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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Fri, 04 Dec 2009 05:04:03 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t1259928312glwh64abuxtb9nj.htm/, Retrieved Sat, 27 Apr 2024 21:58:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63345, Retrieved Sat, 27 Apr 2024 21:58:53 +0000

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Estimated Impact

130

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

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Classical Decomposition] [] [2009-11-27 14:58:37] [b98453cac15ba1066b407e146608df68]
-   PD    [Classical Decomposition] [ws 8 Ad hoc forec...] [2009-12-02 20:02:57] [616e2df490b611f6cb7080068870ecbd]
-   PD        [Classical Decomposition] [Workshop 9] [2009-12-04 12:04:03] [ee8fc1691ecec7724e0ca78f0c288737] [Current]
-   PD          [Classical Decomposition] [WS9] [2009-12-11 12:41:48] [4fe1472705bb0a32f118ba3ca90ffa8e] 

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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	'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63345&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63345&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63345&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	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	130	NA	NA	1.01806069840243	NA
2	136.7	NA	NA	0.978555411369657	NA
3	138.1	NA	NA	0.930529683716758	NA
4	139.5	NA	NA	0.895800101280962	NA
5	140.4	NA	NA	0.903949226659508	NA
6	144.6	NA	NA	0.9341091800984	NA
7	151.4	139.708309263111	143.170833333333	0.975815436778518	1.08368643782576
8	147.9	158.056225054296	144.766666666667	1.09179985070893	0.935742960767242
9	141.5	163.364192191931	146.525	1.11492367986304	0.866162884910276
10	143.8	155.852410486663	149.075	1.04546309231369	0.922667795454507
11	143.6	164.222029637681	152.55	1.07651281309525	0.87442592395686
12	150.5	161.965214612444	156.566666666667	1.03448082571286	0.929211870339699
13	150.1	163.428435530627	160.529166666667	1.01806069840243	0.918444819670755
14	154.9	162.215946005591	165.770833333333	0.978555411369657	0.954899957829431
15	162.1	162.264990805125	174.379166666667	0.930529683716758	0.998983201463812
16	176.7	164.386783585901	183.508333333333	0.895800101280962	1.07490393172432
17	186.6	173.584616704403	192.029166666667	0.903949226659508	1.07498005032186
18	194.8	187.892169455209	201.145833333333	0.9341091800984	1.03676486659779
19	196.3	205.043218653086	210.125	0.975815436778518	0.957359142572382
20	228.8	239.536338079494	219.395833333333	1.09179985070893	0.955178666562354
21	267.2	254.806516002031	228.541666666667	1.11492367986304	1.04863880324736
22	237.2	247.543879778791	236.779166666667	1.04546309231369	0.95821395468135
23	254.7	262.081529818094	243.454166666667	1.07651281309525	0.97183498652798
24	258.2	257.499518867027	248.916666666667	1.03448082571286	1.00272032016236
25	257.9	259.423075550822	254.820833333333	1.01806069840243	0.994128989691497
26	269.6	255.496740594403	261.095833333333	0.978555411369657	1.05519937112617
27	266.9	247.222350928465	265.679166666667	0.930529683716758	1.07959494357057
28	269.6	241.175514767789	269.229166666667	0.895800101280962	1.11785808878475
29	253.9	246.416559187382	272.6	0.903949226659508	1.03036906625633
