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Data X:
8.2 3.7 5.1 6.8 4.9 8.5 4.3 5 5.7 4.9 4.3 5.3 7.9 8.2 4 3.9 8.9 4.5 4 4.5 7.4 9.2 4.6 5.4 4.8 3 4.1 8.8 4.7 6.4 3.6 4.3 7.1 3.5 3.5 6.8 6 9 4.5 4.5 4.7 3.3 4.7 8.5 4.3 6.5 9.5 3.6 5.7 2 4.2 8.9 2.3 6.9 2.5 2.1 6.3 3.7 6.3 6.9 3.6 6.2 4.8 4.3 7 4.6 6.1 9.3 5.9 5.8 4.4 4.4 5.5 4.4 5.8 8.4 5.7 6.4 5.3 4.1 7.4 4 3.7 6.8 6.8 8.7 7.5 3.8 6 3.2 4.9 8.2 3.9 6.1 5.9 3 8.4 4.4 4.5 7.6 6.9 9.5 5.3 5.1 7.6 4.2 2.6 7.1 8.4 9.2 3 4.5 8 5.2 6.2 8.8 6.8 6.3 5.4 4.8 6.6 4.5 3.9 4.9 7.8 8.7 5 4.3 6.4 4.5 6.2 6.2 5.5 5.7 5.4 4.2 7.4 4.8 5.8 8.4 6.4 5.9 6.3 5.7 6.8 4.5 6 9.1 5.7 5.6 6.1 5 7.6 4.4 6.1 8.4 5.3 9.1 6.7 4.5 5.4 3.3 4.9 8.4 4.3 5.2 4.6 3.3 9.9 4.3 3 4.5 8.3 9.6 6.5 4.3 7 4 3.4 3.7 7.3 8.6 6 4.8 8.6 4.5 4.4 6.2 7.2 9.3 4.2 6.7 4.8 4 5.3 8 5.3 6 3.9 4.7 6.6 3.9 6.6 7.1 3.9 6.4 3.7 5.6 6.3 4.4 3.8 4.8 7.6 8.5 6.7 5.3 5.4 3.7 5.2 9 4.8 7 5.9 4.3 6.3 4.4 3.8 4.8 7.6 8.5 6 5.7 5.4 3.5 5.5 7.7 4.2 7.6 7.2 4.7 6.1 3.3 2.7 5.2 6.4 6.9 3.3 3.7 6.4 3 3.5 6.6 5.1 8.1 6.1 3 5.4 3.4 4.5 9.2 5.1 6.7 4.2 3.5 7.3 4.2 6.6 8.7 4.6 8 3.8 4.7 6.3 3.5 4.3 8.4 5.4 6.7 6 2.5 5.4 2.5 2.9 5.6 6.1 8.7 6.5 3.1 7.1 3.5 3.5 6.8 6 9 4.3 3.9 8.7 4.9 4.6 7.7 7.7 9.6 4.4 5.2 7.6 4.5 6.9 9 4.9 8.2 7.1 4.7 6 3.2 4.9 8.2 3.9 6.1 6.8 4.5 7 3.9 5.8 9.1 4.6 8.3 1.7 4.6 7.6 4.1 4.5 8.5 6.5 9.4 6.2 4.1 8.9 4.3 4.6 7.4 6.6 9.3 4.1 4.6 7.6 4.5 6.3 5.9 5.4 5.1 5.2 4.9 5.5 4.7 4.2 5.2 7.7 8 3.9 4.3 7.4 4.8 5.8 8.4 6.4 5.9 5.1 5.2 7.1 3.5 4 3.8 5.4 10 3.7 5 7.6 5.2 7.3 8.2 5.7 5.7 4.8 6.5 8.7 3.9 3.4 6.8 7 9.9 7.2 4.5 8.6 4.3 4.2 4.7 6.9 7.9 3.6 4.1 5.4 2.8 3.6 7.2 4.7 6.7 5.3 4 5.7 4.9 4.3 5.3 7.9 8.2 5 4.5 8.7 4.6 4.6 6.3 7.3 9.4 9.2 4.7 6.1 3.3 2.7 5.2 6.4 6.9 4.4 3.2 7.3 4.2 6.6 8.7 4.6 8 4.2 4.9 7.7 3.4 3.2 7.4 6.4 9.3 5.9 4.1 9 5.5 6.5 9.6 7.2 7.4 7.4 5.7 8.2 4 3.9 4.4 6.6 7.6 6.4 4.6 7.1 3.5 4 3.8 5.4 10 4.5 3.7 7.9 4 4.9 5.4 5.8 9.9 7 5.6 6.6 4.5 3.9 4.9 7.8 8.7 4.5 5.4 8 3.6 5 6.7 4.7 8.4 4.2 2.7 6.3 2.9 3.7 5.8 4.7 8.8 7.2 4.4 6 2.6 3.1 6.2 4.7 7.7 4.7 3.3 5.4 2.8 3.6 7.2 4.7 6.6 3.9 3.5 7.6 5.2 7.3 8.2 5.7 5.7 5 4.7 6.4 4.5 6.2 6.2 5.5 5.7 6.4 5 6.1 4.3 5.9 6 