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Data X:
89.28 89.47 89.53 90.72 90.91 91.38 91.49 90.9 90.93 90.57 91.28 90.83 91.5 91.58 92.49 94.16 95.46 95.8 95.32 95.41 95.35 95.68 95.59 94.96 96.92 96.06 96.59 96.67 97.27 96.38 96.47 96.05 96.76 96.51 96.55 95.97 97 97.46 97.9 98.42 98.54 99 98.94 99.02 100.07 98.72 98.73 98.04 99.08 99.22 99.57 100.44 100.84 100.75 100.49 99.98 99.96 99.76 100.11 99.79 100.29 101.12 102.65 102.71 103.39 102.8 102.07 102.15 101.21 101.27 101.86 101.65 101.94 102.62 102.71 103.39 104.51 104.09 104.29 104.57 105.39 105.15 106.13 105.46 106.47 106.62 106.52 108.04 107.15 107.32 107.76 107.26 107.89
Data Y:
1.79 1.95 2.26 2.04 2.16 2.75 2.79 2.88 3.36 2.97 3.1 2.49 2.2 2.25 2.09 2.79 3.14 2.93 2.65 2.67 2.26 2.35 2.13 2.18 2.9 2.63 2.67 1.81 1.33 0.88 1.28 1.26 1.26 1.29 1.1 1.37 1.21 1.74 1.76 1.48 1.04 1.62 1.49 1.79 1.8 1.58 1.86 1.74 1.59 1.26 1.13 1.92 2.61 2.26 2.41 2.26 2.03 2.86 2.55 2.27 2.26 2.57 3.07 2.76 2.51 2.87 3.14 3.11 3.16 2.47 2.57 2.89 2.63 2.38 1.69 1.96 2.19 1.87 1.6 1.63 1.22 1.21 1.49 1.64 1.66 1.77 1.82 1.78 1.28 1.29 1.37 1.12 1.51
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R Code
bitmap(file='test1.png') histx <- hist(x, plot=FALSE) histy <- hist(y, plot=FALSE) maxcounts <- max(c(histx$counts, histx$counts)) xrange <- c(min(x),max(x)) yrange <- c(min(y),max(y)) nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE) par(mar=c(4,4,1,1)) plot(x, y, xlim=xrange, ylim=yrange, xlab=xlab, ylab=ylab) par(mar=c(0,4,1,1)) barplot(histx$counts, axes=FALSE, ylim=c(0, maxcounts), space=0) par(mar=c(4,0,1,1)) barplot(histy$counts, axes=FALSE, xlim=c(0, maxcounts), space=0, horiz=TRUE) dev.off() lx = length(x) makebiased = (lx-1)/lx varx = var(x)*makebiased vary = var(y)*makebiased corxy <- cor.test(x,y,method='pearson') cxy <- as.matrix(corxy$estimate)[1,1] load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Pearson Product Moment Correlation - Ungrouped Data',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Statistic',1,TRUE) a<-table.element(a,'Variable X',1,TRUE) a<-table.element(a,'Variable Y',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm','Mean',''),header=TRUE) a<-table.element(a,mean(x)) a<-table.element(a,mean(y)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/biased.htm','Biased Variance',''),header=TRUE) a<-table.element(a,varx) a<-table.element(a,vary) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/biased1.htm','Biased Standard Deviation',''),header=TRUE) a<-table.element(a,sqrt(varx)) a<-table.element(a,sqrt(vary)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/covariance.htm','Covariance',''),header=TRUE) a<-table.element(a,cov(x,y),2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/pearson_correlation.htm','Correlation',''),header=TRUE) a<-table.element(a,cxy,2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/coeff_of_determination.htm','Determination',''),header=TRUE) a<-table.element(a,cxy*cxy,2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ttest_statistic.htm','T-Test',''),header=TRUE) a<-table.element(a,as.matrix(corxy$statistic)[1,1],2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value (2 sided)',header=TRUE) a<-table.element(a,(p2 <- as.matrix(corxy$p.value)[1,1]),2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value (1 sided)',header=TRUE) a<-table.element(a,p2/2,2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Degrees of Freedom',header=TRUE) a<-table.element(a,lx-2,2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of Observations',header=TRUE) a<-table.element(a,lx,2) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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