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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationWed, 14 Jan 2009 08:53:43 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Jan/14/t1231948461kbatavtoqf8mcab.htm/, Retrieved Sun, 26 Apr 2026 00:55:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=36877, Retrieved Sun, 26 Apr 2026 00:55:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact494

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [opgave , oef 2 - ...] [2009-01-14 15:53:43] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9,026
9,787
9,536
9,49
9,736
9,694
9,647
9,753
10,07
10,137
9,984
9,732
9,103
9,155
9,308
9,394
9,948
10,177
10,002
9,728
10,002
10,063
10,018
9,96
10,236
10,893
10,756
10,94
10,997
10,827
10,166
10,186
10,457
10,368
10,244
10,511
10,812
10,738
10,171
9,721
9,897
9,828
9,924
10,371
10,846
10,413
10,709
10,662
10,57
10,297
10,635
10,872
10,296
10,383
10,431
10,574
10,653
10,805
10,872
10,625
10,407
10,463
10,556
10,646
10,702
11,353
11,346
11,451
11,964
12,574
13,031
13,812
14,544
14,931
14,886
16,005
17,064
15,168
16,05
15,839
15,137
14,954
15,648
15,305

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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=36877&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=36877&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36877&T=0

As an alternative you can also use a QR Code:  
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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
19.026NANA-0.0057436342592588NA
29.787NANA0.0416938657407400NA
39.536NANA-0.0632575231481491NA
49.49NANA0.0791035879629625NA
59.736NANA0.227318865740741NA
69.694NANA-0.0453825231481485NA
79.6479.51762442129639.71920833333333-0.2015839120370370.129375578703705
89.7539.51162442129639.69608333333333-0.1844589120370370.241375578703703
910.079.724416087962969.660250.0641660879629630.345583912037036
1010.1379.689853587962969.646750.04310358796296380.447146412037037
119.9849.681554976851859.651583333333330.02997164351851860.302445023148149
129.7329.69561053240749.680541666666670.01506886574074110.036389467592592
139.1039.709714699074079.71545833333333-0.0057436342592588-0.606714699074073
149.1559.770902199074079.729208333333330.0416938657407400-0.615902199074075
159.3089.662075810185189.72533333333333-0.0632575231481491-0.354075810185185
169.3949.798520254629639.719416666666670.0791035879629625-0.404520254629629
179.9489.945068865740749.717750.2273188657407410.00293113425925817
1810.1779.683284143518529.72866666666667-0.04538252314814850.493715856481481
1910.0029.583791087962969.785375-0.2015839120370370.418208912037038
209.7289.720541087962969.905-0.1844589120370370.00745891203703941
2110.00210.101916087963010.037750.064166087962963-0.0999160879629617
2210.06310.205603587963010.16250.0431035879629638-0.142603587962965
2310.01810.300596643518510.2706250.0299716435185186-0.282596643518518
249.9610.356485532407410.34141666666670.0150688657407411-0.396485532407407
2510.23610.369589699074110.3753333333333-0.0057436342592588-0.133589699074074
2610.89310.442943865740710.401250.04169386574074000.45005613425926
2710.75610.376034143518510.4392916666667-0.06325752314814910.37996585648148
2810.9410.550061921296310.47095833333330.07910358796296250.389938078703702
2910.99710.720402199074110.49308333333330.2273188657407410.276597800925925
3010.82710.480075810185210.5254583333333-0.04538252314814850.346924189814816
3110.16610.370832754629610.5724166666667-0.201583912037037-0.204832754629630
3210.18610.405499421296310.5899583333333-0.184458912037037-0.219499421296295
3310.45710.623291087963010.5591250.064166087962963-0.166291087962962
3410.36810.527061921296310.48395833333330.0431035879629638-0.159061921296296
3510.24410.417304976851910.38733333333330.0299716435185186-0.173304976851851
3610.51110.314943865740710.2998750.01506886574074110.19605613425926
3710.81210.242423032407410.2481666666667-0.00574363425925880.569576967592592
