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R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationWed, 04 Dec 2013 04:01:24 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/04/t1386147711qpx7v2m84v11916.htm/, Retrieved Fri, 29 Mar 2024 14:46:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=230443, Retrieved Fri, 29 Mar 2024 14:46:54 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-04 09:01:24] [a77fefb2f58fa7cd2e34999d4ef06758] [Current]
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Dataseries X:
-2.5
4.4
13.7
12.3
13.4
2.2
1.7
-7.2
-4.8
-2.9
-2.4
-2.5
-5.3
-7.1
-8
-8.9
-7.7
-1.1
4
9.6
10.9
13
14.9
20.1
10.8
11
3.8
10.8
7.6
10.2
2.2
-0.1
-1.7
-4.8
-9.9
-13.5
-18.1
-18
-15.7
-15.2
-15.1
-17.9
-14.5
-9.4
-4.2
-2.2
4.5
12.4
15.8
11.5
14.1
18.8
26.1
27.9
25.4
23.4
11.5
9.9
8.1
12.6
8.2
5.4
1
-2.9
-3.7
-7
-7.2
-11.8
-2.1
1.2
2.5
4.8
-6.6
-16
-22.7
-17.7
-18.2
-18.9
-16
-12.2
-17.1
-18.6
-17.5
-24.9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230443&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230443&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230443&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1-2.5NANA0.898032NA
24.4NANA-1.94433NA
313.7NANA-4.20752NA
412.3NANA-1.94641NA
513.4NANA-1.04919NA
62.2NANA-0.0887731NA
71.71.942482-0.0575231-0.242477
8-7.20.3334491.40417-1.07072-7.53345
9-4.80.194560.02083330.173727-4.99456
10-2.9-0.365162-1.766671.4015-2.53484
11-2.4-1.11655-3.529172.41262-1.28345
12-2.50.932755-4.545835.47859-3.43275
13-5.3-3.68947-4.58750.898032-1.61053
14-7.1-5.736-3.79167-1.94433-1.364
15-8-6.64502-2.4375-4.20752-1.35498
16-8.9-3.06725-1.12083-1.94641-5.83275
17-7.7-0.786690.2625-1.04919-6.91331
18-1.11.836231.925-0.0887731-2.93623
1943.479983.5375-0.05752310.520023
209.63.891784.9625-1.070725.70822
2110.96.382066.208330.1737274.51794
22138.922347.520831.40154.07766
2314.911.39188.979172.412623.50822
2420.115.566110.08755.478594.53391
2510.811.381410.48330.898032-0.581366
26118.0598410.0042-1.944332.94016
273.84.867489.075-4.20752-1.06748
2810.85.861927.80833-1.946414.93808
297.64.984146.03333-1.049192.61586
3010.23.511233.6-0.08877316.68877
312.20.938310.995833-0.05752311.26169
32-0.1-2.48738-1.41667-1.070722.38738
33-1.7-3.26377-3.43750.1737271.56377
34-4.8-3.93183-5.333331.4015-0.868171
35-9.9-4.94988-7.36252.41262-4.95012
36-13.5-4.00058-9.479175.47859-9.49942
37-18.1-10.4478-11.34580.898032-7.6522
38-18-14.3735-12.4292-1.94433-3.6265
39-15.7-17.1284-12.9208-4.207521.42836
40-15.2-14.8631-12.9167-1.94641-0.336921
41-15.1-13.2575-12.2083-1.04919-1.84248
