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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Wed, 04 Dec 2013 04:05:02 -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/04/t1386148190odxmjh8ew3meqzo.htm/, Retrieved Thu, 25 Apr 2024 05:02:50 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=230450, Retrieved Thu, 25 Apr 2024 05:02:50 +0000

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Estimated Impact

105

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

-       [Classical Decomposition] [] [2013-12-04 09:05:02] [bc709afd059270defb36fb1011c3ea57] [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	'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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230450&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230450&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230450&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	6 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	86.5	NA	NA	-3.44676	NA
2	86.6	NA	NA	-1.96273	NA
3	98.8	NA	NA	8.82546	NA
4	84.4	NA	NA	-1.51273	NA
5	91.4	NA	NA	-1.01829	NA
6	95.7	NA	NA	4.73727	NA
7	78.5	82.0019	90.5542	-8.55231	-3.50185
8	81.7	82.8595	90.9833	-8.12384	-1.15949
9	94.3	96.8081	91.2917	5.51644	-2.5081
10	98.5	97.6477	91.7417	5.90602	0.852315
11	95.4	92.838	92.2	0.637963	2.56204
12	91.7	91.606	92.6125	-1.00648	0.0939815
13	92.8	89.8616	93.3083	-3.44676	2.93843
14	90.6	92.1081	94.0708	-1.96273	-1.5081
15	102.2	103.375	94.55	8.82546	-1.17546
16	91.8	93.6081	95.1208	-1.51273	-1.8081
17	95	94.7484	95.7667	-1.01829	0.25162
18	102	100.871	96.1333	4.73727	1.1294
19	88.9	88.0644	96.6167	-8.55231	0.835648
20	89.6	89.2637	97.3875	-8.12384	0.336343
21	97.9	103.346	97.8292	5.51644	-5.4456
22	108.6	104.314	98.4083	5.90602	4.28565
23	100.8	99.7546	99.1167	0.637963	1.04537
24	95.1	98.3894	99.3958	-1.00648	-3.28935
25	101	96.4032	99.85	-3.44676	4.59676
26	100.9	98.1414	100.104	-1.96273	2.75856
27	102.5	109.284	100.458	8.82546	-6.7838
28	105.4	99.2998	100.813	-1.51273	6.10023
29	98.4	99.3859	100.404	-1.01829	-0.98588
30	105.3	104.821	100.083	4.73727	0.479398
31	96.5	90.8977	99.45	-8.55231	5.60231
32	88.1	90.0262	98.15	-8.12384	-1.92616
33	107.9	102.7	97.1833	5.51644	5.20023
34	107.1	101.964	96.0583	5.90602	5.13565
35	92.5	95.4963	94.8583	0.637963	-2.9963
36	95.7	92.956	93.9625	-1.00648	2.74398
37	85.2	89.4741	92.9208	-3.44676	-4.27407
38	85.5	90.1956	92.1583	-1.96273	-4.6956
39	94.7	100.3	91.475	8.82546	-5.60046
40	86.2	89.0414	90.5542	-1.51273	-2.84144
41	88.8	89.0942	90.1125	-1.01829	-0.294213
42	93.4	94.7414	90.0042	4.73727	-1.34144
