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paper trend dow jones

*The author of this computation has been verified*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sat, 25 Dec 2010 23:02:54 +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/Dec/26/t12933180615l2ifgh8dikqle7.htm/, Retrieved Sun, 26 Dec 2010 00:01:01 +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/Dec/26/t12933180615l2ifgh8dikqle7.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:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
10554,27 10532,54 10324,31 10695,25 10827,81 10872,48 10971,19 11145,65 11234,68 11333,88 10997,97 11036,89 11257,35 11533,59 11963,12 12185,15 12377,62 12512,89 12631,48 12268,53 12754,8 13407,75 13480,21 13673,28 13239,71 13557,69 13901,28 13200,58 13406,97 12538,12 12419,57 12193,88 12656,63 12812,48 12056,67 11322,38 11530,75 11114,08 9181,73 8614,55 8595,56 8396,2 7690,5 7235,47 7992,12 8398,37 8593 8679,75 9374,63 9634,97 9857,34 10238,83 10433,44 10471,24 10214,51 10677,52 11052,15 10500,19 10159,27 10222,24 10350,4
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
110554.27NANA178.343706597222NA
210532.54NANA300.574644097223NA
310324.31NANA73.1373524305562NA
410695.25NANA-82.3670225694447NA
510827.81NANA78.6737065972228NA
610872.48NANA-127.888897569444NA
710971.1910555.985269097210906.5383333333-350.553064236112415.204730902778
811145.6510437.252039930610977.54375-540.291710069445708.397960069444
911234.6811041.239852430611087.5379166667-46.2980642361116193.440147569445
1011333.8811512.333602430611217.9008333333294.432769097223-178.453602430558
1110997.9711549.543498263911344.5554166667204.988081597221-551.57349826389
1211036.8911494.729748263911477.4812517.2484982638883-457.83974826389
1311257.3511793.354123263911615.0104166667178.343706597222-536.004123263889
1411533.5912031.550477430611730.9758333333300.574644097223-497.960477430554
1511963.1211914.238185763911841.100833333373.137352430556248.8818142361142
1612185.1511908.483394097211990.8504166667-82.3670225694447276.666605902778
1712377.6212259.362039930612180.688333333378.6737065972228118.257960069444
1812512.8912266.075685763912393.9645833333-127.888897569444246.814314236113
1912631.4812235.859435763912586.4125-350.553064236112395.620564236113
2012268.5312213.056623263912753.3483333333-540.29171006944555.4733767361122
2112754.812872.144435763912918.4425-46.2980642361116-117.344435763889
2213407.7513335.941519097213041.50875294.43276909722371.8084809027769
2313480.2113331.695998263913126.7079166667204.988081597221148.51400173611
2413673.2813187.897248263913170.6487517.2484982638883485.382751736111
2513239.7113341.214123263913162.8704166667178.343706597222-101.504123263889
2613557.6913451.505060763913150.9304166667300.574644097223106.184939236111
2713901.2813216.866935763913143.729583333373.1373524305562684.41306423611
2813200.5813032.469227430613114.83625-82.3670225694447168.110772569446
2913406.9713109.392873263913030.719166666778.6737065972228297.577126736111
3012538.1212745.561935763912873.4508333333-127.888897569444-207.441935763889
3112419.5712353.736935763912704.29-350.55306423611265.8330642361107
3212193.8811990.974539930612531.26625-540.291710069445202.905460069444
3312656.6312186.503185763912232.80125-46.2980642361116470.126814236111
3412812.4812139.501519097211845.06875294.432769097223672.978480902779
3512056.6711658.496831597211453.50875204.988081597221398.17316840278
3611322.3811097.701831597211080.453333333317.2484982638883224.678168402779
3711530.7510889.172456597210710.82875178.343706597222641.577543402782
3811114.0810607.758394097210307.18375300.574644097223506.321605902778
399181.739979.366102430569906.2287573.1373524305562-797.636102430557
408614.559445.585894097229527.95291666667-82.3670225694447-831.035894097224
418595.569278.385789930559199.7120833333378.6737065972228-682.825789930555
428396.28817.394019097228945.28291666667-127.888897569444-421.194019097222
437690.58394.781935763898745.335-350.553064236112-704.281935763887
447235.478053.575373263898593.86708333333-540.291710069445-818.10537326389
457992.128514.089852430568560.38791666667-46.2980642361116-521.969852430555
468398.378950.649435763898656.21666666667294.432769097223-552.279435763888
4785939005.461414930558800.47333333333204.988081597221-412.461414930554
488679.758980.760164930568963.5116666666717.2484982638883-301.010164930556
499374.639333.482456597229155.13875178.34370659722241.1475434027780
509634.979704.299227430569403.72458333333300.574644097223-69.3292274305568
519857.349747.781935763899674.6445833333373.1373524305562109.558064236111
5210238.839807.354644097229889.72166666667-82.3670225694447431.475355902778
5310433.4410121.232456597210042.5587578.6737065972228312.207543402779
5410471.2410044.201519097210172.0904166667-127.888897569444427.038480902778
5510214.519926.4648524305610277.0179166667-350.553064236112288.045147569444
5610677.52NANA-540.291710069445NA
5711052.15NANA-46.2980642361116NA
5810500.19NANA294.432769097223NA
5910159.27NANA204.988081597221NA
6010222.24NANA17.2484982638883NA
6110350.4NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/26/t12933180615l2ifgh8dikqle7/1k2b81293318170.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t12933180615l2ifgh8dikqle7/1k2b81293318170.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t12933180615l2ifgh8dikqle7/2k2b81293318170.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t12933180615l2ifgh8dikqle7/2k2b81293318170.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t12933180615l2ifgh8dikqle7/3dctt1293318170.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t12933180615l2ifgh8dikqle7/3dctt1293318170.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t12933180615l2ifgh8dikqle7/4dctt1293318170.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t12933180615l2ifgh8dikqle7/4dctt1293318170.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')
 





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