3810.73810.287485532407410.24579166666670.04169386574074000.450514467592591
3910.17110.206450810185210.2697083333333-0.0632575231481491-0.0354508101851856
409.72110.366895254629610.28779166666670.0791035879629625-0.64589525462963
419.89710.536360532407410.30904166666670.227318865740741-0.639360532407407
429.82810.289325810185210.3347083333333-0.0453825231481485-0.461325810185183
439.92410.129332754629610.3309166666667-0.201583912037037-0.205332754629628
4410.37110.117999421296310.3024583333333-0.1844589120370370.253000578703704
4510.84610.367582754629610.30341666666670.0641660879629630.47841724537037
4610.41310.413811921296310.37070833333330.0431035879629638-0.000811921296296703
4710.70910.465263310185210.43529166666670.02997164351851860.243736689814813
4810.66210.490110532407410.47504166666670.01506886574074110.171889467592594
4910.5710.513548032407410.5192916666667-0.00574363425925880.0564519675925919
5010.29710.590568865740710.5488750.0416938657407400-0.29356886574074
5110.63510.486034143518510.5492916666667-0.06325752314814910.148965856481482
5210.87210.636686921296310.55758333333330.07910358796296250.235313078703705
5310.29610.808027199074110.58070833333330.227318865740741-0.512027199074074
5410.38310.540575810185210.5859583333333-0.0453825231481485-0.157575810185184
5510.43110.376041087963010.577625-0.2015839120370370.0549589120370353
5610.57410.393291087963010.57775-0.1844589120370370.180708912037037
5710.65310.645541087963010.5813750.0641660879629630.00745891203703586
5810.80510.611770254629610.56866666666670.04310358796296380.193229745370370
5910.87210.606138310185210.57616666666670.02997164351851860.265861689814816
6010.62510.648568865740710.63350.0150688657407411-0.0235688657407405
6110.40710.706298032407410.7120416666667-0.0057436342592588-0.299298032407407
6210.46310.828402199074110.78670833333330.0416938657407400-0.365402199074074
6310.55610.814617476851910.877875-0.0632575231481491-0.258617476851853
6410.64611.085311921296311.00620833333330.0791035879629625-0.439311921296294
6510.70211.397193865740711.1698750.227318865740741-0.695193865740741
6611.35311.347242476851911.392625-0.04538252314814850.00575752314814615
6711.34611.496207754629611.6977916666667-0.201583912037037-0.15020775462963
6811.45111.871874421296312.0563333333333-0.184458912037037-0.420874421296297
6911.96412.487082754629612.42291666666670.064166087962963-0.523082754629629
7012.57412.869728587963012.8266250.0431035879629638-0.295728587962962
7113.03113.344971643518513.3150.0299716435185186-0.313971643518517
7213.81213.754110532407413.73904166666670.01506886574074110.0578894675925934
7314.54414.088256365740714.094-0.00574363425925880.455743634259262
7414.93114.514527199074114.47283333333330.04169386574074000.416472800925929
7514.88614.724617476851814.787875-0.06325752314814910.161382523148150
7616.00515.098353587963015.019250.07910358796296250.906646412037041
7717.06415.454777199074115.22745833333330.2273188657407411.60922280092593
7815.16815.353325810185215.3987083333333-0.0453825231481485-0.185325810185184
7916.05NANA-0.201583912037037NA
8015.839NANA-0.184458912037037NA
8115.137NANA0.064166087962963NA
8214.954NANA0.0431035879629638NA
8315.648NANA0.0299716435185186NA
8415.305NANA0.0150688657407411NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 9.026 & NA & NA & -0.0057436342592588 & NA \tabularnewline
2 & 9.787 & NA & NA & 0.0416938657407400 & NA \tabularnewline
3 & 9.536 & NA & NA & -0.0632575231481491 & NA \tabularnewline
4 & 9.49 & NA & NA & 0.0791035879629625 & NA \tabularnewline
5 & 9.736 & NA & NA & 0.227318865740741 & NA \tabularnewline
6 & 9.694 & NA & NA & -0.0453825231481485 & NA \tabularnewline
7 & 9.647 & 9.5176244212963 & 9.71920833333333 & -0.201583912037037 & 0.129375578703705 \tabularnewline
8 & 9.753 & 9.5116244212963 & 9.69608333333333 & -0.184458912037037 & 0.241375578703703 \tabularnewline
9 & 10.07 & 9.72441608796296 & 9.66025 & 0.064166087962963 & 0.345583912037036 \tabularnewline
