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Data X:
12.9 149 96 68 86 7.4 152 75 55 62 12.2 139 70 39 70 12.8 148 88 32 71 7.4 158 114 62 108 6.7 128 69 33 64 12.6 224 176 52 119 14.8 159 114 62 97 13.3 105 121 77 129 11.1 159 110 76 153 8.2 167 158 41 78 11.4 165 116 48 80 6.4 159 181 63 99 10.6 119 77 30 68 12.0 176 141 78 147 6.3 54 35 19 40 11.3 91 80 31 57 11.9 163 152 66 120 9.3 124 97 35 71 9.6 137 99 42 84 10.0 121 84 45 68 6.4 153 68 21 55 13.8 148 101 25 137 10.8 221 107 44 79 13.8 188 88 69 116 11.7 149 112 54 101 10.9 244 171 74 111 16.1 148 137 80 189 13.4 92 77 42 66 9.9 150 66 61 81 11.5 153 93 41 63 8.3 94 105 46 69 11.7 156 131 39 71 6.1 146 89 63 70 9.0 132 102 34 64 9.7 161 161 51 143 10.8 105 120 42 85 10.3 97 127 31 86 10.4 151 77 39 55 12.7 131 108 20 69 9.3 166 85 49 120 11.8 157 168 53 96 5.9 111 48 31 60 11.4 145 152 39 95 13.0 162 75 54 100 10.8 163 107 49 68 12.3 59 62 34 57 11.3 187 121 46 105 11.8 109 124 55 85 7.9 90 72 42 103 12.7 105 40 50 57 12.3 83 58 13 51 11.6 116 97 37 69 6.7 42 88 25 41 10.9 148 126 30 49 12.1 155 104 28 50 13.3 125 148 45 93 10.1 116 146 35 58 5.7 128 80 28 54 14.3 138 97 41 74 8.0 49 25 6 15 13.3 96 99 45 69 9.3 164 118 73 107 12.5 162 58 17 65 7.6 99 63 40 58 15.9 202 139 64 107 9.2 186 50 37 70 9.1 66 60 25 53 11.1 183 152 65 136 13.0 214 142 100 126 14.5 188 94 28 95 12.2 104 66 35 69 12.3 177 127 56 136 11.4 126 67 29 58 8.8 76 90 43 59 14.6 99 75 59 118 7.3 157 96 52 110 12.6 139 128 50 82 NA 78 41 3 50 13.0 162 146 59 102 12.6 108 69 27 65 13.2 159 186 61 90 9.9 74 81 28 64 7.7 110 85 51 83 10.5 96 54 35 70 13.4 116 46 29 50 10.9 87 106 48 77 4.3 97 34 25 37 10.3 127 60 44 81 11.8 106 95 64 101 11.2 80 57 32 79 11.4 74 62 20 71 8.6 91 36 28 60 13.2 133 56 34 55 12.6 74 54 31 44 5.6 114 64 26 40 9.9 140 76 58 56 8.8 95 98 23 43 7.7 98 88 21 45 9.0 121 35 21 32 7.3 126 102 33 56 11.4 98 61 16 40 13.6 95 80 20 34 7.9 110 49 37 89 10.7 70 78 35 50 10.3 102 90 33 56 8.3 86 45 27 46 9.6 130 55 41 76 14.2 96 96 40 64 8.5 102 43 35 74 13.5 100 52 28 57 4.9 94 60 32 45 6.4 52 54 22 30 9.6 98 51 44 62 11.6 118 51 27 51 11.1 99 38 17 36 4.35 48 41 12 34 12.7 50 146 45 61 18.1 150 182 37 70 17.85 154 192 37 69 16.6 109 263 108 145 12.6 68 35 10 23 17.1 194 439 68 120 19.1 158 214 72 147 16.1 159 341 143 215 13.35 67 58 9 24 18.4 147 292 55 84 14.7 39 85 17 30 10.6 100 200 37 77 12.6 111 158 27 46 16.2 138 199 37 61 13.6 101 297 58 178 18.9 131 227 66 160 14.1 101 108 21 57 14.5 114 86 19 42 16.15 165 302 78 163 14.75 114 148 35 75 14.8 111 178 48 94 12.45 75 120 27 45 12.65 82 207 43 78 17.35 121 157 30 47 8.6 32 128 25 29 18.4 150 296 69 97 16.1 117 323 72 116 11.6 71 79 23 32 17.75 165 70 13 50 15.25 154 146 61 118 17.65 126 246 43 66 15.6 138 145 22 48 16.35 149 196 51 86 17.65 145 199 67 89 13.6 120 127 36 76 11.7 138 91 21 39 14.35 109 153 44 75 14.75 132 299 45 57 18.25 172 228 34 72 9.9 169 190 36 60 16 114 180 72 109 18.25 156 212 39 76 16.85 172 269 43 65 14.6 68 130 25 40 13.85 89 179 56 58 18.95 167 243 80 123 15.6 113 190 40 71 14.85 115 299 73 102 11.75 78 121 34 80 18.45 118 137 72 97 15.9 87 305 42 46 17.1 173 157 61 93 16.1 2 96 23 19 19.9 162 183 74 140 10.95 49 52 16 78 18.45 122 