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Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSat, 26 Nov 2011 14:33:30 -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/2011/Nov/26/t1322336021qfg19ikuz9dspgi.htm/, Retrieved Mon, 30 Jan 2023 02:10:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147445, Retrieved Mon, 30 Jan 2023 02:10:46 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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- RMPD      [Classical Decomposition] [B3] [2011-11-26 19:33:30] [cdf03f2f7d2bbe3f2da091606ae8e03f] [Current]
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Dataseries X:
39923931
35810356.5
35492936.3
38937434.1
40059102.8
37708710.2
41570965.7
36333563
34181220.1
42593543.9
43119727.6
38497690.9
45473273.4
38399780.4
38882302.6
44051120.6
41677559.9
40699203.5
44150027.6
38225518.7
35447405.7
43075518.3
42302792
39743541.7
44670641.2
37123384
37668266.4
46117528.8
42273156.4
39404153.2
45799994.5
38602505.2
39454830.1
47427901.4
46497980.9
45057149.4
50615569.2
43033396.2
46013056.5
54222266.3
46417306.4
51046271.8
51201279.6
43475288.7
44968981.1
53939345.4
54549319.7
54072107.3
58434230.1
51158751
50039368
57872617.4
51642978.8
54534465.9
56094697.8
48189983.1
47492381
52987449.1
55719803.5
53922860.5
54931231.9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'AstonUniversity' @ aston.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 & 3 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147445&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147445&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147445&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 time3 seconds
R Server'AstonUniversity' @ aston.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
139923931NANA1.10103771417634NA
235810356.5NANA0.930816356211068NA
335492936.3NANA0.942277243646016NA
438937434.1NANA1.09878429132752NA
540059102.8NANA0.98621843886586NA
637708710.2NANA0.994281278646136NA
741570965.741059811.069522338916987.7751.055061386223191.01244902538914
83633356336746940.797073939256103.03750.9360822382693160.98875068813601
934181220.135205765.290262439505219.29583330.8911674436389110.970898369008165
1042593543.940093684.791855539859513.16251.005874924472881.06235044549092
1143119727.641907743.696900340140019.14583331.044038956350391.02892028527867
1238497690.940911216.146055840332058.74583331.014359728172380.94100578097118
1345473273.444662629.866471140564123.54583331.101037714176341.01815037618592
1438399780.437931153.68478640750415.94583330.9308163562110681.01235466548443
1538882302.638522183.143168840882005.16666670.9422772436460161.00934836573236
1644051120.645000540.522884340954845.16666671.098784291327520.978902032912216
1741677559.940376659.092940340940888.450.986218438865861.03221912947441
1840699203.540724528.181703440958759.91666670.9942812786461360.999378146713194
1944150027.643233490.300265940977227.35833331.055061386223191.02119970637042
2038225518.738276965.464271240890601.16666670.9360822382693160.998655934093855
2135447405.736347897.824985940786833.14166670.8911674436389110.975225744021793
2243075518.341062176.85674240822348.64166671.005874924472881.04903153211487
234230279242735923.796983440933265.50416671.044038956350390.989864924903906
2439743541.741491493.663079340904121.59583331.014359728172380.957872040537439
2544670641.245053262.89901740918909.78751.101037714176340.99150734765039
