Ann-Sophie Coeman additief decompositie model verkochte ton koffie per maand

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 18 May 2011 18:27:04 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/18/t1305743098obl2hah0xeqwqh3.htm/, Retrieved Wed, 18 May 2011 20:24:58 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

600 425 398 582 458 455 621 635 589 220 351 379 683 524 536 598 581 632 645 722 689 645 354 486 423 479 684 601 608 463 602 485 563 645 486 435 479 579 563 202 389 467 466 706 546 689 531 528 579 684 651 637 548 496 582 467 693 615 708 648 899 852 745 689 582 674 684 542 489 472 398 486 549 766 654 628 689 648 578 536 548 496 475 687 642 584 596 609 678 694 485 489 537 706 489 598

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 2 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 600 NA NA 29.3164682539683 NA 2 425 NA NA 61.5664682539683 NA 3 398 NA NA 57.1736111111111 NA 4 582 NA NA -11.8382936507936 NA 5 458 NA NA 0.304563492063487 NA 6 455 NA NA -1.96329365079364 NA 7 621 507.477182539683 479.541666666667 27.9355158730159 113.522817460317 8 635 501.72123015873 487.125 14.5962301587302 133.27876984127 9 589 512.899801587302 497 15.8998015873015 76.1001984126984 10 220 470.12003968254 503.416666666667 -33.296626984127 -250.12003968254 11 351 406.012896825397 509.208333333333 -103.195436507937 -55.0128968253968 12 379 465.209325396825 521.708333333333 -56.499007936508 -86.2093253968253 13 683 559.399801587302 530.083333333333 29.3164682539683 123.600198412698 14 524 596.274801587302 534.708333333333 61.5664682539683 -72.2748015873016 15 536 599.673611111111 542.5 57.1736111111111 -63.6736111111111 16 598 552.536706349206 564.375 -11.8382936507936 45.4632936507936 17 581 582.512896825397 582.208333333333 0.304563492063487 -1.51289682539687 18 632 584.828373015873 586.791666666667 -1.96329365079364 47.171626984127 19 645 608.352182539683 580.416666666667 27.9355158730159 36.6478174603174 20 722 582.304563492063 567.708333333333 14.5962301587302 139.695436507937 21 689 587.899801587302 572 15.8998015873015 101.100198412698 22 645 544.99503968254 578.291666666667 -33.296626984127 100.00496031746 23 354 476.34623015873 579.541666666667 -103.195436507937 -122.34623015873 24 486 517.125992063492 573.625 -56.499007936508 -31.1259920634922 25 423 594.108134920635 564.791666666667 29.3164682539683 -171.108134920635 26 479 614.691468253968 553.125 61.5664682539683 -135.691468253968 27 684 595.173611111111 538 57.1736111111111 88.826388888889 28 601 520.911706349206 532.75 -11.8382936507936 80.0882936507936 29 608 538.554563492063 538.25 0.304563492063487 69.4454365079365 30 463 539.661706349206 541.625 -1.96329365079364 -76.6617063492064 31 602 569.768849206349 541.833333333333 27.9355158730159 32.2311507936508 32 485 562.929563492063 548.333333333333 14.5962301587302 -77.9295634920636 33 563 563.358134920635 547.458333333333 15.8998015873015 -0.358134920634939 34 645 492.49503968254 525.791666666667 -33.296626984127 152.50496031746 35 486 396.84623015873 500.041666666667 -103.195436507937 89.1537698412698 36 435 434.584325396825 491.083333333333 -56.499007936508 0.41567460317458 37 479 514.899801587302 485.583333333333 29.3164682539683 -35.8998015873016 38 579 550.691468253968 489.125 61.5664682539683 28.3085317460317 39 563 554.798611111111 497.625 57.1736111111111 8.20138888888891 40 202 486.911706349206 498.75 -11.8382936507936 -284.911706349206 41 389 502.762896825397 502.458333333333 0.304563492063487 -113.762896825397 42 467 506.24503968254 508.208333333333 -1.96329365079364 -39.2450396825398 43 466 544.185515873016 516.25 27.9355158730159 -78.1855158730159 44 706 539.387896825397 524.791666666667 14.5962301587302 166.612103174603 45 546 548.733134920635 532.833333333333 15.8998015873015 -2.73313492063494 46 689 521.328373015873 554.625 -33.296626984127 167.671626984127 47 531 476.179563492063 579.375 -103.195436507937 54.8204365079365 48 528 530.709325396825 587.208333333333 -56.499007936508 -2.70932539682542 49 579 622.566468253968 593.25 29.3164682539683 -43.5664682539683 50 684 