| Classical Deomposition gemiddelde consumptieprijs cola-limonade | | *Unverified author* | | R Software Module: /rwasp_decompose.wasp (opens new window with default values) | | Title produced by software: Classical Decomposition | | Date of computation: Tue, 12 Jan 2010 14:27:23 -0700 | | | | Cite this page as follows: | | Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/12/t1263331912qqqbeajai4krvhv.htm/, Retrieved Tue, 12 Jan 2010 22:31:59 +0100 | | | | 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/Jan/12/t1263331912qqqbeajai4krvhv.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 « | | 1,14
1,15
1,15
1,14
1,14
1,14
1,15
1,14
1,14
1,15
1,15
1,14
1,15
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,17
1,18
1,19
1,19
1,19
1,19
1,18
1,19
1,19
1,2
1,21
1,21
1,2
1,21
1,21
1,21
1,21
1,21
1,21
1,2
1,21
1,22
1,22
1,23
1,22
1,23
1,23
1,23
1,23
1,23
1,22
1,22
1,23
1,24
1,24
1,25
1,25
1,25
1,26
1,26
1,26
1,26
1,27
1,27
1,29
1,31
1,32
1,32
1,33
1,33
1,32
1,32
1,31
1,3
1,31
1,29
1,3
1,3
1,32
1,31
1,35
1,35
1,36
1,37
1,37
1,37
1,32
1,32
1,31
1,31
1,34
1,31
1,26
1,27
1,24
1,25
1,27
1,25
1,26
1,27
1,26
1,26
1,28
1,27
1,28
1,27
1,26
1,27
1,27
1,28
1,27
1,26
1,3
1,31
1,28
1,29
1,31
1,29
1,29
1,32
1,3
1,29
1,31
1,29
1,33
1,35
1,32
1,33
| | | | Output produced by software: |
| Classical Decomposition by Moving Averages | | t | Observations | Fit | Trend | Seasonal | Random | | 1 | 1.14 | NA | NA | 1.00158510866783 | NA | | 2 | 1.15 | NA | NA | 1.00320363896428 | NA | | 3 | 1.15 | NA | NA | 1.00054259510927 | NA | | 4 | 1.14 | NA | NA | 1.00384190243488 | NA | | 5 | 1.14 | NA | NA | 1.00187288851855 | NA | | 6 | 1.14 | NA | NA | 0.998338825906253 | NA | | 7 | 1.15 | 1.13929731626151 | 1.14458333333333 | 0.995381710603431 | 1.00939410949690 | | 8 | 1.14 | 1.13660039508960 | 1.14583333333333 | 0.99194216298729 | 1.00299102914717 | | 9 | 1.14 | 1.14347365652135 | 1.1475 | 0.996491203940177 | 0.996962189289152 | | 10 | 1.15 | 1.15078378041834 | 1.14958333333333 | 1.00104424538022 | 0.999318916001706 | | 11 | 1.15 | 1.15733418344872 | 1.15208333333333 | 1.00455769991932 | 0.993662864578264 | | 12 | 1.14 | 1.15596654445098 | 1.15458333333333 | 1.00119801756851 | 0.986187710598008 | | 13 | 1.15 | 1.15850010902579 | 1.15666666666667 | 1.00158510866783 | 0.992662832778727 | | 14 | 1.17 | 1.16246221664986 | 1.15875 | 1.00320363896428 | 1.00648432546209 | | 15 | 1.17 | 1.16188008857064 | 1.16125 | 1.00054259510927 | 1.00698859676591 | | 16 | 1.17 | 1.16780274649924 | 1.16333333333333 | 1.00384190243488 | 1.00188152794412 | | 17 | 1.17 | 1.16718191512411 | 1.165 | 1.00187288851855 | 1.00241443500741 | | 18 | 1.17 | 1.16514460473476 | 1.16708333333333 | 0.998338825906253 | 1.00416720400671 | | 19 | 1.17 | 1.16376711664718 | 1.16916666666667 | 0.995381710603431 | 1.00535578232420 | | 20 | 1.17 | 1.16098563992971 | 1.17041666666667 | 0.99194216298729 | 1.00776440272839 | | 21 | 1.17 | 1.16755552728324 | 1.17166666666667 | 0.996491203940177 | 1.00209366720437 | | 22 | 1.17 | 1.17455858124612 | 1.17333333333333 | 