10 & 10.137 & 9.68985358796296 & 9.64675 & 0.0431035879629638 & 0.447146412037037 \tabularnewline
11 & 9.984 & 9.68155497685185 & 9.65158333333333 & 0.0299716435185186 & 0.302445023148149 \tabularnewline
12 & 9.732 & 9.6956105324074 & 9.68054166666667 & 0.0150688657407411 & 0.036389467592592 \tabularnewline
13 & 9.103 & 9.70971469907407 & 9.71545833333333 & -0.0057436342592588 & -0.606714699074073 \tabularnewline
14 & 9.155 & 9.77090219907407 & 9.72920833333333 & 0.0416938657407400 & -0.615902199074075 \tabularnewline
15 & 9.308 & 9.66207581018518 & 9.72533333333333 & -0.0632575231481491 & -0.354075810185185 \tabularnewline
16 & 9.394 & 9.79852025462963 & 9.71941666666667 & 0.0791035879629625 & -0.404520254629629 \tabularnewline
17 & 9.948 & 9.94506886574074 & 9.71775 & 0.227318865740741 & 0.00293113425925817 \tabularnewline
18 & 10.177 & 9.68328414351852 & 9.72866666666667 & -0.0453825231481485 & 0.493715856481481 \tabularnewline
19 & 10.002 & 9.58379108796296 & 9.785375 & -0.201583912037037 & 0.418208912037038 \tabularnewline
20 & 9.728 & 9.72054108796296 & 9.905 & -0.184458912037037 & 0.00745891203703941 \tabularnewline
21 & 10.002 & 10.1019160879630 & 10.03775 & 0.064166087962963 & -0.0999160879629617 \tabularnewline
22 & 10.063 & 10.2056035879630 & 10.1625 & 0.0431035879629638 & -0.142603587962965 \tabularnewline
23 & 10.018 & 10.3005966435185 & 10.270625 & 0.0299716435185186 & -0.282596643518518 \tabularnewline
24 & 9.96 & 10.3564855324074 & 10.3414166666667 & 0.0150688657407411 & -0.396485532407407 \tabularnewline
25 & 10.236 & 10.3695896990741 & 10.3753333333333 & -0.0057436342592588 & -0.133589699074074 \tabularnewline
26 & 10.893 & 10.4429438657407 & 10.40125 & 0.0416938657407400 & 0.45005613425926 \tabularnewline
27 & 10.756 & 10.3760341435185 & 10.4392916666667 & -0.0632575231481491 & 0.37996585648148 \tabularnewline
28 & 10.94 & 10.5500619212963 & 10.4709583333333 & 0.0791035879629625 & 0.389938078703702 \tabularnewline
29 & 10.997 & 10.7204021990741 & 10.4930833333333 & 0.227318865740741 & 0.276597800925925 \tabularnewline
30 & 10.827 & 10.4800758101852 & 10.5254583333333 & -0.0453825231481485 & 0.346924189814816 \tabularnewline
31 & 10.166 & 10.3708327546296 & 10.5724166666667 & -0.201583912037037 & -0.204832754629630 \tabularnewline
32 & 10.186 & 10.4054994212963 & 10.5899583333333 & -0.184458912037037 & -0.219499421296295 \tabularnewline
33 & 10.457 & 10.6232910879630 & 10.559125 & 0.064166087962963 & -0.166291087962962 \tabularnewline
34 & 10.368 & 10.5270619212963 & 10.4839583333333 & 0.0431035879629638 & -0.159061921296296 \tabularnewline
35 & 10.244 & 10.4173049768519 & 10.3873333333333 & 0.0299716435185186 & -0.173304976851851 \tabularnewline
36 & 10.511 & 10.3149438657407 & 10.299875 & 0.0150688657407411 & 0.19605613425926 \tabularnewline
37 & 10.812 & 10.2424230324074 & 10.2481666666667 & -0.0057436342592588 & 0.569576967592592 \tabularnewline
38 & 10.738 & 10.2874855324074 & 10.2457916666667 & 0.0416938657407400 & 0.450514467592591 \tabularnewline
39 & 10.171 & 10.2064508101852 & 10.2697083333333 & -0.0632575231481491 & -0.0354508101851856 \tabularnewline
40 & 9.721 & 10.3668952546296 & 10.2877916666667 & 0.0791035879629625 & -0.64589525462963 \tabularnewline
41 & 9.897 & 10.5363605324074 & 10.3090416666667 & 0.227318865740741 & -0.639360532407407 \tabularnewline
42 & 9.828 & 10.2893258101852 & 10.3347083333333 & -0.0453825231481485 & -0.461325810185183 \tabularnewline
43 & 9.924 & 10.1293327546296 & 10.3309166666667 & -0.201583912037037 & -0.205332754629628 \tabularnewline
44 & 10.371 & 10.1179994212963 & 10.3024583333333 & -0.184458912037037 & 0.253000578703704 \tabularnewline
45 & 10.846 & 10.3675827546296 & 10.3034166666667 & 0.064166087962963 & 0.47841724537037 \tabularnewline
46 & 10.413 & 10.4138119212963 & 10.3707083333333 & 0.0431035879629638 & -0.000811921296296703 \tabularnewline
47 & 10.709 & 10.4652633101852 & 10.4352916666667 & 0.0299716435185186 & 0.243736689814813 \tabularnewline
