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Data X:
30 210907 56 79 112285 1418 28 120982 56 58 84786 869 38 176508 54 60 83123 1530 30 179321 89 108 101193 2172 22 123185 40 49 38361 901 26 52746 25 0 68504 463 25 385534 92 121 119182 3201 18 33170 18 1 22807 371 11 101645 63 20 17140 1192 26 149061 44 43 116174 1583 25 165446 33 69 57635 1439 38 237213 84 78 66198 1764 44 173326 88 86 71701 1495 30 133131 55 44 57793 1373 40 258873 60 104 80444 2187 34 180083 66 63 53855 1491 47 324799 154 158 97668 4041 30 230964 53 102 133824 1706 31 236785 119 77 101481 2152 23 135473 41 82 99645 1036 36 202925 61 115 114789 1882 36 215147 58 101 99052 1929 30 344297 75 80 67654 2242 25 153935 33 50 65553 1220 39 132943 40 83 97500 1289 34 174724 92 123 69112 2515 31 174415 100 73 82753 2147 31 225548 112 81 85323 2352 33 223632 73 105 72654 1638 25 124817 40 47 30727 1222 33 221698 45 105 77873 1812 35 210767 60 94 117478 1677 42 170266 62 44 74007 1579 43 260561 75 114 90183 1731 30 84853 31 38 61542 807 33 294424 77 107 101494 2452 13 101011 34 30 27570 829 32 215641 46 71 55813 1940 36 325107 99 84 79215 2662 0 7176 17 0 1423 186 28 167542 66 59 55461 1499 14 106408 30 33 31081 865 17 96560 76 42 22996 1793 32 265769 146 96 83122 2527 30 269651 67 106 70106 2747 35 149112 56 56 60578 1324 20 175824 107 57 39992 2702 28 152871 58 59 79892 1383 28 111665 34 39 49810 1179 39 116408 61 34 71570 2099 34 362301 119 76 100708 4308 26 78800 42 20 33032 918 39 183167 66 91 82875 1831 39 277965 89 115 139077 3373 33 150629 44 85 71595 1713 28 168809 66 76 72260 1438 4 24188 24 8 5950 496 39 329267 259 79 115762 2253 18 65029 17 21 32551 744 14 101097 64 30 31701 1161 29 218946 41 76 80670 2352 44 244052 68 101 143558 2144 21 341570 168 94 117105 4691 16 103597 43 27 23789 1112 28 233328 132 92 120733 2694 35 256462 105 123 105195 1973 28 206161 71 75 73107 1769 38 311473 112 128 132068 3148 23 235800 94 105 149193 2474 36 177939 82 55 46821 2084 32 207176 70 56 87011 1954 29 196553 57 41 95260 1226 25 174184 53 72 55183 1389 27 143246 103 67 106671 1496 36 187559 121 75 73511 2269 28 187681 62 114 92945 1833 23 119016 52 118 78664 1268 40 182192 52 77 70054 1943 23 73566 32 22 22618 893 40 194979 62 66 74011 1762 28 167488 45 69 83737 1403 34 143756 46 105 69094 1425 33 275541 63 116 93133 1857 28 243199 75 88 95536 1840 34 182999 88 73 225920 1502 30 135649 46 99 62133 1441 33 152299 53 62 61370 1420 22 120221 37 53 43836 1416 38 346485 90 118 106117 2970 26 145790 63 30 38692 1317 35 193339 78 100 84651 1644 8 80953 25 49 56622 870 24 122774 45 24 15986 1654 29 130585 46 67 95364 1054 20 112611 41 46 26706 937 29 286468 144 57 89691 3004 45 241066 82 75 67267 2008 37 148446 91 135 126846 2547 33 204713 71 68 41140 1885 33 182079 63 124 102860 1626 25 140344 53 33 51715 1468 32 220516 62 98 55801 2445 29 243060 63 58 111813 1964 28 162765 32 68 120293 1381 28 182613 39 81 138599 1369 31 232138 62 131 161647 1659 52 265318 117 110 115929 2888 21 85574 34 37 24266 1290 24 310839 92 130 162901 2845 41 225060 93 93 109825 1982 33 232317 54 118 129838 1904 32 144966 144 39 37510 1391 19 43287 14 13 43750 602 20 155754 61 74 40652 1743 31 164709 109 81 87771 1559 31 201940 38 109 85872 2014 32 235454 73 151 89275 2143 18 220801 75 51 44418 2146 23 99466 50 28 192565 874 17 92661 61 40 35232 1590 20 133328 55 56 40909 1590 12 61361 77 27 13294 1210 17 125930 75 37 32387 2072 30 100750 72 83 140867 1281 31 224549 50 54 120662 1401 10 82316 32 27 21233 834 13 102010 53 28 44332 1105 22 101523 42 59 61056 1272 42 243511 71 133 101338 1944 1 22938 10 12 1168 391 9 41566 35 0 13497 761 32 152474 65 106 65567 1605 11 61857 25 23 25162 530 25 99923 66 44 32334 1988 36 132487 41 71 40735 1386 31 317394 86 116 91413 2395 0 21054 16 4 855 387 24 209641 42 62 97068 1742 13 22648 19 12 44339 620 8 31414 19 18 14116 449 13 46698 45 14 10288 800 19 131698 65 60 65622 1684 18 91735 35 7 16563 1050 33 244749 95 98 76643 2699 40 184510 49 64 110681 1606 22 79863 37 29 29011 1502 38 128423 64 32 