312	99.8125	0.999185	1.01172
51	100.2	100.607	100.696	0.999122	0.99595
52	101.8	102.211	101.933	1.00272	0.995978
53	105.4	102.863	103.237	0.996373	1.02466
54	106.4	105.856	104.55	1.01249	1.00514
55	105.6	107.012	105.75	1.01193	0.986809
56	107.5	107.888	106.6	1.01208	0.996403
57	109.5	105.99	107.204	0.988673	1.03312
58	108.6	106.653	107.779	0.989555	1.01825
59	109.2	107.83	108.15	0.997045	1.0127
60	110.3	108.589	108.237	1.00325	1.01576
61	110.3	106.884	108.229	0.987573	1.03196
62	107.9	108.037	108.125	0.999185	0.998733
63	107.7	NA	NA	0.999122	NA
64	108.1	NA	NA	1.00272	NA
65	108	NA	NA	0.996373	NA
66	105.9	NA	NA	1.01249	NA
67	105.9	NA	NA	1.01193	NA
68	104.7	NA	NA	1.01208	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 89.8 & NA & NA & 0.987573 & NA \tabularnewline
2 & 89.2 & NA & NA & 0.999185 & NA \tabularnewline
3 & 89.9 & NA & NA & 0.999122 & NA \tabularnewline
4 & 88.9 & NA & NA & 1.00272 & NA \tabularnewline
5 & 84 & NA & NA & 0.996373 & NA \tabularnewline
6 & 86.3 & NA & NA & 1.01249 & NA \tabularnewline
7 & 89.3 & 91.196 & 90.1208 & 1.01193 & 0.97921 \tabularnewline
8 & 90.6 & 91.5092 & 90.4167 & 1.01208 & 0.990064 \tabularnewline
9 & 88.3 & 89.7344 & 90.7625 & 0.988673 & 0.984015 \tabularnewline
10 & 91.6 & 90.132 & 91.0833 & 0.989555 & 1.01629 \tabularnewline
11 & 95.4 & 91.1507 & 91.4208 & 0.997045 & 1.04662 \tabularnewline
12 & 96.8 & 92.0687 & 91.7708 & 1.00325 & 1.05139 \tabularnewline
13 & 92.5 & 90.8773 & 92.0208 & 0.987573 & 1.01786 \tabularnewline
14 & 93.6 & 92.0625 & 92.1375 & 0.999185 & 1.0167 \tabularnewline
15 & 93.8 & 92.1191 & 92.2 & 0.999122 & 1.01825 \tabularnewline
16 & 92.7 & 92.3509 & 92.1 & 1.00272 & 1.00378 \tabularnewline
17 & 88.3 & 91.3798 & 91.7125 & 0.996373 & 0.966296 \tabularnewline
18 & 90.4 & 92.2801 & 91.1417 & 1.01249 & 0.979627 \tabularnewline
19 & 91.2 & 91.6725 & 90.5917 & 1.01193 & 0.994846 \tabularnewline
20 & 91.5 & 91.2815 & 90.1917 & 1.01208 & 1.00239 \tabularnewline
21 & 88.9 & 88.894 & 89.9125 & 0.988673 & 1.00007 \tabularnewline
22 & 88.6 & 88.8002 & 89.7375 & 0.989555 & 0.997745 \tabularnewline
23 & 89.1 & 89.4972 & 89.7625 & 0.997045 & 0.995562 \tabularnewline
24 & 89.4 & 90.2085 & 89.9167 & 1.00325 & 0.991038 \tabularnewline
25 & 86.7 & 88.9886 & 90.1083 & 0.987573 & 0.974283 \tabularnewline
26 & 89.8 & 90.3305 & 90.4042 & 0.999185 & 0.994127 \tabularnewline
27 & 90.9 & 90.7619 & 90.8417 & 0.999122 & 1.00152 \tabularnewline
28 & 91.4 & 91.6657 & 91.4167 & 1.00272 & 0.997101 \tabularnewline
29 & 90.2 & 91.7203 & 92.0542 & 0.996373 & 0.983425 \tabularnewline
30 & 92.2 & 93.8536 & 92.6958 & 1.01249 & 0.982381 \tabularnewline
31 & 94 & 94.5396 & 93.425 & 1.01193 & 0.994292 \tabularnewline
32 & 95.8 & 95.2792 & 94.1417 & 1.01208 & 1.00547 \tabularnewline
