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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Thu, 02 Apr 2015 20:43:17 +0100

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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/Apr/02/t1428003944v3k2nbs9wae4n40.htm/, Retrieved Thu, 09 May 2024 14:30:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=278645, Retrieved Thu, 09 May 2024 14:30:42 +0000

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Classical Deomposition prijsindexcijfers grondstof graan Valerie Weyts Karel de Grote-Hogeschool

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-       [Classical Decomposition] [Classical Deompos...] [2015-04-02 19:43:17] [ab73e159a571dceeee45078a19254ea4] [Current]
- R P     [Classical Decomposition] [eigen reeks addit...] [2015-05-24 12:14:35] [69304374246e9fd5f7a19a35f2b701e6] 

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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	'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=278645&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=278645&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=278645&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	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	123.2	NA	NA	1.00928	NA
2	136.9	NA	NA	1.00273	NA
3	146.8	NA	NA	1.0006	NA
4	149.6	NA	NA	0.991056	NA
5	146.5	NA	NA	1.00276	NA
6	157	NA	NA	0.987578	NA
7	147.9	129.654	128.237	1.01105	1.14073
8	133.6	128.175	125.246	1.02339	1.04233
9	128.7	123.849	121.129	1.02245	1.03917
10	100.8	114.593	116.554	0.983172	0.879636
11	91.8	109.833	112.312	0.977919	0.835818
12	89.3	106.764	108.058	0.988019	0.836426
13	96.7	104.116	103.158	1.00928	0.928773
14	91.6	98.8146	98.5458	1.00273	0.926989
15	93.3	94.5151	94.4583	1.0006	0.987144
16	93.3	91.074	91.8958	0.991056	1.02444
17	101	91.6401	91.3875	1.00276	1.10214
18	100.4	90.5445	91.6833	0.987578	1.10885
19	86.9	92.7592	91.7458	1.01105	0.936834
20	83.9	93.5118	91.375	1.02339	0.897213
21	80.3	92.9706	90.9292	1.02245	0.863714
22	87.7	88.7395	90.2583	0.983172	0.988286
23	92.7	87.2222	89.1917	0.977919	1.0628
24	95.5	86.6946	87.7458	0.988019	1.10157
25	92	87.9421	87.1333	1.00928	1.04614
26	87.4	88.2734	88.0333	1.00273	0.990106
27	86.8	90.2167	90.1625	1.0006	0.962128
28	83.7	92.0774	92.9083	0.991056	0.909018
29	85	95.7472	95.4833	1.00276	0.887754
30	81.7	97.0296	98.25	0.987578	0.842011
31	90.9	102.916	101.792	1.01105	0.883245
32	101.5	108.863	106.375	1.02339	0.932368
33	113.8	113.854	111.354	1.02245	0.999525
34	120.1	114.732	116.696	0.983172	1.04679
35	122.1	119.705	122.408	0.977919	1.02
