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Data:
0.3024438225 0.7526270971 -0.798521809 2.509269247 0.5041515907 0.3076010072 1.085014943 -1.945408534 1.099338245 0.9637793464 -0.2213537153 1.543557424 -0.7905904726 1.138924919 -0.01448671359 -1.582669286 -0.3748682428 -0.8107658471 -1.357368657 0.9540071754 -0.8338719527 -1.43819693 -0.008675436885 -1.617982007 -1.037749004 0.3689678742 1.569535779 0.4154083557 0.6568296879 0.3845208639 0.1260106485 -0.7890094842 0.5317914492 -0.5381986818 -0.5436424075 -0.2526278385 -0.4453407794 1.373661167 -0.8390178502 0.6258599082 0.3474235944 0.07430614953 0.3248965684 1.293237253 1.077289591 2.383011093 0.1194044167 0.6864182321 1.269299159 2.089711268 2.325660143 -0.7010243019 -1.459171992 -0.5175321306 -1.219896359 -1.330455558 -0.7420423072 1.29680896 -1.258230116 -0.7079002865 0.1137226114 1.086945174 0.1814734478 -1.051889576 0.6282323681 -0.9811391521 -0.007132025478 0.4605675517 -1.429331674 -0.9147205222 0.1253808477 -1.130112625 0.3031563715 0.742504741 -0.3609770886 0.03168997645 0.04651334849 1.969136222 -0.3189272772 1.636452184 0.06786952306 -1.253339701 -0.6153636369 -2.348963661 -0.2098347155 -0.03808398007 -1.310918073 0.1820932801 -1.17647615 0.7314356613 1.082348991 -0.2386590637 0.9184974882 1.147660973 -0.7348665391 2.052717352 -0.1588095703 -0.5529664259 0.7369468277 -2.018590748 -0.8297447142 1.599960991 2.304051003 -0.7152447733 0.01576539336 -1.677342474 1.509267663 0.4850241199 -2.340038025 1.510859941 -0.01633706533 -0.7159316054 0.1198760549 0.09541827733 -0.2798929782 0.3798522478 0.6318255705 -1.680238147 -1.731291364 -0.2951449473 1.057787512 -0.5607601824 0.8541246262 0.08634987513 1.21364474 -1.157662754 0.4147447329 -0.6714434871 0.1193084763 -0.4062341804 0.5637507126 0.7166698477 1.170715032 0.399429512 -0.5279486397 -0.057236684 1.233697917 -0.006734311974 -0.3269080999 0.4535671416 -0.3402649045 -3.054039208 0.7895384869 1.220121567 0.2235709658 -0.104458884 -1.818162106 0.9293053561 -0.01728671063 -0.8001613948
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R Code
library(MASS) library(car) chii <- x chi_df <- fitdistr(chii,'chi-squared',start=list(df=3), method='Brent',lower=0.1,upper=100) chi_k <- chi_df[[1]][1] bitmap(file='test1.png') chi_hist <- hist(chii,breaks=50,col='gray',freq=FALSE,main='Histogram scaled to density',xlab='x') curve(dchisq(x,df=chi_k),add=TRUE,col='red') dev.off() f<-chi_df f$estimate f$sd xlab <- paste('Chisq(df = ',round(f$estimate[[1]],2),sep='') xlab <- paste(xlab,')',sep='') xlab bitmap(file='test2.png') qqPlot(x, dist='chisq', df=f$estimate[[1]], ncp=0, main='QQ plot (Chi-squared 1 param.)', xlab=xlab ) 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,'df',header=TRUE) a<-table.element(a,f$estimate[1]) a<-table.element(a,f$sd[1]) 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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