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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 27 Dec 2010 13:16:37 +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/2010/Dec/27/t1293457386kqhn0a317nm4zmz.htm/, Retrieved Mon, 06 May 2024 22:16:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115984, Retrieved Mon, 06 May 2024 22:16:22 +0000

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User-defined keywords

Classical Decomposition - Handelsbalans België (1995-2009)

Estimated Impact

124

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

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Classical Decomposition] [Workshop 8 monthl...] [2010-12-09 10:43:30] [82c18f3ebe9df70882495121eb816e07]
-   PD    [Classical Decomposition] [Paper Statistiek ] [2010-12-26 10:47:22] [82c18f3ebe9df70882495121eb816e07]
-    D        [Classical Decomposition] [Paper Statistiek ] [2010-12-27 13:16:37] [f6fdc0236f011c1845380977efc505f8] [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	'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115984&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115984&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115984&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	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	2540.9	NA	NA	-435.086830357143	NA
2	2370.3	NA	NA	425.647098214286	NA
3	1807.5	2212.05066964286	1919.1125	292.938169642857	-404.550669642857
4	1834.8	1315.2390625	1598.7375	-283.4984375	519.5609375
5	786.8	1005.00066964286	1440.0875	-435.086830357143	-218.200669642857
6	1561.4	1772.57209821429	1346.925	425.647098214286	-211.172098214286
7	1347.2	1700.11316964286	1407.175	292.938169642857	-352.913169642857
8	1549.8	1252.1890625	1535.6875	-283.4984375	297.6109375
9	1553.8	1349.67566964286	1784.7625	-435.086830357143	204.124330357143
10	1822.5	2431.75959821429	2006.1125	425.647098214286	-609.259598214286
11	3078.7	2333.67566964286	2040.7375	292.938169642857	745.024330357143
12	1589.1	1878.9015625	2162.4	-283.4984375	-289.8015625
13	1791.5	1698.40066964286	2133.4875	-435.086830357143	93.0993303571427
14	2558.1	2500.02209821429	2074.375	425.647098214286	58.077901785714
15	2111.8	2461.63816964286	2168.7	292.938169642857	-349.838169642857
16	2083.1	1878.4515625	2161.95	-283.4984375	204.6484375
17	2052.1	1751.31316964286	2186.4	-435.086830357143	300.786830357143
18	2243.5	2659.50959821429	2233.8625	425.647098214286	-416.009598214285
19	2622	2355.08816964286	2062.15	292.938169642857	266.911830357143
20	1952.6	1556.5390625	1840.0375	-283.4984375	396.0609375
21	808.9	1208.25066964286	1643.3375	-435.086830357143	-399.350669642857
22	1709.8	1803.12209821429	1377.475	425.647098214286	-93.3220982142857
23	1582.1	1572.93816964286	1280	292.938169642857	9.16183035714266
24	865.6	961.1015625	1244.6	-283.4984375	-95.5015624999999
25	1116.1	831.700669642857	1266.7875	-435.086830357143	284.399330357143
26	1119.4	1927.17209821429	1501.525	425.647098214286	-807.772098214286
27	2350	2110.76316964286	1817.825	292.938169642857	239.236830357143
28	1975.6	1920.0765625	2203.575	-283.4984375	55.5234374999995