5.3 5.5 2.5 4.5 5.2 3.4 5.4 7.6 4.1 7.5 5.2 4 6.6 3.9 6.6 7.1 3.9 6.4 5.5 4.7 7.6 4.4 6.1 8.4 5.3 9.1 5.7 5.4 5.8 3.1 2.6 5 6.3 6.7 2.5 2.9 7.9 4.6 5.6 8.7 6.3 6.5 6.3 4.6 8.6 3.9 3.4 6.8 7 9.9 4.6 4.1 8.2 3.7 5.1 6.8 4.9 8.5 3.6 4.4 7.1 3.8 4.3 4.9 5.9 9.9 7.6 3.1 6.4 3.9 5.8 7.4 4.6 7.6 6.6 4.5 7.6 4.1 4.5 8.5 6.5 9.4 2.4 4.3 8.9 4.6 4.1 4.6 7.5 9.3 3.1 5.2 5.7 2.7 3.1 7.8 5 7.1 3.5 2.6 7.1 3.8 4.3 4.9 5.9 9.9 6.9 3.2 7.4 4 3.7 6.8 6.8 8.7 5.1 4.3 6.6 3 3 6.3 5.6 8.6 4 2.7 5 1.6 3.7 8.4 2.9 6.4 6.5 2 8.2 4.3 3.9 5.9 7.2 7.7 4.1 4.7 5.2 3.4 5.4 7.6 4.1 7.5 2.8 3.4 5.2 3.1 4.8 8.2 4.2 5 7.6 2.4 8.2 4.3 3.9 5.9 7.2 7.7 7.7 5.1 7.3 3.9 4.3 8.3 6.2 9.1 4.1 4.6 8.2 4.9 6.7 6.3 5.7 5.5 4.9 5.5 7.4 3.3 3 7.3 6.3 9.1 4.6 4.4 4.8 2.4 4 9.9 3.3 7.1 3.5 2 7.6 4.2 2.6 7.1 8.4 9.2 6.6 4.4 8.9 4.6 4.1 4.6 7.5 9.3 4.9 4.8 7.7 3.4 3.2 7.4 6.4 9.3 4.8 3.6 7.3 3.6 3.6 6.7 6 8.6 3.6 4.9 6.3 3.7 5.6 7.2 4.4 7.4 6.4 4.2 5.4 2.5 2.9 5.6 6.1 8.7 4.3 3.1 6.4 3.9 4.9 7.9 5.3 7.8 5.7 4.3 6.4 3.5 5.4 9.7 4.2 7.9 5.8 3.4 5.4 3.5 5.5 7.7 4.2 7.6 5.1 3.1 8.7 4.2 4.6 7.3 6.5 9.2 8.6 5.1 6.1 3.7 4.7 7.7 5.2 7.7 5.4 4 8.4 4.4 4.5 7.6 6.9 9.5 4.4 5.6 7.9 4.6 5.6 8.7 6.3 6.5 6.9 5 7 3.9 5.8 9.1 4.6 8.3 5.2 4.2 8.7 4.9 4.6 7.7 7.7 9.6 5.5 4.4 7.9 5.4 7.5 8.4 5.9 5.9 5.3 5.8 7.1 4.2 3.5 3.8 7.4 8.7 5.7 4.6 5.8 3.1 2.6 5 6.3 6.7 6.5 3.8 8.4 4.1 3.4 6.7 7.5 9.7 5.2 3.7 7.1 3.9 2.3 6.7 8.1 8.8 2.7 4 7.6 4.5 6.9 9 4.9 8.2 4.3 4.5 7.3 4.2 5.9 8.2 5.1 8.9 6.7 4.2 8 3.6 5 6.7 4.7 8.4 6.6 4 6.1 3.7 4.7 7.7 5.2 7.7 7.4 5.1 8.7 4.2 4.6 7.3 6.5 9.2 8.9 4.2 5.8 2.9 3.3 8 5.2 7.3 3.7 2.8 6.4 3.1 3.9 6 4.8 9 4.9 3.3 6.4 3 3.5 6.6 5.1 8.1 6.2 2.6 9 5.5 6.5 9.6 7.2 7.4 4.3 5.7 6.4 3.5 5.4 9.7 4.2 7.9 4.6 4.8 6 2.6 3.1 6.2 4.7 7.7 4.3 3.2 8.7 4.6 4.6 6.3 7.3 9.4 5.4 5.8 5 2.5 4.1 10 3.4 7.2 3.6 3.2 7.4 3.1 2.9 5.3 6.1 8.3 7.4 4.1 8.6 4.3 4.2 4.7 6.9 7.9 6.7 4.6 5.8 2.9 3.3 8 5.2 7.3 2.9 3.3 9.8 4.3 3 4.5 8.2 9.6 4.8 4.4 4.8 2.1 2.5 5.2 5.7 8.3 2.8 1.2 7 4 3.4 3.7 7.3 8.6 5.2 5 5.5 4.7 4.2 5.2 7.7 8 6.8 4.6 5 1.6 3.7 8.4 2.9 6.4 7 2.4 6 3.3 4.1 8.2 5.3 6.6 2.9 4.3 8 4.2 3.8 5.8 7.1 7.6 6.2 3.6 7.9 4.4 3.7 7.6 7.8 9.4 6 5.1 4.8 