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5655 5165 5405 5515 5375 5455 6035 5805 6040 6115 6325 6690 5545 5465 5445 5130 4925 5110 5180 5005 5040 4785 4755 5085 4655 4290 4255 4140 4060 4280 4270 4265 4380 4320 4320 4615 4540 4480 4480 4430 4290 4350 4670 4775 4655 4880 4965 5265 5180 5015 4885 4970 4850 4925 5055 4790 4790 4995 4785 4835 4770 4530 4435 4455 4220 4215 4420 4255 4385 4740 4390 4660 3905 3845 3775 3875 3760 3865 3985 3725 4050 4190 4085 4510 4235 4355 4630 4685 4485 4540 4525 4855 5285 5110 5290 5145 5040 4940 5060 5120 5120 5185 5035 5480 5415 5410 5555 5475 5535 5270 5155 5035 5085 5155 5175 5260 4880 5120 4905 4545 4635 4585 4535 4800 5125 4990
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R Code
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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