1.00104424538022 | 0.996118898351342 | | 23 | 1.17 | 1.18035529740520 | 1.175 | 1.00455769991932 | 0.991226965789058 | | 24 | 1.17 | 1.17807633400562 | 1.17666666666667 | 1.00119801756851 | 0.99314447309356 | | 25 | 1.17 | 1.17978379258498 | 1.17791666666667 | 1.00158510866783 | 0.99170713087731 | | 26 | 1.18 | 1.18294429094538 | 1.17916666666667 | 1.00320363896428 | 0.997511048518584 | | 27 | 1.19 | 1.18147404772486 | 1.18083333333333 | 1.00054259510927 | 1.00721636864691 | | 28 | 1.19 | 1.18746131708859 | 1.18291666666667 | 1.00384190243488 | 1.00213790788371 | | 29 | 1.19 | 1.18805426696824 | 1.18583333333333 | 1.00187288851855 | 1.00163774760620 | | 30 | 1.19 | 1.18719125380685 | 1.18916666666667 | 0.998338825906253 | 1.00236587507206 | | 31 | 1.18 | 1.18657794751517 | 1.19208333333333 | 0.995381710603431 | 0.994456371341682 | | 32 | 1.19 | 1.18495757553523 | 1.19458333333333 | 0.99194216298729 | 1.00425536286604 | | 33 | 1.19 | 1.19246780738174 | 1.19666666666667 | 0.996491203940177 | 0.997930503979673 | | 34 | 1.2 | 1.19958468738062 | 1.19833333333333 | 1.00104424538022 | 1.00034621367190 | | 35 | 1.21 | 1.20546923990319 | 1.2 | 1.00455769991932 | 1.00375850328390 | | 36 | 1.21 | 1.20310628444483 | 1.20166666666667 | 1.00119801756851 | 1.00572993063398 | | 37 | 1.2 | 1.20565807455890 | 1.20375 | 1.00158510866783 | 0.995307065346064 | | 38 | 1.21 | 1.20927838646819 | 1.20541666666667 | 1.00320363896428 | 1.00059673069483 | | 39 | 1.21 | 1.20732139809851 | 1.20666666666667 | 1.00054259510927 | 1.00221863201108 | | 40 | 1.21 | 1.21297563210881 | 1.20833333333333 | 1.00384190243488 | 0.99754683273922 | | 41 | 1.21 | 1.21184874807056 | 1.20958333333333 | 1.00187288851855 | 0.99847443992206 | | 42 | 1.21 | 1.20882192836815 | 1.21083333333333 | 0.998338825906253 | 1.00097456176481 | | 43 | 1.21 | 1.20690032410666 | 1.2125 | 0.995381710603431 | 1.00256829485536 | | 44 | 1.2 | 1.20438310956040 | 1.21416666666667 | 0.99194216298729 | 0.996360701569451 | | 45 | 1.21 | 1.21156722212393 | 1.21583333333333 | 0.996491203940177 | 0.99870645054165 | | 46 | 1.22 | 1.21877136875041 | 1.2175 | 1.00104424538022 | 1.00100809001679 | | 47 | 1.22 | 1.22472326248497 | 1.21916666666667 | 1.00455769991932 | 0.99614340428597 | | 48 | 1.23 | 1.22229591311489 | 1.22083333333333 | 1.00119801756851 | 1.00630296379334 | | 49 | 1.22 | 1.22402046821781 | 1.22208333333333 | 1.00158510866783 | 0.99671535867071 | | 50 | 1.23 | 1.22725245166630 | 1.22333333333333 | 1.00320363896428 | 1.00223878007330 | | 51 | 1.23 | 1.22566467900885 | 1.225 | 1.00054259510927 | 1.00353711832069 | | 52 | 1.23 | 1.23137940032012 | 1.22666666666667 | 1.00384190243488 | 0.998879792597017 | | 53 | 1.23 | 1.23063386473028 | 1.22833333333333 | 1.00187288851855 | 0.999484928256529 | | 54 | 1.23 | 1.22795675586469 | 1.23 | 0.998338825906253 | 1.00166393818475 | | 55 | 1.22 | 1.22639321593931 | 1.23208333333333 | 0.995381710603431 | 0.994786977083517 | | 56 | 1.22 | 1.22422195282015 | 1.23416666666667 | 0.99194216298729 | 0.996551317503805 | | 57 | 1.23 | 