36	132.5	126.302	127.833	0.988019	1.04907
37	140	133.633	132.404	1.00928	1.04764
38	149.4	136.567	136.196	1.00273	1.09397
39	144.3	139.463	139.379	1.0006	1.03468
40	154.4	139.983	141.246	0.991056	1.10299
41	151.4	142.589	142.196	1.00276	1.06179
42	145.5	140.606	142.375	0.987578	1.0348
43	136.8	143.32	141.754	1.01105	0.954508
44	146.6	143.867	140.579	1.02339	1.019
45	145.1	142.444	139.317	1.02245	1.01864
46	133.6	135.424	137.742	0.983172	0.986533
47	131.4	132.577	135.571	0.977919	0.99112
48	127.5	132.172	133.775	0.988019	0.96465
49	130.1	135.122	133.879	1.00928	0.962835
50	131.1	135.744	135.375	1.00273	0.965787
51	132.3	136.761	136.679	1.0006	0.967379
52	128.6	137.047	138.283	0.991056	0.938367
53	125.1	140.696	140.308	1.00276	0.88915
54	128.7	140.615	142.383	0.987578	0.915267
55	156.1	145.822	144.229	1.01105	1.07048
56	163.2	149.056	145.65	1.02339	1.09489
57	159.8	150.083	146.787	1.02245	1.06474
58	157.4	145.128	147.612	0.983172	1.08456
59	156.2	145.16	148.437	0.977919	1.07605
60	152.5	147.474	149.262	0.988019	1.03408
61	149.4	149.34	147.967	1.00928	1.0004
62	145.9	144.606	144.212	1.00273	1.00895
63	144.8	139.976	139.892	1.0006	1.03446
64	135.9	134.424	135.638	0.991056	1.01098
65	137.6	131.68	131.317	1.00276	1.04496
66	136	125.361	126.938	0.987578	1.08487
67	117.7	NA	NA	1.01105	NA
68	111.5	NA	NA	1.02339	NA
69	107.8	NA	NA	1.02245	NA
70	107.3	NA	NA	0.983172	NA
71	102.6	NA	NA	0.977919	NA
72	101	NA	NA	0.988019	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 123.2 & NA & NA & 1.00928 & NA \tabularnewline
2 & 136.9 & NA & NA & 1.00273 & NA \tabularnewline
3 & 146.8 & NA & NA & 1.0006 & NA \tabularnewline
4 & 149.6 & NA & NA & 0.991056 & NA \tabularnewline
5 & 146.5 & NA & NA & 1.00276 & NA \tabularnewline
6 & 157 & NA & NA & 0.987578 & NA \tabularnewline
7 & 147.9 & 129.654 & 128.237 & 1.01105 & 1.14073 \tabularnewline
8 & 133.6 & 128.175 & 125.246 & 1.02339 & 1.04233 \tabularnewline
9 & 128.7 & 123.849 & 121.129 & 1.02245 & 1.03917 \tabularnewline
10 & 100.8 & 114.593 & 116.554 & 0.983172 & 0.879636 \tabularnewline
11 & 91.8 & 109.833 & 112.312 & 0.977919 & 0.835818 \tabularnewline
12 & 89.3 & 106.764 & 108.058 & 0.988019 & 0.836426 \tabularnewline
13 & 96.7 & 104.116 & 103.158 & 1.00928 & 0.928773 \tabularnewline
14 & 91.6 & 98.8146 & 98.5458 & 1.00273 & 0.926989 \tabularnewline
15 & 93.3 & 94.5151 & 94.4583 & 1.0006 & 0.987144 \tabularnewline
16 & 93.3 & 91.074 & 91.8958 & 0.991056 & 1.02444 \tabularnewline
17 & 101 & 91.6401 & 91.3875 & 1.00276 & 1.10214 \tabularnewline
18 & 100.4 & 90.5445 & 91.6833 & 0.987578 & 1.10885 \tabularnewline
19 & 86.9 & 92.7592 & 91.7458 & 1.01105 & 0.936834 \tabularnewline
