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Data X:
210907 56 396 81 3 79 115 94 120982 56 297 55 4 58 109 103 176508 54 559 50 12 60 146 93 179321 89 967 125 2 108 116 103 123185 40 270 40 1 49 68 51 52746 25 143 37 3 0 101 70 385534 92 1562 63 0 121 96 91 33170 18 109 44 0 1 67 22 101645 63 371 88 0 20 44 38 149061 44 656 66 5 43 100 93 165446 33 511 57 0 69 93 60 237213 84 655 74 0 78 140 123 173326 88 465 49 7 86 166 148 133131 55 525 52 7 44 99 90 258873 60 885 88 3 104 139 124 180083 66 497 36 9 63 130 70 324799 154 1436 108 0 158 181 168 230964 53 612 43 4 102 116 115 236785 119 865 75 3 77 116 71 135473 41 385 32 0 82 88 66 202925 61 567 44 7 115 139 134 215147 58 639 85 0 101 135 117 344297 75 963 86 1 80 108 108 153935 33 398 56 5 50 89 84 132943 40 410 50 7 83 156 156 174724 92 966 135 0 123 129 120 174415 100 801 63 0 73 118 114 225548 112 892 81 5 81 118 94 223632 73 513 52 0 105 125 120 124817 40 469 44 0 47 95 81 221698 45 683 113 0 105 126 110 210767 60 643 39 3 94 135 133 170266 62 535 73 4 44 154 122 260561 75 625 48 1 114 165 158 84853 31 264 33 4 38 113 109 294424 77 992 59 2 107 127 124 101011 34 238 41 0 30 52 39 215641 46 818 69 0 71 121 92 325107 99 937 64 0 84 136 126 7176 17 70 1 0 0 0 0 167542 66 507 59 2 59 108 70 106408 30 260 32 1 33 46 37 96560 76 503 129 0 42 54 38 265769 146 927 37 2 96 124 120 269651 67 1269 31 10 106 115 93 149112 56 537 65 6 56 128 95 175824 107 910 107 0 57 80 77 152871 58 532 74 5 59 97 90 111665 34 345 54 4 39 104 80 116408 61 918 76 1 34 59 31 362301 119 1635 715 2 76 125 110 78800 42 330 57 2 20 82 66 183167 66 557 66 0 91 149 138 277965 89 1178 106 8 115 149 133 150629 44 740 54 3 85 122 113 168809 66 452 32 0 76 118 100 24188 24 218 20 0 8 12 7 329267 259 764 71 8 79 144 140 65029 17 255 21 5 21 67 61 101097 64 454 70 3 30 52 41 218946 41 866 112 1 76 108 96 244052 68 574 66 5 101 166 164 341570 168 1276 190 1 94 80 78 103597 43 379 66 1 27 60 49 233328 132 825 165 5 92 107 102 256462 105 798 56 0 123 127 124 206161 71 663 61 12 75 107 99 311473 112 1069 53 8 128 146 129 235800 94 921 127 8 105 84 62 177939 82 858 63 8 55 141 73 207176 70 711 38 8 56 123 114 196553 57 503 50 2 41 111 99 174184 53 382 52 0 72 98 70 143246 103 464 42 5 67 105 104 187559 121 717 76 8 75 135 116 187681 62 690 67 2 114 107 91 119016 52 462 50 5 118 85 74 182192 52 657 53 12 77 155 138 73566 32 385 39 6 22 88 67 194979 62 577 50 7 66 155 151 167488 45 619 77 2 69 104 72 143756 46 479 57 0 105 132 120 275541 63 817 73 4 116 127 115 243199 75 752 34 3 88 108 105 182999 88 430 39 6 73 129 104 135649 46 451 46 2 99 116 108 152299 53 537 63 0 62 122 98 120221 37 519 35 1 53 85 69 346485 90 1000 106 0 118 147 111 145790 63 637 43 5 30 99 99 193339 78 465 47 2 100 87 71 80953 25 437 31 0 49 28 27 122774 45 711 162 0 24 90 69 130585 46 299 57 5 67 109 107 112611 41 248 36 0 46 78 73 286468 144 1162 263 1 57 111 