263712338438166603.8992841003366.17916670.9308163562110680.97266668257849
2737668266.438808677.639257841186049.96666670.9422772436460160.970614529826078
2846117528.845637319.106850641534375.27916671.098784291327521.01052230285537
2942273156.441313207.293502441890524.11250.986218438865861.02323588918376
3039404153.242044897.979476442286723.97083330.9942812786461360.937192265735419
3145799994.545110024.873274942755829.6251.055061386223191.01529526150037
3238602505.240485355.984305143249785.46666670.9360822382693160.95349304116197
3339454830.139072109.740813643843735.56250.8911674436389111.0097952314765
3447427901.444790737.83638344529132.54583331.005874924472881.05887743071459
3546497980.947022995.558754545039502.85833331.044038956350390.988834938044333
3645057149.446353464.339979345697264.051.014359728172380.972034130384053
3750615569.251096304.080876146407405.87083331.101037714176340.990591591906233
3843033396.243595242.062968646835492.06250.9308163562110680.987112220591479
3946013056.544539825.5325654472682810.9422772436460161.03307671168037
4054222266.352488208.594183147769347.45833331.098784291327521.03303709065828
4146417306.447709431.480938248376130.0750.986218438865860.972916778908705
4251046271.848806509.604615749087225.77083330.9942812786461361.04589064478343
4351201279.652530057.350387749788626.55416671.055061386223190.97470442985576
4443475288.747228120.231156850452960.54166670.9360822382693160.920538198158456
4544968981.145413251.261287150959279.97083330.8911674436389110.990217169021196
4653939345.451580401.992395151279140.91251.005874924472881.0457333273198
4754549319.753923542.190475951648975.2251.044038956350391.01160490361174
4854072107.352758931.938540752012052.99583331.014359728172381.02489010511033
4958434230.157651751.575480552361286.84166671.101037714176341.01357250219007
505115875149111383.406165952761624.86666670.9308163562110681.04168824927821
515003936850000257.26407553063212.12916670.9422772436460161.00078221069381
5257872617.458376971.579468753128691.44583331.098784291327520.991360391506742
5351642978.852405477.429320953137799.25833330.986218438865860.985450020365728
5454534465.952876227.19227453180350.80.9942812786461361.03136076070057
5556094697.855947978.690194853028173.9251.055061386223191.00262242020606
5648189983.1NANA0.936082238269316NA
5747492381NANA0.891167443638911NA
5852987449.1NANA1.00587492447288NA
5955719803.5NANA1.04403895635039NA
6053922860.5NANA1.01435972817238NA
6154931231.9NANANANA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 39923931 & NA & NA & 1.10103771417634 & NA \tabularnewline
2 & 35810356.5 & NA & NA & 0.930816356211068 & NA \tabularnewline
3 & 35492936.3 & NA & NA & 0.942277243646016 & NA \tabularnewline
4 & 38937434.1 & NA & NA & 1.09878429132752 & NA \tabularnewline
5 & 40059102.8 & NA & NA & 0.98621843886586 & NA \tabularnewline
6 & 37708710.2 & NA & NA & 0.994281278646136 & NA \tabularnewline
7 & 41570965.7 & 41059811.0695223 & 38916987.775 & 1.05506138622319 & 1.01244902538914 \tabularnewline
8 & 36333563 & 36746940.7970739 & 39256103.0375 & 0.936082238269316 & 0.98875068813601 \tabularnewline
9 & 34181220.1 & 35205765.2902624 & 39505219.2958333 & 0.891167443638911 & 0.970898369008165 \tabularnewline
10 & 42593543.9 & 40093684.7918555 & 39859513.1625 & 1.00587492447288 & 1.06235044549092 \tabularnewline
11 & 43119727.6 & 41907743.6969003 & 40140019.1458333 & 1.04403895635039 & 1.02892028527867 \tabularnewline
