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Data X:
2161.99683 879.5583596 1046.119874 1676.403785 2290.364501 2455.816165 1885.947205 2233.312203 2028.729282 2442.503962 2594.643423 2349.318542 1862.472266 2600.348653 1658.678696 1888.664596 2275.150555 2197.179081 2287.511886 2259.936609 2091.63233 2354.0729 1758.563536 2126.81458 377.9810726 2414.0729 885.2365931 712.9968454 1998.563536 1862.472266 2275.150555 NA NA 2566.740331 1703.038674 2296.40884 2666.740331 1936.024845 1862.472266 2336.980331 2122.7665 1936.801242 1987.0902 1936.801242 2277.232426 2091.63233 1749.038674 2566.740331 1703.038674 2296.40884 2836.740331 2296.40884 2855.765149 2176.41284 2333.11233 2409.223455 2143.11233 2942.265193 2210.41284 2300.987 1985.635359 2231.767956 1511.325967 1703.038674 3042.265193 1934.47205 1998.57205 2299.987 2211.7789 1888.276398 2380.047544 2300.987 2290.011544 2176.41284 2333.11233 2490.047544 1886.335404 2296.40884 2354.41989 2441.8988 2551.8542 1330.353591 1940.331492 2252.486188 1999.9865 2462.486188 2346.132597 2354.41989 1106.353591 2231.767956 2289.413629 3203.038674 2261.60221 2467.127072 2408.287293 NA 1937.57764 1703.038674 NA NA NA 3432.596685 769.0607735 1887.5 3512.154696 3470.718232 3470.718232 3193.922652 3653.867403 3397.790055 3189.779006 1333.149171 1936.801242 2937.016575 2233.701657 3242.81768 1067.955801 3116.850829 3185.635359 2233.701657 2277.052298 1101.104972 2058.839779 2988.674033 1789.779006 3088.674033 2375.414365 1297.513812 2530.935024 2442.503962 2363.581616 2320.792393 2530.935024 2329.350238 2394.96038 2301.77496 2874.033149 2744.034549
Data Y:
5.61 3.79 4.68 9.72 4.03 2.29 2 2.94 2 2.96 2.36 2.04 8.87 8.34 10.5 2 3.02 4.63 9.14 2.5 <2.0 2.03 2 2 2.14 <2.0 3.5 3.42 5.7 2.37 2 9.73 2.52 3.86 8.08 4.52 43.9 5.47 10.3 6.67 9.02 2.8 2.74 7.93 2.5 2.55 4.3 5.34 2.78 2 3.62 5.61 9.89 4.83 7.05 13.7 7.9 2.71 2 5.45 3.51 2 3.66 6.81 6.05 2.62 2 2 2 4.16 6.34 36.4 3.32 3.95 4.07 7.23 3.12 2 22.9 2.37 9.85 4.47 2 4.81 4.08 4.48 6.37 4.38 2 5.73 8.81 11.7 3.23 3.38 2.29 3.02 6.46 2 4.36 6.43 2 5.1 2 2 10.9 5.07 2.69 4.16 4.81 2.59 9.02 2 8.11 5.27 2 5.03 4.68 16.8 2.79 7.26 4.09 5.43 4.53 5.24 8.34 6.94 10.2 10.4 12.2 4.24 4.9 16.7 4.62 9.33 3.67 6.91 13.5 7.1
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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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