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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Sun, 26 Dec 2010 10:47:22 +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/26/t12933605979toyj30uhryyyev.htm/, Retrieved Mon, 06 May 2024 17:53:24 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115508, Retrieved Mon, 06 May 2024 17:53:24 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Bouwgrondprijzen per kwartaal (1995-2009) : gemiddelde prijs(€/m²)

Estimated Impact

154

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] [f6fdc0236f011c1845380977efc505f8] [Current]
-    D        [Classical Decomposition] [Paper Statistiek ] [2010-12-27 13:16:37] [82c18f3ebe9df70882495121eb816e07] 

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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	'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115508&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115508&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115508&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	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	26	NA	NA	0.495535714285714	NA
2	26	NA	NA	0.0223214285714286	NA
3	27	27.5848214285714	26.875	0.709821428571429	-0.584821428571427
4	28	26.1473214285714	27.375	-1.22767857142857	1.85267857142857
5	27	28.2455357142857	27.75	0.495535714285714	-1.24553571428572
6	29	28.0223214285714	28	0.0223214285714286	0.977678571428573
7	27	28.9598214285714	28.25	0.709821428571429	-1.95982142857143
8	30	27.1473214285714	28.375	-1.22767857142857	2.85267857142857
9	27	29.6205357142857	29.125	0.495535714285714	-2.62053571428572
10	30	29.7723214285714	29.75	0.0223214285714286	0.227678571428573
11	32	31.0848214285714	30.375	0.709821428571429	0.915178571428573
12	30	30.1473214285714	31.375	-1.22767857142857	-0.147321428571427
13	32	32.4955357142857	32	0.495535714285714	-0.495535714285715
14	33	32.5223214285714	32.5	0.0223214285714286	0.477678571428569
15	34	33.7098214285714	33	0.709821428571429	0.290178571428569
16	32	32.5223214285714	33.75	-1.22767857142857	-0.522321428571431
17	34	35.1205357142857	34.625	0.495535714285714	-1.12053571428572
18	37	35.5223214285714	35.5	0.0223214285714286	1.47767857142857
19	37	36.7098214285714	36	0.709821428571429	0.290178571428569
20	36	34.8973214285714	36.125	-1.22767857142857	1.10267857142857
21	34	37.2455357142857	36.75	0.495535714285714	-3.24553571428572
22	38	37.8973214285714	37.875	0.0223214285714286	0.102678571428569
23	41	40.4598214285714	39.75	0.709821428571429	0.540178571428569
24	41	40.2723214285714	41.5	-1.22767857142857	0.72767857142857
25	44	42.9955357142857	42.5	0.495535714285714	1.00446428571428
26	42	43.5223214285714	43.5	0.0223214285714286	-1.52232142857143
27	45	45.3348214285714	44.625	0.709821428571429	-0.334821428571431
28	45	45.5223214285714	46.75	-1.22767857142857	-0.522321428571431
29	49	49.6205357142857	49.125	0.495535714285714	-0.620535714285715
30	54	51.0223214285714	51	0.0223214285714286	2.97767857142857