1.23191225087104 | 1.23625 | 0.996491203940177 | 0.998447737759169 | | 58 | 1.24 | 1.24004355896474 | 1.23875 | 1.00104424538022 | 0.999964873036575 | | 59 | 1.24 | 1.24690724502486 | 1.24125 | 1.00455769991932 | 0.99446049812252 | | 60 | 1.25 | 1.24524003435084 | 1.24375 | 1.00119801756851 | 1.00382252860321 | | 61 | 1.25 | 1.24906009593450 | 1.24708333333333 | 1.00158510866783 | 1.00075248906643 | | 62 | 1.25 | 1.25525855325406 | 1.25125 | 1.00320363896428 | 0.995810780782633 | | 63 | 1.26 | 1.25651474235805 | 1.25583333333333 | 1.00054259510927 | 1.00277374990078 | | 64 | 1.26 | 1.26609559944599 | 1.26125 | 1.00384190243488 | 0.995185514072824 | | 65 | 1.26 | 1.26987388619726 | 1.2675 | 1.00187288851855 | 0.992224514335966 | | 66 | 1.26 | 1.27163407949809 | 1.27375 | 0.998338825906253 | 0.990851079185703 | | 67 | 1.27 | 1.27408858957239 | 1.28 | 0.995381710603431 | 0.996790969163483 | | 68 | 1.27 | 1.27629891637698 | 1.28666666666667 | 0.99194216298729 | 0.995064701304565 | | 69 | 1.29 | 1.28796488109268 | 1.2925 | 0.996491203940177 | 1.00158010434694 | | 70 | 1.31 | 1.29885490838083 | 1.2975 | 1.00104424538022 | 1.00858070562559 | | 71 | 1.32 | 1.30801783843662 | 1.30208333333333 | 1.00455769991932 | 1.00916054904703 | | 72 | 1.32 | 1.30739774460821 | 1.30583333333333 | 1.00119801756851 | 1.00963919009633 | | 73 | 1.33 | 1.31124183809763 | 1.30916666666667 | 1.00158510866783 | 1.01430564626399 | | 74 | 1.33 | 1.31586877310815 | 1.31166666666667 | 1.00320363896428 | 1.01073908521932 | | 75 | 1.32 | 1.31362904882887 | 1.31291666666667 | 1.00054259510927 | 1.00484988602894 | | 76 | 1.32 | 1.31796076440513 | 1.31291666666667 | 1.00384190243488 | 1.00154726578359 | | 77 | 1.31 | 1.31495816618059 | 1.3125 | 1.00187288851855 | 0.99622941146866 | | 78 | 1.3 | 1.30990373449116 | 1.31208333333333 | 0.998338825906253 | 0.992439341739102 | | 79 | 1.31 | 1.30643849516700 | 1.3125 | 0.995381710603431 | 1.00272611749131 | | 80 | 1.29 | 1.30357732585913 | 1.31416666666667 | 0.99194216298729 | 0.989584564268036 | | 81 | 1.3 | 1.31204675185457 | 1.31666666666667 | 0.996491203940177 | 0.990818351680275 | | 82 | 1.3 | 1.32179550567079 | 1.32041666666667 | 1.00104424538022 | 0.983510682569819 | | 83 | 1.32 | 1.3310389523931 | 1.325 | 1.00455769991932 | 0.991706514393697 | | 84 | 1.31 | 1.33201052920677 | 1.33041666666667 | 1.00119801756851 | 0.98347570929497 | | 85 | 1.35 | 1.33586413868571 | 1.33375 | 1.00158510866783 | 1.01058181060852 | | 86 | 1.35 | 1.33969485953355 | 1.33541666666667 | 1.00320363896428 | 1.00769215496582 | | 87 | 1.36 | 1.33780882821068 | 1.33708333333333 | 1.00054259510927 | 1.01658770021648 | | 88 | 1.37 | 1.343056811966 | 1.33791666666667 | 1.00384190243488 | 1.02006109331634 | | 89 | 1.37 | 1.34167477654109 | 1.33916666666667 | 1.00187288851855 | 1.02111184018227 | | 90 | 1.37 | 1.33777402671438 | 1.34 | 0.998338825906253 | 1.02408925023367 | | 91 | 1.32 | 1.33007881079383 | 1.33625 | 0.995381710603431 | 0.992422395791856 | | 92 | 1.32 | 1.31845645830394 | 1.32916666666667 | 0.99194216298729 | 