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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sat, 07 Dec 2013 10:08:05 -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/Dec/07/t1386429033kodjl5qvz0lc900.htm/, Retrieved Fri, 26 Apr 2024 23:13:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231398, Retrieved Fri, 26 Apr 2024 23:13:33 +0000

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-       [Classical Decomposition] [] [2013-12-07 15:08:05] [a51ebb1a550b3d5f2cc4b9cf02171f8b] [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	6 seconds
R Server	'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231398&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231398&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231398&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	6 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	71.4	NA	NA	-5.55764	NA
2	75.8	NA	NA	-3.78181	NA
3	79.2	NA	NA	2.29653	NA
4	84.4	NA	NA	5.83403	NA
5	84.4	NA	NA	4.62653	NA
6	87.2	NA	NA	5.01069	NA
7	92.4	99.5149	91.4	8.11486	-7.11486
8	88.5	97.5799	94.7625	2.81736	-9.07986
9	94.8	97.8232	98.4083	-0.585139	-3.02319
10	100.9	97.3974	102.312	-4.91514	3.50264
11	110	101.392	106.85	-5.45847	8.60847
12	107.9	103.865	112.267	-8.40181	4.03514
13	111.2	112.263	117.821	-5.55764	-1.06319
14	116.7	118.781	122.562	-3.78181	-2.08069
15	125.8	127.992	125.696	2.29653	-2.19236
16	131.5	132.142	126.308	5.83403	-0.642361
17	146.2	128.76	124.133	4.62653	17.4401
18	155.4	125.327	120.317	5.01069	30.0726
19	157.5	124.102	115.988	8.11486	33.3976
20	137.2	114.097	111.279	2.81736	23.1035
21	121.3	105.461	106.046	-0.585139	15.8393
22	89.1	95.6224	100.538	-4.91514	-6.52236
23	69.6	89.2499	94.7083	-5.45847	-19.6499
24	56.7	80.3149	88.7167	-8.40181	-23.6149
25	58.5	76.9965	82.5542	-5.55764	-18.4965
26	56.4	73.5265	77.3083	-3.78181	-17.1265
27	60.5	76.0424	73.7458	2.29653	-15.5424
28	64.6	78.1215	72.2875	5.83403	-13.5215
29	73.2	78.039	73.4125	4.62653	-4.83903
30	84.6	80.994	75.9833	5.01069	3.60597
31	80.4	87.2024	79.0875	8.11486	-6.80236
32	88.4	85.0215	82.2042	2.81736	3.37847
33	84.6	84.7232	85.3083	-0.585139	-0.123194
34	90.8	83.6515	88.5667	-4.91514	7.14847
35	94.9	85.7249	91.1833	-5.45847	9.17514
36	93.1	84.119	92.5208	-8.40181	8.98097
37	96.6	87.9882	93.5458	-5.55764	8.61181
38	93.1	90.7474	94.5292	-3.78181	2.35264
39	98.3	97.7507	95.4542	2.29653	0.549306
40	105	102.417	96.5833	5.83403	2.58264
41	95.6	102.314	97.6875	4.62653	-6.71403
42	94.3	104.119	99.1083	5.01069	-9.81903
43	95.3	109.027	100.912	8.11486	-13.7274
