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 07:43:32 -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/t139989518145s48exzp2wxo9n.htm/, Retrieved Thu, 16 May 2024 00:41:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=234815, Retrieved Thu, 16 May 2024 00:41:05 +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] [OP9 - OEF2 - Benj...] [2014-05-12 11:43:32] [bde58939c1a4c9e8add61ba7c57eaa76] [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	'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234815&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234815&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234815&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	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	530.3	NA	NA	0.999993	NA
2	527.76	NA	NA	0.993097	NA
3	521.41	NA	NA	0.989421	NA
4	1601.93	NA	NA	0.979711	NA
5	1577.49	NA	NA	0.983988	NA
6	1551.43	NA	NA	0.988246	NA
7	1551.43	1352.05	1284.75	1.05239	1.14746
8	1516.88	1386.83	1349.23	1.02787	1.09377
9	1485.95	1426.96	1410.45	1.0117	1.04134
10	1438.22	1421.58	1422.98	0.999019	1.01171
11	1385.06	1374.45	1388.07	0.990183	1.00772
12	1329.49	1332.2	1353.34	0.984378	0.997964
13	1329.49	1318.65	1318.66	0.999993	1.00822
14	1276.16	1274.46	1283.32	0.993097	1.00134
15	1242.34	1235.09	1248.29	0.989421	1.00587
16	1181.59	1190.89	1215.56	0.979711	0.992187
17	1160.21	1166.08	1185.05	0.983988	0.99497
18	1135.18	1143.47	1157.07	0.988246	0.992749
19	1135.18	1189.94	1130.7	1.05239	0.953982
20	1084.96	1136.67	1105.85	1.02787	0.954507
21	1077.35	1095.71	1083.04	1.0117	0.983239
22	1061.13	1061.29	1062.33	0.999019	0.999852
23	1029.98	1033.52	1043.77	0.990183	0.996576
24	1013.08	1010.29	1026.33	0.984378	1.00276
25	1013.08	1009.43	1009.44	0.999993	1.00362
26	996.04	987.272	994.134	0.993097	1.00888
27	975.02	969.531	979.897	0.989421	1.00566
28	951.89	945.671	965.255	0.979711	1.00658
29	944.4	936.087	951.319	0.983988	1.00888
30	932.47	927.196	938.224	0.988246	1.00569
31	932.47	973.865	925.386	1.05239	0.957494
32	920.44	938.112	912.674	1.02787	0.981163
33	900.18	911.079	900.538	1.0117	0.988038
34	886.9	888.563	889.435	0.999019	0.998129
35	869.74	870.234	878.862	0.990183	0.999432
36	859.03	855.013	868.582	0.984378	1.0047
37	859.03	858.637	858.643	0.999993	1.00046
38	844.99	843.254	849.115	0.993097	1.00206
39	834.82	831.664	840.556	0.989421	1.0038
40	825.62	815.57	832.46	0.979711	1.01232
41	816.92	811.371	824.574	0.983988	1.00684
42	813.21	807.302	816.903	0.988246	1.00732
43	813.21	851.533	809.143	1.05239	0.954996
44	811.03	823.804	801.466	1.02787	0.984494
45	804.16	803.101	793.81	1.0117	1.00132
46	788.62	785.284	786.055	0.999019	1.00425
47	778.76	770.892	778.535	0.990183	1.01021
48	765.91	759.442	771.495	0.984378	1.00852
49	765.91	764.657	764.662	0.999993	1.00164
50	753.85	752.601	757.832	0.993097	1.00166
51	742.22	743.255	751.202	0.989421	0.998608
52	732.11	730.029	745.147	0.979711	1.00285
53	729.94	727.578	739.418	0.983988	1.00325
54	731.22	725.287	733.913	0.988246	1.00818
55	731.22	NA	NA	1.05239	NA
56	729.11	NA	NA	1.02787	NA
57	726.94	NA	NA	1.0117	NA