33 & 95.1 & 93.6644 & 94.7375 & 0.988673 & 1.01533 \tabularnewline
34 & 96.2 & 94.383 & 95.3792 & 0.989555 & 1.01925 \tabularnewline
35 & 96.8 & 95.9074 & 96.1917 & 0.997045 & 1.00931 \tabularnewline
36 & 97.1 & 97.4277 & 97.1125 & 1.00325 & 0.996637 \tabularnewline
37 & 96.5 & 96.7945 & 98.0125 & 0.987573 & 0.996957 \tabularnewline
38 & 97.2 & 98.578 & 98.6583 & 0.999185 & 0.986021 \tabularnewline
39 & 97.8 & 98.7757 & 98.8625 & 0.999122 & 0.990122 \tabularnewline
40 & 99.9 & 99.0107 & 98.7417 & 1.00272 & 1.00898 \tabularnewline
41 & 101.2 & 98.1137 & 98.4708 & 0.996373 & 1.03146 \tabularnewline
42 & 103.3 & 99.4519 & 98.225 & 1.01249 & 1.03869 \tabularnewline
43 & 104.5 & 99.321 & 98.15 & 1.01193 & 1.05214 \tabularnewline
44 & 100.8 & 99.5089 & 98.3208 & 1.01208 & 1.01297 \tabularnewline
45 & 95 & 97.4584 & 98.575 & 0.988673 & 0.974775 \tabularnewline
46 & 93.4 & 97.7227 & 98.7542 & 0.989555 & 0.955766 \tabularnewline
47 & 93.1 & 98.7157 & 99.0083 & 0.997045 & 0.943112 \tabularnewline
48 & 94.9 & 99.6348 & 99.3125 & 1.00325 & 0.952479 \tabularnewline
49 & 96.9 & 98.2512 & 99.4875 & 0.987573 & 0.986248 \tabularnewline
50 & 100.9 & 99.7312 & 99.8125 & 0.999185 & 1.01172 \tabularnewline
51 & 100.2 & 100.607 & 100.696 & 0.999122 & 0.99595 \tabularnewline
52 & 101.8 & 102.211 & 101.933 & 1.00272 & 0.995978 \tabularnewline
53 & 105.4 & 102.863 & 103.237 & 0.996373 & 1.02466 \tabularnewline
54 & 106.4 & 105.856 & 104.55 & 1.01249 & 1.00514 \tabularnewline
55 & 105.6 & 107.012 & 105.75 & 1.01193 & 0.986809 \tabularnewline
56 & 107.5 & 107.888 & 106.6 & 1.01208 & 0.996403 \tabularnewline
57 & 109.5 & 105.99 & 107.204 & 0.988673 & 1.03312 \tabularnewline
58 & 108.6 & 106.653 & 107.779 & 0.989555 & 1.01825 \tabularnewline
59 & 109.2 & 107.83 & 108.15 & 0.997045 & 1.0127 \tabularnewline
60 & 110.3 & 108.589 & 108.237 & 1.00325 & 1.01576 \tabularnewline
61 & 110.3 & 106.884 & 108.229 & 0.987573 & 1.03196 \tabularnewline
62 & 107.9 & 108.037 & 108.125 & 0.999185 & 0.998733 \tabularnewline
63 & 107.7 & NA & NA & 0.999122 & NA \tabularnewline
64 & 108.1 & NA & NA & 1.00272 & NA \tabularnewline
65 & 108 & NA & NA & 0.996373 & NA \tabularnewline
66 & 105.9 & NA & NA & 1.01249 & NA \tabularnewline
67 & 105.9 & NA & NA & 1.01193 & NA \tabularnewline
68 & 104.7 & NA & NA & 1.01208 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294949&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]89.8[/C][C]NA[/C][C]NA[/C][C]0.987573[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]89.2[/C][C]NA[/C][C]NA[/C][C]0.999185[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]89.9[/C][C]NA[/C][C]NA[/C][C]0.999122[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]88.9[/C][C]NA[/C][C]NA[/C][C]1.00272[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]84[/C][C]NA[/C][C]NA[/C][C]0.996373[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]86.3[/C][C]NA[/C][C]NA[/C][C]1.01249[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]89.3[/C][C]91.196[/C][C]90.1208[/C][C]1.01193[/C][C]0.97921[/C][/ROW]