20 & 83.9 & 93.5118 & 91.375 & 1.02339 & 0.897213 \tabularnewline
21 & 80.3 & 92.9706 & 90.9292 & 1.02245 & 0.863714 \tabularnewline
22 & 87.7 & 88.7395 & 90.2583 & 0.983172 & 0.988286 \tabularnewline
23 & 92.7 & 87.2222 & 89.1917 & 0.977919 & 1.0628 \tabularnewline
24 & 95.5 & 86.6946 & 87.7458 & 0.988019 & 1.10157 \tabularnewline
25 & 92 & 87.9421 & 87.1333 & 1.00928 & 1.04614 \tabularnewline
26 & 87.4 & 88.2734 & 88.0333 & 1.00273 & 0.990106 \tabularnewline
27 & 86.8 & 90.2167 & 90.1625 & 1.0006 & 0.962128 \tabularnewline
28 & 83.7 & 92.0774 & 92.9083 & 0.991056 & 0.909018 \tabularnewline
29 & 85 & 95.7472 & 95.4833 & 1.00276 & 0.887754 \tabularnewline
30 & 81.7 & 97.0296 & 98.25 & 0.987578 & 0.842011 \tabularnewline
31 & 90.9 & 102.916 & 101.792 & 1.01105 & 0.883245 \tabularnewline
32 & 101.5 & 108.863 & 106.375 & 1.02339 & 0.932368 \tabularnewline
33 & 113.8 & 113.854 & 111.354 & 1.02245 & 0.999525 \tabularnewline
34 & 120.1 & 114.732 & 116.696 & 0.983172 & 1.04679 \tabularnewline
35 & 122.1 & 119.705 & 122.408 & 0.977919 & 1.02 \tabularnewline
36 & 132.5 & 126.302 & 127.833 & 0.988019 & 1.04907 \tabularnewline
37 & 140 & 133.633 & 132.404 & 1.00928 & 1.04764 \tabularnewline
38 & 149.4 & 136.567 & 136.196 & 1.00273 & 1.09397 \tabularnewline
39 & 144.3 & 139.463 & 139.379 & 1.0006 & 1.03468 \tabularnewline
40 & 154.4 & 139.983 & 141.246 & 0.991056 & 1.10299 \tabularnewline
41 & 151.4 & 142.589 & 142.196 & 1.00276 & 1.06179 \tabularnewline
42 & 145.5 & 140.606 & 142.375 & 0.987578 & 1.0348 \tabularnewline
43 & 136.8 & 143.32 & 141.754 & 1.01105 & 0.954508 \tabularnewline
44 & 146.6 & 143.867 & 140.579 & 1.02339 & 1.019 \tabularnewline
45 & 145.1 & 142.444 & 139.317 & 1.02245 & 1.01864 \tabularnewline
46 & 133.6 & 135.424 & 137.742 & 0.983172 & 0.986533 \tabularnewline
47 & 131.4 & 132.577 & 135.571 & 0.977919 & 0.99112 \tabularnewline
48 & 127.5 & 132.172 & 133.775 & 0.988019 & 0.96465 \tabularnewline
49 & 130.1 & 135.122 & 133.879 & 1.00928 & 0.962835 \tabularnewline
50 & 131.1 & 135.744 & 135.375 & 1.00273 & 0.965787 \tabularnewline
51 & 132.3 & 136.761 & 136.679 & 1.0006 & 0.967379 \tabularnewline
52 & 128.6 & 137.047 & 138.283 & 0.991056 & 0.938367 \tabularnewline
53 & 125.1 & 140.696 & 140.308 & 1.00276 & 0.88915 \tabularnewline
54 & 128.7 & 140.615 & 142.383 & 0.987578 & 0.915267 \tabularnewline
55 & 156.1 & 145.822 & 144.229 & 1.01105 & 1.07048 \tabularnewline
56 & 163.2 & 149.056 & 145.65 & 1.02339 & 1.09489 \tabularnewline
57 & 159.8 & 150.083 & 146.787 & 1.02245 & 1.06474 \tabularnewline
58 & 157.4 & 145.128 & 147.612 & 0.983172 & 1.08456 \tabularnewline