58	720.52	NA	NA	0.999019	NA
59	709.36	NA	NA	0.990183	NA
60	703.21	NA	NA	0.984378	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 530.3 & NA & NA & 0.999993 & NA \tabularnewline
2 & 527.76 & NA & NA & 0.993097 & NA \tabularnewline
3 & 521.41 & NA & NA & 0.989421 & NA \tabularnewline
4 & 1601.93 & NA & NA & 0.979711 & NA \tabularnewline
5 & 1577.49 & NA & NA & 0.983988 & NA \tabularnewline
6 & 1551.43 & NA & NA & 0.988246 & NA \tabularnewline
7 & 1551.43 & 1352.05 & 1284.75 & 1.05239 & 1.14746 \tabularnewline
8 & 1516.88 & 1386.83 & 1349.23 & 1.02787 & 1.09377 \tabularnewline
9 & 1485.95 & 1426.96 & 1410.45 & 1.0117 & 1.04134 \tabularnewline
10 & 1438.22 & 1421.58 & 1422.98 & 0.999019 & 1.01171 \tabularnewline
11 & 1385.06 & 1374.45 & 1388.07 & 0.990183 & 1.00772 \tabularnewline
12 & 1329.49 & 1332.2 & 1353.34 & 0.984378 & 0.997964 \tabularnewline
13 & 1329.49 & 1318.65 & 1318.66 & 0.999993 & 1.00822 \tabularnewline
14 & 1276.16 & 1274.46 & 1283.32 & 0.993097 & 1.00134 \tabularnewline
15 & 1242.34 & 1235.09 & 1248.29 & 0.989421 & 1.00587 \tabularnewline
16 & 1181.59 & 1190.89 & 1215.56 & 0.979711 & 0.992187 \tabularnewline
17 & 1160.21 & 1166.08 & 1185.05 & 0.983988 & 0.99497 \tabularnewline
18 & 1135.18 & 1143.47 & 1157.07 & 0.988246 & 0.992749 \tabularnewline
19 & 1135.18 & 1189.94 & 1130.7 & 1.05239 & 0.953982 \tabularnewline
20 & 1084.96 & 1136.67 & 1105.85 & 1.02787 & 0.954507 \tabularnewline
21 & 1077.35 & 1095.71 & 1083.04 & 1.0117 & 0.983239 \tabularnewline
22 & 1061.13 & 1061.29 & 1062.33 & 0.999019 & 0.999852 \tabularnewline
23 & 1029.98 & 1033.52 & 1043.77 & 0.990183 & 0.996576 \tabularnewline
24 & 1013.08 & 1010.29 & 1026.33 & 0.984378 & 1.00276 \tabularnewline
25 & 1013.08 & 1009.43 & 1009.44 & 0.999993 & 1.00362 \tabularnewline
26 & 996.04 & 987.272 & 994.134 & 0.993097 & 1.00888 \tabularnewline
27 & 975.02 & 969.531 & 979.897 & 0.989421 & 1.00566 \tabularnewline
28 & 951.89 & 945.671 & 965.255 & 0.979711 & 1.00658 \tabularnewline
29 & 944.4 & 936.087 & 951.319 & 0.983988 & 1.00888 \tabularnewline
30 & 932.47 & 927.196 & 938.224 & 0.988246 & 1.00569 \tabularnewline
31 & 932.47 & 973.865 & 925.386 & 1.05239 & 0.957494 \tabularnewline
32 & 920.44 & 938.112 & 912.674 & 1.02787 & 0.981163 \tabularnewline
33 & 900.18 & 911.079 & 900.538 & 1.0117 & 0.988038 \tabularnewline
34 & 886.9 & 888.563 & 889.435 & 0.999019 & 0.998129 \tabularnewline
35 & 869.74 & 870.234 & 878.862 & 0.990183 & 0.999432 \tabularnewline
36 & 859.03 & 855.013 & 868.582 & 0.984378 & 1.0047 \tabularnewline
37 & 859.03 & 858.637 & 858.643 & 0.999993 & 1.00046 \tabularnewline
38 & 844.99 & 843.254 & 849.115 & 0.993097 & 1.00206 \tabularnewline
39 & 834.82 & 831.664 & 840.556 & 0.989421 & 1.0038 \tabularnewline
40 & 825.62 & 815.57 & 832.46 & 0.979711 & 1.01232 \tabularnewline
41 & 816.92 & 811.371 & 824.574 & 0.983988 & 1.00684 \tabularnewline
42 & 813.21 & 807.302 & 816.903 & 0.988246 & 1.00732 \tabularnewline
43 & 813.21 & 851.533 & 809.143 & 1.05239 & 0.954996 \tabularnewline
44 & 811.03 & 823.804 & 801.466 & 1.02787 & 0.984494 \tabularnewline
