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Data:
'no' 71.33 'yes' 71.07 'yes' 70.80 'yes' 70.80 'yes' 70.73 'yes' 70.73 'yes' 70.73 'yes' 70.73 'yes' 70.67 'yes' 70.67 'yes' 70.47 'no' 70.33 'yes' 70.07 'yes' 70.07 'no' 70.00 'yes' 69.93 'yes' 69.87 'yes' 69.60 'yes' 69.60 'yes' 69.53 'no' 69.00 'yes' 68.87 'yes' 68.80 'yes' 68.80 'yes' 68.73 'yes' 68.67 'no' 68.67 'yes' 68.67 'yes' 68.60 'yes' 68.07 'no' 68.00 'yes' 67.80 'no' 67.67 'yes' 67.60 'yes' 67.60 'yes' 67.53 'yes' 67.47 'yes' 67.47 'yes' 67.33 'no' 67.33 'yes' 67.13 'yes' 67.07 'yes' 66.93 'yes' 66.87 'yes' 66.80 'yes' 66.73 'yes' 66.73 'yes' 66.73 'yes' 66.73 'no' 66.67 'no' 66.67 'no' 66.67 'no' 66.67 'yes' 66.60 'yes' 66.53 'yes' 66.53 'yes' 66.53 'yes' 66.47 'no' 66.33 'no' 66.33 'yes' 66.27 'yes' 66.27 'yes' 66.20 'yes' 66.20 'yes' 66.07 'no' 66.00 'yes' 66.00 'yes' 66.00 'yes' 66.00 'yes' 66.00 'no' 66.00 'no' 66.00 'yes' 66.00 'yes' 66.00 'no' 66.00 'yes' 66.00 'yes' 65.93 'yes' 65.80 'yes' 65.80 'yes' 65.80 'no' 65.67 'no' 65.67 'no' 65.67 'no' 65.67 'no' 65.67 'yes' 65.67 'yes' 65.67 'yes' 65.60 'yes' 65.47 'yes' 65.47 'no' 65.33 'no' 65.33 'yes' 65.20 'yes' 65.07 'yes' 65.07 'yes' 65.00 'no' 65.00 'no' 65.00 'yes' 65.00 'no' 65.00 'yes' 64.87 'yes' 64.80 'yes' 64.73 'yes' 64.67 'yes' 64.67 'yes' 64.67 'yes' 64.67 'yes' 64.67 'no' 64.67 'no' 64.67 'yes' 64.67 'yes' 64.67 'no' 64.67 'no' 64.67 'yes' 64.67 'yes' 64.60 'yes' 64.60 'yes' 64.47 'yes' 64.40 'no' 64.33 'no' 64.33 'yes' 64.27 'yes' 64.27 'yes' 64.20 'yes' 64.20 'yes' 64.13 'yes' 64.07 'yes' 64.07 'yes' 64.07 'no' 64.00 'no' 64.00 'no' 64.00 'no' 64.00 'yes' 64.00 'no' 64.00 'no' 64.00 'no' 64.00 'no' 64.00 'yes' 63.87 'yes' 63.73 'yes' 63.73 'yes' 63.73 'no' 63.67 'no' 63.67 'no' 63.67 'yes' 63.67 'no' 63.67 'no' 63.67 'no' 63.67 'no' 63.67 'yes' 63.67 'no' 63.67 'yes' 63.67 'no' 63.67 'yes' 63.60 'yes' 63.53 'yes' 63.53 'yes' 63.53 'yes' 63.47 'no' 63.33 'yes' 63.33 'no' 63.33 'yes' 63.33 'no' 63.33 'no' 63.33 'no' 63.33 'yes' 63.27 'yes' 63.20 'yes' 63.13 'yes' 63.07 'no' 63.00 'no' 63.00 'yes' 63.00 'no' 63.00 'yes' 63.00 'yes' 62.93 'yes' 62.93 'yes' 62.87 'yes' 62.87 'yes' 62.80 'yes' 62.80 'yes' 62.80 'no' 62.67 'yes' 62.67 'yes' 62.67 'yes' 62.60 'yes' 62.60 'yes' 62.60 'yes' 62.60 'yes' 62.53 'yes' 62.40 'yes' 62.40 'yes' 62.40 'yes' 62.40 'yes' 62.40 'no' 62.33 'no' 62.33 'no' 62.33 'no' 62.33 'yes' 62.20 'yes' 62.13 'no' 62.00 'no' 62.00 'no' 62.00 'no' 62.00 'yes' 62.00 'no' 62.00 'no' 62.00 'yes' 61.93 'yes' 61.93 'yes' 61.93 'yes' 61.87 'yes' 61.80 'no' 61.67 'no' 61.67 'no' 61.67 'no' 61.67 'no' 61.67 'no' 61.67 'no' 61.67 'no' 61.67 'yes' 61.67 'yes' 61.60 'yes' 61.60 'yes' 61.60 'yes' 61.47 'yes' 