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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Wed, 14 Dec 2011 11:47:57 -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/2011/Dec/14/t1323881546b9l0i8veanyhccq.htm/, Retrieved Wed, 01 May 2024 22:17:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155127, Retrieved Wed, 01 May 2024 22:17:34 +0000

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User-defined keywords

KDGP2W92

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

-       [Classical Decomposition] [] [2011-12-14 16:47:57] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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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	'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 & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155127&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155127&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155127&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	2 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	478.34	NA	NA	0.857263888888909	NA
2	485.7	NA	NA	2.55568055555557	NA
3	485.75	NA	NA	2.58926388888888	NA
4	485.85	NA	NA	1.53076388888889	NA
5	485.84	NA	NA	0.887597222222226	NA
6	485.85	NA	NA	-0.137569444444441	NA
7	485.84	486.588097222222	487.2475	-0.6594027777778	-0.74809722222227
8	486	487.878597222222	488.615	-0.736402777777798	-1.87859722222225
9	488.79	488.877763888889	489.70625	-0.828486111111087	-0.087763888888901
10	489.71	489.566763888889	490.79625	-1.22948611111112	0.143236111111037
11	489.71	489.752930555556	491.8825	-2.12956944444445	-0.0429305555556425
12	489.71	490.269097222222	492.96875	-2.69965277777778	-0.559097222222249
13	498.1	494.937263888889	494.08	0.857263888888909	3.16273611111109
14	498.76	497.837763888889	495.282083333333	2.55568055555557	0.92223611111109
15	498.88	499.057597222222	496.468333333333	2.58926388888888	-0.177597222222175
16	498.88	499.065763888889	497.535	1.53076388888889	-0.185763888888857
17	498.88	499.450930555555	498.563333333333	0.887597222222226	-0.570930555555492
18	498.88	499.539513888889	499.677083333333	-0.137569444444441	-0.659513888888853
19	499.48	499.980180555555	500.639583333333	-0.6594027777778	-0.50018055555546
20	501.21	501.021097222222	501.7575	-0.736402777777798	0.188902777777855
21	502.05	502.581930555556	503.410416666667	-0.828486111111087	-0.531930555555562
22	502.05	503.987180555555	505.216666666667	-1.22948611111112	-1.93718055555547
23	502.05	504.903763888889	507.033333333333	-2.12956944444445	-2.85376388888881
24	504.1	506.150347222222	508.85	-2.69965277777778	-2.05034722222211
25	506.81	511.513513888889	510.65625	0.857263888888909	-4.70351388888878
26	516.88	514.944847222222	512.389166666667	2.55568055555557	1.93515277777783
27	520.43	516.613430555556	514.024166666667	2.58926388888888	3.81656944444444
28	520.68	517.154930555556	515.624166666667	1.53076388888889	3.5250694444444
29	520.68	518.128430555555	517.240833333333	0.887597222222226	2.55156944444445
30	520.68	518.651180555555	518.78875	-0.137569444444441	2.02881944444448
31	521.03	519.525597222222	520.185	-0.6594027777778	1.50440277777784
32	521.25	520.190263888889	520.926666666667	-0.736402777777798	1.05973611111119
33	521.25	520.126513888889	520.955	-0.828486111111087	1.12348611111111
34	521.25	519.623013888889	520.8525	-1.22948611111112	1.62698611111114