59 & 156.2 & 145.16 & 148.437 & 0.977919 & 1.07605 \tabularnewline
60 & 152.5 & 147.474 & 149.262 & 0.988019 & 1.03408 \tabularnewline
61 & 149.4 & 149.34 & 147.967 & 1.00928 & 1.0004 \tabularnewline
62 & 145.9 & 144.606 & 144.212 & 1.00273 & 1.00895 \tabularnewline
63 & 144.8 & 139.976 & 139.892 & 1.0006 & 1.03446 \tabularnewline
64 & 135.9 & 134.424 & 135.638 & 0.991056 & 1.01098 \tabularnewline
65 & 137.6 & 131.68 & 131.317 & 1.00276 & 1.04496 \tabularnewline
66 & 136 & 125.361 & 126.938 & 0.987578 & 1.08487 \tabularnewline
67 & 117.7 & NA & NA & 1.01105 & NA \tabularnewline
68 & 111.5 & NA & NA & 1.02339 & NA \tabularnewline
69 & 107.8 & NA & NA & 1.02245 & NA \tabularnewline
70 & 107.3 & NA & NA & 0.983172 & NA \tabularnewline
71 & 102.6 & NA & NA & 0.977919 & NA \tabularnewline
72 & 101 & NA & NA & 0.988019 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=278645&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]123.2[/C][C]NA[/C][C]NA[/C][C]1.00928[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]136.9[/C][C]NA[/C][C]NA[/C][C]1.00273[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]146.8[/C][C]NA[/C][C]NA[/C][C]1.0006[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]149.6[/C][C]NA[/C][C]NA[/C][C]0.991056[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]146.5[/C][C]NA[/C][C]NA[/C][C]1.00276[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]157[/C][C]NA[/C][C]NA[/C][C]0.987578[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]147.9[/C][C]129.654[/C][C]128.237[/C][C]1.01105[/C][C]1.14073[/C][/ROW]
[ROW][C]8[/C][C]133.6[/C][C]128.175[/C][C]125.246[/C][C]1.02339[/C][C]1.04233[/C][/ROW]
[ROW][C]9[/C][C]128.7[/C][C]123.849[/C][C]121.129[/C][C]1.02245[/C][C]1.03917[/C][/ROW]
[ROW][C]10[/C][C]100.8[/C][C]114.593[/C][C]116.554[/C][C]0.983172[/C][C]0.879636[/C][/ROW]
[ROW][C]11[/C][C]91.8[/C][C]109.833[/C][C]112.312[/C][C]0.977919[/C][C]0.835818[/C][/ROW]
[ROW][C]12[/C][C]89.3[/C][C]106.764[/C][C]108.058[/C][C]0.988019[/C][C]0.836426[/C][/ROW]
[ROW][C]13[/C][C]96.7[/C][C]104.116[/C][C]103.158[/C][C]1.00928[/C][C]0.928773[/C][/ROW]
[ROW][C]14[/C][C]91.6[/C][C]98.8146[/C][C]98.5458[/C][C]1.00273[/C][C]0.926989[/C][/ROW]
[ROW][C]15[/C][C]93.3[/C][C]94.5151[/C][C]94.4583[/C][C]1.0006[/C][C]0.987144[/C][/ROW]
[ROW][C]16[/C][C]93.3[/C][C]91.074[/C][C]91.8958[/C][C]0.991056[/C][C]1.02444[/C][/ROW]
[ROW][C]17[/C][C]101[/C][C]91.6401[/C][C]91.3875[/C][C]1.00276[/C][C]1.10214[/C][/ROW]
[ROW][C]18[/C][C]100.4[/C][C]90.5445[/C][C]91.6833[/C][C]0.987578[/C][C]1.10885[/C][/ROW]
[ROW][C]19[/C][C]86.9[/C][C]92.7592[/C][C]91.7458[/C][C]1.01105[/C][C]0.936834[/C][/ROW]