92696 1204 24 97839 38 25 94785 1138 8 38214 34 16 8773 568 35 151101 32 48 83209 1459 43 272458 65 100 93815 2158 43 172494 52 46 86687 1111 14 108043 62 45 34553 1421 41 328107 65 129 105547 2833 38 250579 83 130 103487 1955 45 351067 95 136 213688 2922 31 158015 29 59 71220 1002 13 98866 18 25 23517 1060 28 85439 33 32 56926 956 31 229242 247 63 91721 2186 40 351619 139 95 115168 3604 30 84207 29 14 111194 1035 16 120445 118 36 51009 1417 37 324598 110 113 135777 3261 30 131069 67 47 51513 1587 35 204271 42 92 74163 1424 32 165543 65 70 51633 1701 27 141722 94 19 75345 1249 20 116048 64 50 33416 946 18 250047 81 41 83305 1926 31 299775 95 91 98952 3352 31 195838 67 111 102372 1641 21 173260 63 41 37238 2035 39 254488 83 120 103772 2312 41 104389 45 135 123969 1369 13 136084 30 27 27142 1577 32 199476 70 87 135400 2201 18 92499 32 25 21399 961 39 224330 83 131 130115 1900 14 135781 31 45 24874 1254 7 74408 67 29 34988 1335 17 81240 66 58 45549 1597 0 14688 10 4 6023 207 30 181633 70 47 64466 1645 37 271856 103 109 54990 2429 0 7199 5 7 1644 151 5 46660 20 12 6179 474 1 17547 5 0 3926 141 16 133368 36 37 32755 1639 32 95227 34 37 34777 872 24 152601 48 46 73224 1318 17 98146 40 15 27114 1018 11 79619 43 42 20760 1383 24 59194 31 7 37636 1314 22 139942 42 54 65461 1335 12 118612 46 54 30080 1403 19 72880 33 14 24094 910 13 65475 18 16 69008 616 17 99643 55 33 54968 1407 15 71965 35 32 46090 771 16 77272 59 21 27507 766 24 49289 19 15 10672 473 15 135131 66 38 34029 1376 17 108446 60 22 46300 1232 18 89746 36 28 24760 1521 20 44296 25 10 18779 572 16 77648 47 31 21280 1059 16 181528 54 32 40662 1544 18 134019 53 32 28987 1230 22 124064 40 43 22827 1206 8 92630 40 27 18513 1205 17 121848 39 37 30594 1255 18 52915 14 20 24006 613 16 81872 45 32 27913 721 23 58981 36 0 42744 1109 22 53515 28 5 12934 740 13 60812 44 26 22574 1126 13 56375 30 10 41385 728 16 65490 22 27 18653 689 16 80949 17 11 18472 592 20 76302 31 29 30976 995 22 104011 55 25 63339 1613 17 98104 54 55 25568 2048 18 67989 21 23 33747 705 17 30989 14 5 4154 301 12 135458 81 43 19474 1803 7 73504 35 23 35130 799 17 63123 43 34 39067 861 14 61254 46 36 13310 1186 23 74914 30 35 65892 1451 17 31774 23 0 4143 628 14 81437 38 37 28579 1161 15 87186 54 28 51776 1463 17 50090 20 16 21152 742 21 65745 53 26 38084 979 18 56653 45 38 27717 675 18 158399 39 23 32928 1241 17 46455 20 22 11342 676 17 73624 24 30 19499 1049 16 38395 31 16 16380 620 15 91899 35 18 36874 1081 21 139526 151 28 48259 1688 16 52164 52 32 16734 736 14 51567 30 21 28207 617 15 70551 31 23 30143 812 17 84856 29 29 41369 1051 15 102538 57 50 45833 1656 15 86678 40 12 29156 705 10 85709 44 21 35944 945 6 34662 25 18 36278 554 22 150580 77 27 45588 1597 21 99611 35 41 45097 982 1 19349 11 13 3895 222 18 99373 63 12 28394 1212 17 86230 44 21 18632 1143 4 30837 19 8 2325 435 10 31706 13 26 25139 532 16 89806 42 27 27975 882 16 62088 38 13 14483 608 9 40151 29 16 13127 459 16 27634 20 2 5839 578 17 76990 27 42 24069 826 7 37460 20 5 3738 509 15 54157 19 37 18625 717 14 49862 37 17 36341 637 14 84337 26 38 24548 857 18 64175 42 37 21792 830 12 59382 49 29 26263 652 16 119308 30 32 23686 707 21 76702 49 35 49303 954 19 103425 67 17 25659 1461 16 70344 28 20 28904 672 1 43410 19 7 2781 778 16 104838 49 46 29236 1141 10 62215 27 24 19546 680 19 69304 30 40 22818 1090 12 53117 22 3 32689 616 2 19764 12 10 5752 285 14 86680 31 37 22197 1145 17 84105 20 17 20055 733 19 77945 20 28 25272 888 14 89113 39 19 82206 849 11 91005 29 29 32073 1182 4 40248 16 8 5444 528 16 64187 27 10 20154 642 20 50857 21 15 36944 947 12 56613 19 15 8019 819 15 62792 35 28 30884 757 16 72535 14 17 19540 894
Names of X columns:
compendiums_reviewed time_in_rfc logins blogged_computations totsize pageviews
Sample Range:
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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
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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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