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Data X:
1418 210907 56 396 81 3 79 30 115 94 112285 24188 146283 144 145 869 120982 56 297 55 4 58 28 109 103 84786 18273 98364 103 101 1530 176508 54 559 50 12 60 38 146 93 83123 14130 86146 98 98 2172 179321 89 967 125 2 108 30 116 103 101193 32287 96933 135 132 901 123185 40 270 40 1 49 22 68 51 38361 8654 79234 61 60 463 52746 25 143 37 3 0 26 101 70 68504 9245 42551 39 38 3201 385534 92 1562 63 0 121 25 96 91 119182 33251 195663 150 144 371 33170 18 109 44 0 1 18 67 22 22807 1271 6853 5 5 1192 101645 63 371 88 0 20 11 44 38 17140 5279 21529 28 28 1583 149061 44 656 66 5 43 26 100 93 116174 27101 95757 84 84 1439 165446 33 511 57 0 69 25 93 60 57635 16373 85584 80 79 1764 237213 84 655 74 0 78 38 140 123 66198 19716 143983 130 127 1495 173326 88 465 49 7 86 44 166 148 71701 17753 75851 82 78 1373 133131 55 525 52 7 44 30 99 90 57793 9028 59238 60 60 2187 258873 60 885 88 3 104 40 139 124 80444 18653 93163 131 131 1491 180083 66 497 36 9 63 34 130 70 53855 8828 96037 84 84 4041 324799 154 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1335 139942 42 498 46 0 54 22 88 87 65461 18715 96841 82 72 1403 118612 46 454 72 2 54 12 48 43 30080 7936 41907 90 87 910 72880 33 376 41 0 14 19 76 67 24094 8643 27080 25 21 616 65475 18 225 24 2 16 13 51 46 69008 7294 35885 60 56 1407 99643 55 555 45 1 33 17 67 46 54968 4570 41247 61 59 771 71965 35 252 33 1 32 15 59 56 46090 7185 28313 85 82 766 77272 59 208 27 2 21 16 61 48 27507 10058 36845 43 43 473 49289 19 130 36 1 15 24 76 44 10672 2342 16548 25 25 1376 135131 66 481 87 0 38 15 60 60 34029 8509 36134 41 38 1232 108446 60 389 90 1 22 17 68 65 46300 13275 55764 26 25 1521 89746 36 565 114 3 28 18 71 55 24760 6816 28910 38 38 572 44296 25 173 31 0 10 20 76 38 18779 1930 13339 12 12 1059 77648 47 278 45 0 31 16 62 52 21280 8086 25319 29 29 1544 181528 54 609 69 0 32 16 61 60 40662 10737 66956 49 47 1230 134019 53 422 51 0 32 18 67 54 28987 8033 47487 46 45 1206 124064 40 445 34 1 43 22 88 86 22827 7058 52785 41 40 1205 92630 40 387 60 4 27 8 30 24 18513 6782 44683 31 30 1255 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1656 102538 57 490 79 1 50 15 58 45 45833 9078 48231 51 51 705 86678 40 238 44 0 12 15 59 50 29156 4605 39725 19 18 945 85709 44 343 65 0 21 10 40 35 35944 3238 21455 37 34 554 34662 25 232 10 0 18 6 22 7 36278 8100 23430 33 31 1597 150580 77 530 124 0 27 22 83 71 45588 9653 62991 41 39 982 99611 35 291 81 0 41 21 81 67 45097 8914 49363 54 54 222 19349 11 67 15 0 13 1 2 0 3895 786 9604 14 14 1212 99373 63 397 92 1 12 18 72 62 28394 6700 24552 25 24 1143 86230 44 467 42 0 21 17 61 54 18632 5788 31493 25 24 435 30837 19 178 10 0 8 4 15 4 2325 593 3439 8 8 532 31706 13 175 24 0 26 10 32 25 25139 4506 19555 26 26 882 89806 42 299 64 0 27 16 62 40 27975 6382 21228 20 19 608 62088 38 154 45 1 13 16 58 38 14483 5621 23177 11 11 459 40151 29 106 22 0 16 9 36 19 13127 3997 22094 14 14 578 27634 20 189 56 0 2 16 59 17 5839 520 2342 3 1 826 76990 27 194 94 0 42 17 68 67 24069 8891 38798 40 39 509 37460 20 135 19 0 5 7 21 14 3738 999 3255 5 5 717 54157 19 201 35 0 37 15 55 30 18625 7067 24261 38 37 637 49862 37 207 32 0 17 14 54 54 36341 4639 18511 32 32 857 84337 26 280 35 0 38 14 55 35 24548 5654 40798 41 38 830 64175 42 260 48 0 37 18 72 59 21792 6928 28893 46 47 652 59382 49 227 49 0 29 12 41 24 26263 1514 21425 47 47 707 119308 30 239 48 0 32 16 61 58 23686 9238 50276 37 37 954 76702 49 333 62 0 35 21 67 42 49303 8204 37643 51 51 1461 103425 67 428 96 1 17 19 76 46 25659 5926 30377 49 45 672 70344 28 230 45 0 20 16 64 61 28904 5785 27126 21 21 778 43410 19 292 63 0 7 1 3 3 2781 4 13 1 1 1141 104838 49 350 71 1 46 16 63 52 29236 5930 42097 44 42 680 62215 27 186 26 0 24 10 40 25 19546 3710 24451 26 26 1090 69304 30 326 48 6 40 19 69 40 22818 705 14335 21 21 616 53117 22 155 29 3 3 12 48 32 32689 443 5084 4 4 285 19764 12 75 19 1 10 2 8 4 5752 2416 9927 10 10 1145 86680 31 361 45 2 37 14 52 49 22197 7747 43527 43 43 733 84105 20 261 45 0 17 17 66 63 20055 5432 27184 34 34 888 77945 20 299 67 0 28 19 76 67 25272 4913 21610 32 31 849 89113 39 300 30 0 19 14 43 32 82206 2650 20484 20 19 1182 91005 29 450 36 3 29 11 39 23 32073 2370 20156 34 34 528 40248 16 183 34 1 8 4 14 7 5444 775 6012 6 6 642 64187 27 238 36 0 10 16 61 54 20154 5576 18475 12 11 947 50857 21 165 34 0 15 20 71 37 36944 1352 12645 24 24 819 56613 19 234 37 1 15 12 44 35 8019 3080 11017 16 16 757 62792 35 176 46 0 28 15 60 51 30884 10205 37623 72 72 894 72535 14 329 44 0 17 16 64 39 19540 6095 35873 27 21
Names of X columns:
pageviews time_in_rfc logins compendium_views_info compendium_views_pr shared_compendiums blogged_computations compendiums_reviewed feedback_messages_p1 feedback_messages_p120 totsize totrevisions totseconds tothyperlinks totblogs
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') }
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