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rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Tue, 30 Nov 2010 18:49:04 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/30/t1291144137vuag0s7ze1sg5uh.htm/, Retrieved Mon, 29 Apr 2024 13:30:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103772, Retrieved Mon, 29 Apr 2024 13:30:36 +0000

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Estimated Impact

141

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-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
-  M D    [Classical Decomposition] [Workshop 8 - blog 1] [2010-11-30 18:49:04] [47bfda5353cd53c1cf7ea7aa9038654a] [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 Ronald Aylmer Fisher' @ 193.190.124.24

\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 Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103772&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 Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103772&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103772&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 Ronald Aylmer Fisher' @ 193.190.124.24

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	219.3	NA	NA	-43.7393518518519	NA
2	211.1	NA	NA	-37.0726851851852	NA
3	215.2	NA	NA	-17.6629629629630	NA
4	240.2	NA	NA	-2.18240740740742	NA
5	242.2	NA	NA	23.4037037037037	NA
6	240.7	NA	NA	47.9231481481482	NA
7	255.4	273.855092592593	225.4	48.4550925925926	-18.4550925925925
8	253	252.625925925926	223.775	28.8509259259259	0.374074074074116
9	218.2	236.749537037037	223.383333333333	13.3662037037037	-18.5495370370371
10	203.7	214.464814814815	222.920833333333	-8.45601851851854	-10.7648148148148
11	205.6	205.846759259259	222	-16.1532407407407	-0.246759259259278
12	215.6	184.771759259259	221.504166666667	-36.7324074074074	30.8282407407407
13	188.5	177.952314814815	221.691666666667	-43.7393518518519	10.5476851851852
14	202.9	184.577314814815	221.65	-37.0726851851852	18.3226851851852
15	214	205.941203703704	223.604166666667	-17.6629629629630	8.0587962962963
16	230.3	227.175925925926	229.358333333333	-2.18240740740742	3.12407407407409
17	230	261.224537037037	237.820833333333	23.4037037037037	-31.224537037037
18	241	294.764814814815	246.841666666667	47.9231481481482	-53.7648148148148
19	259.6	304.925925925926	256.470833333333	48.4550925925926	-45.3259259259259
20	247.8	296.084259259259	267.233333333333	28.8509259259259	-48.2842592592592
21	270.3	291.982870370370	278.616666666667	13.3662037037037	-21.6828703703704
22	289.7	282.598148148148	291.054166666667	-8.45601851851854	7.10185185185185
23	322.7	289.788425925926	305.941666666667	-16.1532407407407	32.9115740740741
24	315	287.059259259259	323.791666666667	-36.7324074074074	27.9407407407408
25	320.2	298.060648148148	341.8	-43.7393518518519	22.1393518518519
26	329.5	319.910648148148	356.983333333333	-37.0726851851852	9.58935185185186
27	360.6	349.174537037037	366.8375	-17.6629629629630	11.4254629629631
28	382.2	366.471759259259	368.654166666667	-2.18240740740742	15.7282407407408
29	435.4	384.874537037037	361.470833333333	23.4037037037037	50.525462962963
30	464	396.760648148148	348.8375	47.9231481481482	67.239351851852
31	468.8	383.300925925926	334.845833333333	48.4550925925926	85.4990740740741
32	403	349.167592592593	320.316666666667	28.8509259259259	53.8324074074074
33	351.6	317.978703703704	304.6125	13.3662037037037	33.6212962962964