1.00117071874944 | | 93 | 1.31 | 1.31619879853765 | 1.32083333333333 | 0.996491203940177 | 0.995290378212974 | | 94 | 1.31 | 1.3122021649859 | 1.31083333333333 | 1.00104424538022 | 0.998321779185662 | | 95 | 1.34 | 1.30759927272832 | 1.30166666666667 | 1.00455769991932 | 1.02477878960890 | | 96 | 1.31 | 1.2940484377073 | 1.2925 | 1.00119801756851 | 1.01232686646642 | | 97 | 1.26 | 1.28703686463816 | 1.285 | 1.00158510866783 | 0.978992936891705 | | 98 | 1.27 | 1.28451865939051 | 1.28041666666667 | 1.00320363896428 | 0.988697198530846 | | 99 | 1.24 | 1.2769424870082 | 1.27625 | 1.00054259510927 | 0.97106957644212 | | 100 | 1.25 | 1.27697055338904 | 1.27208333333333 | 1.00384190243488 | 0.978879267562233 | | 101 | 1.27 | 1.26987388619726 | 1.2675 | 1.00187288851855 | 1.00009931206879 | | 102 | 1.25 | 1.26123471672823 | 1.26333333333333 | 0.998338825906253 | 0.991092287122118 | | 103 | 1.26 | 1.25666940963683 | 1.2625 | 0.995381710603431 | 1.00265033137405 | | 104 | 1.27 | 1.25315359924061 | 1.26333333333333 | 0.99194216298729 | 1.01344320502259 | | 105 | 1.26 | 1.25973096364771 | 1.26416666666667 | 0.996491203940177 | 1.00021356651544 | | 106 | 1.26 | 1.26715517394379 | 1.26583333333333 | 1.00104424538022 | 0.994353356170642 | | 107 | 1.28 | 1.27243975323114 | 1.26666666666667 | 1.00455769991932 | 1.00594153613141 | | 108 | 1.27 | 1.26943565310874 | 1.26791666666667 | 1.00119801756851 | 1.00044456518129 | | 109 | 1.28 | 1.27159576087953 | 1.26958333333333 | 1.00158510866783 | 1.00660920661977 | | 110 | 1.27 | 1.27365061996840 | 1.26958333333333 | 1.00320363896428 | 0.997133735177319 | | 111 | 1.26 | 1.27152288128469 | 1.27083333333333 | 1.00054259510927 | 0.990937731869166 | | 112 | 1.27 | 1.27948015814512 | 1.27458333333333 | 1.00384190243488 | 0.992590617302837 | | 113 | 1.27 | 1.27905772100868 | 1.27666666666667 | 1.00187288851855 | 0.992918442334616 | | 114 | 1.28 | 1.27537785009524 | 1.2775 | 0.998338825906253 | 1.00362414158629 | | 115 | 1.27 | 1.27367384719297 | 1.27958333333333 | 0.995381710603431 | 0.99711555104859 | | 116 | 1.26 | 1.27133920556204 | 1.28166666666667 | 0.99194216298729 | 0.99108089681146 | | 117 | 1.3 | 1.27924558305820 | 1.28375 | 0.996491203940177 | 1.01622395044131 | | 118 | 1.31 | 1.28842736415812 | 1.28708333333333 | 1.00104424538022 | 1.01674338534092 | | 119 | 1.28 | 1.29629799860422 | 1.29041666666667 | 1.00455769991932 | 0.987427274730214 | | 120 | 1.29 | 1.29363127186665 | 1.29208333333333 | 1.00119801756851 | 0.997192962209852 | | 121 | 1.31 | 1.29621806146761 | 1.29416666666667 | 1.00158510866783 | 1.01063242284773 | | 122 | 1.29 | 1.30123872003992 | 1.29708333333333 | 1.00320363896428 | 0.991363060546204 | | 123 | 1.29 | 1.30028848089408 | 1.29958333333333 | 1.00054259510927 | 0.99208753976886 | | 124 | 1.32 | 1.30750407792143 | 1.3025 | 1.00384190243488 | 1.00955708076906 | | 125 | 1.3 | 1.30827901359047 | 1.30583333333333 | 1.00187288851855 | 0.993671828788457 | | 126 | 1.29 | 1.30699191291560 | 1.30916666666667 | 0.998338825906253 | 0.986999221075747 | | 127 | 1.31 | NA | NA | 0.995381710603431 | NA | | 128 | 1.29 | NA | NA | 0.99194216298729 | NA | | 129 | 1.33 | NA | NA | 0.996491203940177 | NA | | 130 | 1.35 | NA | NA | 1.00104424538022 | NA | | 131 | 1.32 | NA | NA | 1.00455769991932 | NA | | 132 | 1.33 | NA | NA | 1.00119801756851 | NA |
