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Data X:
1 21 26 50 4 13 12 13 13 21 2 149 96 18 68 12.9 1 21 37 54 5 14 11 11 11 22 0 148 88 39 32 12.8 0 21 67 71 4 16 13 14 10 18 0 158 114 46 62 7.4 1 21 43 54 4 14 11 15 9 23 4 128 69 31 33 6.7 0 21 52 65 9 13 10 14 8 12 0 224 176 67 52 12.6 0 21 52 73 8 15 7 11 26 20 -1 159 114 35 62 14.8 1 23 43 52 11 13 10 13 10 22 0 105 121 52 77 13.3 1 22 84 84 4 20 15 16 10 21 1 159 110 77 76 11.1 0 25 67 42 4 17 12 14 8 19 0 167 158 37 41 8.2 1 21 49 66 6 15 12 14 13 22 3 165 116 32 48 11.4 0 23 70 65 4 16 10 15 11 15 -1 159 181 36 63 6.4 1 21 58 73 4 17 14 13 12 19 4 176 141 69 78 12.0 0 21 68 75 4 11 6 14 24 18 1 54 35 21 19 6.3 0 25 62 72 11 16 12 11 21 15 0 91 80 26 31 11.3 1 21 43 66 4 16 14 12 5 20 -2 163 152 54 66 11.9 1 21 56 70 4 15 11 14 14 21 -4 124 97 36 35 9.3 0 24 74 81 6 14 12 12 9 15 2 121 84 23 45 10.0 1 21 63 69 8 16 13 15 17 23 2 148 101 112 25 13.8 1 24 58 71 5 17 11 14 18 21 -4 221 107 35 44 10.8 0 21 63 68 9 15 7 12 23 25 2 149 112 47 54 11.7 1 22 53 70 4 14 11 12 9 9 2 244 171 37 74 10.9 0 20 57 68 7 14 7 12 14 30 0 148 137 109 80 16.1 1 21 64 67 4 15 12 14 10 23 -3 150 66 20 61 9.9 0 22 53 76 4 17 13 16 8 16 2 153 93 22 41 11.5 1 22 29 70 7 14 9 12 10 16 0 94 105 23 46 8.3 0 21 54 60 12 16 11 12 19 19 4 156 131 32 39 11.7 1 21 58 72 7 15 12 14 11 25 2 132 102 30 34 9.0 1 22 51 71 8 16 12 15 12 23 2 105 120 43 42 10.8 0 20 54 70 4 8 5 14 11 10 -4 151 77 16 39 10.4 0 21 56 64 9 17 13 13 10 14 3 131 108 49 20 12.7 1 21 47 76 4 10 6 16 14 26 2 157 168 43 53 11.8 1 24 50 68 4 16 6 15 11 24 -1 162 75 46 54 13.0 1 22 35 76 4 16 12 13 13 24 -3 163 107 19 49 10.8 1 22 30 65 7 16 11 16 15 18 0 59 62 23 34 12.3 0 21 68 67 4 8 6 16 15 23 1 187 121 59 46 11.3 0 20 56 75 4 14 11 15 14 23 -3 116 97 32 37 11.6 0 23 43 60 4 16 12 13 12 19 3 148 126 19 30 10.9 0 20 67 73 4 19 13 12 13 21 0 155 104 22 28 12.1 0 23 62 63 4 19 14 14 7 18 0 125 148 48 45 13.3 1 21 57 70 4 14 12 14 8 27 0 116 146 23 35 10.1 1 21 54 66 12 13 14 10 20 13 3 138 97 33 41 14.3 0 22 61 64 4 15 11 16 16 28 0 164 118 34 73 9.3 0 21 56 70 5 11 10 14 11 23 2 162 58 48 17 12.5 0 21 41 75 15 9 7 14 26 21 -1 99 63 18 40 7.6 1 21 53 60 10 12 7 15 15 19 3 186 50 33 37 9.2 1 22 46 66 5 13 10 16 20 17 2 188 94 67 28 14.5 1 21 51 59 9 17 12 15 15 25 2 177 127 80 56 12.3 0 21 37 78 4 7 5 13 17 14 -2 139 128 32 50 12.6 0 21 42 67 7 15 10 12 19 16 0 162 146 43 59 13.0 0 21 38 59 5 12 12 12 13 24 -2 108 69 38 27 12.6 0 20 66 66 4 15 11 14 8 20 0 159 186 29 61 13.2 0 21 53 71 4 16 12 15 9 24 6 110 85 32 51 7.7 0 19 49 66 4 14 11 11 12 22 0 96 54 35 35 10.5 1 19 49 72 4 16 12 14 9 22 -2 87 106 29 48 10.9 1 20 59 71 6 13 10 16 14 20 1 97 34 12 25 4.3 1 19 40 59 10 16 9 13 14 10 0 127 60 37 44 10.3 1 20 63 78 4 10 7 11 13 22 2 74 62 51 20 11.4 0 21 34 65 11 12 9 12 16 20 2 114 64 14 26 5.6 0 18 32 65 14 14 10 12 14 22 -3 95 98 20 23 8.8 0 21 67 71 4 16 12 14 11 20 1 121 35 11 21 9.0 0 20 61 72 4 18 14 12 11 17 -4 130 55 35 41 9.6 1 21 60 66 5 12 9 13 14 18 1 52 54 8 22 6.4 1 19 63 69 4 15 12 14 15 19 0 118 51 24 27 11.6
Names of X columns:
geslacht leeftijd intrinsieke extrinsieke demotivatie statistiek software Stress Depressie NumeracyTest TussentijdseTest LFM B PRH CH Totaalscore
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
10
11
12
Chart options
R Code
par3 <- 'No Linear Trend' par2 <- 'Do not include Seasonal Dummies' par1 <- '16' 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') }
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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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