[ROW][C]8[/C][C]90.6[/C][C]91.5092[/C][C]90.4167[/C][C]1.01208[/C][C]0.990064[/C][/ROW]
[ROW][C]9[/C][C]88.3[/C][C]89.7344[/C][C]90.7625[/C][C]0.988673[/C][C]0.984015[/C][/ROW]
[ROW][C]10[/C][C]91.6[/C][C]90.132[/C][C]91.0833[/C][C]0.989555[/C][C]1.01629[/C][/ROW]
[ROW][C]11[/C][C]95.4[/C][C]91.1507[/C][C]91.4208[/C][C]0.997045[/C][C]1.04662[/C][/ROW]
[ROW][C]12[/C][C]96.8[/C][C]92.0687[/C][C]91.7708[/C][C]1.00325[/C][C]1.05139[/C][/ROW]
[ROW][C]13[/C][C]92.5[/C][C]90.8773[/C][C]92.0208[/C][C]0.987573[/C][C]1.01786[/C][/ROW]
[ROW][C]14[/C][C]93.6[/C][C]92.0625[/C][C]92.1375[/C][C]0.999185[/C][C]1.0167[/C][/ROW]
[ROW][C]15[/C][C]93.8[/C][C]92.1191[/C][C]92.2[/C][C]0.999122[/C][C]1.01825[/C][/ROW]
[ROW][C]16[/C][C]92.7[/C][C]92.3509[/C][C]92.1[/C][C]1.00272[/C][C]1.00378[/C][/ROW]
[ROW][C]17[/C][C]88.3[/C][C]91.3798[/C][C]91.7125[/C][C]0.996373[/C][C]0.966296[/C][/ROW]
[ROW][C]18[/C][C]90.4[/C][C]92.2801[/C][C]91.1417[/C][C]1.01249[/C][C]0.979627[/C][/ROW]
[ROW][C]19[/C][C]91.2[/C][C]91.6725[/C][C]90.5917[/C][C]1.01193[/C][C]0.994846[/C][/ROW]
[ROW][C]20[/C][C]91.5[/C][C]91.2815[/C][C]90.1917[/C][C]1.01208[/C][C]1.00239[/C][/ROW]
[ROW][C]21[/C][C]88.9[/C][C]88.894[/C][C]89.9125[/C][C]0.988673[/C][C]1.00007[/C][/ROW]
[ROW][C]22[/C][C]88.6[/C][C]88.8002[/C][C]89.7375[/C][C]0.989555[/C][C]0.997745[/C][/ROW]
[ROW][C]23[/C][C]89.1[/C][C]89.4972[/C][C]89.7625[/C][C]0.997045[/C][C]0.995562[/C][/ROW]
[ROW][C]24[/C][C]89.4[/C][C]90.2085[/C][C]89.9167[/C][C]1.00325[/C][C]0.991038[/C][/ROW]
[ROW][C]25[/C][C]86.7[/C][C]88.9886[/C][C]90.1083[/C][C]0.987573[/C][C]0.974283[/C][/ROW]
[ROW][C]26[/C][C]89.8[/C][C]90.3305[/C][C]90.4042[/C][C]0.999185[/C][C]0.994127[/C][/ROW]
[ROW][C]27[/C][C]90.9[/C][C]90.7619[/C][C]90.8417[/C][C]0.999122[/C][C]1.00152[/C][/ROW]
[ROW][C]28[/C][C]91.4[/C][C]91.6657[/C][C]91.4167[/C][C]1.00272[/C][C]0.997101[/C][/ROW]
[ROW][C]29[/C][C]90.2[/C][C]91.7203[/C][C]92.0542[/C][C]0.996373[/C][C]0.983425[/C][/ROW]
[ROW][C]30[/C][C]92.2[/C][C]93.8536[/C][C]92.6958[/C][C]1.01249[/C][C]0.982381[/C][/ROW]
[ROW][C]31[/C][C]94[/C][C]94.5396[/C][C]93.425[/C][C]1.01193[/C][C]0.994292[/C][/ROW]
[ROW][C]32[/C][C]95.8[/C][C]95.2792[/C][C]94.1417[/C][C]1.01208[/C][C]1.00547[/C][/ROW]
[ROW][C]33[/C][C]95.1[/C][C]93.6644[/C][C]94.7375[/C][C]0.988673[/C][C]1.01533[/C][/ROW]
[ROW][C]34[/C][C]96.2[/C][C]94.383[/C][C]95.3792[/C][C]0.989555[/C][C]1.01925[/C][/ROW]
[ROW][C]35[/C][C]96.8[/C][C]95.9074[/C][C]96.1917[/C][C]0.997045[/C][C]1.00931[/C][/ROW]
[ROW][C]36[/C][C]97.1[/C][C]97.4277[/C][C]97.1125[/C][C]1.00325[/C][C]0.996637[/C][/ROW]