35	521.65	518.694180555556	520.82375	-2.12956944444445	2.95581944444439
36	521.65	518.174930555556	520.874583333333	-2.69965277777778	3.47506944444444
37	522.77	521.768097222222	520.910833333333	0.857263888888909	1.00190277777779
38	518.72	523.569847222222	521.014166666667	2.55568055555557	-4.84984722222214
39	519.27	523.804680555555	521.215416666667	2.58926388888888	-4.5346805555555
40	519.38	522.975347222222	521.444583333333	1.53076388888889	-3.59534722222213
41	521.29	522.556347222222	521.66875	0.887597222222226	-1.26634722222218
42	521.29	521.738680555555	521.87625	-0.137569444444441	-0.448680555555484
43	521.29	521.813513888889	522.472916666667	-0.6594027777778	-0.523513888888829
44	523.47	522.907347222222	523.64375	-0.736402777777798	0.562652777777885
45	523.86	524.164847222222	524.993333333333	-0.828486111111087	-0.304847222222293
46	524.14	525.102597222222	526.332083333333	-1.22948611111112	-0.962597222222257
47	524.14	525.457097222222	527.586666666667	-2.12956944444445	-1.31709722222217
48	524.14	526.062013888889	528.761666666667	-2.69965277777778	-1.92201388888884
49	534.6	530.793930555556	529.936666666667	0.857263888888909	3.80606944444446
50	534.99	533.586930555556	531.03125	2.55568055555557	1.40306944444444
51	535.39	534.636763888889	532.0475	2.58926388888888	0.75323611111105
52	535.39	534.656597222222	533.125833333333	1.53076388888889	0.733402777777656
53	535.39	535.151763888889	534.264166666667	0.887597222222226	0.238236111111178
54	535.39	535.264930555555	535.4025	-0.137569444444441	0.125069444444534
55	535.39	535.447680555556	536.107083333333	-0.6594027777778	-0.0576805555555211
56	535.64	535.897763888889	536.634166666667	-0.736402777777798	-0.257763888888917
57	536.08	536.604013888889	537.4325	-0.828486111111087	-0.524013888888817
58	537.8	536.995513888889	538.225	-1.22948611111112	0.804486111111146
59	537.8	536.867097222222	538.996666666667	-2.12956944444445	0.932902777777826
60	537.8	537.068680555555	539.768333333333	-2.69965277777778	0.73131944444458
61	537.85	541.442263888889	540.585	0.857263888888909	-3.59226388888897
62	544.39	544.125680555556	541.57	2.55568055555557	0.264319444444482
63	545.15	545.332597222222	542.743333333333	2.58926388888888	-0.182597222222284
64	544.65	545.452430555556	543.921666666667	1.53076388888889	-0.802430555555588
65	544.65	545.927597222222	545.04	0.887597222222226	-1.2775972222222
66	544.65	546.020763888889	546.158333333333	-0.137569444444441	-1.37076388888886
67	545.73	NA	NA	-0.6594027777778	NA
68	548.94	NA	NA	-0.736402777777798	NA
69	550.94	NA	NA	-0.828486111111087	NA
70	551.22	NA	NA	-1.22948611111112	NA
71	551.22	NA	NA	-2.12956944444445	NA
72	551.22	NA	NA	-2.69965277777778	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 478.34 & NA & NA & 0.857263888888909 & NA \tabularnewline
2 & 485.7 & NA & NA & 2.55568055555557 & NA \tabularnewline
3 & 485.75 & NA & NA & 2.58926388888888 & NA \tabularnewline
4 & 485.85 & NA & NA & 1.53076388888889 & NA \tabularnewline
5 & 485.84 & NA & NA & 0.887597222222226 & NA \tabularnewline
6 & 485.85 & NA & NA & -0.137569444444441 & NA \tabularnewline
7 & 485.84 & 486.588097222222 & 487.2475 & -0.6594027777778 & -0.74809722222227 \tabularnewline
8 & 486 & 487.878597222222 & 488.615 & -0.736402777777798 & -1.87859722222225 \tabularnewline
9 & 488.79 & 488.877763888889 & 489.70625 & -0.828486111111087 & -0.087763888888901 \tabularnewline