42-17.9-10.6179-10.5292-0.0887731-7.28206
43-14.5-8.09502-8.0375-0.0575231-6.40498
44-9.4-6.46655-5.39583-1.07072-2.93345
45-4.2-2.75127-2.9250.173727-1.44873
46-2.21.13484-0.2666671.4015-3.33484
474.55.279282.866672.41262-0.779282
4812.411.97036.491675.478590.429745
4915.810.960510.06250.8980324.83947
5011.511.147313.0917-1.944330.352662
5114.110.90515.1125-4.207523.19502
5218.814.324416.2708-1.946414.47558
5326.115.875816.925-1.0491910.2242
5427.916.994617.0833-0.088773110.9054
5525.416.717516.775-0.05752318.68252
5623.415.133416.2042-1.070728.26655
5711.515.577915.40420.173727-4.07789
589.915.355713.95421.4015-5.45567
598.114.220911.80832.41262-6.12095
6012.614.59119.11255.47859-1.99109
618.27.198036.30.8980321.00197
625.41.530673.475-1.944333.86933
631-2.765861.44167-4.207523.76586
64-2.9-1.433910.5125-1.94641-1.46609
65-3.7-1.13252-0.0833333-1.04919-2.56748
66-7-0.73044-0.641667-0.0887731-6.26956
67-7.2-1.64086-1.58333-0.0575231-5.55914
68-11.8-4.16238-3.09167-1.07072-7.63762
69-2.1-4.79711-4.970830.1737272.69711
701.2-5.1735-6.5751.40156.3735
712.5-5.38322-7.795832.412627.88322
724.8-3.41725-8.895835.478598.21725
73-6.6-8.8603-9.758330.8980322.2603
74-16-12.086-10.1417-1.94433-3.914
75-22.7-14.9909-10.7833-4.20752-7.70914
76-17.7-14.1797-12.2333-1.94641-3.52025
77-18.2-14.9409-13.8917-1.04919-3.25914
78-18.9-16.0513-15.9625-0.0887731-2.84873
79-16NANA-0.0575231NA
80-12.2NANA-1.07072NA
81-17.1NANA0.173727NA
82-18.6NANA1.4015NA
83-17.5NANA2.41262NA
84-24.9NANA5.47859NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & -2.5 & NA & NA & 0.898032 & NA \tabularnewline
2 & 4.4 & NA & NA & -1.94433 & NA \tabularnewline
3 & 13.7 & NA & NA & -4.20752 & NA \tabularnewline
4 & 12.3 & NA & NA & -1.94641 & NA \tabularnewline
5 & 13.4 & NA & NA & -1.04919 & NA \tabularnewline
6 & 2.2 & NA & NA & -0.0887731 & NA \tabularnewline
7 & 1.7 & 1.94248 & 2 & -0.0575231 & -0.242477 \tabularnewline
8 & -7.2 & 0.333449 & 1.40417 & -1.07072 & -7.53345 \tabularnewline
9 & -4.8 & 0.19456 & 0.0208333 & 0.173727 & -4.99456 \tabularnewline
10 & -2.9 & -0.365162 & -1.76667 & 1.4015 & -2.53484 \tabularnewline
11 & -2.4 & -1.11655 & -3.52917 & 2.41262 & -1.28345 \tabularnewline
12 & -2.5 & 0.932755 & -4.54583 & 5.47859 & -3.43275 \tabularnewline
13 & -5.3 & -3.68947 & -4.5875 & 0.898032 & -1.61053 \tabularnewline
14 & -7.1 & -5.736 & -3.79167 & -1.94433 & -1.364 \tabularnewline
15 & -8 & -6.64502 & -2.4375 & -4.20752 & -1.35498 \tabularnewline
16 & -8.9 & -3.06725 & -1.12083 & -1.94641 & -5.83275 \tabularnewline