44	97.1	105.942	103.125	2.81736	-8.84236
45	98.1	105.327	105.912	-0.585139	-7.22736
46	104.4	104.052	108.967	-4.91514	0.348472
47	107.8	106.658	112.117	-5.45847	1.14181
48	114.3	106.836	115.238	-8.40181	7.46431
49	118.7	112.788	118.346	-5.55764	5.91181
50	124.1	117.343	121.125	-3.78181	6.75681
51	134.2	125.792	123.496	2.29653	8.40764
52	142.4	131.263	125.429	5.83403	11.1368
53	133.8	131.602	126.975	4.62653	2.19847
54	131	133.207	128.196	5.01069	-2.20653
55	133.2	137.136	129.021	8.11486	-3.93569
56	125.9	132.642	129.825	2.81736	-6.74236
57	126.2	129.848	130.433	-0.585139	-3.64819
58	122.7	125.36	130.275	-4.91514	-2.65986
59	126.6	124.058	129.517	-5.45847	2.54181
60	124.8	119.848	128.25	-8.40181	4.95181
61	128	121.147	126.704	-5.55764	6.85347
62	134.1	122.185	125.967	-3.78181	11.9151
63	138.8	128.205	125.908	2.29653	10.5951
64	134	131.738	125.904	5.83403	2.26181
65	124	130.268	125.642	4.62653	-6.26819
66	110.4	130.236	125.225	5.01069	-19.8357
67	116.7	NA	NA	8.11486	NA
68	124.7	NA	NA	2.81736	NA
69	126	NA	NA	-0.585139	NA
70	122.8	NA	NA	-4.91514	NA
71	120.2	NA	NA	-5.45847	NA
72	121.2	NA	NA	-8.40181	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 71.4 & NA & NA & -5.55764 & NA \tabularnewline
2 & 75.8 & NA & NA & -3.78181 & NA \tabularnewline
3 & 79.2 & NA & NA & 2.29653 & NA \tabularnewline
4 & 84.4 & NA & NA & 5.83403 & NA \tabularnewline
5 & 84.4 & NA & NA & 4.62653 & NA \tabularnewline
6 & 87.2 & NA & NA & 5.01069 & NA \tabularnewline
7 & 92.4 & 99.5149 & 91.4 & 8.11486 & -7.11486 \tabularnewline
8 & 88.5 & 97.5799 & 94.7625 & 2.81736 & -9.07986 \tabularnewline
9 & 94.8 & 97.8232 & 98.4083 & -0.585139 & -3.02319 \tabularnewline
10 & 100.9 & 97.3974 & 102.312 & -4.91514 & 3.50264 \tabularnewline
11 & 110 & 101.392 & 106.85 & -5.45847 & 8.60847 \tabularnewline
12 & 107.9 & 103.865 & 112.267 & -8.40181 & 4.03514 \tabularnewline
13 & 111.2 & 112.263 & 117.821 & -5.55764 & -1.06319 \tabularnewline
14 & 116.7 & 118.781 & 122.562 & -3.78181 & -2.08069 \tabularnewline
15 & 125.8 & 127.992 & 125.696 & 2.29653 & -2.19236 \tabularnewline
16 & 131.5 & 132.142 & 126.308 & 5.83403 & -0.642361 \tabularnewline
17 & 146.2 & 128.76 & 124.133 & 4.62653 & 17.4401 \tabularnewline
18 & 155.4 & 125.327 & 120.317 & 5.01069 & 30.0726 \tabularnewline
19 & 157.5 & 124.102 & 115.988 & 8.11486 & 33.3976 \tabularnewline
20 & 137.2 & 114.097 & 111.279 & 2.81736 & 23.1035 \tabularnewline
21 & 121.3 & 105.461 & 106.046 & -0.585139 & 15.8393 \tabularnewline
22 & 89.1 & 95.6224 & 100.538 & -4.91514 & -6.52236 \tabularnewline