12 & 38497690.9 & 40911216.1460558 & 40332058.7458333 & 1.01435972817238 & 0.94100578097118 \tabularnewline
13 & 45473273.4 & 44662629.8664711 & 40564123.5458333 & 1.10103771417634 & 1.01815037618592 \tabularnewline
14 & 38399780.4 & 37931153.684786 & 40750415.9458333 & 0.930816356211068 & 1.01235466548443 \tabularnewline
15 & 38882302.6 & 38522183.1431688 & 40882005.1666667 & 0.942277243646016 & 1.00934836573236 \tabularnewline
16 & 44051120.6 & 45000540.5228843 & 40954845.1666667 & 1.09878429132752 & 0.978902032912216 \tabularnewline
17 & 41677559.9 & 40376659.0929403 & 40940888.45 & 0.98621843886586 & 1.03221912947441 \tabularnewline
18 & 40699203.5 & 40724528.1817034 & 40958759.9166667 & 0.994281278646136 & 0.999378146713194 \tabularnewline
19 & 44150027.6 & 43233490.3002659 & 40977227.3583333 & 1.05506138622319 & 1.02119970637042 \tabularnewline
20 & 38225518.7 & 38276965.4642712 & 40890601.1666667 & 0.936082238269316 & 0.998655934093855 \tabularnewline
21 & 35447405.7 & 36347897.8249859 & 40786833.1416667 & 0.891167443638911 & 0.975225744021793 \tabularnewline
22 & 43075518.3 & 41062176.856742 & 40822348.6416667 & 1.00587492447288 & 1.04903153211487 \tabularnewline
23 & 42302792 & 42735923.7969834 & 40933265.5041667 & 1.04403895635039 & 0.989864924903906 \tabularnewline
24 & 39743541.7 & 41491493.6630793 & 40904121.5958333 & 1.01435972817238 & 0.957872040537439 \tabularnewline
25 & 44670641.2 & 45053262.899017 & 40918909.7875 & 1.10103771417634 & 0.99150734765039 \tabularnewline
26 & 37123384 & 38166603.89928 & 41003366.1791667 & 0.930816356211068 & 0.97266668257849 \tabularnewline
27 & 37668266.4 & 38808677.6392578 & 41186049.9666667 & 0.942277243646016 & 0.970614529826078 \tabularnewline
28 & 46117528.8 & 45637319.1068506 & 41534375.2791667 & 1.09878429132752 & 1.01052230285537 \tabularnewline
29 & 42273156.4 & 41313207.2935024 & 41890524.1125 & 0.98621843886586 & 1.02323588918376 \tabularnewline
30 & 39404153.2 & 42044897.9794764 & 42286723.9708333 & 0.994281278646136 & 0.937192265735419 \tabularnewline
31 & 45799994.5 & 45110024.8732749 & 42755829.625 & 1.05506138622319 & 1.01529526150037 \tabularnewline
32 & 38602505.2 & 40485355.9843051 & 43249785.4666667 & 0.936082238269316 & 0.95349304116197 \tabularnewline
33 & 39454830.1 & 39072109.7408136 & 43843735.5625 & 0.891167443638911 & 1.0097952314765 \tabularnewline
34 & 47427901.4 & 44790737.836383 & 44529132.5458333 & 1.00587492447288 & 1.05887743071459 \tabularnewline
35 & 46497980.9 & 47022995.5587545 & 45039502.8583333 & 1.04403895635039 & 0.988834938044333 \tabularnewline
36 & 45057149.4 & 46353464.3399793 & 45697264.05 & 1.01435972817238 & 0.972034130384053 \tabularnewline
37 & 50615569.2 & 51096304.0808761 & 46407405.8708333 & 1.10103771417634 & 0.990591591906233 \tabularnewline
38 & 43033396.2 & 43595242.0629686 & 46835492.0625 & 0.930816356211068 & 0.987112220591479 \tabularnewline
39 & 46013056.5 & 44539825.5325654 & 47268281 & 0.942277243646016 & 1.03307671168037 \tabularnewline
40 & 54222266.3 & 52488208.5941831 & 47769347.4583333 & 1.09878429132752 & 1.03303709065828 \tabularnewline
41 & 46417306.4 & 47709431.4809382 & 48376130.075 & 0.98621843886586 & 0.972916778908705 \tabularnewline
42 & 51046271.8 & 48806509.6046157 & 49087225.7708333 & 0.994281278646136 & 1.04589064478343 \tabularnewline
43 & 51201279.6 & 52530057.3503877 & 49788626.5541667 & 1.05506138622319 & 0.97470442985576 \tabularnewline