34	252	279.460648148148	287.916666666667	-8.45601851851854	-27.4606481481482
35	188	253.659259259259	269.8125	-16.1532407407407	-65.6592592592593
36	146.5	214.396759259259	251.129166666667	-36.7324074074074	-67.8967592592593
37	152.9	188.252314814815	231.991666666667	-43.7393518518519	-35.3523148148148
38	148.1	178.677314814815	215.75	-37.0726851851852	-30.5773148148148
39	165.1	187.249537037037	204.9125	-17.6629629629630	-22.1495370370371
40	177	198.517592592593	200.7	-2.18240740740742	-21.5175925925926
41	206.1	228.066203703704	204.6625	23.4037037037037	-21.9662037037037
42	244.9	261.039814814815	213.116666666667	47.9231481481482	-16.1398148148148
43	228.6	271.438425925926	222.983333333333	48.4550925925926	-42.8384259259259
44	253.4	261.613425925926	232.7625	28.8509259259259	-8.21342592592589
45	241.1	255.703703703704	242.3375	13.3662037037037	-14.6037037037037
46	261.4	243.706481481481	252.1625	-8.45601851851854	17.6935185185185
47	273.7	243.617592592593	259.770833333333	-16.1532407407407	30.0824074074074
48	263.7	226.409259259259	263.141666666667	-36.7324074074074	37.2907407407408
49	272.5	NA	265.4375	NA	NA
50	263.2	NA	267.5625	NA	NA
51	279.8	NA	269.366666666667	NA	NA
52	298.1	NA	271.666666666667	NA	NA
53	267.6	NA	NA	NA	NA
54	264.3	NA	NA	NA	NA
55	264.3	NA	NA	NA	NA
56	268.7	NA	NA	NA	NA
57	269.1	NA	NA	NA	NA
58	288.6	NA	NA	NA	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 219.3 & NA & NA & -43.7393518518519 & NA \tabularnewline
2 & 211.1 & NA & NA & -37.0726851851852 & NA \tabularnewline
3 & 215.2 & NA & NA & -17.6629629629630 & NA \tabularnewline
4 & 240.2 & NA & NA & -2.18240740740742 & NA \tabularnewline
5 & 242.2 & NA & NA & 23.4037037037037 & NA \tabularnewline
6 & 240.7 & NA & NA & 47.9231481481482 & NA \tabularnewline
7 & 255.4 & 273.855092592593 & 225.4 & 48.4550925925926 & -18.4550925925925 \tabularnewline
8 & 253 & 252.625925925926 & 223.775 & 28.8509259259259 & 0.374074074074116 \tabularnewline
9 & 218.2 & 236.749537037037 & 223.383333333333 & 13.3662037037037 & -18.5495370370371 \tabularnewline
10 & 203.7 & 214.464814814815 & 222.920833333333 & -8.45601851851854 & -10.7648148148148 \tabularnewline
11 & 205.6 & 205.846759259259 & 222 & -16.1532407407407 & -0.246759259259278 \tabularnewline
12 & 215.6 & 184.771759259259 & 221.504166666667 & -36.7324074074074 & 30.8282407407407 \tabularnewline
13 & 188.5 & 177.952314814815 & 221.691666666667 & -43.7393518518519 & 10.5476851851852 \tabularnewline
14 & 202.9 & 184.577314814815 & 221.65 & -37.0726851851852 & 18.3226851851852 \tabularnewline
15 & 214 & 205.941203703704 & 223.604166666667 & -17.6629629629630 & 8.0587962962963 \tabularnewline
16 & 230.3 & 227.175925925926 & 229.358333333333 & -2.18240740740742 & 3.12407407407409 \tabularnewline
17 & 230 & 261.224537037037 & 237.820833333333 & 23.4037037037037 & -31.224537037037 \tabularnewline
18 & 241 & 294.764814814815 & 246.841666666667 & 47.9231481481482 & -53.7648148148148 \tabularnewline
19 & 259.6 & 304.925925925926 & 256.470833333333 & 48.4550925925926 & -45.3259259259259 \tabularnewline
20 & 247.8 & 296.084259259259 & 267.233333333333 & 28.8509259259259 & -48.2842592592592 \tabularnewline
21 & 270.3 & 291.982870370370 & 278.616666666667 & 13.3662037037037 & -21.6828703703704 \tabularnewline
22 & 289.7 & 282.598148148148 & 291.054166666667 & -8.45601851851854 & 7.10185185185185 \tabularnewline
