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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Thu, 26 Nov 2015 11:53:14 +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/2015/Nov/26/t1448539089dh14oldypnusbso.htm/, Retrieved Tue, 14 May 2024 20:58:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284195, Retrieved Tue, 14 May 2024 20:58:14 +0000

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Estimated Impact

106

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

-       [Classical Decomposition] [] [2015-11-26 11:53:14] [cb8108074d5ede30ed5e3c15decd01d7] [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' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284195&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' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284195&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284195&T=0

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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' @ yule.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	143.7	NA	NA	17.086	NA
2	149.3	NA	NA	32.956	NA
3	121.7	NA	NA	10.6877	NA
4	81	NA	NA	-16.8865	NA
5	68.1	NA	NA	-21.7781	NA
6	92.3	NA	NA	-10.6123	NA
7	107.7	103.942	102.554	1.38771	3.75813
8	114.4	112.819	101.287	11.531	1.58146
9	98.6	106.959	100.937	6.02187	-8.35937
10	106.7	106.652	100.867	5.78521	0.048125
11	73.9	79.4227	100.929	-21.5065	-5.52271
12	85.9	86.5652	101.237	-14.6723	-0.665208
13	118.4	118.04	100.954	17.086	0.359792
14	144.2	133.044	100.087	32.956	11.1565
15	118.4	110.609	99.9208	10.6877	7.79146
16	82.6	82.876	99.7625	-16.8865	-0.276042
17	68	77.8385	99.6167	-21.7781	-9.83854
18	99.8	89.2544	99.8667	-10.6123	10.5456
19	93.4	100.842	99.4542	1.38771	-7.44187
20	107.9	109.631	98.1	11.531	-1.73104
21	101.1	103.043	97.0208	6.02187	-1.94271
22	100.4	102.802	97.0167	5.78521	-2.40187
23	76.7	76.6352	98.1417	-21.5065	0.0647917
24	89.1	83.6902	98.3625	-14.6723	5.40979
25	105.3	114.715	97.6292	17.086	-9.41521
26	124.8	130.914	97.9583	32.956	-6.11437
27	111.9	109.175	98.4875	10.6877	2.72479
28	89	81.3802	98.2667	-16.8865	7.61979
29	88.6	75.8927	97.6708	-21.7781	12.7073
30	84.5	86.0002	96.6125	-10.6123	-1.50021
31	91.1	97.5877	96.2	1.38771	-6.48771
32	118.1	108.127	96.5958	11.531	9.97312
33	103.6	102.097	96.075	6.02187	1.50312
34	92.6	100.335	94.55	5.78521	-7.73521
35	70.2	71.1185	92.625	-21.5065	-0.918542
36	70.2	76.4569	91.1292	-14.6723	-6.25688
37	114.3	107.39	90.3042	17.086	6.90979
38	125.3	122.344	89.3875	32.956	2.95646
39	98.9	98.9377	88.25	10.6877	-0.0377083