[ROW][C]37[/C][C]96.5[/C][C]96.7945[/C][C]98.0125[/C][C]0.987573[/C][C]0.996957[/C][/ROW]
[ROW][C]38[/C][C]97.2[/C][C]98.578[/C][C]98.6583[/C][C]0.999185[/C][C]0.986021[/C][/ROW]
[ROW][C]39[/C][C]97.8[/C][C]98.7757[/C][C]98.8625[/C][C]0.999122[/C][C]0.990122[/C][/ROW]
[ROW][C]40[/C][C]99.9[/C][C]99.0107[/C][C]98.7417[/C][C]1.00272[/C][C]1.00898[/C][/ROW]
[ROW][C]41[/C][C]101.2[/C][C]98.1137[/C][C]98.4708[/C][C]0.996373[/C][C]1.03146[/C][/ROW]
[ROW][C]42[/C][C]103.3[/C][C]99.4519[/C][C]98.225[/C][C]1.01249[/C][C]1.03869[/C][/ROW]
[ROW][C]43[/C][C]104.5[/C][C]99.321[/C][C]98.15[/C][C]1.01193[/C][C]1.05214[/C][/ROW]
[ROW][C]44[/C][C]100.8[/C][C]99.5089[/C][C]98.3208[/C][C]1.01208[/C][C]1.01297[/C][/ROW]
[ROW][C]45[/C][C]95[/C][C]97.4584[/C][C]98.575[/C][C]0.988673[/C][C]0.974775[/C][/ROW]
[ROW][C]46[/C][C]93.4[/C][C]97.7227[/C][C]98.7542[/C][C]0.989555[/C][C]0.955766[/C][/ROW]
[ROW][C]47[/C][C]93.1[/C][C]98.7157[/C][C]99.0083[/C][C]0.997045[/C][C]0.943112[/C][/ROW]
[ROW][C]48[/C][C]94.9[/C][C]99.6348[/C][C]99.3125[/C][C]1.00325[/C][C]0.952479[/C][/ROW]
[ROW][C]49[/C][C]96.9[/C][C]98.2512[/C][C]99.4875[/C][C]0.987573[/C][C]0.986248[/C][/ROW]
[ROW][C]50[/C][C]100.9[/C][C]99.7312[/C][C]99.8125[/C][C]0.999185[/C][C]1.01172[/C][/ROW]
[ROW][C]51[/C][C]100.2[/C][C]100.607[/C][C]100.696[/C][C]0.999122[/C][C]0.99595[/C][/ROW]
[ROW][C]52[/C][C]101.8[/C][C]102.211[/C][C]101.933[/C][C]1.00272[/C][C]0.995978[/C][/ROW]
[ROW][C]53[/C][C]105.4[/C][C]102.863[/C][C]103.237[/C][C]0.996373[/C][C]1.02466[/C][/ROW]
[ROW][C]54[/C][C]106.4[/C][C]105.856[/C][C]104.55[/C][C]1.01249[/C][C]1.00514[/C][/ROW]
[ROW][C]55[/C][C]105.6[/C][C]107.012[/C][C]105.75[/C][C]1.01193[/C][C]0.986809[/C][/ROW]
[ROW][C]56[/C][C]107.5[/C][C]107.888[/C][C]106.6[/C][C]1.01208[/C][C]0.996403[/C][/ROW]
[ROW][C]57[/C][C]109.5[/C][C]105.99[/C][C]107.204[/C][C]0.988673[/C][C]1.03312[/C][/ROW]
[ROW][C]58[/C][C]108.6[/C][C]106.653[/C][C]107.779[/C][C]0.989555[/C][C]1.01825[/C][/ROW]
[ROW][C]59[/C][C]109.2[/C][C]107.83[/C][C]108.15[/C][C]0.997045[/C][C]1.0127[/C][/ROW]
[ROW][C]60[/C][C]110.3[/C][C]108.589[/C][C]108.237[/C][C]1.00325[/C][C]1.01576[/C][/ROW]
[ROW][C]61[/C][C]110.3[/C][C]106.884[/C][C]108.229[/C][C]0.987573[/C][C]1.03196[/C][/ROW]
[ROW][C]62[/C][C]107.9[/C][C]108.037[/C][C]108.125[/C][C]0.999185[/C][C]0.998733[/C][/ROW]
[ROW][C]63[/C][C]107.7[/C][C]NA[/C][C]NA[/C][C]0.999122[/C][C]NA[/C][/ROW]
[ROW][C]64[/C][C]108.1[/C][C]NA[/C][C]NA[/C][C]1.00272[/C][C]NA[/C][/ROW]
[ROW][C]65[/C][C]108[/C][C]NA[/C][C]NA[/C][C]0.996373[/C][C]NA[/C][/ROW]
[ROW][C]66[/C][C]105.9[/C][C]NA[/C][C]NA[/C][C]1.01249[/C][C]NA[/C][/ROW]