107 241066 82 714 78 0 75 158 93 148446 91 905 63 1 135 141 129 204713 71 649 54 1 68 122 69 182079 63 512 63 2 124 124 118 140344 53 472 77 6 33 93 73 220516 62 905 79 1 98 124 119 243060 63 786 110 4 58 112 104 162765 32 489 56 2 68 108 107 182613 39 479 56 3 81 99 99 232138 62 617 43 0 131 117 90 265318 117 925 111 10 110 199 197 85574 34 351 71 0 37 78 36 310839 92 1144 62 9 130 91 85 225060 93 669 56 7 93 158 139 232317 54 707 74 0 118 126 106 144966 144 458 60 0 39 122 50 43287 14 214 43 4 13 71 64 155754 61 599 68 4 74 75 31 164709 109 572 53 0 81 115 63 201940 38 897 87 0 109 119 92 235454 73 819 46 0 151 124 106 220801 75 720 105 1 51 72 63 99466 50 273 32 0 28 91 69 92661 61 508 133 1 40 45 41 133328 55 506 79 0 56 78 56 61361 77 451 51 0 27 39 25 125930 75 699 207 4 37 68 65 100750 72 407 67 0 83 119 93 224549 50 465 47 4 54 117 114 82316 32 245 34 4 27 39 38 102010 53 370 66 3 28 50 44 101523 42 316 76 0 59 88 87 243511 71 603 65 0 133 155 110 22938 10 154 9 0 12 0 0 41566 35 229 42 5 0 36 27 152474 65 577 45 0 106 123 83 61857 25 192 25 4 23 32 30 99923 66 617 115 0 44 99 80 132487 41 411 97 0 71 136 98 317394 86 975 53 1 116 117 82 21054 16 146 2 0 4 0 0 209641 42 705 52 5 62 88 60 22648 19 184 44 0 12 39 28 31414 19 200 22 0 18 25 9 46698 45 274 35 0 14 52 33 131698 65 502 74 0 60 75 59 91735 35 382 103 0 7 71 49 244749 95 964 144 2 98 124 115 184510 49 537 60 7 64 151 140 79863 37 438 134 1 29 71 49 128423 64 369 89 8 32 145 120 97839 38 417 42 2 25 87 66 38214 34 276 52 0 16 27 21 151101 32 514 98 2 48 131 124 272458 65 822 99 0 100 162 152 172494 52 389 52 0 46 165 139 108043 62 466 29 1 45 54 38 328107 65 1255 125 3 129 159 144 250579 83 694 106 0 130 147 120 351067 95 1024 95 3 136 170 160 158015 29 400 40 0 59 119 114 98866 18 397 140 0 25 49 39 85439 33 350 43 0 32 104 78 229242 247 719 128 4 63 120 119 351619 139 1277 142 4 95 150 141 84207 29 356 73 11 14 112 101 120445 118 457 72 0 36 59 56 324598 110 1402 128 0 113 136 133 131069 67 600 61 4 47 107 83 204271 42 480 73 0 92 130 116 165543 65 595 148 1 70 115 90 141722 94 436 64 0 19 107 36 116048 64 230 45 0 50 75 50 250047 81 651 58 0 41 71 61 299775 95 1367 97 9 91 120 97 195838 67 564 50 1 111 116 98 173260 63 716 37 3 41 79 78 254488 83 747 50 10 120 150 117 104389 45 467 105 5 135 156 148 136084 30 671 69 0 27 51 41 199476 70 861 46 2 87 118 105 92499 32 319 57 0 25 71 55 224330 83 612 52 1 131 144 132 135781 31 433 98 2 45 47 44 74408 67 434 61 4 29 28 21 81240 66 503 89 0 58 68 50 14688 10 85 0 0 4 0 0 181633 70 564 48 2 47 110 73 271856 103 824 91 1 109 147 86 7199 5 74 0 0 7 0 0 46660 20 259 7 0 12 15 13 17547 5 69 3 0 0 4 4 133368 36 535 54 1 37 64 57 95227 34 239 70 0 37 111 48 152601 48 438 36 2 46 85 46 98146 40 459 37 0 15 68 48 79619 43 426 123 3 42 40 32 59194 31 288 247 6 7 80 68 139942 42 498 46 0 54 88 87 118612 46 454 72 2 54 48 43 72880 33 376 41 0 14 