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Data X:
'Average' 'Data' 'Female' 'Average' 'Data' 'Female' 'Low' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Female' 'Low' 'Data' 'Male' 'High' 'Data' 'Female' 'Average' 'Data' 'Female' 'Average' 'Data' 'Male' 'Average' 'Data' 'Female' 'Average' 'Data' 'Female' 'Average' 'Data' 'Male' 'Low' 'Data' 'Female' 'Average' 'Data' 'Male' 'Low' 'Data' 'Male' 'Low' 'Data' 'Female' 'Low' 'Data' 'Female' 'Average' 'Data' 'Female' 'Average' 'Data' 'Female' 'Average' 'Data' 'Female' 'Average' 'Data' 'Female' 'Average' 'Data' 'Female' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Female' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Low' 'Data' 'Male' 'Average' 'Data' 'Female' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Female' 'Average' 'Data' 'Female' 'Average' 'Data' 'Male' 'Average' 'Data' 'Female' 'Average' 'Data' 'Male' 'Low' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Low' 'Data' 'Male' 'Average' 'Data' 'Female' 'Average' 'Data' 'Female' 'Low' 'Data' 'Male' 'Low' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Female' 'Average' 'Data' 'Male' 'High' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Female' 'Low' 'Data' 'Female' 'Low' 'Data' 'Male' 'Average' 'Data' 'Female' 'Average' 'Data' 'Male' 'Average' 'Data' 'Female' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Data' 'Male' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Low' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Female' 'High' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'High' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'High' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'High' 'Blog' 'Female' 'Average' 'Blog' 'Female' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male' 'Average' 'Blog' 'Male'
Names of X columns:
Score Treatment Gender
Factor 1


Factor 2


Type of test to use (?)


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')

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center

Tabulation of Expected Results
Score x Treatment
	Blog	Data
Average	42.21	53.79
High	2.64	3.36
Low	6.16	7.84

Tabulation of Results
Score x Treatment
	Blog	Data
Average	46	50
High	4	2
Low	1	13

Statistical Results
Pearson's Chi-squared test
Pearson Chi Square Statistic	9.57
Degrees of Freedom	2
P value	0.01