29	2536.5	2035.40066964286	2470.4875	-435.086830357143	501.099330357143
30	2785	2936.58459821429	2510.9375	425.647098214286	-151.584598214286
31	2819.7	2563.33816964286	2270.4	292.938169642857	256.361830357142
32	1829.5	1781.7015625	2065.2	-283.4984375	47.7984375000001
33	758.3	1604.97566964286	2040.0625	-435.086830357143	-846.675669642857
34	2921.6	2431.39709821429	2005.75	425.647098214286	490.202901785714
35	2482	2443.68816964286	2150.75	292.938169642857	38.3118303571428
36	1892.7	1908.0640625	2191.5625	-283.4984375	-15.3640624999998
37	1855.1	1555.21316964286	1990.3	-435.086830357143	299.886830357143
38	2151.3	2279.44709821429	1853.8	425.647098214286	-128.147098214286
39	1642.2	2054.08816964286	1761.15	292.938169642857	-411.888169642857
40	1640.5	1339.2140625	1622.7125	-283.4984375	301.2859375
41	1366.1	1008.08816964286	1443.175	-435.086830357143	358.011830357143
42	1532.8	1496.69709821429	1071.05	425.647098214286	36.1029017857143
43	824.4	801.013169642857	508.075	292.938169642857	23.3868303571427
44	-518.7	-114.7859375	168.7125	-283.4984375	-403.9140625
45	-978.5	-260.286830357143	174.8	-435.086830357143	-718.213169642857
46	1162.5	867.597098214286	441.95	425.647098214286	294.902901785714
47	1243.4	1182.36316964286	889.425	292.938169642857	61.0368303571429
48	1199.5	872.9640625	1156.4625	-283.4984375	326.5359375
49	883.1	667.100669642857	1102.1875	-435.086830357143	215.999330357143
50	1437.2	1051.54709821429	625.9	425.647098214286	385.652901785714
51	534.5	105.638169642857	-187.3	292.938169642857	428.861830357143
52	-1901.9	-1291.1109375	-1007.6125	-283.4984375	-610.7890625
53	-2521.1	-2291.11183035714	-1856.025	-435.086830357143	-229.988169642857
54	-1721.1	-2108.11540178571	-2533.7625	425.647098214286	387.015401785714
55	-3094.5	-2461.31183035714	-2754.25	292.938169642857	-633.188169642857
56	-3694.8	-2877.0609375	-2593.5625	-283.4984375	-817.7390625
57	-2492.1	-2563.03683035714	-2127.95	-435.086830357143	70.9368303571428
58	-464.6	-1145.82790178571	-1571.475	425.647098214286	681.227901785714
59	-626.1	NA	NA	292.938169642857	NA
60	-1711.4	NA	NA	-283.4984375	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 2540.9 & NA & NA & -435.086830357143 & NA \tabularnewline
2 & 2370.3 & NA & NA & 425.647098214286 & NA \tabularnewline
3 & 1807.5 & 2212.05066964286 & 1919.1125 & 292.938169642857 & -404.550669642857 \tabularnewline
4 & 1834.8 & 1315.2390625 & 1598.7375 & -283.4984375 & 519.5609375 \tabularnewline
5 & 786.8 & 1005.00066964286 & 1440.0875 & -435.086830357143 & -218.200669642857 \tabularnewline
6 & 1561.4 & 1772.57209821429 & 1346.925 & 425.647098214286 & -211.172098214286 \tabularnewline
7 & 1347.2 & 1700.11316964286 & 1407.175 & 292.938169642857 & -352.913169642857 \tabularnewline
8 & 1549.8 & 1252.1890625 & 1535.6875 & -283.4984375 & 297.6109375 \tabularnewline
9 & 1553.8 & 1349.67566964286 & 1784.7625 & -435.086830357143 & 204.124330357143 \tabularnewline
10 & 1822.5 & 2431.75959821429 & 2006.1125 & 425.647098214286 & -609.259598214286 \tabularnewline
11 & 3078.7 & 2333.67566964286 & 2040.7375 & 292.938169642857 & 745.024330357143 \tabularnewline
12 & 1589.1 & 1878.9015625 & 2162.4 & -283.4984375 & -289.8015625 \tabularnewline
