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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 01 May 2017 14:01:08 +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/2017/May/01/t1493643755eujjh3ujfbr8ldw.htm/, Retrieved Wed, 15 May 2024 21:21:55 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 15 May 2024 21:21:55 +0200

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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 Maurice George Kendall' @ kendall.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 Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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 Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	97.91	NA	NA	0.99938	NA
2	98.51	NA	NA	1.00021	NA
3	98.54	NA	NA	1.00167	NA
4	98.52	NA	NA	1.00095	NA
5	98.66	NA	NA	1.00096	NA
6	98.53	NA	NA	1.00091	NA
7	98.71	98.8219	98.8304	0.999913	0.998868
8	98.92	98.8319	98.9354	0.998953	1.00089
9	98.96	98.8722	99.0312	0.998394	1.00089
10	99.25	99.1396	99.1346	1.00005	1.00111
11	99.32	99.1922	99.2437	0.999481	1.00129
12	99.41	99.283	99.3704	0.99912	1.00128
13	99.36	99.442	99.5037	0.99938	0.999175
14	99.58	99.6381	99.6167	1.00021	0.999417
15	99.77	99.8772	99.7104	1.00167	0.998926
16	99.77	99.8889	99.7938	1.00095	0.998809
17	100.03	99.9688	99.8729	1.00096	1.00061
18	100.2	100.046	99.955	1.00091	1.00154
19	100.24	100.04	100.048	0.999913	1.002
20	100.1	100.044	100.149	0.998953	1.00056
21	100.03	100.079	100.24	0.998394	0.99951
22	100.18	100.315	100.31	1.00005	0.998649
23	100.29	100.297	100.349	0.999481	0.999929
24	100.41	100.277	100.366	0.99912	1.00132
25	100.6	100.319	100.381	0.99938	1.00281
26	100.75	100.412	100.391	1.00021	1.00336
27	100.79	100.561	100.393	1.00167	1.00228
28	100.44	100.494	100.398	1.00095	0.999462
29	100.29	100.499	100.402	1.00096	0.997925
30	100.34	100.492	100.401	1.00091	0.998486
31	100.46	100.393	100.401	0.999913	1.00067
32	100.12	100.302	100.408	0.998953	0.998181
33	100.06	100.25	100.411	0.998394	0.998109
34	100.28	100.442	100.437	1.00005	0.99839
35	100.28	100.449	100.501	0.999481	0.998317
36	100.4	100.49	100.579	0.99912	0.999102
37	100.61	100.595	100.658	0.99938	1.00014
38	100.89	100.78	100.758	1.00021	1.0011
39	100.73	101.063	100.895	1.00167	0.996701
40	101.12	101.148	101.052	1.00095	0.999719
41	101.16	101.316	101.218	1.00096	0.998465
42	101.33	101.47	101.378	1.00091	0.998623
43	101.37	101.521	101.53	0.999913	0.998515
44	101.61	101.567	101.673	0.998953	1.00042
45	101.85	101.692	101.856	0.998394	1.00155
46	102.27	102.076	102.07	1.00005	1.0019
47	102.28	102.227	102.28	0.999481	1.00052
48	102.23	102.399	102.489	0.99912	0.998354
49	102.42	102.632	102.696	0.99938	0.997933
50	102.53	102.923	102.901	1.00021	0.996182
51	103.47	103.248	103.075	1.00167	1.00215
52	103.53	103.316	103.218	1.00095	1.00207
53	103.77	103.454	103.355	1.00096	1.00306
54	103.74	103.595	103.501	1.00091	1.0014
55	103.93	NA	NA	0.999913	NA
56	103.97	NA	NA	0.998953	NA
57	103.68	NA	NA	0.998394	NA