44 & 43475288.7 & 47228120.2311568 & 50452960.5416667 & 0.936082238269316 & 0.920538198158456 \tabularnewline
45 & 44968981.1 & 45413251.2612871 & 50959279.9708333 & 0.891167443638911 & 0.990217169021196 \tabularnewline
46 & 53939345.4 & 51580401.9923951 & 51279140.9125 & 1.00587492447288 & 1.0457333273198 \tabularnewline
47 & 54549319.7 & 53923542.1904759 & 51648975.225 & 1.04403895635039 & 1.01160490361174 \tabularnewline
48 & 54072107.3 & 52758931.9385407 & 52012052.9958333 & 1.01435972817238 & 1.02489010511033 \tabularnewline
49 & 58434230.1 & 57651751.5754805 & 52361286.8416667 & 1.10103771417634 & 1.01357250219007 \tabularnewline
50 & 51158751 & 49111383.4061659 & 52761624.8666667 & 0.930816356211068 & 1.04168824927821 \tabularnewline
51 & 50039368 & 50000257.264075 & 53063212.1291667 & 0.942277243646016 & 1.00078221069381 \tabularnewline
52 & 57872617.4 & 58376971.5794687 & 53128691.4458333 & 1.09878429132752 & 0.991360391506742 \tabularnewline
53 & 51642978.8 & 52405477.4293209 & 53137799.2583333 & 0.98621843886586 & 0.985450020365728 \tabularnewline
54 & 54534465.9 & 52876227.192274 & 53180350.8 & 0.994281278646136 & 1.03136076070057 \tabularnewline
55 & 56094697.8 & 55947978.6901948 & 53028173.925 & 1.05506138622319 & 1.00262242020606 \tabularnewline
56 & 48189983.1 & NA & NA & 0.936082238269316 & NA \tabularnewline
57 & 47492381 & NA & NA & 0.891167443638911 & NA \tabularnewline
58 & 52987449.1 & NA & NA & 1.00587492447288 & NA \tabularnewline
59 & 55719803.5 & NA & NA & 1.04403895635039 & NA \tabularnewline
60 & 53922860.5 & NA & NA & 1.01435972817238 & NA \tabularnewline
61 & 54931231.9 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147445&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]39923931[/C][C]NA[/C][C]NA[/C][C]1.10103771417634[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]35810356.5[/C][C]NA[/C][C]NA[/C][C]0.930816356211068[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]35492936.3[/C][C]NA[/C][C]NA[/C][C]0.942277243646016[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]38937434.1[/C][C]NA[/C][C]NA[/C][C]1.09878429132752[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]40059102.8[/C][C]NA[/C][C]NA[/C][C]0.98621843886586[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]37708710.2[/C][C]NA[/C][C]NA[/C][C]0.994281278646136[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]41570965.7[/C][C]41059811.0695223[/C][C]38916987.775[/C][C]1.05506138622319[/C][C]1.01244902538914[/C][/ROW]
[ROW][C]8[/C][C]36333563[/C][C]36746940.7970739[/C][C]39256103.0375[/C][C]0.936082238269316[/C][C]0.98875068813601[/C][/ROW]
[ROW][C]9[/C][C]34181220.1[/C][C]35205765.2902624[/C][C]39505219.2958333[/C][C]0.891167443638911[/C][C]0.970898369008165[/C][/ROW]
[ROW][C]10[/C][C]42593543.9[/C][C]40093684.7918555[/C][C]39859513.1625[/C][C]1.00587492447288[/C][C]1.06235044549092[/C][/ROW]
[ROW][C]11[/C][C]43119727.6[/C][C]41907743.6969003[/C][C]40140019.1458333[/C][C]1.04403895635039[/C][C]1.02892028527867[/C][/ROW]
[ROW][C]12[/C][C]38497690.9[/C][C]40911216.1460558[/C][C]40332058.7458333[/C][C]1.01435972817238[/C][C]0.94100578097118[/C][/ROW]
[ROW][C]13[/C][C]45473273.4[/C][C]44662629.8664711[/C][C]40564123.5458333[/C][C]1.10103771417634[/C][C]1.01815037618592[/C][/ROW]
[ROW][C]14[/C][C]38399780.4[/C][C]37931153.684786[/C][C]40750415.9458333[/C][C]0.930816356211068[/C][C]1.01235466548443[/C][/ROW]