13 & 1791.5 & 1698.40066964286 & 2133.4875 & -435.086830357143 & 93.0993303571427 \tabularnewline
14 & 2558.1 & 2500.02209821429 & 2074.375 & 425.647098214286 & 58.077901785714 \tabularnewline
15 & 2111.8 & 2461.63816964286 & 2168.7 & 292.938169642857 & -349.838169642857 \tabularnewline
16 & 2083.1 & 1878.4515625 & 2161.95 & -283.4984375 & 204.6484375 \tabularnewline
17 & 2052.1 & 1751.31316964286 & 2186.4 & -435.086830357143 & 300.786830357143 \tabularnewline
18 & 2243.5 & 2659.50959821429 & 2233.8625 & 425.647098214286 & -416.009598214285 \tabularnewline
19 & 2622 & 2355.08816964286 & 2062.15 & 292.938169642857 & 266.911830357143 \tabularnewline
20 & 1952.6 & 1556.5390625 & 1840.0375 & -283.4984375 & 396.0609375 \tabularnewline
21 & 808.9 & 1208.25066964286 & 1643.3375 & -435.086830357143 & -399.350669642857 \tabularnewline
22 & 1709.8 & 1803.12209821429 & 1377.475 & 425.647098214286 & -93.3220982142857 \tabularnewline
23 & 1582.1 & 1572.93816964286 & 1280 & 292.938169642857 & 9.16183035714266 \tabularnewline
24 & 865.6 & 961.1015625 & 1244.6 & -283.4984375 & -95.5015624999999 \tabularnewline
25 & 1116.1 & 831.700669642857 & 1266.7875 & -435.086830357143 & 284.399330357143 \tabularnewline
26 & 1119.4 & 1927.17209821429 & 1501.525 & 425.647098214286 & -807.772098214286 \tabularnewline
27 & 2350 & 2110.76316964286 & 1817.825 & 292.938169642857 & 239.236830357143 \tabularnewline
28 & 1975.6 & 1920.0765625 & 2203.575 & -283.4984375 & 55.5234374999995 \tabularnewline
29 & 2536.5 & 2035.40066964286 & 2470.4875 & -435.086830357143 & 501.099330357143 \tabularnewline
30 & 2785 & 2936.58459821429 & 2510.9375 & 425.647098214286 & -151.584598214286 \tabularnewline
31 & 2819.7 & 2563.33816964286 & 2270.4 & 292.938169642857 & 256.361830357142 \tabularnewline
32 & 1829.5 & 1781.7015625 & 2065.2 & -283.4984375 & 47.7984375000001 \tabularnewline
33 & 758.3 & 1604.97566964286 & 2040.0625 & -435.086830357143 & -846.675669642857 \tabularnewline
34 & 2921.6 & 2431.39709821429 & 2005.75 & 425.647098214286 & 490.202901785714 \tabularnewline
35 & 2482 & 2443.68816964286 & 2150.75 & 292.938169642857 & 38.3118303571428 \tabularnewline
36 & 1892.7 & 1908.0640625 & 2191.5625 & -283.4984375 & -15.3640624999998 \tabularnewline
37 & 1855.1 & 1555.21316964286 & 1990.3 & -435.086830357143 & 299.886830357143 \tabularnewline
38 & 2151.3 & 2279.44709821429 & 1853.8 & 425.647098214286 & -128.147098214286 \tabularnewline
39 & 1642.2 & 2054.08816964286 & 1761.15 & 292.938169642857 & -411.888169642857 \tabularnewline
40 & 1640.5 & 1339.2140625 & 1622.7125 & -283.4984375 & 301.2859375 \tabularnewline
41 & 1366.1 & 1008.08816964286 & 1443.175 & -435.086830357143 & 358.011830357143 \tabularnewline
42 & 1532.8 & 1496.69709821429 & 1071.05 & 425.647098214286 & 36.1029017857143 \tabularnewline
43 & 824.4 & 801.013169642857 & 508.075 & 292.938169642857 & 23.3868303571427 \tabularnewline
44 & -518.7 & -114.7859375 & 168.7125 & -283.4984375 & -403.9140625 \tabularnewline
45 & -978.5 & -260.286830357143 & 174.8 & -435.086830357143 & -718.213169642857 \tabularnewline