43	83.4	81.5394	90.0917	-8.55231	1.86065
44	82.9	82.6095	90.7333	-8.12384	0.290509
45	96.7	97.2164	91.7	5.51644	-0.516435
46	96.2	98.631	92.725	5.90602	-2.43102
47	92.8	94.113	93.475	0.637963	-1.31296
48	92.8	93.356	94.3625	-1.00648	-0.556019
49	90.2	91.8949	95.3417	-3.44676	-1.69491
50	95.9	94.0956	96.0583	-1.96273	1.8044
51	107.5	105.734	96.9083	8.82546	1.7662
52	98	96.2498	97.7625	-1.51273	1.75023
53	95	97.615	98.6333	-1.01829	-2.61505
54	108.5	104.304	99.5667	4.73727	4.19606
55	91.8	91.7977	100.35	-8.55231	0.00231481
56	91.7	92.847	100.971	-8.12384	-1.14699
57	108.3	107.329	101.813	5.51644	0.971065
58	105.1	108.481	102.575	5.90602	-3.38102
59	104.8	103.984	103.346	0.637963	0.816204
60	103.2	102.794	103.8	-1.00648	0.406481
61	98.6	100.187	103.633	-3.44676	-1.58657
62	102.4	101.958	103.921	-1.96273	0.441898
63	121.2	113.213	104.387	8.82546	7.98704
64	102.6	102.996	104.508	-1.51273	-0.395602
65	108.9	103.403	104.421	-1.01829	5.49745
66	105.5	109.025	104.287	4.73727	-3.52477
67	90.8	95.681	104.233	-8.55231	-4.88102
68	99.6	96.0762	104.2	-8.12384	3.52384
69	111.6	109.383	103.867	5.51644	2.2169
70	104.7	109.244	103.338	5.90602	-4.54352
71	103.1	103.296	102.658	0.637963	-0.196296
72	101.7	101.181	102.187	-1.00648	0.518981
73	98.8	98.8616	102.308	-3.44676	-0.0615741
74	101.4	100.283	102.246	-1.96273	1.1169
75	114.2	110.475	101.65	8.82546	3.72454
76	96.9	99.7873	101.3	-1.51273	-2.88727
77	98.3	100.236	101.254	-1.01829	-1.93588
78	104.8	105.821	101.083	4.73727	-1.0206
79	94.4	NA	NA	-8.55231	NA
80	94.5	NA	NA	-8.12384	NA
81	102.4	NA	NA	5.51644	NA
82	105.5	NA	NA	5.90602	NA
83	101.2	NA	NA	0.637963	NA
84	99.5	NA	NA	-1.00648	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 86.5 & NA & NA & -3.44676 & NA \tabularnewline
2 & 86.6 & NA & NA & -1.96273 & NA \tabularnewline
3 & 98.8 & NA & NA & 8.82546 & NA \tabularnewline
4 & 84.4 & NA & NA & -1.51273 & NA \tabularnewline
5 & 91.4 & NA & NA & -1.01829 & NA \tabularnewline
6 & 95.7 & NA & NA & 4.73727 & NA \tabularnewline
7 & 78.5 & 82.0019 & 90.5542 & -8.55231 & -3.50185 \tabularnewline
8 & 81.7 & 82.8595 & 90.9833 & -8.12384 & -1.15949 \tabularnewline
9 & 94.3 & 96.8081 & 91.2917 & 5.51644 & -2.5081 \tabularnewline
10 & 98.5 & 97.6477 & 91.7417 & 5.90602 & 0.852315 \tabularnewline
11 & 95.4 & 92.838 & 92.2 & 0.637963 & 2.56204 \tabularnewline
12 & 91.7 & 91.606 & 92.6125 & -1.00648 & 0.0939815 \tabularnewline
13 & 92.8 & 89.8616 & 93.3083 & -3.44676 & 2.93843 \tabularnewline
14 & 90.6 & 92.1081 & 94.0708 & -1.96273 & -1.5081 \tabularnewline
15 & 102.2 & 103.375 & 94.55 & 8.82546 & -1.17546 \tabularnewline