[ROW][C]15[/C][C]38882302.6[/C][C]38522183.1431688[/C][C]40882005.1666667[/C][C]0.942277243646016[/C][C]1.00934836573236[/C][/ROW]
[ROW][C]16[/C][C]44051120.6[/C][C]45000540.5228843[/C][C]40954845.1666667[/C][C]1.09878429132752[/C][C]0.978902032912216[/C][/ROW]
[ROW][C]17[/C][C]41677559.9[/C][C]40376659.0929403[/C][C]40940888.45[/C][C]0.98621843886586[/C][C]1.03221912947441[/C][/ROW]
[ROW][C]18[/C][C]40699203.5[/C][C]40724528.1817034[/C][C]40958759.9166667[/C][C]0.994281278646136[/C][C]0.999378146713194[/C][/ROW]
[ROW][C]19[/C][C]44150027.6[/C][C]43233490.3002659[/C][C]40977227.3583333[/C][C]1.05506138622319[/C][C]1.02119970637042[/C][/ROW]
[ROW][C]20[/C][C]38225518.7[/C][C]38276965.4642712[/C][C]40890601.1666667[/C][C]0.936082238269316[/C][C]0.998655934093855[/C][/ROW]
[ROW][C]21[/C][C]35447405.7[/C][C]36347897.8249859[/C][C]40786833.1416667[/C][C]0.891167443638911[/C][C]0.975225744021793[/C][/ROW]
[ROW][C]22[/C][C]43075518.3[/C][C]41062176.856742[/C][C]40822348.6416667[/C][C]1.00587492447288[/C][C]1.04903153211487[/C][/ROW]
[ROW][C]23[/C][C]42302792[/C][C]42735923.7969834[/C][C]40933265.5041667[/C][C]1.04403895635039[/C][C]0.989864924903906[/C][/ROW]
[ROW][C]24[/C][C]39743541.7[/C][C]41491493.6630793[/C][C]40904121.5958333[/C][C]1.01435972817238[/C][C]0.957872040537439[/C][/ROW]
[ROW][C]25[/C][C]44670641.2[/C][C]45053262.899017[/C][C]40918909.7875[/C][C]1.10103771417634[/C][C]0.99150734765039[/C][/ROW]
[ROW][C]26[/C][C]37123384[/C][C]38166603.89928[/C][C]41003366.1791667[/C][C]0.930816356211068[/C][C]0.97266668257849[/C][/ROW]
[ROW][C]27[/C][C]37668266.4[/C][C]38808677.6392578[/C][C]41186049.9666667[/C][C]0.942277243646016[/C][C]0.970614529826078[/C][/ROW]
[ROW][C]28[/C][C]46117528.8[/C][C]45637319.1068506[/C][C]41534375.2791667[/C][C]1.09878429132752[/C][C]1.01052230285537[/C][/ROW]
[ROW][C]29[/C][C]42273156.4[/C][C]41313207.2935024[/C][C]41890524.1125[/C][C]0.98621843886586[/C][C]1.02323588918376[/C][/ROW]
[ROW][C]30[/C][C]39404153.2[/C][C]42044897.9794764[/C][C]42286723.9708333[/C][C]0.994281278646136[/C][C]0.937192265735419[/C][/ROW]
[ROW][C]31[/C][C]45799994.5[/C][C]45110024.8732749[/C][C]42755829.625[/C][C]1.05506138622319[/C][C]1.01529526150037[/C][/ROW]
[ROW][C]32[/C][C]38602505.2[/C][C]40485355.9843051[/C][C]43249785.4666667[/C][C]0.936082238269316[/C][C]0.95349304116197[/C][/ROW]
[ROW][C]33[/C][C]39454830.1[/C][C]39072109.7408136[/C][C]43843735.5625[/C][C]0.891167443638911[/C][C]1.0097952314765[/C][/ROW]
[ROW][C]34[/C][C]47427901.4[/C][C]44790737.836383[/C][C]44529132.5458333[/C][C]1.00587492447288[/C][C]1.05887743071459[/C][/ROW]
[ROW][C]35[/C][C]46497980.9[/C][C]47022995.5587545[/C][C]45039502.8583333[/C][C]1.04403895635039[/C][C]0.988834938044333[/C][/ROW]
[ROW][C]36[/C][C]45057149.4[/C][C]46353464.3399793[/C][C]45697264.05[/C][C]1.01435972817238[/C][C]0.972034130384053[/C][/ROW]
[ROW][C]37[/C][C]50615569.2[/C][C]51096304.0808761[/C][C]46407405.8708333[/C][C]1.10103771417634[/C][C]0.990591591906233[/C][/ROW]
[ROW][C]38[/C][C]43033396.2[/C][C]43595242.0629686[/C][C]46835492.0625[/C][C]0.930816356211068[/C][C]0.987112220591479[/C][/ROW]
[ROW][C]39[/C][C]46013056.5[/C][C]44539825.5325654[/C][C]47268281[/C][C]0.942277243646016[/C][C]1.03307671168037[/C][/ROW]
[ROW][C]40[/C][C]54222266.3[/C][C]52488208.5941831[/C][C]47769347.4583333[/C][C]1.09878429132752[/C][C]1.03303709065828[/C][/ROW]
