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3910 3955 3825 4020 4270 4290 4340 4325 4370 4435 4410 4455 4600 4630 4660 4745 4790 4780 4780 4810 4940 4745 4805 4850 4870 4915 4980 4915 4950 4870 4835 4830 5015 4980 4935 4910 4910 4965 4765 4805 4710 4745 4585 4730 4690 4905 4995 4865 4900 4950 4930 4875 5010 4970 4940 5075 5030 5050 5125 5115 5040 5050 5115 5195 5190 5160 5160 5115 5105 5135 5040 5065 5325 5015 5050 5145 4990 5010 5085 5015 5020 5055 5060 4855 5265 5235 5115 5050 5095 5165 5125 5085 5265 5000 5035 5035 4865 4870 4790 4715 4660 4705 4610 4670 4690 4660 4590 4690 4610 4630 4715 4730 4850 4620 4700 4675
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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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Big Analytics Cloud Computing Center
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