649.691468253968 588.125 61.5664682539683 34.3085317460318 51 651 641.465277777778 584.291666666667 57.1736111111111 9.53472222222229 52 637 575.49503968254 587.333333333333 -11.8382936507936 61.5049603174602 53 548 591.929563492063 591.625 0.304563492063487 -43.9295634920635 54 496 602.036706349206 604 -1.96329365079364 -106.036706349206 55 582 650.268849206349 622.333333333333 27.9355158730159 -68.2688492063492 56 467 657.262896825397 642.666666666667 14.5962301587302 -190.262896825397 57 693 669.483134920635 653.583333333333 15.8998015873015 23.5168650793651 58 615 626.37003968254 659.666666666667 -33.296626984127 -11.3700396825396 59 708 560.054563492063 663.25 -103.195436507937 147.945436507937 60 648 615.584325396825 672.083333333333 -56.499007936508 32.4156746031747 61 899 713.066468253968 683.75 29.3164682539683 185.933531746032 62 852 752.691468253968 691.125 61.5664682539683 99.3085317460317 63 745 742.923611111111 685.75 57.1736111111111 2.07638888888891 64 689 659.453373015873 671.291666666667 -11.8382936507936 29.546626984127 65 582 652.72123015873 652.416666666667 0.304563492063487 -70.7212301587301 66 674 630.786706349206 632.75 -1.96329365079364 43.2132936507937 67 684 639.352182539683 611.416666666667 27.9355158730159 44.6478174603175 68 542 607.84623015873 593.25 14.5962301587302 -65.8462301587301 69 489 601.774801587302 585.875 15.8998015873015 -112.774801587302 70 472 546.24503968254 579.541666666667 -33.296626984127 -74.2450396825398 71 398 478.262896825397 581.458333333333 -103.195436507937 -80.2628968253969 72 486 528.334325396825 584.833333333333 -56.499007936508 -42.3343253968254 73 549 608.649801587302 579.333333333333 29.3164682539683 -59.6498015873016 74 766 636.233134920635 574.666666666667 61.5664682539683 129.766865079365 75 654 634.048611111111 576.875 57.1736111111111 19.9513888888889 76 628 568.49503968254 580.333333333333 -11.8382936507936 59.5049603174604 77 689 584.84623015873 584.541666666667 0.304563492063487 104.15376984127 78 648 594.161706349206 596.125 -1.96329365079364 53.8382936507936 79 578 636.310515873016 608.375 27.9355158730159 -58.3105158730159 80 536 619.262896825397 604.666666666667 14.5962301587302 -83.2628968253968 81 548 610.566468253968 594.666666666667 15.8998015873015 -62.5664682539683 82 496 558.161706349206 591.458333333333 -33.296626984127 -62.1617063492063 83 475 487.012896825397 590.208333333333 -103.195436507937 -12.0128968253969 84 687 535.167658730159 591.666666666667 -56.499007936508 151.832341269841 85 642 619.024801587302 589.708333333333 29.3164682539683 22.9751984126984 86 584 645.441468253968 583.875 61.5664682539683 -61.4414682539683 87 596 638.631944444445 581.458333333333 57.1736111111111 -42.6319444444446 88 609 577.911706349206 589.75 -11.8382936507936 31.0882936507936 89 678 599.387896825397 599.083333333333 0.304563492063487 78.6121031746031 90 694 593.99503968254 595.958333333333 -1.96329365079364 100.00496031746 91 485 NA NA 27.9355158730159 NA 92 489 NA NA 14.5962301587302 NA 93 537 NA NA 15.8998015873015 NA 94 706 NA NA -33.296626984127 NA 95 489 NA NA -103.195436507937 NA 96 598 NA NA -56.499007936508 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/18/t1305743098obl2hah0xeqwqh3/1g68a1305743221.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305743098obl2hah0xeqwqh3/1g68a1305743221.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305743098obl2hah0xeqwqh3/221ec1305743221.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305743098obl2hah0xeqwqh3/221ec1305743221.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305743098obl2hah0xeqwqh3/3cw201305743221.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305743098obl2hah0xeqwqh3/3cw201305743221.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305743098obl2hah0xeqwqh3/4gw4v1305743221.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305743098obl2hah0xeqwqh3/4gw4v1305743221.ps (open in new window)

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')

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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