[ROW][C]41[/C][C]46417306.4[/C][C]47709431.4809382[/C][C]48376130.075[/C][C]0.98621843886586[/C][C]0.972916778908705[/C][/ROW]
[ROW][C]42[/C][C]51046271.8[/C][C]48806509.6046157[/C][C]49087225.7708333[/C][C]0.994281278646136[/C][C]1.04589064478343[/C][/ROW]
[ROW][C]43[/C][C]51201279.6[/C][C]52530057.3503877[/C][C]49788626.5541667[/C][C]1.05506138622319[/C][C]0.97470442985576[/C][/ROW]
[ROW][C]44[/C][C]43475288.7[/C][C]47228120.2311568[/C][C]50452960.5416667[/C][C]0.936082238269316[/C][C]0.920538198158456[/C][/ROW]
[ROW][C]45[/C][C]44968981.1[/C][C]45413251.2612871[/C][C]50959279.9708333[/C][C]0.891167443638911[/C][C]0.990217169021196[/C][/ROW]
[ROW][C]46[/C][C]53939345.4[/C][C]51580401.9923951[/C][C]51279140.9125[/C][C]1.00587492447288[/C][C]1.0457333273198[/C][/ROW]
[ROW][C]47[/C][C]54549319.7[/C][C]53923542.1904759[/C][C]51648975.225[/C][C]1.04403895635039[/C][C]1.01160490361174[/C][/ROW]
[ROW][C]48[/C][C]54072107.3[/C][C]52758931.9385407[/C][C]52012052.9958333[/C][C]1.01435972817238[/C][C]1.02489010511033[/C][/ROW]
[ROW][C]49[/C][C]58434230.1[/C][C]57651751.5754805[/C][C]52361286.8416667[/C][C]1.10103771417634[/C][C]1.01357250219007[/C][/ROW]
[ROW][C]50[/C][C]51158751[/C][C]49111383.4061659[/C][C]52761624.8666667[/C][C]0.930816356211068[/C][C]1.04168824927821[/C][/ROW]
[ROW][C]51[/C][C]50039368[/C][C]50000257.264075[/C][C]53063212.1291667[/C][C]0.942277243646016[/C][C]1.00078221069381[/C][/ROW]
[ROW][C]52[/C][C]57872617.4[/C][C]58376971.5794687[/C][C]53128691.4458333[/C][C]1.09878429132752[/C][C]0.991360391506742[/C][/ROW]
[ROW][C]53[/C][C]51642978.8[/C][C]52405477.4293209[/C][C]53137799.2583333[/C][C]0.98621843886586[/C][C]0.985450020365728[/C][/ROW]
[ROW][C]54[/C][C]54534465.9[/C][C]52876227.192274[/C][C]53180350.8[/C][C]0.994281278646136[/C][C]1.03136076070057[/C][/ROW]
[ROW][C]55[/C][C]56094697.8[/C][C]55947978.6901948[/C][C]53028173.925[/C][C]1.05506138622319[/C][C]1.00262242020606[/C][/ROW]
[ROW][C]56[/C][C]48189983.1[/C][C]NA[/C][C]NA[/C][C]0.936082238269316[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]47492381[/C][C]NA[/C][C]NA[/C][C]0.891167443638911[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]52987449.1[/C][C]NA[/C][C]NA[/C][C]1.00587492447288[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]55719803.5[/C][C]NA[/C][C]NA[/C][C]1.04403895635039[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]53922860.5[/C][C]NA[/C][C]NA[/C][C]1.01435972817238[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]54931231.9[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147445&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147445&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
139923931NANA1.10103771417634NA
235810356.5NANA0.930816356211068NA
335492936.3NANA0.942277243646016NA
438937434.1NANA1.09878429132752NA
540059102.8NANA0.98621843886586NA
637708710.2NANA0.994281278646136NA
741570965.741059811.069522338916987.7751.055061386223191.01244902538914
83633356336746940.797073939256103.03750.9360822382693160.98875068813601
934181220.135205765.290262439505219.29583330.8911674436389110.970898369008165
1042593543.940093684.791855539859513.16251.005874924472881.06235044549092
1143119727.641907743.696900340140019.14583331.044038956350391.02892028527867
1238497690.940911216.146055840332058.74583331.014359728172380.94100578097118
1345473273.444662629.866471140564123.54583331.101037714176341.01815037618592
1438399780.437931153.68478640750415.94583330.9308163562110681.01235466548443