16 & 91.8 & 93.6081 & 95.1208 & -1.51273 & -1.8081 \tabularnewline
17 & 95 & 94.7484 & 95.7667 & -1.01829 & 0.25162 \tabularnewline
18 & 102 & 100.871 & 96.1333 & 4.73727 & 1.1294 \tabularnewline
19 & 88.9 & 88.0644 & 96.6167 & -8.55231 & 0.835648 \tabularnewline
20 & 89.6 & 89.2637 & 97.3875 & -8.12384 & 0.336343 \tabularnewline
21 & 97.9 & 103.346 & 97.8292 & 5.51644 & -5.4456 \tabularnewline
22 & 108.6 & 104.314 & 98.4083 & 5.90602 & 4.28565 \tabularnewline
23 & 100.8 & 99.7546 & 99.1167 & 0.637963 & 1.04537 \tabularnewline
24 & 95.1 & 98.3894 & 99.3958 & -1.00648 & -3.28935 \tabularnewline
25 & 101 & 96.4032 & 99.85 & -3.44676 & 4.59676 \tabularnewline
26 & 100.9 & 98.1414 & 100.104 & -1.96273 & 2.75856 \tabularnewline
27 & 102.5 & 109.284 & 100.458 & 8.82546 & -6.7838 \tabularnewline
28 & 105.4 & 99.2998 & 100.813 & -1.51273 & 6.10023 \tabularnewline
29 & 98.4 & 99.3859 & 100.404 & -1.01829 & -0.98588 \tabularnewline
30 & 105.3 & 104.821 & 100.083 & 4.73727 & 0.479398 \tabularnewline
31 & 96.5 & 90.8977 & 99.45 & -8.55231 & 5.60231 \tabularnewline
32 & 88.1 & 90.0262 & 98.15 & -8.12384 & -1.92616 \tabularnewline
33 & 107.9 & 102.7 & 97.1833 & 5.51644 & 5.20023 \tabularnewline
34 & 107.1 & 101.964 & 96.0583 & 5.90602 & 5.13565 \tabularnewline
35 & 92.5 & 95.4963 & 94.8583 & 0.637963 & -2.9963 \tabularnewline
36 & 95.7 & 92.956 & 93.9625 & -1.00648 & 2.74398 \tabularnewline
37 & 85.2 & 89.4741 & 92.9208 & -3.44676 & -4.27407 \tabularnewline
38 & 85.5 & 90.1956 & 92.1583 & -1.96273 & -4.6956 \tabularnewline
39 & 94.7 & 100.3 & 91.475 & 8.82546 & -5.60046 \tabularnewline
40 & 86.2 & 89.0414 & 90.5542 & -1.51273 & -2.84144 \tabularnewline
41 & 88.8 & 89.0942 & 90.1125 & -1.01829 & -0.294213 \tabularnewline
42 & 93.4 & 94.7414 & 90.0042 & 4.73727 & -1.34144 \tabularnewline
43 & 83.4 & 81.5394 & 90.0917 & -8.55231 & 1.86065 \tabularnewline
44 & 82.9 & 82.6095 & 90.7333 & -8.12384 & 0.290509 \tabularnewline
45 & 96.7 & 97.2164 & 91.7 & 5.51644 & -0.516435 \tabularnewline
46 & 96.2 & 98.631 & 92.725 & 5.90602 & -2.43102 \tabularnewline
47 & 92.8 & 94.113 & 93.475 & 0.637963 & -1.31296 \tabularnewline
48 & 92.8 & 93.356 & 94.3625 & -1.00648 & -0.556019 \tabularnewline
49 & 90.2 & 91.8949 & 95.3417 & -3.44676 & -1.69491 \tabularnewline
50 & 95.9 & 94.0956 & 96.0583 & -1.96273 & 1.8044 \tabularnewline
51 & 107.5 & 105.734 & 96.9083 & 8.82546 & 1.7662 \tabularnewline
52 & 98 & 96.2498 & 97.7625 & -1.51273 & 1.75023 \tabularnewline
53 & 95 & 97.615 & 98.6333 & -1.01829 & -2.61505 \tabularnewline
54 & 108.5 & 104.304 & 99.5667 & 4.73727 & 4.19606 \tabularnewline