30	258.6	256.206687493072	274.279166666667	0.9341091800984	1.00934133503831
31	274.2	268.194741003269	274.841666666667	0.975815436778518	1.02239141220393
32	301.5	300.649834722926	275.370833333333	1.09179985070893	1.00282775900362
33	304.5	306.947780096959	275.308333333333	1.11492367986304	0.992025418472856
34	285.1	286.147604462474	273.704166666667	1.04546309231369	0.996338936806961
35	287.7	293.10752618551	272.275	1.07651281309525	0.981551049691956
36	265.5	282.283955316398	272.875	1.03448082571286	0.940542297922723
37	264.1	279.56795162046	274.608333333333	1.01806069840243	0.944671942793144
38	276.1	269.424845949565	275.329166666667	0.978555411369657	1.02477556970260
39	258.9	255.131853240058	274.179166666667	0.930529683716758	1.01476940927637
40	239.1	244.460115139153	272.895833333333	0.895800101280962	0.978073661889174
41	250.1	246.032380766052	272.175	0.903949226659508	1.01653286133022
42	276.8	253.505555113955	271.3875	0.9341091800984	1.0918892876946
43	297.6	263.181489196820	269.704166666667	0.975815436778518	1.13077861557900
44	295.4	288.608192202815	264.341666666667	1.09179985070893	1.02353296954375
45	283	284.965201542326	255.591666666667	1.11492367986304	0.993103713956336
46	275.8	257.314603595706	246.125	1.04546309231369	1.07183967076093
47	279.7	254.119820471244	236.058333333333	1.07651281309525	1.10066188257697
48	254.6	232.111635269324	224.375	1.03448082571286	1.09688598636851
49	234.6	214.666582097306	210.858333333333	1.01806069840243	1.09285757339565
50	176.9	193.285080316577	197.520833333333	0.978555411369657	0.915228426892853
51	148.1	172.861397578450	185.766666666667	0.930529683716758	0.856755771240295
52	122.7	157.455530302239	175.770833333333	0.895800101280962	0.779267643152799
53	124.9	150.805096192583	166.829166666667	0.903949226659508	0.828221347642644
54	121.6	149.733809448190	160.295833333333	0.9341091800984	0.812107836220352
55	128.4	152.934674329113	156.725	0.975815436778518	0.839574155195734
56	144.5	NA	NA	1.09179985070893	NA
57	151.8	NA	NA	1.11492367986304	NA
58	167.1	NA	NA	1.04546309231369	NA
59	173.8	NA	NA	1.07651281309525	NA
60	203.7	NA	NA	1.03448082571286	NA
61	199.8	NA	NA	NA	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 130 & NA & NA & 1.01806069840243 & NA \tabularnewline
2 & 136.7 & NA & NA & 0.978555411369657 & NA \tabularnewline
3 & 138.1 & NA & NA & 0.930529683716758 & NA \tabularnewline
4 & 139.5 & NA & NA & 0.895800101280962 & NA \tabularnewline
5 & 140.4 & NA & NA & 0.903949226659508 & NA \tabularnewline
6 & 144.6 & NA & NA & 0.9341091800984 & NA \tabularnewline
7 & 151.4 & 139.708309263111 & 143.170833333333 & 0.975815436778518 & 1.08368643782576 \tabularnewline
8 & 147.9 & 158.056225054296 & 144.766666666667 & 1.09179985070893 & 0.935742960767242 \tabularnewline
9 & 141.5 & 163.364192191931 & 146.525 & 1.11492367986304 & 0.866162884910276 \tabularnewline
10 & 143.8 & 155.852410486663 & 149.075 & 1.04546309231369 & 0.922667795454507 \tabularnewline
11 & 143.6 & 164.222029637681 & 152.55 & 1.07651281309525 & 0.87442592395686 \tabularnewline
12 & 150.5 & 161.965214612444 & 156.566666666667 & 1.03448082571286 & 0.929211870339699 \tabularnewline
13 & 150.1 & 163.428435530627 & 160.529166666667 & 1.01806069840243 & 0.918444819670755 \tabularnewline