45 & 804.16 & 803.101 & 793.81 & 1.0117 & 1.00132 \tabularnewline
46 & 788.62 & 785.284 & 786.055 & 0.999019 & 1.00425 \tabularnewline
47 & 778.76 & 770.892 & 778.535 & 0.990183 & 1.01021 \tabularnewline
48 & 765.91 & 759.442 & 771.495 & 0.984378 & 1.00852 \tabularnewline
49 & 765.91 & 764.657 & 764.662 & 0.999993 & 1.00164 \tabularnewline
50 & 753.85 & 752.601 & 757.832 & 0.993097 & 1.00166 \tabularnewline
51 & 742.22 & 743.255 & 751.202 & 0.989421 & 0.998608 \tabularnewline
52 & 732.11 & 730.029 & 745.147 & 0.979711 & 1.00285 \tabularnewline
53 & 729.94 & 727.578 & 739.418 & 0.983988 & 1.00325 \tabularnewline
54 & 731.22 & 725.287 & 733.913 & 0.988246 & 1.00818 \tabularnewline
55 & 731.22 & NA & NA & 1.05239 & NA \tabularnewline
56 & 729.11 & NA & NA & 1.02787 & NA \tabularnewline
57 & 726.94 & NA & NA & 1.0117 & NA \tabularnewline
58 & 720.52 & NA & NA & 0.999019 & NA \tabularnewline
59 & 709.36 & NA & NA & 0.990183 & NA \tabularnewline
60 & 703.21 & NA & NA & 0.984378 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234815&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]530.3[/C][C]NA[/C][C]NA[/C][C]0.999993[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]527.76[/C][C]NA[/C][C]NA[/C][C]0.993097[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]521.41[/C][C]NA[/C][C]NA[/C][C]0.989421[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]1601.93[/C][C]NA[/C][C]NA[/C][C]0.979711[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]1577.49[/C][C]NA[/C][C]NA[/C][C]0.983988[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]1551.43[/C][C]NA[/C][C]NA[/C][C]0.988246[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]1551.43[/C][C]1352.05[/C][C]1284.75[/C][C]1.05239[/C][C]1.14746[/C][/ROW]
[ROW][C]8[/C][C]1516.88[/C][C]1386.83[/C][C]1349.23[/C][C]1.02787[/C][C]1.09377[/C][/ROW]
[ROW][C]9[/C][C]1485.95[/C][C]1426.96[/C][C]1410.45[/C][C]1.0117[/C][C]1.04134[/C][/ROW]
[ROW][C]10[/C][C]1438.22[/C][C]1421.58[/C][C]1422.98[/C][C]0.999019[/C][C]1.01171[/C][/ROW]
[ROW][C]11[/C][C]1385.06[/C][C]1374.45[/C][C]1388.07[/C][C]0.990183[/C][C]1.00772[/C][/ROW]
[ROW][C]12[/C][C]1329.49[/C][C]1332.2[/C][C]1353.34[/C][C]0.984378[/C][C]0.997964[/C][/ROW]
[ROW][C]13[/C][C]1329.49[/C][C]1318.65[/C][C]1318.66[/C][C]0.999993[/C][C]1.00822[/C][/ROW]
[ROW][C]14[/C][C]1276.16[/C][C]1274.46[/C][C]1283.32[/C][C]0.993097[/C][C]1.00134[/C][/ROW]
[ROW][C]15[/C][C]1242.34[/C][C]1235.09[/C][C]1248.29[/C][C]0.989421[/C][C]1.00587[/C][/ROW]
[ROW][C]16[/C][C]1181.59[/C][C]1190.89[/C][C]1215.56[/C][C]0.979711[/C][C]0.992187[/C][/ROW]
[ROW][C]17[/C][C]1160.21[/C][C]1166.08[/C][C]1185.05[/C][C]0.983988[/C][C]0.99497[/C][/ROW]
[ROW][C]18[/C][C]1135.18[/C][C]1143.47[/C][C]1157.07[/C][C]0.988246[/C][C]0.992749[/C][/ROW]
[ROW][C]19[/C][C]1135.18[/C][C]1189.94[/C][C]1130.7[/C][C]1.05239[/C][C]0.953982[/C][/ROW]
[ROW][C]20[/C][C]1084.96[/C][C]1136.67[/C][C]1105.85[/C][C]1.02787[/C][C]0.954507[/C][/ROW]
[ROW][C]21[/C][C]1077.35[/C][C]1095.71[/C][C]1083.04[/C][C]1.0117[/C][C]0.983239[/C][/ROW]