23 & 69.6 & 89.2499 & 94.7083 & -5.45847 & -19.6499 \tabularnewline
24 & 56.7 & 80.3149 & 88.7167 & -8.40181 & -23.6149 \tabularnewline
25 & 58.5 & 76.9965 & 82.5542 & -5.55764 & -18.4965 \tabularnewline
26 & 56.4 & 73.5265 & 77.3083 & -3.78181 & -17.1265 \tabularnewline
27 & 60.5 & 76.0424 & 73.7458 & 2.29653 & -15.5424 \tabularnewline
28 & 64.6 & 78.1215 & 72.2875 & 5.83403 & -13.5215 \tabularnewline
29 & 73.2 & 78.039 & 73.4125 & 4.62653 & -4.83903 \tabularnewline
30 & 84.6 & 80.994 & 75.9833 & 5.01069 & 3.60597 \tabularnewline
31 & 80.4 & 87.2024 & 79.0875 & 8.11486 & -6.80236 \tabularnewline
32 & 88.4 & 85.0215 & 82.2042 & 2.81736 & 3.37847 \tabularnewline
33 & 84.6 & 84.7232 & 85.3083 & -0.585139 & -0.123194 \tabularnewline
34 & 90.8 & 83.6515 & 88.5667 & -4.91514 & 7.14847 \tabularnewline
35 & 94.9 & 85.7249 & 91.1833 & -5.45847 & 9.17514 \tabularnewline
36 & 93.1 & 84.119 & 92.5208 & -8.40181 & 8.98097 \tabularnewline
37 & 96.6 & 87.9882 & 93.5458 & -5.55764 & 8.61181 \tabularnewline
38 & 93.1 & 90.7474 & 94.5292 & -3.78181 & 2.35264 \tabularnewline
39 & 98.3 & 97.7507 & 95.4542 & 2.29653 & 0.549306 \tabularnewline
40 & 105 & 102.417 & 96.5833 & 5.83403 & 2.58264 \tabularnewline
41 & 95.6 & 102.314 & 97.6875 & 4.62653 & -6.71403 \tabularnewline
42 & 94.3 & 104.119 & 99.1083 & 5.01069 & -9.81903 \tabularnewline
43 & 95.3 & 109.027 & 100.912 & 8.11486 & -13.7274 \tabularnewline
44 & 97.1 & 105.942 & 103.125 & 2.81736 & -8.84236 \tabularnewline
45 & 98.1 & 105.327 & 105.912 & -0.585139 & -7.22736 \tabularnewline
46 & 104.4 & 104.052 & 108.967 & -4.91514 & 0.348472 \tabularnewline
47 & 107.8 & 106.658 & 112.117 & -5.45847 & 1.14181 \tabularnewline
48 & 114.3 & 106.836 & 115.238 & -8.40181 & 7.46431 \tabularnewline
49 & 118.7 & 112.788 & 118.346 & -5.55764 & 5.91181 \tabularnewline
50 & 124.1 & 117.343 & 121.125 & -3.78181 & 6.75681 \tabularnewline
51 & 134.2 & 125.792 & 123.496 & 2.29653 & 8.40764 \tabularnewline
52 & 142.4 & 131.263 & 125.429 & 5.83403 & 11.1368 \tabularnewline
53 & 133.8 & 131.602 & 126.975 & 4.62653 & 2.19847 \tabularnewline
54 & 131 & 133.207 & 128.196 & 5.01069 & -2.20653 \tabularnewline
55 & 133.2 & 137.136 & 129.021 & 8.11486 & -3.93569 \tabularnewline
56 & 125.9 & 132.642 & 129.825 & 2.81736 & -6.74236 \tabularnewline
57 & 126.2 & 129.848 & 130.433 & -0.585139 & -3.64819 \tabularnewline
58 & 122.7 & 125.36 & 130.275 & -4.91514 & -2.65986 \tabularnewline
59 & 126.6 & 124.058 & 129.517 & -5.45847 & 2.54181 \tabularnewline
60 & 124.8 & 119.848 & 128.25 & -8.40181 & 4.95181 \tabularnewline