46 & 1162.5 & 867.597098214286 & 441.95 & 425.647098214286 & 294.902901785714 \tabularnewline
47 & 1243.4 & 1182.36316964286 & 889.425 & 292.938169642857 & 61.0368303571429 \tabularnewline
48 & 1199.5 & 872.9640625 & 1156.4625 & -283.4984375 & 326.5359375 \tabularnewline
49 & 883.1 & 667.100669642857 & 1102.1875 & -435.086830357143 & 215.999330357143 \tabularnewline
50 & 1437.2 & 1051.54709821429 & 625.9 & 425.647098214286 & 385.652901785714 \tabularnewline
51 & 534.5 & 105.638169642857 & -187.3 & 292.938169642857 & 428.861830357143 \tabularnewline
52 & -1901.9 & -1291.1109375 & -1007.6125 & -283.4984375 & -610.7890625 \tabularnewline
53 & -2521.1 & -2291.11183035714 & -1856.025 & -435.086830357143 & -229.988169642857 \tabularnewline
54 & -1721.1 & -2108.11540178571 & -2533.7625 & 425.647098214286 & 387.015401785714 \tabularnewline
55 & -3094.5 & -2461.31183035714 & -2754.25 & 292.938169642857 & -633.188169642857 \tabularnewline
56 & -3694.8 & -2877.0609375 & -2593.5625 & -283.4984375 & -817.7390625 \tabularnewline
57 & -2492.1 & -2563.03683035714 & -2127.95 & -435.086830357143 & 70.9368303571428 \tabularnewline
58 & -464.6 & -1145.82790178571 & -1571.475 & 425.647098214286 & 681.227901785714 \tabularnewline
59 & -626.1 & NA & NA & 292.938169642857 & NA \tabularnewline
60 & -1711.4 & NA & NA & -283.4984375 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115984&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]2540.9[/C][C]NA[/C][C]NA[/C][C]-435.086830357143[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]2370.3[/C][C]NA[/C][C]NA[/C][C]425.647098214286[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]1807.5[/C][C]2212.05066964286[/C][C]1919.1125[/C][C]292.938169642857[/C][C]-404.550669642857[/C][/ROW]
[ROW][C]4[/C][C]1834.8[/C][C]1315.2390625[/C][C]1598.7375[/C][C]-283.4984375[/C][C]519.5609375[/C][/ROW]
[ROW][C]5[/C][C]786.8[/C][C]1005.00066964286[/C][C]1440.0875[/C][C]-435.086830357143[/C][C]-218.200669642857[/C][/ROW]
[ROW][C]6[/C][C]1561.4[/C][C]1772.57209821429[/C][C]1346.925[/C][C]425.647098214286[/C][C]-211.172098214286[/C][/ROW]
[ROW][C]7[/C][C]1347.2[/C][C]1700.11316964286[/C][C]1407.175[/C][C]292.938169642857[/C][C]-352.913169642857[/C][/ROW]
[ROW][C]8[/C][C]1549.8[/C][C]1252.1890625[/C][C]1535.6875[/C][C]-283.4984375[/C][C]297.6109375[/C][/ROW]
[ROW][C]9[/C][C]1553.8[/C][C]1349.67566964286[/C][C]1784.7625[/C][C]-435.086830357143[/C][C]204.124330357143[/C][/ROW]
[ROW][C]10[/C][C]1822.5[/C][C]2431.75959821429[/C][C]2006.1125[/C][C]425.647098214286[/C][C]-609.259598214286[/C][/ROW]
[ROW][C]11[/C][C]3078.7[/C][C]2333.67566964286[/C][C]2040.7375[/C][C]292.938169642857[/C][C]745.024330357143[/C][/ROW]
[ROW][C]12[/C][C]1589.1[/C][C]1878.9015625[/C][C]2162.4[/C][C]-283.4984375[/C][C]-289.8015625[/C][/ROW]
[ROW][C]13[/C][C]1791.5[/C][C]1698.40066964286[/C][C]2133.4875[/C][C]-435.086830357143[/C][C]93.0993303571427[/C][/ROW]
[ROW][C]14[/C][C]2558.1[/C][C]2500.02209821429[/C][C]2074.375[/C][C]425.647098214286[/C][C]58.077901785714[/C][/ROW]
[ROW][C]15[/C][C]2111.8[/C][C]2461.63816964286[/C][C]2168.7[/C][C]292.938169642857[/C][C]-349.838169642857[/C][/ROW]
