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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Tue, 26 Nov 2013 09:08:26 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/26/t13854749130wcoqlxiitrhj5c.htm/, Retrieved Fri, 09 May 2025 14:24:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=228837, Retrieved Fri, 09 May 2025 14:24:33 +0000

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Estimated Impact

176

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

-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- R P         [Univariate Data Series] [WS8 Time Series A...] [2010-11-30 09:10:08] [afe9379cca749d06b3d6872e02cc47ed]
- R PD          [Univariate Data Series] [] [2013-11-26 13:58:05] [fcc5c79267206050edde2804b94bb45f]
- RMP               [Classical Decomposition] [] [2013-11-26 14:08:26] [42d3493b68e44bd1b6f64c892e56509b] [Current]

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Dataseries X:

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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	4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=228837&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]4 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=228837&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=228837&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	4 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	37	NA	NA	11.4653	NA
2	30	NA	NA	-0.444444	NA
3	47	NA	NA	-13.3014	NA
4	35	NA	NA	1.97361	NA
5	30	NA	NA	-3.05139	NA
6	43	NA	NA	2.69861	NA
7	82	64.2431	51.625	12.6181	17.7569
8	40	50.5625	53.9167	-3.35417	-10.5625
9	47	57.4028	54.7083	2.69444	-10.4028
10	19	43.4514	56.2917	-12.8403	-24.4514
11	52	44.2014	59.7083	-15.5069	7.79861
12	136	79.7153	62.6667	17.0486	56.2847
13	80	77.2569	65.7917	11.4653	2.74306
14	42	68.9722	69.4167	-0.444444	-26.9722
15	54	59.9486	73.25	-13.3014	-5.94861
16	66	79.3486	77.375	1.97361	-13.3486
17	81	75.2819	78.3333	-3.05139	5.71806
18	63	76.8653	74.1667	2.69861	-13.8653
19	137	83.2431	70.625	12.6181	53.7569
20	72	68.6875	72.0417	-3.35417	3.3125
21	107	76.1944	73.5	2.69444	30.8056
22	58	61.4097	74.25	-12.8403	-3.40972
23	36	58.1181	73.625	-15.5069	-22.1181
24	52	90.6736	73.625	17.0486	-38.6736
25	79	84.2153	72.75	11.4653	-5.21528
26	77	69.8056	70.25	-0.444444	7.19444
27	54	54.7819	68.0833	-13.3014	-0.781944
28	84	67.9319	65.9583	1.97361	16.0681
29	48	62.4486	65.5	-3.05139	-14.4486
30	96	68.8653	66.1667	2.69861	27.1347
31	83	79.2847	66.6667	12.6181	3.71528
32	66	62.1458	65.5	-3.35417	3.85417
33	61	66.9444	64.25	2.69444	-5.94444
34	53	50.1181	62.9583	-12.8403	2.88194
35	30	47.5764	63.0833	-15.5069	-17.5764
36	74	81.2153	64.1667	17.0486	-7.21528
37	69	74.9653	63.5	11.4653	-5.96528
38	59	63.8472	64.2917	-0.444444	-4.84722
39	42	53.4903	66.7917	-13.3014	-11.4903
40	65	70.8069	68.8333	1.97361	-5.80694