48 & 10.662 & 10.4901105324074 & 10.4750416666667 & 0.0150688657407411 & 0.171889467592594 \tabularnewline
49 & 10.57 & 10.5135480324074 & 10.5192916666667 & -0.0057436342592588 & 0.0564519675925919 \tabularnewline
50 & 10.297 & 10.5905688657407 & 10.548875 & 0.0416938657407400 & -0.29356886574074 \tabularnewline
51 & 10.635 & 10.4860341435185 & 10.5492916666667 & -0.0632575231481491 & 0.148965856481482 \tabularnewline
52 & 10.872 & 10.6366869212963 & 10.5575833333333 & 0.0791035879629625 & 0.235313078703705 \tabularnewline
53 & 10.296 & 10.8080271990741 & 10.5807083333333 & 0.227318865740741 & -0.512027199074074 \tabularnewline
54 & 10.383 & 10.5405758101852 & 10.5859583333333 & -0.0453825231481485 & -0.157575810185184 \tabularnewline
55 & 10.431 & 10.3760410879630 & 10.577625 & -0.201583912037037 & 0.0549589120370353 \tabularnewline
56 & 10.574 & 10.3932910879630 & 10.57775 & -0.184458912037037 & 0.180708912037037 \tabularnewline
57 & 10.653 & 10.6455410879630 & 10.581375 & 0.064166087962963 & 0.00745891203703586 \tabularnewline
58 & 10.805 & 10.6117702546296 & 10.5686666666667 & 0.0431035879629638 & 0.193229745370370 \tabularnewline
59 & 10.872 & 10.6061383101852 & 10.5761666666667 & 0.0299716435185186 & 0.265861689814816 \tabularnewline
60 & 10.625 & 10.6485688657407 & 10.6335 & 0.0150688657407411 & -0.0235688657407405 \tabularnewline
61 & 10.407 & 10.7062980324074 & 10.7120416666667 & -0.0057436342592588 & -0.299298032407407 \tabularnewline
62 & 10.463 & 10.8284021990741 & 10.7867083333333 & 0.0416938657407400 & -0.365402199074074 \tabularnewline
63 & 10.556 & 10.8146174768519 & 10.877875 & -0.0632575231481491 & -0.258617476851853 \tabularnewline
64 & 10.646 & 11.0853119212963 & 11.0062083333333 & 0.0791035879629625 & -0.439311921296294 \tabularnewline
65 & 10.702 & 11.3971938657407 & 11.169875 & 0.227318865740741 & -0.695193865740741 \tabularnewline
66 & 11.353 & 11.3472424768519 & 11.392625 & -0.0453825231481485 & 0.00575752314814615 \tabularnewline
67 & 11.346 & 11.4962077546296 & 11.6977916666667 & -0.201583912037037 & -0.15020775462963 \tabularnewline
68 & 11.451 & 11.8718744212963 & 12.0563333333333 & -0.184458912037037 & -0.420874421296297 \tabularnewline
69 & 11.964 & 12.4870827546296 & 12.4229166666667 & 0.064166087962963 & -0.523082754629629 \tabularnewline
70 & 12.574 & 12.8697285879630 & 12.826625 & 0.0431035879629638 & -0.295728587962962 \tabularnewline
71 & 13.031 & 13.3449716435185 & 13.315 & 0.0299716435185186 & -0.313971643518517 \tabularnewline
72 & 13.812 & 13.7541105324074 & 13.7390416666667 & 0.0150688657407411 & 0.0578894675925934 \tabularnewline
73 & 14.544 & 14.0882563657407 & 14.094 & -0.0057436342592588 & 0.455743634259262 \tabularnewline
74 & 14.931 & 14.5145271990741 & 14.4728333333333 & 0.0416938657407400 & 0.416472800925929 \tabularnewline
75 & 14.886 & 14.7246174768518 & 14.787875 & -0.0632575231481491 & 0.161382523148150 \tabularnewline
76 & 16.005 & 15.0983535879630 & 15.01925 & 0.0791035879629625 & 0.906646412037041 \tabularnewline
77 & 17.064 & 15.4547771990741 & 15.2274583333333 & 0.227318865740741 & 1.60922280092593 \tabularnewline
78 & 15.168 & 15.3533258101852 & 15.3987083333333 & -0.0453825231481485 & -0.185325810185184 \tabularnewline
79 & 16.05 & NA & NA & -0.201583912037037 & NA \tabularnewline
80 & 15.839 & NA & NA & -0.184458912037037 & NA \tabularnewline
81 & 15.137 & NA & NA & 0.064166087962963 & NA \tabularnewline
82 & 14.954 & NA & NA & 0.0431035879629638 & NA \tabularnewline
83 & 15.648 & NA & NA & 0.0299716435185186 & NA \tabularnewline
84 & 15.305 & NA & NA & 0.0150688657407411 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36877&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]9.026[/C][C]NA[/C][C]NA[/C][C]-0.0057436342592588[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]9.787[/C][C]NA[/C][C]NA[/C][C]0.0416938657407400[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]9.536[/C][C]NA[/C][C]NA[/C][C]-0.0632575231481491[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]9.49[/C][C]NA[/C][C]NA[/C][C]0.0791035879629625[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]9.736[/C][C]NA[/C][C]NA[/C][C]0.227318865740741[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]9.694[/C][C]NA[/C][C]NA[/C][C]-0.0453825231481485[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]9.647[/C][C]9.5176244212963[/C][C]9.71920833333333[/C][C]-0.201583912037037[/C][C]0.129375578703705[/C][/ROW]