17 & -7.7 & -0.78669 & 0.2625 & -1.04919 & -6.91331 \tabularnewline
18 & -1.1 & 1.83623 & 1.925 & -0.0887731 & -2.93623 \tabularnewline
19 & 4 & 3.47998 & 3.5375 & -0.0575231 & 0.520023 \tabularnewline
20 & 9.6 & 3.89178 & 4.9625 & -1.07072 & 5.70822 \tabularnewline
21 & 10.9 & 6.38206 & 6.20833 & 0.173727 & 4.51794 \tabularnewline
22 & 13 & 8.92234 & 7.52083 & 1.4015 & 4.07766 \tabularnewline
23 & 14.9 & 11.3918 & 8.97917 & 2.41262 & 3.50822 \tabularnewline
24 & 20.1 & 15.5661 & 10.0875 & 5.47859 & 4.53391 \tabularnewline
25 & 10.8 & 11.3814 & 10.4833 & 0.898032 & -0.581366 \tabularnewline
26 & 11 & 8.05984 & 10.0042 & -1.94433 & 2.94016 \tabularnewline
27 & 3.8 & 4.86748 & 9.075 & -4.20752 & -1.06748 \tabularnewline
28 & 10.8 & 5.86192 & 7.80833 & -1.94641 & 4.93808 \tabularnewline
29 & 7.6 & 4.98414 & 6.03333 & -1.04919 & 2.61586 \tabularnewline
30 & 10.2 & 3.51123 & 3.6 & -0.0887731 & 6.68877 \tabularnewline
31 & 2.2 & 0.93831 & 0.995833 & -0.0575231 & 1.26169 \tabularnewline
32 & -0.1 & -2.48738 & -1.41667 & -1.07072 & 2.38738 \tabularnewline
33 & -1.7 & -3.26377 & -3.4375 & 0.173727 & 1.56377 \tabularnewline
34 & -4.8 & -3.93183 & -5.33333 & 1.4015 & -0.868171 \tabularnewline
35 & -9.9 & -4.94988 & -7.3625 & 2.41262 & -4.95012 \tabularnewline
36 & -13.5 & -4.00058 & -9.47917 & 5.47859 & -9.49942 \tabularnewline
37 & -18.1 & -10.4478 & -11.3458 & 0.898032 & -7.6522 \tabularnewline
38 & -18 & -14.3735 & -12.4292 & -1.94433 & -3.6265 \tabularnewline
39 & -15.7 & -17.1284 & -12.9208 & -4.20752 & 1.42836 \tabularnewline
40 & -15.2 & -14.8631 & -12.9167 & -1.94641 & -0.336921 \tabularnewline
41 & -15.1 & -13.2575 & -12.2083 & -1.04919 & -1.84248 \tabularnewline
42 & -17.9 & -10.6179 & -10.5292 & -0.0887731 & -7.28206 \tabularnewline
43 & -14.5 & -8.09502 & -8.0375 & -0.0575231 & -6.40498 \tabularnewline
44 & -9.4 & -6.46655 & -5.39583 & -1.07072 & -2.93345 \tabularnewline
45 & -4.2 & -2.75127 & -2.925 & 0.173727 & -1.44873 \tabularnewline
46 & -2.2 & 1.13484 & -0.266667 & 1.4015 & -3.33484 \tabularnewline
47 & 4.5 & 5.27928 & 2.86667 & 2.41262 & -0.779282 \tabularnewline
48 & 12.4 & 11.9703 & 6.49167 & 5.47859 & 0.429745 \tabularnewline
49 & 15.8 & 10.9605 & 10.0625 & 0.898032 & 4.83947 \tabularnewline
50 & 11.5 & 11.1473 & 13.0917 & -1.94433 & 0.352662 \tabularnewline
51 & 14.1 & 10.905 & 15.1125 & -4.20752 & 3.19502 \tabularnewline
52 & 18.8 & 14.3244 & 16.2708 & -1.94641 & 4.47558 \tabularnewline
53 & 26.1 & 15.8758 & 16.925 & -1.04919 & 10.2242 \tabularnewline
54 & 27.9 & 16.9946 & 17.0833 & -0.0887731 & 10.9054 \tabularnewline