[ROW][C]22[/C][C]1061.13[/C][C]1061.29[/C][C]1062.33[/C][C]0.999019[/C][C]0.999852[/C][/ROW]
[ROW][C]23[/C][C]1029.98[/C][C]1033.52[/C][C]1043.77[/C][C]0.990183[/C][C]0.996576[/C][/ROW]
[ROW][C]24[/C][C]1013.08[/C][C]1010.29[/C][C]1026.33[/C][C]0.984378[/C][C]1.00276[/C][/ROW]
[ROW][C]25[/C][C]1013.08[/C][C]1009.43[/C][C]1009.44[/C][C]0.999993[/C][C]1.00362[/C][/ROW]
[ROW][C]26[/C][C]996.04[/C][C]987.272[/C][C]994.134[/C][C]0.993097[/C][C]1.00888[/C][/ROW]
[ROW][C]27[/C][C]975.02[/C][C]969.531[/C][C]979.897[/C][C]0.989421[/C][C]1.00566[/C][/ROW]
[ROW][C]28[/C][C]951.89[/C][C]945.671[/C][C]965.255[/C][C]0.979711[/C][C]1.00658[/C][/ROW]
[ROW][C]29[/C][C]944.4[/C][C]936.087[/C][C]951.319[/C][C]0.983988[/C][C]1.00888[/C][/ROW]
[ROW][C]30[/C][C]932.47[/C][C]927.196[/C][C]938.224[/C][C]0.988246[/C][C]1.00569[/C][/ROW]
[ROW][C]31[/C][C]932.47[/C][C]973.865[/C][C]925.386[/C][C]1.05239[/C][C]0.957494[/C][/ROW]
[ROW][C]32[/C][C]920.44[/C][C]938.112[/C][C]912.674[/C][C]1.02787[/C][C]0.981163[/C][/ROW]
[ROW][C]33[/C][C]900.18[/C][C]911.079[/C][C]900.538[/C][C]1.0117[/C][C]0.988038[/C][/ROW]
[ROW][C]34[/C][C]886.9[/C][C]888.563[/C][C]889.435[/C][C]0.999019[/C][C]0.998129[/C][/ROW]
[ROW][C]35[/C][C]869.74[/C][C]870.234[/C][C]878.862[/C][C]0.990183[/C][C]0.999432[/C][/ROW]
[ROW][C]36[/C][C]859.03[/C][C]855.013[/C][C]868.582[/C][C]0.984378[/C][C]1.0047[/C][/ROW]
[ROW][C]37[/C][C]859.03[/C][C]858.637[/C][C]858.643[/C][C]0.999993[/C][C]1.00046[/C][/ROW]
[ROW][C]38[/C][C]844.99[/C][C]843.254[/C][C]849.115[/C][C]0.993097[/C][C]1.00206[/C][/ROW]
[ROW][C]39[/C][C]834.82[/C][C]831.664[/C][C]840.556[/C][C]0.989421[/C][C]1.0038[/C][/ROW]
[ROW][C]40[/C][C]825.62[/C][C]815.57[/C][C]832.46[/C][C]0.979711[/C][C]1.01232[/C][/ROW]
[ROW][C]41[/C][C]816.92[/C][C]811.371[/C][C]824.574[/C][C]0.983988[/C][C]1.00684[/C][/ROW]
[ROW][C]42[/C][C]813.21[/C][C]807.302[/C][C]816.903[/C][C]0.988246[/C][C]1.00732[/C][/ROW]
[ROW][C]43[/C][C]813.21[/C][C]851.533[/C][C]809.143[/C][C]1.05239[/C][C]0.954996[/C][/ROW]
[ROW][C]44[/C][C]811.03[/C][C]823.804[/C][C]801.466[/C][C]1.02787[/C][C]0.984494[/C][/ROW]
[ROW][C]45[/C][C]804.16[/C][C]803.101[/C][C]793.81[/C][C]1.0117[/C][C]1.00132[/C][/ROW]
[ROW][C]46[/C][C]788.62[/C][C]785.284[/C][C]786.055[/C][C]0.999019[/C][C]1.00425[/C][/ROW]
[ROW][C]47[/C][C]778.76[/C][C]770.892[/C][C]778.535[/C][C]0.990183[/C][C]1.01021[/C][/ROW]
[ROW][C]48[/C][C]765.91[/C][C]759.442[/C][C]771.495[/C][C]0.984378[/C][C]1.00852[/C][/ROW]
[ROW][C]49[/C][C]765.91[/C][C]764.657[/C][C]764.662[/C][C]0.999993[/C][C]1.00164[/C][/ROW]
[ROW][C]50[/C][C]753.85[/C][C]752.601[/C][C]757.832[/C][C]0.993097[/C][C]1.00166[/C][/ROW]
[ROW][C]51[/C][C]742.22[/C][C]743.255[/C][C]751.202[/C][C]0.989421[/C][C]0.998608[/C][/ROW]
[ROW][C]52[/C][C]732.11[/C][C]730.029[/C][C]745.147[/C][C]0.979711[/C][C]1.00285[/C][/ROW]
[ROW][C]53[/C][C]729.94[/C][C]727.578[/C][C]739.418[/C][C]0.983988[/C][C]1.00325[/C][/ROW]
[ROW][C]54[/C][C]731.22[/C][C]725.287[/C][C]733.913[/C][C]0.988246[/C][C]1.00818[/C][/ROW]