58	103.86	NA	NA	1.00005	NA
59	103.97	NA	NA	0.999481	NA
60	104.05	NA	NA	0.99912	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 97.91 & NA & NA & 0.99938 & NA \tabularnewline
2 & 98.51 & NA & NA & 1.00021 & NA \tabularnewline
3 & 98.54 & NA & NA & 1.00167 & NA \tabularnewline
4 & 98.52 & NA & NA & 1.00095 & NA \tabularnewline
5 & 98.66 & NA & NA & 1.00096 & NA \tabularnewline
6 & 98.53 & NA & NA & 1.00091 & NA \tabularnewline
7 & 98.71 & 98.8219 & 98.8304 & 0.999913 & 0.998868 \tabularnewline
8 & 98.92 & 98.8319 & 98.9354 & 0.998953 & 1.00089 \tabularnewline
9 & 98.96 & 98.8722 & 99.0312 & 0.998394 & 1.00089 \tabularnewline
10 & 99.25 & 99.1396 & 99.1346 & 1.00005 & 1.00111 \tabularnewline
11 & 99.32 & 99.1922 & 99.2437 & 0.999481 & 1.00129 \tabularnewline
12 & 99.41 & 99.283 & 99.3704 & 0.99912 & 1.00128 \tabularnewline
13 & 99.36 & 99.442 & 99.5037 & 0.99938 & 0.999175 \tabularnewline
14 & 99.58 & 99.6381 & 99.6167 & 1.00021 & 0.999417 \tabularnewline
15 & 99.77 & 99.8772 & 99.7104 & 1.00167 & 0.998926 \tabularnewline
16 & 99.77 & 99.8889 & 99.7938 & 1.00095 & 0.998809 \tabularnewline
17 & 100.03 & 99.9688 & 99.8729 & 1.00096 & 1.00061 \tabularnewline
18 & 100.2 & 100.046 & 99.955 & 1.00091 & 1.00154 \tabularnewline
19 & 100.24 & 100.04 & 100.048 & 0.999913 & 1.002 \tabularnewline
20 & 100.1 & 100.044 & 100.149 & 0.998953 & 1.00056 \tabularnewline
21 & 100.03 & 100.079 & 100.24 & 0.998394 & 0.99951 \tabularnewline
22 & 100.18 & 100.315 & 100.31 & 1.00005 & 0.998649 \tabularnewline
23 & 100.29 & 100.297 & 100.349 & 0.999481 & 0.999929 \tabularnewline
24 & 100.41 & 100.277 & 100.366 & 0.99912 & 1.00132 \tabularnewline
25 & 100.6 & 100.319 & 100.381 & 0.99938 & 1.00281 \tabularnewline
26 & 100.75 & 100.412 & 100.391 & 1.00021 & 1.00336 \tabularnewline
27 & 100.79 & 100.561 & 100.393 & 1.00167 & 1.00228 \tabularnewline
28 & 100.44 & 100.494 & 100.398 & 1.00095 & 0.999462 \tabularnewline
29 & 100.29 & 100.499 & 100.402 & 1.00096 & 0.997925 \tabularnewline
30 & 100.34 & 100.492 & 100.401 & 1.00091 & 0.998486 \tabularnewline
31 & 100.46 & 100.393 & 100.401 & 0.999913 & 1.00067 \tabularnewline
32 & 100.12 & 100.302 & 100.408 & 0.998953 & 0.998181 \tabularnewline
33 & 100.06 & 100.25 & 100.411 & 0.998394 & 0.998109 \tabularnewline
34 & 100.28 & 100.442 & 100.437 & 1.00005 & 0.99839 \tabularnewline
35 & 100.28 & 100.449 & 100.501 & 0.999481 & 0.998317 \tabularnewline
36 & 100.4 & 100.49 & 100.579 & 0.99912 & 0.999102 \tabularnewline
37 & 100.61 & 100.595 & 100.658 & 0.99938 & 1.00014 \tabularnewline
38 & 100.89 & 100.78 & 100.758 & 1.00021 & 1.0011 \tabularnewline
39 & 100.73 & 101.063 & 100.895 & 1.00167 & 0.996701 \tabularnewline
40 & 101.12 & 101.148 & 101.052 & 1.00095 & 0.999719 \tabularnewline
41 & 101.16 & 101.316 & 101.218 & 1.00096 & 0.998465 \tabularnewline
42 & 101.33 & 101.47 & 101.378 & 1.00091 & 0.998623 \tabularnewline
43 & 101.37 & 101.521 & 101.53 & 0.999913 & 0.998515 \tabularnewline
