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Data X:
210907 56 79 30 112285 24188 120982 56 58 28 84786 18273 176508 54 60 38 83123 14130 179321 89 108 30 101193 32287 123185 40 49 22 38361 8654 52746 25 0 26 68504 9245 385534 92 121 25 119182 33251 33170 18 1 18 22807 1271 101645 63 20 11 17140 5279 149061 44 43 26 116174 27101 165446 33 69 25 57635 16373 237213 84 78 38 66198 19716 173326 88 86 44 71701 17753 133131 55 44 30 57793 9028 258873 60 104 40 80444 18653 180083 66 63 34 53855 8828 324799 154 158 47 97668 29498 230964 53 102 30 133824 27563 236785 119 77 31 101481 18293 135473 41 82 23 99645 22530 202925 61 115 36 114789 15977 215147 58 101 36 99052 35082 344297 75 80 30 67654 16116 153935 33 50 25 65553 15849 132943 40 83 39 97500 16026 174724 92 123 34 69112 26569 174415 100 73 31 82753 24785 225548 112 81 31 85323 17569 223632 73 105 33 72654 23825 124817 40 47 25 30727 7869 221698 45 105 33 77873 14975 210767 60 94 35 117478 37791 170266 62 44 42 74007 9605 260561 75 114 43 90183 27295 84853 31 38 30 61542 2746 294424 77 107 33 101494 34461 101011 34 30 13 27570 8098 215641 46 71 32 55813 4787 325107 99 84 36 79215 24919 7176 17 0 0 1423 603 167542 66 59 28 55461 16329 106408 30 33 14 31081 12558 96560 76 42 17 22996 7784 265769 146 96 32 83122 28522 269651 67 106 30 70106 22265 149112 56 56 35 60578 14459 175824 107 57 20 39992 14526 152871 58 59 28 79892 22240 111665 34 39 28 49810 11802 116408 61 34 39 71570 7623 362301 119 76 34 100708 11912 78800 42 20 26 33032 7935 183167 66 91 39 82875 18220 277965 89 115 39 139077 19199 150629 44 85 33 71595 19918 168809 66 76 28 72260 21884 24188 24 8 4 5950 2694 329267 259 79 39 115762 15808 65029 17 21 18 32551 3597 101097 64 30 14 31701 5296 218946 41 76 29 80670 25239 244052 68 101 44 143558 29801 341570 168 94 21 117105 18450 103597 43 27 16 23789 7132 233328 132 92 28 120733 34861 256462 105 123 35 105195 35940 206161 71 75 28 73107 16688 311473 112 128 38 132068 24683 235800 94 105 23 149193 46230 177939 82 55 36 46821 10387 207176 70 56 32 87011 21436 196553 57 41 29 95260 30546 174184 53 72 25 55183 19746 143246 103 67 27 106671 15977 187559 121 75 36 73511 22583 187681 62 114 28 92945 17274 119016 52 118 23 78664 16469 182192 52 77 40 70054 14251 73566 32 22 23 22618 3007 194979 62 66 40 74011 16851 167488 45 69 28 83737 21113 143756 46 105 34 69094 17401 275541 63 116 33 93133 23958 243199 75 88 28 95536 23567 182999 88 73 34 225920 13065 135649 46 99 30 62133 15358 152299 53 62 33 61370 14587 120221 37 53 22 43836 12770 346485 90 118 38 106117 24021 145790 63 30 26 38692 9648 193339 78 100 35 84651 20537 80953 25 49 8 56622 7905 122774 45 24 24 15986 4527 130585 46 67 29 95364 30495 112611 41 46 20 26706 7117 286468 144 57 29 89691 17719 241066 82 75 45 67267 27056 148446 91 135 37 126846 33473 204713 71 68 33 41140 9758 182079 63 124 33 102860 21115 140344 53 33 25 51715 7236 220516 62 98 32 55801 13790 243060 63 58 29 111813 32902 162765 32 68 28 120293 25131 182613 39 81 28 138599 30910 232138 62 131 31 161647 35947 265318 117 110 52 115929 29848 85574 34 37 21 24266 6943 310839 92 130 24 162901 42705 225060 93 93 41 109825 31808 232317 54 118 33 129838 26675 144966 144 39 32 37510 8435 43287 14 13 19 43750 7409 155754 61 74 20 40652 14993 164709 109 81 31 87771 36867 201940 38 109 31 85872 33835 235454 73 151 32 89275 24164 220801 75 51 18 44418 12607 99466 50 28 23 192565 22609 92661 61 40 17 35232 5892 133328 55 56 20 40909 17014 61361 77 27 12 13294 5394 125930 75 37 17 32387 9178 100750 72 83 30 140867 6440 224549 50 54 31 120662 21916 82316 32 27 10 21233 4011 102010 53 28 13 44332 5818 101523 42 59 22 61056 18647 243511 71 133 42 101338 20556 22938 10 12 1 1168 238 41566 35 0 9 13497 70 152474 65 106 32 65567 22392 61857 25 23 11 25162 3913 99923 66 44 25 32334 12237 132487 41 71 36 40735 8388 317394 86 116 31 91413 22120 21054 16 4 0 855 338 209641 42 62 24 97068 11727 22648 19 12 13 44339 3704 31414 19 18 8 14116 3988 46698 45 14 13 10288 3030 131698 65 60 19 65622 13520 91735 35 7 18 16563 1421 244749 95 98 33 76643 20923 