10 & 489.71 & 489.566763888889 & 490.79625 & -1.22948611111112 & 0.143236111111037 \tabularnewline
11 & 489.71 & 489.752930555556 & 491.8825 & -2.12956944444445 & -0.0429305555556425 \tabularnewline
12 & 489.71 & 490.269097222222 & 492.96875 & -2.69965277777778 & -0.559097222222249 \tabularnewline
13 & 498.1 & 494.937263888889 & 494.08 & 0.857263888888909 & 3.16273611111109 \tabularnewline
14 & 498.76 & 497.837763888889 & 495.282083333333 & 2.55568055555557 & 0.92223611111109 \tabularnewline
15 & 498.88 & 499.057597222222 & 496.468333333333 & 2.58926388888888 & -0.177597222222175 \tabularnewline
16 & 498.88 & 499.065763888889 & 497.535 & 1.53076388888889 & -0.185763888888857 \tabularnewline
17 & 498.88 & 499.450930555555 & 498.563333333333 & 0.887597222222226 & -0.570930555555492 \tabularnewline
18 & 498.88 & 499.539513888889 & 499.677083333333 & -0.137569444444441 & -0.659513888888853 \tabularnewline
19 & 499.48 & 499.980180555555 & 500.639583333333 & -0.6594027777778 & -0.50018055555546 \tabularnewline
20 & 501.21 & 501.021097222222 & 501.7575 & -0.736402777777798 & 0.188902777777855 \tabularnewline
21 & 502.05 & 502.581930555556 & 503.410416666667 & -0.828486111111087 & -0.531930555555562 \tabularnewline
22 & 502.05 & 503.987180555555 & 505.216666666667 & -1.22948611111112 & -1.93718055555547 \tabularnewline
23 & 502.05 & 504.903763888889 & 507.033333333333 & -2.12956944444445 & -2.85376388888881 \tabularnewline
24 & 504.1 & 506.150347222222 & 508.85 & -2.69965277777778 & -2.05034722222211 \tabularnewline
25 & 506.81 & 511.513513888889 & 510.65625 & 0.857263888888909 & -4.70351388888878 \tabularnewline
26 & 516.88 & 514.944847222222 & 512.389166666667 & 2.55568055555557 & 1.93515277777783 \tabularnewline
27 & 520.43 & 516.613430555556 & 514.024166666667 & 2.58926388888888 & 3.81656944444444 \tabularnewline
28 & 520.68 & 517.154930555556 & 515.624166666667 & 1.53076388888889 & 3.5250694444444 \tabularnewline
29 & 520.68 & 518.128430555555 & 517.240833333333 & 0.887597222222226 & 2.55156944444445 \tabularnewline
30 & 520.68 & 518.651180555555 & 518.78875 & -0.137569444444441 & 2.02881944444448 \tabularnewline
31 & 521.03 & 519.525597222222 & 520.185 & -0.6594027777778 & 1.50440277777784 \tabularnewline
32 & 521.25 & 520.190263888889 & 520.926666666667 & -0.736402777777798 & 1.05973611111119 \tabularnewline
33 & 521.25 & 520.126513888889 & 520.955 & -0.828486111111087 & 1.12348611111111 \tabularnewline
34 & 521.25 & 519.623013888889 & 520.8525 & -1.22948611111112 & 1.62698611111114 \tabularnewline
35 & 521.65 & 518.694180555556 & 520.82375 & -2.12956944444445 & 2.95581944444439 \tabularnewline
36 & 521.65 & 518.174930555556 & 520.874583333333 & -2.69965277777778 & 3.47506944444444 \tabularnewline
37 & 522.77 & 521.768097222222 & 520.910833333333 & 0.857263888888909 & 1.00190277777779 \tabularnewline
38 & 518.72 & 523.569847222222 & 521.014166666667 & 2.55568055555557 & -4.84984722222214 \tabularnewline
39 & 519.27 & 523.804680555555 & 521.215416666667 & 2.58926388888888 & -4.5346805555555 \tabularnewline
40 & 519.38 & 522.975347222222 & 521.444583333333 & 1.53076388888889 & -3.59534722222213 \tabularnewline
41 & 521.29 & 522.556347222222 & 521.66875 & 0.887597222222226 & -1.26634722222218 \tabularnewline
42 & 521.29 & 521.738680555555 & 521.87625 & -0.137569444444441 & -0.448680555555484 \tabularnewline