55 & 91.8 & 91.7977 & 100.35 & -8.55231 & 0.00231481 \tabularnewline
56 & 91.7 & 92.847 & 100.971 & -8.12384 & -1.14699 \tabularnewline
57 & 108.3 & 107.329 & 101.813 & 5.51644 & 0.971065 \tabularnewline
58 & 105.1 & 108.481 & 102.575 & 5.90602 & -3.38102 \tabularnewline
59 & 104.8 & 103.984 & 103.346 & 0.637963 & 0.816204 \tabularnewline
60 & 103.2 & 102.794 & 103.8 & -1.00648 & 0.406481 \tabularnewline
61 & 98.6 & 100.187 & 103.633 & -3.44676 & -1.58657 \tabularnewline
62 & 102.4 & 101.958 & 103.921 & -1.96273 & 0.441898 \tabularnewline
63 & 121.2 & 113.213 & 104.387 & 8.82546 & 7.98704 \tabularnewline
64 & 102.6 & 102.996 & 104.508 & -1.51273 & -0.395602 \tabularnewline
65 & 108.9 & 103.403 & 104.421 & -1.01829 & 5.49745 \tabularnewline
66 & 105.5 & 109.025 & 104.287 & 4.73727 & -3.52477 \tabularnewline
67 & 90.8 & 95.681 & 104.233 & -8.55231 & -4.88102 \tabularnewline
68 & 99.6 & 96.0762 & 104.2 & -8.12384 & 3.52384 \tabularnewline
69 & 111.6 & 109.383 & 103.867 & 5.51644 & 2.2169 \tabularnewline
70 & 104.7 & 109.244 & 103.338 & 5.90602 & -4.54352 \tabularnewline
71 & 103.1 & 103.296 & 102.658 & 0.637963 & -0.196296 \tabularnewline
72 & 101.7 & 101.181 & 102.187 & -1.00648 & 0.518981 \tabularnewline
73 & 98.8 & 98.8616 & 102.308 & -3.44676 & -0.0615741 \tabularnewline
74 & 101.4 & 100.283 & 102.246 & -1.96273 & 1.1169 \tabularnewline
75 & 114.2 & 110.475 & 101.65 & 8.82546 & 3.72454 \tabularnewline
76 & 96.9 & 99.7873 & 101.3 & -1.51273 & -2.88727 \tabularnewline
77 & 98.3 & 100.236 & 101.254 & -1.01829 & -1.93588 \tabularnewline
78 & 104.8 & 105.821 & 101.083 & 4.73727 & -1.0206 \tabularnewline
79 & 94.4 & NA & NA & -8.55231 & NA \tabularnewline
80 & 94.5 & NA & NA & -8.12384 & NA \tabularnewline
81 & 102.4 & NA & NA & 5.51644 & NA \tabularnewline
82 & 105.5 & NA & NA & 5.90602 & NA \tabularnewline
83 & 101.2 & NA & NA & 0.637963 & NA \tabularnewline
84 & 99.5 & NA & NA & -1.00648 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230450&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]86.5[/C][C]NA[/C][C]NA[/C][C]-3.44676[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]86.6[/C][C]NA[/C][C]NA[/C][C]-1.96273[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]98.8[/C][C]NA[/C][C]NA[/C][C]8.82546[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]84.4[/C][C]NA[/C][C]NA[/C][C]-1.51273[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]91.4[/C][C]NA[/C][C]NA[/C][C]-1.01829[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]95.7[/C][C]NA[/C][C]NA[/C][C]4.73727[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]78.5[/C][C]82.0019[/C][C]90.5542[/C][C]-8.55231[/C][C]-3.50185[/C][/ROW]
