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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 30 Mar 2015 16:15:59 +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/Mar/30/t1427728590cqw7sabduyy1o7m.htm/, Retrieved Sun, 19 May 2024 13:09:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=278476, Retrieved Sun, 19 May 2024 13:09:33 +0000

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141

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-       [Classical Decomposition] [decompositie werk...] [2015-03-30 15:15:59] [cab9dc260884be88f444bea8f40c034b] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'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=278476&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=278476&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=278476&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	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	3862.5	NA	NA	-0.625	NA
2	3875.7	NA	NA	-0.366667	NA
3	3875.9	3875.7	3875.19	0.514583	0.197917
4	3877.7	3878.86	3878.39	0.477083	-1.16458
5	3880.4	3879.76	3880.39	-0.625	0.6375
6	3883.4	3881.95	3882.31	-0.366667	1.45417
7	3884.2	3885.53	3885.01	0.514583	-1.32708
8	3884.8	3889.79	3889.31	0.477083	-4.98958
9	3894.9	3894.55	3895.18	-0.625	0.35
10	3903.3	3903.7	3904.06	-0.366667	-0.395833
11	3911.2	3916.43	3915.91	0.514583	-5.22708
12	3928.9	3930.53	3930.05	0.477083	-1.62708
13	3945.6	3947.36	3947.99	-0.625	-1.7625
14	3965.7	3967.73	3968.1	-0.366667	-2.03333
15	3992.3	3987.24	3986.73	0.514583	5.06042
16	4008.7	4002.71	4002.24	0.477083	5.98542
17	4014.8	4014.13	4014.75	-0.625	0.675
18	4020.6	4026.26	4026.63	-0.366667	-5.65833
19	4037.5	4041.8	4041.29	0.514583	-4.30208
20	4058.5	4060.43	4059.95	0.477083	-1.92708
21	4082.3	4080.75	4081.38	-0.625	1.55
22	4102.4	4102.95	4103.31	-0.366667	-0.545833
23	4127.1	4124.4	4123.89	0.514583	2.69792
24	4144.4	4142.43	4141.95	0.477083	1.97292
25	4161	4155.95	4156.58	-0.625	5.05
26	4168.2	4166.32	4166.69	-0.366667	1.87917
27	4178.3	4171.5	4170.99	0.514583	6.79792
28	4174.1	4172.01	4171.54	0.477083	2.08542
29	4165.7	4168.38	4169	-0.625	-2.675
30	4167.9	4164.16	4164.52	-0.366667	3.74167
31	4158.3	4160.31	4159.8	0.514583	-2.01458
32	4158.3	4156.23	4155.75	0.477083	2.07292
33	4143.7	4154.64	4155.26	-0.625	-10.9375
34	4157.5	4157.66	4158.03	-0.366667	-0.158333
35	4164.8	4165.18	4164.66	0.514583	-0.377083
36	4173.9	4173.98	4173.5	0.477083	-0.0770833
37	4181.2	4182.25	4182.88	-0.625	-1.05