14 & 154.9 & 162.215946005591 & 165.770833333333 & 0.978555411369657 & 0.954899957829431 \tabularnewline
15 & 162.1 & 162.264990805125 & 174.379166666667 & 0.930529683716758 & 0.998983201463812 \tabularnewline
16 & 176.7 & 164.386783585901 & 183.508333333333 & 0.895800101280962 & 1.07490393172432 \tabularnewline
17 & 186.6 & 173.584616704403 & 192.029166666667 & 0.903949226659508 & 1.07498005032186 \tabularnewline
18 & 194.8 & 187.892169455209 & 201.145833333333 & 0.9341091800984 & 1.03676486659779 \tabularnewline
19 & 196.3 & 205.043218653086 & 210.125 & 0.975815436778518 & 0.957359142572382 \tabularnewline
20 & 228.8 & 239.536338079494 & 219.395833333333 & 1.09179985070893 & 0.955178666562354 \tabularnewline
21 & 267.2 & 254.806516002031 & 228.541666666667 & 1.11492367986304 & 1.04863880324736 \tabularnewline
22 & 237.2 & 247.543879778791 & 236.779166666667 & 1.04546309231369 & 0.95821395468135 \tabularnewline
23 & 254.7 & 262.081529818094 & 243.454166666667 & 1.07651281309525 & 0.97183498652798 \tabularnewline
24 & 258.2 & 257.499518867027 & 248.916666666667 & 1.03448082571286 & 1.00272032016236 \tabularnewline
25 & 257.9 & 259.423075550822 & 254.820833333333 & 1.01806069840243 & 0.994128989691497 \tabularnewline
26 & 269.6 & 255.496740594403 & 261.095833333333 & 0.978555411369657 & 1.05519937112617 \tabularnewline
27 & 266.9 & 247.222350928465 & 265.679166666667 & 0.930529683716758 & 1.07959494357057 \tabularnewline
28 & 269.6 & 241.175514767789 & 269.229166666667 & 0.895800101280962 & 1.11785808878475 \tabularnewline
29 & 253.9 & 246.416559187382 & 272.6 & 0.903949226659508 & 1.03036906625633 \tabularnewline
30 & 258.6 & 256.206687493072 & 274.279166666667 & 0.9341091800984 & 1.00934133503831 \tabularnewline
31 & 274.2 & 268.194741003269 & 274.841666666667 & 0.975815436778518 & 1.02239141220393 \tabularnewline
32 & 301.5 & 300.649834722926 & 275.370833333333 & 1.09179985070893 & 1.00282775900362 \tabularnewline
33 & 304.5 & 306.947780096959 & 275.308333333333 & 1.11492367986304 & 0.992025418472856 \tabularnewline
34 & 285.1 & 286.147604462474 & 273.704166666667 & 1.04546309231369 & 0.996338936806961 \tabularnewline
35 & 287.7 & 293.10752618551 & 272.275 & 1.07651281309525 & 0.981551049691956 \tabularnewline
36 & 265.5 & 282.283955316398 & 272.875 & 1.03448082571286 & 0.940542297922723 \tabularnewline
37 & 264.1 & 279.56795162046 & 274.608333333333 & 1.01806069840243 & 0.944671942793144 \tabularnewline
38 & 276.1 & 269.424845949565 & 275.329166666667 & 0.978555411369657 & 1.02477556970260 \tabularnewline
39 & 258.9 & 255.131853240058 & 274.179166666667 & 0.930529683716758 & 1.01476940927637 \tabularnewline
40 & 239.1 & 244.460115139153 & 272.895833333333 & 0.895800101280962 & 0.978073661889174 \tabularnewline
41 & 250.1 & 246.032380766052 & 272.175 & 0.903949226659508 & 1.01653286133022 \tabularnewline
42 & 276.8 & 253.505555113955 & 271.3875 & 0.9341091800984 & 1.0918892876946 \tabularnewline
43 & 297.6 & 263.181489196820 & 269.704166666667 & 0.975815436778518 & 1.13077861557900 \tabularnewline
44 & 295.4 & 288.608192202815 & 264.341666666667 & 1.09179985070893 & 1.02353296954375 \tabularnewline
45 & 283 & 284.965201542326 & 255.591666666667 & 1.11492367986304 & 0.993103713956336 \tabularnewline