[ROW][C]20[/C][C]83.9[/C][C]93.5118[/C][C]91.375[/C][C]1.02339[/C][C]0.897213[/C][/ROW]
[ROW][C]21[/C][C]80.3[/C][C]92.9706[/C][C]90.9292[/C][C]1.02245[/C][C]0.863714[/C][/ROW]
[ROW][C]22[/C][C]87.7[/C][C]88.7395[/C][C]90.2583[/C][C]0.983172[/C][C]0.988286[/C][/ROW]
[ROW][C]23[/C][C]92.7[/C][C]87.2222[/C][C]89.1917[/C][C]0.977919[/C][C]1.0628[/C][/ROW]
[ROW][C]24[/C][C]95.5[/C][C]86.6946[/C][C]87.7458[/C][C]0.988019[/C][C]1.10157[/C][/ROW]
[ROW][C]25[/C][C]92[/C][C]87.9421[/C][C]87.1333[/C][C]1.00928[/C][C]1.04614[/C][/ROW]
[ROW][C]26[/C][C]87.4[/C][C]88.2734[/C][C]88.0333[/C][C]1.00273[/C][C]0.990106[/C][/ROW]
[ROW][C]27[/C][C]86.8[/C][C]90.2167[/C][C]90.1625[/C][C]1.0006[/C][C]0.962128[/C][/ROW]
[ROW][C]28[/C][C]83.7[/C][C]92.0774[/C][C]92.9083[/C][C]0.991056[/C][C]0.909018[/C][/ROW]
[ROW][C]29[/C][C]85[/C][C]95.7472[/C][C]95.4833[/C][C]1.00276[/C][C]0.887754[/C][/ROW]
[ROW][C]30[/C][C]81.7[/C][C]97.0296[/C][C]98.25[/C][C]0.987578[/C][C]0.842011[/C][/ROW]
[ROW][C]31[/C][C]90.9[/C][C]102.916[/C][C]101.792[/C][C]1.01105[/C][C]0.883245[/C][/ROW]
[ROW][C]32[/C][C]101.5[/C][C]108.863[/C][C]106.375[/C][C]1.02339[/C][C]0.932368[/C][/ROW]
[ROW][C]33[/C][C]113.8[/C][C]113.854[/C][C]111.354[/C][C]1.02245[/C][C]0.999525[/C][/ROW]
[ROW][C]34[/C][C]120.1[/C][C]114.732[/C][C]116.696[/C][C]0.983172[/C][C]1.04679[/C][/ROW]
[ROW][C]35[/C][C]122.1[/C][C]119.705[/C][C]122.408[/C][C]0.977919[/C][C]1.02[/C][/ROW]
[ROW][C]36[/C][C]132.5[/C][C]126.302[/C][C]127.833[/C][C]0.988019[/C][C]1.04907[/C][/ROW]
[ROW][C]37[/C][C]140[/C][C]133.633[/C][C]132.404[/C][C]1.00928[/C][C]1.04764[/C][/ROW]
[ROW][C]38[/C][C]149.4[/C][C]136.567[/C][C]136.196[/C][C]1.00273[/C][C]1.09397[/C][/ROW]
[ROW][C]39[/C][C]144.3[/C][C]139.463[/C][C]139.379[/C][C]1.0006[/C][C]1.03468[/C][/ROW]
[ROW][C]40[/C][C]154.4[/C][C]139.983[/C][C]141.246[/C][C]0.991056[/C][C]1.10299[/C][/ROW]
[ROW][C]41[/C][C]151.4[/C][C]142.589[/C][C]142.196[/C][C]1.00276[/C][C]1.06179[/C][/ROW]
[ROW][C]42[/C][C]145.5[/C][C]140.606[/C][C]142.375[/C][C]0.987578[/C][C]1.0348[/C][/ROW]
[ROW][C]43[/C][C]136.8[/C][C]143.32[/C][C]141.754[/C][C]1.01105[/C][C]0.954508[/C][/ROW]
[ROW][C]44[/C][C]146.6[/C][C]143.867[/C][C]140.579[/C][C]1.02339[/C][C]1.019[/C][/ROW]
[ROW][C]45[/C][C]145.1[/C][C]142.444[/C][C]139.317[/C][C]1.02245[/C][C]1.01864[/C][/ROW]
[ROW][C]46[/C][C]133.6[/C][C]135.424[/C][C]137.742[/C][C]0.983172[/C][C]0.986533[/C][/ROW]
[ROW][C]47[/C][C]131.4[/C][C]132.577[/C][C]135.571[/C][C]0.977919[/C][C]0.99112[/C][/ROW]
[ROW][C]48[/C][C]127.5[/C][C]132.172[/C][C]133.775[/C][C]0.988019[/C][C]0.96465[/C][/ROW]