38	4190.7	4193.76	4194.12	-0.366667	-3.05833
39	4206.6	4208.74	4208.22	0.514583	-2.13958
40	4222.1	4224.86	4224.39	0.477083	-2.76458
41	4245.8	4239.29	4239.91	-0.625	6.5125
42	4255.4	4253.42	4253.79	-0.366667	1.97917
43	4266.1	4265.28	4264.76	0.514583	0.822917
44	4273.6	4275.33	4274.85	0.477083	-1.72708
45	4282.1	4285.98	4286.6	-0.625	-3.875
46	4299.8	4299.7	4300.06	-0.366667	0.104167
47	4315.7	4316.13	4315.61	0.514583	-0.427083
48	4331.7	4332.88	4332.4	0.477083	-1.17708
49	4348.4	4349.21	4349.84	-0.625	-0.8125
50	4367.8	4368.31	4368.68	-0.366667	-0.508333
51	4387.2	4390.04	4389.52	0.514583	-2.83958
52	4410.9	4411.7	4411.23	0.477083	-0.802083
53	4436	4431.59	4432.21	-0.625	4.4125
54	4453.8	4449.72	4450.09	-0.366667	4.07917
55	4469.1	4461.01	4460.5	0.514583	8.08542
56	4472	4463.15	4462.68	0.477083	8.84792
57	4458.2	4458	4458.62	-0.625	0.2
58	4449	4451.52	4451.89	-0.366667	-2.52083
59	4441.5	4448.58	4448.06	0.514583	-7.07708
60	4445.7	4450.59	4450.11	0.477083	-4.88958
61	4453.9	4457.82	4458.45	-0.625	-3.925
62	4469.7	4471.12	4471.49	-0.366667	-1.42083
63	4487.5	4488.06	4487.55	0.514583	-0.564583
64	4504	4505.65	4505.18	0.477083	-1.65208
65	4524.1	4521.01	4521.64	-0.625	3.0875
66	4540.5	4535.16	4535.52	-0.366667	5.34167
67	4548.4	4546.55	4546.04	0.514583	1.84792
68	4554.2	4552.88	4552.4	0.477083	1.32292
69	4558	4554.66	4555.29	-0.625	3.3375
70	4557.5	4555.16	4555.52	-0.366667	2.34167
71	4554.5	4553.74	4553.23	0.514583	0.760417
72	4550	4549.51	4549.04	0.477083	0.485417
73	4543.8	4544.6	4545.23	-0.625	-0.8
74	4538.2	4542.85	4543.21	-0.366667	-4.64583
75	4543.3	NA	NA	0.514583	NA
76	4545.1	NA	NA	0.477083	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 3862.5 & NA & NA & -0.625 & NA \tabularnewline
2 & 3875.7 & NA & NA & -0.366667 & NA \tabularnewline
3 & 3875.9 & 3875.7 & 3875.19 & 0.514583 & 0.197917 \tabularnewline
4 & 3877.7 & 3878.86 & 3878.39 & 0.477083 & -1.16458 \tabularnewline
5 & 3880.4 & 3879.76 & 3880.39 & -0.625 & 0.6375 \tabularnewline
6 & 3883.4 & 3881.95 & 3882.31 & -0.366667 & 1.45417 \tabularnewline
7 & 3884.2 & 3885.53 & 3885.01 & 0.514583 & -1.32708 \tabularnewline
8 & 3884.8 & 3889.79 & 3889.31 & 0.477083 & -4.98958 \tabularnewline
9 & 3894.9 & 3894.55 & 3895.18 & -0.625 & 0.35 \tabularnewline
10 & 3903.3 & 3903.7 & 3904.06 & -0.366667 & -0.395833 \tabularnewline
11 & 3911.2 & 3916.43 & 3915.91 & 0.514583 & -5.22708 \tabularnewline
12 & 3928.9 & 3930.53 & 3930.05 & 0.477083 & -1.62708 \tabularnewline