31	52	52.9598214285714	52.25	0.709821428571429	-0.95982142857143
32	53	51.3973214285714	52.625	-1.22767857142857	1.60267857142857
33	51	54.2455357142857	53.75	0.495535714285714	-3.24553571428572
34	55	55.6473214285714	55.625	0.0223214285714286	-0.64732142857143
35	60	58.7098214285714	58	0.709821428571429	1.29017857142857
36	60	58.8973214285714	60.125	-1.22767857142857	1.10267857142857
37	63	61.7455357142857	61.25	0.495535714285714	1.25446428571428
38	60	62.3973214285714	62.375	0.0223214285714286	-2.39732142857143
39	64	65.2098214285714	64.5	0.709821428571429	-1.20982142857143
40	65	66.0223214285714	67.25	-1.22767857142857	-1.02232142857143
41	75	69.9955357142857	69.5	0.495535714285714	5.00446428571429
42	70	71.0223214285714	71	0.0223214285714286	-1.02232142857143
43	72	72.2098214285714	71.5	0.709821428571429	-0.209821428571431
44	69	70.7723214285714	72	-1.22767857142857	-1.77232142857143
45	75	73.2455357142857	72.75	0.495535714285714	1.75446428571429
46	74	73.7723214285714	73.75	0.0223214285714286	0.227678571428569
47	74	75.7098214285714	75	0.709821428571429	-1.70982142857143
48	75	74.8973214285714	76.125	-1.22767857142857	0.102678571428569
49	79	78.6205357142857	78.125	0.495535714285714	0.379464285714292
50	79	79.8973214285714	79.875	0.0223214285714286	-0.89732142857143
51	85	81.5848214285714	80.875	0.709821428571429	3.41517857142857
52	78	81.0223214285714	82.25	-1.22767857142857	-3.02232142857143
53	84	83.4955357142857	83	0.495535714285714	0.504464285714292
54	85	83.5223214285714	83.5	0.0223214285714286	1.47767857142857
55	85	85.5848214285714	84.875	0.709821428571429	-0.58482142857143
56	82	85.1473214285714	86.375	-1.22767857142857	-3.14732142857143
57	91	89.1205357142857	88.625	0.495535714285714	1.87946428571429
58	90	92.2723214285714	92.25	0.0223214285714286	-2.27232142857143
59	98	NA	NA	0.709821428571429	NA
60	98	NA	NA	-1.22767857142857	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 26 & NA & NA & 0.495535714285714 & NA \tabularnewline
2 & 26 & NA & NA & 0.0223214285714286 & NA \tabularnewline
3 & 27 & 27.5848214285714 & 26.875 & 0.709821428571429 & -0.584821428571427 \tabularnewline
4 & 28 & 26.1473214285714 & 27.375 & -1.22767857142857 & 1.85267857142857 \tabularnewline
5 & 27 & 28.2455357142857 & 27.75 & 0.495535714285714 & -1.24553571428572 \tabularnewline
6 & 29 & 28.0223214285714 & 28 & 0.0223214285714286 & 0.977678571428573 \tabularnewline
7 & 27 & 28.9598214285714 & 28.25 & 0.709821428571429 & -1.95982142857143 \tabularnewline
8 & 30 & 27.1473214285714 & 28.375 & -1.22767857142857 & 2.85267857142857 \tabularnewline
9 & 27 & 29.6205357142857 & 29.125 & 0.495535714285714 & -2.62053571428572 \tabularnewline
10 & 30 & 29.7723214285714 & 29.75 & 0.0223214285714286 & 0.227678571428573 \tabularnewline
11 & 32 & 31.0848214285714 & 30.375 & 0.709821428571429 & 0.915178571428573 \tabularnewline
12 & 30 & 30.1473214285714 & 31.375 & -1.22767857142857 & -0.147321428571427 \tabularnewline
13 & 32 & 32.4955357142857 & 32 & 0.495535714285714 & -0.495535714285715 \tabularnewline
