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Data X:
140824 279055 73 1818 504 130 110459 212408 75 1433 510 143 105079 233939 83 2059 710 118 112098 222117 106 2733 1154 146 43929 189911 56 1399 415 73 76173 70849 28 631 179 89 187326 605767 135 5460 2563 146 22807 33186 19 381 111 22 144408 227332 62 2150 763 132 66485 267925 49 2042 661 92 79089 371987 122 2536 981 147 81625 264989 131 2377 733 203 68788 212638 87 2100 785 113 103297 368577 85 3020 1186 171 69446 269455 88 2265 724 87 114948 398124 191 5139 1774 208 167949 335567 77 2363 845 153 125081 432711 173 3564 1390 97 125818 182016 58 1477 514 95 136588 267365 89 2398 692 197 112431 279428 73 2546 847 160 103037 508849 111 3150 1397 148 82317 220142 49 1705 569 84 118906 200004 58 1787 636 227 83515 257139 133 3792 1370 154 104581 270941 138 3108 1092 151 103129 324969 134 3230 1201 142 83243 329962 92 2348 763 148 37110 190867 60 1780 652 110 113344 393860 79 3218 1213 149 139165 327660 89 2692 1111 179 86652 269239 83 2187 758 149 112302 396136 106 2577 906 187 69652 130446 49 1293 456 153 119442 430118 104 3567 1293 163 69867 273950 56 2764 1186 127 101629 428077 128 3755 1348 151 70168 254312 93 2075 695 100 31081 120351 35 995 306 46 103925 395658 212 3750 1319 156 92622 345875 86 3413 1566 128 79011 216827 82 2053 784 111 93487 224524 83 1984 730 119 64520 182485 69 1825 488 148 93473 157164 85 2599 1051 65 114360 459455 157 5572 2089 134 33032 78800 42 918 330 66 96125 255072 85 2685 764 201 151911 368086 123 4145 1410 177 89256 230299 70 2841 1187 156 95676 244782 81 2175 691 158 5950 24188 24 496 218 7 149695 400109 334 2699 865 175 32551 65029 17 744 255 61 31701 101097 64 1161 454 41 100087 309810 67 3333 1229 133 169707 375638 91 2970 790 228 150491 367127 204 3968 1208 140 120192 381998 155 2878 1102 155 95893 280106 90 2399 919 141 151715 400971 153 4121 1352 181 176225 315924 122 3294 1190 75 59900 291391 124 3132 1257 97 104767 295075 93 2868 1030 142 114799 280018 81 1778 669 136 72128 267432 71 2109 542 87 143592 217181 141 2148 652 140 89626 258166 159 3009 894 169 131072 264771 88 2562 917 129 126817 182961 73 1737 637 92 81351 256967 74 2680 900 160 22618 73566 32 893 385 67 88977 272362 93 2389 784 179 92059 229056 62 2197 910 90 81897 229851 70 2227 781 144 108146 371391 91 2370 1001 144 126372 398210 104 3226 1265 144 249771 220419 111 1978 587 134 71154 231884 72 2516 767 146 71571 219381 73 2147 746 121 55918 206169 54 2150 795 112 160141 483074 132 4229 1272 145 38692 146100 72 1380 657 99 102812 295224 109 2449 703 96 56622 80953 25 870 437 27 15986 217384 63 2700 1060 77 123534 179344 62 1574 459 137 108535 415550 222 4046 1586 151 93879 389059 129 3259 1084 126 144551 180679 106 3098 1051 159 56750 299505 104 2615 846 101 127654 292260 84 2404 732 144 65594 199481 68 1932 632 102 59938 282361 78 3147 1128 135 146975 329281 89 2598 971 147 165904 234577 48 2108 711 155 169265 297995 67 2193 738 138 183500 342490 90 2478 898 113 165986 416463 163 4198 1369 248 184923 429565 120 4165 1538 116 140358 297080 142 2842 893 176 149959 331792 71 2562 926 140 57224 229772 202 2449 800 59 43750 43287 14 602 214 64 48029 238089 87 2579 833 40 104978 263322 160 2591 906 98 100046 302082 61 2957 1288 139 101047 321797 95 2786 1079 135 197426 193926 96 1477 490 97 160902 175138 105 3350 990 142 147172 354041 78 2107 677 155 109432 303273 91 2332 696 115 1168 23668 13 400 156 0 83248 196743 79 2233 785 103 25162 61857 25 530 192 30 45724 217543 54 2033 641 130 110529 440711 128 3246 1251 102 855 21054 16 387 146 0 101382 252805 52 2137 866 77 14116 31961 22 492 200 9 89506 360436 125 3838 1351 150 135356 251948 77 2193 740 163 116066 187320 97 1796 524 148 144244 180842 58 1907 724 94 8773 38214 34 568 276 21 102153 280392 56 2602 862 151 117440 358276 84 2819 1031 187 104128 211775 67 1464 511 171 134238 447335 90 3946 1716 170 134047 348017 99 2554 884 145 279488 441946 133 3506 1201 198 79756 215177 43 1552 575 152 66089 130177 47 1389 481 112 102070 318037 365 3101 1031 173 146760 466139 198 4541 1574 177 154771 162279 62 1872 575 153 165933 416643 140 4403 1827 161 64593 178322 86 2113 790 115 92280 292443 54 2046 668 147 67150 283913 100 2564 905 124 128692 244931 127 2073 689 57 124089 387072 125 4112 1613 144 125386 246963 93 2340 811 126 37238 173260 63 2035 716 78 140015 346748 108 3241 1034 153 150047 178402 60 1991 739 196 154451 268750 96 2828 1086 130 156349 314070 112 2748 852 159 0 1 0 2 0 0 6023 14688 10 207 85 0 0 98 1 5 0 0 0 455 2 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 84601 291847 95 2449 816 94 68946 415421 168 3490 1142 129 0 0 0 0 0 0 0 203 4 4 0 0 1644 7199 5 151 74 0 6179 46660 21 475 259 13 3926 17547 5 141 69 4 52789 121550 46 1145 309 89 0 969 2 29 0 0 100350 242774 75 2080 695 71
Names of X columns:
Totalsize TotalTime Logins Pageviews CompendiumViews LongPR
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
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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
1 seconds
R Server
Big Analytics Cloud Computing Center
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