44 & 101.61 & 101.567 & 101.673 & 0.998953 & 1.00042 \tabularnewline
45 & 101.85 & 101.692 & 101.856 & 0.998394 & 1.00155 \tabularnewline
46 & 102.27 & 102.076 & 102.07 & 1.00005 & 1.0019 \tabularnewline
47 & 102.28 & 102.227 & 102.28 & 0.999481 & 1.00052 \tabularnewline
48 & 102.23 & 102.399 & 102.489 & 0.99912 & 0.998354 \tabularnewline
49 & 102.42 & 102.632 & 102.696 & 0.99938 & 0.997933 \tabularnewline
50 & 102.53 & 102.923 & 102.901 & 1.00021 & 0.996182 \tabularnewline
51 & 103.47 & 103.248 & 103.075 & 1.00167 & 1.00215 \tabularnewline
52 & 103.53 & 103.316 & 103.218 & 1.00095 & 1.00207 \tabularnewline
53 & 103.77 & 103.454 & 103.355 & 1.00096 & 1.00306 \tabularnewline
54 & 103.74 & 103.595 & 103.501 & 1.00091 & 1.0014 \tabularnewline
55 & 103.93 & NA & NA & 0.999913 & NA \tabularnewline
56 & 103.97 & NA & NA & 0.998953 & NA \tabularnewline
57 & 103.68 & NA & NA & 0.998394 & NA \tabularnewline
58 & 103.86 & NA & NA & 1.00005 & NA \tabularnewline
59 & 103.97 & NA & NA & 0.999481 & NA \tabularnewline
60 & 104.05 & NA & NA & 0.99912 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]97.91[/C][C]NA[/C][C]NA[/C][C]0.99938[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]98.51[/C][C]NA[/C][C]NA[/C][C]1.00021[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]98.54[/C][C]NA[/C][C]NA[/C][C]1.00167[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]98.52[/C][C]NA[/C][C]NA[/C][C]1.00095[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]98.66[/C][C]NA[/C][C]NA[/C][C]1.00096[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]98.53[/C][C]NA[/C][C]NA[/C][C]1.00091[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]98.71[/C][C]98.8219[/C][C]98.8304[/C][C]0.999913[/C][C]0.998868[/C][/ROW]
[ROW][C]8[/C][C]98.92[/C][C]98.8319[/C][C]98.9354[/C][C]0.998953[/C][C]1.00089[/C][/ROW]
[ROW][C]9[/C][C]98.96[/C][C]98.8722[/C][C]99.0312[/C][C]0.998394[/C][C]1.00089[/C][/ROW]
[ROW][C]10[/C][C]99.25[/C][C]99.1396[/C][C]99.1346[/C][C]1.00005[/C][C]1.00111[/C][/ROW]
[ROW][C]11[/C][C]99.32[/C][C]99.1922[/C][C]99.2437[/C][C]0.999481[/C][C]1.00129[/C][/ROW]
[ROW][C]12[/C][C]99.41[/C][C]99.283[/C][C]99.3704[/C][C]0.99912[/C][C]1.00128[/C][/ROW]
[ROW][C]13[/C][C]99.36[/C][C]99.442[/C][C]99.5037[/C][C]0.99938[/C][C]0.999175[/C][/ROW]
[ROW][C]14[/C][C]99.58[/C][C]99.6381[/C][C]99.6167[/C][C]1.00021[/C][C]0.999417[/C][/ROW]
[ROW][C]15[/C][C]99.77[/C][C]99.8772[/C][C]99.7104[/C][C]1.00167[/C][C]0.998926[/C][/ROW]
[ROW][C]16[/C][C]99.77[/C][C]99.8889[/C][C]99.7938[/C][C]1.00095[/C][C]0.998809[/C][/ROW]
[ROW][C]17[/C][C]100.03[/C][C]99.9688[/C][C]99.8729[/C][C]1.00096[/C][C]1.00061[/C][/ROW]
[ROW][C]18[/C][C]100.2[/C][C]100.046[/C][C]99.955[/C][C]1.00091[/C][C]1.00154[/C][/ROW]
[ROW][C]19[/C][C]100.24[/C][C]100.04[/C][C]100.048[/C][C]0.999913[/C][C]1.002[/C][/ROW]
[ROW][C]20[/C][C]100.1[/C][C]100.044[/C][C]100.149[/C][C]0.998953[/C][C]1.00056[/C][/ROW]
[ROW][C]21[/C][C]100.03[/C][C]100.079[/C][C]100.24[/C][C]0.998394[/C][C]0.99951[/C][/ROW]