14 & 33 & 32.5223214285714 & 32.5 & 0.0223214285714286 & 0.477678571428569 \tabularnewline
15 & 34 & 33.7098214285714 & 33 & 0.709821428571429 & 0.290178571428569 \tabularnewline
16 & 32 & 32.5223214285714 & 33.75 & -1.22767857142857 & -0.522321428571431 \tabularnewline
17 & 34 & 35.1205357142857 & 34.625 & 0.495535714285714 & -1.12053571428572 \tabularnewline
18 & 37 & 35.5223214285714 & 35.5 & 0.0223214285714286 & 1.47767857142857 \tabularnewline
19 & 37 & 36.7098214285714 & 36 & 0.709821428571429 & 0.290178571428569 \tabularnewline
20 & 36 & 34.8973214285714 & 36.125 & -1.22767857142857 & 1.10267857142857 \tabularnewline
21 & 34 & 37.2455357142857 & 36.75 & 0.495535714285714 & -3.24553571428572 \tabularnewline
22 & 38 & 37.8973214285714 & 37.875 & 0.0223214285714286 & 0.102678571428569 \tabularnewline
23 & 41 & 40.4598214285714 & 39.75 & 0.709821428571429 & 0.540178571428569 \tabularnewline
24 & 41 & 40.2723214285714 & 41.5 & -1.22767857142857 & 0.72767857142857 \tabularnewline
25 & 44 & 42.9955357142857 & 42.5 & 0.495535714285714 & 1.00446428571428 \tabularnewline
26 & 42 & 43.5223214285714 & 43.5 & 0.0223214285714286 & -1.52232142857143 \tabularnewline
27 & 45 & 45.3348214285714 & 44.625 & 0.709821428571429 & -0.334821428571431 \tabularnewline
28 & 45 & 45.5223214285714 & 46.75 & -1.22767857142857 & -0.522321428571431 \tabularnewline
29 & 49 & 49.6205357142857 & 49.125 & 0.495535714285714 & -0.620535714285715 \tabularnewline
30 & 54 & 51.0223214285714 & 51 & 0.0223214285714286 & 2.97767857142857 \tabularnewline
31 & 52 & 52.9598214285714 & 52.25 & 0.709821428571429 & -0.95982142857143 \tabularnewline
32 & 53 & 51.3973214285714 & 52.625 & -1.22767857142857 & 1.60267857142857 \tabularnewline
33 & 51 & 54.2455357142857 & 53.75 & 0.495535714285714 & -3.24553571428572 \tabularnewline
34 & 55 & 55.6473214285714 & 55.625 & 0.0223214285714286 & -0.64732142857143 \tabularnewline
35 & 60 & 58.7098214285714 & 58 & 0.709821428571429 & 1.29017857142857 \tabularnewline
36 & 60 & 58.8973214285714 & 60.125 & -1.22767857142857 & 1.10267857142857 \tabularnewline
37 & 63 & 61.7455357142857 & 61.25 & 0.495535714285714 & 1.25446428571428 \tabularnewline
38 & 60 & 62.3973214285714 & 62.375 & 0.0223214285714286 & -2.39732142857143 \tabularnewline
39 & 64 & 65.2098214285714 & 64.5 & 0.709821428571429 & -1.20982142857143 \tabularnewline
40 & 65 & 66.0223214285714 & 67.25 & -1.22767857142857 & -1.02232142857143 \tabularnewline
41 & 75 & 69.9955357142857 & 69.5 & 0.495535714285714 & 5.00446428571429 \tabularnewline
42 & 70 & 71.0223214285714 & 71 & 0.0223214285714286 & -1.02232142857143 \tabularnewline
43 & 72 & 72.2098214285714 & 71.5 & 0.709821428571429 & -0.209821428571431 \tabularnewline
44 & 69 & 70.7723214285714 & 72 & -1.22767857142857 & -1.77232142857143 \tabularnewline
45 & 75 & 73.2455357142857 & 72.75 & 0.495535714285714 & 1.75446428571429 \tabularnewline
46 & 74 & 73.7723214285714 & 73.75 & 0.0223214285714286 & 0.227678571428569 \tabularnewline