[ROW][C]16[/C][C]2083.1[/C][C]1878.4515625[/C][C]2161.95[/C][C]-283.4984375[/C][C]204.6484375[/C][/ROW]
[ROW][C]17[/C][C]2052.1[/C][C]1751.31316964286[/C][C]2186.4[/C][C]-435.086830357143[/C][C]300.786830357143[/C][/ROW]
[ROW][C]18[/C][C]2243.5[/C][C]2659.50959821429[/C][C]2233.8625[/C][C]425.647098214286[/C][C]-416.009598214285[/C][/ROW]
[ROW][C]19[/C][C]2622[/C][C]2355.08816964286[/C][C]2062.15[/C][C]292.938169642857[/C][C]266.911830357143[/C][/ROW]
[ROW][C]20[/C][C]1952.6[/C][C]1556.5390625[/C][C]1840.0375[/C][C]-283.4984375[/C][C]396.0609375[/C][/ROW]
[ROW][C]21[/C][C]808.9[/C][C]1208.25066964286[/C][C]1643.3375[/C][C]-435.086830357143[/C][C]-399.350669642857[/C][/ROW]
[ROW][C]22[/C][C]1709.8[/C][C]1803.12209821429[/C][C]1377.475[/C][C]425.647098214286[/C][C]-93.3220982142857[/C][/ROW]
[ROW][C]23[/C][C]1582.1[/C][C]1572.93816964286[/C][C]1280[/C][C]292.938169642857[/C][C]9.16183035714266[/C][/ROW]
[ROW][C]24[/C][C]865.6[/C][C]961.1015625[/C][C]1244.6[/C][C]-283.4984375[/C][C]-95.5015624999999[/C][/ROW]
[ROW][C]25[/C][C]1116.1[/C][C]831.700669642857[/C][C]1266.7875[/C][C]-435.086830357143[/C][C]284.399330357143[/C][/ROW]
[ROW][C]26[/C][C]1119.4[/C][C]1927.17209821429[/C][C]1501.525[/C][C]425.647098214286[/C][C]-807.772098214286[/C][/ROW]
[ROW][C]27[/C][C]2350[/C][C]2110.76316964286[/C][C]1817.825[/C][C]292.938169642857[/C][C]239.236830357143[/C][/ROW]
[ROW][C]28[/C][C]1975.6[/C][C]1920.0765625[/C][C]2203.575[/C][C]-283.4984375[/C][C]55.5234374999995[/C][/ROW]
[ROW][C]29[/C][C]2536.5[/C][C]2035.40066964286[/C][C]2470.4875[/C][C]-435.086830357143[/C][C]501.099330357143[/C][/ROW]
[ROW][C]30[/C][C]2785[/C][C]2936.58459821429[/C][C]2510.9375[/C][C]425.647098214286[/C][C]-151.584598214286[/C][/ROW]
[ROW][C]31[/C][C]2819.7[/C][C]2563.33816964286[/C][C]2270.4[/C][C]292.938169642857[/C][C]256.361830357142[/C][/ROW]
[ROW][C]32[/C][C]1829.5[/C][C]1781.7015625[/C][C]2065.2[/C][C]-283.4984375[/C][C]47.7984375000001[/C][/ROW]
[ROW][C]33[/C][C]758.3[/C][C]1604.97566964286[/C][C]2040.0625[/C][C]-435.086830357143[/C][C]-846.675669642857[/C][/ROW]
[ROW][C]34[/C][C]2921.6[/C][C]2431.39709821429[/C][C]2005.75[/C][C]425.647098214286[/C][C]490.202901785714[/C][/ROW]
[ROW][C]35[/C][C]2482[/C][C]2443.68816964286[/C][C]2150.75[/C][C]292.938169642857[/C][C]38.3118303571428[/C][/ROW]
[ROW][C]36[/C][C]1892.7[/C][C]1908.0640625[/C][C]2191.5625[/C][C]-283.4984375[/C][C]-15.3640624999998[/C][/ROW]
[ROW][C]37[/C][C]1855.1[/C][C]1555.21316964286[/C][C]1990.3[/C][C]-435.086830357143[/C][C]299.886830357143[/C][/ROW]
[ROW][C]38[/C][C]2151.3[/C][C]2279.44709821429[/C][C]1853.8[/C][C]425.647098214286[/C][C]-128.147098214286[/C][/ROW]
[ROW][C]39[/C][C]1642.2[/C][C]2054.08816964286[/C][C]1761.15[/C][C]292.938169642857[/C][C]-411.888169642857[/C][/ROW]
[ROW][C]40[/C][C]1640.5[/C][C]1339.2140625[/C][C]1622.7125[/C][C]-283.4984375[/C][C]301.2859375[/C][/ROW]
[ROW][C]41[/C][C]1366.1[/C][C]1008.08816964286[/C][C]1443.175[/C][C]-435.086830357143[/C][C]358.011830357143[/C][/ROW]
[ROW][C]42[/C][C]1532.8[/C][C]1496.69709821429[/C][C]1071.05[/C][C]425.647098214286[/C][C]36.1029017857143[/C][/ROW]
[ROW][C]43[/C][C]824.4[/C][C]801.013169642857[/C][C]508.075[/C][C]292.938169642857[/C][C]23.3868303571427[/C][/ROW]