43 & 521.29 & 521.813513888889 & 522.472916666667 & -0.6594027777778 & -0.523513888888829 \tabularnewline
44 & 523.47 & 522.907347222222 & 523.64375 & -0.736402777777798 & 0.562652777777885 \tabularnewline
45 & 523.86 & 524.164847222222 & 524.993333333333 & -0.828486111111087 & -0.304847222222293 \tabularnewline
46 & 524.14 & 525.102597222222 & 526.332083333333 & -1.22948611111112 & -0.962597222222257 \tabularnewline
47 & 524.14 & 525.457097222222 & 527.586666666667 & -2.12956944444445 & -1.31709722222217 \tabularnewline
48 & 524.14 & 526.062013888889 & 528.761666666667 & -2.69965277777778 & -1.92201388888884 \tabularnewline
49 & 534.6 & 530.793930555556 & 529.936666666667 & 0.857263888888909 & 3.80606944444446 \tabularnewline
50 & 534.99 & 533.586930555556 & 531.03125 & 2.55568055555557 & 1.40306944444444 \tabularnewline
51 & 535.39 & 534.636763888889 & 532.0475 & 2.58926388888888 & 0.75323611111105 \tabularnewline
52 & 535.39 & 534.656597222222 & 533.125833333333 & 1.53076388888889 & 0.733402777777656 \tabularnewline
53 & 535.39 & 535.151763888889 & 534.264166666667 & 0.887597222222226 & 0.238236111111178 \tabularnewline
54 & 535.39 & 535.264930555555 & 535.4025 & -0.137569444444441 & 0.125069444444534 \tabularnewline
55 & 535.39 & 535.447680555556 & 536.107083333333 & -0.6594027777778 & -0.0576805555555211 \tabularnewline
56 & 535.64 & 535.897763888889 & 536.634166666667 & -0.736402777777798 & -0.257763888888917 \tabularnewline
57 & 536.08 & 536.604013888889 & 537.4325 & -0.828486111111087 & -0.524013888888817 \tabularnewline
58 & 537.8 & 536.995513888889 & 538.225 & -1.22948611111112 & 0.804486111111146 \tabularnewline
59 & 537.8 & 536.867097222222 & 538.996666666667 & -2.12956944444445 & 0.932902777777826 \tabularnewline
60 & 537.8 & 537.068680555555 & 539.768333333333 & -2.69965277777778 & 0.73131944444458 \tabularnewline
61 & 537.85 & 541.442263888889 & 540.585 & 0.857263888888909 & -3.59226388888897 \tabularnewline
62 & 544.39 & 544.125680555556 & 541.57 & 2.55568055555557 & 0.264319444444482 \tabularnewline
63 & 545.15 & 545.332597222222 & 542.743333333333 & 2.58926388888888 & -0.182597222222284 \tabularnewline
64 & 544.65 & 545.452430555556 & 543.921666666667 & 1.53076388888889 & -0.802430555555588 \tabularnewline
65 & 544.65 & 545.927597222222 & 545.04 & 0.887597222222226 & -1.2775972222222 \tabularnewline
66 & 544.65 & 546.020763888889 & 546.158333333333 & -0.137569444444441 & -1.37076388888886 \tabularnewline
67 & 545.73 & NA & NA & -0.6594027777778 & NA \tabularnewline
68 & 548.94 & NA & NA & -0.736402777777798 & NA \tabularnewline
69 & 550.94 & NA & NA & -0.828486111111087 & NA \tabularnewline
70 & 551.22 & NA & NA & -1.22948611111112 & NA \tabularnewline
71 & 551.22 & NA & NA & -2.12956944444445 & NA \tabularnewline
72 & 551.22 & NA & NA & -2.69965277777778 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155127&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]478.34[/C][C]NA[/C][C]NA[/C][C]0.857263888888909[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]485.7[/C][C]NA[/C][C]NA[/C][C]2.55568055555557[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]485.75[/C][C]NA[/C][C]NA[/C][C]2.58926388888888[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]485.85[/C][C]NA[/C][C]NA[/C][C]1.53076388888889[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]485.84[/C][C]NA[/C][C]NA[/C][C]0.887597222222226[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]485.85[/C][C]NA[/C][C]NA[/C][C]-0.137569444444441[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]485.84[/C][C]486.588097222222[/C][C]487.2475[/C][C]-0.6594027777778[/C][C]-0.74809722222227[/C][/ROW]