61 & 128 & 121.147 & 126.704 & -5.55764 & 6.85347 \tabularnewline
62 & 134.1 & 122.185 & 125.967 & -3.78181 & 11.9151 \tabularnewline
63 & 138.8 & 128.205 & 125.908 & 2.29653 & 10.5951 \tabularnewline
64 & 134 & 131.738 & 125.904 & 5.83403 & 2.26181 \tabularnewline
65 & 124 & 130.268 & 125.642 & 4.62653 & -6.26819 \tabularnewline
66 & 110.4 & 130.236 & 125.225 & 5.01069 & -19.8357 \tabularnewline
67 & 116.7 & NA & NA & 8.11486 & NA \tabularnewline
68 & 124.7 & NA & NA & 2.81736 & NA \tabularnewline
69 & 126 & NA & NA & -0.585139 & NA \tabularnewline
70 & 122.8 & NA & NA & -4.91514 & NA \tabularnewline
71 & 120.2 & NA & NA & -5.45847 & NA \tabularnewline
72 & 121.2 & NA & NA & -8.40181 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231398&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]71.4[/C][C]NA[/C][C]NA[/C][C]-5.55764[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]75.8[/C][C]NA[/C][C]NA[/C][C]-3.78181[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]79.2[/C][C]NA[/C][C]NA[/C][C]2.29653[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]84.4[/C][C]NA[/C][C]NA[/C][C]5.83403[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]84.4[/C][C]NA[/C][C]NA[/C][C]4.62653[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]87.2[/C][C]NA[/C][C]NA[/C][C]5.01069[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]92.4[/C][C]99.5149[/C][C]91.4[/C][C]8.11486[/C][C]-7.11486[/C][/ROW]
[ROW][C]8[/C][C]88.5[/C][C]97.5799[/C][C]94.7625[/C][C]2.81736[/C][C]-9.07986[/C][/ROW]
[ROW][C]9[/C][C]94.8[/C][C]97.8232[/C][C]98.4083[/C][C]-0.585139[/C][C]-3.02319[/C][/ROW]
[ROW][C]10[/C][C]100.9[/C][C]97.3974[/C][C]102.312[/C][C]-4.91514[/C][C]3.50264[/C][/ROW]
[ROW][C]11[/C][C]110[/C][C]101.392[/C][C]106.85[/C][C]-5.45847[/C][C]8.60847[/C][/ROW]
[ROW][C]12[/C][C]107.9[/C][C]103.865[/C][C]112.267[/C][C]-8.40181[/C][C]4.03514[/C][/ROW]
[ROW][C]13[/C][C]111.2[/C][C]112.263[/C][C]117.821[/C][C]-5.55764[/C][C]-1.06319[/C][/ROW]
[ROW][C]14[/C][C]116.7[/C][C]118.781[/C][C]122.562[/C][C]-3.78181[/C][C]-2.08069[/C][/ROW]
[ROW][C]15[/C][C]125.8[/C][C]127.992[/C][C]125.696[/C][C]2.29653[/C][C]-2.19236[/C][/ROW]
[ROW][C]16[/C][C]131.5[/C][C]132.142[/C][C]126.308[/C][C]5.83403[/C][C]-0.642361[/C][/ROW]
[ROW][C]17[/C][C]146.2[/C][C]128.76[/C][C]124.133[/C][C]4.62653[/C][C]17.4401[/C][/ROW]
[ROW][C]18[/C][C]155.4[/C][C]125.327[/C][C]120.317[/C][C]5.01069[/C][C]30.0726[/C][/ROW]
[ROW][C]19[/C][C]157.5[/C][C]124.102[/C][C]115.988[/C][C]8.11486[/C][C]33.3976[/C][/ROW]
[ROW][C]20[/C][C]137.2[/C][C]114.097[/C][C]111.279[/C][C]2.81736[/C][C]23.1035[/C][/ROW]
