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Data X:
0.56 12.9 0.79 7.4 0.68 12.2 0.66 12.8 0.37 7.4 0.71 6.7 0.35 12.6 0.55 14.8 0.76 13.3 0.49 11.1 0.56 8.2 0.60 11.4 0.44 6.4 0.55 10.6 0.58 12.0 0.40 6.3 0.42 11.3 0.58 11.9 0.64 9.3 0.58 9.6 0.44 10.0 0.46 6.4 0.64 13.8 0.59 10.8 0.54 13.8 0.71 11.7 0.20 10.9 0.89 16.1 0.55 13.4 0.71 9.9 0.48 11.5 0.49 8.3 0.58 11.7 0.78 6.1 0.71 9.0 0.51 9.7 0.65 10.8 0.68 10.3 0.24 10.4 0.36 12.7 0.65 9.3 0.79 11.8 0.67 5.9 0.74 11.4 0.72 13.0 0.73 10.8 0.58 12.3 0.67 11.3 0.43 11.8 0.59 7.9 0.43 12.7 0.80 12.3 0.74 11.6 0.43 6.7 0.72 10.9 0.50 12.1 0.45 13.3 0.88 10.1 0.67 5.7 0.32 14.3 0.20 8.0 0.84 13.3 0.83 9.3 0.65 12.5 0.74 7.6 0.53 15.9 0.58 9.2 0.65 9.1 0.64 11.1 0.60 13.0 0.52 14.5 0.53 12.2 0.73 12.3 0.52 11.4 0.61 8.8 0.73 14.6 0.79 7.3 0.29 12.6 0.86 NA 0.37 13.0 0.68 12.6 0.52 13.2 0.26 9.9 0.74 7.7 0.72 10.5 0.24 13.4 0.71 10.9 0.59 4.3 0.27 10.3 0.57 11.8 0.51 11.2 0.69 11.4 0.69 8.6 0.50 13.2 0.63 12.6 0.65 5.6 0.54 9.9 0.69 8.8 0.52 7.7 0.53 9.0 0.74 7.3 0.73 11.4 0.75 13.6 0.70 7.9 0.69 10.7 0.57 10.3 0.14 8.3 0.42 9.6 0.48 14.2 0.27 8.5 0.21 13.5 0.41 4.9 0.56 6.4 0.44 9.6 0.52 11.6 0.59 11.1 0.73 4.35 0.79 12.7 0.67 18.1 0.88 17.85 0.96 16.6 0.43 12.6 0.84 17.1 0.81 19.1 0.67 16.1 0.45 13.35 0.58 18.4 0.70 14.7 0.61 10.6 0.44 12.6 0.54 16.2 0.41 13.6 0.66 18.9 0.83 14.1 0.88 14.5 0.40 16.15 0.54 14.75 0.60 14.8 0.57 12.45 0.59 12.65 0.81 17.35 0.51 8.6 0.65 18.4 0.59 16.1 0.68 11.6 0.65 17.75 0.06 15.25 0.74 17.65 0.29 15.6 0.73 16.35 0.54 17.65 0.39 13.6 0.27 11.7 0.40 14.35 0.20 14.75 0.85 18.25 0.42 9.9 0.68 16 0.72 18.25 0.52 16.85 0.78 14.6 0.60 13.85 0.93 18.95 0.73 15.6 0.81 14.85 0.51 11.75 0.86 18.45 0.67 15.9 0.50 17.1 0.74 16.1 0.85 19.9 0.75 10.95 0.83 18.45 0.82 15.1 0.58 15 0.72 11.35 0.89 15.95 0.51 18.1 0.75 14.6 0.84 15.4 0.84 15.4 0.59 17.6 0.64 13.35 0.45 19.1 0.57 15.35 0.58 7.6 0.72 13.4 0.38 13.9 0.68 19.1 0.45 15.25 0.55 12.9 0.73 16.1 0.73 17.35 0.73 13.15 0.71 12.15 0.38 12.6 0.79 10.35 0.32 15.4 0.62 9.6 0.42 18.2 0.45 13.6 0.97 14.85 0.67 14.75 0.08 14.1 0.49 14.9 0.66 16.25 0.67 19.25 0.55 13.6 0.55 13.6 0.49 15.65 0.56 12.75 0.69 14.6 0.47 9.85 0.68 12.65 0.43 11.9 0.00 19.2 0.48 16.6 0.77 11.2 0.71 15.25 0.43 11.9 0.50 13.2 0.68 16.35 0.34 12.4 0.47 15.85 0.33 14.35 0.80 18.15 0.74 11.15 0.82 15.65 0.57 17.75 0.46 7.65 0.91 12.35 0.41 15.6 0.64 19.3 0.58 15.2 0.45 17.1 0.77 15.6 0.67 18.4 0.53 19.05 0.07 18.55 0.65 19.1 0.76 13.1 0.56 12.85 0.07 9.5 0.72 4.5 0.61 11.85 0.47 13.6 0.06 11.7 0.37 12.4 0.76 13.35 0.47 11.4 0.55 14.9 0.85 19.9 0.77 17.75 0.79 11.2 0.70 14.6 0.46 17.6 0.51 14.05 0.65 16.1 0.57 13.35 0.68 11.85 0.52 11.95 0.70 14.75 0.46 15.15 0.88 13.2 0.76 16.85 0.74 7.85 0.56 7.7 0.47 12.6 0.44 7.85 0.75 10.95 0.78 12.35 0.26 9.95 0.55 14.9 0.49 16.65 0.81 13.4 0.45 13.95 0.39 15.7 0.89 16.85 0.66 10.95 0.34 15.35 0.84 12.2 0.05 15.1 0.79 17.75 0.60 15.2 0.69 14.6 0.58 16.65 0.66 8.1
Names of X columns:
Totaal TOT
Column number of first sample
Column number of second sample
Confidence
Alternative
two.sided
less
greater
Are observations paired?
unpaired
paired
Null Hypothesis
Chart options
Title:
R Code