2.1 2.5 5.2 5.7 8.3 4.2 1.8 6.4 3.9 4.9 7.9 5.3 7.8 5.2 4.1 4.8 2.4 4 9.9 3.3 7.1 3.1 2.8 6.4 3.9 5.8 7.4 4.6 7.6 5.3 4.4 6.8 4.5 6 9.1 5.7 5.6 6 4.5 7.9 4 4.9 5.4 5.8 9.9 6.8 4 8.9 4.5 4 4.5 7.4 9.2 6.1 4.2 7.4 4.2 3.4 7.3 7.5 9.1 5.2 4.5 7 3.5 4 3.8 5.4 9.9 8 3.8 7 3.5 4 3.8 5.4 9.9 6.2 4.1 6 3.3 4.1 8.2 5.3 6.6 3.5 4.6 7.4 3.3 3 7.3 6.3 9.1 6.5 3.7 7.6 4.5 6.3 5.9 5.4 5.1 3.9 5.1 4.8 4 5.3 8 5.3 6 3.6 4.3 7.3 4.2 5.9 8.2 5.1 8.9 3.8 5 6.3 3.7 6.3 6.9 3.6 6.2 4.7 4 5 2.5 4.1 10 3.4 7.2 2.9 3 7.1 3.9 2.3 6.7 8.1 8.8 5.6 4.1 6.3 3.4 5.1 8.4 4.1 6.3 4.2 4.4 6.8 3.6 4.1 4.8 5.7 9.7 1.6 4 5.2 3.1 4.8 8.2 4.2 5 4 3.7 6.3 3.7 5.6 7.2 4.4 7.4 5.1 4 6.1 4.3 5.9 6 5.3 5.5 6 4.3 7.3 3.9 4.3 8.3 6.2 9.1 6.1 4.6 5.4 3.4 4.5 9.2 5.1 6.7 5.6 3.7 8 5.2 6.2 8.8 6.8 6.3 6.5 6.4 7.4 3.1 2.9 5.3 6.1 8.3 5.5 3.6 7.3 3 2.8 5.2 6 8.2 5.9 4.7 7.3 3 2.8 5.2 6 8.2 6.2 4 6.4 3.1 3.9 6 4.8 9 5.6 4.3 5.7 2.7 3.1 7.8 5 7.1 7.2 3.6 5.7 2 4.2 8.9 2.3 6.9 3.4 2.7 6.6 3 3 6.3 5.6 8.6 5.1 4 6.3 3.5 4.3 8.4 5.4 6.7 4 3.8 5.4 3.7 5.2 9 4.8 7 5.3 3.3 7.4 3.8 4.7 5.2 5.6 9.7 8.4 4.5 8.6 3.9 3.4 6.8 7 9.9 8 5 7.3 3.6 3.6 6.7 6 8.6 2.8 4.8 6.3 3.4 5.1 8.4 4.1 6.3 2.4 2.8 8.7 3.9 3.4 6.8 7 9.9 5.2 4.3 8.6 4.5 4.4 6.2 7.2 9.3 4.1 4 8.4 4.1 3.4 6.7 7.5 9.7 6.1 4.9 7.4 3.8 4.7 5.2 5.6 9.7 7.1 4.6 9.9 4.3 3 4.5 8.3 9.6 6.2 4 8 4.2 3.8 5.8 7.1 7.6 5.5 4.4 7.9 4.4 3.7 7.6 7.8 9.4 6.5 4.7 9.8 4.3 3 4.5 8.2 9.6 5.6 4.6 8.9 4.3 4.6 7.4 6.6 9.3 5.7 4.4 6.8 3.6 4.1 4.8 5.7 9.7 6.3 4.7 7.4 4.2 3.4 7.3 7.5 9.1 5.1 6 4.7 3.3 4.7 8.5 4.3 6.5 4.8 4.3 5.4 2.8 3.6 7.2 4.7 6.6 4.8 3.2 7 4.6 6.1 9.3 5.9 5.8 3.4 5.9 7.1 4.2 3.5 3.8 7.4 8.7 3.6 5.5 6.3 2.9 3.7 5.8 4.7 8.8 5.8 3.8 5.5 4.4 5.8 8.4 5.7 6.4 5 4 5.4 2.8 3.6 7.2 4.7 6.7 5 2.9 5.4 3.3 4.9 8.4 4.3 5.2 3.6 4.3 4.8 3 4.1 8.8 4.7 6.4 7 3.6 8.2 4 3.9 4.4 6.6 7.6 6.8 4.4 7.9 5.4 7.5 8.4 5.9 5.9 6.6 6 8.6 4.2 3.5 6.8 7.6 9.7 5.2 4.4 8.2 4.9 6.7 6.3 5.7 5.5 5.3 5.9 8.6 4.2 3.5 6.8 7.6 9.7 1.2 4.3
Names of X columns:
Klantentevredenheid Leveringssnelheid Prijsflexibiliteit Prijszetting Productgamma Productkwaliteit Productontwikkeling Facturatie
Factor 1
Factor 2
Type of test to use
(?)