40	65.4	71.0719	87.9583	-16.8865	-5.67188
41	66	66.126	87.9042	-21.7781	-0.126042
42	71.2	76.7085	87.3208	-10.6123	-5.50854
43	84.6	87.6252	86.2375	1.38771	-3.02521
44	102.6	96.1644	84.6333	11.531	6.43563
45	91.8	89.4469	83.425	6.02187	2.35313
46	97.4	88.8435	83.0583	5.78521	8.55646
47	64.1	61.2019	82.7083	-21.5065	2.89812
48	62.3	67.6652	82.3375	-14.6723	-5.36521
49	96.2	99.7527	82.6667	17.086	-3.55271
50	104.9	114.798	81.8417	32.956	-9.89771
51	90.3	91.3169	80.6292	10.6877	-1.01688
52	65.2	63.1969	80.0833	-16.8865	2.00312
53	57.8	57.5719	79.35	-21.7781	0.228125
54	70.5	68.771	79.3833	-10.6123	1.72896
55	93.2	81.1002	79.7125	1.38771	12.0998
56	74.2	91.556	80.025	11.531	-17.356
57	91.1	85.751	79.7292	6.02187	5.34896
58	85	84.5644	78.7792	5.78521	0.435625
59	58.9	56.5185	78.025	-21.5065	2.38146
60	68.3	62.5194	77.1917	-14.6723	5.78062
61	98.1	93.4985	76.4125	17.086	4.60146
62	110.5	109.698	76.7417	32.956	0.802292
63	77.6	88.1585	77.4708	10.6877	-10.5585
64	55.1	59.8719	76.7583	-16.8865	-4.77187
65	49.8	53.8677	75.6458	-21.7781	-4.06771
66	58.5	64.8627	75.475	-10.6123	-6.36271
67	86.5	NA	NA	1.38771	NA
68	88.8	NA	NA	11.531	NA
69	94	NA	NA	6.02187	NA
70	65	NA	NA	5.78521	NA
71	52.2	NA	NA	-21.5065	NA
72	70.9	NA	NA	-14.6723	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 143.7 & NA & NA & 17.086 & NA \tabularnewline
2 & 149.3 & NA & NA & 32.956 & NA \tabularnewline
3 & 121.7 & NA & NA & 10.6877 & NA \tabularnewline
4 & 81 & NA & NA & -16.8865 & NA \tabularnewline
5 & 68.1 & NA & NA & -21.7781 & NA \tabularnewline
6 & 92.3 & NA & NA & -10.6123 & NA \tabularnewline
7 & 107.7 & 103.942 & 102.554 & 1.38771 & 3.75813 \tabularnewline
8 & 114.4 & 112.819 & 101.287 & 11.531 & 1.58146 \tabularnewline
9 & 98.6 & 106.959 & 100.937 & 6.02187 & -8.35937 \tabularnewline
10 & 106.7 & 106.652 & 100.867 & 5.78521 & 0.048125 \tabularnewline
11 & 73.9 & 79.4227 & 100.929 & -21.5065 & -5.52271 \tabularnewline
12 & 85.9 & 86.5652 & 101.237 & -14.6723 & -0.665208 \tabularnewline
13 & 118.4 & 118.04 & 100.954 & 17.086 & 0.359792 \tabularnewline
14 & 144.2 & 133.044 & 100.087 & 32.956 & 11.1565 \tabularnewline
15 & 118.4 & 110.609 & 99.9208 & 10.6877 & 7.79146 \tabularnewline
16 & 82.6 & 82.876 & 99.7625 & -16.8865 & -0.276042 \tabularnewline
17 & 68 & 77.8385 & 99.6167 & -21.7781 & -9.83854 \tabularnewline
18 & 99.8 & 89.2544 & 99.8667 & -10.6123 & 10.5456 \tabularnewline
19 & 93.4 & 100.842 & 99.4542 & 1.38771 & -7.44187 \tabularnewline
20 & 107.9 & 109.631 & 98.1 & 11.531 & -1.73104 \tabularnewline