13 & 3945.6 & 3947.36 & 3947.99 & -0.625 & -1.7625 \tabularnewline
14 & 3965.7 & 3967.73 & 3968.1 & -0.366667 & -2.03333 \tabularnewline
15 & 3992.3 & 3987.24 & 3986.73 & 0.514583 & 5.06042 \tabularnewline
16 & 4008.7 & 4002.71 & 4002.24 & 0.477083 & 5.98542 \tabularnewline
17 & 4014.8 & 4014.13 & 4014.75 & -0.625 & 0.675 \tabularnewline
18 & 4020.6 & 4026.26 & 4026.63 & -0.366667 & -5.65833 \tabularnewline
19 & 4037.5 & 4041.8 & 4041.29 & 0.514583 & -4.30208 \tabularnewline
20 & 4058.5 & 4060.43 & 4059.95 & 0.477083 & -1.92708 \tabularnewline
21 & 4082.3 & 4080.75 & 4081.38 & -0.625 & 1.55 \tabularnewline
22 & 4102.4 & 4102.95 & 4103.31 & -0.366667 & -0.545833 \tabularnewline
23 & 4127.1 & 4124.4 & 4123.89 & 0.514583 & 2.69792 \tabularnewline
24 & 4144.4 & 4142.43 & 4141.95 & 0.477083 & 1.97292 \tabularnewline
25 & 4161 & 4155.95 & 4156.58 & -0.625 & 5.05 \tabularnewline
26 & 4168.2 & 4166.32 & 4166.69 & -0.366667 & 1.87917 \tabularnewline
27 & 4178.3 & 4171.5 & 4170.99 & 0.514583 & 6.79792 \tabularnewline
28 & 4174.1 & 4172.01 & 4171.54 & 0.477083 & 2.08542 \tabularnewline
29 & 4165.7 & 4168.38 & 4169 & -0.625 & -2.675 \tabularnewline
30 & 4167.9 & 4164.16 & 4164.52 & -0.366667 & 3.74167 \tabularnewline
31 & 4158.3 & 4160.31 & 4159.8 & 0.514583 & -2.01458 \tabularnewline
32 & 4158.3 & 4156.23 & 4155.75 & 0.477083 & 2.07292 \tabularnewline
33 & 4143.7 & 4154.64 & 4155.26 & -0.625 & -10.9375 \tabularnewline
34 & 4157.5 & 4157.66 & 4158.03 & -0.366667 & -0.158333 \tabularnewline
35 & 4164.8 & 4165.18 & 4164.66 & 0.514583 & -0.377083 \tabularnewline
36 & 4173.9 & 4173.98 & 4173.5 & 0.477083 & -0.0770833 \tabularnewline
37 & 4181.2 & 4182.25 & 4182.88 & -0.625 & -1.05 \tabularnewline
38 & 4190.7 & 4193.76 & 4194.12 & -0.366667 & -3.05833 \tabularnewline
39 & 4206.6 & 4208.74 & 4208.22 & 0.514583 & -2.13958 \tabularnewline
40 & 4222.1 & 4224.86 & 4224.39 & 0.477083 & -2.76458 \tabularnewline
41 & 4245.8 & 4239.29 & 4239.91 & -0.625 & 6.5125 \tabularnewline
42 & 4255.4 & 4253.42 & 4253.79 & -0.366667 & 1.97917 \tabularnewline
43 & 4266.1 & 4265.28 & 4264.76 & 0.514583 & 0.822917 \tabularnewline
44 & 4273.6 & 4275.33 & 4274.85 & 0.477083 & -1.72708 \tabularnewline
45 & 4282.1 & 4285.98 & 4286.6 & -0.625 & -3.875 \tabularnewline
46 & 4299.8 & 4299.7 & 4300.06 & -0.366667 & 0.104167 \tabularnewline
47 & 4315.7 & 4316.13 & 4315.61 & 0.514583 & -0.427083 \tabularnewline
48 & 4331.7 & 4332.88 & 4332.4 & 0.477083 & -1.17708 \tabularnewline