46 & 275.8 & 257.314603595706 & 246.125 & 1.04546309231369 & 1.07183967076093 \tabularnewline
47 & 279.7 & 254.119820471244 & 236.058333333333 & 1.07651281309525 & 1.10066188257697 \tabularnewline
48 & 254.6 & 232.111635269324 & 224.375 & 1.03448082571286 & 1.09688598636851 \tabularnewline
49 & 234.6 & 214.666582097306 & 210.858333333333 & 1.01806069840243 & 1.09285757339565 \tabularnewline
50 & 176.9 & 193.285080316577 & 197.520833333333 & 0.978555411369657 & 0.915228426892853 \tabularnewline
51 & 148.1 & 172.861397578450 & 185.766666666667 & 0.930529683716758 & 0.856755771240295 \tabularnewline
52 & 122.7 & 157.455530302239 & 175.770833333333 & 0.895800101280962 & 0.779267643152799 \tabularnewline
53 & 124.9 & 150.805096192583 & 166.829166666667 & 0.903949226659508 & 0.828221347642644 \tabularnewline
54 & 121.6 & 149.733809448190 & 160.295833333333 & 0.9341091800984 & 0.812107836220352 \tabularnewline
55 & 128.4 & 152.934674329113 & 156.725 & 0.975815436778518 & 0.839574155195734 \tabularnewline
56 & 144.5 & NA & NA & 1.09179985070893 & NA \tabularnewline
57 & 151.8 & NA & NA & 1.11492367986304 & NA \tabularnewline
58 & 167.1 & NA & NA & 1.04546309231369 & NA \tabularnewline
59 & 173.8 & NA & NA & 1.07651281309525 & NA \tabularnewline
60 & 203.7 & NA & NA & 1.03448082571286 & NA \tabularnewline
61 & 199.8 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63345&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]130[/C][C]NA[/C][C]NA[/C][C]1.01806069840243[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]136.7[/C][C]NA[/C][C]NA[/C][C]0.978555411369657[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]138.1[/C][C]NA[/C][C]NA[/C][C]0.930529683716758[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]139.5[/C][C]NA[/C][C]NA[/C][C]0.895800101280962[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]140.4[/C][C]NA[/C][C]NA[/C][C]0.903949226659508[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]144.6[/C][C]NA[/C][C]NA[/C][C]0.9341091800984[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]151.4[/C][C]139.708309263111[/C][C]143.170833333333[/C][C]0.975815436778518[/C][C]1.08368643782576[/C][/ROW]
[ROW][C]8[/C][C]147.9[/C][C]158.056225054296[/C][C]144.766666666667[/C][C]1.09179985070893[/C][C]0.935742960767242[/C][/ROW]
[ROW][C]9[/C][C]141.5[/C][C]163.364192191931[/C][C]146.525[/C][C]1.11492367986304[/C][C]0.866162884910276[/C][/ROW]
[ROW][C]10[/C][C]143.8[/C][C]155.852410486663[/C][C]149.075[/C][C]1.04546309231369[/C][C]0.922667795454507[/C][/ROW]
[ROW][C]11[/C][C]143.6[/C][C]164.222029637681[/C][C]152.55[/C][C]1.07651281309525[/C][C]0.87442592395686[/C][/ROW]
[ROW][C]12[/C][C]150.5[/C][C]161.965214612444[/C][C]156.566666666667[/C][C]1.03448082571286[/C][C]0.929211870339699[/C][/ROW]
[ROW][C]13[/C][C]150.1[/C][C]163.428435530627[/C][C]160.529166666667[/C][C]1.01806069840243[/C][C]0.918444819670755[/C][/ROW]
[ROW][C]14[/C][C]154.9[/C][C]162.215946005591[/C][C]165.770833333333[/C][C]0.978555411369657[/C][C]0.954899957829431[/C][/ROW]
[ROW][C]15[/C][C]162.1[/C][C]162.264990805125[/C][C]174.379166666667[/C][C]0.930529683716758[/C][C]0.998983201463812[/C][/ROW]
[ROW][C]16[/C][C]176.7[/C][C]164.386783585901[/C][C]183.508333333333[/C][C]0.895800101280962[/C][C]1.07490393172432[/C][/ROW]