[ROW][C]8[/C][C]81.7[/C][C]82.8595[/C][C]90.9833[/C][C]-8.12384[/C][C]-1.15949[/C][/ROW]
[ROW][C]9[/C][C]94.3[/C][C]96.8081[/C][C]91.2917[/C][C]5.51644[/C][C]-2.5081[/C][/ROW]
[ROW][C]10[/C][C]98.5[/C][C]97.6477[/C][C]91.7417[/C][C]5.90602[/C][C]0.852315[/C][/ROW]
[ROW][C]11[/C][C]95.4[/C][C]92.838[/C][C]92.2[/C][C]0.637963[/C][C]2.56204[/C][/ROW]
[ROW][C]12[/C][C]91.7[/C][C]91.606[/C][C]92.6125[/C][C]-1.00648[/C][C]0.0939815[/C][/ROW]
[ROW][C]13[/C][C]92.8[/C][C]89.8616[/C][C]93.3083[/C][C]-3.44676[/C][C]2.93843[/C][/ROW]
[ROW][C]14[/C][C]90.6[/C][C]92.1081[/C][C]94.0708[/C][C]-1.96273[/C][C]-1.5081[/C][/ROW]
[ROW][C]15[/C][C]102.2[/C][C]103.375[/C][C]94.55[/C][C]8.82546[/C][C]-1.17546[/C][/ROW]
[ROW][C]16[/C][C]91.8[/C][C]93.6081[/C][C]95.1208[/C][C]-1.51273[/C][C]-1.8081[/C][/ROW]
[ROW][C]17[/C][C]95[/C][C]94.7484[/C][C]95.7667[/C][C]-1.01829[/C][C]0.25162[/C][/ROW]
[ROW][C]18[/C][C]102[/C][C]100.871[/C][C]96.1333[/C][C]4.73727[/C][C]1.1294[/C][/ROW]
[ROW][C]19[/C][C]88.9[/C][C]88.0644[/C][C]96.6167[/C][C]-8.55231[/C][C]0.835648[/C][/ROW]
[ROW][C]20[/C][C]89.6[/C][C]89.2637[/C][C]97.3875[/C][C]-8.12384[/C][C]0.336343[/C][/ROW]
[ROW][C]21[/C][C]97.9[/C][C]103.346[/C][C]97.8292[/C][C]5.51644[/C][C]-5.4456[/C][/ROW]
[ROW][C]22[/C][C]108.6[/C][C]104.314[/C][C]98.4083[/C][C]5.90602[/C][C]4.28565[/C][/ROW]
[ROW][C]23[/C][C]100.8[/C][C]99.7546[/C][C]99.1167[/C][C]0.637963[/C][C]1.04537[/C][/ROW]
[ROW][C]24[/C][C]95.1[/C][C]98.3894[/C][C]99.3958[/C][C]-1.00648[/C][C]-3.28935[/C][/ROW]
[ROW][C]25[/C][C]101[/C][C]96.4032[/C][C]99.85[/C][C]-3.44676[/C][C]4.59676[/C][/ROW]
[ROW][C]26[/C][C]100.9[/C][C]98.1414[/C][C]100.104[/C][C]-1.96273[/C][C]2.75856[/C][/ROW]
[ROW][C]27[/C][C]102.5[/C][C]109.284[/C][C]100.458[/C][C]8.82546[/C][C]-6.7838[/C][/ROW]
[ROW][C]28[/C][C]105.4[/C][C]99.2998[/C][C]100.813[/C][C]-1.51273[/C][C]6.10023[/C][/ROW]
[ROW][C]29[/C][C]98.4[/C][C]99.3859[/C][C]100.404[/C][C]-1.01829[/C][C]-0.98588[/C][/ROW]
[ROW][C]30[/C][C]105.3[/C][C]104.821[/C][C]100.083[/C][C]4.73727[/C][C]0.479398[/C][/ROW]
[ROW][C]31[/C][C]96.5[/C][C]90.8977[/C][C]99.45[/C][C]-8.55231[/C][C]5.60231[/C][/ROW]
[ROW][C]32[/C][C]88.1[/C][C]90.0262[/C][C]98.15[/C][C]-8.12384[/C][C]-1.92616[/C][/ROW]
[ROW][C]33[/C][C]107.9[/C][C]102.7[/C][C]97.1833[/C][C]5.51644[/C][C]5.20023[/C][/ROW]
[ROW][C]34[/C][C]107.1[/C][C]101.964[/C][C]96.0583[/C][C]5.90602[/C][C]5.13565[/C][/ROW]
[ROW][C]35[/C][C]92.5[/C][C]95.4963[/C][C]94.8583[/C][C]0.637963[/C][C]-2.9963[/C][/ROW]
[ROW][C]36[/C][C]95.7[/C][C]92.956[/C][C]93.9625[/C][C]-1.00648[/C][C]2.74398[/C][/ROW]