par1 <- as.numeric(par1) #column number of first sample par2 <- as.numeric(par2) #column number of second sample par3 <- as.numeric(par3) #confidence (= 1 - alpha) if (par5 == 'unpaired') paired <- FALSE else paired <- TRUE par6 <- as.numeric(par6) #H0 z <- t(y) if (par1 == par2) stop('Please, select two different column numbers') if (par1 < 1) stop('Please, select a column number greater than zero for the first sample') if (par2 < 1) stop('Please, select a column number greater than zero for the second sample') if (par1 > length(z[1,])) stop('The column number for the first sample should be smaller') if (par2 > length(z[1,])) stop('The column number for the second sample should be smaller') if (par3 <= 0) stop('The confidence level should be larger than zero') if (par3 >= 1) stop('The confidence level should be smaller than zero') (r.t <- t.test(z[,par1],z[,par2],var.equal=TRUE,alternative=par4,paired=paired,mu=par6,conf.level=par3)) (v.t <- var.test(z[,par1],z[,par2],conf.level=par3)) (r.w <- t.test(z[,par1],z[,par2],var.equal=FALSE,alternative=par4,paired=paired,mu=par6,conf.level=par3)) (w.t <- wilcox.test(z[,par1],z[,par2],alternative=par4,paired=paired,mu=par6,conf.level=par3)) (ks.t <- ks.test(z[,par1],z[,par2],alternative=par4)) m1 <- mean(z[,par1],na.rm=T) m2 <- mean(z[,par2],na.rm=T) mdiff <- m1 - m2 newsam1 <- z[!is.na(z[,par1]),par1] newsam2 <- z[,par2]+mdiff newsam2 <- newsam2[!is.na(newsam2)] (ks1.t <- ks.test(newsam1,newsam2,alternative=par4)) mydf <- data.frame(cbind(z[,par1],z[,par2])) colnames(mydf) <- c('Variable 1','Variable 2') bitmap(file='test1.png') boxplot(mydf, notch=TRUE, ylab='value',main=main) dev.off() bitmap(file='test2.png') qqnorm(z[,par1],main='Normal QQplot - Variable 1') qqline(z[,par1]) dev.off() bitmap(file='test3.png') qqnorm(z[,par2],main='Normal QQplot - Variable 2') qqline(z[,par2]) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Two Sample t-test (',par5,')',sep=''),2,TRUE) a<-table.row.end(a) if(!paired){ a<-table.row.start(a) a<-table.element(a,'Mean of Sample 1',header=TRUE) a<-table.element(a,r.t$estimate[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Mean of Sample 2',header=TRUE) a<-table.element(a,r.t$estimate[[2]]) a<-table.row.end(a) } else { a<-table.row.start(a) a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE) a<-table.element(a,r.t$estimate) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'t-stat',header=TRUE) a<-table.element(a,r.t$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'df',header=TRUE) a<-table.element(a,r.t$parameter[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,r.t$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'H0 value',header=TRUE) a<-table.element(a,r.t$null.value[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Alternative',header=TRUE) a<-table.element(a,r.t$alternative) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI Level',header=TRUE) a<-table.element(a,attr(r.t$conf.int,'conf.level')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI',header=TRUE) a<-table.element(a,paste('[',r.t$conf.int[1],',',r.t$conf.int[2],']',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'F-test to compare two