[ROW][C]21[/C][C]121.3[/C][C]105.461[/C][C]106.046[/C][C]-0.585139[/C][C]15.8393[/C][/ROW]
[ROW][C]22[/C][C]89.1[/C][C]95.6224[/C][C]100.538[/C][C]-4.91514[/C][C]-6.52236[/C][/ROW]
[ROW][C]23[/C][C]69.6[/C][C]89.2499[/C][C]94.7083[/C][C]-5.45847[/C][C]-19.6499[/C][/ROW]
[ROW][C]24[/C][C]56.7[/C][C]80.3149[/C][C]88.7167[/C][C]-8.40181[/C][C]-23.6149[/C][/ROW]
[ROW][C]25[/C][C]58.5[/C][C]76.9965[/C][C]82.5542[/C][C]-5.55764[/C][C]-18.4965[/C][/ROW]
[ROW][C]26[/C][C]56.4[/C][C]73.5265[/C][C]77.3083[/C][C]-3.78181[/C][C]-17.1265[/C][/ROW]
[ROW][C]27[/C][C]60.5[/C][C]76.0424[/C][C]73.7458[/C][C]2.29653[/C][C]-15.5424[/C][/ROW]
[ROW][C]28[/C][C]64.6[/C][C]78.1215[/C][C]72.2875[/C][C]5.83403[/C][C]-13.5215[/C][/ROW]
[ROW][C]29[/C][C]73.2[/C][C]78.039[/C][C]73.4125[/C][C]4.62653[/C][C]-4.83903[/C][/ROW]
[ROW][C]30[/C][C]84.6[/C][C]80.994[/C][C]75.9833[/C][C]5.01069[/C][C]3.60597[/C][/ROW]
[ROW][C]31[/C][C]80.4[/C][C]87.2024[/C][C]79.0875[/C][C]8.11486[/C][C]-6.80236[/C][/ROW]
[ROW][C]32[/C][C]88.4[/C][C]85.0215[/C][C]82.2042[/C][C]2.81736[/C][C]3.37847[/C][/ROW]
[ROW][C]33[/C][C]84.6[/C][C]84.7232[/C][C]85.3083[/C][C]-0.585139[/C][C]-0.123194[/C][/ROW]
[ROW][C]34[/C][C]90.8[/C][C]83.6515[/C][C]88.5667[/C][C]-4.91514[/C][C]7.14847[/C][/ROW]
[ROW][C]35[/C][C]94.9[/C][C]85.7249[/C][C]91.1833[/C][C]-5.45847[/C][C]9.17514[/C][/ROW]
[ROW][C]36[/C][C]93.1[/C][C]84.119[/C][C]92.5208[/C][C]-8.40181[/C][C]8.98097[/C][/ROW]
[ROW][C]37[/C][C]96.6[/C][C]87.9882[/C][C]93.5458[/C][C]-5.55764[/C][C]8.61181[/C][/ROW]
[ROW][C]38[/C][C]93.1[/C][C]90.7474[/C][C]94.5292[/C][C]-3.78181[/C][C]2.35264[/C][/ROW]
[ROW][C]39[/C][C]98.3[/C][C]97.7507[/C][C]95.4542[/C][C]2.29653[/C][C]0.549306[/C][/ROW]
[ROW][C]40[/C][C]105[/C][C]102.417[/C][C]96.5833[/C][C]5.83403[/C][C]2.58264[/C][/ROW]
[ROW][C]41[/C][C]95.6[/C][C]102.314[/C][C]97.6875[/C][C]4.62653[/C][C]-6.71403[/C][/ROW]
[ROW][C]42[/C][C]94.3[/C][C]104.119[/C][C]99.1083[/C][C]5.01069[/C][C]-9.81903[/C][/ROW]
[ROW][C]43[/C][C]95.3[/C][C]109.027[/C][C]100.912[/C][C]8.11486[/C][C]-13.7274[/C][/ROW]
[ROW][C]44[/C][C]97.1[/C][C]105.942[/C][C]103.125[/C][C]2.81736[/C][C]-8.84236[/C][/ROW]
[ROW][C]45[/C][C]98.1[/C][C]105.327[/C][C]105.912[/C][C]-0.585139[/C][C]-7.22736[/C][/ROW]
[ROW][C]46[/C][C]104.4[/C][C]104.052[/C][C]108.967[/C][C]-4.91514[/C][C]0.348472[/C][/ROW]
[ROW][C]47[/C][C]107.8[/C][C]106.658[/C][C]112.117[/C][C]-5.45847[/C][C]1.14181[/C][/ROW]
[ROW][C]48[/C][C]114.3[/C][C]106.836[/C][C]115.238[/C][C]-8.40181[/C][C]7.46431[/C][/ROW]
