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Data:
13768040.14 17487530.67 16198106.13 17535166.38 16571771.60 16198892.67 16554237.93 19554176.37 15903762.33 18003781.65 18329610.38 16260733.42 14851949.20 18174068.44 18406552.23 18466459.42 16016524.60 17428458.32 17167191.42 19629987.60 17183629.01 18344657.85 19301440.71 18147463.68 16192909.22 18374420.60 20515191.95 18957217.20 16471529.53 18746813.27 19009453.59 19211178.55 20547653.75 19325754.03 20605542.58 20056915.06 16141449.72 20359793.22 19711553.27 15638580.70 14384486.00 13855616.12 14308336.46 15290621.44 14423755.53 13779681.49 15686348.94 14733828.17 12522497.94 16189383.57 16059123.25 16007123.26 15806842.33 15159951.13 15692144.17 18908869.11 16969881.42 16997477.78 19858875.65 17681170.13
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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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