55 & 25.4 & 16.7175 & 16.775 & -0.0575231 & 8.68252 \tabularnewline
56 & 23.4 & 15.1334 & 16.2042 & -1.07072 & 8.26655 \tabularnewline
57 & 11.5 & 15.5779 & 15.4042 & 0.173727 & -4.07789 \tabularnewline
58 & 9.9 & 15.3557 & 13.9542 & 1.4015 & -5.45567 \tabularnewline
59 & 8.1 & 14.2209 & 11.8083 & 2.41262 & -6.12095 \tabularnewline
60 & 12.6 & 14.5911 & 9.1125 & 5.47859 & -1.99109 \tabularnewline
61 & 8.2 & 7.19803 & 6.3 & 0.898032 & 1.00197 \tabularnewline
62 & 5.4 & 1.53067 & 3.475 & -1.94433 & 3.86933 \tabularnewline
63 & 1 & -2.76586 & 1.44167 & -4.20752 & 3.76586 \tabularnewline
64 & -2.9 & -1.43391 & 0.5125 & -1.94641 & -1.46609 \tabularnewline
65 & -3.7 & -1.13252 & -0.0833333 & -1.04919 & -2.56748 \tabularnewline
66 & -7 & -0.73044 & -0.641667 & -0.0887731 & -6.26956 \tabularnewline
67 & -7.2 & -1.64086 & -1.58333 & -0.0575231 & -5.55914 \tabularnewline
68 & -11.8 & -4.16238 & -3.09167 & -1.07072 & -7.63762 \tabularnewline
69 & -2.1 & -4.79711 & -4.97083 & 0.173727 & 2.69711 \tabularnewline
70 & 1.2 & -5.1735 & -6.575 & 1.4015 & 6.3735 \tabularnewline
71 & 2.5 & -5.38322 & -7.79583 & 2.41262 & 7.88322 \tabularnewline
72 & 4.8 & -3.41725 & -8.89583 & 5.47859 & 8.21725 \tabularnewline
73 & -6.6 & -8.8603 & -9.75833 & 0.898032 & 2.2603 \tabularnewline
74 & -16 & -12.086 & -10.1417 & -1.94433 & -3.914 \tabularnewline
75 & -22.7 & -14.9909 & -10.7833 & -4.20752 & -7.70914 \tabularnewline
76 & -17.7 & -14.1797 & -12.2333 & -1.94641 & -3.52025 \tabularnewline
77 & -18.2 & -14.9409 & -13.8917 & -1.04919 & -3.25914 \tabularnewline
78 & -18.9 & -16.0513 & -15.9625 & -0.0887731 & -2.84873 \tabularnewline
79 & -16 & NA & NA & -0.0575231 & NA \tabularnewline
80 & -12.2 & NA & NA & -1.07072 & NA \tabularnewline
81 & -17.1 & NA & NA & 0.173727 & NA \tabularnewline
82 & -18.6 & NA & NA & 1.4015 & NA \tabularnewline
83 & -17.5 & NA & NA & 2.41262 & NA \tabularnewline
84 & -24.9 & NA & NA & 5.47859 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230443&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]-2.5[/C][C]NA[/C][C]NA[/C][C]0.898032[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]4.4[/C][C]NA[/C][C]NA[/C][C]-1.94433[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]13.7[/C][C]NA[/C][C]NA[/C][C]-4.20752[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]12.3[/C][C]NA[/C][C]NA[/C][C]-1.94641[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]13.4[/C][C]NA[/C][C]NA[/C][C]-1.04919[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]2.2[/C][C]NA[/C][C]NA[/C][C]-0.0887731[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]1.7[/C][C]1.94248[/C][C]2[/C][C]-0.0575231[/C][C]-0.242477[/C][/ROW]