[ROW][C]8[/C][C]486[/C][C]487.878597222222[/C][C]488.615[/C][C]-0.736402777777798[/C][C]-1.87859722222225[/C][/ROW]
[ROW][C]9[/C][C]488.79[/C][C]488.877763888889[/C][C]489.70625[/C][C]-0.828486111111087[/C][C]-0.087763888888901[/C][/ROW]
[ROW][C]10[/C][C]489.71[/C][C]489.566763888889[/C][C]490.79625[/C][C]-1.22948611111112[/C][C]0.143236111111037[/C][/ROW]
[ROW][C]11[/C][C]489.71[/C][C]489.752930555556[/C][C]491.8825[/C][C]-2.12956944444445[/C][C]-0.0429305555556425[/C][/ROW]
[ROW][C]12[/C][C]489.71[/C][C]490.269097222222[/C][C]492.96875[/C][C]-2.69965277777778[/C][C]-0.559097222222249[/C][/ROW]
[ROW][C]13[/C][C]498.1[/C][C]494.937263888889[/C][C]494.08[/C][C]0.857263888888909[/C][C]3.16273611111109[/C][/ROW]
[ROW][C]14[/C][C]498.76[/C][C]497.837763888889[/C][C]495.282083333333[/C][C]2.55568055555557[/C][C]0.92223611111109[/C][/ROW]
[ROW][C]15[/C][C]498.88[/C][C]499.057597222222[/C][C]496.468333333333[/C][C]2.58926388888888[/C][C]-0.177597222222175[/C][/ROW]
[ROW][C]16[/C][C]498.88[/C][C]499.065763888889[/C][C]497.535[/C][C]1.53076388888889[/C][C]-0.185763888888857[/C][/ROW]
[ROW][C]17[/C][C]498.88[/C][C]499.450930555555[/C][C]498.563333333333[/C][C]0.887597222222226[/C][C]-0.570930555555492[/C][/ROW]
[ROW][C]18[/C][C]498.88[/C][C]499.539513888889[/C][C]499.677083333333[/C][C]-0.137569444444441[/C][C]-0.659513888888853[/C][/ROW]
[ROW][C]19[/C][C]499.48[/C][C]499.980180555555[/C][C]500.639583333333[/C][C]-0.6594027777778[/C][C]-0.50018055555546[/C][/ROW]
[ROW][C]20[/C][C]501.21[/C][C]501.021097222222[/C][C]501.7575[/C][C]-0.736402777777798[/C][C]0.188902777777855[/C][/ROW]
[ROW][C]21[/C][C]502.05[/C][C]502.581930555556[/C][C]503.410416666667[/C][C]-0.828486111111087[/C][C]-0.531930555555562[/C][/ROW]
[ROW][C]22[/C][C]502.05[/C][C]503.987180555555[/C][C]505.216666666667[/C][C]-1.22948611111112[/C][C]-1.93718055555547[/C][/ROW]
[ROW][C]23[/C][C]502.05[/C][C]504.903763888889[/C][C]507.033333333333[/C][C]-2.12956944444445[/C][C]-2.85376388888881[/C][/ROW]
[ROW][C]24[/C][C]504.1[/C][C]506.150347222222[/C][C]508.85[/C][C]-2.69965277777778[/C][C]-2.05034722222211[/C][/ROW]
[ROW][C]25[/C][C]506.81[/C][C]511.513513888889[/C][C]510.65625[/C][C]0.857263888888909[/C][C]-4.70351388888878[/C][/ROW]
[ROW][C]26[/C][C]516.88[/C][C]514.944847222222[/C][C]512.389166666667[/C][C]2.55568055555557[/C][C]1.93515277777783[/C][/ROW]
[ROW][C]27[/C][C]520.43[/C][C]516.613430555556[/C][C]514.024166666667[/C][C]2.58926388888888[/C][C]3.81656944444444[/C][/ROW]
[ROW][C]28[/C][C]520.68[/C][C]517.154930555556[/C][C]515.624166666667[/C][C]1.53076388888889[/C][C]3.5250694444444[/C][/ROW]
[ROW][C]29[/C][C]520.68[/C][C]518.128430555555[/C][C]517.240833333333[/C][C]0.887597222222226[/C][C]2.55156944444445[/C][/ROW]
[ROW][C]30[/C][C]520.68[/C][C]518.651180555555[/C][C]518.78875[/C][C]-0.137569444444441[/C][C]2.02881944444448[/C][/ROW]
[ROW][C]31[/C][C]521.03[/C][C]519.525597222222[/C][C]520.185[/C][C]-0.6594027777778[/C][C]1.50440277777784[/C][/ROW]
[ROW][C]32[/C][C]521.25[/C][C]520.190263888889[/C][C]520.926666666667[/C][C]-0.736402777777798[/C][C]1.05973611111119[/C][/ROW]