[ROW][C]8[/C][C]9.753[/C][C]9.5116244212963[/C][C]9.69608333333333[/C][C]-0.184458912037037[/C][C]0.241375578703703[/C][/ROW]
[ROW][C]9[/C][C]10.07[/C][C]9.72441608796296[/C][C]9.66025[/C][C]0.064166087962963[/C][C]0.345583912037036[/C][/ROW]
[ROW][C]10[/C][C]10.137[/C][C]9.68985358796296[/C][C]9.64675[/C][C]0.0431035879629638[/C][C]0.447146412037037[/C][/ROW]
[ROW][C]11[/C][C]9.984[/C][C]9.68155497685185[/C][C]9.65158333333333[/C][C]0.0299716435185186[/C][C]0.302445023148149[/C][/ROW]
[ROW][C]12[/C][C]9.732[/C][C]9.6956105324074[/C][C]9.68054166666667[/C][C]0.0150688657407411[/C][C]0.036389467592592[/C][/ROW]
[ROW][C]13[/C][C]9.103[/C][C]9.70971469907407[/C][C]9.71545833333333[/C][C]-0.0057436342592588[/C][C]-0.606714699074073[/C][/ROW]
[ROW][C]14[/C][C]9.155[/C][C]9.77090219907407[/C][C]9.72920833333333[/C][C]0.0416938657407400[/C][C]-0.615902199074075[/C][/ROW]
[ROW][C]15[/C][C]9.308[/C][C]9.66207581018518[/C][C]9.72533333333333[/C][C]-0.0632575231481491[/C][C]-0.354075810185185[/C][/ROW]
[ROW][C]16[/C][C]9.394[/C][C]9.79852025462963[/C][C]9.71941666666667[/C][C]0.0791035879629625[/C][C]-0.404520254629629[/C][/ROW]
[ROW][C]17[/C][C]9.948[/C][C]9.94506886574074[/C][C]9.71775[/C][C]0.227318865740741[/C][C]0.00293113425925817[/C][/ROW]
[ROW][C]18[/C][C]10.177[/C][C]9.68328414351852[/C][C]9.72866666666667[/C][C]-0.0453825231481485[/C][C]0.493715856481481[/C][/ROW]
[ROW][C]19[/C][C]10.002[/C][C]9.58379108796296[/C][C]9.785375[/C][C]-0.201583912037037[/C][C]0.418208912037038[/C][/ROW]
[ROW][C]20[/C][C]9.728[/C][C]9.72054108796296[/C][C]9.905[/C][C]-0.184458912037037[/C][C]0.00745891203703941[/C][/ROW]
[ROW][C]21[/C][C]10.002[/C][C]10.1019160879630[/C][C]10.03775[/C][C]0.064166087962963[/C][C]-0.0999160879629617[/C][/ROW]
[ROW][C]22[/C][C]10.063[/C][C]10.2056035879630[/C][C]10.1625[/C][C]0.0431035879629638[/C][C]-0.142603587962965[/C][/ROW]
[ROW][C]23[/C][C]10.018[/C][C]10.3005966435185[/C][C]10.270625[/C][C]0.0299716435185186[/C][C]-0.282596643518518[/C][/ROW]
[ROW][C]24[/C][C]9.96[/C][C]10.3564855324074[/C][C]10.3414166666667[/C][C]0.0150688657407411[/C][C]-0.396485532407407[/C][/ROW]
[ROW][C]25[/C][C]10.236[/C][C]10.3695896990741[/C][C]10.3753333333333[/C][C]-0.0057436342592588[/C][C]-0.133589699074074[/C][/ROW]
[ROW][C]26[/C][C]10.893[/C][C]10.4429438657407[/C][C]10.40125[/C][C]0.0416938657407400[/C][C]0.45005613425926[/C][/ROW]
[ROW][C]27[/C][C]10.756[/C][C]10.3760341435185[/C][C]10.4392916666667[/C][C]-0.0632575231481491[/C][C]0.37996585648148[/C][/ROW]
[ROW][C]28[/C][C]10.94[/C][C]10.5500619212963[/C][C]10.4709583333333[/C][C]0.0791035879629625[/C][C]0.389938078703702[/C][/ROW]
[ROW][C]29[/C][C]10.997[/C][C]10.7204021990741[/C][C]10.4930833333333[/C][C]0.227318865740741[/C][C]0.276597800925925[/C][/ROW]
[ROW][C]30[/C][C]10.827[/C][C]10.4800758101852[/C][C]10.5254583333333[/C][C]-0.0453825231481485[/C][C]0.346924189814816[/C][/ROW]
[ROW][C]31[/C][C]10.166[/C][C]10.3708327546296[/C][C]10.5724166666667[/C][C]-0.201583912037037[/C][C]-0.204832754629630[/C][/ROW]
[ROW][C]32[/C][C]10.186[/C][C]10.4054994212963[/C][C]10.5899583333333[/C][C]-0.184458912037037[/C][C]-0.219499421296295[/C][/ROW]
[ROW][C]33[/C][C]10.457[/C][C]10.6232910879630[/C][C]10.559125[/C][C]0.064166087962963[/C][C]-0.166291087962962[/C][/ROW]
[ROW][C]34[/C][C]10.368[/C][C]10.5270619212963[/C][C]10.4839583333333[/C][C]0.0431035879629638[/C][C]-0.159061921296296[/C][/ROW]
[ROW][C]35[/C][C]10.244[/C][C]10.4173049768519[/C][C]10.3873333333333[/C][C]0.0299716435185186[/C][C]-0.173304976851851[/C][/ROW]