| | | Charts produced by software: |  | | http://www.freestatistics.org/blog/date/2010/Jan/12/t1263331912qqqbeajai4krvhv/1wm281263331640.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Jan/12/t1263331912qqqbeajai4krvhv/1wm281263331640.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Jan/12/t1263331912qqqbeajai4krvhv/2td371263331640.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Jan/12/t1263331912qqqbeajai4krvhv/2td371263331640.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Jan/12/t1263331912qqqbeajai4krvhv/3rwfj1263331640.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Jan/12/t1263331912qqqbeajai4krvhv/3rwfj1263331640.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Jan/12/t1263331912qqqbeajai4krvhv/4l2b41263331640.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Jan/12/t1263331912qqqbeajai4krvhv/4l2b41263331640.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')
| | |
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and destruction. However, at any time, and under any circumstance you
are solely responsible for managing your passwords, and keeping them
secret.
We store a unique ANONYMOUS USER ID in the form of a small
'Cookie' on your computer. This allows us to track your progress when
using this website which is necessary to create state-dependent
features. The cookie is used for NO OTHER PURPOSE. At any time you may
opt to disallow cookies from this website - this will not affect other
features of this website.
We examine cookies that are used by third-parties (banner and
online ads) very closely: abuse from third-parties automatically
results in termination of the advertising contract without refund. We
have very good reason to believe that the cookies that are produced by
third parties (banner ads) do NOT cause any privacy or security risk.
FreeStatistics.org is safe. There is no need to download any
software to use the applications and services contained in this
website. Hence, your system's security is not compromised by their use,
and your personal data - other than data you submit in the account
application form, and the user-agent information that is transmitted by
your browser - is never transmitted to our servers.
As a general rule, we do not log on-line behavior of
individuals (other than normal logging of webserver 'hits'). However,
in cases of abuse, hacking, unauthorized access, Denial of Service
attacks, illegal copying, hotlinking, non-compliance with international
webstandards (such as robots.txt), or any other harmful behavior, our
system engineers are empowered to log, track, identify, publish, and
ban misbehaving individuals - even if this leads to ban entire blocks
of IP addresses, or disclosing user's identity.
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