238 66 98 15.1 96 40 9 40 15 100 226 41 80 11.35 82 190 57 76 15.95 100 214 48 79 18.1 115 145 51 87 14.6 141 119 53 95 15.4 165 222 29 49 15.4 165 222 29 49 17.6 110 159 55 80 13.35 118 165 54 86 19.1 158 249 43 69 15.35 146 125 51 79 7.6 49 122 20 52 13.4 90 186 79 120 13.9 121 148 39 69 19.1 155 274 61 94 15.25 104 172 55 72 12.9 147 84 30 43 16.1 110 168 55 87 17.35 108 102 22 52 13.15 113 106 37 71 12.15 115 2 2 61 12.6 61 139 38 51 10.35 60 95 27 50 15.4 109 130 56 67 9.6 68 72 25 30 18.2 111 141 39 70 13.6 77 113 33 52 14.85 73 206 43 75 14.75 151 268 57 87 14.1 89 175 43 69 14.9 78 77 23 72 16.25 110 125 44 79 19.25 220 255 54 121 13.6 65 111 28 43 13.6 141 132 36 58 15.65 117 211 39 57 12.75 122 92 16 50 14.6 63 76 23 69 9.85 44 171 40 64 12.65 52 83 24 38 11.9 62 119 29 53 19.2 131 266 78 90 16.6 101 186 57 96 11.2 42 50 37 49 15.25 152 117 27 56 11.9 107 219 61 102 13.2 77 246 27 40 16.35 154 279 69 100 12.4 103 148 34 67 15.85 96 137 44 78 14.35 154 130 21 62 18.15 175 181 34 55 11.15 57 98 39 59 15.65 112 226 51 96 17.75 143 234 34 86 7.65 49 138 31 38 12.35 110 85 13 43 15.6 131 66 12 23 19.3 167 236 51 77 15.2 56 106 24 48 17.1 137 135 19 26 15.6 86 122 30 91 18.4 121 218 81 94 19.05 149 199 42 62 18.55 168 112 22 74 19.1 140 278 85 114 13.1 88 94 27 52 12.85 168 113 25 64 9.5 94 84 22 31 4.5 51 86 19 38 11.85 48 62 14 27 13.6 145 222 45 105 11.7 66 167 45 64 12.4 85 82 28 62 13.35 109 207 51 65 11.4 63 184 41 58 14.9 102 83 31 76 19.9 162 183 74 140 17.75 128 85 24 48 11.2 86 89 19 68 14.6 114 225 51 80 17.6 164 237 73 71 14.05 119 102 24 76 16.1 126 221 61 63 13.35 132 128 23 46 11.85 142 91 14 53 11.95 83 198 54 74 14.75 94 204 51 70 15.15 81 158 62 78 13.2 166 138 36 56 16.85 110 226 59 100 7.85 64 44 24 51 7.7 93 196 26 52 12.6 104 83 54 102 7.85 105 79 39 78 10.95 49 52 16 78 12.35 88 105 36 55 9.95 95 116 31 98 14.9 102 83 31 76 16.65 99 196 42 73 13.4 63 153 39 47 13.95 76 157 25 45 15.7 109 75 31 83 16.85 117 106 38 60 10.95 57 58 31 48 15.35 120 75 17 50 12.2 73 74 22 56 15.1 91 185 55 77 17.75 108 265 62 91 15.2 105 131 51 76 14.6 117 139 30 68 16.65 119 196 49 74 8.1 31 78 16 29
Names of X columns:
Totaalscore Feedback Blogs UrenCompendia UrenRFC
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
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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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) 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, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) 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,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) 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,signif(numsignificant1,6)) a<-table.element(a,signif(numsignificant1/numgqtests,6)) 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,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) 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,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) 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
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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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