49 & 4348.4 & 4349.21 & 4349.84 & -0.625 & -0.8125 \tabularnewline
50 & 4367.8 & 4368.31 & 4368.68 & -0.366667 & -0.508333 \tabularnewline
51 & 4387.2 & 4390.04 & 4389.52 & 0.514583 & -2.83958 \tabularnewline
52 & 4410.9 & 4411.7 & 4411.23 & 0.477083 & -0.802083 \tabularnewline
53 & 4436 & 4431.59 & 4432.21 & -0.625 & 4.4125 \tabularnewline
54 & 4453.8 & 4449.72 & 4450.09 & -0.366667 & 4.07917 \tabularnewline
55 & 4469.1 & 4461.01 & 4460.5 & 0.514583 & 8.08542 \tabularnewline
56 & 4472 & 4463.15 & 4462.68 & 0.477083 & 8.84792 \tabularnewline
57 & 4458.2 & 4458 & 4458.62 & -0.625 & 0.2 \tabularnewline
58 & 4449 & 4451.52 & 4451.89 & -0.366667 & -2.52083 \tabularnewline
59 & 4441.5 & 4448.58 & 4448.06 & 0.514583 & -7.07708 \tabularnewline
60 & 4445.7 & 4450.59 & 4450.11 & 0.477083 & -4.88958 \tabularnewline
61 & 4453.9 & 4457.82 & 4458.45 & -0.625 & -3.925 \tabularnewline
62 & 4469.7 & 4471.12 & 4471.49 & -0.366667 & -1.42083 \tabularnewline
63 & 4487.5 & 4488.06 & 4487.55 & 0.514583 & -0.564583 \tabularnewline
64 & 4504 & 4505.65 & 4505.18 & 0.477083 & -1.65208 \tabularnewline
65 & 4524.1 & 4521.01 & 4521.64 & -0.625 & 3.0875 \tabularnewline
66 & 4540.5 & 4535.16 & 4535.52 & -0.366667 & 5.34167 \tabularnewline
67 & 4548.4 & 4546.55 & 4546.04 & 0.514583 & 1.84792 \tabularnewline
68 & 4554.2 & 4552.88 & 4552.4 & 0.477083 & 1.32292 \tabularnewline
69 & 4558 & 4554.66 & 4555.29 & -0.625 & 3.3375 \tabularnewline
70 & 4557.5 & 4555.16 & 4555.52 & -0.366667 & 2.34167 \tabularnewline
71 & 4554.5 & 4553.74 & 4553.23 & 0.514583 & 0.760417 \tabularnewline
72 & 4550 & 4549.51 & 4549.04 & 0.477083 & 0.485417 \tabularnewline
73 & 4543.8 & 4544.6 & 4545.23 & -0.625 & -0.8 \tabularnewline
74 & 4538.2 & 4542.85 & 4543.21 & -0.366667 & -4.64583 \tabularnewline
75 & 4543.3 & NA & NA & 0.514583 & NA \tabularnewline
76 & 4545.1 & NA & NA & 0.477083 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=278476&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]3862.5[/C][C]NA[/C][C]NA[/C][C]-0.625[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]3875.7[/C][C]NA[/C][C]NA[/C][C]-0.366667[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]3875.9[/C][C]3875.7[/C][C]3875.19[/C][C]0.514583[/C][C]0.197917[/C][/ROW]
[ROW][C]4[/C][C]3877.7[/C][C]3878.86[/C][C]3878.39[/C][C]0.477083[/C][C]-1.16458[/C][/ROW]
[ROW][C]5[/C][C]3880.4[/C][C]3879.76[/C][C]3880.39[/C][C]-0.625[/C][C]0.6375[/C][/ROW]