[ROW][C]36[/C][C]10.511[/C][C]10.3149438657407[/C][C]10.299875[/C][C]0.0150688657407411[/C][C]0.19605613425926[/C][/ROW]
[ROW][C]37[/C][C]10.812[/C][C]10.2424230324074[/C][C]10.2481666666667[/C][C]-0.0057436342592588[/C][C]0.569576967592592[/C][/ROW]
[ROW][C]38[/C][C]10.738[/C][C]10.2874855324074[/C][C]10.2457916666667[/C][C]0.0416938657407400[/C][C]0.450514467592591[/C][/ROW]
[ROW][C]39[/C][C]10.171[/C][C]10.2064508101852[/C][C]10.2697083333333[/C][C]-0.0632575231481491[/C][C]-0.0354508101851856[/C][/ROW]
[ROW][C]40[/C][C]9.721[/C][C]10.3668952546296[/C][C]10.2877916666667[/C][C]0.0791035879629625[/C][C]-0.64589525462963[/C][/ROW]
[ROW][C]41[/C][C]9.897[/C][C]10.5363605324074[/C][C]10.3090416666667[/C][C]0.227318865740741[/C][C]-0.639360532407407[/C][/ROW]
[ROW][C]42[/C][C]9.828[/C][C]10.2893258101852[/C][C]10.3347083333333[/C][C]-0.0453825231481485[/C][C]-0.461325810185183[/C][/ROW]
[ROW][C]43[/C][C]9.924[/C][C]10.1293327546296[/C][C]10.3309166666667[/C][C]-0.201583912037037[/C][C]-0.205332754629628[/C][/ROW]
[ROW][C]44[/C][C]10.371[/C][C]10.1179994212963[/C][C]10.3024583333333[/C][C]-0.184458912037037[/C][C]0.253000578703704[/C][/ROW]
[ROW][C]45[/C][C]10.846[/C][C]10.3675827546296[/C][C]10.3034166666667[/C][C]0.064166087962963[/C][C]0.47841724537037[/C][/ROW]
[ROW][C]46[/C][C]10.413[/C][C]10.4138119212963[/C][C]10.3707083333333[/C][C]0.0431035879629638[/C][C]-0.000811921296296703[/C][/ROW]
[ROW][C]47[/C][C]10.709[/C][C]10.4652633101852[/C][C]10.4352916666667[/C][C]0.0299716435185186[/C][C]0.243736689814813[/C][/ROW]
[ROW][C]48[/C][C]10.662[/C][C]10.4901105324074[/C][C]10.4750416666667[/C][C]0.0150688657407411[/C][C]0.171889467592594[/C][/ROW]
[ROW][C]49[/C][C]10.57[/C][C]10.5135480324074[/C][C]10.5192916666667[/C][C]-0.0057436342592588[/C][C]0.0564519675925919[/C][/ROW]
[ROW][C]50[/C][C]10.297[/C][C]10.5905688657407[/C][C]10.548875[/C][C]0.0416938657407400[/C][C]-0.29356886574074[/C][/ROW]
[ROW][C]51[/C][C]10.635[/C][C]10.4860341435185[/C][C]10.5492916666667[/C][C]-0.0632575231481491[/C][C]0.148965856481482[/C][/ROW]
[ROW][C]52[/C][C]10.872[/C][C]10.6366869212963[/C][C]10.5575833333333[/C][C]0.0791035879629625[/C][C]0.235313078703705[/C][/ROW]
[ROW][C]53[/C][C]10.296[/C][C]10.8080271990741[/C][C]10.5807083333333[/C][C]0.227318865740741[/C][C]-0.512027199074074[/C][/ROW]
[ROW][C]54[/C][C]10.383[/C][C]10.5405758101852[/C][C]10.5859583333333[/C][C]-0.0453825231481485[/C][C]-0.157575810185184[/C][/ROW]
[ROW][C]55[/C][C]10.431[/C][C]10.3760410879630[/C][C]10.577625[/C][C]-0.201583912037037[/C][C]0.0549589120370353[/C][/ROW]
[ROW][C]56[/C][C]10.574[/C][C]10.3932910879630[/C][C]10.57775[/C][C]-0.184458912037037[/C][C]0.180708912037037[/C][/ROW]
[ROW][C]57[/C][C]10.653[/C][C]10.6455410879630[/C][C]10.581375[/C][C]0.064166087962963[/C][C]0.00745891203703586[/C][/ROW]
[ROW][C]58[/C][C]10.805[/C][C]10.6117702546296[/C][C]10.5686666666667[/C][C]0.0431035879629638[/C][C]0.193229745370370[/C][/ROW]
[ROW][C]59[/C][C]10.872[/C][C]10.6061383101852[/C][C]10.5761666666667[/C][C]0.0299716435185186[/C][C]0.265861689814816[/C][/ROW]
[ROW][C]60[/C][C]10.625[/C][C]10.6485688657407[/C][C]10.6335[/C][C]0.0150688657407411[/C][C]-0.0235688657407405[/C][/ROW]
[ROW][C]61[/C][C]10.407[/C][C]10.7062980324074[/C][C]10.7120416666667[/C][C]-0.0057436342592588[/C][C]-0.299298032407407[/C][/ROW]
[ROW][C]62[/C][C]10.463[/C][C]10.8284021990741[/C][C]10.7867083333333[/C][C]0.0416938657407400[/C][C]-0.365402199074074[/C][/ROW]
[ROW][C]63[/C][C]10.556[/C][C]10.8146174768519[/C][C]10.877875[/C][C]-0.0632575231481491[/C][C]-0.258617476851853[/C][/ROW]
[ROW][C]64[/C][C]10.646[/C][C]11.0853119212963[/C][C]11.0062083333333[/C][C]0.0791035879629625[/C][C]-0.439311921296294[/C][/ROW]
[ROW][C]65[/C][C]10.702[/C][C]11.3971938657407[/C][C]11.169875[/C][C]0.227318865740741[/C][C]-0.695193865740741[/C][/ROW]
[ROW][C]66[/C][C]11.353[/C][C]11.3472424768519[/C][C]11.392625[/C][C]-0.0453825231481485[/C][C]0.00575752314814615[/C][/ROW]
[ROW][C]67[/C][C]11.346[/C][C]11.4962077546296[/C][C]11.6977916666667[/C][C]-0.201583912037037[/C][C]-0.15020775462963[/C][/ROW]
