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Data X:
0.79 2.21 2.12 0.93 5.38 3.14 2.23 11.88 9.31 6.06 2.31 6.84 7.49 0.72 4.48 5.09 7.44 1.41 5.77 4.84 2.96 3.12 3.83 3.11 2.86 4.06 3.32 1.21 0.8 2.52 1.21 1.17 8.17 5.65 1.24 1.46 4.36 3.38 1.87 1.03 1.29 0.82 2.84 1.27 3.92 1.95 4.21 5.19 5.51 2.19 2.57 1.53 2.17 2.15 2.07 3.97 0.42 6.86 1.02 2.9 5.87 5.14 2.34 4.73 2.02 1.03 1.58 5.3 1.97 4.38 2.98 3.23 1.89 1.41 1.53 3.07 0.61 1.68 2.92 1.16 1.58 2.79 1.88 5.57 6.22 4.61 1.89 5.02 2.1 5.55 1.03 1.17 5.69 8.13 1.91 1.22 6.29 3.84 1.66 1.21 3.69 5.83 15.82 3.26 0.99 0.81 3.71 1.53 2.08 2.54 3.46 2.89 1.78 6.08 3.78 7.78 1.68 0.87 1.43 2.48 2.94 0.98 5.28 3.58 5.6 1.39 1.56 1.16 4.98 7.52 0.79 2.79 1.91 4.16 2.28 1.1 4.44 3.88 10.8 3.65 2.71 5.69 0.87 4.94 2.45 3.11 2.77 1.49 5.61 1.21 2.7 1.24 7.97 4.06 5.81 1.29 1.24 3.31 3.67 1.32 4.25 2.01 7.25 5.79 1.51 0.91 1.32 2.66 0.48 1.13 2.7 7.92 2.34 3.33 5.47 1.24 2.84 4.94 7.93 8.22 2.91 2.32 3.57 1.65 2.07 1.03 0.99 1.37
Data Y:
614.66 4534.37 5430.57 4665.91 13205.1 13540 3426.39 NA 66604.2 51274.1 7106.04 22647.3 24299 857.5 15722.8 6300.45 48053.3 746.83 70626.3 2395 2253.09 4708.85 7743.5 13237.6 NA 47097.4 7615.28 671.07 276.69 3801.45 877.64 1271.21 52145.4 NA 495.04 1161.22 14525.8 5560.94 7305.22 860.24 1943.69 338.63 8979.96 1016.83 14522.8 5175.94 31454.7 21676.3 61413.6 1433.17 7088.01 6085.89 5192.88 2930.33 3696.33 24064 439.73 17304.4 379.38 4201.37 50960.2 45430.3 NA NA 11989 505.76 3710.7 46822.4 1627.9 25987.4 7410.48 NA 3233.8 459.09 681.25 3269.46 749.13 2269.51 13964.2 1513.85 3688.53 7511.1 5848.54 52853.6 33718.9 38412 5226.3 46201.6 4615.17 11278 1062.11 NA 24155.8 41830.5 1116.37 1236.24 13732 9143.86 1338.42 397.38 5859.43 14373.7 114665 5174.89 456.33 493.84 10252.6 741.22 NA 1524.39 8811.15 10123.9 1971.03 3736.07 7251.6 NA 3149.43 538.82 1117.58 5880.8 NA 700.07 53589.9 NA 37488.3 1626.85 410.91 2612.12 100172 22622.8 1218.6 8410.77 1871.21 3557.31 5684.73 2379.44 13769.5 23217.3 99431.5 NA 9213.94 13320.2 628.08 12952.5 7737.2 6171.48 4067.15 1384.53 23593.8 1079.27 6426.18 499.89 53122.4 18103.1 25040.5 1647.86 NA 8089.87 32008.7 2880.03 8190.7 4657.48 59381.9 88506.2 NA 836.17 765.33 5479.29 5167.86 580.86 4330.9 18310.8 4305.07 10437.7 5290.14 601.35 3589.63 40980.5 40817.4 49725 14238.1 1560.85 10237.8 1532.31 NA 1302.3 1740.64 865.91
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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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