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Data X:
1801 159261 91 19 6200 0 37 1717 189672 59 20 10265 1 43 192 7215 18 0 603 0 0 2295 129098 95 27 8874 0 54 3450 230632 136 31 20323 0 86 6861 515038 263 36 26258 1 181 1795 180745 56 23 10165 1 42 1681 185559 59 30 8247 0 59 1897 154581 44 30 8683 0 46 2974 298001 96 26 16957 1 77 1946 121844 75 24 8058 2 49 2148 184039 69 30 20488 0 79 1832 100324 98 22 7945 0 37 3183 220269 119 28 13448 4 92 1476 168265 58 18 5389 4 31 1567 154647 88 22 6185 3 28 1756 142018 57 33 24369 0 103 1247 79030 61 15 70 5 2 2779 167047 87 34 17327 0 48 726 27997 24 18 3878 0 25 1048 73019 59 15 3149 0 16 2805 241082 100 30 20517 0 106 1760 195820 72 25 2570 0 35 2266 142001 54 34 5162 1 33 1848 145433 86 21 5299 1 45 1665 183744 32 21 7233 0 64 2084 202357 163 25 15657 0 73 1440 199532 93 31 15329 0 78 2741 354924 118 31 14881 0 63 2112 192399 44 20 16318 0 69 1684 182286 44 28 9556 0 36 1616 181590 45 22 10462 2 41 2227 133801 105 17 7192 4 59 3088 233686 123 25 4362 0 33 2389 219428 53 24 14349 1 76 1 0 1 0 0 0 0 2099 223044 63 28 10881 0 27 1669 100129 51 14 8022 3 44 2137 145864 49 35 13073 9 43 2153 249965 64 34 26641 0 104 2390 242379 71 22 14426 2 120 1701 145794 59 34 15604 0 44 983 96404 32 23 9184 2 71 2161 195891 78 24 5989 1 78 1276 117156 50 26 11270 2 106 1190 157787 95 22 13958 2 61 745 81293 32 35 7162 1 53 2330 237435 101 24 13275 0 51 2289 233155 89 31 21224 1 46 2639 160344 59 26 10615 8 55 658 48188 28 22 2102 0 14 1917 161922 69 21 12396 0 44 2557 307432 74 27 18717 0 113 2026 235223 79 30 9724 0 55 1911 195583 59 33 9863 1 46 1716 146061 56 11 8374 8 39 1852 208834 67 26 8030 0 51 981 93764 24 26 7509 1 31 1177 151985 66 23 14146 0 36 2833 193222 96 38 7768 10 47 1688 148922 60 31 13823 6 53 2097 132856 80 20 7230 0 38 1331 129561 61 22 10170 11 52 1244 112718 37 26 7573 3 37 1256 160930 35 26 5753 0 11 1294 99184 41 33 9791 0 45 2303 192535 70 36 19365 8 59 2897 138708 65 25 9422 2 82 1103 114408 38 24 12310 0 49 340 31970 15 21 1283 0 6 2791 225558 112 19 6372 3 81 1338 139220 72 12 5413 1 56 1441 113612 68 30 10837 2 105 1623 108641 71 21 3394 1 46 2650 162203 67 34 12964 0 46 1499 100098 44 32 3495 2 2 2302 174768 60 28 11580 1 51 2540 158459 97 28 9970 0 95 1000 80934 30 21 4911 0 18 1234 84971 71 31 10138 0 55 927 80545 68 26 14697 0 48 2176 287191 64 29 8464 0 48 957 62974 28 23 4204 1 39 1551 134091 40 25 10226 0 40 1014 75555 46 22 3456 0 36 1771 162154 54 26 8895 0 60 2613 226638 227 33 22557 0 114 1205 115367 112 24 6900 0 39 1337 108749 62 24 8620 7 45 1524 155537 52 21 7820 0 59 1829 153133 41 28 12112 5 59 2229 165618 78 27 13178 1 93 1233 151517 57 25 7028 0 35 1365 133686 58 15 6616 0 47 950 61342 40 13 9570 0 36 2319 245196 117 36 14612 0 59 1857 195576 70 24 11219 0 79 223 19349 12 1 786 0 14 2390 225371 105 24 11252 3 42 1985 153213 78 31 9289 0 41 700 59117 29 4 593 0 8 1062 91762 24 21 6562 0 41 1311 136769 54 23 8208 0 24 1157 114798 61 23 7488 1 22 823 85338 40 12 4574 1 18 596 27676 22 16 522 0 1 1545 153535 48 29 12840 0 53 1130 122417 37 26 1350 0 6 0 0 0 0 0 0 0 1082 91529 32 25 10623 0 49 1135 107205 67 21 5322 0 33 1367 144664 45 23 7987 0 50 1506 146445 63 21 10566 1 64 870 76656 60 21 1900 0 53 78 3616 5 0 0 0 0 0 0 0 0 0 0 0 1130 183088 44 23 10698 0 48 1582 144677 84 33 14884 0 90 2034 159104 98 30 6852 2 46 919 113273 38 23 6873 0 29 778 43410 19 1 4 0 1 1752 175774 73 29 9188 1 64 957 95401 42 18 5141 0 29 2098 134837 55 33 4260 8 27 731 60493 40 12 443 3 4 285 19764 12 2 2416 1 10 1834 164062 56 21 9831 3 47 1148 132696 33 28 5953 0 44 1646 155367 54 29 9435 0 51 256 11796 9 2 0 0 0 98 10674 9 0 0 0 0 1404 142261 57 18 7642 0 38 41 6836 3 1 0 0 0 1824 162563 63 21 6837 6 57 42 5118 3 0 0 0 0 528 40248 16 4 775 1 6 0 0 0 0 0 0 0 1073 122641 47 25 8191 0 22 1305 88837 38 26 1661 0 34 81 7131 4 0 0 1 0 261 9056 14 4 548 0 10 934 76611 24 17 3080 1 16 1180 132697 51 21 13400 0 93 1147 100681 19 22 8181 1 22
Names of X columns:
Total_number_of_Pageviews Total_Time_spent_in_RFC_in_seconds Number_of_Logins Total_Number_of_Reviewed_Compendiums Compendium_Writing_total_number_of_revisions Total_number_of_Compendiums_that_have_been_shared_by_other_Authors Compendium_Writing_total_number_of_included_blogs
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
0
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
1
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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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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