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4790.92 4795.33 4822.62 4797.52 4822.17 4843.08 4850.79 4827.02 4796.65 4854.96 4870.81 4891.06 4881.38 4921.43 4956.21 4962.81 4949.38 4977.99 4992.73 5009.02 4990.98 5014.96 5022.23 5028.83 4894.36 4918.13 4936.4 4899.87 4862.89 4882.69 4895.46 4883.8 4855.4 4874.33 4880.94 4861.79 4851.44 4840.22 4842.42 4827.02 4749.77 4866.63 4734.37 4726.44 4753.51 4867.29 4793.35 4822.4 4865.09 4987.67 4900.96 4904.71 4889.52 5015.63 4938.81 4924.73 4871.48 4998.24 4891.06 4876.54 4824.15
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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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