21 & 101.1 & 103.043 & 97.0208 & 6.02187 & -1.94271 \tabularnewline
22 & 100.4 & 102.802 & 97.0167 & 5.78521 & -2.40187 \tabularnewline
23 & 76.7 & 76.6352 & 98.1417 & -21.5065 & 0.0647917 \tabularnewline
24 & 89.1 & 83.6902 & 98.3625 & -14.6723 & 5.40979 \tabularnewline
25 & 105.3 & 114.715 & 97.6292 & 17.086 & -9.41521 \tabularnewline
26 & 124.8 & 130.914 & 97.9583 & 32.956 & -6.11437 \tabularnewline
27 & 111.9 & 109.175 & 98.4875 & 10.6877 & 2.72479 \tabularnewline
28 & 89 & 81.3802 & 98.2667 & -16.8865 & 7.61979 \tabularnewline
29 & 88.6 & 75.8927 & 97.6708 & -21.7781 & 12.7073 \tabularnewline
30 & 84.5 & 86.0002 & 96.6125 & -10.6123 & -1.50021 \tabularnewline
31 & 91.1 & 97.5877 & 96.2 & 1.38771 & -6.48771 \tabularnewline
32 & 118.1 & 108.127 & 96.5958 & 11.531 & 9.97312 \tabularnewline
33 & 103.6 & 102.097 & 96.075 & 6.02187 & 1.50312 \tabularnewline
34 & 92.6 & 100.335 & 94.55 & 5.78521 & -7.73521 \tabularnewline
35 & 70.2 & 71.1185 & 92.625 & -21.5065 & -0.918542 \tabularnewline
36 & 70.2 & 76.4569 & 91.1292 & -14.6723 & -6.25688 \tabularnewline
37 & 114.3 & 107.39 & 90.3042 & 17.086 & 6.90979 \tabularnewline
38 & 125.3 & 122.344 & 89.3875 & 32.956 & 2.95646 \tabularnewline
39 & 98.9 & 98.9377 & 88.25 & 10.6877 & -0.0377083 \tabularnewline
40 & 65.4 & 71.0719 & 87.9583 & -16.8865 & -5.67188 \tabularnewline
41 & 66 & 66.126 & 87.9042 & -21.7781 & -0.126042 \tabularnewline
42 & 71.2 & 76.7085 & 87.3208 & -10.6123 & -5.50854 \tabularnewline
43 & 84.6 & 87.6252 & 86.2375 & 1.38771 & -3.02521 \tabularnewline
44 & 102.6 & 96.1644 & 84.6333 & 11.531 & 6.43563 \tabularnewline
45 & 91.8 & 89.4469 & 83.425 & 6.02187 & 2.35313 \tabularnewline
46 & 97.4 & 88.8435 & 83.0583 & 5.78521 & 8.55646 \tabularnewline
47 & 64.1 & 61.2019 & 82.7083 & -21.5065 & 2.89812 \tabularnewline
48 & 62.3 & 67.6652 & 82.3375 & -14.6723 & -5.36521 \tabularnewline
49 & 96.2 & 99.7527 & 82.6667 & 17.086 & -3.55271 \tabularnewline
50 & 104.9 & 114.798 & 81.8417 & 32.956 & -9.89771 \tabularnewline
51 & 90.3 & 91.3169 & 80.6292 & 10.6877 & -1.01688 \tabularnewline
52 & 65.2 & 63.1969 & 80.0833 & -16.8865 & 2.00312 \tabularnewline
53 & 57.8 & 57.5719 & 79.35 & -21.7781 & 0.228125 \tabularnewline
54 & 70.5 & 68.771 & 79.3833 & -10.6123 & 1.72896 \tabularnewline
55 & 93.2 & 81.1002 & 79.7125 & 1.38771 & 12.0998 \tabularnewline
56 & 74.2 & 91.556 & 80.025 & 11.531 & -17.356 \tabularnewline
57 & 91.1 & 85.751 & 79.7292 & 6.02187 & 5.34896 \tabularnewline
58 & 85 & 84.5644 & 78.7792 & 5.78521 & 0.435625 \tabularnewline
59 & 58.9 & 56.5185 & 78.025 & -21.5065 & 2.38146 \tabularnewline