[ROW][C]37[/C][C]85.2[/C][C]89.4741[/C][C]92.9208[/C][C]-3.44676[/C][C]-4.27407[/C][/ROW]
[ROW][C]38[/C][C]85.5[/C][C]90.1956[/C][C]92.1583[/C][C]-1.96273[/C][C]-4.6956[/C][/ROW]
[ROW][C]39[/C][C]94.7[/C][C]100.3[/C][C]91.475[/C][C]8.82546[/C][C]-5.60046[/C][/ROW]
[ROW][C]40[/C][C]86.2[/C][C]89.0414[/C][C]90.5542[/C][C]-1.51273[/C][C]-2.84144[/C][/ROW]
[ROW][C]41[/C][C]88.8[/C][C]89.0942[/C][C]90.1125[/C][C]-1.01829[/C][C]-0.294213[/C][/ROW]
[ROW][C]42[/C][C]93.4[/C][C]94.7414[/C][C]90.0042[/C][C]4.73727[/C][C]-1.34144[/C][/ROW]
[ROW][C]43[/C][C]83.4[/C][C]81.5394[/C][C]90.0917[/C][C]-8.55231[/C][C]1.86065[/C][/ROW]
[ROW][C]44[/C][C]82.9[/C][C]82.6095[/C][C]90.7333[/C][C]-8.12384[/C][C]0.290509[/C][/ROW]
[ROW][C]45[/C][C]96.7[/C][C]97.2164[/C][C]91.7[/C][C]5.51644[/C][C]-0.516435[/C][/ROW]
[ROW][C]46[/C][C]96.2[/C][C]98.631[/C][C]92.725[/C][C]5.90602[/C][C]-2.43102[/C][/ROW]
[ROW][C]47[/C][C]92.8[/C][C]94.113[/C][C]93.475[/C][C]0.637963[/C][C]-1.31296[/C][/ROW]
[ROW][C]48[/C][C]92.8[/C][C]93.356[/C][C]94.3625[/C][C]-1.00648[/C][C]-0.556019[/C][/ROW]
[ROW][C]49[/C][C]90.2[/C][C]91.8949[/C][C]95.3417[/C][C]-3.44676[/C][C]-1.69491[/C][/ROW]
[ROW][C]50[/C][C]95.9[/C][C]94.0956[/C][C]96.0583[/C][C]-1.96273[/C][C]1.8044[/C][/ROW]
[ROW][C]51[/C][C]107.5[/C][C]105.734[/C][C]96.9083[/C][C]8.82546[/C][C]1.7662[/C][/ROW]
[ROW][C]52[/C][C]98[/C][C]96.2498[/C][C]97.7625[/C][C]-1.51273[/C][C]1.75023[/C][/ROW]
[ROW][C]53[/C][C]95[/C][C]97.615[/C][C]98.6333[/C][C]-1.01829[/C][C]-2.61505[/C][/ROW]
[ROW][C]54[/C][C]108.5[/C][C]104.304[/C][C]99.5667[/C][C]4.73727[/C][C]4.19606[/C][/ROW]
[ROW][C]55[/C][C]91.8[/C][C]91.7977[/C][C]100.35[/C][C]-8.55231[/C][C]0.00231481[/C][/ROW]
[ROW][C]56[/C][C]91.7[/C][C]92.847[/C][C]100.971[/C][C]-8.12384[/C][C]-1.14699[/C][/ROW]
[ROW][C]57[/C][C]108.3[/C][C]107.329[/C][C]101.813[/C][C]5.51644[/C][C]0.971065[/C][/ROW]
[ROW][C]58[/C][C]105.1[/C][C]108.481[/C][C]102.575[/C][C]5.90602[/C][C]-3.38102[/C][/ROW]
[ROW][C]59[/C][C]104.8[/C][C]103.984[/C][C]103.346[/C][C]0.637963[/C][C]0.816204[/C][/ROW]
[ROW][C]60[/C][C]103.2[/C][C]102.794[/C][C]103.8[/C][C]-1.00648[/C][C]0.406481[/C][/ROW]
[ROW][C]61[/C][C]98.6[/C][C]100.187[/C][C]103.633[/C][C]-3.44676[/C][C]-1.58657[/C][/ROW]
[ROW][C]62[/C][C]102.4[/C][C]101.958[/C][C]103.921[/C][C]-1.96273[/C][C]0.441898[/C][/ROW]
[ROW][C]63[/C][C]121.2[/C][C]113.213[/C][C]104.387[/C][C]8.82546[/C][C]7.98704[/C][/ROW]
[ROW][C]64[/C][C]102.6[/C][C]102.996[/C][C]104.508[/C][C]-1.51273[/C][C]-0.395602[/C][/ROW]
[ROW][C]65[/C][C]108.9[/C][C]103.403[/C][C]104.421[/C][C]-1.01829[/C][C]5.49745[/C][/ROW]