[ROW][C]49[/C][C]130.1[/C][C]135.122[/C][C]133.879[/C][C]1.00928[/C][C]0.962835[/C][/ROW]
[ROW][C]50[/C][C]131.1[/C][C]135.744[/C][C]135.375[/C][C]1.00273[/C][C]0.965787[/C][/ROW]
[ROW][C]51[/C][C]132.3[/C][C]136.761[/C][C]136.679[/C][C]1.0006[/C][C]0.967379[/C][/ROW]
[ROW][C]52[/C][C]128.6[/C][C]137.047[/C][C]138.283[/C][C]0.991056[/C][C]0.938367[/C][/ROW]
[ROW][C]53[/C][C]125.1[/C][C]140.696[/C][C]140.308[/C][C]1.00276[/C][C]0.88915[/C][/ROW]
[ROW][C]54[/C][C]128.7[/C][C]140.615[/C][C]142.383[/C][C]0.987578[/C][C]0.915267[/C][/ROW]
[ROW][C]55[/C][C]156.1[/C][C]145.822[/C][C]144.229[/C][C]1.01105[/C][C]1.07048[/C][/ROW]
[ROW][C]56[/C][C]163.2[/C][C]149.056[/C][C]145.65[/C][C]1.02339[/C][C]1.09489[/C][/ROW]
[ROW][C]57[/C][C]159.8[/C][C]150.083[/C][C]146.787[/C][C]1.02245[/C][C]1.06474[/C][/ROW]
[ROW][C]58[/C][C]157.4[/C][C]145.128[/C][C]147.612[/C][C]0.983172[/C][C]1.08456[/C][/ROW]
[ROW][C]59[/C][C]156.2[/C][C]145.16[/C][C]148.437[/C][C]0.977919[/C][C]1.07605[/C][/ROW]
[ROW][C]60[/C][C]152.5[/C][C]147.474[/C][C]149.262[/C][C]0.988019[/C][C]1.03408[/C][/ROW]
[ROW][C]61[/C][C]149.4[/C][C]149.34[/C][C]147.967[/C][C]1.00928[/C][C]1.0004[/C][/ROW]
[ROW][C]62[/C][C]145.9[/C][C]144.606[/C][C]144.212[/C][C]1.00273[/C][C]1.00895[/C][/ROW]
[ROW][C]63[/C][C]144.8[/C][C]139.976[/C][C]139.892[/C][C]1.0006[/C][C]1.03446[/C][/ROW]
[ROW][C]64[/C][C]135.9[/C][C]134.424[/C][C]135.638[/C][C]0.991056[/C][C]1.01098[/C][/ROW]
[ROW][C]65[/C][C]137.6[/C][C]131.68[/C][C]131.317[/C][C]1.00276[/C][C]1.04496[/C][/ROW]
[ROW][C]66[/C][C]136[/C][C]125.361[/C][C]126.938[/C][C]0.987578[/C][C]1.08487[/C][/ROW]
[ROW][C]67[/C][C]117.7[/C][C]NA[/C][C]NA[/C][C]1.01105[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]111.5[/C][C]NA[/C][C]NA[/C][C]1.02339[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]107.8[/C][C]NA[/C][C]NA[/C][C]1.02245[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]107.3[/C][C]NA[/C][C]NA[/C][C]0.983172[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]102.6[/C][C]NA[/C][C]NA[/C][C]0.977919[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]101[/C][C]NA[/C][C]NA[/C][C]0.988019[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=278645&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=278645&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	123.2	NA	NA	1.00928	NA
2	136.9	NA	NA	1.00273	NA
3	146.8	NA	NA	1.0006	NA
4	149.6	NA	NA	0.991056	NA
5	146.5	NA	NA	1.00276	NA
6	157	NA	NA	0.987578	NA
7	147.9	129.654	128.237	1.01105	1.14073
8	133.6	128.175	125.246	1.02339	1.04233
9	128.7	123.849	121.129	1.02245	1.03917
10	100.8	114.593	116.554	0.983172	0.879636
11	91.8	109.833	112.312	0.977919	0.835818