61.47 'yes' 61.40 'yes' 61.40 'yes' 61.33 'no' 61.33 'no' 61.33 'no' 61.33 'no' 61.33 'no' 61.33 'no' 61.33 'no' 61.33 'yes' 61.27 'yes' 61.27 'no' 61.00 'no' 61.00 'yes' 61.00 'no' 61.00 'no' 61.00 'yes' 61.00 'no' 61.00 'no' 61.00 'yes' 60.87 'yes' 60.80 'yes' 60.73 'no' 60.67 'no' 60.67 'no' 60.67 'no' 60.67 'no' 60.67 'yes' 60.47 'yes' 60.40 'no' 60.33 'no' 60.33 'no' 60.33 'no' 60.33 'no' 60.33 'no' 60.33 'yes' 60.33 'yes' 60.13 'yes' 60.13 'yes' 60.13 'no' 60.00 'no' 60.00 'no' 60.00 'no' 60.00 'no' 60.00 'no' 60.00 'no' 60.00 'no' 60.00 'no' 60.00 'yes' 59.87 'yes' 59.80 'yes' 59.80 'yes' 59.73 'yes' 59.73 'yes' 59.73 'no' 59.67 'yes' 59.67 'yes' 59.67 'no' 59.67 'no' 59.67 'no' 59.67 'no' 59.67 'no' 59.67 'yes' 59.60 'yes' 59.53 'yes' 59.53 'yes' 59.53 'yes' 59.47 'yes' 59.40 'no' 59.33 'no' 59.33 'no' 59.33 'no' 59.33 'no' 59.33 'yes' 59.13 'yes' 59.13 'no' 59.00 'no' 59.00 'yes' 59.00 'yes' 58.93 'yes' 58.80 'yes' 58.80 'no' 58.67 'no' 58.67 'no' 58.67 'no' 58.67 'no' 58.67 'no' 58.67 'no' 58.67 'yes' 58.40 'no' 58.33 'yes' 58.33 'no' 58.33 'yes' 58.27 'yes' 58.27 'yes' 58.20 'yes' 58.07 'no' 58.00 'no' 58.00 'no' 58.00 'no' 58.00 'no' 58.00 'no' 58.00 'no' 58.00 'no' 58.00 'yes' 57.87 'yes' 57.80 'yes' 57.67 'no' 57.67 'no' 57.67 'yes' 57.67 'yes' 57.47 'yes' 57.47 'no' 57.33 'no' 57.33 'yes' 57.13 'no' 57.00 'yes' 56.67 'no' 56.67 'no' 56.67 'no' 56.67 'no' 56.67 'yes' 56.33 'no' 56.33 'no' 56.33 'yes' 56.20 'no' 56.00 'no' 56.00 'no' 56.00 'yes' 55.93 'no' 55.67 'no' 55.67 'yes' 55.47 'no' 55.33 'no' 55.33 'no' 55.33 'yes' 55.07 'yes' 55.00 'no' 55.00 'no' 55.00 'yes' 54.73 'no' 54.67 'no' 54.67 'no' 54.67 'no' 54.33 'no' 54.33 'yes' 54.20 'no' 54.00 'no' 54.00 'yes' 53.87 'no' 53.67 'no' 53.67 'no' 53.33 'no' 52.67 'yes' 52.67 'yes' 52.33 'yes' 51.53 'no' 51.00 'no' 51.00 'no' 51.00 'no' 51.00 'no' 49.67 'no' 49.33 'no' 49.00 'no' 48.33 'yes' 47.20 'no' 47.00 'no' 47.00
Number of Bins
(?)
Chart options
Title:
Label x-axis:
R Code
bitmap(file='test1.png') par1 <- as.numeric(par1) myhist<-hist(x, breaks=par1, col=2) dev.off() bitmap(file='test2.png') qqnorm(x) qqline(x, col=2, xlab=xlab, ylab=ylab) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Descriptive Statistics',3,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,hyperlink('http://www.xycoon.com/arithmetic_mean.htm','formula','click to see the formula')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'standard deviation',header=TRUE) a<-table.element(a,sd(x)) a<-table.element(a,hyperlink('http://www.xycoon.com/unbiased1.htm','formula','click to see the formula')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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