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Pearson verband huwelijken

*The author of this computation has been verified*
R Software Module: /rwasp_pairs.wasp (opens new window with default values)
Title produced by software: Kendall tau Correlation Matrix
Date of computation: Tue, 21 Dec 2010 16:00:13 +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/21/t1292947097mww3lr964kvgkqy.htm/, Retrieved Tue, 21 Dec 2010 16:58:19 +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/21/t1292947097mww3lr964kvgkqy.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 «
3111 5140 17153 2.5 766 332 2.4 3995 4749 15579 1.8 294 369 2.4 5245 3635 16755 7.3 235 384 2.4 5588 4305 16585 9.9 462 373 2.1 10681 5805 16572 13.2 919 378 2 10516 4260 16325 17.8 346 426 2 7496 3869 17913 18.8 298 423 2.1 9935 7325 17572 19.3 92 397 2.1 10249 9280 17338 13.9 516 422 2 6271 6222 17087 7.5 843 409 2 3616 3272 15864 8 395 430 2 3724 7598 15554 4 961 412 1.7 2886 1345 16229 3.6 1231 470 1.3 3318 1900 15180 4.8 794 491 1.2 4166 1480 16215 5.9 420 504 1.1 6401 1472 15801 10.4 331 484 1.4 9209 3823 15751 12.3 312 474 1.5 9820 4454 16477 15.5 692 508 1.4 7470 3357 17324 16.7 1221 492 1.1 8207 5393 16919 18.8 1272 452 1.1 9564 8329 16438 15.2 622 457 1 5309 4152 16239 11.3 479 457 1.4 3385 4042 15613 6.3 757 471 1.3 3706 7747 15821 3.2 463 451 1.2 2733 1451 15678 5.3 534 493 1.5 3045 911 14671 2.4 731 514 1.6 3449 406 15876 6.5 498 522 1.8 5542 1387 15563 10.4 629 490 1.5 10072 2150 15711 12.6 542 484 1.3 9418 1577 15583 16.8 519 506 1.6 7516 2642 16405 17.7 1585 501 1 etc...
 
Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Correlations for all pairs of data series (method=pearson)
HuwelijkenBevolkingsgroeiGeborenenTemperatuurNeerslagWerkloosheidInflatie
Huwelijken10.4340.4790.83-0.015-0.0510.006
Bevolkingsgroei0.43410.2910.2610.004-0.4520.109
Geborenen0.4790.29110.520.0920.1950.041
Temperatuur0.830.2610.5210.091-0.019-0.077
Neerslag-0.0150.0040.0920.0911-0.024-0.016
Werkloosheid-0.051-0.4520.195-0.019-0.0241-0.472
Inflatie0.0060.1090.041-0.077-0.016-0.4721


Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Huwelijken;Bevolkingsgroei0.43380.41770.2943
p-value(0)(1e-04)(1e-04)
Huwelijken;Geborenen0.4790.51970.3609
p-value(0)(0)(0)
Huwelijken;Temperatuur0.83020.82690.5936
p-value(0)(0)(0)
Huwelijken;Neerslag-0.015-0.084-0.0559
p-value(0.8921)(0.4475)(0.4513)
Huwelijken;Werkloosheid-0.0507-0.0396-0.0227
p-value(0.6473)(0.7208)(0.7602)
Huwelijken;Inflatie0.0058-0.0323-0.0313
p-value(0.9582)(0.7707)(0.6813)
Bevolkingsgroei;Geborenen0.2910.31060.2071
p-value(0.0073)(0.0042)(0.0053)
Bevolkingsgroei;Temperatuur0.26120.26050.1752
p-value(0.0164)(0.0167)(0.0186)
Bevolkingsgroei;Neerslag0.00410.0020.0014
p-value(0.9706)(0.9853)(0.9846)
Bevolkingsgroei;Werkloosheid-0.4522-0.4795-0.3164
p-value(0)(0)(0)
Bevolkingsgroei;Inflatie0.10860.15920.1375
p-value(0.3254)(0.1479)(0.071)
Geborenen;Temperatuur0.52040.54010.3801
p-value(0)(0)(0)
Geborenen;Neerslag0.09210.0660.0416
p-value(0.4046)(0.5508)(0.5754)
Geborenen;Werkloosheid0.19540.21060.1359
p-value(0.0749)(0.0545)(0.0676)
Geborenen;Inflatie0.04080.0037-0.0153
p-value(0.7125)(0.9732)(0.8403)
Temperatuur;Neerslag0.09070.02990.0213
p-value(0.4119)(0.7874)(0.7749)
Temperatuur;Werkloosheid-0.0187-0.0213-0.0138
p-value(0.8663)(0.8475)(0.8529)
Temperatuur;Inflatie-0.0766-0.0904-0.0645
p-value(0.4885)(0.4137)(0.3982)
Neerslag;Werkloosheid-0.0238-0.0143-0.0063
p-value(0.8296)(0.8972)(0.9323)
Neerslag;Inflatie-0.0159-0.1178-0.0823
p-value(0.8858)(0.2857)(0.2796)
Werkloosheid;Inflatie-0.4719-0.4488-0.3056
p-value(0)(0)(1e-04)
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292947097mww3lr964kvgkqy/1egar1292947210.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292947097mww3lr964kvgkqy/1egar1292947210.ps (open in new window)


 
Parameters (Session):
par1 = pearson ;
 
Parameters (R input):
par1 = pearson ;
 
R code (references can be found in the software module):
panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method=par1)
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
cor.test(y[1,],y[2,],method=par1)
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
a<-table.element(a,dum,header=TRUE)
rp <- cor.test(y[i,],y[j,],method='pearson')
a<-table.element(a,round(rp$estimate,4))
rs <- cor.test(y[i,],y[j,],method='spearman')
a<-table.element(a,round(rs$estimate,4))
rk <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,round(rk$estimate,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=T)
a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 





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