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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sat, 26 Nov 2016 18:20:06 +0000

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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/Nov/26/t1480184561fxxhdfcnriapd9q.htm/, Retrieved Fri, 03 May 2024 18:55:11 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Fri, 03 May 2024 18:55:11 +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	2 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 & 2 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]2 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	2 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	18347.7	NA	NA	-894.063	NA
2	19372.7	NA	NA	-44.3977	NA
3	22263.8	NA	NA	1642.69	NA
4	19422.9	NA	NA	603.183	NA
5	21268.6	NA	NA	119.943	NA
6	20310	NA	NA	818.291	NA
7	19256	19875	19614.5	260.484	-618.975
8	17535.9	17253.7	19684.7	-2431	282.203
9	19857.4	20303.9	19734.6	569.321	-446.479
10	19628.4	20926.1	19751.2	1174.96	-1297.74
11	19727.5	19582.9	19713.4	-130.486	144.586
12	18112.2	17970.2	19659.1	-1688.92	141.979
13	18889.3	18770.4	19664.5	-894.063	118.859
14	20516.1	19643.1	19687.5	-44.3977	872.977
15	22317	21315.7	19673	1642.69	1001.33
16	19768.8	20298.2	19695	603.183	-529.395
17	20015.8	19865.1	19745.2	119.943	150.67
18	20260.5	20535.7	19717.4	818.291	-275.216
19	19434.3	19963.2	19702.7	260.484	-528.934
20	17910	17226	19657	-2431	684.024
21	19134.4	20127.8	19558.5	569.321	-993.404
22	20880.1	20726.5	19551.6	1174.96	153.59
23	19680	19475.8	19606.3	-130.486	204.203
24	17493.4	17919.7	19608.6	-1688.92	-426.254
25	19155.9	18761.9	19656	-894.063	394.009
26	19151	19643.4	19687.8	-44.3977	-492.407
27	21318.2	21364.6	19721.9	1642.69	-46.419
28	20601.3	20422.5	19819.4	603.183	178.751
29	20496.8	19980.1	19860.2	119.943	516.653
30	19834.4	20746	19927.7	818.291	-911.603
31	20997.6	20267.5	20007	260.484	730.146
32	17111.1	17628.3	20059.3	-2431	-517.18
33	20752.3	20642.2	20072.9	569.321	110.108
34	21600.7	21243	20068.1	1174.96	357.682
35	19939.5	19928.5	20059	-130.486	10.957
36	18854.1	18381	20069.9	-1688.92	473.104
37	19697.4	19190.7	20084.8	-894.063	506.676
38	19865	20017.5	20061.9	-44.3977	-152.54
39	20930.3	21739	20096.3	1642.69	-808.661
40	20873.8	20747.9	20144.7	603.183	125.888
41	20007.5	20244.9	20124.9	119.943	-237.389
42	20584.9	20876.5	20058.3	818.291	-291.641
43	20604.1	20204.8	19944.3	260.484	399.3
44	16956.2	17423.7	19854.7	-2431	-467.51
45	21731.2	20419.9	19850.6	569.321	1311.31
46	21784.8	21016.8	19841.8	1174.96	768.007
47	19280.6	19658.8	19789.3	-130.486	-378.21
48	17912.3	18119.6	19808.5	-1688.92	-207.292
49	17904.8	18942.8	19836.9	-894.063	-1038.01
50	19507.1	19753.6	19798	-44.3977	-246.494
51	21188.7	21353.4	19710.7	1642.69	-164.711
52	20405.9	20199.6	19596.4	603.183	206.292
53	19214.4	19662.8	19542.9	119.943	-448.397
54	21839.1	20379.1	19560.8	818.291	1460
55	20030.6	NA	NA	260.484	NA
56	16596.6	NA	NA	-2431	NA
57	19996.3	NA	NA	569.321	NA
58	20776.6	NA	NA	1174.96	NA