41	70	68.8236	71.875	-3.05139	1.17639
42	100	77.6153	74.9167	2.69861	22.3847
43	63	90.8681	78.25	12.6181	-27.8681
44	105	78.6875	82.0417	-3.35417	26.3125
45	82	87.7778	85.0833	2.69444	-5.77778
46	81	74.1597	87	-12.8403	6.84028
47	75	71.7014	87.2083	-15.5069	3.29861
48	102	101.34	84.2917	17.0486	0.659722
49	121	91.9653	80.5	11.4653	29.0347
50	98	75.4722	75.9167	-0.444444	22.5278
51	76	57.4486	70.75	-13.3014	18.5514
52	77	68.8069	66.8333	1.97361	8.19306
53	63	60.0319	63.0833	-3.05139	2.96806
54	37	62.0736	59.375	2.69861	-25.0736
55	35	65.6597	53.0417	12.6181	-30.6597
56	23	42.5625	45.9167	-3.35417	-19.5625
57	40	43.0694	40.375	2.69444	-3.06944
58	29	22.6181	35.4583	-12.8403	6.38194
59	37	16.0764	31.5833	-15.5069	20.9236
60	51	46.0486	29	17.0486	4.95139
61	20	38.6736	27.2083	11.4653	-18.6736
62	28	25.5556	26	-0.444444	2.44444
63	13	11.2819	24.5833	-13.3014	1.71806
64	22	25.0569	23.0833	1.97361	-3.05694
65	25	18.3653	21.4167	-3.05139	6.63472
66	13	21.5319	18.8333	2.69861	-8.53194
67	16	30.2431	17.625	12.6181	-14.2431
68	13	13.8958	17.25	-3.35417	-0.895833
69	16	19.1528	16.4583	2.69444	-3.15278
70	17	2.78472	15.625	-12.8403	14.2153
71	9	-1.13194	14.375	-15.5069	10.1319
72	17	30.5486	13.5	17.0486	-13.5486
73	25	24.4653	13	11.4653	0.534722
74	14	11.8889	12.3333	-0.444444	2.11111
75	8	NA	NA	-13.3014	NA
76	7	NA	NA	1.97361	NA
77	10	NA	NA	-3.05139	NA
78	7	NA	NA	2.69861	NA
79	10	NA	NA	12.6181	NA
80	3	NA	NA	-3.35417	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 37 & NA & NA & 11.4653 & NA \tabularnewline
2 & 30 & NA & NA & -0.444444 & NA \tabularnewline
3 & 47 & NA & NA & -13.3014 & NA \tabularnewline
4 & 35 & NA & NA & 1.97361 & NA \tabularnewline
5 & 30 & NA & NA & -3.05139 & NA \tabularnewline
6 & 43 & NA & NA & 2.69861 & NA \tabularnewline
7 & 82 & 64.2431 & 51.625 & 12.6181 & 17.7569 \tabularnewline
8 & 40 & 50.5625 & 53.9167 & -3.35417 & -10.5625 \tabularnewline
9 & 47 & 57.4028 & 54.7083 & 2.69444 & -10.4028 \tabularnewline
10 & 19 & 43.4514 & 56.2917 & -12.8403 & -24.4514 \tabularnewline
11 & 52 & 44.2014 & 59.7083 & -15.5069 & 7.79861 \tabularnewline
12 & 136 & 79.7153 & 62.6667 & 17.0486 & 56.2847 \tabularnewline
13 & 80 & 77.2569 & 65.7917 & 11.4653 & 2.74306 \tabularnewline
14 & 42 & 68.9722 & 69.4167 & -0.444444 & -26.9722 \tabularnewline
15 & 54 & 59.9486 & 73.25 & -13.3014 & -5.94861 \tabularnewline
16 & 66 & 79.3486 & 77.375 & 1.97361 & -13.3486 \tabularnewline
17 & 81 & 75.2819 & 78.3333 & -3.05139 & 5.71806 \tabularnewline
18 & 63 & 76.8653 & 74.1667 & 2.69861 & -13.8653 \tabularnewline
19 & 137 & 83.2431 & 70.625 & 12.6181 & 53.7569 \tabularnewline
20 & 72 & 68.6875 & 72.0417 & -3.35417 & 3.3125 \tabularnewline
21 & 107 & 76.1944 & 73.5 & 2.69444 & 30.8056 \tabularnewline
22 & 58 & 61.4097 & 74.25 & -12.8403 & -3.40972 \tabularnewline