[ROW][C]8[/C][C]-7.2[/C][C]0.333449[/C][C]1.40417[/C][C]-1.07072[/C][C]-7.53345[/C][/ROW]
[ROW][C]9[/C][C]-4.8[/C][C]0.19456[/C][C]0.0208333[/C][C]0.173727[/C][C]-4.99456[/C][/ROW]
[ROW][C]10[/C][C]-2.9[/C][C]-0.365162[/C][C]-1.76667[/C][C]1.4015[/C][C]-2.53484[/C][/ROW]
[ROW][C]11[/C][C]-2.4[/C][C]-1.11655[/C][C]-3.52917[/C][C]2.41262[/C][C]-1.28345[/C][/ROW]
[ROW][C]12[/C][C]-2.5[/C][C]0.932755[/C][C]-4.54583[/C][C]5.47859[/C][C]-3.43275[/C][/ROW]
[ROW][C]13[/C][C]-5.3[/C][C]-3.68947[/C][C]-4.5875[/C][C]0.898032[/C][C]-1.61053[/C][/ROW]
[ROW][C]14[/C][C]-7.1[/C][C]-5.736[/C][C]-3.79167[/C][C]-1.94433[/C][C]-1.364[/C][/ROW]
[ROW][C]15[/C][C]-8[/C][C]-6.64502[/C][C]-2.4375[/C][C]-4.20752[/C][C]-1.35498[/C][/ROW]
[ROW][C]16[/C][C]-8.9[/C][C]-3.06725[/C][C]-1.12083[/C][C]-1.94641[/C][C]-5.83275[/C][/ROW]
[ROW][C]17[/C][C]-7.7[/C][C]-0.78669[/C][C]0.2625[/C][C]-1.04919[/C][C]-6.91331[/C][/ROW]
[ROW][C]18[/C][C]-1.1[/C][C]1.83623[/C][C]1.925[/C][C]-0.0887731[/C][C]-2.93623[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]3.47998[/C][C]3.5375[/C][C]-0.0575231[/C][C]0.520023[/C][/ROW]
[ROW][C]20[/C][C]9.6[/C][C]3.89178[/C][C]4.9625[/C][C]-1.07072[/C][C]5.70822[/C][/ROW]
[ROW][C]21[/C][C]10.9[/C][C]6.38206[/C][C]6.20833[/C][C]0.173727[/C][C]4.51794[/C][/ROW]
[ROW][C]22[/C][C]13[/C][C]8.92234[/C][C]7.52083[/C][C]1.4015[/C][C]4.07766[/C][/ROW]
[ROW][C]23[/C][C]14.9[/C][C]11.3918[/C][C]8.97917[/C][C]2.41262[/C][C]3.50822[/C][/ROW]
[ROW][C]24[/C][C]20.1[/C][C]15.5661[/C][C]10.0875[/C][C]5.47859[/C][C]4.53391[/C][/ROW]
[ROW][C]25[/C][C]10.8[/C][C]11.3814[/C][C]10.4833[/C][C]0.898032[/C][C]-0.581366[/C][/ROW]
[ROW][C]26[/C][C]11[/C][C]8.05984[/C][C]10.0042[/C][C]-1.94433[/C][C]2.94016[/C][/ROW]
[ROW][C]27[/C][C]3.8[/C][C]4.86748[/C][C]9.075[/C][C]-4.20752[/C][C]-1.06748[/C][/ROW]
[ROW][C]28[/C][C]10.8[/C][C]5.86192[/C][C]7.80833[/C][C]-1.94641[/C][C]4.93808[/C][/ROW]
[ROW][C]29[/C][C]7.6[/C][C]4.98414[/C][C]6.03333[/C][C]-1.04919[/C][C]2.61586[/C][/ROW]
[ROW][C]30[/C][C]10.2[/C][C]3.51123[/C][C]3.6[/C][C]-0.0887731[/C][C]6.68877[/C][/ROW]
[ROW][C]31[/C][C]2.2[/C][C]0.93831[/C][C]0.995833[/C][C]-0.0575231[/C][C]1.26169[/C][/ROW]
[ROW][C]32[/C][C]-0.1[/C][C]-2.48738[/C][C]-1.41667[/C][C]-1.07072[/C][C]2.38738[/C][/ROW]
[ROW][C]33[/C][C]-1.7[/C][C]-3.26377[/C][C]-3.4375[/C][C]0.173727[/C][C]1.56377[/C][/ROW]
[ROW][C]34[/C][C]-4.8[/C][C]-3.93183[/C][C]-5.33333[/C][C]1.4015[/C][C]-0.868171[/C][/ROW]
[ROW][C]35[/C][C]-9.9[/C][C]-4.94988[/C][C]-7.3625[/C][C]2.41262[/C][C]-4.95012[/C][/ROW]