[ROW][C]6[/C][C]3883.4[/C][C]3881.95[/C][C]3882.31[/C][C]-0.366667[/C][C]1.45417[/C][/ROW]
[ROW][C]7[/C][C]3884.2[/C][C]3885.53[/C][C]3885.01[/C][C]0.514583[/C][C]-1.32708[/C][/ROW]
[ROW][C]8[/C][C]3884.8[/C][C]3889.79[/C][C]3889.31[/C][C]0.477083[/C][C]-4.98958[/C][/ROW]
[ROW][C]9[/C][C]3894.9[/C][C]3894.55[/C][C]3895.18[/C][C]-0.625[/C][C]0.35[/C][/ROW]
[ROW][C]10[/C][C]3903.3[/C][C]3903.7[/C][C]3904.06[/C][C]-0.366667[/C][C]-0.395833[/C][/ROW]
[ROW][C]11[/C][C]3911.2[/C][C]3916.43[/C][C]3915.91[/C][C]0.514583[/C][C]-5.22708[/C][/ROW]
[ROW][C]12[/C][C]3928.9[/C][C]3930.53[/C][C]3930.05[/C][C]0.477083[/C][C]-1.62708[/C][/ROW]
[ROW][C]13[/C][C]3945.6[/C][C]3947.36[/C][C]3947.99[/C][C]-0.625[/C][C]-1.7625[/C][/ROW]
[ROW][C]14[/C][C]3965.7[/C][C]3967.73[/C][C]3968.1[/C][C]-0.366667[/C][C]-2.03333[/C][/ROW]
[ROW][C]15[/C][C]3992.3[/C][C]3987.24[/C][C]3986.73[/C][C]0.514583[/C][C]5.06042[/C][/ROW]
[ROW][C]16[/C][C]4008.7[/C][C]4002.71[/C][C]4002.24[/C][C]0.477083[/C][C]5.98542[/C][/ROW]
[ROW][C]17[/C][C]4014.8[/C][C]4014.13[/C][C]4014.75[/C][C]-0.625[/C][C]0.675[/C][/ROW]
[ROW][C]18[/C][C]4020.6[/C][C]4026.26[/C][C]4026.63[/C][C]-0.366667[/C][C]-5.65833[/C][/ROW]
[ROW][C]19[/C][C]4037.5[/C][C]4041.8[/C][C]4041.29[/C][C]0.514583[/C][C]-4.30208[/C][/ROW]
[ROW][C]20[/C][C]4058.5[/C][C]4060.43[/C][C]4059.95[/C][C]0.477083[/C][C]-1.92708[/C][/ROW]
[ROW][C]21[/C][C]4082.3[/C][C]4080.75[/C][C]4081.38[/C][C]-0.625[/C][C]1.55[/C][/ROW]
[ROW][C]22[/C][C]4102.4[/C][C]4102.95[/C][C]4103.31[/C][C]-0.366667[/C][C]-0.545833[/C][/ROW]
[ROW][C]23[/C][C]4127.1[/C][C]4124.4[/C][C]4123.89[/C][C]0.514583[/C][C]2.69792[/C][/ROW]
[ROW][C]24[/C][C]4144.4[/C][C]4142.43[/C][C]4141.95[/C][C]0.477083[/C][C]1.97292[/C][/ROW]
[ROW][C]25[/C][C]4161[/C][C]4155.95[/C][C]4156.58[/C][C]-0.625[/C][C]5.05[/C][/ROW]
[ROW][C]26[/C][C]4168.2[/C][C]4166.32[/C][C]4166.69[/C][C]-0.366667[/C][C]1.87917[/C][/ROW]
[ROW][C]27[/C][C]4178.3[/C][C]4171.5[/C][C]4170.99[/C][C]0.514583[/C][C]6.79792[/C][/ROW]
[ROW][C]28[/C][C]4174.1[/C][C]4172.01[/C][C]4171.54[/C][C]0.477083[/C][C]2.08542[/C][/ROW]
[ROW][C]29[/C][C]4165.7[/C][C]4168.38[/C][C]4169[/C][C]-0.625[/C][C]-2.675[/C][/ROW]
[ROW][C]30[/C][C]4167.9[/C][C]4164.16[/C][C]4164.52[/C][C]-0.366667[/C][C]3.74167[/C][/ROW]
[ROW][C]31[/C][C]4158.3[/C][C]4160.31[/C][C]4159.8[/C][C]0.514583[/C][C]-2.01458[/C][/ROW]
[ROW][C]32[/C][C]4158.3[/C][C]4156.23[/C][C]4155.75[/C][C]0.477083[/C][C]2.07292[/C][/ROW]
[ROW][C]33[/C][C]4143.7[/C][C]4154.64[/C][C]4155.26[/C][C]-0.625[/C][C]-10.9375[/C][/ROW]