[ROW][C]33[/C][C]521.25[/C][C]520.126513888889[/C][C]520.955[/C][C]-0.828486111111087[/C][C]1.12348611111111[/C][/ROW]
[ROW][C]34[/C][C]521.25[/C][C]519.623013888889[/C][C]520.8525[/C][C]-1.22948611111112[/C][C]1.62698611111114[/C][/ROW]
[ROW][C]35[/C][C]521.65[/C][C]518.694180555556[/C][C]520.82375[/C][C]-2.12956944444445[/C][C]2.95581944444439[/C][/ROW]
[ROW][C]36[/C][C]521.65[/C][C]518.174930555556[/C][C]520.874583333333[/C][C]-2.69965277777778[/C][C]3.47506944444444[/C][/ROW]
[ROW][C]37[/C][C]522.77[/C][C]521.768097222222[/C][C]520.910833333333[/C][C]0.857263888888909[/C][C]1.00190277777779[/C][/ROW]
[ROW][C]38[/C][C]518.72[/C][C]523.569847222222[/C][C]521.014166666667[/C][C]2.55568055555557[/C][C]-4.84984722222214[/C][/ROW]
[ROW][C]39[/C][C]519.27[/C][C]523.804680555555[/C][C]521.215416666667[/C][C]2.58926388888888[/C][C]-4.5346805555555[/C][/ROW]
[ROW][C]40[/C][C]519.38[/C][C]522.975347222222[/C][C]521.444583333333[/C][C]1.53076388888889[/C][C]-3.59534722222213[/C][/ROW]
[ROW][C]41[/C][C]521.29[/C][C]522.556347222222[/C][C]521.66875[/C][C]0.887597222222226[/C][C]-1.26634722222218[/C][/ROW]
[ROW][C]42[/C][C]521.29[/C][C]521.738680555555[/C][C]521.87625[/C][C]-0.137569444444441[/C][C]-0.448680555555484[/C][/ROW]
[ROW][C]43[/C][C]521.29[/C][C]521.813513888889[/C][C]522.472916666667[/C][C]-0.6594027777778[/C][C]-0.523513888888829[/C][/ROW]
[ROW][C]44[/C][C]523.47[/C][C]522.907347222222[/C][C]523.64375[/C][C]-0.736402777777798[/C][C]0.562652777777885[/C][/ROW]
[ROW][C]45[/C][C]523.86[/C][C]524.164847222222[/C][C]524.993333333333[/C][C]-0.828486111111087[/C][C]-0.304847222222293[/C][/ROW]
[ROW][C]46[/C][C]524.14[/C][C]525.102597222222[/C][C]526.332083333333[/C][C]-1.22948611111112[/C][C]-0.962597222222257[/C][/ROW]
[ROW][C]47[/C][C]524.14[/C][C]525.457097222222[/C][C]527.586666666667[/C][C]-2.12956944444445[/C][C]-1.31709722222217[/C][/ROW]
[ROW][C]48[/C][C]524.14[/C][C]526.062013888889[/C][C]528.761666666667[/C][C]-2.69965277777778[/C][C]-1.92201388888884[/C][/ROW]
[ROW][C]49[/C][C]534.6[/C][C]530.793930555556[/C][C]529.936666666667[/C][C]0.857263888888909[/C][C]3.80606944444446[/C][/ROW]
[ROW][C]50[/C][C]534.99[/C][C]533.586930555556[/C][C]531.03125[/C][C]2.55568055555557[/C][C]1.40306944444444[/C][/ROW]
[ROW][C]51[/C][C]535.39[/C][C]534.636763888889[/C][C]532.0475[/C][C]2.58926388888888[/C][C]0.75323611111105[/C][/ROW]
[ROW][C]52[/C][C]535.39[/C][C]534.656597222222[/C][C]533.125833333333[/C][C]1.53076388888889[/C][C]0.733402777777656[/C][/ROW]
[ROW][C]53[/C][C]535.39[/C][C]535.151763888889[/C][C]534.264166666667[/C][C]0.887597222222226[/C][C]0.238236111111178[/C][/ROW]
[ROW][C]54[/C][C]535.39[/C][C]535.264930555555[/C][C]535.4025[/C][C]-0.137569444444441[/C][C]0.125069444444534[/C][/ROW]
[ROW][C]55[/C][C]535.39[/C][C]535.447680555556[/C][C]536.107083333333[/C][C]-0.6594027777778[/C][C]-0.0576805555555211[/C][/ROW]
[ROW][C]56[/C][C]535.64[/C][C]535.897763888889[/C][C]536.634166666667[/C][C]-0.736402777777798[/C][C]-0.257763888888917[/C][/ROW]
[ROW][C]57[/C][C]536.08[/C][C]536.604013888889[/C][C]537.4325[/C][C]-0.828486111111087[/C][C]-0.524013888888817[/C][/ROW]
[ROW][C]58[/C][C]537.8[/C][C]536.995513888889[/C][C]538.225[/C][C]-1.22948611111112[/C][C]0.804486111111146[/C][/ROW]
[ROW][C]59[/C][C]537.8[/C][C]536.867097222222[/C][C]538.996666666667[/C][C]-2.12956944444445[/C][C]0.932902777777826[/C][/ROW]