59	19003.1	NA	NA	-130.486	NA
60	18620.8	NA	NA	-1688.92	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 18347.7 & NA & NA & -894.063 & NA \tabularnewline
2 & 19372.7 & NA & NA & -44.3977 & NA \tabularnewline
3 & 22263.8 & NA & NA & 1642.69 & NA \tabularnewline
4 & 19422.9 & NA & NA & 603.183 & NA \tabularnewline
5 & 21268.6 & NA & NA & 119.943 & NA \tabularnewline
6 & 20310 & NA & NA & 818.291 & NA \tabularnewline
7 & 19256 & 19875 & 19614.5 & 260.484 & -618.975 \tabularnewline
8 & 17535.9 & 17253.7 & 19684.7 & -2431 & 282.203 \tabularnewline
9 & 19857.4 & 20303.9 & 19734.6 & 569.321 & -446.479 \tabularnewline
10 & 19628.4 & 20926.1 & 19751.2 & 1174.96 & -1297.74 \tabularnewline
11 & 19727.5 & 19582.9 & 19713.4 & -130.486 & 144.586 \tabularnewline
12 & 18112.2 & 17970.2 & 19659.1 & -1688.92 & 141.979 \tabularnewline
13 & 18889.3 & 18770.4 & 19664.5 & -894.063 & 118.859 \tabularnewline
14 & 20516.1 & 19643.1 & 19687.5 & -44.3977 & 872.977 \tabularnewline
15 & 22317 & 21315.7 & 19673 & 1642.69 & 1001.33 \tabularnewline
16 & 19768.8 & 20298.2 & 19695 & 603.183 & -529.395 \tabularnewline
17 & 20015.8 & 19865.1 & 19745.2 & 119.943 & 150.67 \tabularnewline
18 & 20260.5 & 20535.7 & 19717.4 & 818.291 & -275.216 \tabularnewline
19 & 19434.3 & 19963.2 & 19702.7 & 260.484 & -528.934 \tabularnewline
20 & 17910 & 17226 & 19657 & -2431 & 684.024 \tabularnewline
21 & 19134.4 & 20127.8 & 19558.5 & 569.321 & -993.404 \tabularnewline
22 & 20880.1 & 20726.5 & 19551.6 & 1174.96 & 153.59 \tabularnewline
23 & 19680 & 19475.8 & 19606.3 & -130.486 & 204.203 \tabularnewline
24 & 17493.4 & 17919.7 & 19608.6 & -1688.92 & -426.254 \tabularnewline
25 & 19155.9 & 18761.9 & 19656 & -894.063 & 394.009 \tabularnewline
26 & 19151 & 19643.4 & 19687.8 & -44.3977 & -492.407 \tabularnewline
27 & 21318.2 & 21364.6 & 19721.9 & 1642.69 & -46.419 \tabularnewline
28 & 20601.3 & 20422.5 & 19819.4 & 603.183 & 178.751 \tabularnewline
29 & 20496.8 & 19980.1 & 19860.2 & 119.943 & 516.653 \tabularnewline
30 & 19834.4 & 20746 & 19927.7 & 818.291 & -911.603 \tabularnewline
31 & 20997.6 & 20267.5 & 20007 & 260.484 & 730.146 \tabularnewline
32 & 17111.1 & 17628.3 & 20059.3 & -2431 & -517.18 \tabularnewline
33 & 20752.3 & 20642.2 & 20072.9 & 569.321 & 110.108 \tabularnewline
34 & 21600.7 & 21243 & 20068.1 & 1174.96 & 357.682 \tabularnewline
35 & 19939.5 & 19928.5 & 20059 & -130.486 & 10.957 \tabularnewline
36 & 18854.1 & 18381 & 20069.9 & -1688.92 & 473.104 \tabularnewline
37 & 19697.4 & 19190.7 & 20084.8 & -894.063 & 506.676 \tabularnewline
38 & 19865 & 20017.5 & 20061.9 & -44.3977 & -152.54 \tabularnewline
39 & 20930.3 & 21739 & 20096.3 & 1642.69 & -808.661 \tabularnewline
40 & 20873.8 & 20747.9 & 20144.7 & 603.183 & 125.888 \tabularnewline
41 & 20007.5 & 20244.9 & 20124.9 & 119.943 & -237.389 \tabularnewline
42 & 20584.9 & 20876.5 & 20058.3 & 818.291 & -291.641 \tabularnewline
43 & 20604.1 & 20204.8 & 19944.3 & 260.484 & 399.3 \tabularnewline
44 & 16956.2 & 17423.7 & 19854.7 & -2431 & -467.51 \tabularnewline