[ROW][C]36[/C][C]-13.5[/C][C]-4.00058[/C][C]-9.47917[/C][C]5.47859[/C][C]-9.49942[/C][/ROW]
[ROW][C]37[/C][C]-18.1[/C][C]-10.4478[/C][C]-11.3458[/C][C]0.898032[/C][C]-7.6522[/C][/ROW]
[ROW][C]38[/C][C]-18[/C][C]-14.3735[/C][C]-12.4292[/C][C]-1.94433[/C][C]-3.6265[/C][/ROW]
[ROW][C]39[/C][C]-15.7[/C][C]-17.1284[/C][C]-12.9208[/C][C]-4.20752[/C][C]1.42836[/C][/ROW]
[ROW][C]40[/C][C]-15.2[/C][C]-14.8631[/C][C]-12.9167[/C][C]-1.94641[/C][C]-0.336921[/C][/ROW]
[ROW][C]41[/C][C]-15.1[/C][C]-13.2575[/C][C]-12.2083[/C][C]-1.04919[/C][C]-1.84248[/C][/ROW]
[ROW][C]42[/C][C]-17.9[/C][C]-10.6179[/C][C]-10.5292[/C][C]-0.0887731[/C][C]-7.28206[/C][/ROW]
[ROW][C]43[/C][C]-14.5[/C][C]-8.09502[/C][C]-8.0375[/C][C]-0.0575231[/C][C]-6.40498[/C][/ROW]
[ROW][C]44[/C][C]-9.4[/C][C]-6.46655[/C][C]-5.39583[/C][C]-1.07072[/C][C]-2.93345[/C][/ROW]
[ROW][C]45[/C][C]-4.2[/C][C]-2.75127[/C][C]-2.925[/C][C]0.173727[/C][C]-1.44873[/C][/ROW]
[ROW][C]46[/C][C]-2.2[/C][C]1.13484[/C][C]-0.266667[/C][C]1.4015[/C][C]-3.33484[/C][/ROW]
[ROW][C]47[/C][C]4.5[/C][C]5.27928[/C][C]2.86667[/C][C]2.41262[/C][C]-0.779282[/C][/ROW]
[ROW][C]48[/C][C]12.4[/C][C]11.9703[/C][C]6.49167[/C][C]5.47859[/C][C]0.429745[/C][/ROW]
[ROW][C]49[/C][C]15.8[/C][C]10.9605[/C][C]10.0625[/C][C]0.898032[/C][C]4.83947[/C][/ROW]
[ROW][C]50[/C][C]11.5[/C][C]11.1473[/C][C]13.0917[/C][C]-1.94433[/C][C]0.352662[/C][/ROW]
[ROW][C]51[/C][C]14.1[/C][C]10.905[/C][C]15.1125[/C][C]-4.20752[/C][C]3.19502[/C][/ROW]
[ROW][C]52[/C][C]18.8[/C][C]14.3244[/C][C]16.2708[/C][C]-1.94641[/C][C]4.47558[/C][/ROW]
[ROW][C]53[/C][C]26.1[/C][C]15.8758[/C][C]16.925[/C][C]-1.04919[/C][C]10.2242[/C][/ROW]
[ROW][C]54[/C][C]27.9[/C][C]16.9946[/C][C]17.0833[/C][C]-0.0887731[/C][C]10.9054[/C][/ROW]
[ROW][C]55[/C][C]25.4[/C][C]16.7175[/C][C]16.775[/C][C]-0.0575231[/C][C]8.68252[/C][/ROW]
[ROW][C]56[/C][C]23.4[/C][C]15.1334[/C][C]16.2042[/C][C]-1.07072[/C][C]8.26655[/C][/ROW]
[ROW][C]57[/C][C]11.5[/C][C]15.5779[/C][C]15.4042[/C][C]0.173727[/C][C]-4.07789[/C][/ROW]
[ROW][C]58[/C][C]9.9[/C][C]15.3557[/C][C]13.9542[/C][C]1.4015[/C][C]-5.45567[/C][/ROW]
[ROW][C]59[/C][C]8.1[/C][C]14.2209[/C][C]11.8083[/C][C]2.41262[/C][C]-6.12095[/C][/ROW]
[ROW][C]60[/C][C]12.6[/C][C]14.5911[/C][C]9.1125[/C][C]5.47859[/C][C]-1.99109[/C][/ROW]
[ROW][C]61[/C][C]8.2[/C][C]7.19803[/C][C]6.3[/C][C]0.898032[/C][C]1.00197[/C][/ROW]
[ROW][C]62[/C][C]5.4[/C][C]1.53067[/C][C]3.475[/C][C]-1.94433[/C][C]3.86933[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]-2.76586[/C][C]1.44167[/C][C]-4.20752[/C][C]3.76586[/C][/ROW]