[ROW][C]17[/C][C]186.6[/C][C]173.584616704403[/C][C]192.029166666667[/C][C]0.903949226659508[/C][C]1.07498005032186[/C][/ROW]
[ROW][C]18[/C][C]194.8[/C][C]187.892169455209[/C][C]201.145833333333[/C][C]0.9341091800984[/C][C]1.03676486659779[/C][/ROW]
[ROW][C]19[/C][C]196.3[/C][C]205.043218653086[/C][C]210.125[/C][C]0.975815436778518[/C][C]0.957359142572382[/C][/ROW]
[ROW][C]20[/C][C]228.8[/C][C]239.536338079494[/C][C]219.395833333333[/C][C]1.09179985070893[/C][C]0.955178666562354[/C][/ROW]
[ROW][C]21[/C][C]267.2[/C][C]254.806516002031[/C][C]228.541666666667[/C][C]1.11492367986304[/C][C]1.04863880324736[/C][/ROW]
[ROW][C]22[/C][C]237.2[/C][C]247.543879778791[/C][C]236.779166666667[/C][C]1.04546309231369[/C][C]0.95821395468135[/C][/ROW]
[ROW][C]23[/C][C]254.7[/C][C]262.081529818094[/C][C]243.454166666667[/C][C]1.07651281309525[/C][C]0.97183498652798[/C][/ROW]
[ROW][C]24[/C][C]258.2[/C][C]257.499518867027[/C][C]248.916666666667[/C][C]1.03448082571286[/C][C]1.00272032016236[/C][/ROW]
[ROW][C]25[/C][C]257.9[/C][C]259.423075550822[/C][C]254.820833333333[/C][C]1.01806069840243[/C][C]0.994128989691497[/C][/ROW]
[ROW][C]26[/C][C]269.6[/C][C]255.496740594403[/C][C]261.095833333333[/C][C]0.978555411369657[/C][C]1.05519937112617[/C][/ROW]
[ROW][C]27[/C][C]266.9[/C][C]247.222350928465[/C][C]265.679166666667[/C][C]0.930529683716758[/C][C]1.07959494357057[/C][/ROW]
[ROW][C]28[/C][C]269.6[/C][C]241.175514767789[/C][C]269.229166666667[/C][C]0.895800101280962[/C][C]1.11785808878475[/C][/ROW]
[ROW][C]29[/C][C]253.9[/C][C]246.416559187382[/C][C]272.6[/C][C]0.903949226659508[/C][C]1.03036906625633[/C][/ROW]
[ROW][C]30[/C][C]258.6[/C][C]256.206687493072[/C][C]274.279166666667[/C][C]0.9341091800984[/C][C]1.00934133503831[/C][/ROW]
[ROW][C]31[/C][C]274.2[/C][C]268.194741003269[/C][C]274.841666666667[/C][C]0.975815436778518[/C][C]1.02239141220393[/C][/ROW]
[ROW][C]32[/C][C]301.5[/C][C]300.649834722926[/C][C]275.370833333333[/C][C]1.09179985070893[/C][C]1.00282775900362[/C][/ROW]
[ROW][C]33[/C][C]304.5[/C][C]306.947780096959[/C][C]275.308333333333[/C][C]1.11492367986304[/C][C]0.992025418472856[/C][/ROW]
[ROW][C]34[/C][C]285.1[/C][C]286.147604462474[/C][C]273.704166666667[/C][C]1.04546309231369[/C][C]0.996338936806961[/C][/ROW]
[ROW][C]35[/C][C]287.7[/C][C]293.10752618551[/C][C]272.275[/C][C]1.07651281309525[/C][C]0.981551049691956[/C][/ROW]
[ROW][C]36[/C][C]265.5[/C][C]282.283955316398[/C][C]272.875[/C][C]1.03448082571286[/C][C]0.940542297922723[/C][/ROW]
[ROW][C]37[/C][C]264.1[/C][C]279.56795162046[/C][C]274.608333333333[/C][C]1.01806069840243[/C][C]0.944671942793144[/C][/ROW]
[ROW][C]38[/C][C]276.1[/C][C]269.424845949565[/C][C]275.329166666667[/C][C]0.978555411369657[/C][C]1.02477556970260[/C][/ROW]
[ROW][C]39[/C][C]258.9[/C][C]255.131853240058[/C][C]274.179166666667[/C][C]0.930529683716758[/C][C]1.01476940927637[/C][/ROW]
[ROW][C]40[/C][C]239.1[/C][C]244.460115139153[/C][C]272.895833333333[/C][C]0.895800101280962[/C][C]0.978073661889174[/C][/ROW]
[ROW][C]41[/C][C]250.1[/C][C]246.032380766052[/C][C]272.175[/C][C]0.903949226659508[/C][C]1.01653286133022[/C][/ROW]
[ROW][C]42[/C][C]276.8[/C][C]253.505555113955[/C][C]271.3875[/C][C]0.9341091800984[/C][C]1.0918892876946[/C][/ROW]