23 & 36 & 58.1181 & 73.625 & -15.5069 & -22.1181 \tabularnewline
24 & 52 & 90.6736 & 73.625 & 17.0486 & -38.6736 \tabularnewline
25 & 79 & 84.2153 & 72.75 & 11.4653 & -5.21528 \tabularnewline
26 & 77 & 69.8056 & 70.25 & -0.444444 & 7.19444 \tabularnewline
27 & 54 & 54.7819 & 68.0833 & -13.3014 & -0.781944 \tabularnewline
28 & 84 & 67.9319 & 65.9583 & 1.97361 & 16.0681 \tabularnewline
29 & 48 & 62.4486 & 65.5 & -3.05139 & -14.4486 \tabularnewline
30 & 96 & 68.8653 & 66.1667 & 2.69861 & 27.1347 \tabularnewline
31 & 83 & 79.2847 & 66.6667 & 12.6181 & 3.71528 \tabularnewline
32 & 66 & 62.1458 & 65.5 & -3.35417 & 3.85417 \tabularnewline
33 & 61 & 66.9444 & 64.25 & 2.69444 & -5.94444 \tabularnewline
34 & 53 & 50.1181 & 62.9583 & -12.8403 & 2.88194 \tabularnewline
35 & 30 & 47.5764 & 63.0833 & -15.5069 & -17.5764 \tabularnewline
36 & 74 & 81.2153 & 64.1667 & 17.0486 & -7.21528 \tabularnewline
37 & 69 & 74.9653 & 63.5 & 11.4653 & -5.96528 \tabularnewline
38 & 59 & 63.8472 & 64.2917 & -0.444444 & -4.84722 \tabularnewline
39 & 42 & 53.4903 & 66.7917 & -13.3014 & -11.4903 \tabularnewline
40 & 65 & 70.8069 & 68.8333 & 1.97361 & -5.80694 \tabularnewline
41 & 70 & 68.8236 & 71.875 & -3.05139 & 1.17639 \tabularnewline
42 & 100 & 77.6153 & 74.9167 & 2.69861 & 22.3847 \tabularnewline
43 & 63 & 90.8681 & 78.25 & 12.6181 & -27.8681 \tabularnewline
44 & 105 & 78.6875 & 82.0417 & -3.35417 & 26.3125 \tabularnewline
45 & 82 & 87.7778 & 85.0833 & 2.69444 & -5.77778 \tabularnewline
46 & 81 & 74.1597 & 87 & -12.8403 & 6.84028 \tabularnewline
47 & 75 & 71.7014 & 87.2083 & -15.5069 & 3.29861 \tabularnewline
48 & 102 & 101.34 & 84.2917 & 17.0486 & 0.659722 \tabularnewline
49 & 121 & 91.9653 & 80.5 & 11.4653 & 29.0347 \tabularnewline
50 & 98 & 75.4722 & 75.9167 & -0.444444 & 22.5278 \tabularnewline
51 & 76 & 57.4486 & 70.75 & -13.3014 & 18.5514 \tabularnewline
52 & 77 & 68.8069 & 66.8333 & 1.97361 & 8.19306 \tabularnewline
53 & 63 & 60.0319 & 63.0833 & -3.05139 & 2.96806 \tabularnewline
54 & 37 & 62.0736 & 59.375 & 2.69861 & -25.0736 \tabularnewline
55 & 35 & 65.6597 & 53.0417 & 12.6181 & -30.6597 \tabularnewline
56 & 23 & 42.5625 & 45.9167 & -3.35417 & -19.5625 \tabularnewline
57 & 40 & 43.0694 & 40.375 & 2.69444 & -3.06944 \tabularnewline
58 & 29 & 22.6181 & 35.4583 & -12.8403 & 6.38194 \tabularnewline
59 & 37 & 16.0764 & 31.5833 & -15.5069 & 20.9236 \tabularnewline
60 & 51 & 46.0486 & 29 & 17.0486 & 4.95139 \tabularnewline
61 & 20 & 38.6736 & 27.2083 & 11.4653 & -18.6736 \tabularnewline
62 & 28 & 25.5556 & 26 & -0.444444 & 2.44444 \tabularnewline
63 & 13 & 11.2819 & 24.5833 & -13.3014 & 1.71806 \tabularnewline
64 & 22 & 25.0569 & 23.0833 & 1.97361 & -3.05694 \tabularnewline
65 & 25 & 18.3653 & 21.4167 & -3.05139 & 6.63472 \tabularnewline
66 & 13 & 21.5319 & 18.8333 & 2.69861 & -8.53194 \tabularnewline
67 & 16 & 30.2431 & 17.625 & 12.6181 & -14.2431 \tabularnewline