184510 49 64 40 110681 20237 79863 37 29 22 29011 3219 128423 64 32 38 92696 3769 97839 38 25 24 94785 12252 38214 34 16 8 8773 1888 151101 32 48 35 83209 14497 272458 65 100 43 93815 28864 172494 52 46 43 86687 21721 108043 62 45 14 34553 4821 328107 65 129 41 105547 33644 250579 83 130 38 103487 15923 351067 95 136 45 213688 42935 158015 29 59 31 71220 18864 98866 18 25 13 23517 4977 85439 33 32 28 56926 7785 229242 247 63 31 91721 17939 351619 139 95 40 115168 23436 84207 29 14 30 111194 325 120445 118 36 16 51009 13539 324598 110 113 37 135777 34538 131069 67 47 30 51513 12198 204271 42 92 35 74163 26924 165543 65 70 32 51633 12716 141722 94 19 27 75345 8172 116048 64 50 20 33416 10855 250047 81 41 18 83305 11932 299775 95 91 31 98952 14300 195838 67 111 31 102372 25515 173260 63 41 21 37238 2805 254488 83 120 39 103772 29402 104389 45 135 41 123969 16440 136084 30 27 13 27142 11221 199476 70 87 32 135400 28732 92499 32 25 18 21399 5250 224330 83 131 39 130115 28608 135781 31 45 14 24874 8092 74408 67 29 7 34988 4473 81240 66 58 17 45549 1572 14688 10 4 0 6023 2065 181633 70 47 30 64466 14817 271856 103 109 37 54990 16714 7199 5 7 0 1644 556 46660 20 12 5 6179 2089 17547 5 0 1 3926 2658 133368 36 37 16 32755 10695 95227 34 37 32 34777 1669 152601 48 46 24 73224 16267 98146 40 15 17 27114 7768 79619 43 42 11 20760 7252 59194 31 7 24 37636 6387 139942 42 54 22 65461 18715 118612 46 54 12 30080 7936 72880 33 14 19 24094 8643 65475 18 16 13 69008 7294 99643 55 33 17 54968 4570 71965 35 32 15 46090 7185 77272 59 21 16 27507 10058 49289 19 15 24 10672 2342 135131 66 38 15 34029 8509 108446 60 22 17 46300 13275 89746 36 28 18 24760 6816 44296 25 10 20 18779 1930 77648 47 31 16 21280 8086 181528 54 32 16 40662 10737 134019 53 32 18 28987 8033 124064 40 43 22 22827 7058 92630 40 27 8 18513 6782 121848 39 37 17 30594 5401 52915 14 20 18 24006 6521 81872 45 32 16 27913 10856 58981 36 0 23 42744 2154 53515 28 5 22 12934 6117 60812 44 26 13 22574 5238 56375 30 10 13 41385 4820 65490 22 27 16 18653 5615 80949 17 11 16 18472 4272 76302 31 29 20 30976 8702 104011 55 25 22 63339 15340 98104 54 55 17 25568 8030 67989 21 23 18 33747 9526 30989 14 5 17 4154 1278 135458 81 43 12 19474 4236 73504 35 23 7 35130 3023 63123 43 34 17 39067 7196 61254 46 36 14 13310 3394 74914 30 35 23 65892 6371 31774 23 0 17 4143 1574 81437 38 37 14 28579 9620 87186 54 28 15 51776 6978 50090 20 16 17 21152 4911 65745 53 26 21 38084 8645 56653 45 38 18 27717 8987 158399 39 23 18 32928 5544 46455 20 22 17 11342 3083 73624 24 30 17 19499 6909 38395 31 16 16 16380 3189 91899 35 18 15 36874 6745 139526 151 28 21 48259 16724 52164 52 32 16 16734 4850 51567 30 21 14 28207 7025 70551 31 23 15 30143 6047 84856 29 29 17 41369 7377 102538 57 50 15 45833 9078 86678 40 12 15 29156 4605 85709 44 21 10 35944 3238 34662 25 18 6 36278 8100 150580 77 27 22 45588 9653 99611 35 41 21 45097 8914 19349 11 13 1 3895 786 99373 63 12 18 28394 6700 86230 44 21 17 18632 5788 30837 19 8 4 2325 593 31706 13 26 10 25139 4506 89806 42 27 16 27975 6382 62088 38 13 16 14483 5621 40151 29 16 9 13127 3997 27634 20 2 16 5839 520 76990 27 42 17 24069 8891 37460 20 5 7 3738 999 54157 19 37 15 18625 7067 49862 37 17 14 36341 4639 84337 26 38 14 24548 5654 64175 42 37 18 21792 6928 59382 49 29 12 26263 1514 119308 30 32 16 23686 9238 76702 49 35 21 49303 8204 103425 67 17 19 25659 5926 70344 28 20 16 28904 5785 43410 19 7 1 2781 4 104838 49 46 16 29236 5930 62215 27 24 10 19546 3710 69304 30 40 19 22818 705 53117 22 3 12 32689 443 19764 12 10 2 5752 2416 86680 31 37 14 22197 7747 84105 20 17 17 20055 5432 77945 20 28 19 25272 4913 89113 39 19 14 82206 2650 91005 29 29 11 32073 2370 40248 16 8 4 5444 775 64187 27 10 16 20154 5576 50857 21 15 20 36944 1352 56613 19 15 12 8019 3080 62792 35 28 15 30884 10205 72535 14 17 16 19540 6095
Names of X columns:
Time_in_RFC Logins blogged_computations compendiums_reviewed totsize totrevisions
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
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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