60 & 68.3 & 62.5194 & 77.1917 & -14.6723 & 5.78062 \tabularnewline
61 & 98.1 & 93.4985 & 76.4125 & 17.086 & 4.60146 \tabularnewline
62 & 110.5 & 109.698 & 76.7417 & 32.956 & 0.802292 \tabularnewline
63 & 77.6 & 88.1585 & 77.4708 & 10.6877 & -10.5585 \tabularnewline
64 & 55.1 & 59.8719 & 76.7583 & -16.8865 & -4.77187 \tabularnewline
65 & 49.8 & 53.8677 & 75.6458 & -21.7781 & -4.06771 \tabularnewline
66 & 58.5 & 64.8627 & 75.475 & -10.6123 & -6.36271 \tabularnewline
67 & 86.5 & NA & NA & 1.38771 & NA \tabularnewline
68 & 88.8 & NA & NA & 11.531 & NA \tabularnewline
69 & 94 & NA & NA & 6.02187 & NA \tabularnewline
70 & 65 & NA & NA & 5.78521 & NA \tabularnewline
71 & 52.2 & NA & NA & -21.5065 & NA \tabularnewline
72 & 70.9 & NA & NA & -14.6723 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284195&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]143.7[/C][C]NA[/C][C]NA[/C][C]17.086[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]149.3[/C][C]NA[/C][C]NA[/C][C]32.956[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]121.7[/C][C]NA[/C][C]NA[/C][C]10.6877[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]81[/C][C]NA[/C][C]NA[/C][C]-16.8865[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]68.1[/C][C]NA[/C][C]NA[/C][C]-21.7781[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]92.3[/C][C]NA[/C][C]NA[/C][C]-10.6123[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]107.7[/C][C]103.942[/C][C]102.554[/C][C]1.38771[/C][C]3.75813[/C][/ROW]
[ROW][C]8[/C][C]114.4[/C][C]112.819[/C][C]101.287[/C][C]11.531[/C][C]1.58146[/C][/ROW]
[ROW][C]9[/C][C]98.6[/C][C]106.959[/C][C]100.937[/C][C]6.02187[/C][C]-8.35937[/C][/ROW]
[ROW][C]10[/C][C]106.7[/C][C]106.652[/C][C]100.867[/C][C]5.78521[/C][C]0.048125[/C][/ROW]
[ROW][C]11[/C][C]73.9[/C][C]79.4227[/C][C]100.929[/C][C]-21.5065[/C][C]-5.52271[/C][/ROW]
[ROW][C]12[/C][C]85.9[/C][C]86.5652[/C][C]101.237[/C][C]-14.6723[/C][C]-0.665208[/C][/ROW]
[ROW][C]13[/C][C]118.4[/C][C]118.04[/C][C]100.954[/C][C]17.086[/C][C]0.359792[/C][/ROW]
[ROW][C]14[/C][C]144.2[/C][C]133.044[/C][C]100.087[/C][C]32.956[/C][C]11.1565[/C][/ROW]
[ROW][C]15[/C][C]118.4[/C][C]110.609[/C][C]99.9208[/C][C]10.6877[/C][C]7.79146[/C][/ROW]
[ROW][C]16[/C][C]82.6[/C][C]82.876[/C][C]99.7625[/C][C]-16.8865[/C][C]-0.276042[/C][/ROW]
[ROW][C]17[/C][C]68[/C][C]77.8385[/C][C]99.6167[/C][C]-21.7781[/C][C]-9.83854[/C][/ROW]
[ROW][C]18[/C][C]99.8[/C][C]89.2544[/C][C]99.8667[/C][C]-10.6123[/C][C]10.5456[/C][/ROW]
[ROW][C]19[/C][C]93.4[/C][C]100.842[/C][C]99.4542[/C][C]1.38771[/C][C]-7.44187[/C][/ROW]
