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Data:
0.06637033912 0.8853102477 0.626449832 1.940332931 0.1911544302 0.2222410583 5.136945351 0.820246095 0.8587168475 0.0351997513 1.541056659 0.567997407 2.572860893 0.07322382302 0.02277049657 0.4342521132 1.24786071 1.84728994 0.8395992894 2.917275088 0.6017203193 0.926113182 0.1115987587 2.62869434 0.9884238725 3.379661954 0.6551312127 0.07808288248 0.08375056244 0.01987268567 0.9304715246 0.8908997093 0.01660574813 0.7944787013 0.2533344972 5.134134411 0.7369078182 1.0024994 4.593211768 0.1412883937 0.1213487092 1.512874436 0.3426962801 3.497718739 0.01531668179 0.06571248604 0.8132356616 0.2969312727 0.0004173593227 2.789405424 0.0181954063 2.111367988 0.8469493576 0.07162338456 0.1308246045 0.0006458818571 2.129553076 0.4677555371 0.6168951865 0.07954722915 0.2502612292 0.6487977098 0.003022899133 0.3429234546 5.443013317 0.4684984042 1.428007276 1.253288003 0.4066663673 4.329079678 2.294226442 0.4218887894 0.001328532994 0.244733653 3.69153603 2.176780508 0.07646248814 1.086779142 2.050430542 0.02172040945 0.08749920232 0.2730334826 0.03087365692 2.009657297 0.008453893525 2.821697025 0.3712782036 0.006888276577 0.0001931396985 0.4971004454 0.1082450327 1.260831448 0.3542168768 0.103383066 0.1056515987 0.2100594431 2.345209709 0.000009469250772 0.288886307 0.1826031484 0.03137535565 1.128556535 0.5777045951 2.928637089 0.7233022502 0.04804374491 0.0008145265148 0.1425016866 0.3046951296 2.073305308 0.2567365406 3.978802461 1.186620784 3.131577405 0.04994358782 1.485985622 0.06672825866 0.9652671514 0.03478908291 0.00006513699193 1.051998382 0.2247196328 0.03185225506 0.05890993765 0.528271352 0.5431984003 0.0110425919 0.1055201884 0.8186604091 8.86234345 0.5328201904 1.660274473 0.2056527288 0.03130537079 0.947362146 0.6414822963 2.155353002 0.294205906 2.892597104 1.5122846 0.009076589618 0.4234215098 2.315234531 0.001724113696 1.281192361 3.439717352 0.03579309387 0.3161543148 0.2243528741 0.948421459
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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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1 seconds
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Big Analytics Cloud Computing Center
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