23 & 322.7 & 289.788425925926 & 305.941666666667 & -16.1532407407407 & 32.9115740740741 \tabularnewline
24 & 315 & 287.059259259259 & 323.791666666667 & -36.7324074074074 & 27.9407407407408 \tabularnewline
25 & 320.2 & 298.060648148148 & 341.8 & -43.7393518518519 & 22.1393518518519 \tabularnewline
26 & 329.5 & 319.910648148148 & 356.983333333333 & -37.0726851851852 & 9.58935185185186 \tabularnewline
27 & 360.6 & 349.174537037037 & 366.8375 & -17.6629629629630 & 11.4254629629631 \tabularnewline
28 & 382.2 & 366.471759259259 & 368.654166666667 & -2.18240740740742 & 15.7282407407408 \tabularnewline
29 & 435.4 & 384.874537037037 & 361.470833333333 & 23.4037037037037 & 50.525462962963 \tabularnewline
30 & 464 & 396.760648148148 & 348.8375 & 47.9231481481482 & 67.239351851852 \tabularnewline
31 & 468.8 & 383.300925925926 & 334.845833333333 & 48.4550925925926 & 85.4990740740741 \tabularnewline
32 & 403 & 349.167592592593 & 320.316666666667 & 28.8509259259259 & 53.8324074074074 \tabularnewline
33 & 351.6 & 317.978703703704 & 304.6125 & 13.3662037037037 & 33.6212962962964 \tabularnewline
34 & 252 & 279.460648148148 & 287.916666666667 & -8.45601851851854 & -27.4606481481482 \tabularnewline
35 & 188 & 253.659259259259 & 269.8125 & -16.1532407407407 & -65.6592592592593 \tabularnewline
36 & 146.5 & 214.396759259259 & 251.129166666667 & -36.7324074074074 & -67.8967592592593 \tabularnewline
37 & 152.9 & 188.252314814815 & 231.991666666667 & -43.7393518518519 & -35.3523148148148 \tabularnewline
38 & 148.1 & 178.677314814815 & 215.75 & -37.0726851851852 & -30.5773148148148 \tabularnewline
39 & 165.1 & 187.249537037037 & 204.9125 & -17.6629629629630 & -22.1495370370371 \tabularnewline
40 & 177 & 198.517592592593 & 200.7 & -2.18240740740742 & -21.5175925925926 \tabularnewline
41 & 206.1 & 228.066203703704 & 204.6625 & 23.4037037037037 & -21.9662037037037 \tabularnewline
42 & 244.9 & 261.039814814815 & 213.116666666667 & 47.9231481481482 & -16.1398148148148 \tabularnewline
43 & 228.6 & 271.438425925926 & 222.983333333333 & 48.4550925925926 & -42.8384259259259 \tabularnewline
44 & 253.4 & 261.613425925926 & 232.7625 & 28.8509259259259 & -8.21342592592589 \tabularnewline
45 & 241.1 & 255.703703703704 & 242.3375 & 13.3662037037037 & -14.6037037037037 \tabularnewline
46 & 261.4 & 243.706481481481 & 252.1625 & -8.45601851851854 & 17.6935185185185 \tabularnewline
47 & 273.7 & 243.617592592593 & 259.770833333333 & -16.1532407407407 & 30.0824074074074 \tabularnewline
48 & 263.7 & 226.409259259259 & 263.141666666667 & -36.7324074074074 & 37.2907407407408 \tabularnewline
49 & 272.5 & NA & 265.4375 & NA & NA \tabularnewline
50 & 263.2 & NA & 267.5625 & NA & NA \tabularnewline
51 & 279.8 & NA & 269.366666666667 & NA & NA \tabularnewline
52 & 298.1 & NA & 271.666666666667 & NA & NA \tabularnewline
53 & 267.6 & NA & NA & NA & NA \tabularnewline
54 & 264.3 & NA & NA & NA & NA \tabularnewline
55 & 264.3 & NA & NA & NA & NA \tabularnewline
56 & 268.7 & NA & NA & NA & NA \tabularnewline
57 & 269.1 & NA & NA & NA & NA \tabularnewline