[ROW][C]20[/C][C]107.9[/C][C]109.631[/C][C]98.1[/C][C]11.531[/C][C]-1.73104[/C][/ROW]
[ROW][C]21[/C][C]101.1[/C][C]103.043[/C][C]97.0208[/C][C]6.02187[/C][C]-1.94271[/C][/ROW]
[ROW][C]22[/C][C]100.4[/C][C]102.802[/C][C]97.0167[/C][C]5.78521[/C][C]-2.40187[/C][/ROW]
[ROW][C]23[/C][C]76.7[/C][C]76.6352[/C][C]98.1417[/C][C]-21.5065[/C][C]0.0647917[/C][/ROW]
[ROW][C]24[/C][C]89.1[/C][C]83.6902[/C][C]98.3625[/C][C]-14.6723[/C][C]5.40979[/C][/ROW]
[ROW][C]25[/C][C]105.3[/C][C]114.715[/C][C]97.6292[/C][C]17.086[/C][C]-9.41521[/C][/ROW]
[ROW][C]26[/C][C]124.8[/C][C]130.914[/C][C]97.9583[/C][C]32.956[/C][C]-6.11437[/C][/ROW]
[ROW][C]27[/C][C]111.9[/C][C]109.175[/C][C]98.4875[/C][C]10.6877[/C][C]2.72479[/C][/ROW]
[ROW][C]28[/C][C]89[/C][C]81.3802[/C][C]98.2667[/C][C]-16.8865[/C][C]7.61979[/C][/ROW]
[ROW][C]29[/C][C]88.6[/C][C]75.8927[/C][C]97.6708[/C][C]-21.7781[/C][C]12.7073[/C][/ROW]
[ROW][C]30[/C][C]84.5[/C][C]86.0002[/C][C]96.6125[/C][C]-10.6123[/C][C]-1.50021[/C][/ROW]
[ROW][C]31[/C][C]91.1[/C][C]97.5877[/C][C]96.2[/C][C]1.38771[/C][C]-6.48771[/C][/ROW]
[ROW][C]32[/C][C]118.1[/C][C]108.127[/C][C]96.5958[/C][C]11.531[/C][C]9.97312[/C][/ROW]
[ROW][C]33[/C][C]103.6[/C][C]102.097[/C][C]96.075[/C][C]6.02187[/C][C]1.50312[/C][/ROW]
[ROW][C]34[/C][C]92.6[/C][C]100.335[/C][C]94.55[/C][C]5.78521[/C][C]-7.73521[/C][/ROW]
[ROW][C]35[/C][C]70.2[/C][C]71.1185[/C][C]92.625[/C][C]-21.5065[/C][C]-0.918542[/C][/ROW]
[ROW][C]36[/C][C]70.2[/C][C]76.4569[/C][C]91.1292[/C][C]-14.6723[/C][C]-6.25688[/C][/ROW]
[ROW][C]37[/C][C]114.3[/C][C]107.39[/C][C]90.3042[/C][C]17.086[/C][C]6.90979[/C][/ROW]
[ROW][C]38[/C][C]125.3[/C][C]122.344[/C][C]89.3875[/C][C]32.956[/C][C]2.95646[/C][/ROW]
[ROW][C]39[/C][C]98.9[/C][C]98.9377[/C][C]88.25[/C][C]10.6877[/C][C]-0.0377083[/C][/ROW]
[ROW][C]40[/C][C]65.4[/C][C]71.0719[/C][C]87.9583[/C][C]-16.8865[/C][C]-5.67188[/C][/ROW]
[ROW][C]41[/C][C]66[/C][C]66.126[/C][C]87.9042[/C][C]-21.7781[/C][C]-0.126042[/C][/ROW]
[ROW][C]42[/C][C]71.2[/C][C]76.7085[/C][C]87.3208[/C][C]-10.6123[/C][C]-5.50854[/C][/ROW]
[ROW][C]43[/C][C]84.6[/C][C]87.6252[/C][C]86.2375[/C][C]1.38771[/C][C]-3.02521[/C][/ROW]
[ROW][C]44[/C][C]102.6[/C][C]96.1644[/C][C]84.6333[/C][C]11.531[/C][C]6.43563[/C][/ROW]
[ROW][C]45[/C][C]91.8[/C][C]89.4469[/C][C]83.425[/C][C]6.02187[/C][C]2.35313[/C][/ROW]
[ROW][C]46[/C][C]97.4[/C][C]88.8435[/C][C]83.0583[/C][C]5.78521[/C][C]8.55646[/C][/ROW]
[ROW][C]47[/C][C]64.1[/C][C]61.2019[/C][C]82.7083[/C][C]-21.5065[/C][C]2.89812[/C][/ROW]
