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694 694 1486 2974 2642 2052 2052 2056 2052 3700 2974 1452 2052 2052 2124 2974 2124 2052 2124 2052 2124 2758 2974 2758 1770 2032 1890 2032 1882 2032 2850 2032 94 1366 808 2114 1302 1494 2720 2720 776 2114 1344 3800 1928 1080 940 1791 2620 2000 1750 1750 1380 2104 1264 1214 1848 749 1320 1266 1266 1266 1440 1494 1440 1848 1566 3959 1560 1250 2550 1700 952 1566 1052 2240 1748 2394 2244 1056 2002 2144 1504 1956 1150 2002 1748 684 684 684 826 1968 1178 1874 2016 1882 3574 2600 1974 3100 1950 2674 3782 2758 2600 1974 686 1590 1200 2674 1950 2602 1950 688 394 700 490 320 700 3114 2501 2020 1950 3114 4370 1950 3634 2501 1800 4370 2744 3114 2744 3114 5400 2501 2435 2501 2852 2076 2435 3114 208 208 208 296 382 388 296 66 800 1480 1287 66 1960 158 167 308
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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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