47 & 74 & 75.7098214285714 & 75 & 0.709821428571429 & -1.70982142857143 \tabularnewline
48 & 75 & 74.8973214285714 & 76.125 & -1.22767857142857 & 0.102678571428569 \tabularnewline
49 & 79 & 78.6205357142857 & 78.125 & 0.495535714285714 & 0.379464285714292 \tabularnewline
50 & 79 & 79.8973214285714 & 79.875 & 0.0223214285714286 & -0.89732142857143 \tabularnewline
51 & 85 & 81.5848214285714 & 80.875 & 0.709821428571429 & 3.41517857142857 \tabularnewline
52 & 78 & 81.0223214285714 & 82.25 & -1.22767857142857 & -3.02232142857143 \tabularnewline
53 & 84 & 83.4955357142857 & 83 & 0.495535714285714 & 0.504464285714292 \tabularnewline
54 & 85 & 83.5223214285714 & 83.5 & 0.0223214285714286 & 1.47767857142857 \tabularnewline
55 & 85 & 85.5848214285714 & 84.875 & 0.709821428571429 & -0.58482142857143 \tabularnewline
56 & 82 & 85.1473214285714 & 86.375 & -1.22767857142857 & -3.14732142857143 \tabularnewline
57 & 91 & 89.1205357142857 & 88.625 & 0.495535714285714 & 1.87946428571429 \tabularnewline
58 & 90 & 92.2723214285714 & 92.25 & 0.0223214285714286 & -2.27232142857143 \tabularnewline
59 & 98 & NA & NA & 0.709821428571429 & NA \tabularnewline
60 & 98 & NA & NA & -1.22767857142857 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115508&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]26[/C][C]NA[/C][C]NA[/C][C]0.495535714285714[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]26[/C][C]NA[/C][C]NA[/C][C]0.0223214285714286[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]27[/C][C]27.5848214285714[/C][C]26.875[/C][C]0.709821428571429[/C][C]-0.584821428571427[/C][/ROW]
[ROW][C]4[/C][C]28[/C][C]26.1473214285714[/C][C]27.375[/C][C]-1.22767857142857[/C][C]1.85267857142857[/C][/ROW]
[ROW][C]5[/C][C]27[/C][C]28.2455357142857[/C][C]27.75[/C][C]0.495535714285714[/C][C]-1.24553571428572[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]28.0223214285714[/C][C]28[/C][C]0.0223214285714286[/C][C]0.977678571428573[/C][/ROW]
[ROW][C]7[/C][C]27[/C][C]28.9598214285714[/C][C]28.25[/C][C]0.709821428571429[/C][C]-1.95982142857143[/C][/ROW]
[ROW][C]8[/C][C]30[/C][C]27.1473214285714[/C][C]28.375[/C][C]-1.22767857142857[/C][C]2.85267857142857[/C][/ROW]
[ROW][C]9[/C][C]27[/C][C]29.6205357142857[/C][C]29.125[/C][C]0.495535714285714[/C][C]-2.62053571428572[/C][/ROW]
[ROW][C]10[/C][C]30[/C][C]29.7723214285714[/C][C]29.75[/C][C]0.0223214285714286[/C][C]0.227678571428573[/C][/ROW]
[ROW][C]11[/C][C]32[/C][C]31.0848214285714[/C][C]30.375[/C][C]0.709821428571429[/C][C]0.915178571428573[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]30.1473214285714[/C][C]31.375[/C][C]-1.22767857142857[/C][C]-0.147321428571427[/C][/ROW]
[ROW][C]13[/C][C]32[/C][C]32.4955357142857[/C][C]32[/C][C]0.495535714285714[/C][C]-0.495535714285715[/C][/ROW]
[ROW][C]14[/C][C]33[/C][C]32.5223214285714[/C][C]32.5[/C][C]0.0223214285714286[/C][C]0.477678571428569[/C][/ROW]
