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Data X:
146283 94 24188 144 1418 112285 98364 103 18273 103 869 84786 86146 93 14130 98 1530 83123 96933 103 32287 135 2172 101193 79234 51 8654 61 901 38361 42551 70 9245 39 463 68504 195663 91 33251 150 3201 119182 6853 22 1271 5 371 22807 21529 38 5279 28 1192 17140 95757 93 27101 84 1583 116174 85584 60 16373 80 1439 57635 143983 123 19716 130 1764 66198 75851 148 17753 82 1495 71701 59238 90 9028 60 1373 57793 93163 124 18653 131 2187 80444 96037 70 8828 84 1491 53855 151511 168 29498 140 4041 97668 136368 115 27563 151 1706 133824 112642 71 18293 91 2152 101481 94728 66 22530 138 1036 99645 105499 134 15977 150 1882 114789 121527 117 35082 124 1929 99052 127766 108 16116 119 2242 67654 98958 84 15849 73 1220 65553 77900 156 16026 110 1289 97500 85646 120 26569 123 2515 69112 98579 114 24785 90 2147 82753 130767 94 17569 116 2352 85323 131741 120 23825 113 1638 72654 53907 81 7869 56 1222 30727 178812 110 14975 115 1812 77873 146761 133 37791 119 1677 117478 82036 122 9605 129 1579 74007 163253 158 27295 127 1731 90183 27032 109 2746 27 807 61542 171975 124 34461 175 2452 101494 65990 39 8098 35 829 27570 86572 92 4787 64 1940 55813 159676 126 24919 96 2662 79215 1929 0 603 0 186 1423 85371 70 16329 84 1499 55461 58391 37 12558 41 865 31081 31580 38 7784 47 1793 22996 136815 120 28522 126 2527 83122 120642 93 22265 105 2747 70106 69107 95 14459 80 1324 60578 50495 77 14526 70 2702 39992 108016 90 22240 73 1383 79892 46341 80 11802 57 1179 49810 78348 31 7623 40 2099 71570 79336 110 11912 68 4308 100708 56968 66 7935 21 918 33032 93176 138 18220 127 1831 82875 161632 133 19199 154 3373 139077 87850 113 19918 116 1713 71595 127969 100 21884 102 1438 72260 15049 7 2694 7 496 5950 155135 140 15808 148 2253 115762 25109 61 3597 21 744 32551 45824 41 5296 35 1161 31701 102996 96 25239 112 2352 80670 160604 164 29801 137 2144 143558 158051 78 18450 135 4691 117105 44547 49 7132 26 1112 23789 162647 102 34861 230 2694 120733 174141 124 35940 181 1973 105195 60622 99 16688 71 1769 73107 179566 129 24683 147 3148 132068 184301 62 46230 190 2474 149193 75661 73 10387 64 2084 46821 96144 114 21436 105 1954 87011 129847 99 30546 107 1226 95260 117286 70 19746 94 1389 55183 71180 104 15977 116 1496 106671 109377 116 22583 106 2269 73511 85298 91 17274 143 1833 92945 73631 74 16469 81 1268 78664 86767 138 14251 89 1943 70054 23824 67 3007 26 893 22618 93487 151 16851 84 1762 74011 82981 72 21113 113 1403 83737 73815 120 17401 120 1425 69094 94552 115 23958 110 1857 93133 132190 105 23567 134 1840 95536 128754 104 13065 54 1502 225920 66363 108 15358 96 1441 62133 67808 98 14587 78 1420 61370 61724 69 12770 51 1416 43836 131722 111 24021 121 2970 106117 68580 99 9648 38 1317 38692 106175 71 20537 145 1644 84651 55792 27 7905 59 870 56622 25157 69 4527 27 1654 15986 76669 107 30495 91 1054 95364 57283 73 7117 48 937 26706 105805 107 17719 68 3004 89691 129484 93 27056 58 2008 67267 72413 129 33473 150 2547 126846 87831 69 9758 74 1885 41140 96971 118 21115 181 1626 102860 71299 73 7236 65 1468 51715 77494 119 13790 97 2445 55801 120336 104 32902 121 1964 111813 93913 107 25131 99 1381 120293 136048 99 30910 152 1369 138599 181248 90 35947 188 1659 161647 146123 197 29848 138 2888 115929 32036 36 6943 40 1290 24266 186646 85 42705 254 2845 162901 102255 139 31808 87 1982 109825 168237 106 26675 178 1904 129838 64219 50 8435 51 1391 37510 19630 64 7409 49 602 43750 76825 31 14993 73 1743 40652 115338 63 36867 176 1559 87771 109427 92 33835 94 2014 85872 118168 106 24164 120 2143 89275 84845 63 12607 66 2146 44418 153197 69 22609 56 874 192565 29877 41 5892 39 1590 35232 63506 56 17014 66 1590 40909 22445 25 5394 27 1210 13294 47695 65 9178 65 2072 32387 68370 93 6440 58 1281 140867 146304 114 21916 98 1401 120662 38233 38 4011 25 834 21233 42071 44 5818 26 1105 44332 50517 87 18647 77 1272 61056 103950 110 20556 130 1944 101338 5841 0 238 11 391 1168 2341 27 70 2 761 13497 84396 83 22392 101 1605 65567 24610 30 3913 31 530 25162 35753 80 12237 36 1988 32334 55515 98 8388 120 1386 40735 209056 82 22120 195 2395 91413 6622 0 338 4 387 855 115814 60 11727 89 1742 97068 11609 28 3704 24 620 44339 13155 9 3988 39 449 14116 18274 33 3030 14 800 10288 72875 59 13520 78 1684 65622 10112 49 1421 15 1050 16563 142775 115 20923 106 2699 76643 68847 140 20237 83 1606 110681 17659 49 3219 