12	89.3	106.764	108.058	0.988019	0.836426
13	96.7	104.116	103.158	1.00928	0.928773
14	91.6	98.8146	98.5458	1.00273	0.926989
15	93.3	94.5151	94.4583	1.0006	0.987144
16	93.3	91.074	91.8958	0.991056	1.02444
17	101	91.6401	91.3875	1.00276	1.10214
18	100.4	90.5445	91.6833	0.987578	1.10885
19	86.9	92.7592	91.7458	1.01105	0.936834
20	83.9	93.5118	91.375	1.02339	0.897213
21	80.3	92.9706	90.9292	1.02245	0.863714
22	87.7	88.7395	90.2583	0.983172	0.988286
23	92.7	87.2222	89.1917	0.977919	1.0628
24	95.5	86.6946	87.7458	0.988019	1.10157
25	92	87.9421	87.1333	1.00928	1.04614
26	87.4	88.2734	88.0333	1.00273	0.990106
27	86.8	90.2167	90.1625	1.0006	0.962128
28	83.7	92.0774	92.9083	0.991056	0.909018
29	85	95.7472	95.4833	1.00276	0.887754
30	81.7	97.0296	98.25	0.987578	0.842011
31	90.9	102.916	101.792	1.01105	0.883245
32	101.5	108.863	106.375	1.02339	0.932368
33	113.8	113.854	111.354	1.02245	0.999525
34	120.1	114.732	116.696	0.983172	1.04679
35	122.1	119.705	122.408	0.977919	1.02
36	132.5	126.302	127.833	0.988019	1.04907
37	140	133.633	132.404	1.00928	1.04764
38	149.4	136.567	136.196	1.00273	1.09397
39	144.3	139.463	139.379	1.0006	1.03468
40	154.4	139.983	141.246	0.991056	1.10299
41	151.4	142.589	142.196	1.00276	1.06179
42	145.5	140.606	142.375	0.987578	1.0348
43	136.8	143.32	141.754	1.01105	0.954508
44	146.6	143.867	140.579	1.02339	1.019
45	145.1	142.444	139.317	1.02245	1.01864
46	133.6	135.424	137.742	0.983172	0.986533
47	131.4	132.577	135.571	0.977919	0.99112
48	127.5	132.172	133.775	0.988019	0.96465
49	130.1	135.122	133.879	1.00928	0.962835
50	131.1	135.744	135.375	1.00273	0.965787
51	132.3	136.761	136.679	1.0006	0.967379
52	128.6	137.047	138.283	0.991056	0.938367
53	125.1	140.696	140.308	1.00276	0.88915
54	128.7	140.615	142.383	0.987578	0.915267
55	156.1	145.822	144.229	1.01105	1.07048
56	163.2	149.056	145.65	1.02339	1.09489
57	159.8	150.083	146.787	1.02245	1.06474
58	157.4	145.128	147.612	0.983172	1.08456
59	156.2	145.16	148.437	0.977919	1.07605
60	152.5	147.474	149.262	0.988019	1.03408
61	149.4	149.34	147.967	1.00928	1.0004
62	145.9	144.606	144.212	1.00273	1.00895
63	144.8	139.976	139.892	1.0006	1.03446
64	135.9	134.424	135.638	0.991056	1.01098
65	137.6	131.68	131.317	1.00276	1.04496
66	136	125.361	126.938	0.987578	1.08487
67	117.7	NA	NA	1.01105	NA
68	111.5	NA	NA	1.02339	NA
69	107.8	NA	NA	1.02245	NA
70	107.3	NA	NA	0.983172	NA
71	102.6	NA	NA	0.977919	NA
72	101	NA	NA	0.988019	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 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- '12'
par1 <- 'multiplicative'
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