[ROW][C]15[/C][C]34[/C][C]33.7098214285714[/C][C]33[/C][C]0.709821428571429[/C][C]0.290178571428569[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]32.5223214285714[/C][C]33.75[/C][C]-1.22767857142857[/C][C]-0.522321428571431[/C][/ROW]
[ROW][C]17[/C][C]34[/C][C]35.1205357142857[/C][C]34.625[/C][C]0.495535714285714[/C][C]-1.12053571428572[/C][/ROW]
[ROW][C]18[/C][C]37[/C][C]35.5223214285714[/C][C]35.5[/C][C]0.0223214285714286[/C][C]1.47767857142857[/C][/ROW]
[ROW][C]19[/C][C]37[/C][C]36.7098214285714[/C][C]36[/C][C]0.709821428571429[/C][C]0.290178571428569[/C][/ROW]
[ROW][C]20[/C][C]36[/C][C]34.8973214285714[/C][C]36.125[/C][C]-1.22767857142857[/C][C]1.10267857142857[/C][/ROW]
[ROW][C]21[/C][C]34[/C][C]37.2455357142857[/C][C]36.75[/C][C]0.495535714285714[/C][C]-3.24553571428572[/C][/ROW]
[ROW][C]22[/C][C]38[/C][C]37.8973214285714[/C][C]37.875[/C][C]0.0223214285714286[/C][C]0.102678571428569[/C][/ROW]
[ROW][C]23[/C][C]41[/C][C]40.4598214285714[/C][C]39.75[/C][C]0.709821428571429[/C][C]0.540178571428569[/C][/ROW]
[ROW][C]24[/C][C]41[/C][C]40.2723214285714[/C][C]41.5[/C][C]-1.22767857142857[/C][C]0.72767857142857[/C][/ROW]
[ROW][C]25[/C][C]44[/C][C]42.9955357142857[/C][C]42.5[/C][C]0.495535714285714[/C][C]1.00446428571428[/C][/ROW]
[ROW][C]26[/C][C]42[/C][C]43.5223214285714[/C][C]43.5[/C][C]0.0223214285714286[/C][C]-1.52232142857143[/C][/ROW]
[ROW][C]27[/C][C]45[/C][C]45.3348214285714[/C][C]44.625[/C][C]0.709821428571429[/C][C]-0.334821428571431[/C][/ROW]
[ROW][C]28[/C][C]45[/C][C]45.5223214285714[/C][C]46.75[/C][C]-1.22767857142857[/C][C]-0.522321428571431[/C][/ROW]
[ROW][C]29[/C][C]49[/C][C]49.6205357142857[/C][C]49.125[/C][C]0.495535714285714[/C][C]-0.620535714285715[/C][/ROW]
[ROW][C]30[/C][C]54[/C][C]51.0223214285714[/C][C]51[/C][C]0.0223214285714286[/C][C]2.97767857142857[/C][/ROW]
[ROW][C]31[/C][C]52[/C][C]52.9598214285714[/C][C]52.25[/C][C]0.709821428571429[/C][C]-0.95982142857143[/C][/ROW]
[ROW][C]32[/C][C]53[/C][C]51.3973214285714[/C][C]52.625[/C][C]-1.22767857142857[/C][C]1.60267857142857[/C][/ROW]
[ROW][C]33[/C][C]51[/C][C]54.2455357142857[/C][C]53.75[/C][C]0.495535714285714[/C][C]-3.24553571428572[/C][/ROW]
[ROW][C]34[/C][C]55[/C][C]55.6473214285714[/C][C]55.625[/C][C]0.0223214285714286[/C][C]-0.64732142857143[/C][/ROW]
[ROW][C]35[/C][C]60[/C][C]58.7098214285714[/C][C]58[/C][C]0.709821428571429[/C][C]1.29017857142857[/C][/ROW]
[ROW][C]36[/C][C]60[/C][C]58.8973214285714[/C][C]60.125[/C][C]-1.22767857142857[/C][C]1.10267857142857[/C][/ROW]
[ROW][C]37[/C][C]63[/C][C]61.7455357142857[/C][C]61.25[/C][C]0.495535714285714[/C][C]1.25446428571428[/C][/ROW]
[ROW][C]38[/C][C]60[/C][C]62.3973214285714[/C][C]62.375[/C][C]0.0223214285714286[/C][C]-2.39732142857143[/C][/ROW]
[ROW][C]39[/C][C]64[/C][C]65.2098214285714[/C][C]64.5[/C][C]0.709821428571429[/C][C]-1.20982142857143[/C][/ROW]
[ROW][C]40[/C][C]65[/C][C]66.0223214285714[/C][C]67.25[/C][C]-1.22767857142857[/C][C]-1.02232142857143[/C][/ROW]
[ROW][C]41[/C][C]75[/C][C]69.9955357142857[/C][C]69.5[/C][C]0.495535714285714[/C][C]5.00446428571429[/C][/ROW]
