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Data:
13663 11635 9606 8784.5 9415.5 10418 11344.5 11271 11895 12152.5 12731 12951 10692 8563.5 6217 5562 6294.5 7422 9254.5 10607 11268 12041 12962.5 12200.5 10400.5 8765 7000 6677 7318 7999 8762 9696 10373 10682.5 10935.5 10815.5 8669 7079.5 5640 5238.5 5777.5 6479 7290 7343 7810.5 8171.5 8532 8719 7281.5 5923.5 4837 4675.5 4585.5 5083 5766 6201 6778 7393.5 7849.5 8282.5 7610 6192.5 4693.5 4869 5149 5648.5 6230.5 7032 7727 8087.5 8443 9002 7717.5 6374.5 4995.5 4655 5198 5501 6119.5 6922 7390 7466.5 7773 7865 6567 5132.5 3656.5 3623 4045.5 4617 5374 6022.5 6464.5 7058 7484.5 7955 6801 5499 4179.5 4305.5 3304 5773.5 6419.5 6938 7760 8224 8381 8667 7304.5 5565.5 4023 3932.5 4508.5 5491 6284
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R Code
par2 <- '0' par1 <- '8' library(MASS) library(car) 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') print(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$estimate[2])^2),min(x),max(x),add=T) dev.off() bitmap(file='test3.png') qqPlot(x,dist='norm',main='QQ plot (Normal) with confidence intervals') grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Parameter',1,TRUE) a<-table.element(a,'Estimated Value',1,TRUE) a<-table.element(a,'Standard Deviation',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,r$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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