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Data X:
210907 56 112285 24188 146283 144 145 120982 56 84786 18273 98364 103 101 176508 54 83123 14130 86146 98 98 179321 89 101193 32287 96933 135 132 123185 40 38361 8654 79234 61 60 52746 25 68504 9245 42551 39 38 385534 92 119182 33251 195663 150 144 33170 18 22807 1271 6853 5 5 101645 63 17140 5279 21529 28 28 149061 44 116174 27101 95757 84 84 165446 33 57635 16373 85584 80 79 237213 84 66198 19716 143983 130 127 173326 88 71701 17753 75851 82 78 133131 55 57793 9028 59238 60 60 258873 60 80444 18653 93163 131 131 180083 66 53855 8828 96037 84 84 324799 154 97668 29498 151511 140 133 230964 53 133824 27563 136368 151 150 236785 119 101481 18293 112642 91 91 135473 41 99645 22530 94728 138 132 202925 61 114789 15977 105499 150 136 215147 58 99052 35082 121527 124 124 344297 75 67654 16116 127766 119 118 153935 33 65553 15849 98958 73 70 132943 40 97500 16026 77900 110 107 174724 92 69112 26569 85646 123 119 174415 100 82753 24785 98579 90 89 225548 112 85323 17569 130767 116 112 223632 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329267 259 115762 15808 155135 148 141 65029 17 32551 3597 25109 21 21 101097 64 31701 5296 45824 35 35 218946 41 80670 25239 102996 112 109 244052 68 143558 29801 160604 137 133 341570 168 117105 18450 158051 135 123 103597 43 23789 7132 44547 26 26 233328 132 120733 34861 162647 230 230 256462 105 105195 35940 174141 181 166 206161 71 73107 16688 60622 71 68 311473 112 132068 24683 179566 147 147 235800 94 149193 46230 184301 190 179 177939 82 46821 10387 75661 64 61 207176 70 87011 21436 96144 105 101 196553 57 95260 30546 129847 107 108 174184 53 55183 19746 117286 94 90 143246 103 106671 15977 71180 116 114 187559 121 73511 22583 109377 106 103 187681 62 92945 17274 85298 143 142 119016 52 78664 16469 73631 81 79 182192 52 70054 14251 86767 89 88 73566 32 22618 3007 23824 26 25 194979 62 74011 16851 93487 84 83 167488 45 83737 21113 82981 113 113 143756 46 69094 17401 73815 120 118 275541 63 93133 23958 94552 110 110 243199 75 95536 23567 132190 134 129 182999 88 225920 13065 128754 54 51 135649 46 62133 15358 66363 96 93 152299 53 61370 14587 67808 78 76 120221 37 43836 12770 61724 51 49 346485 90 106117 24021 131722 121 118 145790 63 38692 9648 68580 38 38 193339 78 84651 20537 106175 145 141 80953 25 56622 7905 55792 59 58 122774 45 15986 4527 25157 27 27 130585 46 95364 30495 76669 91 91 112611 41 26706 7117 57283 48 48 286468 144 89691 17719 105805 68 63 241066 82 67267 27056 129484 58 56 148446 91 126846 33473 72413 150 144 204713 71 41140 9758 87831 74 73 182079 63 102860 21115 96971 181 168 140344 53 51715 7236 71299 65 64 220516 62 55801 13790 77494 97 97 243060 63 111813 32902 120336 121 117 162765 32 120293 25131 93913 99 100 182613 39 138599 30910 136048 152 149 232138 62 161647 35947 181248 188 187 265318 117 115929 29848 146123 138 127 85574 34 24266 6943 32036 40 37 310839 92 162901 42705 186646 254 245 225060 93 109825 31808 102255 87 87 232317 54 129838 26675 168237 178 177 144966 