[ROW][C]67[/C][C]105.9[/C][C]NA[/C][C]NA[/C][C]1.01193[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]104.7[/C][C]NA[/C][C]NA[/C][C]1.01208[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294949&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294949&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	89.8	NA	NA	0.987573	NA
2	89.2	NA	NA	0.999185	NA
3	89.9	NA	NA	0.999122	NA
4	88.9	NA	NA	1.00272	NA
5	84	NA	NA	0.996373	NA
6	86.3	NA	NA	1.01249	NA
7	89.3	91.196	90.1208	1.01193	0.97921
8	90.6	91.5092	90.4167	1.01208	0.990064
9	88.3	89.7344	90.7625	0.988673	0.984015
10	91.6	90.132	91.0833	0.989555	1.01629
11	95.4	91.1507	91.4208	0.997045	1.04662
12	96.8	92.0687	91.7708	1.00325	1.05139
13	92.5	90.8773	92.0208	0.987573	1.01786
14	93.6	92.0625	92.1375	0.999185	1.0167
15	93.8	92.1191	92.2	0.999122	1.01825
16	92.7	92.3509	92.1	1.00272	1.00378
17	88.3	91.3798	91.7125	0.996373	0.966296
18	90.4	92.2801	91.1417	1.01249	0.979627
19	91.2	91.6725	90.5917	1.01193	0.994846
20	91.5	91.2815	90.1917	1.01208	1.00239
21	88.9	88.894	89.9125	0.988673	1.00007
22	88.6	88.8002	89.7375	0.989555	0.997745
23	89.1	89.4972	89.7625	0.997045	0.995562
24	89.4	90.2085	89.9167	1.00325	0.991038
25	86.7	88.9886	90.1083	0.987573	0.974283
26	89.8	90.3305	90.4042	0.999185	0.994127
27	90.9	90.7619	90.8417	0.999122	1.00152
28	91.4	91.6657	91.4167	1.00272	0.997101
29	90.2	91.7203	92.0542	0.996373	0.983425
30	92.2	93.8536	92.6958	1.01249	0.982381
31	94	94.5396	93.425	1.01193	0.994292
32	95.8	95.2792	94.1417	1.01208	1.00547
33	95.1	93.6644	94.7375	0.988673	1.01533
34	96.2	94.383	95.3792	0.989555	1.01925
35	96.8	95.9074	96.1917	0.997045	1.00931
36	97.1	97.4277	97.1125	1.00325	0.996637
37	96.5	96.7945	98.0125	0.987573	0.996957
38	97.2	98.578	98.6583	0.999185	0.986021
39	97.8	98.7757	98.8625	0.999122	0.990122
40	99.9	99.0107	98.7417	1.00272	1.00898
41	101.2	98.1137	98.4708	0.996373	1.03146
42	103.3	99.4519	98.225	1.01249	1.03869
43	104.5	99.321	98.15	1.01193	1.05214
44	100.8	99.5089	98.3208	1.01208	1.01297
45	95	97.4584	98.575	0.988673	0.974775
46	93.4	97.7227	98.7542	0.989555	0.955766
47	93.1	98.7157	99.0083	0.997045	0.943112
48	94.9	99.6348	99.3125	1.00325	0.952479
49	96.9	98.2512	99.4875	0.987573	0.986248
50	100.9	99.7312	99.8125	0.999185	1.01172
51	100.2	100.607	100.696	0.999122	0.99595
52	101.8	102.211	101.933	1.00272	0.995978
53	105.4	102.863	103.237	0.996373	1.02466
54	106.4	105.856	104.55	1.01249	1.00514
55	105.6	107.012	105.75	1.01193	0.986809
56	107.5	107.888	106.6	1.01208	0.996403
57	109.5	105.99	107.204	0.988673	1.03312
58	108.6	106.653	107.779	0.989555	1.01825
59	109.2	107.83	108.15	0.997045	1.0127
60	110.3	108.589	108.237	1.00325	1.01576
61	110.3	106.884	108.229	0.987573	1.03196
62	107.9	108.037	108.125	0.999185	0.998733
63	107.7	NA	NA	0.999122	NA
64	108.1	NA	NA	1.00272	NA
65	108	NA	NA	0.996373	NA
66	105.9	NA	NA	1.01249	NA
67	105.9	NA	NA	1.01193	NA
68	104.7	NA	NA	1.01208	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