[ROW][C]66[/C][C]105.5[/C][C]109.025[/C][C]104.287[/C][C]4.73727[/C][C]-3.52477[/C][/ROW]
[ROW][C]67[/C][C]90.8[/C][C]95.681[/C][C]104.233[/C][C]-8.55231[/C][C]-4.88102[/C][/ROW]
[ROW][C]68[/C][C]99.6[/C][C]96.0762[/C][C]104.2[/C][C]-8.12384[/C][C]3.52384[/C][/ROW]
[ROW][C]69[/C][C]111.6[/C][C]109.383[/C][C]103.867[/C][C]5.51644[/C][C]2.2169[/C][/ROW]
[ROW][C]70[/C][C]104.7[/C][C]109.244[/C][C]103.338[/C][C]5.90602[/C][C]-4.54352[/C][/ROW]
[ROW][C]71[/C][C]103.1[/C][C]103.296[/C][C]102.658[/C][C]0.637963[/C][C]-0.196296[/C][/ROW]
[ROW][C]72[/C][C]101.7[/C][C]101.181[/C][C]102.187[/C][C]-1.00648[/C][C]0.518981[/C][/ROW]
[ROW][C]73[/C][C]98.8[/C][C]98.8616[/C][C]102.308[/C][C]-3.44676[/C][C]-0.0615741[/C][/ROW]
[ROW][C]74[/C][C]101.4[/C][C]100.283[/C][C]102.246[/C][C]-1.96273[/C][C]1.1169[/C][/ROW]
[ROW][C]75[/C][C]114.2[/C][C]110.475[/C][C]101.65[/C][C]8.82546[/C][C]3.72454[/C][/ROW]
[ROW][C]76[/C][C]96.9[/C][C]99.7873[/C][C]101.3[/C][C]-1.51273[/C][C]-2.88727[/C][/ROW]
[ROW][C]77[/C][C]98.3[/C][C]100.236[/C][C]101.254[/C][C]-1.01829[/C][C]-1.93588[/C][/ROW]
[ROW][C]78[/C][C]104.8[/C][C]105.821[/C][C]101.083[/C][C]4.73727[/C][C]-1.0206[/C][/ROW]
[ROW][C]79[/C][C]94.4[/C][C]NA[/C][C]NA[/C][C]-8.55231[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]94.5[/C][C]NA[/C][C]NA[/C][C]-8.12384[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]102.4[/C][C]NA[/C][C]NA[/C][C]5.51644[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]105.5[/C][C]NA[/C][C]NA[/C][C]5.90602[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]101.2[/C][C]NA[/C][C]NA[/C][C]0.637963[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]99.5[/C][C]NA[/C][C]NA[/C][C]-1.00648[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230450&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230450&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	86.5	NA	NA	-3.44676	NA
2	86.6	NA	NA	-1.96273	NA
3	98.8	NA	NA	8.82546	NA
4	84.4	NA	NA	-1.51273	NA
5	91.4	NA	NA	-1.01829	NA
6	95.7	NA	NA	4.73727	NA
7	78.5	82.0019	90.5542	-8.55231	-3.50185
8	81.7	82.8595	90.9833	-8.12384	-1.15949
9	94.3	96.8081	91.2917	5.51644	-2.5081
10	98.5	97.6477	91.7417	5.90602	0.852315
11	95.4	92.838	92.2	0.637963	2.56204
12	91.7	91.606	92.6125	-1.00648	0.0939815
13	92.8	89.8616	93.3083	-3.44676	2.93843
14	90.6	92.1081	94.0708	-1.96273	-1.5081
15	102.2	103.375	94.55	8.82546	-1.17546
16	91.8	93.6081	95.1208	-1.51273	-1.8081
17	95	94.7484	95.7667	-1.01829	0.25162
18	102	100.871	96.1333	4.73727	1.1294
19	88.9	88.0644	96.6167	-8.55231	0.835648
20	89.6	89.2637	97.3875	-8.12384	0.336343
21	97.9	103.346	97.8292	5.51644	-5.4456