[ROW][C]49[/C][C]118.7[/C][C]112.788[/C][C]118.346[/C][C]-5.55764[/C][C]5.91181[/C][/ROW]
[ROW][C]50[/C][C]124.1[/C][C]117.343[/C][C]121.125[/C][C]-3.78181[/C][C]6.75681[/C][/ROW]
[ROW][C]51[/C][C]134.2[/C][C]125.792[/C][C]123.496[/C][C]2.29653[/C][C]8.40764[/C][/ROW]
[ROW][C]52[/C][C]142.4[/C][C]131.263[/C][C]125.429[/C][C]5.83403[/C][C]11.1368[/C][/ROW]
[ROW][C]53[/C][C]133.8[/C][C]131.602[/C][C]126.975[/C][C]4.62653[/C][C]2.19847[/C][/ROW]
[ROW][C]54[/C][C]131[/C][C]133.207[/C][C]128.196[/C][C]5.01069[/C][C]-2.20653[/C][/ROW]
[ROW][C]55[/C][C]133.2[/C][C]137.136[/C][C]129.021[/C][C]8.11486[/C][C]-3.93569[/C][/ROW]
[ROW][C]56[/C][C]125.9[/C][C]132.642[/C][C]129.825[/C][C]2.81736[/C][C]-6.74236[/C][/ROW]
[ROW][C]57[/C][C]126.2[/C][C]129.848[/C][C]130.433[/C][C]-0.585139[/C][C]-3.64819[/C][/ROW]
[ROW][C]58[/C][C]122.7[/C][C]125.36[/C][C]130.275[/C][C]-4.91514[/C][C]-2.65986[/C][/ROW]
[ROW][C]59[/C][C]126.6[/C][C]124.058[/C][C]129.517[/C][C]-5.45847[/C][C]2.54181[/C][/ROW]
[ROW][C]60[/C][C]124.8[/C][C]119.848[/C][C]128.25[/C][C]-8.40181[/C][C]4.95181[/C][/ROW]
[ROW][C]61[/C][C]128[/C][C]121.147[/C][C]126.704[/C][C]-5.55764[/C][C]6.85347[/C][/ROW]
[ROW][C]62[/C][C]134.1[/C][C]122.185[/C][C]125.967[/C][C]-3.78181[/C][C]11.9151[/C][/ROW]
[ROW][C]63[/C][C]138.8[/C][C]128.205[/C][C]125.908[/C][C]2.29653[/C][C]10.5951[/C][/ROW]
[ROW][C]64[/C][C]134[/C][C]131.738[/C][C]125.904[/C][C]5.83403[/C][C]2.26181[/C][/ROW]
[ROW][C]65[/C][C]124[/C][C]130.268[/C][C]125.642[/C][C]4.62653[/C][C]-6.26819[/C][/ROW]
[ROW][C]66[/C][C]110.4[/C][C]130.236[/C][C]125.225[/C][C]5.01069[/C][C]-19.8357[/C][/ROW]
[ROW][C]67[/C][C]116.7[/C][C]NA[/C][C]NA[/C][C]8.11486[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]124.7[/C][C]NA[/C][C]NA[/C][C]2.81736[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]126[/C][C]NA[/C][C]NA[/C][C]-0.585139[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]122.8[/C][C]NA[/C][C]NA[/C][C]-4.91514[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]120.2[/C][C]NA[/C][C]NA[/C][C]-5.45847[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]121.2[/C][C]NA[/C][C]NA[/C][C]-8.40181[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231398&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231398&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	71.4	NA	NA	-5.55764	NA
2	75.8	NA	NA	-3.78181	NA
3	79.2	NA	NA	2.29653	NA
4	84.4	NA	NA	5.83403	NA
5	84.4	NA	NA	4.62653	NA
6	87.2	NA	NA	5.01069	NA
7	92.4	99.5149	91.4	8.11486	-7.11486
8	88.5	97.5799	94.7625	2.81736	-9.07986
9	94.8	97.8232	98.4083	-0.585139	-3.02319