[ROW][C]22[/C][C]100.18[/C][C]100.315[/C][C]100.31[/C][C]1.00005[/C][C]0.998649[/C][/ROW]
[ROW][C]23[/C][C]100.29[/C][C]100.297[/C][C]100.349[/C][C]0.999481[/C][C]0.999929[/C][/ROW]
[ROW][C]24[/C][C]100.41[/C][C]100.277[/C][C]100.366[/C][C]0.99912[/C][C]1.00132[/C][/ROW]
[ROW][C]25[/C][C]100.6[/C][C]100.319[/C][C]100.381[/C][C]0.99938[/C][C]1.00281[/C][/ROW]
[ROW][C]26[/C][C]100.75[/C][C]100.412[/C][C]100.391[/C][C]1.00021[/C][C]1.00336[/C][/ROW]
[ROW][C]27[/C][C]100.79[/C][C]100.561[/C][C]100.393[/C][C]1.00167[/C][C]1.00228[/C][/ROW]
[ROW][C]28[/C][C]100.44[/C][C]100.494[/C][C]100.398[/C][C]1.00095[/C][C]0.999462[/C][/ROW]
[ROW][C]29[/C][C]100.29[/C][C]100.499[/C][C]100.402[/C][C]1.00096[/C][C]0.997925[/C][/ROW]
[ROW][C]30[/C][C]100.34[/C][C]100.492[/C][C]100.401[/C][C]1.00091[/C][C]0.998486[/C][/ROW]
[ROW][C]31[/C][C]100.46[/C][C]100.393[/C][C]100.401[/C][C]0.999913[/C][C]1.00067[/C][/ROW]
[ROW][C]32[/C][C]100.12[/C][C]100.302[/C][C]100.408[/C][C]0.998953[/C][C]0.998181[/C][/ROW]
[ROW][C]33[/C][C]100.06[/C][C]100.25[/C][C]100.411[/C][C]0.998394[/C][C]0.998109[/C][/ROW]
[ROW][C]34[/C][C]100.28[/C][C]100.442[/C][C]100.437[/C][C]1.00005[/C][C]0.99839[/C][/ROW]
[ROW][C]35[/C][C]100.28[/C][C]100.449[/C][C]100.501[/C][C]0.999481[/C][C]0.998317[/C][/ROW]
[ROW][C]36[/C][C]100.4[/C][C]100.49[/C][C]100.579[/C][C]0.99912[/C][C]0.999102[/C][/ROW]
[ROW][C]37[/C][C]100.61[/C][C]100.595[/C][C]100.658[/C][C]0.99938[/C][C]1.00014[/C][/ROW]
[ROW][C]38[/C][C]100.89[/C][C]100.78[/C][C]100.758[/C][C]1.00021[/C][C]1.0011[/C][/ROW]
[ROW][C]39[/C][C]100.73[/C][C]101.063[/C][C]100.895[/C][C]1.00167[/C][C]0.996701[/C][/ROW]
[ROW][C]40[/C][C]101.12[/C][C]101.148[/C][C]101.052[/C][C]1.00095[/C][C]0.999719[/C][/ROW]
[ROW][C]41[/C][C]101.16[/C][C]101.316[/C][C]101.218[/C][C]1.00096[/C][C]0.998465[/C][/ROW]
[ROW][C]42[/C][C]101.33[/C][C]101.47[/C][C]101.378[/C][C]1.00091[/C][C]0.998623[/C][/ROW]
[ROW][C]43[/C][C]101.37[/C][C]101.521[/C][C]101.53[/C][C]0.999913[/C][C]0.998515[/C][/ROW]
[ROW][C]44[/C][C]101.61[/C][C]101.567[/C][C]101.673[/C][C]0.998953[/C][C]1.00042[/C][/ROW]
[ROW][C]45[/C][C]101.85[/C][C]101.692[/C][C]101.856[/C][C]0.998394[/C][C]1.00155[/C][/ROW]
[ROW][C]46[/C][C]102.27[/C][C]102.076[/C][C]102.07[/C][C]1.00005[/C][C]1.0019[/C][/ROW]
[ROW][C]47[/C][C]102.28[/C][C]102.227[/C][C]102.28[/C][C]0.999481[/C][C]1.00052[/C][/ROW]
[ROW][C]48[/C][C]102.23[/C][C]102.399[/C][C]102.489[/C][C]0.99912[/C][C]0.998354[/C][/ROW]
[ROW][C]49[/C][C]102.42[/C][C]102.632[/C][C]102.696[/C][C]0.99938[/C][C]0.997933[/C][/ROW]
[ROW][C]50[/C][C]102.53[/C][C]102.923[/C][C]102.901[/C][C]1.00021[/C][C]0.996182[/C][/ROW]
[ROW][C]51[/C][C]103.47[/C][C]103.248[/C][C]103.075[/C][C]1.00167[/C][C]1.00215[/C][/ROW]
[ROW][C]52[/C][C]103.53[/C][C]103.316[/C][C]103.218[/C][C]1.00095[/C][C]1.00207[/C][/ROW]
[ROW][C]53[/C][C]103.77[/C][C]103.454[/C][C]103.355[/C][C]1.00096[/C][C]1.00306[/C][/ROW]
[ROW][C]54[/C][C]103.74[/C][C]103.595[/C][C]103.501[/C][C]1.00091[/C][C]1.0014[/C][/ROW]