22	108.6	104.314	98.4083	5.90602	4.28565
23	100.8	99.7546	99.1167	0.637963	1.04537
24	95.1	98.3894	99.3958	-1.00648	-3.28935
25	101	96.4032	99.85	-3.44676	4.59676
26	100.9	98.1414	100.104	-1.96273	2.75856
27	102.5	109.284	100.458	8.82546	-6.7838
28	105.4	99.2998	100.813	-1.51273	6.10023
29	98.4	99.3859	100.404	-1.01829	-0.98588
30	105.3	104.821	100.083	4.73727	0.479398
31	96.5	90.8977	99.45	-8.55231	5.60231
32	88.1	90.0262	98.15	-8.12384	-1.92616
33	107.9	102.7	97.1833	5.51644	5.20023
34	107.1	101.964	96.0583	5.90602	5.13565
35	92.5	95.4963	94.8583	0.637963	-2.9963
36	95.7	92.956	93.9625	-1.00648	2.74398
37	85.2	89.4741	92.9208	-3.44676	-4.27407
38	85.5	90.1956	92.1583	-1.96273	-4.6956
39	94.7	100.3	91.475	8.82546	-5.60046
40	86.2	89.0414	90.5542	-1.51273	-2.84144
41	88.8	89.0942	90.1125	-1.01829	-0.294213
42	93.4	94.7414	90.0042	4.73727	-1.34144
43	83.4	81.5394	90.0917	-8.55231	1.86065
44	82.9	82.6095	90.7333	-8.12384	0.290509
45	96.7	97.2164	91.7	5.51644	-0.516435
46	96.2	98.631	92.725	5.90602	-2.43102
47	92.8	94.113	93.475	0.637963	-1.31296
48	92.8	93.356	94.3625	-1.00648	-0.556019
49	90.2	91.8949	95.3417	-3.44676	-1.69491
50	95.9	94.0956	96.0583	-1.96273	1.8044
51	107.5	105.734	96.9083	8.82546	1.7662
52	98	96.2498	97.7625	-1.51273	1.75023
53	95	97.615	98.6333	-1.01829	-2.61505
54	108.5	104.304	99.5667	4.73727	4.19606
55	91.8	91.7977	100.35	-8.55231	0.00231481
56	91.7	92.847	100.971	-8.12384	-1.14699
57	108.3	107.329	101.813	5.51644	0.971065
58	105.1	108.481	102.575	5.90602	-3.38102
59	104.8	103.984	103.346	0.637963	0.816204
60	103.2	102.794	103.8	-1.00648	0.406481
61	98.6	100.187	103.633	-3.44676	-1.58657
62	102.4	101.958	103.921	-1.96273	0.441898
63	121.2	113.213	104.387	8.82546	7.98704
64	102.6	102.996	104.508	-1.51273	-0.395602
65	108.9	103.403	104.421	-1.01829	5.49745
66	105.5	109.025	104.287	4.73727	-3.52477
67	90.8	95.681	104.233	-8.55231	-4.88102
68	99.6	96.0762	104.2	-8.12384	3.52384
69	111.6	109.383	103.867	5.51644	2.2169
70	104.7	109.244	103.338	5.90602	-4.54352
71	103.1	103.296	102.658	0.637963	-0.196296
72	101.7	101.181	102.187	-1.00648	0.518981
73	98.8	98.8616	102.308	-3.44676	-0.0615741
74	101.4	100.283	102.246	-1.96273	1.1169
75	114.2	110.475	101.65	8.82546	3.72454
76	96.9	99.7873	101.3	-1.51273	-2.88727
77	98.3	100.236	101.254	-1.01829	-1.93588
78	104.8	105.821	101.083	4.73727	-1.0206
79	94.4	NA	NA	-8.55231	NA
80	94.5	NA	NA	-8.12384	NA
81	102.4	NA	NA	5.51644	NA
82	105.5	NA	NA	5.90602	NA
83	101.2	NA	NA	0.637963	NA
84	99.5	NA	NA	-1.00648	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 <- '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