Exact Pearson Chi-Squared by Simulation
Pearson Chi-Squared
Exact Pearson Chi-Squared by Simulation
McNemar Chi-Squared
Fisher Exact Test
Chart options
Title:
R Code
library(vcd) cat1 <- as.numeric(par1) # cat2<- as.numeric(par2) # simulate.p.value=FALSE if (par3 == 'Exact Pearson Chi-Squared by Simulation') simulate.p.value=TRUE x <- t(x) (z <- array(unlist(x),dim=c(length(x[,1]),length(x[1,])))) (table1 <- table(z[,cat1],z[,cat2])) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) bitmap(file='pic1.png') assoc(ftable(z[,cat1],z[,cat2],row.vars=1,dnn=c(V1,V2)),shade=T) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tabulation of Results',ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste(V1,' x ', V2),ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ', 1,TRUE) for(nc in 1:ncol(table1)){ a<-table.element(a, colnames(table1)[nc], 1, TRUE) } a<-table.row.end(a) for(nr in 1:nrow(table1) ){ a<-table.element(a, rownames(table1)[nr], 1, TRUE) for(nc in 1:ncol(table1) ){ a<-table.element(a, table1[nr, nc], 1, FALSE) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') (cst<-chisq.test(table1, simulate.p.value=simulate.p.value) ) if (par3 == 'McNemar Chi-Squared') { (cst <- mcnemar.test(table1)) } if (par3=='Fisher Exact Test') { (cst <- fisher.test(table1)) } if ((par3 != 'McNemar Chi-Squared') & (par3 != 'Fisher Exact Test')) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tabulation of Expected Results',ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste(V1,' x ', V2),ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ', 1,TRUE) for(nc in 1:ncol(table1)){ a<-table.element(a, colnames(table1)[nc], 1, TRUE) } a<-table.row.end(a) for(nr in 1:nrow(table1) ){ a<-table.element(a, rownames(table1)[nr], 1, TRUE) for(nc in 1:ncol(table1) ){ a<-table.element(a, round(cst$expected[nr, nc], digits=2), 1, FALSE) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab') } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Statistical Results',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, cst$method, 2,TRUE) a<-table.row.end(a) a<-table.row.start(a) if (par3=='Pearson Chi-Squared') a<-table.element(a, 'Pearson Chi Square Statistic', 1, TRUE) if (par3=='Exact Pearson Chi-Squared by Simulation') a<-table.element(a, 'Exact Pearson Chi Square Statistic', 1, TRUE) if (par3=='McNemar Chi-Squared') a<-table.element(a, 'McNemar Chi Square Statistic', 1, TRUE) if (par3=='Fisher Exact Test') a<-table.element(a, 'Odds Ratio', 1, TRUE) if (par3=='Fisher Exact Test') { if ((ncol(table1) == 2) & (nrow(table1) == 2)) { a<-table.element(a, round(cst$estimate, digits=2), 1,FALSE) } else { a<-table.element(a, '--', 1,FALSE) } } else { a<-table.element(a, round(cst$statistic, digits=2), 1,FALSE) } a<-table.row.end(a) if(!simulate.p.value){ if(par3!='Fisher Exact Test') { a<-table.row.start(a) a<-table.element(a, 'Degrees of Freedom', 1, TRUE) a<-table.element(a, cst$parameter, 1,FALSE) a<-table.row.end(a) } } a<-table.row.start(a) a<-table.element(a, 'P value', 1, TRUE) a<-table.element(a, round(cst$p.value, digits=2), 1,FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable2.tab')
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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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