[ROW][C]48[/C][C]62.3[/C][C]67.6652[/C][C]82.3375[/C][C]-14.6723[/C][C]-5.36521[/C][/ROW]
[ROW][C]49[/C][C]96.2[/C][C]99.7527[/C][C]82.6667[/C][C]17.086[/C][C]-3.55271[/C][/ROW]
[ROW][C]50[/C][C]104.9[/C][C]114.798[/C][C]81.8417[/C][C]32.956[/C][C]-9.89771[/C][/ROW]
[ROW][C]51[/C][C]90.3[/C][C]91.3169[/C][C]80.6292[/C][C]10.6877[/C][C]-1.01688[/C][/ROW]
[ROW][C]52[/C][C]65.2[/C][C]63.1969[/C][C]80.0833[/C][C]-16.8865[/C][C]2.00312[/C][/ROW]
[ROW][C]53[/C][C]57.8[/C][C]57.5719[/C][C]79.35[/C][C]-21.7781[/C][C]0.228125[/C][/ROW]
[ROW][C]54[/C][C]70.5[/C][C]68.771[/C][C]79.3833[/C][C]-10.6123[/C][C]1.72896[/C][/ROW]
[ROW][C]55[/C][C]93.2[/C][C]81.1002[/C][C]79.7125[/C][C]1.38771[/C][C]12.0998[/C][/ROW]
[ROW][C]56[/C][C]74.2[/C][C]91.556[/C][C]80.025[/C][C]11.531[/C][C]-17.356[/C][/ROW]
[ROW][C]57[/C][C]91.1[/C][C]85.751[/C][C]79.7292[/C][C]6.02187[/C][C]5.34896[/C][/ROW]
[ROW][C]58[/C][C]85[/C][C]84.5644[/C][C]78.7792[/C][C]5.78521[/C][C]0.435625[/C][/ROW]
[ROW][C]59[/C][C]58.9[/C][C]56.5185[/C][C]78.025[/C][C]-21.5065[/C][C]2.38146[/C][/ROW]
[ROW][C]60[/C][C]68.3[/C][C]62.5194[/C][C]77.1917[/C][C]-14.6723[/C][C]5.78062[/C][/ROW]
[ROW][C]61[/C][C]98.1[/C][C]93.4985[/C][C]76.4125[/C][C]17.086[/C][C]4.60146[/C][/ROW]
[ROW][C]62[/C][C]110.5[/C][C]109.698[/C][C]76.7417[/C][C]32.956[/C][C]0.802292[/C][/ROW]
[ROW][C]63[/C][C]77.6[/C][C]88.1585[/C][C]77.4708[/C][C]10.6877[/C][C]-10.5585[/C][/ROW]
[ROW][C]64[/C][C]55.1[/C][C]59.8719[/C][C]76.7583[/C][C]-16.8865[/C][C]-4.77187[/C][/ROW]
[ROW][C]65[/C][C]49.8[/C][C]53.8677[/C][C]75.6458[/C][C]-21.7781[/C][C]-4.06771[/C][/ROW]
[ROW][C]66[/C][C]58.5[/C][C]64.8627[/C][C]75.475[/C][C]-10.6123[/C][C]-6.36271[/C][/ROW]
[ROW][C]67[/C][C]86.5[/C][C]NA[/C][C]NA[/C][C]1.38771[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]88.8[/C][C]NA[/C][C]NA[/C][C]11.531[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]94[/C][C]NA[/C][C]NA[/C][C]6.02187[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]65[/C][C]NA[/C][C]NA[/C][C]5.78521[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]52.2[/C][C]NA[/C][C]NA[/C][C]-21.5065[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]70.9[/C][C]NA[/C][C]NA[/C][C]-14.6723[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284195&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284195&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	143.7	NA	NA	17.086	NA
2	149.3	NA	NA	32.956	NA
3	121.7	NA	NA	10.6877	NA
4	81	NA	NA	-16.8865	NA
5	68.1	NA	NA	-21.7781	NA
6	92.3	NA	NA	-10.6123	NA
7	107.7	103.942	102.554	1.38771	3.75813
8	114.4	112.819	101.287	11.531	1.58146