144 37510 8435 64219 51 49 43287 14 43750 7409 19630 49 49 155754 61 40652 14993 76825 73 73 164709 109 87771 36867 115338 176 177 201940 38 85872 33835 109427 94 94 235454 73 89275 24164 118168 120 117 220801 75 44418 12607 84845 66 60 99466 50 192565 22609 153197 56 55 92661 61 35232 5892 29877 39 39 133328 55 40909 17014 63506 66 64 61361 77 13294 5394 22445 27 26 125930 75 32387 9178 47695 65 64 100750 72 140867 6440 68370 58 58 224549 50 120662 21916 146304 98 95 82316 32 21233 4011 38233 25 25 102010 53 44332 5818 42071 26 26 101523 42 61056 18647 50517 77 76 243511 71 101338 20556 103950 130 129 22938 10 1168 238 5841 11 11 41566 35 13497 70 2341 2 2 152474 65 65567 22392 84396 101 101 61857 25 25162 3913 24610 31 28 99923 66 32334 12237 35753 36 36 132487 41 40735 8388 55515 120 89 317394 86 91413 22120 209056 195 193 21054 16 855 338 6622 4 4 209641 42 97068 11727 115814 89 84 22648 19 44339 3704 11609 24 23 31414 19 14116 3988 13155 39 39 46698 45 10288 3030 18274 14 14 131698 65 65622 13520 72875 78 78 91735 35 16563 1421 10112 15 14 244749 95 76643 20923 142775 106 101 184510 49 110681 20237 68847 83 82 79863 37 29011 3219 17659 24 24 128423 64 92696 3769 20112 37 36 97839 38 94785 12252 61023 77 75 38214 34 8773 1888 13983 16 16 151101 32 83209 14497 65176 56 55 272458 65 93815 28864 132432 132 131 172494 52 86687 21721 112494 144 131 108043 62 34553 4821 45109 40 39 328107 65 105547 33644 170875 153 144 250579 83 103487 15923 180759 143 139 351067 95 213688 42935 214921 220 211 158015 29 71220 18864 100226 79 78 98866 18 23517 4977 32043 50 50 85439 33 56926 7785 54454 39 39 229242 247 91721 17939 78876 95 90 351619 139 115168 23436 170745 169 166 84207 29 111194 325 6940 12 12 120445 118 51009 13539 49025 63 57 324598 110 135777 34538 122037 134 133 131069 67 51513 12198 53782 69 69 204271 42 74163 26924 127748 119 119 165543 65 51633 12716 86839 119 119 141722 94 75345 8172 44830 75 65 116048 64 33416 10855 77395 63 61 250047 81 83305 11932 89324 55 49 299775 95 98952 14300 103300 103 101 195838 67 102372 25515 112283 197 196 173260 63 37238 2805 10901 16 15 254488 83 103772 29402 120691 140 136 104389 45 123969 16440 58106 89 89 136084 30 27142 11221 57140 40 40 199476 70 135400 28732 122422 125 123 92499 32 21399 5250 25899 21 21 224330 83 130115 28608 139296 167 163 135781 31 24874 8092 52678 32 29 74408 67 34988 4473 23853 36 35 81240 66 45549 1572 17306 13 13 14688 10 6023 2065 7953 5 5 181633 70 64466 14817 89455 96 96 271856 103 54990 16714 147866 151 151 7199 5 1644 556 4245 6 6 46660 20 6179 2089 21509 13 13 17547 5 3926 2658 7670 3 3 133368 36 32755 10695 66675 57 56 95227 34 34777 1669 14336 23 23 152601 48 73224 16267 53608 61 57 98146 40 27114 7768 30059 21 14 79619 43 20760 7252 29668 43 43 59194 31 37636 6387 22097 20 20 139942 42 65461 18715 96841 82 72 118612 46 30080 7936 41907 90 87 72880 33 24094 8643 27080 25 21 65475 18 69008 7294 35885 60 56 99643 55 54968 4570 41247 61 59 71965 35 46090 7185 28313 85 82 77272 59 27507 10058 36845 43 43 49289 19 10672 2342 