[ROW][C]34[/C][C]4157.5[/C][C]4157.66[/C][C]4158.03[/C][C]-0.366667[/C][C]-0.158333[/C][/ROW]
[ROW][C]35[/C][C]4164.8[/C][C]4165.18[/C][C]4164.66[/C][C]0.514583[/C][C]-0.377083[/C][/ROW]
[ROW][C]36[/C][C]4173.9[/C][C]4173.98[/C][C]4173.5[/C][C]0.477083[/C][C]-0.0770833[/C][/ROW]
[ROW][C]37[/C][C]4181.2[/C][C]4182.25[/C][C]4182.88[/C][C]-0.625[/C][C]-1.05[/C][/ROW]
[ROW][C]38[/C][C]4190.7[/C][C]4193.76[/C][C]4194.12[/C][C]-0.366667[/C][C]-3.05833[/C][/ROW]
[ROW][C]39[/C][C]4206.6[/C][C]4208.74[/C][C]4208.22[/C][C]0.514583[/C][C]-2.13958[/C][/ROW]
[ROW][C]40[/C][C]4222.1[/C][C]4224.86[/C][C]4224.39[/C][C]0.477083[/C][C]-2.76458[/C][/ROW]
[ROW][C]41[/C][C]4245.8[/C][C]4239.29[/C][C]4239.91[/C][C]-0.625[/C][C]6.5125[/C][/ROW]
[ROW][C]42[/C][C]4255.4[/C][C]4253.42[/C][C]4253.79[/C][C]-0.366667[/C][C]1.97917[/C][/ROW]
[ROW][C]43[/C][C]4266.1[/C][C]4265.28[/C][C]4264.76[/C][C]0.514583[/C][C]0.822917[/C][/ROW]
[ROW][C]44[/C][C]4273.6[/C][C]4275.33[/C][C]4274.85[/C][C]0.477083[/C][C]-1.72708[/C][/ROW]
[ROW][C]45[/C][C]4282.1[/C][C]4285.98[/C][C]4286.6[/C][C]-0.625[/C][C]-3.875[/C][/ROW]
[ROW][C]46[/C][C]4299.8[/C][C]4299.7[/C][C]4300.06[/C][C]-0.366667[/C][C]0.104167[/C][/ROW]
[ROW][C]47[/C][C]4315.7[/C][C]4316.13[/C][C]4315.61[/C][C]0.514583[/C][C]-0.427083[/C][/ROW]
[ROW][C]48[/C][C]4331.7[/C][C]4332.88[/C][C]4332.4[/C][C]0.477083[/C][C]-1.17708[/C][/ROW]
[ROW][C]49[/C][C]4348.4[/C][C]4349.21[/C][C]4349.84[/C][C]-0.625[/C][C]-0.8125[/C][/ROW]
[ROW][C]50[/C][C]4367.8[/C][C]4368.31[/C][C]4368.68[/C][C]-0.366667[/C][C]-0.508333[/C][/ROW]
[ROW][C]51[/C][C]4387.2[/C][C]4390.04[/C][C]4389.52[/C][C]0.514583[/C][C]-2.83958[/C][/ROW]
[ROW][C]52[/C][C]4410.9[/C][C]4411.7[/C][C]4411.23[/C][C]0.477083[/C][C]-0.802083[/C][/ROW]
[ROW][C]53[/C][C]4436[/C][C]4431.59[/C][C]4432.21[/C][C]-0.625[/C][C]4.4125[/C][/ROW]
[ROW][C]54[/C][C]4453.8[/C][C]4449.72[/C][C]4450.09[/C][C]-0.366667[/C][C]4.07917[/C][/ROW]
[ROW][C]55[/C][C]4469.1[/C][C]4461.01[/C][C]4460.5[/C][C]0.514583[/C][C]8.08542[/C][/ROW]
[ROW][C]56[/C][C]4472[/C][C]4463.15[/C][C]4462.68[/C][C]0.477083[/C][C]8.84792[/C][/ROW]
[ROW][C]57[/C][C]4458.2[/C][C]4458[/C][C]4458.62[/C][C]-0.625[/C][C]0.2[/C][/ROW]
[ROW][C]58[/C][C]4449[/C][C]4451.52[/C][C]4451.89[/C][C]-0.366667[/C][C]-2.52083[/C][/ROW]
[ROW][C]59[/C][C]4441.5[/C][C]4448.58[/C][C]4448.06[/C][C]0.514583[/C][C]-7.07708[/C][/ROW]
[ROW][C]60[/C][C]4445.7[/C][C]4450.59[/C][C]4450.11[/C][C]0.477083[/C][C]-4.88958[/C][/ROW]
