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Data X:
1738 157382 86 563 109 0 48 18 20465 1446 168465 52 426 74 1 45 20 33629 192 7215 18 72 1 0 0 0 1423 2181 122259 91 618 154 0 49 26 25629 3335 221399 129 1118 124 0 76 30 54002 6406 454489 242 1788 278 1 118 36 151036 1479 134379 53 498 89 1 42 23 33287 1410 150416 54 384 54 0 62 30 31172 1591 121391 41 468 87 0 48 30 28113 2811 275326 91 919 129 1 67 26 57803 1932 121593 72 629 158 2 50 24 49830 1967 172071 64 611 113 0 71 30 52143 1621 86249 94 574 83 0 41 21 21055 2933 201902 100 987 255 4 77 25 47007 1332 144113 50 384 50 4 45 17 28735 1502 144677 77 452 81 3 54 19 59147 1636 134153 52 391 92 0 75 33 78950 1076 64149 52 305 72 5 0 15 13497 2376 122294 82 721 142 0 54 34 46154 710 27918 24 215 49 0 13 18 53249 902 52197 52 310 40 0 16 15 10726 2332 191463 90 697 94 0 78 27 83700 1647 176034 68 597 127 0 35 25 40400 1876 98629 49 560 164 1 38 34 33797 1800 143546 81 569 41 1 50 21 36205 1385 139780 25 513 160 0 39 21 30165 1913 174181 149 616 90 0 58 25 58534 1243 163773 79 311 55 0 70 28 44663 2483 312831 111 810 84 0 55 28 92556 1953 184024 41 815 90 0 52 20 40078 1514 151621 41 427 76 0 50 28 34711 1488 164516 42 496 111 2 54 20 31076 2073 120414 95 661 87 4 53 17 74608 2874 214975 117 861 302 0 76 25 58092 2248 200609 48 742 84 1 54 24 42009 1 0 1 0 0 0 0 0 0 1888 191923 58 890 58 0 46 27 36022 1563 93107 49 483 137 3 44 14 23333 2054 129419 45 495 267 9 35 32 53349 2053 233497 61 760 60 0 82 31 92596 1950 178228 67 628 94 2 73 21 49598 1572 126602 53 597 62 0 31 34 44093 957 94332 29 350 35 2 25 23 84205 1974 164183 75 742 59 1 57 24 63369 1036 95704 42 322 46 2 44 22 60132 1105 139901 85 282 40 2 40 22 37403 744 81293 31 212 49 1 23 35 24460 1926 189007 87 651 114 0 63 21 46456 1853 173779 80 588 113 1 43 31 66616 2445 146552 55 875 171 7 62 26 41554 658 48188 28 205 37 0 12 22 22346 1489 113870 61 381 51 0 67 21 30874 2276 266451 68 769 89 0 60 27 68701 1955 229437 77 664 67 0 55 26 35728 1630 174876 54 584 49 1 53 33 29010 1506 119070 50 465 74 6 35 11 23110 1720 186704 60 456 58 0 50 26 38844 852 72559 22 251 72 0 25 26 27084 917 111940 59 315 32 0 47 23 35139 2614 166226 86 880 59 10 30 38 57476 1558 130414 52 454 65 6 50 29 33277 1792 102141 71 673 81 0 36 19 31141 1271 115753 58 348 84 11 43 19 61281 1135 102194 33 384 46 3 44 24 25820 1169 148531 32 395 56 0 25 26 23284 1229 94982 37 391 36 0 38 29 35378 2226 178613 67 483 86 8 68 36 74990 2752 128907 65 843 152 2 83 25 29653 1007 102378 36 248 48 0 48 24 64622 340 31970 15 101 40 0 5 21 4157 2606 204812 108 910 135 3 53 19 29245 1159 104972 61 352 80 1 36 12 50008 1264 95276 62 410 60 2 62 28 52338 1516 101560 65 442 89 1 46 21 13310 2474 144193 60 627 89 0 67 34 92901 1288 71921 37 345 79 2 2 32 10956 1911 126905 54 538 111 1 64 27 34241 2337 140303 89 756 69 0 59 28 75043 816 60138 23 253 76 0 16 21 21152 1234 84971 71 395 105 0 34 31 42249 907 80420 64 211 49 0 54 26 42005 1912 244190 58 712 60 0 39 29 41152 842 56252 26 244 49 0 26 23 14399 1309 97181 32 438 132 0 37 25 28263 770 50913 42 256 49 0 17 22 17215 1567 143910 46 496 71 0 32 26 48140 2543 218900 217 624 100 0 55 33 62897 1095 90772 96 263 71 0 50 22 22883 1154 90385 54 360 49 6 39 24 41622 1324 136220 48 518 72 0 30 21 40715 1509 115572 37 535 59 5 45 28 65897 2014 139075 69 587 87 1 66 23 76542 1216 148950 56 407 68 0 39 25 37477 1265 124626 57 466 81 0 27 15 53216 762 49176 34 291 30 0 22 13 40911 2178 215480 104 694 166 0 45 36 57021 1752 182328 61 526 94 0 95 24 73116 223 19349 12 67 15 0 13 1 3895 2117 183873 95 726 104 3 26 24 46609 1888 146020 71 771 61 0 40 31 29351 619 51201 27 263 11 0 13 4 2325 802 58280 20 240 44 0 41 20 31747 1131 115944 44 360 84 0 51 23 32665 982 94341 53 249 66 1 24 23 19249 709 72904 37 190 27 0 30 12 15292 596 27676 22 194 59 0 2 16 5842 1302 125728 42 298 127 0 78 29 33994 868 89920 31 229 32 0 12 10 13018 0 0 0 0 0 0 0 0 0 1030 85610 31 306 58 0 46 25 98177 1116 106742 65 366 57 0 25 21 37941 1237 126825 42 412 59 0 49 23 31032 1214 109807 56 372 65 0 52 21 32683 849 71894 57 287 71 0 36 21 34545 78 3616 5 14 5 0 0 0 0 0 0 0 0 0 0 0 0 0 924 154806 38 301 70 0 35 23 27525 1495 136333 75 539 72 0 68 29 66856 1898 147766 92 535 119 1 26 28 28549 912 113245 37 287 56 0 36 23 38610 778 43410 19 292 63 0 7 1 2781 1567 152455 65 474 88 1 67 25 41211 890 88874 39 241 46 0 30 17 22698 1705 111924 49 497 60 8 55 29 41194 700 60373 39 165 29 3 3 12 32689 285 19764 12 75 19 1 10 2 5752 1528 125760 49 471 61 2 46 20 26757 982 108685 27 341 66 0 23 25 22527 1496 141868 38 497 97 0 48 29 44810 256 11796 9 79 22 0 1 2 0 98 10674 9 33 7 0 0 0 0 1317 131263 52 449 37 0 33 18 100674 41 6836 3 11 5 0 0 1 0 1769 153278 56 606 48 5 48 21 57786 42 5118 3 6 1 0 5 0 0 528 40248 16 183 34 1 8 4 5444 0 0 0 0 0 0 0 0 0 946 100798 43 312 49 0 25 25 28470 1252 84315 36 248 44 0 21 26 61849 81 7131 4 27 0 1 0 0 0 257 8812 13 97 18 0 0 4 2179 892 63952 23 247 48 1 15 17 8019 1114 120111 47 273 54 0 47 21 39644 1079 94127 18 386 50 1 17 22 23494
Names of X columns:
pageviews time_spent_in_RFC_in_seconds logins compendium_views compendium_views_(PR) shared_compendiums blogged_computations reviewed_compendiums compendium_writing_total_characters
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
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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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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