[ROW][C]60[/C][C]537.8[/C][C]537.068680555555[/C][C]539.768333333333[/C][C]-2.69965277777778[/C][C]0.73131944444458[/C][/ROW]
[ROW][C]61[/C][C]537.85[/C][C]541.442263888889[/C][C]540.585[/C][C]0.857263888888909[/C][C]-3.59226388888897[/C][/ROW]
[ROW][C]62[/C][C]544.39[/C][C]544.125680555556[/C][C]541.57[/C][C]2.55568055555557[/C][C]0.264319444444482[/C][/ROW]
[ROW][C]63[/C][C]545.15[/C][C]545.332597222222[/C][C]542.743333333333[/C][C]2.58926388888888[/C][C]-0.182597222222284[/C][/ROW]
[ROW][C]64[/C][C]544.65[/C][C]545.452430555556[/C][C]543.921666666667[/C][C]1.53076388888889[/C][C]-0.802430555555588[/C][/ROW]
[ROW][C]65[/C][C]544.65[/C][C]545.927597222222[/C][C]545.04[/C][C]0.887597222222226[/C][C]-1.2775972222222[/C][/ROW]
[ROW][C]66[/C][C]544.65[/C][C]546.020763888889[/C][C]546.158333333333[/C][C]-0.137569444444441[/C][C]-1.37076388888886[/C][/ROW]
[ROW][C]67[/C][C]545.73[/C][C]NA[/C][C]NA[/C][C]-0.6594027777778[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]548.94[/C][C]NA[/C][C]NA[/C][C]-0.736402777777798[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]550.94[/C][C]NA[/C][C]NA[/C][C]-0.828486111111087[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]551.22[/C][C]NA[/C][C]NA[/C][C]-1.22948611111112[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]551.22[/C][C]NA[/C][C]NA[/C][C]-2.12956944444445[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]551.22[/C][C]NA[/C][C]NA[/C][C]-2.69965277777778[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155127&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155127&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	478.34	NA	NA	0.857263888888909	NA
2	485.7	NA	NA	2.55568055555557	NA
3	485.75	NA	NA	2.58926388888888	NA
4	485.85	NA	NA	1.53076388888889	NA
5	485.84	NA	NA	0.887597222222226	NA
6	485.85	NA	NA	-0.137569444444441	NA
7	485.84	486.588097222222	487.2475	-0.6594027777778	-0.74809722222227
8	486	487.878597222222	488.615	-0.736402777777798	-1.87859722222225
9	488.79	488.877763888889	489.70625	-0.828486111111087	-0.087763888888901
10	489.71	489.566763888889	490.79625	-1.22948611111112	0.143236111111037
11	489.71	489.752930555556	491.8825	-2.12956944444445	-0.0429305555556425
12	489.71	490.269097222222	492.96875	-2.69965277777778	-0.559097222222249
13	498.1	494.937263888889	494.08	0.857263888888909	3.16273611111109
14	498.76	497.837763888889	495.282083333333	2.55568055555557	0.92223611111109
15	498.88	499.057597222222	496.468333333333	2.58926388888888	-0.177597222222175
16	498.88	499.065763888889	497.535	1.53076388888889	-0.185763888888857
17	498.88	499.450930555555	498.563333333333	0.887597222222226	-0.570930555555492
18	498.88	499.539513888889	499.677083333333	-0.137569444444441	-0.659513888888853
19	499.48	499.980180555555	500.639583333333	-0.6594027777778	-0.50018055555546
20	501.21	501.021097222222	501.7575	-0.736402777777798	0.188902777777855
21	502.05	502.581930555556	503.410416666667	-0.828486111111087	-0.531930555555562
22	502.05	503.987180555555	505.216666666667	-1.22948611111112	-1.93718055555547
23	502.05	504.903763888889	507.033333333333	-2.12956944444445	-2.85376388888881
24	504.1	506.150347222222	508.85	-2.69965277777778	-2.05034722222211
25	506.81	511.513513888889	510.65625	0.857263888888909	-4.70351388888878
26	516.88	514.944847222222	512.389166666667	2.55568055555557	1.93515277777783
27	520.43	516.613430555556	514.024166666667	2.58926388888888	3.81656944444444