[ROW][C]55[/C][C]103.93[/C][C]NA[/C][C]NA[/C][C]0.999913[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]103.97[/C][C]NA[/C][C]NA[/C][C]0.998953[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]103.68[/C][C]NA[/C][C]NA[/C][C]0.998394[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]103.86[/C][C]NA[/C][C]NA[/C][C]1.00005[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]103.97[/C][C]NA[/C][C]NA[/C][C]0.999481[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]104.05[/C][C]NA[/C][C]NA[/C][C]0.99912[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	97.91	NA	NA	0.99938	NA
2	98.51	NA	NA	1.00021	NA
3	98.54	NA	NA	1.00167	NA
4	98.52	NA	NA	1.00095	NA
5	98.66	NA	NA	1.00096	NA
6	98.53	NA	NA	1.00091	NA
7	98.71	98.8219	98.8304	0.999913	0.998868
8	98.92	98.8319	98.9354	0.998953	1.00089
9	98.96	98.8722	99.0312	0.998394	1.00089
10	99.25	99.1396	99.1346	1.00005	1.00111
11	99.32	99.1922	99.2437	0.999481	1.00129
12	99.41	99.283	99.3704	0.99912	1.00128
13	99.36	99.442	99.5037	0.99938	0.999175
14	99.58	99.6381	99.6167	1.00021	0.999417
15	99.77	99.8772	99.7104	1.00167	0.998926
16	99.77	99.8889	99.7938	1.00095	0.998809
17	100.03	99.9688	99.8729	1.00096	1.00061
18	100.2	100.046	99.955	1.00091	1.00154
19	100.24	100.04	100.048	0.999913	1.002
20	100.1	100.044	100.149	0.998953	1.00056
21	100.03	100.079	100.24	0.998394	0.99951
22	100.18	100.315	100.31	1.00005	0.998649
23	100.29	100.297	100.349	0.999481	0.999929
24	100.41	100.277	100.366	0.99912	1.00132
25	100.6	100.319	100.381	0.99938	1.00281
26	100.75	100.412	100.391	1.00021	1.00336
27	100.79	100.561	100.393	1.00167	1.00228
28	100.44	100.494	100.398	1.00095	0.999462
29	100.29	100.499	100.402	1.00096	0.997925
30	100.34	100.492	100.401	1.00091	0.998486
31	100.46	100.393	100.401	0.999913	1.00067
32	100.12	100.302	100.408	0.998953	0.998181
33	100.06	100.25	100.411	0.998394	0.998109
34	100.28	100.442	100.437	1.00005	0.99839
35	100.28	100.449	100.501	0.999481	0.998317
36	100.4	100.49	100.579	0.99912	0.999102
37	100.61	100.595	100.658	0.99938	1.00014
38	100.89	100.78	100.758	1.00021	1.0011
39	100.73	101.063	100.895	1.00167	0.996701
40	101.12	101.148	101.052	1.00095	0.999719
41	101.16	101.316	101.218	1.00096	0.998465
42	101.33	101.47	101.378	1.00091	0.998623
43	101.37	101.521	101.53	0.999913	0.998515
44	101.61	101.567	101.673	0.998953	1.00042
45	101.85	101.692	101.856	0.998394	1.00155
46	102.27	102.076	102.07	1.00005	1.0019
47	102.28	102.227	102.28	0.999481	1.00052
48	102.23	102.399	102.489	0.99912	0.998354
49	102.42	102.632	102.696	0.99938	0.997933
50	102.53	102.923	102.901	1.00021	0.996182
51	103.47	103.248	103.075	1.00167	1.00215
52	103.53	103.316	103.218	1.00095	1.00207
53	103.77	103.454	103.355	1.00096	1.00306
54	103.74	103.595	103.501	1.00091	1.0014
55	103.93	NA	NA	0.999913	NA
56	103.97	NA	NA	0.998953	NA
57	103.68	NA	NA	0.998394	NA
58	103.86	NA	NA	1.00005	NA
59	103.97	NA	NA	0.999481	NA
60	104.05	NA	NA	0.99912	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