Het verkoopcijfer van auto's

*Unverified author*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Mon, 24 May 2010 09:06:56 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/May/24/t1274692047opv42mui5z7i3su.htm/, Retrieved Mon, 24 May 2010 11:07:33 +0200

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/May/24/t1274692047opv42mui5z7i3su.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

68897 38683 44720 39525 45315 50380 40600 36279 42438 38064 31879 11379 70249 39253 47060 41697 38708 49267 39018 32228 40870 39383 34571 12066 70938 34077 45409 40809 37013 44953 37848 32745 43412 34931 33008 8620 68906 39556 50669 36432 40891 48428 36222 33425 39401 37967 34801 12657 69116 41519 51321 38529 41547 52073 38401 40898 40439 41888 37898 8771 68184 50530 47221 41756 45633 48138 39486 39341 41117 41629 29722 7054 56676 34870 35117 30169 30936 35699 33228 27733 33666 35429 27438 8170 62557

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 68897 NA NA 1.68837863231135 NA 2 38683 NA NA 1.00290954572447 NA 3 44720 NA NA 1.16069965886865 NA 4 39525 NA NA 0.964955734768908 NA 5 45315 NA NA 0.987777612114035 NA 6 50380 NA NA 1.17336251478437 NA 7 40600 38735.8124602678 40736.25 0.950892938360006 1.04812568580159 8 36279 36673.4086884129 40816.3333333333 0.898498363116388 0.989245376895181 9 42438 40731.1298634693 40937.5833333333 0.994956872070757 1.04190578906728 10 38064 40465.3947354235 41125.5833333333 0.983947009515736 0.940655595945012 11 31879 35198.5533772435 40940.7916666667 0.859742861443043 0.905690630473761 12 11379 13561.8426527085 40619.125 0.333878256922288 0.839045275143891 13 70249 68390.8718625972 40506.8333333333 1.68837863231135 1.02716924184175 14 39253 40389.2985891089 40272.125 1.00290954572447 0.971866345076482 15 47060 46472.0929417829 40038 1.16069965886865 1.01265075491551 16 41697 38624.8862929293 40027.625 0.964955734768908 1.07953715860215 17 38708 39703.4741745206 40194.75 0.987777612114035 0.974927277896516 18 49267 47328.2126051899 40335.5416666667 1.17336251478437 1.04096472881795 19 39018 38409.2995975584 40392.875 0.950892938360006 1.01584773502301 20 32228 36124.9503125939 40205.9166666667 0.898498363116388 0.892125794530565 21 40870 39720.1293118364 39921.4583333333 0.994956872070757 1.02894931884879 22 39383 39176.5061485421 39815.6666666667 0.983947009515736 1.00527085929192 23 34571 34138.7053647996 39708.0416666667 0.859742861443043 1.01266288895789 24 12066 13174.0569688873 39457.6666666667 0.333878256922288 0.915890983961570 25 70938 66233.6867633807 39229.1666666667 1.68837863231135 1.07102599094967 26 34077 39316.0182235928 39201.9583333333 1.00290954572447 0.866745961053376 27 45409 45649.6405085028 39329.4166666667 1.16069965886865 0.99472853442388 28 40809 37874.3517637238 39249.8333333333 0.964955734768908 1.07748378783045 29 37013 38522.5448818378 38999.2083333333 0.987777612114035 0.960813988627489 30 44953 45515.3186297433 38790.5 1.17336251478437 0.9876455082229 31 37848 36668.5712122731 38562.25 0.950892938360006 1.03216456896832 32 32745 34777.1653304875 38705.875 0.898498363116388 0.94156610203345 33 43412 38955.8780644770 39153.3333333333 0.994956872070757 1.11438894864974 34 34931 38561.0062962979 39190.125 0.983947009515736 0.90586328924081 35 33008 33675.5547208164 39169.3333333333 0.859742861443043 0.980176875292756 36 8620 13180.0806891060 39475.7083333333 0.333878256922288 0.654017240359149 37 68906 66780.0179491528 39552.75 1.68837863231135 1.03183560166854 38 39556 39628.2991833927 39513.3333333333 1.00290954572447 0.998175566832729 39 50669 45702.0170806093 39374.5416666667 1.16069965886865 1.10868191901968 40 36432 37955.4884584223 39333.9166666667 0.964955734768908 0.959861181602466 41 40891 39051.9113671299 39535.125 0.987777612114035 1.04709343457176 42 48428 46674.0630031976 39778.0416666667 1.17336251478437 1.03757840830532 