58 & 288.6 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103772&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]219.3[/C][C]NA[/C][C]NA[/C][C]-43.7393518518519[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]211.1[/C][C]NA[/C][C]NA[/C][C]-37.0726851851852[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]215.2[/C][C]NA[/C][C]NA[/C][C]-17.6629629629630[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]240.2[/C][C]NA[/C][C]NA[/C][C]-2.18240740740742[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]242.2[/C][C]NA[/C][C]NA[/C][C]23.4037037037037[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]240.7[/C][C]NA[/C][C]NA[/C][C]47.9231481481482[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]255.4[/C][C]273.855092592593[/C][C]225.4[/C][C]48.4550925925926[/C][C]-18.4550925925925[/C][/ROW]
[ROW][C]8[/C][C]253[/C][C]252.625925925926[/C][C]223.775[/C][C]28.8509259259259[/C][C]0.374074074074116[/C][/ROW]
[ROW][C]9[/C][C]218.2[/C][C]236.749537037037[/C][C]223.383333333333[/C][C]13.3662037037037[/C][C]-18.5495370370371[/C][/ROW]
[ROW][C]10[/C][C]203.7[/C][C]214.464814814815[/C][C]222.920833333333[/C][C]-8.45601851851854[/C][C]-10.7648148148148[/C][/ROW]
[ROW][C]11[/C][C]205.6[/C][C]205.846759259259[/C][C]222[/C][C]-16.1532407407407[/C][C]-0.246759259259278[/C][/ROW]
[ROW][C]12[/C][C]215.6[/C][C]184.771759259259[/C][C]221.504166666667[/C][C]-36.7324074074074[/C][C]30.8282407407407[/C][/ROW]
[ROW][C]13[/C][C]188.5[/C][C]177.952314814815[/C][C]221.691666666667[/C][C]-43.7393518518519[/C][C]10.5476851851852[/C][/ROW]
[ROW][C]14[/C][C]202.9[/C][C]184.577314814815[/C][C]221.65[/C][C]-37.0726851851852[/C][C]18.3226851851852[/C][/ROW]
[ROW][C]15[/C][C]214[/C][C]205.941203703704[/C][C]223.604166666667[/C][C]-17.6629629629630[/C][C]8.0587962962963[/C][/ROW]
[ROW][C]16[/C][C]230.3[/C][C]227.175925925926[/C][C]229.358333333333[/C][C]-2.18240740740742[/C][C]3.12407407407409[/C][/ROW]
[ROW][C]17[/C][C]230[/C][C]261.224537037037[/C][C]237.820833333333[/C][C]23.4037037037037[/C][C]-31.224537037037[/C][/ROW]
[ROW][C]18[/C][C]241[/C][C]294.764814814815[/C][C]246.841666666667[/C][C]47.9231481481482[/C][C]-53.7648148148148[/C][/ROW]
[ROW][C]19[/C][C]259.6[/C][C]304.925925925926[/C][C]256.470833333333[/C][C]48.4550925925926[/C][C]-45.3259259259259[/C][/ROW]
[ROW][C]20[/C][C]247.8[/C][C]296.084259259259[/C][C]267.233333333333[/C][C]28.8509259259259[/C][C]-48.2842592592592[/C][/ROW]
[ROW][C]21[/C][C]270.3[/C][C]291.982870370370[/C][C]278.616666666667[/C][C]13.3662037037037[/C][C]-21.6828703703704[/C][/ROW]
[ROW][C]22[/C][C]289.7[/C][C]282.598148148148[/C][C]291.054166666667[/C][C]-8.45601851851854[/C][C]7.10185185185185[/C][/ROW]
[ROW][C]23[/C][C]322.7[/C][C]289.788425925926[/C][C]305.941666666667[/C][C]-16.1532407407407[/C][C]32.9115740740741[/C][/ROW]
[ROW][C]24[/C][C]315[/C][C]287.059259259259[/C][C]323.791666666667[/C][C]-36.7324074074074[/C][C]27.9407407407408[/C][/ROW]
[ROW][C]25[/C][C]320.2[/C][C]298.060648148148[/C][C]341.8[/C][C]-43.7393518518519[/C][C]22.1393518518519[/C][/ROW]
[ROW][C]26[/C][C]329.5[/C][C]319.910648148148[/C][C]356.983333333333[/C][C]-37.0726851851852[/C][C]9.58935185185186[/C][/ROW]
[ROW][C]27[/C][C]360.6[/C][C]349.174537037037[/C][C]366.8375[/C][C]-17.6629629629630[/C][C]11.4254629629631[/C][/ROW]
[ROW][C]28[/C][C]382.2[/C][C]366.471759259259[/C][C]368.654166666667[/C][C]-2.18240740740742[/C][C]15.7282407407408[/C][/ROW]
[ROW][C]29[/C][C]435.4[/C][C]384.874537037037[/C][C]361.470833333333[/C][C]23.4037037037037[/C][C]50.525462962963[/C][/ROW]
[ROW][C]30[/C][C]464[/C][C]396.760648148148[/C][C]348.8375[/C][C]47.9231481481482[/C][C]67.239351851852[/C][/ROW]
[ROW][C]31[/C][C]468.8[/C][C]383.300925925926[/C][C]334.845833333333[/C][C]48.4550925925926[/C][C]85.4990740740741[/C][/ROW]