10	100.9	97.3974	102.312	-4.91514	3.50264
11	110	101.392	106.85	-5.45847	8.60847
12	107.9	103.865	112.267	-8.40181	4.03514
13	111.2	112.263	117.821	-5.55764	-1.06319
14	116.7	118.781	122.562	-3.78181	-2.08069
15	125.8	127.992	125.696	2.29653	-2.19236
16	131.5	132.142	126.308	5.83403	-0.642361
17	146.2	128.76	124.133	4.62653	17.4401
18	155.4	125.327	120.317	5.01069	30.0726
19	157.5	124.102	115.988	8.11486	33.3976
20	137.2	114.097	111.279	2.81736	23.1035
21	121.3	105.461	106.046	-0.585139	15.8393
22	89.1	95.6224	100.538	-4.91514	-6.52236
23	69.6	89.2499	94.7083	-5.45847	-19.6499
24	56.7	80.3149	88.7167	-8.40181	-23.6149
25	58.5	76.9965	82.5542	-5.55764	-18.4965
26	56.4	73.5265	77.3083	-3.78181	-17.1265
27	60.5	76.0424	73.7458	2.29653	-15.5424
28	64.6	78.1215	72.2875	5.83403	-13.5215
29	73.2	78.039	73.4125	4.62653	-4.83903
30	84.6	80.994	75.9833	5.01069	3.60597
31	80.4	87.2024	79.0875	8.11486	-6.80236
32	88.4	85.0215	82.2042	2.81736	3.37847
33	84.6	84.7232	85.3083	-0.585139	-0.123194
34	90.8	83.6515	88.5667	-4.91514	7.14847
35	94.9	85.7249	91.1833	-5.45847	9.17514
36	93.1	84.119	92.5208	-8.40181	8.98097
37	96.6	87.9882	93.5458	-5.55764	8.61181
38	93.1	90.7474	94.5292	-3.78181	2.35264
39	98.3	97.7507	95.4542	2.29653	0.549306
40	105	102.417	96.5833	5.83403	2.58264
41	95.6	102.314	97.6875	4.62653	-6.71403
42	94.3	104.119	99.1083	5.01069	-9.81903
43	95.3	109.027	100.912	8.11486	-13.7274
44	97.1	105.942	103.125	2.81736	-8.84236
45	98.1	105.327	105.912	-0.585139	-7.22736
46	104.4	104.052	108.967	-4.91514	0.348472
47	107.8	106.658	112.117	-5.45847	1.14181
48	114.3	106.836	115.238	-8.40181	7.46431
49	118.7	112.788	118.346	-5.55764	5.91181
50	124.1	117.343	121.125	-3.78181	6.75681
51	134.2	125.792	123.496	2.29653	8.40764
52	142.4	131.263	125.429	5.83403	11.1368
53	133.8	131.602	126.975	4.62653	2.19847
54	131	133.207	128.196	5.01069	-2.20653
55	133.2	137.136	129.021	8.11486	-3.93569
56	125.9	132.642	129.825	2.81736	-6.74236
57	126.2	129.848	130.433	-0.585139	-3.64819
58	122.7	125.36	130.275	-4.91514	-2.65986
59	126.6	124.058	129.517	-5.45847	2.54181
60	124.8	119.848	128.25	-8.40181	4.95181
61	128	121.147	126.704	-5.55764	6.85347
62	134.1	122.185	125.967	-3.78181	11.9151
63	138.8	128.205	125.908	2.29653	10.5951
64	134	131.738	125.904	5.83403	2.26181
65	124	130.268	125.642	4.62653	-6.26819
66	110.4	130.236	125.225	5.01069	-19.8357
67	116.7	NA	NA	8.11486	NA
68	124.7	NA	NA	2.81736	NA
69	126	NA	NA	-0.585139	NA
70	122.8	NA	NA	-4.91514	NA
71	120.2	NA	NA	-5.45847	NA
72	121.2	NA	NA	-8.40181	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