1538882302.638522183.143168840882005.16666670.9422772436460161.00934836573236
1644051120.645000540.522884340954845.16666671.098784291327520.978902032912216
1741677559.940376659.092940340940888.450.986218438865861.03221912947441
1840699203.540724528.181703440958759.91666670.9942812786461360.999378146713194
1944150027.643233490.300265940977227.35833331.055061386223191.02119970637042
2038225518.738276965.464271240890601.16666670.9360822382693160.998655934093855
2135447405.736347897.824985940786833.14166670.8911674436389110.975225744021793
2243075518.341062176.85674240822348.64166671.005874924472881.04903153211487
234230279242735923.796983440933265.50416671.044038956350390.989864924903906
2439743541.741491493.663079340904121.59583331.014359728172380.957872040537439
2544670641.245053262.89901740918909.78751.101037714176340.99150734765039
263712338438166603.8992841003366.17916670.9308163562110680.97266668257849
2737668266.438808677.639257841186049.96666670.9422772436460160.970614529826078
2846117528.845637319.106850641534375.27916671.098784291327521.01052230285537
2942273156.441313207.293502441890524.11250.986218438865861.02323588918376
3039404153.242044897.979476442286723.97083330.9942812786461360.937192265735419
3145799994.545110024.873274942755829.6251.055061386223191.01529526150037
3238602505.240485355.984305143249785.46666670.9360822382693160.95349304116197
3339454830.139072109.740813643843735.56250.8911674436389111.0097952314765
3447427901.444790737.83638344529132.54583331.005874924472881.05887743071459
3546497980.947022995.558754545039502.85833331.044038956350390.988834938044333
3645057149.446353464.339979345697264.051.014359728172380.972034130384053
3750615569.251096304.080876146407405.87083331.101037714176340.990591591906233
3843033396.243595242.062968646835492.06250.9308163562110680.987112220591479
3946013056.544539825.5325654472682810.9422772436460161.03307671168037
4054222266.352488208.594183147769347.45833331.098784291327521.03303709065828
4146417306.447709431.480938248376130.0750.986218438865860.972916778908705
4251046271.848806509.604615749087225.77083330.9942812786461361.04589064478343
4351201279.652530057.350387749788626.55416671.055061386223190.97470442985576
4443475288.747228120.231156850452960.54166670.9360822382693160.920538198158456
4544968981.145413251.261287150959279.97083330.8911674436389110.990217169021196
4653939345.451580401.992395151279140.91251.005874924472881.0457333273198
4754549319.753923542.190475951648975.2251.044038956350391.01160490361174
4854072107.352758931.938540752012052.99583331.014359728172381.02489010511033
4958434230.157651751.575480552361286.84166671.101037714176341.01357250219007
505115875149111383.406165952761624.86666670.9308163562110681.04168824927821
515003936850000257.26407553063212.12916670.9422772436460161.00078221069381
5257872617.458376971.579468753128691.44583331.098784291327520.991360391506742
5351642978.852405477.429320953137799.25833330.986218438865860.985450020365728
5454534465.952876227.19227453180350.80.9942812786461361.03136076070057
5556094697.855947978.690194853028173.9251.055061386223191.00262242020606
5648189983.1NANA0.936082238269316NA
5747492381NANA0.891167443638911NA
5852987449.1NANA1.00587492447288NA
5955719803.5NANA1.04403895635039NA
6053922860.5NANA1.01435972817238NA
6154931231.9NANANANA



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
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')