[ROW][C]42[/C][C]70[/C][C]71.0223214285714[/C][C]71[/C][C]0.0223214285714286[/C][C]-1.02232142857143[/C][/ROW]
[ROW][C]43[/C][C]72[/C][C]72.2098214285714[/C][C]71.5[/C][C]0.709821428571429[/C][C]-0.209821428571431[/C][/ROW]
[ROW][C]44[/C][C]69[/C][C]70.7723214285714[/C][C]72[/C][C]-1.22767857142857[/C][C]-1.77232142857143[/C][/ROW]
[ROW][C]45[/C][C]75[/C][C]73.2455357142857[/C][C]72.75[/C][C]0.495535714285714[/C][C]1.75446428571429[/C][/ROW]
[ROW][C]46[/C][C]74[/C][C]73.7723214285714[/C][C]73.75[/C][C]0.0223214285714286[/C][C]0.227678571428569[/C][/ROW]
[ROW][C]47[/C][C]74[/C][C]75.7098214285714[/C][C]75[/C][C]0.709821428571429[/C][C]-1.70982142857143[/C][/ROW]
[ROW][C]48[/C][C]75[/C][C]74.8973214285714[/C][C]76.125[/C][C]-1.22767857142857[/C][C]0.102678571428569[/C][/ROW]
[ROW][C]49[/C][C]79[/C][C]78.6205357142857[/C][C]78.125[/C][C]0.495535714285714[/C][C]0.379464285714292[/C][/ROW]
[ROW][C]50[/C][C]79[/C][C]79.8973214285714[/C][C]79.875[/C][C]0.0223214285714286[/C][C]-0.89732142857143[/C][/ROW]
[ROW][C]51[/C][C]85[/C][C]81.5848214285714[/C][C]80.875[/C][C]0.709821428571429[/C][C]3.41517857142857[/C][/ROW]
[ROW][C]52[/C][C]78[/C][C]81.0223214285714[/C][C]82.25[/C][C]-1.22767857142857[/C][C]-3.02232142857143[/C][/ROW]
[ROW][C]53[/C][C]84[/C][C]83.4955357142857[/C][C]83[/C][C]0.495535714285714[/C][C]0.504464285714292[/C][/ROW]
[ROW][C]54[/C][C]85[/C][C]83.5223214285714[/C][C]83.5[/C][C]0.0223214285714286[/C][C]1.47767857142857[/C][/ROW]
[ROW][C]55[/C][C]85[/C][C]85.5848214285714[/C][C]84.875[/C][C]0.709821428571429[/C][C]-0.58482142857143[/C][/ROW]
[ROW][C]56[/C][C]82[/C][C]85.1473214285714[/C][C]86.375[/C][C]-1.22767857142857[/C][C]-3.14732142857143[/C][/ROW]
[ROW][C]57[/C][C]91[/C][C]89.1205357142857[/C][C]88.625[/C][C]0.495535714285714[/C][C]1.87946428571429[/C][/ROW]
[ROW][C]58[/C][C]90[/C][C]92.2723214285714[/C][C]92.25[/C][C]0.0223214285714286[/C][C]-2.27232142857143[/C][/ROW]
[ROW][C]59[/C][C]98[/C][C]NA[/C][C]NA[/C][C]0.709821428571429[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]98[/C][C]NA[/C][C]NA[/C][C]-1.22767857142857[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115508&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115508&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	26	NA	NA	0.495535714285714	NA
2	26	NA	NA	0.0223214285714286	NA
3	27	27.5848214285714	26.875	0.709821428571429	-0.584821428571427
4	28	26.1473214285714	27.375	-1.22767857142857	1.85267857142857
5	27	28.2455357142857	27.75	0.495535714285714	-1.24553571428572
6	29	28.0223214285714	28	0.0223214285714286	0.977678571428573
7	27	28.9598214285714	28.25	0.709821428571429	-1.95982142857143
8	30	27.1473214285714	28.375	-1.22767857142857	2.85267857142857
9	27	29.6205357142857	29.125	0.495535714285714	-2.62053571428572
10	30	29.7723214285714	29.75	0.0223214285714286	0.227678571428573
11	32	31.0848214285714	30.375	0.709821428571429	0.915178571428573
12	30	30.1473214285714	31.375	-1.22767857142857	-0.147321428571427
13	32	32.4955357142857	32	0.495535714285714	-0.495535714285715
