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Data X:
1418 210907 56 112285 3 145 869 120982 56 84786 4 101 1530 176508 54 83123 12 98 2172 179321 89 101193 2 132 901 123185 40 38361 1 60 463 52746 25 68504 3 38 3201 385534 92 119182 0 144 371 33170 18 22807 0 5 1192 101645 63 17140 0 28 1583 149061 44 116174 5 84 1439 165446 33 57635 0 79 1764 237213 84 66198 0 127 1495 173326 88 71701 7 78 1373 133131 55 57793 7 60 2187 258873 60 80444 3 131 1491 180083 66 53855 9 84 4041 324799 154 97668 0 133 1706 230964 53 133824 4 150 2152 236785 119 101481 3 91 1036 135473 41 99645 0 132 1882 202925 61 114789 7 136 1929 215147 58 99052 0 124 2242 344297 75 67654 1 118 1220 153935 33 65553 5 70 1289 132943 40 97500 7 107 2515 174724 92 69112 0 119 2147 174415 100 82753 0 89 2352 225548 112 85323 5 112 1638 223632 73 72654 0 108 1222 124817 40 30727 0 52 1812 221698 45 77873 0 112 1677 210767 60 117478 3 116 1579 170266 62 74007 4 123 1731 260561 75 90183 1 125 807 84853 31 61542 4 27 2452 294424 77 101494 2 162 829 101011 34 27570 0 32 1940 215641 46 55813 0 64 2662 325107 99 79215 0 92 186 7176 17 1423 0 0 1499 167542 66 55461 2 83 865 106408 30 31081 1 41 1793 96560 76 22996 0 47 2527 265769 146 83122 2 120 2747 269651 67 70106 10 105 1324 149112 56 60578 6 79 2702 175824 107 39992 0 65 1383 152871 58 79892 5 70 1179 111665 34 49810 4 55 2099 116408 61 71570 1 39 4308 362301 119 100708 2 67 918 78800 42 33032 2 21 1831 183167 66 82875 0 127 3373 277965 89 139077 8 152 1713 150629 44 71595 3 113 1438 168809 66 72260 0 99 496 24188 24 5950 0 7 2253 329267 259 115762 8 141 744 65029 17 32551 5 21 1161 101097 64 31701 3 35 2352 218946 41 80670 1 109 2144 244052 68 143558 5 133 4691 341570 168 117105 1 123 1112 103597 43 23789 1 26 2694 233328 132 120733 5 230 1973 256462 105 105195 0 166 1769 206161 71 73107 12 68 3148 311473 112 132068 8 147 2474 235800 94 149193 8 179 2084 177939 82 46821 8 61 1954 207176 70 87011 8 101 1226 196553 57 95260 2 108 1389 174184 53 55183 0 90 1496 143246 103 106671 5 114 2269 187559 121 73511 8 103 1833 187681 62 92945 2 142 1268 119016 52 78664 5 79 1943 182192 52 70054 12 88 893 73566 32 22618 6 25 1762 194979 62 74011 7 83 1403 167488 45 83737 2 113 1425 143756 46 69094 0 118 1857 275541 63 93133 4 110 1840 243199 75 95536 3 129 1502 182999 88 225920 6 51 1441 135649 46 62133 2 93 1420 152299 53 61370 0 76 1416 120221 37 43836 1 49 2970 346485 90 106117 0 118 1317 145790 63 38692 5 38 1644 193339 78 84651 2 141 870 80953 25 56622 0 58 1654 122774 45 15986 0 27 1054 130585 46 95364 5 91 937 112611 41 26706 0 48 3004 286468 144 89691 1 63 2008 241066 82 67267 0 56 2547 148446 91 126846 1 144 1885 204713 71 41140 1 73 1626 182079 63 102860 2 168 1468 140344 53 51715 6 64 2445 220516 62 55801 1 97 1964 243060 63 111813 4 117 1381 162765 32 120293 2 100 1369 182613 39 138599 3 149 1659 232138 62 161647 0 187 2888 265318 117 115929 10 127 1290 85574 34 24266 0 37 2845 310839 92 162901 9 245 1982 225060 93 109825 7 87 1904 232317 54 129838 0 177 1391 144966 144 37510 0 49 602 43287 14 43750 4 49 1743 155754 61 40652 4 73 1559 164709 109 87771 0 177 2014 201940 38 85872 0 94 2143 235454 73 89275 0 117 2146 220801 75 44418 1 60 874 99466 50 192565 0 55 1590 92661 61 35232 1 39 1590 133328 55 40909 0 64 1210 61361 77 13294 0 26 2072 125930 75 32387 4 64 1281 100750 72 140867 0 58 1401 224549 50 120662 4 95 834 82316 32 21233 4 25 1105 102010 53 44332 3 26 1272 101523 42 61056 0 76 1944 243511 71 101338 0 129 391 22938 10 1168 0 11 761 41566 35 13497 5 2 1605 152474 65 65567 0 101 530 61857 25 25162 4 28 1988 99923 66 32334 0 36 1386 132487 41 40735 0 89 2395 317394 86 91413 1 193 387 21054 16 855 0 4 1742 209641 42 97068 5 84 620 22648 19 44339 0 23 449 31414 19 14116 0 39 800 46698 45 10288 0 14 1684 131698 65 65622 0 78 1050 91735 35 16563 0 14 2699 244749 95 76643 2 101 1606 184510 