24 1502 29011 20112 120 3769 37 1204 92696 61023 66 12252 77 1138 94785 13983 21 1888 16 568 8773 65176 124 14497 56 1459 83209 132432 152 28864 132 2158 93815 112494 139 21721 144 1111 86687 45109 38 4821 40 1421 34553 170875 144 33644 153 2833 105547 180759 120 15923 143 1955 103487 214921 160 42935 220 2922 213688 100226 114 18864 79 1002 71220 32043 39 4977 50 1060 23517 54454 78 7785 39 956 56926 78876 119 17939 95 2186 91721 170745 141 23436 169 3604 115168 6940 101 325 12 1035 111194 49025 56 13539 63 1417 51009 122037 133 34538 134 3261 135777 53782 83 12198 69 1587 51513 127748 116 26924 119 1424 74163 86839 90 12716 119 1701 51633 44830 36 8172 75 1249 75345 77395 50 10855 63 946 33416 89324 61 11932 55 1926 83305 103300 97 14300 103 3352 98952 112283 98 25515 197 1641 102372 10901 78 2805 16 2035 37238 120691 117 29402 140 2312 103772 58106 148 16440 89 1369 123969 57140 41 11221 40 1577 27142 122422 105 28732 125 2201 135400 25899 55 5250 21 961 21399 139296 132 28608 167 1900 130115 52678 44 8092 32 1254 24874 23853 21 4473 36 1335 34988 17306 50 1572 13 1597 45549 7953 0 2065 5 207 6023 89455 73 14817 96 1645 64466 147866 86 16714 151 2429 54990 4245 0 556 6 151 1644 21509 13 2089 13 474 6179 7670 4 2658 3 141 3926 66675 57 10695 57 1639 32755 14336 48 1669 23 872 34777 53608 46 16267 61 1318 73224 30059 48 7768 21 1018 27114 29668 32 7252 43 1383 20760 22097 68 6387 20 1314 37636 96841 87 18715 82 1335 65461 41907 43 7936 90 1403 30080 27080 67 8643 25 910 24094 35885 46 7294 60 616 69008 41247 46 4570 61 1407 54968 28313 56 7185 85 771 46090 36845 48 10058 43 766 27507 16548 44 2342 25 473 10672 36134 60 8509 41 1376 34029 55764 65 13275 26 1232 46300 28910 55 6816 38 1521 24760 13339 38 1930 12 572 18779 25319 52 8086 29 1059 21280 66956 60 10737 49 1544 40662 47487 54 8033 46 1230 28987 52785 86 7058 41 1206 22827 44683 24 6782 31 1205 18513 35619 52 5401 41 1255 30594 21920 49 6521 26 613 24006 45608 61 10856 23 721 27913 7721 61 2154 14 1109 42744 20634 81 6117 16 740 12934 29788 43 5238 25 1126 22574 31931 40 4820 21 728 41385 37754 40 5615 32 689 18653 32505 56 4272 9 592 18472 40557 68 8702 35 995 30976 94238 79 15340 42 1613 63339 44197 47 8030 68 2048 25568 43228 57 9526 32 705 33747 4103 41 1278 6 301 4154 44144 29 4236 68 1803 19474 32868 3 3023 33 799 35130 27640 60 7196 84 861 39067 14063 30 3394 46 1186 13310 28990 79 6371 30 1451 65892 4694 47 1574 0 628 4143 42648 40 9620 36 1161 28579 64329 48 6978 47 1463 51776 21928 36 4911 20 742 21152 25836 42 8645 50 979 38084 22779 49 8987 30 675 27717 40820 57 5544 30 1241 32928 27530 12 3083 34 676 11342 32378 40 6909 33 1049 19499 10824 43 3189 34 620 16380 39613 33 6745 37 1081 36874 60865 77 16724 83 1688 48259 19787 43 4850 32 736 16734 20107 45 7025 30 617 28207 36605 47 6047 43 812 30143 40961 43 7377 41 1051 41369 48231 45 9078 51 1656 45833 39725 50 4605 19 705 29156 21455 35 3238 37 945 35944 23430 7 8100 33 554 36278 62991 71 9653 41 1597 45588 49363 67 8914 54 982 45097 9604 0 786 14 222 3895 24552 62 6700 25 1212 28394 31493 54 5788 25 1143 18632 3439 4 593 8 435 2325 19555 25 4506 26 532 25139 21228 40 6382 20 882 27975 23177 38 5621 11 608 14483 22094 19 3997 14 459 13127 2342 17 520 3 578 5839 38798 67 8891 40 826 24069 3255 14 999 5 509 3738 24261 30 7067 38 717 18625 18511 54 4639 32 637 36341 40798 35 5654 41 857 24548 28893 59 6928 46 830 21792 21425 24 1514 47 652 26263 50276 58 9238 37 707 23686 37643 42 8204 51 954 49303 30377 46 5926 49 1461 25659 27126 61 5785 21 672 28904 13 3 4 1 778 2781 42097 52 5930 44 1141 29236 24451 25 3710 26 680 19546 14335 40 705 21 1090 22818 5084 32 443 4 616 32689 9927 4 2416 10 285 5752 43527 49 7747 43 1145 22197 27184 63 5432 34 733 20055 21610 67 4913 32 888 25272 20484 32 2650 20 849 82206 20156 23 2370 34 1182 32073 6012 7 775 6 528 5444 18475 54 5576 12 642 20154 12645 37 1352 24 947 36944 11017 35 3080 16 819 8019 37623 51 10205 72 757 30884 35873 39 6095 27 894 19540
Names of X columns:
totseconds feedback_messages_p120 totrevisions tothyperlinks pageviews totsize
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
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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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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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