[ROW][C]32[/C][C]403[/C][C]349.167592592593[/C][C]320.316666666667[/C][C]28.8509259259259[/C][C]53.8324074074074[/C][/ROW]
[ROW][C]33[/C][C]351.6[/C][C]317.978703703704[/C][C]304.6125[/C][C]13.3662037037037[/C][C]33.6212962962964[/C][/ROW]
[ROW][C]34[/C][C]252[/C][C]279.460648148148[/C][C]287.916666666667[/C][C]-8.45601851851854[/C][C]-27.4606481481482[/C][/ROW]
[ROW][C]35[/C][C]188[/C][C]253.659259259259[/C][C]269.8125[/C][C]-16.1532407407407[/C][C]-65.6592592592593[/C][/ROW]
[ROW][C]36[/C][C]146.5[/C][C]214.396759259259[/C][C]251.129166666667[/C][C]-36.7324074074074[/C][C]-67.8967592592593[/C][/ROW]
[ROW][C]37[/C][C]152.9[/C][C]188.252314814815[/C][C]231.991666666667[/C][C]-43.7393518518519[/C][C]-35.3523148148148[/C][/ROW]
[ROW][C]38[/C][C]148.1[/C][C]178.677314814815[/C][C]215.75[/C][C]-37.0726851851852[/C][C]-30.5773148148148[/C][/ROW]
[ROW][C]39[/C][C]165.1[/C][C]187.249537037037[/C][C]204.9125[/C][C]-17.6629629629630[/C][C]-22.1495370370371[/C][/ROW]
[ROW][C]40[/C][C]177[/C][C]198.517592592593[/C][C]200.7[/C][C]-2.18240740740742[/C][C]-21.5175925925926[/C][/ROW]
[ROW][C]41[/C][C]206.1[/C][C]228.066203703704[/C][C]204.6625[/C][C]23.4037037037037[/C][C]-21.9662037037037[/C][/ROW]
[ROW][C]42[/C][C]244.9[/C][C]261.039814814815[/C][C]213.116666666667[/C][C]47.9231481481482[/C][C]-16.1398148148148[/C][/ROW]
[ROW][C]43[/C][C]228.6[/C][C]271.438425925926[/C][C]222.983333333333[/C][C]48.4550925925926[/C][C]-42.8384259259259[/C][/ROW]
[ROW][C]44[/C][C]253.4[/C][C]261.613425925926[/C][C]232.7625[/C][C]28.8509259259259[/C][C]-8.21342592592589[/C][/ROW]
[ROW][C]45[/C][C]241.1[/C][C]255.703703703704[/C][C]242.3375[/C][C]13.3662037037037[/C][C]-14.6037037037037[/C][/ROW]
[ROW][C]46[/C][C]261.4[/C][C]243.706481481481[/C][C]252.1625[/C][C]-8.45601851851854[/C][C]17.6935185185185[/C][/ROW]
[ROW][C]47[/C][C]273.7[/C][C]243.617592592593[/C][C]259.770833333333[/C][C]-16.1532407407407[/C][C]30.0824074074074[/C][/ROW]
[ROW][C]48[/C][C]263.7[/C][C]226.409259259259[/C][C]263.141666666667[/C][C]-36.7324074074074[/C][C]37.2907407407408[/C][/ROW]
[ROW][C]49[/C][C]272.5[/C][C]NA[/C][C]265.4375[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]50[/C][C]263.2[/C][C]NA[/C][C]267.5625[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]51[/C][C]279.8[/C][C]NA[/C][C]269.366666666667[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]52[/C][C]298.1[/C][C]NA[/C][C]271.666666666667[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]53[/C][C]267.6[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]54[/C][C]264.3[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]55[/C][C]264.3[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]268.7[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]269.1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]288.6[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103772&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103772&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	219.3	NA	NA	-43.7393518518519	NA
2	211.1	NA	NA	-37.0726851851852	NA
3	215.2	NA	NA	-17.6629629629630	NA
4	240.2	NA	NA	-2.18240740740742	NA
5	242.2	NA	NA	23.4037037037037	NA
6	240.7	NA	NA	47.9231481481482	NA
7	255.4	273.855092592593	225.4	48.4550925925926	-18.4550925925925
8	253	252.625925925926	223.775	28.8509259259259	0.374074074074116