variances',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'F-stat',header=TRUE) a<-table.element(a,v.t$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'df',header=TRUE) a<-table.element(a,v.t$parameter[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,v.t$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'H0 value',header=TRUE) a<-table.element(a,v.t$null.value[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Alternative',header=TRUE) a<-table.element(a,v.t$alternative) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI Level',header=TRUE) a<-table.element(a,attr(v.t$conf.int,'conf.level')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI',header=TRUE) a<-table.element(a,paste('[',v.t$conf.int[1],',',v.t$conf.int[2],']',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Welch Two Sample t-test (',par5,')',sep=''),2,TRUE) a<-table.row.end(a) if(!paired){ a<-table.row.start(a) a<-table.element(a,'Mean of Sample 1',header=TRUE) a<-table.element(a,r.w$estimate[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Mean of Sample 2',header=TRUE) a<-table.element(a,r.w$estimate[[2]]) a<-table.row.end(a) } else { a<-table.row.start(a) a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE) a<-table.element(a,r.w$estimate) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'t-stat',header=TRUE) a<-table.element(a,r.w$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'df',header=TRUE) a<-table.element(a,r.w$parameter[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,r.w$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'H0 value',header=TRUE) a<-table.element(a,r.w$null.value[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Alternative',header=TRUE) a<-table.element(a,r.w$alternative) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI Level',header=TRUE) a<-table.element(a,attr(r.w$conf.int,'conf.level')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI',header=TRUE) a<-table.element(a,paste('[',r.w$conf.int[1],',',r.w$conf.int[2],']',sep='')) 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,paste('Wicoxon rank sum test with continuity correction (',par5,')',sep=''),2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'W',header=TRUE) a<-table.element(a,w.t$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,w.t$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'H0 value',header=TRUE) a<-table.element(a,w.t$null.value[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Alternative',header=TRUE) a<-table.element(a,w.t$alternative) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Kolmogorov-Smirnov Test to compare <i>Distributions</i> of two Samples',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'KS Statistic',header=TRUE) a<-table.element(a,ks.t$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,ks.t$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Kolmogorov-Smirnov Test to compare <i>Distributional Shape</i> of two Samples',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'KS Statistic',header=TRUE) a<-table.element(a,ks1.t$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,ks1.t$p.value) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable2.tab')
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Big Analytics Cloud Computing Center
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