[ROW][C]44[/C][C]-518.7[/C][C]-114.7859375[/C][C]168.7125[/C][C]-283.4984375[/C][C]-403.9140625[/C][/ROW]
[ROW][C]45[/C][C]-978.5[/C][C]-260.286830357143[/C][C]174.8[/C][C]-435.086830357143[/C][C]-718.213169642857[/C][/ROW]
[ROW][C]46[/C][C]1162.5[/C][C]867.597098214286[/C][C]441.95[/C][C]425.647098214286[/C][C]294.902901785714[/C][/ROW]
[ROW][C]47[/C][C]1243.4[/C][C]1182.36316964286[/C][C]889.425[/C][C]292.938169642857[/C][C]61.0368303571429[/C][/ROW]
[ROW][C]48[/C][C]1199.5[/C][C]872.9640625[/C][C]1156.4625[/C][C]-283.4984375[/C][C]326.5359375[/C][/ROW]
[ROW][C]49[/C][C]883.1[/C][C]667.100669642857[/C][C]1102.1875[/C][C]-435.086830357143[/C][C]215.999330357143[/C][/ROW]
[ROW][C]50[/C][C]1437.2[/C][C]1051.54709821429[/C][C]625.9[/C][C]425.647098214286[/C][C]385.652901785714[/C][/ROW]
[ROW][C]51[/C][C]534.5[/C][C]105.638169642857[/C][C]-187.3[/C][C]292.938169642857[/C][C]428.861830357143[/C][/ROW]
[ROW][C]52[/C][C]-1901.9[/C][C]-1291.1109375[/C][C]-1007.6125[/C][C]-283.4984375[/C][C]-610.7890625[/C][/ROW]
[ROW][C]53[/C][C]-2521.1[/C][C]-2291.11183035714[/C][C]-1856.025[/C][C]-435.086830357143[/C][C]-229.988169642857[/C][/ROW]
[ROW][C]54[/C][C]-1721.1[/C][C]-2108.11540178571[/C][C]-2533.7625[/C][C]425.647098214286[/C][C]387.015401785714[/C][/ROW]
[ROW][C]55[/C][C]-3094.5[/C][C]-2461.31183035714[/C][C]-2754.25[/C][C]292.938169642857[/C][C]-633.188169642857[/C][/ROW]
[ROW][C]56[/C][C]-3694.8[/C][C]-2877.0609375[/C][C]-2593.5625[/C][C]-283.4984375[/C][C]-817.7390625[/C][/ROW]
[ROW][C]57[/C][C]-2492.1[/C][C]-2563.03683035714[/C][C]-2127.95[/C][C]-435.086830357143[/C][C]70.9368303571428[/C][/ROW]
[ROW][C]58[/C][C]-464.6[/C][C]-1145.82790178571[/C][C]-1571.475[/C][C]425.647098214286[/C][C]681.227901785714[/C][/ROW]
[ROW][C]59[/C][C]-626.1[/C][C]NA[/C][C]NA[/C][C]292.938169642857[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]-1711.4[/C][C]NA[/C][C]NA[/C][C]-283.4984375[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115984&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115984&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	2540.9	NA	NA	-435.086830357143	NA
2	2370.3	NA	NA	425.647098214286	NA
3	1807.5	2212.05066964286	1919.1125	292.938169642857	-404.550669642857
4	1834.8	1315.2390625	1598.7375	-283.4984375	519.5609375
5	786.8	1005.00066964286	1440.0875	-435.086830357143	-218.200669642857
6	1561.4	1772.57209821429	1346.925	425.647098214286	-211.172098214286
7	1347.2	1700.11316964286	1407.175	292.938169642857	-352.913169642857
8	1549.8	1252.1890625	1535.6875	-283.4984375	297.6109375
9	1553.8	1349.67566964286	1784.7625	-435.086830357143	204.124330357143
10	1822.5	2431.75959821429	2006.1125	425.647098214286	-609.259598214286
11	3078.7	2333.67566964286	2040.7375	292.938169642857	745.024330357143
12	1589.1	1878.9015625	2162.4	-283.4984375	-289.8015625
13	1791.5	1698.40066964286	2133.4875	-435.086830357143	93.0993303571427
14	2558.1	2500.02209821429	2074.375	425.647098214286	58.077901785714
15	2111.8	2461.63816964286	2168.7	292.938169642857	-349.838169642857
16	2083.1	1878.4515625	2161.95	-283.4984375	204.6484375
17	2052.1	1751.31316964286	2186.4	-435.086830357143	300.786830357143
