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Data:
1418.7 1344.1 1574.6 1621.6 1887.2 2055.3 1606.8 1494.8 1636 1485.7 1369.7 1333.8 1614.9 1297.3 1226.2 1098.5 1258.5 1065.2 1000.4 1820.2 1224.8 1428.4 1144 1166.9 1902.3 1949.4 1784.5 1671.5 1923.8 1882.8 2165 1826.9 1511.2 2063.1 2169.6 2495.3 2936.9 3076.9 3365.7 3846 3436.2 3561.1 3328 2762.9 2923 2731.1 2571.5 3282.4 4606.5 4698.7 5093.3 4477.3 3850.1 4275.2 3975 4495.9 4042.4 5221.3 2555 2694.6 2757.7 2760.9 3872.9 2888.7 2529.2 3458.3 2882.8 2958.5 2652.4 2869.8 2501.7 2576.1 3347.5 3036.1 3345.2 3223.2 4087 4157.2 3368 3957.5 3469 4501.6 3181.4 3464.5 4186.9 3064.7 4011.7 3537.1 4879.5 4488.7 4632.9 4405.8 2615.2 3338 2825.2 3012.7 4537.5 5676.7 5575.4 6643.4 5590.6 4697.6 5078.1 5769.9 5561.4 7268.8 6496.7 6489.3 10883.5 7998.6 7340 7814.4 5729.6 6463.5 6315.4 5357.1
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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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Big Analytics Cloud Computing Center
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