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Data:
162556 29790 87550 84738 54660 42634 40949 45187 37704 16275 25830 12679 18014 43556 24811 6575 7123 21950 37597 17821 12988 22330 13326 16189 7146 15824 27664 11920 8568 14416 3369 11819 6984 4519 2220 18562 10327 5336 2365 4069 8636 13718 4525 6869 4628 3689 4891 7489 4901 2284 3160 4150 7285 1134 4658 2384 3748 5371 1285 9327 5565 1528 3122 7561 2675 13253 880 2053 1424 4036 3045 5119 1431 554 1975 1765 1012 810 1280 666 1380 4677 876 814 514 5692 3642 540 2099 567 2001 2949 2253 6533 1889 3055 272 1414 2564 1383 1261 975 3366 576 1686 746 3192 2045 5702 1932 936 3437 5131 2397 1389 1503 402 2239 2234 837 10579 875 1585 1659 2647 3294 0 94 422 0 34 1558 0 43 645 316 115 5 897 0 389 0 1002 36 460 309 0 9 271 14 520 1766 0 458 20 0 0 98 405 0 0 0 0 483 454 47 0 757 4655 0 0 36 0 203 0 126 400 71 0 0 972 531 2461 378 23 638 2300 149 226 0 275 0 141 0 28 0 4980 0 0 472 0 0 0 203 496 10 63 0 1136 265 0 0 267 474 534 0 15 397 0 1866 288 0 3 468 20 278 61 0 192 0 317 738 0 368 0 2 0 53 0 0 0 94 0 24 2332 0 0 131 0 0 206 0 167 622 2328 0 365 364 0 0 0 0 226 307 0 0 0 188 0 138 0 0 0 125 0 282 335 0 1324 176 0 0 249 0 333 0 601 30 0 249 0 165 453 0 53 382 0 0 0 0 30 290 0 0 366 2 0 209 384 0 0 365 0 49 3 133 32 368 1 0 0 0 0 0 0 22 0 0 0 0 0 0 0 96 1 314 844 0 26 125 304 0 0 0 621 0 119 0 0 1595 312 60 587 135 0 0 514 0 0 0 1 0 0 1763 180 0 0 0 0 218 0 448 227 174 0 0 121 607 2212 0 0 530 571 0 78 2489 131 923 72 572 397 450 622 694 3425 562 4917 1442 529 2126 1061 776 611 1526 592 1182 621 989 438 726 1303 7419 1164 3310 1920 965 3256 1135 1270 661 1013 2844 11528 6526 2264 5109 3999 35624 9252 15236 18073
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bandwidth of density plot
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# lags (autocorrelation function)
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R Code
par1 <- as.numeric(par1) par2 <- as.numeric(par2) x <- as.ts(x) library(lattice) bitmap(file='pic1.png') plot(x,type='l',main='Run Sequence Plot',xlab='time or index',ylab='value') grid() dev.off() bitmap(file='pic2.png') hist(x) grid() dev.off() bitmap(file='pic3.png') if (par1 > 0) { densityplot(~x,col='black',main=paste('Density Plot bw = ',par1),bw=par1) } else { densityplot(~x,col='black',main='Density Plot') } dev.off() bitmap(file='pic4.png') qqnorm(x) qqline(x) grid() dev.off() if (par2 > 0) { bitmap(file='lagplot1.png') dum <- cbind(lag(x,k=1),x) dum dum1 <- dum[2:length(x),] dum1 z <- as.data.frame(dum1) z plot(z,main='Lag plot (k=1), lowess, and regression line') lines(lowess(z)) abline(lm(z)) dev.off() if (par2 > 1) { bitmap(file='lagplotpar2.png') dum <- cbind(lag(x,k=par2),x) dum dum1 <- dum[(par2+1):length(x),] dum1 z <- as.data.frame(dum1) z mylagtitle <- 'Lag plot (k=' mylagtitle <- paste(mylagtitle,par2,sep='') mylagtitle <- paste(mylagtitle,'), and lowess',sep='') plot(z,main=mylagtitle) lines(lowess(z)) dev.off() } bitmap(file='pic5.png') acf(x,lag.max=par2,main='Autocorrelation Function') grid() dev.off() } summary(x) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Descriptive Statistics',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'# observations',header=TRUE) a<-table.element(a,length(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'minimum',header=TRUE) a<-table.element(a,min(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Q1',header=TRUE) a<-table.element(a,quantile(x,0.25)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) a<-table.element(a,median(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) a<-table.element(a,mean(x)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Q3',header=TRUE) a<-table.element(a,quantile(x,0.75)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'maximum',header=TRUE) a<-table.element(a,max(x)) 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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