16548 25 25 135131 66 34029 8509 36134 41 38 108446 60 46300 13275 55764 26 25 89746 36 24760 6816 28910 38 38 44296 25 18779 1930 13339 12 12 77648 47 21280 8086 25319 29 29 181528 54 40662 10737 66956 49 47 134019 53 28987 8033 47487 46 45 124064 40 22827 7058 52785 41 40 92630 40 18513 6782 44683 31 30 121848 39 30594 5401 35619 41 41 52915 14 24006 6521 21920 26 25 81872 45 27913 10856 45608 23 23 58981 36 42744 2154 7721 14 14 53515 28 12934 6117 20634 16 16 60812 44 22574 5238 29788 25 26 56375 30 41385 4820 31931 21 21 65490 22 18653 5615 37754 32 27 80949 17 18472 4272 32505 9 9 76302 31 30976 8702 40557 35 33 104011 55 63339 15340 94238 42 42 98104 54 25568 8030 44197 68 68 67989 21 33747 9526 43228 32 32 30989 14 4154 1278 4103 6 6 135458 81 19474 4236 44144 68 67 73504 35 35130 3023 32868 33 33 63123 43 39067 7196 27640 84 77 61254 46 13310 3394 14063 46 46 74914 30 65892 6371 28990 30 30 31774 23 4143 1574 4694 0 0 81437 38 28579 9620 42648 36 36 87186 54 51776 6978 64329 47 46 50090 20 21152 4911 21928 20 18 65745 53 38084 8645 25836 50 48 56653 45 27717 8987 22779 30 29 158399 39 32928 5544 40820 30 28 46455 20 11342 3083 27530 34 34 73624 24 19499 6909 32378 33 33 38395 31 16380 3189 10824 34 34 91899 35 36874 6745 39613 37 33 139526 151 48259 16724 60865 83 80 52164 52 16734 4850 19787 32 32 51567 30 28207 7025 20107 30 30 70551 31 30143 6047 36605 43 41 84856 29 41369 7377 40961 41 41 102538 57 45833 9078 48231 51 51 86678 40 29156 4605 39725 19 18 85709 44 35944 3238 21455 37 34 34662 25 36278 8100 23430 33 31 150580 77 45588 9653 62991 41 39 99611 35 45097 8914 49363 54 54 19349 11 3895 786 9604 14 14 99373 63 28394 6700 24552 25 24 86230 44 18632 5788 31493 25 24 30837 19 2325 593 3439 8 8 31706 13 25139 4506 19555 26 26 89806 42 27975 6382 21228 20 19 62088 38 14483 5621 23177 11 11 40151 29 13127 3997 22094 14 14 27634 20 5839 520 2342 3 1 76990 27 24069 8891 38798 40 39 37460 20 3738 999 3255 5 5 54157 19 18625 7067 24261 38 37 49862 37 36341 4639 18511 32 32 84337 26 24548 5654 40798 41 38 64175 42 21792 6928 28893 46 47 59382 49 26263 1514 21425 47 47 119308 30 23686 9238 50276 37 37 76702 49 49303 8204 37643 51 51 103425 67 25659 5926 30377 49 45 70344 28 28904 5785 27126 21 21 43410 19 2781 4 13 1 1 104838 49 29236 5930 42097 44 42 62215 27 19546 3710 24451 26 26 69304 30 22818 705 14335 21 21 53117 22 32689 443 5084 4 4 19764 12 5752 2416 9927 10 10 86680 31 22197 7747 43527 43 43 84105 20 20055 5432 27184 34 34 77945 20 25272 4913 21610 32 31 89113 39 82206 2650 20484 20 19 91005 29 32073 2370 20156 34 34 40248 16 5444 775 6012 6 6 64187 27 20154 5576 18475 12 11 50857 21 36944 1352 12645 24 24 56613 19 8019 3080 11017 16 16 62792 35 30884 10205 37623 72 72 72535 14 19540 6095 35873 27 21
Names of X columns:
TimeRFCSEC #Logins CW#characters CW#revisions CW#seconds CWIncludedHyperlinks CWIncludedBlogs
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') }
Compute
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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