9	218.2	236.749537037037	223.383333333333	13.3662037037037	-18.5495370370371
10	203.7	214.464814814815	222.920833333333	-8.45601851851854	-10.7648148148148
11	205.6	205.846759259259	222	-16.1532407407407	-0.246759259259278
12	215.6	184.771759259259	221.504166666667	-36.7324074074074	30.8282407407407
13	188.5	177.952314814815	221.691666666667	-43.7393518518519	10.5476851851852
14	202.9	184.577314814815	221.65	-37.0726851851852	18.3226851851852
15	214	205.941203703704	223.604166666667	-17.6629629629630	8.0587962962963
16	230.3	227.175925925926	229.358333333333	-2.18240740740742	3.12407407407409
17	230	261.224537037037	237.820833333333	23.4037037037037	-31.224537037037
18	241	294.764814814815	246.841666666667	47.9231481481482	-53.7648148148148
19	259.6	304.925925925926	256.470833333333	48.4550925925926	-45.3259259259259
20	247.8	296.084259259259	267.233333333333	28.8509259259259	-48.2842592592592
21	270.3	291.982870370370	278.616666666667	13.3662037037037	-21.6828703703704
22	289.7	282.598148148148	291.054166666667	-8.45601851851854	7.10185185185185
23	322.7	289.788425925926	305.941666666667	-16.1532407407407	32.9115740740741
24	315	287.059259259259	323.791666666667	-36.7324074074074	27.9407407407408
25	320.2	298.060648148148	341.8	-43.7393518518519	22.1393518518519
26	329.5	319.910648148148	356.983333333333	-37.0726851851852	9.58935185185186
27	360.6	349.174537037037	366.8375	-17.6629629629630	11.4254629629631
28	382.2	366.471759259259	368.654166666667	-2.18240740740742	15.7282407407408
29	435.4	384.874537037037	361.470833333333	23.4037037037037	50.525462962963
30	464	396.760648148148	348.8375	47.9231481481482	67.239351851852
31	468.8	383.300925925926	334.845833333333	48.4550925925926	85.4990740740741
32	403	349.167592592593	320.316666666667	28.8509259259259	53.8324074074074
33	351.6	317.978703703704	304.6125	13.3662037037037	33.6212962962964
34	252	279.460648148148	287.916666666667	-8.45601851851854	-27.4606481481482
35	188	253.659259259259	269.8125	-16.1532407407407	-65.6592592592593
36	146.5	214.396759259259	251.129166666667	-36.7324074074074	-67.8967592592593
37	152.9	188.252314814815	231.991666666667	-43.7393518518519	-35.3523148148148
38	148.1	178.677314814815	215.75	-37.0726851851852	-30.5773148148148
39	165.1	187.249537037037	204.9125	-17.6629629629630	-22.1495370370371
40	177	198.517592592593	200.7	-2.18240740740742	-21.5175925925926
41	206.1	228.066203703704	204.6625	23.4037037037037	-21.9662037037037
42	244.9	261.039814814815	213.116666666667	47.9231481481482	-16.1398148148148
43	228.6	271.438425925926	222.983333333333	48.4550925925926	-42.8384259259259
44	253.4	261.613425925926	232.7625	28.8509259259259	-8.21342592592589
45	241.1	255.703703703704	242.3375	13.3662037037037	-14.6037037037037
46	261.4	243.706481481481	252.1625	-8.45601851851854	17.6935185185185
47	273.7	243.617592592593	259.770833333333	-16.1532407407407	30.0824074074074
48	263.7	226.409259259259	263.141666666667	-36.7324074074074	37.2907407407408
49	272.5	NA	265.4375	NA	NA
50	263.2	NA	267.5625	NA	NA
51	279.8	NA	269.366666666667	NA	NA
52	298.1	NA	271.666666666667	NA	NA
53	267.6	NA	NA	NA	NA
54	264.3	NA	NA	NA	NA
55	264.3	NA	NA	NA	NA
56	268.7	NA	NA	NA	NA
57	269.1	NA	NA	NA	NA
58	288.6	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 = 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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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