[ROW][C]43[/C][C]297.6[/C][C]263.181489196820[/C][C]269.704166666667[/C][C]0.975815436778518[/C][C]1.13077861557900[/C][/ROW]
[ROW][C]44[/C][C]295.4[/C][C]288.608192202815[/C][C]264.341666666667[/C][C]1.09179985070893[/C][C]1.02353296954375[/C][/ROW]
[ROW][C]45[/C][C]283[/C][C]284.965201542326[/C][C]255.591666666667[/C][C]1.11492367986304[/C][C]0.993103713956336[/C][/ROW]
[ROW][C]46[/C][C]275.8[/C][C]257.314603595706[/C][C]246.125[/C][C]1.04546309231369[/C][C]1.07183967076093[/C][/ROW]
[ROW][C]47[/C][C]279.7[/C][C]254.119820471244[/C][C]236.058333333333[/C][C]1.07651281309525[/C][C]1.10066188257697[/C][/ROW]
[ROW][C]48[/C][C]254.6[/C][C]232.111635269324[/C][C]224.375[/C][C]1.03448082571286[/C][C]1.09688598636851[/C][/ROW]
[ROW][C]49[/C][C]234.6[/C][C]214.666582097306[/C][C]210.858333333333[/C][C]1.01806069840243[/C][C]1.09285757339565[/C][/ROW]
[ROW][C]50[/C][C]176.9[/C][C]193.285080316577[/C][C]197.520833333333[/C][C]0.978555411369657[/C][C]0.915228426892853[/C][/ROW]
[ROW][C]51[/C][C]148.1[/C][C]172.861397578450[/C][C]185.766666666667[/C][C]0.930529683716758[/C][C]0.856755771240295[/C][/ROW]
[ROW][C]52[/C][C]122.7[/C][C]157.455530302239[/C][C]175.770833333333[/C][C]0.895800101280962[/C][C]0.779267643152799[/C][/ROW]
[ROW][C]53[/C][C]124.9[/C][C]150.805096192583[/C][C]166.829166666667[/C][C]0.903949226659508[/C][C]0.828221347642644[/C][/ROW]
[ROW][C]54[/C][C]121.6[/C][C]149.733809448190[/C][C]160.295833333333[/C][C]0.9341091800984[/C][C]0.812107836220352[/C][/ROW]
[ROW][C]55[/C][C]128.4[/C][C]152.934674329113[/C][C]156.725[/C][C]0.975815436778518[/C][C]0.839574155195734[/C][/ROW]
[ROW][C]56[/C][C]144.5[/C][C]NA[/C][C]NA[/C][C]1.09179985070893[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]151.8[/C][C]NA[/C][C]NA[/C][C]1.11492367986304[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]167.1[/C][C]NA[/C][C]NA[/C][C]1.04546309231369[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]173.8[/C][C]NA[/C][C]NA[/C][C]1.07651281309525[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]203.7[/C][C]NA[/C][C]NA[/C][C]1.03448082571286[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]199.8[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63345&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63345&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	130	NA	NA	1.01806069840243	NA
2	136.7	NA	NA	0.978555411369657	NA
3	138.1	NA	NA	0.930529683716758	NA
4	139.5	NA	NA	0.895800101280962	NA
5	140.4	NA	NA	0.903949226659508	NA
6	144.6	NA	NA	0.9341091800984	NA
7	151.4	139.708309263111	143.170833333333	0.975815436778518	1.08368643782576
8	147.9	158.056225054296	144.766666666667	1.09179985070893	0.935742960767242
9	141.5	163.364192191931	146.525	1.11492367986304	0.866162884910276
10	143.8	155.852410486663	149.075	1.04546309231369	0.922667795454507
11	143.6	164.222029637681	152.55	1.07651281309525	0.87442592395686
12	150.5	161.965214612444	156.566666666667	1.03448082571286	0.929211870339699
13	150.1	163.428435530627	160.529166666667	1.01806069840243	0.918444819670755
14	154.9	162.215946005591	165.770833333333	0.978555411369657	0.954899957829431
15	162.1	162.264990805125	174.379166666667	0.930529683716758	0.998983201463812
16	176.7	164.386783585901	183.508333333333	0.895800101280962	1.07490393172432
17	186.6	173.584616704403	192.029166666667	0.903949226659508	1.07498005032186