[ROW][C]55[/C][C]731.22[/C][C]NA[/C][C]NA[/C][C]1.05239[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]729.11[/C][C]NA[/C][C]NA[/C][C]1.02787[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]726.94[/C][C]NA[/C][C]NA[/C][C]1.0117[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]720.52[/C][C]NA[/C][C]NA[/C][C]0.999019[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]709.36[/C][C]NA[/C][C]NA[/C][C]0.990183[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]703.21[/C][C]NA[/C][C]NA[/C][C]0.984378[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234815&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234815&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	530.3	NA	NA	0.999993	NA
2	527.76	NA	NA	0.993097	NA
3	521.41	NA	NA	0.989421	NA
4	1601.93	NA	NA	0.979711	NA
5	1577.49	NA	NA	0.983988	NA
6	1551.43	NA	NA	0.988246	NA
7	1551.43	1352.05	1284.75	1.05239	1.14746
8	1516.88	1386.83	1349.23	1.02787	1.09377
9	1485.95	1426.96	1410.45	1.0117	1.04134
10	1438.22	1421.58	1422.98	0.999019	1.01171
11	1385.06	1374.45	1388.07	0.990183	1.00772
12	1329.49	1332.2	1353.34	0.984378	0.997964
13	1329.49	1318.65	1318.66	0.999993	1.00822
14	1276.16	1274.46	1283.32	0.993097	1.00134
15	1242.34	1235.09	1248.29	0.989421	1.00587
16	1181.59	1190.89	1215.56	0.979711	0.992187
17	1160.21	1166.08	1185.05	0.983988	0.99497
18	1135.18	1143.47	1157.07	0.988246	0.992749
19	1135.18	1189.94	1130.7	1.05239	0.953982
20	1084.96	1136.67	1105.85	1.02787	0.954507
21	1077.35	1095.71	1083.04	1.0117	0.983239
22	1061.13	1061.29	1062.33	0.999019	0.999852
23	1029.98	1033.52	1043.77	0.990183	0.996576
24	1013.08	1010.29	1026.33	0.984378	1.00276
25	1013.08	1009.43	1009.44	0.999993	1.00362
26	996.04	987.272	994.134	0.993097	1.00888
27	975.02	969.531	979.897	0.989421	1.00566
28	951.89	945.671	965.255	0.979711	1.00658
29	944.4	936.087	951.319	0.983988	1.00888
30	932.47	927.196	938.224	0.988246	1.00569
31	932.47	973.865	925.386	1.05239	0.957494
32	920.44	938.112	912.674	1.02787	0.981163
33	900.18	911.079	900.538	1.0117	0.988038
34	886.9	888.563	889.435	0.999019	0.998129
35	869.74	870.234	878.862	0.990183	0.999432
36	859.03	855.013	868.582	0.984378	1.0047
37	859.03	858.637	858.643	0.999993	1.00046
38	844.99	843.254	849.115	0.993097	1.00206
39	834.82	831.664	840.556	0.989421	1.0038
40	825.62	815.57	832.46	0.979711	1.01232
41	816.92	811.371	824.574	0.983988	1.00684
42	813.21	807.302	816.903	0.988246	1.00732
43	813.21	851.533	809.143	1.05239	0.954996
44	811.03	823.804	801.466	1.02787	0.984494
45	804.16	803.101	793.81	1.0117	1.00132
46	788.62	785.284	786.055	0.999019	1.00425
47	778.76	770.892	778.535	0.990183	1.01021
48	765.91	759.442	771.495	0.984378	1.00852
49	765.91	764.657	764.662	0.999993	1.00164
50	753.85	752.601	757.832	0.993097	1.00166
51	742.22	743.255	751.202	0.989421	0.998608
52	732.11	730.029	745.147	0.979711	1.00285
53	729.94	727.578	739.418	0.983988	1.00325
54	731.22	725.287	733.913	0.988246	1.00818
55	731.22	NA	NA	1.05239	NA
56	729.11	NA	NA	1.02787	NA
57	726.94	NA	NA	1.0117	NA
58	720.52	NA	NA	0.999019	NA
59	709.36	NA	NA	0.990183	NA
60	703.21	NA	NA	0.984378	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