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745.52 1962.64 2092.88 2034.73 568.28 1482.67 1282.26 1534.16 1621.42 1465.32 1373.15 1386.28 756.10 1359.02 1600.69 1299.97 161.31 622.74 552.60 570.36 806.43 1212.24 1806.54 1433.47 543.97 1453.00 1795.89 1608.99 484.29 1541.01 1094.14 1412.93 1612.35 1309.29 1626.17 874.45 794.25 1416.23 1575.25 1255.68 -787.86 1038.52 934.94 581.78 1069.98 1104.77 1152.28 880.48 169.49 953.93 1009.01 297.91 259.98 1164.90 924.60 840.77 1042.04 1026.08 637.53 1338.82
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R Code
library(MASS) par1 <- as.numeric(par1) if (par2 == '0') par2 = 'Sturges' else par2 <- as.numeric(par2) x <- as.ts(x) #otherwise the fitdistr function does not work properly r <- fitdistr(x,'normal') r bitmap(file='test1.png') myhist<-hist(x,col=par1,breaks=par2,main=main,ylab=ylab,xlab=xlab,freq=F) curve(1/(r$estimate[2]*sqrt(2*pi))*exp(-1/2*((x-r$estimate[1])/r$estimate[2])^2),min(x),max(x),add=T) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Parameter',1,TRUE) a<-table.element(a,'Estimated Value',1,TRUE) a<-table.element(a,'Standard Deviation',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,r$estimate[1]) a<-table.element(a,r$sd[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'standard deviation',header=TRUE) a<-table.element(a,r$estimate[2]) a<-table.element(a,r$sd[2]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Big Analytics Cloud Computing Center
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