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Paper Statistiek

*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: Sun, 26 Dec 2010 10:47:22 +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/t12933605979toyj30uhryyyev.htm/, Retrieved Sun, 26 Dec 2010 11:50:02 +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/t12933605979toyj30uhryyyev.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:

Bouwgrondprijzen per kwartaal (1995-2009) : gemiddelde prijs(€/m²)

Dataseries X:

» Textbox « » Textfile « » CSV «

26 26 27 28 27 29 27 30 27 30 32 30 32 33 34 32 34 37 37 36 34 38 41 41 44 42 45 45 49 54 52 53 51 55 60 60 63 60 64 65 75 70 72 69 75 74 74 75 79 79 85 78 84 85 85 82 91 90 98 98

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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	26	NA	NA	0.495535714285714	NA
2	26	NA	NA	0.0223214285714286	NA
3	27	27.5848214285714	26.875	0.709821428571429	-0.584821428571427
4	28	26.1473214285714	27.375	-1.22767857142857	1.85267857142857
5	27	28.2455357142857	27.75	0.495535714285714	-1.24553571428572
6	29	28.0223214285714	28	0.0223214285714286	0.977678571428573
7	27	28.9598214285714	28.25	0.709821428571429	-1.95982142857143
8	30	27.1473214285714	28.375	-1.22767857142857	2.85267857142857
9	27	29.6205357142857	29.125	0.495535714285714	-2.62053571428572
10	30	29.7723214285714	29.75	0.0223214285714286	0.227678571428573
11	32	31.0848214285714	30.375	0.709821428571429	0.915178571428573
12	30	30.1473214285714	31.375	-1.22767857142857	-0.147321428571427
13	32	32.4955357142857	32	0.495535714285714	-0.495535714285715
14	33	32.5223214285714	32.5	0.0223214285714286	0.477678571428569
15	34	33.7098214285714	33	0.709821428571429	0.290178571428569
16	32	32.5223214285714	33.75	-1.22767857142857	-0.522321428571431
17	34	35.1205357142857	34.625	0.495535714285714	-1.12053571428572
18	37	35.5223214285714	35.5	0.0223214285714286	1.47767857142857
19	37	36.7098214285714	36	0.709821428571429	0.290178571428569
20	36	34.8973214285714	36.125	-1.22767857142857	1.10267857142857
21	34	37.2455357142857	36.75	0.495535714285714	-3.24553571428572
22	38	37.8973214285714	37.875	0.0223214285714286	0.102678571428569
23	41	40.4598214285714	39.75	0.709821428571429	0.540178571428569
24	41	40.2723214285714	41.5	-1.22767857142857	0.72767857142857
25	44	42.9955357142857	42.5	0.495535714285714	1.00446428571428
26	42	43.5223214285714	43.5	0.0223214285714286	-1.52232142857143
27	45	45.3348214285714	44.625	0.709821428571429	-0.334821428571431
28	45	45.5223214285714	46.75	-1.22767857142857	-0.522321428571431
29	49	49.6205357142857	49.125	0.495535714285714	-0.620535714285715
30	54	51.0223214285714	51	0.0223214285714286	2.97767857142857
31	52	52.9598214285714	52.25	0.709821428571429	-0.95982142857143
32	53	51.3973214285714	52.625	-1.22767857142857	1.60267857142857
33	51	54.2455357142857	53.75	0.495535714285714	-3.24553571428572
34	55	55.6473214285714	55.625	0.0223214285714286	-0.64732142857143
35	60	58.7098214285714	58	0.709821428571429	1.29017857142857
36	60	58.8973214285714	60.125	-1.22767857142857	1.10267857142857
37	63	61.7455357142857	61.25	0.495535714285714	1.25446428571428
38	60	62.3973214285714	62.375	0.0223214285714286	-2.39732142857143
39	64	65.2098214285714	64.5	0.709821428571429	-1.20982142857143
40	65	66.0223214285714	67.25	-1.22767857142857	-1.02232142857143
41	75	69.9955357142857	69.5	0.495535714285714	5.00446428571429
42	70	71.0223214285714	71	0.0223214285714286	-1.02232142857143
43	72	72.2098214285714	71.5	0.709821428571429	-0.209821428571431
44	69	70.7723214285714	72	-1.22767857142857	-1.77232142857143
45	75	73.2455357142857	72.75	0.495535714285714	1.75446428571429
46	74	73.7723214285714	73.75	0.0223214285714286	0.227678571428569
47	74	75.7098214285714	75	0.709821428571429	-1.70982142857143
48	75	74.8973214285714	76.125	-1.22767857142857	0.102678571428569
49	79	78.6205357142857	78.125	0.495535714285714	0.379464285714292
50	79	79.8973214285714	79.875	0.0223214285714286	-0.89732142857143
51	85	81.5848214285714	80.875	0.709821428571429	3.41517857142857
52	78	81.0223214285714	82.25	-1.22767857142857	-3.02232142857143
53	84	83.4955357142857	83	0.495535714285714	0.504464285714292
54	85	83.5223214285714	83.5	0.0223214285714286	1.47767857142857
55	85	85.5848214285714	84.875	0.709821428571429	-0.58482142857143
56	82	85.1473214285714	86.375	-1.22767857142857	-3.14732142857143
57	91	89.1205357142857	88.625	0.495535714285714	1.87946428571429
58	90	92.2723214285714	92.25	0.0223214285714286	-2.27232142857143
59	98	NA	NA	0.709821428571429	NA
60	98	NA	NA	-1.22767857142857	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t12933605979toyj30uhryyyev/1uwnf1293360439.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t12933605979toyj30uhryyyev/1uwnf1293360439.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t12933605979toyj30uhryyyev/2uwnf1293360439.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t12933605979toyj30uhryyyev/2uwnf1293360439.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t12933605979toyj30uhryyyev/3n5ni1293360439.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t12933605979toyj30uhryyyev/3n5ni1293360439.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t12933605979toyj30uhryyyev/4fxm31293360439.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t12933605979toyj30uhryyyev/4fxm31293360439.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 4 ;

Parameters (R input):

par1 = additive ; par2 = 4 ;

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')

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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