[ROW][C]61[/C][C]4453.9[/C][C]4457.82[/C][C]4458.45[/C][C]-0.625[/C][C]-3.925[/C][/ROW]
[ROW][C]62[/C][C]4469.7[/C][C]4471.12[/C][C]4471.49[/C][C]-0.366667[/C][C]-1.42083[/C][/ROW]
[ROW][C]63[/C][C]4487.5[/C][C]4488.06[/C][C]4487.55[/C][C]0.514583[/C][C]-0.564583[/C][/ROW]
[ROW][C]64[/C][C]4504[/C][C]4505.65[/C][C]4505.18[/C][C]0.477083[/C][C]-1.65208[/C][/ROW]
[ROW][C]65[/C][C]4524.1[/C][C]4521.01[/C][C]4521.64[/C][C]-0.625[/C][C]3.0875[/C][/ROW]
[ROW][C]66[/C][C]4540.5[/C][C]4535.16[/C][C]4535.52[/C][C]-0.366667[/C][C]5.34167[/C][/ROW]
[ROW][C]67[/C][C]4548.4[/C][C]4546.55[/C][C]4546.04[/C][C]0.514583[/C][C]1.84792[/C][/ROW]
[ROW][C]68[/C][C]4554.2[/C][C]4552.88[/C][C]4552.4[/C][C]0.477083[/C][C]1.32292[/C][/ROW]
[ROW][C]69[/C][C]4558[/C][C]4554.66[/C][C]4555.29[/C][C]-0.625[/C][C]3.3375[/C][/ROW]
[ROW][C]70[/C][C]4557.5[/C][C]4555.16[/C][C]4555.52[/C][C]-0.366667[/C][C]2.34167[/C][/ROW]
[ROW][C]71[/C][C]4554.5[/C][C]4553.74[/C][C]4553.23[/C][C]0.514583[/C][C]0.760417[/C][/ROW]
[ROW][C]72[/C][C]4550[/C][C]4549.51[/C][C]4549.04[/C][C]0.477083[/C][C]0.485417[/C][/ROW]
[ROW][C]73[/C][C]4543.8[/C][C]4544.6[/C][C]4545.23[/C][C]-0.625[/C][C]-0.8[/C][/ROW]
[ROW][C]74[/C][C]4538.2[/C][C]4542.85[/C][C]4543.21[/C][C]-0.366667[/C][C]-4.64583[/C][/ROW]
[ROW][C]75[/C][C]4543.3[/C][C]NA[/C][C]NA[/C][C]0.514583[/C][C]NA[/C][/ROW]
[ROW][C]76[/C][C]4545.1[/C][C]NA[/C][C]NA[/C][C]0.477083[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=278476&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=278476&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	3862.5	NA	NA	-0.625	NA
2	3875.7	NA	NA	-0.366667	NA
3	3875.9	3875.7	3875.19	0.514583	0.197917
4	3877.7	3878.86	3878.39	0.477083	-1.16458
5	3880.4	3879.76	3880.39	-0.625	0.6375
6	3883.4	3881.95	3882.31	-0.366667	1.45417
7	3884.2	3885.53	3885.01	0.514583	-1.32708
8	3884.8	3889.79	3889.31	0.477083	-4.98958
9	3894.9	3894.55	3895.18	-0.625	0.35
10	3903.3	3903.7	3904.06	-0.366667	-0.395833
11	3911.2	3916.43	3915.91	0.514583	-5.22708
12	3928.9	3930.53	3930.05	0.477083	-1.62708
13	3945.6	3947.36	3947.99	-0.625	-1.7625
14	3965.7	3967.73	3968.1	-0.366667	-2.03333
15	3992.3	3987.24	3986.73	0.514583	5.06042
16	4008.7	4002.71	4002.24	0.477083	5.98542
17	4014.8	4014.13	4014.75	-0.625	0.675
18	4020.6	4026.26	4026.63	-0.366667	-5.65833
19	4037.5	4041.8	4041.29	0.514583	-4.30208
20	4058.5	4060.43	4059.95	0.477083	-1.92708
21	4082.3	4080.75	4081.38	-0.625	1.55
