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Data:
1962.3 2095.2 2161 2115.1 1929 2004.5 2009.9 1524.9 2061.1 2261.6 2103.6 2224.3 2173.8 2119.2 2226.4 2159.6 1918.3 2116.1 1948.3 1514.3 2180.5 2312.6 2019.8 2200.8 2028.9 2178.7 2433.7 2230.5 1884.2 2372.7 1918.6 1679.4 2327.3 2225.2 2211.7 2463.6 2029.5 2173.6 2387 2234 2179.9 2397 1960.2 1824.1 2479.3 2234.9 2345.9 2428.9 2179.4 2216.9 2642.3 2340.5 2474.6 2641.8 2165.1 1996.2 2562.9 2529.9 2549.6 2455.1 2472 2424.7 2820.1 2666 2654.6 2732.2 2546.9
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R Code
library(MASS) 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$estimate[2])^2),min(x),max(x),add=T) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Parameter',1,TRUE) a<-table.element(a,'Estimated Value',1,TRUE) a<-table.element(a,'Standard Deviation',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,r$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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