68 & 13 & 13.8958 & 17.25 & -3.35417 & -0.895833 \tabularnewline
69 & 16 & 19.1528 & 16.4583 & 2.69444 & -3.15278 \tabularnewline
70 & 17 & 2.78472 & 15.625 & -12.8403 & 14.2153 \tabularnewline
71 & 9 & -1.13194 & 14.375 & -15.5069 & 10.1319 \tabularnewline
72 & 17 & 30.5486 & 13.5 & 17.0486 & -13.5486 \tabularnewline
73 & 25 & 24.4653 & 13 & 11.4653 & 0.534722 \tabularnewline
74 & 14 & 11.8889 & 12.3333 & -0.444444 & 2.11111 \tabularnewline
75 & 8 & NA & NA & -13.3014 & NA \tabularnewline
76 & 7 & NA & NA & 1.97361 & NA \tabularnewline
77 & 10 & NA & NA & -3.05139 & NA \tabularnewline
78 & 7 & NA & NA & 2.69861 & NA \tabularnewline
79 & 10 & NA & NA & 12.6181 & NA \tabularnewline
80 & 3 & NA & NA & -3.35417 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=228837&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]37[/C][C]NA[/C][C]NA[/C][C]11.4653[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]30[/C][C]NA[/C][C]NA[/C][C]-0.444444[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]47[/C][C]NA[/C][C]NA[/C][C]-13.3014[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]35[/C][C]NA[/C][C]NA[/C][C]1.97361[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]NA[/C][C]NA[/C][C]-3.05139[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]NA[/C][C]NA[/C][C]2.69861[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]82[/C][C]64.2431[/C][C]51.625[/C][C]12.6181[/C][C]17.7569[/C][/ROW]
[ROW][C]8[/C][C]40[/C][C]50.5625[/C][C]53.9167[/C][C]-3.35417[/C][C]-10.5625[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]57.4028[/C][C]54.7083[/C][C]2.69444[/C][C]-10.4028[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]43.4514[/C][C]56.2917[/C][C]-12.8403[/C][C]-24.4514[/C][/ROW]
[ROW][C]11[/C][C]52[/C][C]44.2014[/C][C]59.7083[/C][C]-15.5069[/C][C]7.79861[/C][/ROW]
[ROW][C]12[/C][C]136[/C][C]79.7153[/C][C]62.6667[/C][C]17.0486[/C][C]56.2847[/C][/ROW]
[ROW][C]13[/C][C]80[/C][C]77.2569[/C][C]65.7917[/C][C]11.4653[/C][C]2.74306[/C][/ROW]
[ROW][C]14[/C][C]42[/C][C]68.9722[/C][C]69.4167[/C][C]-0.444444[/C][C]-26.9722[/C][/ROW]
[ROW][C]15[/C][C]54[/C][C]59.9486[/C][C]73.25[/C][C]-13.3014[/C][C]-5.94861[/C][/ROW]
[ROW][C]16[/C][C]66[/C][C]79.3486[/C][C]77.375[/C][C]1.97361[/C][C]-13.3486[/C][/ROW]
[ROW][C]17[/C][C]81[/C][C]75.2819[/C][C]78.3333[/C][C]-3.05139[/C][C]5.71806[/C][/ROW]
[ROW][C]18[/C][C]63[/C][C]76.8653[/C][C]74.1667[/C][C]2.69861[/C][C]-13.8653[/C][/ROW]
[ROW][C]19[/C][C]137[/C][C]83.2431[/C][C]70.625[/C][C]12.6181[/C][C]53.7569[/C][/ROW]
[ROW][C]20[/C][C]72[/C][C]68.6875[/C][C]72.0417[/C][C]-3.35417[/C][C]3.3125[/C][/ROW]
[ROW][C]21[/C][C]107[/C][C]76.1944[/C][C]73.5[/C][C]2.69444[/C][C]30.8056[/C][/ROW]
[ROW][C]22[/C][C]58[/C][C]61.4097[/C][C]74.25[/C][C]-12.8403[/C][C]-3.40972[/C][/ROW]
