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2246 1512 2290 2622 1162 2818 2626 2122 1666 1464 1762 5224 930 1510 2682 1212 1916 3122 1686 1480 2202 1032 1694 2656 1868 798 1526 1644 2570 2386 2014 2336 2990 2976 954 1064 3684 2614 6884 3554 5786 4998 6464 3712 2138 3018 1100 4844 4082 1650 3450 3642 3496 3444 1382 512 290 420 4690 2334 1170 530 1900 888 1200 660 2086 3748 2604 5260 4620 2876 4150 3798 4716 5296 6370 6284 2694 3474 2988 2348 1204 2594 1238 1528 830 2394 1848 1336 1766 5792 1500 7152 1024 2526 2500 4146 1708 1510 3922 5886 6882 6120 3436 2952 6440 6598 3706 7220 9544 9220 5592 9554 5064 5236 2692 4788 8262 11810 6832 10192 15300 4006 10308 8562
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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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