43 36222 37992.9273521740 39955 0.950892938360006 0.95338797308882 44 33425 35980.8536376091 40045.5416666667 0.898498363116388 0.92896628681045 45 39401 39951.9957195652 40154.5 0.994956872070757 0.986208555801997 46 37967 39622.6031239813 40269.0416666667 0.983947009515736 0.958215690200849 47 34801 34719.6407808005 40383.75 0.859742861443043 1.00234331972825 48 12657 13543.0898239447 40562.9583333333 0.333878256922288 0.93457255061706 49 69116 68895.3453281099 40805.625 1.68837863231135 1.00320275151883 50 41519 41327.6876207251 41207.7916666667 1.00290954572447 1.00462915760085 51 51321 48241.4828467565 41562.4166666667 1.16069965886865 1.06383545802325 52 38529 40305.2762920515 41769.0416666667 0.964955734768908 0.955929435164255 53 41547 41547.3668745340 42061.4583333333 0.987777612114035 0.999991169728396 54 52073 49314.7642328246 42028.5833333333 1.17336251478437 1.05593123702576 55 38401 39773.7913435659 41827.8333333333 0.950892938360006 0.965485026767809 56 40898 37884.6967941892 42164.4583333333 0.898498363116388 1.07953879694962 57 40439 42155.4106258386 42369.0833333333 0.994956872070757 0.959283740797284 58 41888 41653.1417692852 42332.7083333333 0.983947009515736 1.00563842775692 59 37898 36657.2146095393 42637.4166666667 0.859742861443043 1.03384832709406 60 8771 14237.8070070358 42643.7083333333 0.333878256922288 0.616035882187875 61 68184 71798.2309899305 42524.9583333333 1.68837863231135 0.949661280785074 62 50530 42628.9627563026 42505.2916666667 1.00290954572447 1.18534434649197 63 47221 49293.3669126062 42468.6666666667 1.16069965886865 0.957958503498444 64 41756 40997.2299668587 42486.125 0.964955734768908 1.01850783659664 65 45633 41619.6804272208 42134.6666666667 0.987777612114035 1.09642840914642 66 48138 48955.5686329863 41722.4583333333 1.17336251478437 0.983299782725118 67 39486 39149.6093706108 41171.4166666667 0.950892938360006 1.00859243897442 68 39341 35975.350335135 40039.4166666667 0.898498363116388 1.09355432632376 69 41117 38686.4934913639 38882.5833333333 0.994956872070757 1.06282571226515 70 41629 37287.1229013115 37895.4583333333 0.983947009515736 1.11644441192688 71 29722 31638.7880594386 36800.2916666667 0.859742861443043 0.939416514443044 72 7054 11909.3122200717 35669.625 0.333878256922288 0.592309603581587 73 56676 58908.5153688786 34890.5833333333 1.68837863231135 0.962101992302831 74 34870 34245.5164998986 34146.1666666667 1.00290954572447 1.01823548201130 75 35117 38711.7033850729 33352.0416666667 1.16069965886865 0.907141689185938 76 30169 31634.3850918628 32783.25 0.964955734768908 0.953677459270744 77 30936 32033.3810164551 32429.75 0.987777612114035 0.965742579096118 78 35699 37994.7493714424 32381.0833333333 1.17336251478437 0.939577193969651 79 33228 31068.1683901846 32672.625 0.950892938360006 1.06951911624432 80 27733 NA NA 0.898498363116388 NA 81 33666 NA NA 0.994956872070757 NA 82 35429 NA NA 0.983947009515736 NA 83 27438 NA NA 0.859742861443043 NA 84 8170 NA NA 0.333878256922288 NA 85 62557 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/24/t1274692047opv42mui5z7i3su/1vehc1274692014.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274692047opv42mui5z7i3su/1vehc1274692014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274692047opv42mui5z7i3su/2vehc1274692014.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274692047opv42mui5z7i3su/2vehc1274692014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274692047opv42mui5z7i3su/3tr5l1274692014.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274692047opv42mui5z7i3su/3tr5l1274692014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274692047opv42mui5z7i3su/4tr5l1274692014.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274692047opv42mui5z7i3su/4tr5l1274692014.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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