[ROW][C]23[/C][C]36[/C][C]58.1181[/C][C]73.625[/C][C]-15.5069[/C][C]-22.1181[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]90.6736[/C][C]73.625[/C][C]17.0486[/C][C]-38.6736[/C][/ROW]
[ROW][C]25[/C][C]79[/C][C]84.2153[/C][C]72.75[/C][C]11.4653[/C][C]-5.21528[/C][/ROW]
[ROW][C]26[/C][C]77[/C][C]69.8056[/C][C]70.25[/C][C]-0.444444[/C][C]7.19444[/C][/ROW]
[ROW][C]27[/C][C]54[/C][C]54.7819[/C][C]68.0833[/C][C]-13.3014[/C][C]-0.781944[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]67.9319[/C][C]65.9583[/C][C]1.97361[/C][C]16.0681[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]62.4486[/C][C]65.5[/C][C]-3.05139[/C][C]-14.4486[/C][/ROW]
[ROW][C]30[/C][C]96[/C][C]68.8653[/C][C]66.1667[/C][C]2.69861[/C][C]27.1347[/C][/ROW]
[ROW][C]31[/C][C]83[/C][C]79.2847[/C][C]66.6667[/C][C]12.6181[/C][C]3.71528[/C][/ROW]
[ROW][C]32[/C][C]66[/C][C]62.1458[/C][C]65.5[/C][C]-3.35417[/C][C]3.85417[/C][/ROW]
[ROW][C]33[/C][C]61[/C][C]66.9444[/C][C]64.25[/C][C]2.69444[/C][C]-5.94444[/C][/ROW]
[ROW][C]34[/C][C]53[/C][C]50.1181[/C][C]62.9583[/C][C]-12.8403[/C][C]2.88194[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]47.5764[/C][C]63.0833[/C][C]-15.5069[/C][C]-17.5764[/C][/ROW]
[ROW][C]36[/C][C]74[/C][C]81.2153[/C][C]64.1667[/C][C]17.0486[/C][C]-7.21528[/C][/ROW]
[ROW][C]37[/C][C]69[/C][C]74.9653[/C][C]63.5[/C][C]11.4653[/C][C]-5.96528[/C][/ROW]
[ROW][C]38[/C][C]59[/C][C]63.8472[/C][C]64.2917[/C][C]-0.444444[/C][C]-4.84722[/C][/ROW]
[ROW][C]39[/C][C]42[/C][C]53.4903[/C][C]66.7917[/C][C]-13.3014[/C][C]-11.4903[/C][/ROW]
[ROW][C]40[/C][C]65[/C][C]70.8069[/C][C]68.8333[/C][C]1.97361[/C][C]-5.80694[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]68.8236[/C][C]71.875[/C][C]-3.05139[/C][C]1.17639[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]77.6153[/C][C]74.9167[/C][C]2.69861[/C][C]22.3847[/C][/ROW]
[ROW][C]43[/C][C]63[/C][C]90.8681[/C][C]78.25[/C][C]12.6181[/C][C]-27.8681[/C][/ROW]
[ROW][C]44[/C][C]105[/C][C]78.6875[/C][C]82.0417[/C][C]-3.35417[/C][C]26.3125[/C][/ROW]
[ROW][C]45[/C][C]82[/C][C]87.7778[/C][C]85.0833[/C][C]2.69444[/C][C]-5.77778[/C][/ROW]
[ROW][C]46[/C][C]81[/C][C]74.1597[/C][C]87[/C][C]-12.8403[/C][C]6.84028[/C][/ROW]
[ROW][C]47[/C][C]75[/C][C]71.7014[/C][C]87.2083[/C][C]-15.5069[/C][C]3.29861[/C][/ROW]
[ROW][C]48[/C][C]102[/C][C]101.34[/C][C]84.2917[/C][C]17.0486[/C][C]0.659722[/C][/ROW]
[ROW][C]49[/C][C]121[/C][C]91.9653[/C][C]80.5[/C][C]11.4653[/C][C]29.0347[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]75.4722[/C][C]75.9167[/C][C]-0.444444[/C][C]22.5278[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]57.4486[/C][C]70.75[/C][C]-13.3014[/C][C]18.5514[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]68.8069[/C][C]66.8333[/C][C]1.97361[/C][C]8.19306[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]60.0319[/C][C]63.0833[/C][C]-3.05139[/C][C]2.96806[/C][/ROW]
[ROW][C]54[/C][C]37[/C][C]62.0736[/C][C]59.375[/C][C]2.69861[/C][C]-25.0736[/C][/ROW]
