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325.87 302.25 294.00 285.43 286.19 276.70 267.77 267.03 257.87 257.19 275.60 305.68 358.06 320.07 295.90 291.27 272.87 269.27 271.32 267.45 260.33 277.94 277.07 312.65 319.71 318.39 304.90 303.73 273.29 274.33 270.45 278.23 274.03 279.00 287.50 336.87 334.10 296.07 286.84 277.63 261.32 264.07 261.94 252.84 257.83 271.16 273.63 304.87 323.90 336.11 335.65 282.23 273.03 270.07 246.03 242.35 250.33 267.45 268.80 302.68 313.10 306.39 305.61 277.27 264.94 268.63 293.90 248.65 256.00 258.52 266.90 281.23 306.00 325.46 291.13 282.53 256.52 258.63 252.74 245.16 255.03 268.35 293.73 278.39
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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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Big Analytics Cloud Computing Center
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