45 & 21731.2 & 20419.9 & 19850.6 & 569.321 & 1311.31 \tabularnewline
46 & 21784.8 & 21016.8 & 19841.8 & 1174.96 & 768.007 \tabularnewline
47 & 19280.6 & 19658.8 & 19789.3 & -130.486 & -378.21 \tabularnewline
48 & 17912.3 & 18119.6 & 19808.5 & -1688.92 & -207.292 \tabularnewline
49 & 17904.8 & 18942.8 & 19836.9 & -894.063 & -1038.01 \tabularnewline
50 & 19507.1 & 19753.6 & 19798 & -44.3977 & -246.494 \tabularnewline
51 & 21188.7 & 21353.4 & 19710.7 & 1642.69 & -164.711 \tabularnewline
52 & 20405.9 & 20199.6 & 19596.4 & 603.183 & 206.292 \tabularnewline
53 & 19214.4 & 19662.8 & 19542.9 & 119.943 & -448.397 \tabularnewline
54 & 21839.1 & 20379.1 & 19560.8 & 818.291 & 1460 \tabularnewline
55 & 20030.6 & NA & NA & 260.484 & NA \tabularnewline
56 & 16596.6 & NA & NA & -2431 & NA \tabularnewline
57 & 19996.3 & NA & NA & 569.321 & NA \tabularnewline
58 & 20776.6 & NA & NA & 1174.96 & NA \tabularnewline
59 & 19003.1 & NA & NA & -130.486 & NA \tabularnewline
60 & 18620.8 & NA & NA & -1688.92 & 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]18347.7[/C][C]NA[/C][C]NA[/C][C]-894.063[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]19372.7[/C][C]NA[/C][C]NA[/C][C]-44.3977[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]22263.8[/C][C]NA[/C][C]NA[/C][C]1642.69[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]19422.9[/C][C]NA[/C][C]NA[/C][C]603.183[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]21268.6[/C][C]NA[/C][C]NA[/C][C]119.943[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]20310[/C][C]NA[/C][C]NA[/C][C]818.291[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]19256[/C][C]19875[/C][C]19614.5[/C][C]260.484[/C][C]-618.975[/C][/ROW]
[ROW][C]8[/C][C]17535.9[/C][C]17253.7[/C][C]19684.7[/C][C]-2431[/C][C]282.203[/C][/ROW]
[ROW][C]9[/C][C]19857.4[/C][C]20303.9[/C][C]19734.6[/C][C]569.321[/C][C]-446.479[/C][/ROW]
[ROW][C]10[/C][C]19628.4[/C][C]20926.1[/C][C]19751.2[/C][C]1174.96[/C][C]-1297.74[/C][/ROW]
[ROW][C]11[/C][C]19727.5[/C][C]19582.9[/C][C]19713.4[/C][C]-130.486[/C][C]144.586[/C][/ROW]
[ROW][C]12[/C][C]18112.2[/C][C]17970.2[/C][C]19659.1[/C][C]-1688.92[/C][C]141.979[/C][/ROW]
[ROW][C]13[/C][C]18889.3[/C][C]18770.4[/C][C]19664.5[/C][C]-894.063[/C][C]118.859[/C][/ROW]
[ROW][C]14[/C][C]20516.1[/C][C]19643.1[/C][C]19687.5[/C][C]-44.3977[/C][C]872.977[/C][/ROW]
[ROW][C]15[/C][C]22317[/C][C]21315.7[/C][C]19673[/C][C]1642.69[/C][C]1001.33[/C][/ROW]
[ROW][C]16[/C][C]19768.8[/C][C]20298.2[/C][C]19695[/C][C]603.183[/C][C]-529.395[/C][/ROW]
[ROW][C]17[/C][C]20015.8[/C][C]19865.1[/C][C]19745.2[/C][C]119.943[/C][C]150.67[/C][/ROW]
[ROW][C]18[/C][C]20260.5[/C][C]20535.7[/C][C]19717.4[/C][C]818.291[/C][C]-275.216[/C][/ROW]
[ROW][C]19[/C][C]19434.3[/C][C]19963.2[/C][C]19702.7[/C][C]260.484[/C][C]-528.934[/C][/ROW]
[ROW][C]20[/C][C]17910[/C][C]17226[/C][C]19657[/C][C]-2431[/C][C]684.024[/C][/ROW]
[ROW][C]21[/C][C]19134.4[/C][C]20127.8[/C][C]19558.5[/C][C]569.321[/C][C]-993.404[/C][/ROW]
[ROW][C]22[/C][C]20880.1[/C][C]20726.5[/C][C]19551.6[/C][C]1174.96[/C][C]153.59[/C][/ROW]