[ROW][C]55[/C][C]35[/C][C]65.6597[/C][C]53.0417[/C][C]12.6181[/C][C]-30.6597[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]42.5625[/C][C]45.9167[/C][C]-3.35417[/C][C]-19.5625[/C][/ROW]
[ROW][C]57[/C][C]40[/C][C]43.0694[/C][C]40.375[/C][C]2.69444[/C][C]-3.06944[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]22.6181[/C][C]35.4583[/C][C]-12.8403[/C][C]6.38194[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]16.0764[/C][C]31.5833[/C][C]-15.5069[/C][C]20.9236[/C][/ROW]
[ROW][C]60[/C][C]51[/C][C]46.0486[/C][C]29[/C][C]17.0486[/C][C]4.95139[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]38.6736[/C][C]27.2083[/C][C]11.4653[/C][C]-18.6736[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]25.5556[/C][C]26[/C][C]-0.444444[/C][C]2.44444[/C][/ROW]
[ROW][C]63[/C][C]13[/C][C]11.2819[/C][C]24.5833[/C][C]-13.3014[/C][C]1.71806[/C][/ROW]
[ROW][C]64[/C][C]22[/C][C]25.0569[/C][C]23.0833[/C][C]1.97361[/C][C]-3.05694[/C][/ROW]
[ROW][C]65[/C][C]25[/C][C]18.3653[/C][C]21.4167[/C][C]-3.05139[/C][C]6.63472[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]21.5319[/C][C]18.8333[/C][C]2.69861[/C][C]-8.53194[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]30.2431[/C][C]17.625[/C][C]12.6181[/C][C]-14.2431[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]13.8958[/C][C]17.25[/C][C]-3.35417[/C][C]-0.895833[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]19.1528[/C][C]16.4583[/C][C]2.69444[/C][C]-3.15278[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]2.78472[/C][C]15.625[/C][C]-12.8403[/C][C]14.2153[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]-1.13194[/C][C]14.375[/C][C]-15.5069[/C][C]10.1319[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]30.5486[/C][C]13.5[/C][C]17.0486[/C][C]-13.5486[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]24.4653[/C][C]13[/C][C]11.4653[/C][C]0.534722[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]11.8889[/C][C]12.3333[/C][C]-0.444444[/C][C]2.11111[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]NA[/C][C]NA[/C][C]-13.3014[/C][C]NA[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]NA[/C][C]NA[/C][C]1.97361[/C][C]NA[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]NA[/C][C]NA[/C][C]-3.05139[/C][C]NA[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]NA[/C][C]NA[/C][C]2.69861[/C][C]NA[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]NA[/C][C]NA[/C][C]12.6181[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]NA[/C][C]NA[/C][C]-3.35417[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=228837&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=228837&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	37	NA	NA	11.4653	NA
2	30	NA	NA	-0.444444	NA
3	47	NA	NA	-13.3014	NA
4	35	NA	NA	1.97361	NA
5	30	NA	NA	-3.05139	NA
6	43	NA	NA	2.69861	NA