22	4102.4	4102.95	4103.31	-0.366667	-0.545833
23	4127.1	4124.4	4123.89	0.514583	2.69792
24	4144.4	4142.43	4141.95	0.477083	1.97292
25	4161	4155.95	4156.58	-0.625	5.05
26	4168.2	4166.32	4166.69	-0.366667	1.87917
27	4178.3	4171.5	4170.99	0.514583	6.79792
28	4174.1	4172.01	4171.54	0.477083	2.08542
29	4165.7	4168.38	4169	-0.625	-2.675
30	4167.9	4164.16	4164.52	-0.366667	3.74167
31	4158.3	4160.31	4159.8	0.514583	-2.01458
32	4158.3	4156.23	4155.75	0.477083	2.07292
33	4143.7	4154.64	4155.26	-0.625	-10.9375
34	4157.5	4157.66	4158.03	-0.366667	-0.158333
35	4164.8	4165.18	4164.66	0.514583	-0.377083
36	4173.9	4173.98	4173.5	0.477083	-0.0770833
37	4181.2	4182.25	4182.88	-0.625	-1.05
38	4190.7	4193.76	4194.12	-0.366667	-3.05833
39	4206.6	4208.74	4208.22	0.514583	-2.13958
40	4222.1	4224.86	4224.39	0.477083	-2.76458
41	4245.8	4239.29	4239.91	-0.625	6.5125
42	4255.4	4253.42	4253.79	-0.366667	1.97917
43	4266.1	4265.28	4264.76	0.514583	0.822917
44	4273.6	4275.33	4274.85	0.477083	-1.72708
45	4282.1	4285.98	4286.6	-0.625	-3.875
46	4299.8	4299.7	4300.06	-0.366667	0.104167
47	4315.7	4316.13	4315.61	0.514583	-0.427083
48	4331.7	4332.88	4332.4	0.477083	-1.17708
49	4348.4	4349.21	4349.84	-0.625	-0.8125
50	4367.8	4368.31	4368.68	-0.366667	-0.508333
51	4387.2	4390.04	4389.52	0.514583	-2.83958
52	4410.9	4411.7	4411.23	0.477083	-0.802083
53	4436	4431.59	4432.21	-0.625	4.4125
54	4453.8	4449.72	4450.09	-0.366667	4.07917
55	4469.1	4461.01	4460.5	0.514583	8.08542
56	4472	4463.15	4462.68	0.477083	8.84792
57	4458.2	4458	4458.62	-0.625	0.2
58	4449	4451.52	4451.89	-0.366667	-2.52083
59	4441.5	4448.58	4448.06	0.514583	-7.07708
60	4445.7	4450.59	4450.11	0.477083	-4.88958
61	4453.9	4457.82	4458.45	-0.625	-3.925
62	4469.7	4471.12	4471.49	-0.366667	-1.42083
63	4487.5	4488.06	4487.55	0.514583	-0.564583
64	4504	4505.65	4505.18	0.477083	-1.65208
65	4524.1	4521.01	4521.64	-0.625	3.0875
66	4540.5	4535.16	4535.52	-0.366667	5.34167
67	4548.4	4546.55	4546.04	0.514583	1.84792
68	4554.2	4552.88	4552.4	0.477083	1.32292
69	4558	4554.66	4555.29	-0.625	3.3375
70	4557.5	4555.16	4555.52	-0.366667	2.34167
71	4554.5	4553.74	4553.23	0.514583	0.760417
72	4550	4549.51	4549.04	0.477083	0.485417
73	4543.8	4544.6	4545.23	-0.625	-0.8
74	4538.2	4542.85	4543.21	-0.366667	-4.64583
75	4543.3	NA	NA	0.514583	NA
76	4545.1	NA	NA	0.477083	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 = 4 ;

Parameters (R input):

par1 = additive ; par2 = 4 ;

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