[ROW][C]68[/C][C]11.451[/C][C]11.8718744212963[/C][C]12.0563333333333[/C][C]-0.184458912037037[/C][C]-0.420874421296297[/C][/ROW]
[ROW][C]69[/C][C]11.964[/C][C]12.4870827546296[/C][C]12.4229166666667[/C][C]0.064166087962963[/C][C]-0.523082754629629[/C][/ROW]
[ROW][C]70[/C][C]12.574[/C][C]12.8697285879630[/C][C]12.826625[/C][C]0.0431035879629638[/C][C]-0.295728587962962[/C][/ROW]
[ROW][C]71[/C][C]13.031[/C][C]13.3449716435185[/C][C]13.315[/C][C]0.0299716435185186[/C][C]-0.313971643518517[/C][/ROW]
[ROW][C]72[/C][C]13.812[/C][C]13.7541105324074[/C][C]13.7390416666667[/C][C]0.0150688657407411[/C][C]0.0578894675925934[/C][/ROW]
[ROW][C]73[/C][C]14.544[/C][C]14.0882563657407[/C][C]14.094[/C][C]-0.0057436342592588[/C][C]0.455743634259262[/C][/ROW]
[ROW][C]74[/C][C]14.931[/C][C]14.5145271990741[/C][C]14.4728333333333[/C][C]0.0416938657407400[/C][C]0.416472800925929[/C][/ROW]
[ROW][C]75[/C][C]14.886[/C][C]14.7246174768518[/C][C]14.787875[/C][C]-0.0632575231481491[/C][C]0.161382523148150[/C][/ROW]
[ROW][C]76[/C][C]16.005[/C][C]15.0983535879630[/C][C]15.01925[/C][C]0.0791035879629625[/C][C]0.906646412037041[/C][/ROW]
[ROW][C]77[/C][C]17.064[/C][C]15.4547771990741[/C][C]15.2274583333333[/C][C]0.227318865740741[/C][C]1.60922280092593[/C][/ROW]
[ROW][C]78[/C][C]15.168[/C][C]15.3533258101852[/C][C]15.3987083333333[/C][C]-0.0453825231481485[/C][C]-0.185325810185184[/C][/ROW]
[ROW][C]79[/C][C]16.05[/C][C]NA[/C][C]NA[/C][C]-0.201583912037037[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]15.839[/C][C]NA[/C][C]NA[/C][C]-0.184458912037037[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]15.137[/C][C]NA[/C][C]NA[/C][C]0.064166087962963[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]14.954[/C][C]NA[/C][C]NA[/C][C]0.0431035879629638[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]15.648[/C][C]NA[/C][C]NA[/C][C]0.0299716435185186[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]15.305[/C][C]NA[/C][C]NA[/C][C]0.0150688657407411[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36877&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36877&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
19.026NANA-0.0057436342592588NA
29.787NANA0.0416938657407400NA
39.536NANA-0.0632575231481491NA
49.49NANA0.0791035879629625NA
59.736NANA0.227318865740741NA
69.694NANA-0.0453825231481485NA
79.6479.51762442129639.71920833333333-0.2015839120370370.129375578703705
89.7539.51162442129639.69608333333333-0.1844589120370370.241375578703703
910.079.724416087962969.660250.0641660879629630.345583912037036
1010.1379.689853587962969.646750.04310358796296380.447146412037037
119.9849.681554976851859.651583333333330.02997164351851860.302445023148149
129.7329.69561053240749.680541666666670.01506886574074110.036389467592592
139.1039.709714699074079.71545833333333-0.0057436342592588-0.606714699074073
149.1559.770902199074079.729208333333330.0416938657407400-0.615902199074075
159.3089.662075810185189.72533333333333-0.0632575231481491-0.354075810185185
169.3949.798520254629639.719416666666670.0791035879629625-0.404520254629629
179.9489.945068865740749.717750.2273188657407410.00293113425925817
1810.1779.683284143518529.72866666666667-0.04538252314814850.493715856481481
1910.0029.583791087962969.785375-0.2015839120370370.418208912037038
209.7289.720541087962969.905-0.1844589120370370.00745891203703941
2110.00210.101916087963010.037750.064166087962963-0.0999160879629617
2210.06310.205603587963010.16250.0431035879629638-0.142603587962965
2310.01810.300596643518510.2706250.0299716435185186-0.282596643518518
249.9610.356485532407410.34141666666670.0150688657407411-0.396485532407407
2510.23610.369589699074110.3753333333333-0.0057436342592588-0.133589699074074
2610.89310.442943865740710.401250.04169386574074000.45005613425926
2710.75610.376034143518510.4392916666667-0.06325752314814910.37996585648148
2810.9410.550061921296310.47095833333330.07910358796296250.389938078703702
2910.99710.720402199074110.49308333333330.2273188657407410.276597800925925
