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Data X:
84 64 65 81 60 65 23 24 75 64 74 56 25 60 32 71 62 39 65 58 59 83 43 80 75 19 74 81 33 61 64 73 81 44 65 52 53 41 41 80 12 69 70 66 65 65 84 76 27 66 15 28 69 63 42 20 59 55 73 72 84 63 80 71 24 54 24 59 62 72 82 27 39 48 56 27 51 69 73 59 71 71 42 72 59 68 83 41 19 63 75 68 53 68 46 73 49 72 67 63 55 57 77 71 47 69 18 13 75 66 73 86 16 48 67 63 72 71 65 64 40 36 38 53 72 44 51 55 61 70 15 50 40 57 43 42 62 84 57 62 15 15 87 37 75 64 64 65 70 59 57 59 80 74 68 30 20 29 30 77 82 67 86 65 78
Data Y:
29.7 59.8 35 36.2 70.2 47.9 90.9 82.9 26.5 27.1 55.7 31.3 77.8 57.4 53 57 49.8 78.1 69.7 70.3 47.5 23.3 78.8 35.9 32.8 91.5 15.2 16.1 79.7 33.2 69.6 37.1 33.1 80.7 41.5 65.7 30 75.9 76.9 14.9 92.2 28.7 65.4 62.7 33.6 59.6 19.2 23.7 79.7 33.5 91.4 78 36.1 43.1 55 86.8 66.3 72.9 56 31.4 19.8 66.7 49.1 56.3 61.5 66.3 95.8 72.3 63.9 24.5 40.6 91.5 77.9 77.1 70.2 79.9 39.3 29.4 51.1 38.5 51.1 23.7 73.8 46.3 66.4 53.5 21.9 75 88.1 58.7 52.2 54.9 68.8 54.1 82.1 38.2 82.2 61.9 58.5 65 57.4 49.9 38.5 38.3 62.5 51.8 88.9 92.6 36.3 37.6 44.4 10.8 98.7 30.4 41.7 70.5 60.3 62.4 66 67.1 66.7 78.4 31.9 63.8 29.4 33.5 19.3 61.5 64.1 46.6 63.8 71 75 72.4 80 80.8 61.9 21.5 69.8 30.3 93.9 90.3 14.3 77.3 19.3 54.1 46.3 71.9 31 71.6 64.1 43.7 17.2 52 56.9 27.6 85.3 79.6 83.8 20.1 31.6 30.8 19.5 56.1 31.6
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R Code
library(psychometric) x <- x[!is.na(y)] y <- y[!is.na(y)] y <- y[!is.na(x)] x <- x[!is.na(x)] 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, sub=main) 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', na.rm = T) cxy <- as.matrix(corxy$estimate)[1,1] load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Pearson Product Moment Correlation - Ungrouped Data',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Statistic',1,TRUE) a<-table.element(a,'Variable X',1,TRUE) a<-table.element(a,'Variable Y',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Mean',header=TRUE) a<-table.element(a,mean(x)) a<-table.element(a,mean(y)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Biased Variance',header=TRUE) a<-table.element(a,varx) a<-table.element(a,vary) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Biased Standard Deviation',header=TRUE) a<-table.element(a,sqrt(varx)) a<-table.element(a,sqrt(vary)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Covariance',header=TRUE) a<-table.element(a,cov(x,y),2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',header=TRUE) a<-table.element(a,cxy,2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Determination',header=TRUE) a<-table.element(a,cxy*cxy,2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'T-Test',header=TRUE) a<-table.element(a,as.matrix(corxy$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,'95% CI of Correlation',header=TRUE) a<-table.element(a,paste('[',CIr(r=cxy, n = lx, level = .95)[1],', ', CIr(r=cxy, n = lx, level = .95)[2],']',sep=''),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') library(moments) library(nortest) jarque.x <- jarque.test(x) jarque.y <- jarque.test(y) if(lx>7) { ad.x <- ad.test(x) ad.y <- ad.test(y) } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Normality Tests',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('jarque.x'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('jarque.y'),'</pre>',sep='')) a<-table.row.end(a) if(lx>7) { a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('ad.x'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('ad.y'),'</pre>',sep='')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab') library(car) bitmap(file='test2.png') qqPlot(x,main='QQplot of variable x') dev.off() bitmap(file='test3.png') qqPlot(y,main='QQplot of variable y') dev.off()
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