14	33	32.5223214285714	32.5	0.0223214285714286	0.477678571428569
15	34	33.7098214285714	33	0.709821428571429	0.290178571428569
16	32	32.5223214285714	33.75	-1.22767857142857	-0.522321428571431
17	34	35.1205357142857	34.625	0.495535714285714	-1.12053571428572
18	37	35.5223214285714	35.5	0.0223214285714286	1.47767857142857
19	37	36.7098214285714	36	0.709821428571429	0.290178571428569
20	36	34.8973214285714	36.125	-1.22767857142857	1.10267857142857
21	34	37.2455357142857	36.75	0.495535714285714	-3.24553571428572
22	38	37.8973214285714	37.875	0.0223214285714286	0.102678571428569
23	41	40.4598214285714	39.75	0.709821428571429	0.540178571428569
24	41	40.2723214285714	41.5	-1.22767857142857	0.72767857142857
25	44	42.9955357142857	42.5	0.495535714285714	1.00446428571428
26	42	43.5223214285714	43.5	0.0223214285714286	-1.52232142857143
27	45	45.3348214285714	44.625	0.709821428571429	-0.334821428571431
28	45	45.5223214285714	46.75	-1.22767857142857	-0.522321428571431
29	49	49.6205357142857	49.125	0.495535714285714	-0.620535714285715
30	54	51.0223214285714	51	0.0223214285714286	2.97767857142857
31	52	52.9598214285714	52.25	0.709821428571429	-0.95982142857143
32	53	51.3973214285714	52.625	-1.22767857142857	1.60267857142857
33	51	54.2455357142857	53.75	0.495535714285714	-3.24553571428572
34	55	55.6473214285714	55.625	0.0223214285714286	-0.64732142857143
35	60	58.7098214285714	58	0.709821428571429	1.29017857142857
36	60	58.8973214285714	60.125	-1.22767857142857	1.10267857142857
37	63	61.7455357142857	61.25	0.495535714285714	1.25446428571428
38	60	62.3973214285714	62.375	0.0223214285714286	-2.39732142857143
39	64	65.2098214285714	64.5	0.709821428571429	-1.20982142857143
40	65	66.0223214285714	67.25	-1.22767857142857	-1.02232142857143
41	75	69.9955357142857	69.5	0.495535714285714	5.00446428571429
42	70	71.0223214285714	71	0.0223214285714286	-1.02232142857143
43	72	72.2098214285714	71.5	0.709821428571429	-0.209821428571431
44	69	70.7723214285714	72	-1.22767857142857	-1.77232142857143
45	75	73.2455357142857	72.75	0.495535714285714	1.75446428571429
46	74	73.7723214285714	73.75	0.0223214285714286	0.227678571428569
47	74	75.7098214285714	75	0.709821428571429	-1.70982142857143
48	75	74.8973214285714	76.125	-1.22767857142857	0.102678571428569
49	79	78.6205357142857	78.125	0.495535714285714	0.379464285714292
50	79	79.8973214285714	79.875	0.0223214285714286	-0.89732142857143
51	85	81.5848214285714	80.875	0.709821428571429	3.41517857142857
52	78	81.0223214285714	82.25	-1.22767857142857	-3.02232142857143
53	84	83.4955357142857	83	0.495535714285714	0.504464285714292
54	85	83.5223214285714	83.5	0.0223214285714286	1.47767857142857
55	85	85.5848214285714	84.875	0.709821428571429	-0.58482142857143
56	82	85.1473214285714	86.375	-1.22767857142857	-3.14732142857143
57	91	89.1205357142857	88.625	0.495535714285714	1.87946428571429
58	90	92.2723214285714	92.25	0.0223214285714286	-2.27232142857143
59	98	NA	NA	0.709821428571429	NA
60	98	NA	NA	-1.22767857142857	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