3010.82710.480075810185210.5254583333333-0.04538252314814850.346924189814816
3110.16610.370832754629610.5724166666667-0.201583912037037-0.204832754629630
3210.18610.405499421296310.5899583333333-0.184458912037037-0.219499421296295
3310.45710.623291087963010.5591250.064166087962963-0.166291087962962
3410.36810.527061921296310.48395833333330.0431035879629638-0.159061921296296
3510.24410.417304976851910.38733333333330.0299716435185186-0.173304976851851
3610.51110.314943865740710.2998750.01506886574074110.19605613425926
3710.81210.242423032407410.2481666666667-0.00574363425925880.569576967592592
3810.73810.287485532407410.24579166666670.04169386574074000.450514467592591
3910.17110.206450810185210.2697083333333-0.0632575231481491-0.0354508101851856
409.72110.366895254629610.28779166666670.0791035879629625-0.64589525462963
419.89710.536360532407410.30904166666670.227318865740741-0.639360532407407
429.82810.289325810185210.3347083333333-0.0453825231481485-0.461325810185183
439.92410.129332754629610.3309166666667-0.201583912037037-0.205332754629628
4410.37110.117999421296310.3024583333333-0.1844589120370370.253000578703704
4510.84610.367582754629610.30341666666670.0641660879629630.47841724537037
4610.41310.413811921296310.37070833333330.0431035879629638-0.000811921296296703
4710.70910.465263310185210.43529166666670.02997164351851860.243736689814813
4810.66210.490110532407410.47504166666670.01506886574074110.171889467592594
4910.5710.513548032407410.5192916666667-0.00574363425925880.0564519675925919
5010.29710.590568865740710.5488750.0416938657407400-0.29356886574074
5110.63510.486034143518510.5492916666667-0.06325752314814910.148965856481482
5210.87210.636686921296310.55758333333330.07910358796296250.235313078703705
5310.29610.808027199074110.58070833333330.227318865740741-0.512027199074074
5410.38310.540575810185210.5859583333333-0.0453825231481485-0.157575810185184
5510.43110.376041087963010.577625-0.2015839120370370.0549589120370353
5610.57410.393291087963010.57775-0.1844589120370370.180708912037037
5710.65310.645541087963010.5813750.0641660879629630.00745891203703586
5810.80510.611770254629610.56866666666670.04310358796296380.193229745370370
5910.87210.606138310185210.57616666666670.02997164351851860.265861689814816
6010.62510.648568865740710.63350.0150688657407411-0.0235688657407405
6110.40710.706298032407410.7120416666667-0.0057436342592588-0.299298032407407
6210.46310.828402199074110.78670833333330.0416938657407400-0.365402199074074
6310.55610.814617476851910.877875-0.0632575231481491-0.258617476851853
6410.64611.085311921296311.00620833333330.0791035879629625-0.439311921296294
6510.70211.397193865740711.1698750.227318865740741-0.695193865740741
6611.35311.347242476851911.392625-0.04538252314814850.00575752314814615
6711.34611.496207754629611.6977916666667-0.201583912037037-0.15020775462963
6811.45111.871874421296312.0563333333333-0.184458912037037-0.420874421296297
6911.96412.487082754629612.42291666666670.064166087962963-0.523082754629629
7012.57412.869728587963012.8266250.0431035879629638-0.295728587962962
7113.03113.344971643518513.3150.0299716435185186-0.313971643518517
7213.81213.754110532407413.73904166666670.01506886574074110.0578894675925934
7314.54414.088256365740714.094-0.00574363425925880.455743634259262
7414.93114.514527199074114.47283333333330.04169386574074000.416472800925929
7514.88614.724617476851814.787875-0.06325752314814910.161382523148150
7616.00515.098353587963015.019250.07910358796296250.906646412037041
7717.06415.454777199074115.22745833333330.2273188657407411.60922280092593
7815.16815.353325810185215.3987083333333-0.0453825231481485-0.185325810185184
7916.05NANA-0.201583912037037NA
8015.839NANA-0.184458912037037NA
8115.137NANA0.064166087962963NA
8214.954NANA0.0431035879629638NA
8315.648NANA0.0299716435185186NA
8415.305NANA0.0150688657407411NA


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = additive ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')