28	520.68	517.154930555556	515.624166666667	1.53076388888889	3.5250694444444
29	520.68	518.128430555555	517.240833333333	0.887597222222226	2.55156944444445
30	520.68	518.651180555555	518.78875	-0.137569444444441	2.02881944444448
31	521.03	519.525597222222	520.185	-0.6594027777778	1.50440277777784
32	521.25	520.190263888889	520.926666666667	-0.736402777777798	1.05973611111119
33	521.25	520.126513888889	520.955	-0.828486111111087	1.12348611111111
34	521.25	519.623013888889	520.8525	-1.22948611111112	1.62698611111114
35	521.65	518.694180555556	520.82375	-2.12956944444445	2.95581944444439
36	521.65	518.174930555556	520.874583333333	-2.69965277777778	3.47506944444444
37	522.77	521.768097222222	520.910833333333	0.857263888888909	1.00190277777779
38	518.72	523.569847222222	521.014166666667	2.55568055555557	-4.84984722222214
39	519.27	523.804680555555	521.215416666667	2.58926388888888	-4.5346805555555
40	519.38	522.975347222222	521.444583333333	1.53076388888889	-3.59534722222213
41	521.29	522.556347222222	521.66875	0.887597222222226	-1.26634722222218
42	521.29	521.738680555555	521.87625	-0.137569444444441	-0.448680555555484
43	521.29	521.813513888889	522.472916666667	-0.6594027777778	-0.523513888888829
44	523.47	522.907347222222	523.64375	-0.736402777777798	0.562652777777885
45	523.86	524.164847222222	524.993333333333	-0.828486111111087	-0.304847222222293
46	524.14	525.102597222222	526.332083333333	-1.22948611111112	-0.962597222222257
47	524.14	525.457097222222	527.586666666667	-2.12956944444445	-1.31709722222217
48	524.14	526.062013888889	528.761666666667	-2.69965277777778	-1.92201388888884
49	534.6	530.793930555556	529.936666666667	0.857263888888909	3.80606944444446
50	534.99	533.586930555556	531.03125	2.55568055555557	1.40306944444444
51	535.39	534.636763888889	532.0475	2.58926388888888	0.75323611111105
52	535.39	534.656597222222	533.125833333333	1.53076388888889	0.733402777777656
53	535.39	535.151763888889	534.264166666667	0.887597222222226	0.238236111111178
54	535.39	535.264930555555	535.4025	-0.137569444444441	0.125069444444534
55	535.39	535.447680555556	536.107083333333	-0.6594027777778	-0.0576805555555211
56	535.64	535.897763888889	536.634166666667	-0.736402777777798	-0.257763888888917
57	536.08	536.604013888889	537.4325	-0.828486111111087	-0.524013888888817
58	537.8	536.995513888889	538.225	-1.22948611111112	0.804486111111146
59	537.8	536.867097222222	538.996666666667	-2.12956944444445	0.932902777777826
60	537.8	537.068680555555	539.768333333333	-2.69965277777778	0.73131944444458
61	537.85	541.442263888889	540.585	0.857263888888909	-3.59226388888897
62	544.39	544.125680555556	541.57	2.55568055555557	0.264319444444482
63	545.15	545.332597222222	542.743333333333	2.58926388888888	-0.182597222222284
64	544.65	545.452430555556	543.921666666667	1.53076388888889	-0.802430555555588
65	544.65	545.927597222222	545.04	0.887597222222226	-1.2775972222222
66	544.65	546.020763888889	546.158333333333	-0.137569444444441	-1.37076388888886
67	545.73	NA	NA	-0.6594027777778	NA
68	548.94	NA	NA	-0.736402777777798	NA
69	550.94	NA	NA	-0.828486111111087	NA
70	551.22	NA	NA	-1.22948611111112	NA
71	551.22	NA	NA	-2.12956944444445	NA
72	551.22	NA	NA	-2.69965277777778	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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$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