9	98.6	106.959	100.937	6.02187	-8.35937
10	106.7	106.652	100.867	5.78521	0.048125
11	73.9	79.4227	100.929	-21.5065	-5.52271
12	85.9	86.5652	101.237	-14.6723	-0.665208
13	118.4	118.04	100.954	17.086	0.359792
14	144.2	133.044	100.087	32.956	11.1565
15	118.4	110.609	99.9208	10.6877	7.79146
16	82.6	82.876	99.7625	-16.8865	-0.276042
17	68	77.8385	99.6167	-21.7781	-9.83854
18	99.8	89.2544	99.8667	-10.6123	10.5456
19	93.4	100.842	99.4542	1.38771	-7.44187
20	107.9	109.631	98.1	11.531	-1.73104
21	101.1	103.043	97.0208	6.02187	-1.94271
22	100.4	102.802	97.0167	5.78521	-2.40187
23	76.7	76.6352	98.1417	-21.5065	0.0647917
24	89.1	83.6902	98.3625	-14.6723	5.40979
25	105.3	114.715	97.6292	17.086	-9.41521
26	124.8	130.914	97.9583	32.956	-6.11437
27	111.9	109.175	98.4875	10.6877	2.72479
28	89	81.3802	98.2667	-16.8865	7.61979
29	88.6	75.8927	97.6708	-21.7781	12.7073
30	84.5	86.0002	96.6125	-10.6123	-1.50021
31	91.1	97.5877	96.2	1.38771	-6.48771
32	118.1	108.127	96.5958	11.531	9.97312
33	103.6	102.097	96.075	6.02187	1.50312
34	92.6	100.335	94.55	5.78521	-7.73521
35	70.2	71.1185	92.625	-21.5065	-0.918542
36	70.2	76.4569	91.1292	-14.6723	-6.25688
37	114.3	107.39	90.3042	17.086	6.90979
38	125.3	122.344	89.3875	32.956	2.95646
39	98.9	98.9377	88.25	10.6877	-0.0377083
40	65.4	71.0719	87.9583	-16.8865	-5.67188
41	66	66.126	87.9042	-21.7781	-0.126042
42	71.2	76.7085	87.3208	-10.6123	-5.50854
43	84.6	87.6252	86.2375	1.38771	-3.02521
44	102.6	96.1644	84.6333	11.531	6.43563
45	91.8	89.4469	83.425	6.02187	2.35313
46	97.4	88.8435	83.0583	5.78521	8.55646
47	64.1	61.2019	82.7083	-21.5065	2.89812
48	62.3	67.6652	82.3375	-14.6723	-5.36521
49	96.2	99.7527	82.6667	17.086	-3.55271
50	104.9	114.798	81.8417	32.956	-9.89771
51	90.3	91.3169	80.6292	10.6877	-1.01688
52	65.2	63.1969	80.0833	-16.8865	2.00312
53	57.8	57.5719	79.35	-21.7781	0.228125
54	70.5	68.771	79.3833	-10.6123	1.72896
55	93.2	81.1002	79.7125	1.38771	12.0998
56	74.2	91.556	80.025	11.531	-17.356
57	91.1	85.751	79.7292	6.02187	5.34896
58	85	84.5644	78.7792	5.78521	0.435625
59	58.9	56.5185	78.025	-21.5065	2.38146
60	68.3	62.5194	77.1917	-14.6723	5.78062
61	98.1	93.4985	76.4125	17.086	4.60146
62	110.5	109.698	76.7417	32.956	0.802292
63	77.6	88.1585	77.4708	10.6877	-10.5585
64	55.1	59.8719	76.7583	-16.8865	-4.77187
65	49.8	53.8677	75.6458	-21.7781	-4.06771
66	58.5	64.8627	75.475	-10.6123	-6.36271
67	86.5	NA	NA	1.38771	NA
68	88.8	NA	NA	11.531	NA
69	94	NA	NA	6.02187	NA
70	65	NA	NA	5.78521	NA
71	52.2	NA	NA	-21.5065	NA
72	70.9	NA	NA	-14.6723	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