7	82	64.2431	51.625	12.6181	17.7569
8	40	50.5625	53.9167	-3.35417	-10.5625
9	47	57.4028	54.7083	2.69444	-10.4028
10	19	43.4514	56.2917	-12.8403	-24.4514
11	52	44.2014	59.7083	-15.5069	7.79861
12	136	79.7153	62.6667	17.0486	56.2847
13	80	77.2569	65.7917	11.4653	2.74306
14	42	68.9722	69.4167	-0.444444	-26.9722
15	54	59.9486	73.25	-13.3014	-5.94861
16	66	79.3486	77.375	1.97361	-13.3486
17	81	75.2819	78.3333	-3.05139	5.71806
18	63	76.8653	74.1667	2.69861	-13.8653
19	137	83.2431	70.625	12.6181	53.7569
20	72	68.6875	72.0417	-3.35417	3.3125
21	107	76.1944	73.5	2.69444	30.8056
22	58	61.4097	74.25	-12.8403	-3.40972
23	36	58.1181	73.625	-15.5069	-22.1181
24	52	90.6736	73.625	17.0486	-38.6736
25	79	84.2153	72.75	11.4653	-5.21528
26	77	69.8056	70.25	-0.444444	7.19444
27	54	54.7819	68.0833	-13.3014	-0.781944
28	84	67.9319	65.9583	1.97361	16.0681
29	48	62.4486	65.5	-3.05139	-14.4486
30	96	68.8653	66.1667	2.69861	27.1347
31	83	79.2847	66.6667	12.6181	3.71528
32	66	62.1458	65.5	-3.35417	3.85417
33	61	66.9444	64.25	2.69444	-5.94444
34	53	50.1181	62.9583	-12.8403	2.88194
35	30	47.5764	63.0833	-15.5069	-17.5764
36	74	81.2153	64.1667	17.0486	-7.21528
37	69	74.9653	63.5	11.4653	-5.96528
38	59	63.8472	64.2917	-0.444444	-4.84722
39	42	53.4903	66.7917	-13.3014	-11.4903
40	65	70.8069	68.8333	1.97361	-5.80694
41	70	68.8236	71.875	-3.05139	1.17639
42	100	77.6153	74.9167	2.69861	22.3847
43	63	90.8681	78.25	12.6181	-27.8681
44	105	78.6875	82.0417	-3.35417	26.3125
45	82	87.7778	85.0833	2.69444	-5.77778
46	81	74.1597	87	-12.8403	6.84028
47	75	71.7014	87.2083	-15.5069	3.29861
48	102	101.34	84.2917	17.0486	0.659722
49	121	91.9653	80.5	11.4653	29.0347
50	98	75.4722	75.9167	-0.444444	22.5278
51	76	57.4486	70.75	-13.3014	18.5514
52	77	68.8069	66.8333	1.97361	8.19306
53	63	60.0319	63.0833	-3.05139	2.96806
54	37	62.0736	59.375	2.69861	-25.0736
55	35	65.6597	53.0417	12.6181	-30.6597
56	23	42.5625	45.9167	-3.35417	-19.5625
57	40	43.0694	40.375	2.69444	-3.06944
58	29	22.6181	35.4583	-12.8403	6.38194
59	37	16.0764	31.5833	-15.5069	20.9236
60	51	46.0486	29	17.0486	4.95139
61	20	38.6736	27.2083	11.4653	-18.6736
62	28	25.5556	26	-0.444444	2.44444
63	13	11.2819	24.5833	-13.3014	1.71806
64	22	25.0569	23.0833	1.97361	-3.05694
65	25	18.3653	21.4167	-3.05139	6.63472
66	13	21.5319	18.8333	2.69861	-8.53194
67	16	30.2431	17.625	12.6181	-14.2431
68	13	13.8958	17.25	-3.35417	-0.895833
69	16	19.1528	16.4583	2.69444	-3.15278
70	17	2.78472	15.625	-12.8403	14.2153
71	9	-1.13194	14.375	-15.5069	10.1319
72	17	30.5486	13.5	17.0486	-13.5486
73	25	24.4653	13	11.4653	0.534722
74	14	11.8889	12.3333	-0.444444	2.11111
75	8	NA	NA	-13.3014	NA
76	7	NA	NA	1.97361	NA
77	10	NA	NA	-3.05139	NA
78	7	NA	NA	2.69861	NA
79	10	NA	NA	12.6181	NA
80	3	NA	NA	-3.35417	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 <- 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