18	2243.5	2659.50959821429	2233.8625	425.647098214286	-416.009598214285
19	2622	2355.08816964286	2062.15	292.938169642857	266.911830357143
20	1952.6	1556.5390625	1840.0375	-283.4984375	396.0609375
21	808.9	1208.25066964286	1643.3375	-435.086830357143	-399.350669642857
22	1709.8	1803.12209821429	1377.475	425.647098214286	-93.3220982142857
23	1582.1	1572.93816964286	1280	292.938169642857	9.16183035714266
24	865.6	961.1015625	1244.6	-283.4984375	-95.5015624999999
25	1116.1	831.700669642857	1266.7875	-435.086830357143	284.399330357143
26	1119.4	1927.17209821429	1501.525	425.647098214286	-807.772098214286
27	2350	2110.76316964286	1817.825	292.938169642857	239.236830357143
28	1975.6	1920.0765625	2203.575	-283.4984375	55.5234374999995
29	2536.5	2035.40066964286	2470.4875	-435.086830357143	501.099330357143
30	2785	2936.58459821429	2510.9375	425.647098214286	-151.584598214286
31	2819.7	2563.33816964286	2270.4	292.938169642857	256.361830357142
32	1829.5	1781.7015625	2065.2	-283.4984375	47.7984375000001
33	758.3	1604.97566964286	2040.0625	-435.086830357143	-846.675669642857
34	2921.6	2431.39709821429	2005.75	425.647098214286	490.202901785714
35	2482	2443.68816964286	2150.75	292.938169642857	38.3118303571428
36	1892.7	1908.0640625	2191.5625	-283.4984375	-15.3640624999998
37	1855.1	1555.21316964286	1990.3	-435.086830357143	299.886830357143
38	2151.3	2279.44709821429	1853.8	425.647098214286	-128.147098214286
39	1642.2	2054.08816964286	1761.15	292.938169642857	-411.888169642857
40	1640.5	1339.2140625	1622.7125	-283.4984375	301.2859375
41	1366.1	1008.08816964286	1443.175	-435.086830357143	358.011830357143
42	1532.8	1496.69709821429	1071.05	425.647098214286	36.1029017857143
43	824.4	801.013169642857	508.075	292.938169642857	23.3868303571427
44	-518.7	-114.7859375	168.7125	-283.4984375	-403.9140625
45	-978.5	-260.286830357143	174.8	-435.086830357143	-718.213169642857
46	1162.5	867.597098214286	441.95	425.647098214286	294.902901785714
47	1243.4	1182.36316964286	889.425	292.938169642857	61.0368303571429
48	1199.5	872.9640625	1156.4625	-283.4984375	326.5359375
49	883.1	667.100669642857	1102.1875	-435.086830357143	215.999330357143
50	1437.2	1051.54709821429	625.9	425.647098214286	385.652901785714
51	534.5	105.638169642857	-187.3	292.938169642857	428.861830357143
52	-1901.9	-1291.1109375	-1007.6125	-283.4984375	-610.7890625
53	-2521.1	-2291.11183035714	-1856.025	-435.086830357143	-229.988169642857
54	-1721.1	-2108.11540178571	-2533.7625	425.647098214286	387.015401785714
55	-3094.5	-2461.31183035714	-2754.25	292.938169642857	-633.188169642857
56	-3694.8	-2877.0609375	-2593.5625	-283.4984375	-817.7390625
57	-2492.1	-2563.03683035714	-2127.95	-435.086830357143	70.9368303571428
58	-464.6	-1145.82790178571	-1571.475	425.647098214286	681.227901785714
59	-626.1	NA	NA	292.938169642857	NA
60	-1711.4	NA	NA	-283.4984375	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 = 4 ;

Parameters (R input):

par1 = additive ; par2 = 4 ;

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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
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