[ROW][C]23[/C][C]19680[/C][C]19475.8[/C][C]19606.3[/C][C]-130.486[/C][C]204.203[/C][/ROW]
[ROW][C]24[/C][C]17493.4[/C][C]17919.7[/C][C]19608.6[/C][C]-1688.92[/C][C]-426.254[/C][/ROW]
[ROW][C]25[/C][C]19155.9[/C][C]18761.9[/C][C]19656[/C][C]-894.063[/C][C]394.009[/C][/ROW]
[ROW][C]26[/C][C]19151[/C][C]19643.4[/C][C]19687.8[/C][C]-44.3977[/C][C]-492.407[/C][/ROW]
[ROW][C]27[/C][C]21318.2[/C][C]21364.6[/C][C]19721.9[/C][C]1642.69[/C][C]-46.419[/C][/ROW]
[ROW][C]28[/C][C]20601.3[/C][C]20422.5[/C][C]19819.4[/C][C]603.183[/C][C]178.751[/C][/ROW]
[ROW][C]29[/C][C]20496.8[/C][C]19980.1[/C][C]19860.2[/C][C]119.943[/C][C]516.653[/C][/ROW]
[ROW][C]30[/C][C]19834.4[/C][C]20746[/C][C]19927.7[/C][C]818.291[/C][C]-911.603[/C][/ROW]
[ROW][C]31[/C][C]20997.6[/C][C]20267.5[/C][C]20007[/C][C]260.484[/C][C]730.146[/C][/ROW]
[ROW][C]32[/C][C]17111.1[/C][C]17628.3[/C][C]20059.3[/C][C]-2431[/C][C]-517.18[/C][/ROW]
[ROW][C]33[/C][C]20752.3[/C][C]20642.2[/C][C]20072.9[/C][C]569.321[/C][C]110.108[/C][/ROW]
[ROW][C]34[/C][C]21600.7[/C][C]21243[/C][C]20068.1[/C][C]1174.96[/C][C]357.682[/C][/ROW]
[ROW][C]35[/C][C]19939.5[/C][C]19928.5[/C][C]20059[/C][C]-130.486[/C][C]10.957[/C][/ROW]
[ROW][C]36[/C][C]18854.1[/C][C]18381[/C][C]20069.9[/C][C]-1688.92[/C][C]473.104[/C][/ROW]
[ROW][C]37[/C][C]19697.4[/C][C]19190.7[/C][C]20084.8[/C][C]-894.063[/C][C]506.676[/C][/ROW]
[ROW][C]38[/C][C]19865[/C][C]20017.5[/C][C]20061.9[/C][C]-44.3977[/C][C]-152.54[/C][/ROW]
[ROW][C]39[/C][C]20930.3[/C][C]21739[/C][C]20096.3[/C][C]1642.69[/C][C]-808.661[/C][/ROW]
[ROW][C]40[/C][C]20873.8[/C][C]20747.9[/C][C]20144.7[/C][C]603.183[/C][C]125.888[/C][/ROW]
[ROW][C]41[/C][C]20007.5[/C][C]20244.9[/C][C]20124.9[/C][C]119.943[/C][C]-237.389[/C][/ROW]
[ROW][C]42[/C][C]20584.9[/C][C]20876.5[/C][C]20058.3[/C][C]818.291[/C][C]-291.641[/C][/ROW]
[ROW][C]43[/C][C]20604.1[/C][C]20204.8[/C][C]19944.3[/C][C]260.484[/C][C]399.3[/C][/ROW]
[ROW][C]44[/C][C]16956.2[/C][C]17423.7[/C][C]19854.7[/C][C]-2431[/C][C]-467.51[/C][/ROW]
[ROW][C]45[/C][C]21731.2[/C][C]20419.9[/C][C]19850.6[/C][C]569.321[/C][C]1311.31[/C][/ROW]
[ROW][C]46[/C][C]21784.8[/C][C]21016.8[/C][C]19841.8[/C][C]1174.96[/C][C]768.007[/C][/ROW]
[ROW][C]47[/C][C]19280.6[/C][C]19658.8[/C][C]19789.3[/C][C]-130.486[/C][C]-378.21[/C][/ROW]
[ROW][C]48[/C][C]17912.3[/C][C]18119.6[/C][C]19808.5[/C][C]-1688.92[/C][C]-207.292[/C][/ROW]
[ROW][C]49[/C][C]17904.8[/C][C]18942.8[/C][C]19836.9[/C][C]-894.063[/C][C]-1038.01[/C][/ROW]
[ROW][C]50[/C][C]19507.1[/C][C]19753.6[/C][C]19798[/C][C]-44.3977[/C][C]-246.494[/C][/ROW]
[ROW][C]51[/C][C]21188.7[/C][C]21353.4[/C][C]19710.7[/C][C]1642.69[/C][C]-164.711[/C][/ROW]
[ROW][C]52[/C][C]20405.9[/C][C]20199.6[/C][C]19596.4[/C][C]603.183[/C][C]206.292[/C][/ROW]
[ROW][C]53[/C][C]19214.4[/C][C]19662.8[/C][C]19542.9[/C][C]119.943[/C][C]-448.397[/C][/ROW]
[ROW][C]54[/C][C]21839.1[/C][C]20379.1[/C][C]19560.8[/C][C]818.291[/C][C]1460[/C][/ROW]