[ROW][C]64[/C][C]-2.9[/C][C]-1.43391[/C][C]0.5125[/C][C]-1.94641[/C][C]-1.46609[/C][/ROW]
[ROW][C]65[/C][C]-3.7[/C][C]-1.13252[/C][C]-0.0833333[/C][C]-1.04919[/C][C]-2.56748[/C][/ROW]
[ROW][C]66[/C][C]-7[/C][C]-0.73044[/C][C]-0.641667[/C][C]-0.0887731[/C][C]-6.26956[/C][/ROW]
[ROW][C]67[/C][C]-7.2[/C][C]-1.64086[/C][C]-1.58333[/C][C]-0.0575231[/C][C]-5.55914[/C][/ROW]
[ROW][C]68[/C][C]-11.8[/C][C]-4.16238[/C][C]-3.09167[/C][C]-1.07072[/C][C]-7.63762[/C][/ROW]
[ROW][C]69[/C][C]-2.1[/C][C]-4.79711[/C][C]-4.97083[/C][C]0.173727[/C][C]2.69711[/C][/ROW]
[ROW][C]70[/C][C]1.2[/C][C]-5.1735[/C][C]-6.575[/C][C]1.4015[/C][C]6.3735[/C][/ROW]
[ROW][C]71[/C][C]2.5[/C][C]-5.38322[/C][C]-7.79583[/C][C]2.41262[/C][C]7.88322[/C][/ROW]
[ROW][C]72[/C][C]4.8[/C][C]-3.41725[/C][C]-8.89583[/C][C]5.47859[/C][C]8.21725[/C][/ROW]
[ROW][C]73[/C][C]-6.6[/C][C]-8.8603[/C][C]-9.75833[/C][C]0.898032[/C][C]2.2603[/C][/ROW]
[ROW][C]74[/C][C]-16[/C][C]-12.086[/C][C]-10.1417[/C][C]-1.94433[/C][C]-3.914[/C][/ROW]
[ROW][C]75[/C][C]-22.7[/C][C]-14.9909[/C][C]-10.7833[/C][C]-4.20752[/C][C]-7.70914[/C][/ROW]
[ROW][C]76[/C][C]-17.7[/C][C]-14.1797[/C][C]-12.2333[/C][C]-1.94641[/C][C]-3.52025[/C][/ROW]
[ROW][C]77[/C][C]-18.2[/C][C]-14.9409[/C][C]-13.8917[/C][C]-1.04919[/C][C]-3.25914[/C][/ROW]
[ROW][C]78[/C][C]-18.9[/C][C]-16.0513[/C][C]-15.9625[/C][C]-0.0887731[/C][C]-2.84873[/C][/ROW]
[ROW][C]79[/C][C]-16[/C][C]NA[/C][C]NA[/C][C]-0.0575231[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]-12.2[/C][C]NA[/C][C]NA[/C][C]-1.07072[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]-17.1[/C][C]NA[/C][C]NA[/C][C]0.173727[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]-18.6[/C][C]NA[/C][C]NA[/C][C]1.4015[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]-17.5[/C][C]NA[/C][C]NA[/C][C]2.41262[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]-24.9[/C][C]NA[/C][C]NA[/C][C]5.47859[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230443&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230443&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
tObservationsFitTrendSeasonalRandom
1-2.5NANA0.898032NA
24.4NANA-1.94433NA
313.7NANA-4.20752NA
412.3NANA-1.94641NA
513.4NANA-1.04919NA
62.2NANA-0.0887731NA
71.71.942482-0.0575231-0.242477
8-7.20.3334491.40417-1.07072-7.53345
9-4.80.194560.02083330.173727-4.99456
10-2.9-0.365162-1.766671.4015-2.53484
11-2.4-1.11655-3.529172.41262-1.28345
12-2.50.932755-4.545835.47859-3.43275
13-5.3-3.68947-4.58750.898032-1.61053
14-7.1-5.736-3.79167-1.94433-1.364
15-8-6.64502-2.4375-4.20752-1.35498
16-8.9-3.06725-1.12083-1.94641-5.83275
