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Data:
14.15 13.95 13.96 13.99 14.08 14.03 13.93 13.95 13.94 14.01 13.98 13.84 14.16 13.92 13.97 14 14 13.87 13.94 13.98 13.95 14.01 13.96 13.88 13.76 13.79 13.97 13.84 13.94 13.97 13.92 13.87 13.9 13.85 13.7 13.87 13.74 13.64 13.83 13.88 14.01 13.98 14.02 14.1 14.11 14.14 14.04 14.19 14.17 14.14 13.94 14.09 14.06 14.07 14.07 14.07 14.05 13.99 13.85 13.95 14.13 14.2 14.2 14.18 14.13 14.07 14.06 14.07 14.1 14.1 14 14.16 13.85 13.97 13.91 13.9 13.83 13.9 13.79 13.88 13.94 13.95 14.08 13.87 14.16 13.9 13.74 13.82 13.82 13.88 13.9 14.04 13.92 13.96 13.8 13.74 13.84 13.71 13.78 13.78 13.76 13.81 13.87 13.76 13.82 13.82 13.83 13.91 13.92 14 13.99 14.01 14.09 14.13 14.03 14.1 14.05 14.02 14.11 14.21 14.38 14.23 14.1 14.04 14.12 14 14.11 14.03 14.03 14.04 14.06 14.1 14.11 14.12 14.24 14.17 14.08 14.07 14.09 14.02 14.01 13.98 13.92 14.03 14.01 14.19 13.73 13.92 13.94 14.03 14.04 14.03 14.07 14.04 13.93 14.17 14.06 14.2 14.16 14.11 14.16 14.13 14.01 14.05 14.04 14.1 14.05 14.02 14.11 14.21 14.38 14.23 14.1 14.04 14.12 14 14.11 14.03 14.03 14.04 14.06 14.1 14.11 14.12 14.24 14.17 14.08 14.07 14.09 14.02 14.01 13.98 13.92 14.03 14.01 14.19 13.73 13.92 13.94 14.03 14.04 14.03 14.07 14.04 13.93 14.17 14.06 14.2 14.16 14.11 14.16 14.13 14.01 14.05 14.04 14.03 14.04 13.9 14.09 14.16 14.09 14.08 13.95 14.01 14 13.99 14 14.02 14.06 14.02 13.97 14.19 13.97 13.98 14.03 14.04 14.13 14.22 14.21 14.15 14.17 14.03 14.02 13.91 13.81 13.78 13.83 13.96 13.9 14.1 13.99 13.9 13.88 13.89 14.03 14.19 14.16 14.1 14.03 14.06 14.07 14.11 14.17 14.23 14.11 14.25 14.03 14.07 13.99 14.01 13.98 13.93 14.06 13.98 14 13.86 13.98 13.8 13.8 13.89 13.88 13.78 13.89 13.93 13.95 13.92 13.96 13.91 13.76 13.79 13.99 13.99 13.99 14.04 14.01 14.13 14.01 14.07 14.04 14.18 14.26 14.31 14.26 14.2 14.18 14.14 14.08 14 14.04 14.08 14 13.94 13.83 13.75 13.92 13.91 13.91 13.9 13.95 14.02 13.89 13.89 13.89 13.87 14.03 13.96 14.06 13.98 14.08 13.95 13.95 13.84 13.94 13.88 13.83 13.8 13.92 13.9 13.73 13.87 13.76 13.86 13.9 13.85 13.9 13.75 13.87 13.97 13.97 14.14 14.18 14.17 14.2 14.17 14.15 14.1 14.04 14.01 14.15 14.03 14.04 14.05 14.12 14.09 13.98 13.94 14.04 13.86 14.03 13.99 14.08 14.01 14.04 13.9 14.09 14.04 13.97 14.08 13.99 14.11 14.16 14.18 14.18 14.38 14.18 14.22 14.13 14.2 14.25 14.14 14.15 14.13 14.1 14.09 14.23 14.11 14.4 14.3 14.37 14.24 14.14 14.17 14.19 14.24 14.11 14.07 14.15 14.28 14.03 14.06 13.94 14.05 14.12 14 14.12 13.99 14.04 14.05 14.06 14.33 14.45 14.39 14.39 14.23 14.25 14.15 14.12 14.26 14.28 14.12 14.29 14.12 14.22 14.09 14.17 14.01 14.22 13.98 14.12 14.09 14.11 14.05 13.96 13.81 14.09 13.87 14.1 14.08 14.09 14.08 13.95 14.08 14 14.05 13.98 14.04 14.24 14.28 14.23 14.16 14.11 14.07 14.07 14.08 14.02 14.08 14.01 14.08 14.23 14.39 14.13 14.21 14.21 14.26 14.36 14.18 14.34 14.26 14.22 14.46 14.51 14.32 14.44 14.35 14.3 14.32 14.24 14.27 14.26 14.26 14.05 14.22 14.11 14.25 14.26 14.16 14.07 14.06 14.22 14.24 14.25 14.23 14.14 14.29 14.33 14.34 14.65 14.43 14.32 14.31 14.34 14.28 14.23 14.4 14.45 14.39 14.35 14.43 14.29 14.41 14.31 14.42 14.43 14.3 14.36 14.22 14.16 14.2 14.38 14.37 14.34 14.19 14.22 14.15 14 14.01 13.94 14 13.93 14.13 14.28 14.26 14.3 14.18 14.18 14.1 14.09 14.03 14.02 14.16 14 14.14 14.28 13.94 14.25 14.26 14.22 14.29 14.2 14.19 14.25 14.38 14.37 14.29 14.44 14.7 14.44 14.34 14.11 14.33 14.46 14.37 14.24 14.42 14.37 14.26 14.23 14.43 14.25 14.2 14.21 14.18 14.3 14.32 14.16 14.15 14.28 14.31 14.27 14.31 14.46 14.33 14.31 14.43 14.28 14.36 14.45 14.5 14.55 14.53 14.55 14.83 14.56 14.58 14.59 14.59 14.67 14.6 14.43 14.42 14.4 14.51 14.45 14.64 14.27 14.28 14.23 14.28 14.26 14.27 14.25 14.3 14.32 14.37 14.21 14.49 14.46 14.5 14.3 14.31 14.28 14.37 14.29 14.21 14.21 14.19 14.38 14.4 14.56 14.42 14.47 14.45 14.46 14.45 14.45 14.43 14.68 14.47 14.74 14.75 14.81 14.54 14.51 14.43 14.53 14.43 14.46 14.48 14.51 14.33
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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') 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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