[ROW][C]55[/C][C]20030.6[/C][C]NA[/C][C]NA[/C][C]260.484[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]16596.6[/C][C]NA[/C][C]NA[/C][C]-2431[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]19996.3[/C][C]NA[/C][C]NA[/C][C]569.321[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]20776.6[/C][C]NA[/C][C]NA[/C][C]1174.96[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]19003.1[/C][C]NA[/C][C]NA[/C][C]-130.486[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]18620.8[/C][C]NA[/C][C]NA[/C][C]-1688.92[/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	18347.7	NA	NA	-894.063	NA
2	19372.7	NA	NA	-44.3977	NA
3	22263.8	NA	NA	1642.69	NA
4	19422.9	NA	NA	603.183	NA
5	21268.6	NA	NA	119.943	NA
6	20310	NA	NA	818.291	NA
7	19256	19875	19614.5	260.484	-618.975
8	17535.9	17253.7	19684.7	-2431	282.203
9	19857.4	20303.9	19734.6	569.321	-446.479
10	19628.4	20926.1	19751.2	1174.96	-1297.74
11	19727.5	19582.9	19713.4	-130.486	144.586
12	18112.2	17970.2	19659.1	-1688.92	141.979
13	18889.3	18770.4	19664.5	-894.063	118.859
14	20516.1	19643.1	19687.5	-44.3977	872.977
15	22317	21315.7	19673	1642.69	1001.33
16	19768.8	20298.2	19695	603.183	-529.395
17	20015.8	19865.1	19745.2	119.943	150.67
18	20260.5	20535.7	19717.4	818.291	-275.216
19	19434.3	19963.2	19702.7	260.484	-528.934
20	17910	17226	19657	-2431	684.024
21	19134.4	20127.8	19558.5	569.321	-993.404
22	20880.1	20726.5	19551.6	1174.96	153.59
23	19680	19475.8	19606.3	-130.486	204.203
24	17493.4	17919.7	19608.6	-1688.92	-426.254
25	19155.9	18761.9	19656	-894.063	394.009
26	19151	19643.4	19687.8	-44.3977	-492.407
27	21318.2	21364.6	19721.9	1642.69	-46.419
28	20601.3	20422.5	19819.4	603.183	178.751
29	20496.8	19980.1	19860.2	119.943	516.653
30	19834.4	20746	19927.7	818.291	-911.603
31	20997.6	20267.5	20007	260.484	730.146
32	17111.1	17628.3	20059.3	-2431	-517.18
33	20752.3	20642.2	20072.9	569.321	110.108
34	21600.7	21243	20068.1	1174.96	357.682
35	19939.5	19928.5	20059	-130.486	10.957
36	18854.1	18381	20069.9	-1688.92	473.104
37	19697.4	19190.7	20084.8	-894.063	506.676
38	19865	20017.5	20061.9	-44.3977	-152.54
39	20930.3	21739	20096.3	1642.69	-808.661
40	20873.8	20747.9	20144.7	603.183	125.888
41	20007.5	20244.9	20124.9	119.943	-237.389
42	20584.9	20876.5	20058.3	818.291	-291.641
43	20604.1	20204.8	19944.3	260.484	399.3
44	16956.2	17423.7	19854.7	-2431	-467.51
45	21731.2	20419.9	19850.6	569.321	1311.31
46	21784.8	21016.8	19841.8	1174.96	768.007
47	19280.6	19658.8	19789.3	-130.486	-378.21
48	17912.3	18119.6	19808.5	-1688.92	-207.292
49	17904.8	18942.8	19836.9	-894.063	-1038.01
50	19507.1	19753.6	19798	-44.3977	-246.494
51	21188.7	21353.4	19710.7	1642.69	-164.711
52	20405.9	20199.6	19596.4	603.183	206.292
53	19214.4	19662.8	19542.9	119.943	-448.397
54	21839.1	20379.1	19560.8	818.291	1460
55	20030.6	NA	NA	260.484	NA
56	16596.6	NA	NA	-2431	NA
57	19996.3	NA	NA	569.321	NA
58	20776.6	NA	NA	1174.96	NA
59	19003.1	NA	NA	-130.486	NA
60	18620.8	NA	NA	-1688.92	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 <- 'additive'
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