17-7.7-0.786690.2625-1.04919-6.91331
18-1.11.836231.925-0.0887731-2.93623
1943.479983.5375-0.05752310.520023
209.63.891784.9625-1.070725.70822
2110.96.382066.208330.1737274.51794
22138.922347.520831.40154.07766
2314.911.39188.979172.412623.50822
2420.115.566110.08755.478594.53391
2510.811.381410.48330.898032-0.581366
26118.0598410.0042-1.944332.94016
273.84.867489.075-4.20752-1.06748
2810.85.861927.80833-1.946414.93808
297.64.984146.03333-1.049192.61586
3010.23.511233.6-0.08877316.68877
312.20.938310.995833-0.05752311.26169
32-0.1-2.48738-1.41667-1.070722.38738
33-1.7-3.26377-3.43750.1737271.56377
34-4.8-3.93183-5.333331.4015-0.868171
35-9.9-4.94988-7.36252.41262-4.95012
36-13.5-4.00058-9.479175.47859-9.49942
37-18.1-10.4478-11.34580.898032-7.6522
38-18-14.3735-12.4292-1.94433-3.6265
39-15.7-17.1284-12.9208-4.207521.42836
40-15.2-14.8631-12.9167-1.94641-0.336921
41-15.1-13.2575-12.2083-1.04919-1.84248
42-17.9-10.6179-10.5292-0.0887731-7.28206
43-14.5-8.09502-8.0375-0.0575231-6.40498
44-9.4-6.46655-5.39583-1.07072-2.93345
45-4.2-2.75127-2.9250.173727-1.44873
46-2.21.13484-0.2666671.4015-3.33484
474.55.279282.866672.41262-0.779282
4812.411.97036.491675.478590.429745
4915.810.960510.06250.8980324.83947
5011.511.147313.0917-1.944330.352662
5114.110.90515.1125-4.207523.19502
5218.814.324416.2708-1.946414.47558
5326.115.875816.925-1.0491910.2242
5427.916.994617.0833-0.088773110.9054
5525.416.717516.775-0.05752318.68252
5623.415.133416.2042-1.070728.26655
5711.515.577915.40420.173727-4.07789
589.915.355713.95421.4015-5.45567
598.114.220911.80832.41262-6.12095
6012.614.59119.11255.47859-1.99109
618.27.198036.30.8980321.00197
625.41.530673.475-1.944333.86933
631-2.765861.44167-4.207523.76586
64-2.9-1.433910.5125-1.94641-1.46609
65-3.7-1.13252-0.0833333-1.04919-2.56748
66-7-0.73044-0.641667-0.0887731-6.26956
67-7.2-1.64086-1.58333-0.0575231-5.55914
68-11.8-4.16238-3.09167-1.07072-7.63762
69-2.1-4.79711-4.970830.1737272.69711
701.2-5.1735-6.5751.40156.3735
712.5-5.38322-7.795832.412627.88322
724.8-3.41725-8.895835.478598.21725
73-6.6-8.8603-9.758330.8980322.2603
74-16-12.086-10.1417-1.94433-3.914
75-22.7-14.9909-10.7833-4.20752-7.70914
76-17.7-14.1797-12.2333-1.94641-3.52025
77-18.2-14.9409-13.8917-1.04919-3.25914
78-18.9-16.0513-15.9625-0.0887731-2.84873
79-16NANA-0.0575231NA
80-12.2NANA-1.07072NA
81-17.1NANA0.173727NA
82-18.6NANA1.4015NA
83-17.5NANA2.41262NA
84-24.9NANA5.47859NA



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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$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')