76 67 65475 18 225 24 2 16 51 46 99643 55 555 45 1 33 67 46 71965 35 252 33 1 32 59 56 77272 59 208 27 2 21 61 48 49289 19 130 36 1 15 76 44 135131 66 481 87 0 38 60 60 108446 60 389 90 1 22 68 65 89746 36 565 114 3 28 71 55 44296 25 173 31 0 10 76 38 77648 47 278 45 0 31 62 52 181528 54 609 69 0 32 61 60 134019 53 422 51 0 32 67 54 124064 40 445 34 1 43 88 86 92630 40 387 60 4 27 30 24 121848 39 339 45 0 37 64 52 52915 14 181 54 0 20 68 49 81872 45 245 25 0 32 64 61 58981 36 384 38 7 0 91 61 53515 28 212 52 2 5 88 81 60812 44 399 67 0 26 52 43 56375 30 229 74 7 10 49 40 65490 22 224 38 3 27 62 40 80949 17 203 30 0 11 61 56 76302 31 333 26 0 29 76 68 104011 55 384 67 6 25 88 79 98104 54 636 132 2 55 66 47 67989 21 185 42 0 23 71 57 30989 14 93 35 0 5 68 41 135458 81 581 118 3 43 48 29 73504 35 248 68 0 23 25 3 63123 43 304 43 1 34 68 60 61254 46 344 76 1 36 41 30 74914 30 407 64 0 35 90 79 31774 23 170 48 1 0 66 47 81437 38 312 64 0 37 54 40 87186 54 507 56 0 28 59 48 50090 20 224 71 0 16 60 36 65745 53 340 75 0 26 77 42 56653 45 168 39 0 38 68 49 158399 39 443 42 0 23 72 57 46455 20 204 39 0 22 67 12 73624 24 367 93 0 30 64 40 38395 31 210 38 0 16 63 43 91899 35 335 60 0 18 59 33 139526 151 364 71 0 28 84 77 52164 52 178 52 0 32 64 43 51567 30 206 27 2 21 56 45 70551 31 279 59 0 23 54 47 84856 29 387 40 1 29 67 43 102538 57 490 79 1 50 58 45 86678 40 238 44 0 12 59 50 85709 44 343 65 0 21 40 35 34662 25 232 10 0 18 22 7 150580 77 530 124 0 27 83 71 99611 35 291 81 0 41 81 67 19349 11 67 15 0 13 2 0 99373 63 397 92 1 12 72 62 86230 44 467 42 0 21 61 54 30837 19 178 10 0 8 15 4 31706 13 175 24 0 26 32 25 89806 42 299 64 0 27 62 40 62088 38 154 45 1 13 58 38 40151 29 106 22 0 16 36 19 27634 20 189 56 0 2 59 17 76990 27 194 94 0 42 68 67 37460 20 135 19 0 5 21 14 54157 19 201 35 0 37 55 30 49862 37 207 32 0 17 54 54 84337 26 280 35 0 38 55 35 64175 42 260 48 0 37 72 59 59382 49 227 49 0 29 41 24 119308 30 239 48 0 32 61 58 76702 49 333 62 0 35 67 42 103425 67 428 96 1 17 76 46 70344 28 230 45 0 20 64 61 43410 19 292 63 0 7 3 3 104838 49 350 71 1 46 63 52 62215 27 186 26 0 24 40 25 69304 30 326 48 6 40 69 40 53117 22 155 29 3 3 48 32 19764 12 75 19 1 10 8 4 86680 31 361 45 2 37 52 49 84105 20 261 45 0 17 66 63 77945 20 299 67 0 28 76 67 89113 39 300 30 0 19 43 32 91005 29 450 36 3 29 39 23 40248 16 183 34 1 8 14 7 64187 27 238 36 0 10 61 54 50857 21 165 34 0 15 71 37 56613 19 234 37 1 15 44 35 62792 35 176 46 0 28 60 51 72535 14 329 44 0 17 64 39
Names of X columns:
time_in_rfc logins compendium_views_info compendium_views_pr shared_compendiums blogged_computations feedback_messages_p1 feedback_messages_p120
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
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Summary of computational transaction
Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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