49 110681 7 82 1502 79863 37 29011 1 24 1204 128423 64 92696 8 36 1138 97839 38 94785 2 75 568 38214 34 8773 0 16 1459 151101 32 83209 2 55 2158 272458 65 93815 0 131 1111 172494 52 86687 0 131 1421 108043 62 34553 1 39 2833 328107 65 105547 3 144 1955 250579 83 103487 0 139 2922 351067 95 213688 3 211 1002 158015 29 71220 0 78 1060 98866 18 23517 0 50 956 85439 33 56926 0 39 2186 229242 247 91721 4 90 3604 351619 139 115168 4 166 1035 84207 29 111194 11 12 1417 120445 118 51009 0 57 3261 324598 110 135777 0 133 1587 131069 67 51513 4 69 1424 204271 42 74163 0 119 1701 165543 65 51633 1 119 1249 141722 94 75345 0 65 946 116048 64 33416 0 61 1926 250047 81 83305 0 49 3352 299775 95 98952 9 101 1641 195838 67 102372 1 196 2035 173260 63 37238 3 15 2312 254488 83 103772 10 136 1369 104389 45 123969 5 89 1577 136084 30 27142 0 40 2201 199476 70 135400 2 123 961 92499 32 21399 0 21 1900 224330 83 130115 1 163 1254 135781 31 24874 2 29 1335 74408 67 34988 4 35 1597 81240 66 45549 0 13 207 14688 10 6023 0 5 1645 181633 70 64466 2 96 2429 271856 103 54990 1 151 151 7199 5 1644 0 6 474 46660 20 6179 0 13 141 17547 5 3926 0 3 1639 133368 36 32755 1 56 872 95227 34 34777 0 23 1318 152601 48 73224 2 57 1018 98146 40 27114 0 14 1383 79619 43 20760 3 43 1314 59194 31 37636 6 20 1335 139942 42 65461 0 72 1403 118612 46 30080 2 87 910 72880 33 24094 0 21 616 65475 18 69008 2 56 1407 99643 55 54968 1 59 771 71965 35 46090 1 82 766 77272 59 27507 2 43 473 49289 19 10672 1 25 1376 135131 66 34029 0 38 1232 108446 60 46300 1 25 1521 89746 36 24760 3 38 572 44296 25 18779 0 12 1059 77648 47 21280 0 29 1544 181528 54 40662 0 47 1230 134019 53 28987 0 45 1206 124064 40 22827 1 40 1205 92630 40 18513 4 30 1255 121848 39 30594 0 41 613 52915 14 24006 0 25 721 81872 45 27913 0 23 1109 58981 36 42744 7 14 740 53515 28 12934 2 16 1126 60812 44 22574 0 26 728 56375 30 41385 7 21 689 65490 22 18653 3 27 592 80949 17 18472 0 9 995 76302 31 30976 0 33 1613 104011 55 63339 6 42 2048 98104 54 25568 2 68 705 67989 21 33747 0 32 301 30989 14 4154 0 6 1803 135458 81 19474 3 67 799 73504 35 35130 0 33 861 63123 43 39067 1 77 1186 61254 46 13310 1 46 1451 74914 30 65892 0 30 628 31774 23 4143 1 0 1161 81437 38 28579 0 36 1463 87186 54 51776 0 46 742 50090 20 21152 0 18 979 65745 53 38084 0 48 675 56653 45 27717 0 29 1241 158399 39 32928 0 28 676 46455 20 11342 0 34 1049 73624 24 19499 0 33 620 38395 31 16380 0 34 1081 91899 35 36874 0 33 1688 139526 151 48259 0 80 736 52164 52 16734 0 32 617 51567 30 28207 2 30 812 70551 31 30143 0 41 1051 84856 29 41369 1 41 1656 102538 57 45833 1 51 705 86678 40 29156 0 18 945 85709 44 35944 0 34 554 34662 25 36278 0 31 1597 150580 77 45588 0 39 982 99611 35 45097 0 54 222 19349 11 3895 0 14 1212 99373 63 28394 1 24 1143 86230 44 18632 0 24 435 30837 19 2325 0 8 532 31706 13 25139 0 26 882 89806 42 27975 0 19 608 62088 38 14483 1 11 459 40151 29 13127 0 14 578 27634 20 5839 0 1 826 76990 27 24069 0 39 509 37460 20 3738 0 5 717 54157 19 18625 0 37 637 49862 37 36341 0 32 857 84337 26 24548 0 38 830 64175 42 21792 0 47 652 59382 49 26263 0 47 707 119308 30 23686 0 37 954 76702 49 49303 0 51 1461 103425 67 25659 1 45 672 70344 28 28904 0 21 778 43410 19 2781 0 1 1141 104838 49 29236 1 42 680 62215 27 19546 0 26 1090 69304 30 22818 6 21 616 53117 22 32689 3 4 285 19764 12 5752 1 10 1145 86680 31 22197 2 43 733 84105 20 20055 0 34 888 77945 20 25272 0 31 849 89113 39 82206 0 19 1182 91005 29 32073 3 34 528 40248 16 5444 1 6 642 64187 27 20154 0 11 947 50857 21 36944 0 24 819 56613 19 8019 1 16 757 62792 35 30884 0 72 894 72535 14 19540 0 21
Names of X columns:
views time logins totsize shared blogs
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
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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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