18	194.8	187.892169455209	201.145833333333	0.9341091800984	1.03676486659779
19	196.3	205.043218653086	210.125	0.975815436778518	0.957359142572382
20	228.8	239.536338079494	219.395833333333	1.09179985070893	0.955178666562354
21	267.2	254.806516002031	228.541666666667	1.11492367986304	1.04863880324736
22	237.2	247.543879778791	236.779166666667	1.04546309231369	0.95821395468135
23	254.7	262.081529818094	243.454166666667	1.07651281309525	0.97183498652798
24	258.2	257.499518867027	248.916666666667	1.03448082571286	1.00272032016236
25	257.9	259.423075550822	254.820833333333	1.01806069840243	0.994128989691497
26	269.6	255.496740594403	261.095833333333	0.978555411369657	1.05519937112617
27	266.9	247.222350928465	265.679166666667	0.930529683716758	1.07959494357057
28	269.6	241.175514767789	269.229166666667	0.895800101280962	1.11785808878475
29	253.9	246.416559187382	272.6	0.903949226659508	1.03036906625633
30	258.6	256.206687493072	274.279166666667	0.9341091800984	1.00934133503831
31	274.2	268.194741003269	274.841666666667	0.975815436778518	1.02239141220393
32	301.5	300.649834722926	275.370833333333	1.09179985070893	1.00282775900362
33	304.5	306.947780096959	275.308333333333	1.11492367986304	0.992025418472856
34	285.1	286.147604462474	273.704166666667	1.04546309231369	0.996338936806961
35	287.7	293.10752618551	272.275	1.07651281309525	0.981551049691956
36	265.5	282.283955316398	272.875	1.03448082571286	0.940542297922723
37	264.1	279.56795162046	274.608333333333	1.01806069840243	0.944671942793144
38	276.1	269.424845949565	275.329166666667	0.978555411369657	1.02477556970260
39	258.9	255.131853240058	274.179166666667	0.930529683716758	1.01476940927637
40	239.1	244.460115139153	272.895833333333	0.895800101280962	0.978073661889174
41	250.1	246.032380766052	272.175	0.903949226659508	1.01653286133022
42	276.8	253.505555113955	271.3875	0.9341091800984	1.0918892876946
43	297.6	263.181489196820	269.704166666667	0.975815436778518	1.13077861557900
44	295.4	288.608192202815	264.341666666667	1.09179985070893	1.02353296954375
45	283	284.965201542326	255.591666666667	1.11492367986304	0.993103713956336
46	275.8	257.314603595706	246.125	1.04546309231369	1.07183967076093
47	279.7	254.119820471244	236.058333333333	1.07651281309525	1.10066188257697
48	254.6	232.111635269324	224.375	1.03448082571286	1.09688598636851
49	234.6	214.666582097306	210.858333333333	1.01806069840243	1.09285757339565
50	176.9	193.285080316577	197.520833333333	0.978555411369657	0.915228426892853
51	148.1	172.861397578450	185.766666666667	0.930529683716758	0.856755771240295
52	122.7	157.455530302239	175.770833333333	0.895800101280962	0.779267643152799
53	124.9	150.805096192583	166.829166666667	0.903949226659508	0.828221347642644
54	121.6	149.733809448190	160.295833333333	0.9341091800984	0.812107836220352
55	128.4	152.934674329113	156.725	0.975815436778518	0.839574155195734
56	144.5	NA	NA	1.09179985070893	NA
57	151.8	NA	NA	1.11492367986304	NA
58	167.1	NA	NA	1.04546309231369	NA
59	173.8	NA	NA	1.07651281309525	NA
60	203.7	NA	NA	1.03448082571286	NA
61	199.8	NA	NA	NA	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 = FALSE ; par2 = 0.0 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = multiplicative ; 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