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Data X:
1 25 25 11 11 7 7 8 8 25 23 23 1 17 17 6 6 17 17 8 8 30 25 25 1 18 18 8 8 12 12 9 9 22 19 19 1 16 16 10 10 12 12 7 7 22 29 29 1 20 20 10 10 11 11 4 4 25 25 25 1 16 16 11 11 11 11 11 11 23 21 21 1 18 18 16 16 12 12 7 7 17 22 22 1 17 17 11 11 13 13 7 7 21 25 25 1 30 30 12 12 16 16 10 10 19 18 18 1 23 23 8 8 11 11 10 10 15 22 22 1 18 18 12 12 10 10 8 8 16 15 15 1 21 21 9 9 9 9 9 9 22 20 20 1 31 31 14 14 17 17 11 11 23 20 20 1 27 27 15 15 11 11 9 9 23 21 21 1 21 21 9 9 14 14 13 13 19 21 21 1 16 16 8 8 15 15 9 9 23 24 24 1 20 20 9 9 15 15 6 6 25 24 24 1 17 17 9 9 13 13 6 6 22 23 23 1 25 25 16 16 18 18 16 16 26 24 24 1 26 26 11 11 18 18 5 5 29 18 18 1 25 25 8 8 12 12 7 7 32 25 25 1 17 17 9 9 17 17 9 9 25 21 21 1 32 32 12 12 18 18 12 12 28 22 22 1 22 22 9 9 14 14 9 9 25 23 23 1 17 17 9 9 16 16 5 5 25 23 23 1 20 20 14 14 14 14 10 10 18 24 24 1 29 29 10 10 12 12 8 8 25 23 23 1 23 23 14 14 17 17 7 7 25 21 21 1 20 20 10 10 12 12 8 8 20 28 28 1 11 11 6 6 6 6 4 4 15 16 16 1 26 26 13 13 12 12 8 8 24 29 29 1 22 22 10 10 12 12 8 8 26 27 27 1 14 14 15 15 13 13 8 8 14 16 16 1 19 19 12 12 14 14 7 7 24 28 28 1 20 20 11 11 11 11 8 8 25 25 25 1 28 28 8 8 12 12 7 7 20 22 22 1 19 19 9 9 9 9 7 7 21 23 23 1 30 30 9 9 15 15 9 9 27 26 26 1 29 29 15 15 18 18 11 11 23 23 23 1 26 26 9 9 15 15 6 6 25 25 25 1 23 23 10 10 12 12 8 8 20 21 21 1 21 21 12 12 14 14 9 9 22 24 24 1 28 28 11 11 13 13 6 6 25 22 22 1 23 23 14 14 13 13 10 10 25 27 27 1 18 18 6 6 11 11 8 8 17 26 26 1 20 20 8 8 16 16 10 10 25 24 24 1 21 21 10 10 11 11 5 5 26 24 24 1 28 28 12 12 16 16 14 14 27 22 22 1 10 10 5 5 8 8 6 6 19 24 24 1 22 22 10 10 15 15 6 6 22 20 20 1 31 31 10 10 21 21 12 12 32 26 26 1 29 29 13 13 18 18 12 12 21 21 21 1 22 22 10 10 13 13 8 8 18 19 19 1 23 23 10 10 15 15 10 10 23 21 21 1 20 20 9 9 19 19 10 10 20 16 16 1 18 18 8 8 15 15 10 10 21 22 22 1 25 25 14 14 11 11 5 5 17 15 15 1 21 21 8 8 10 10 7 7 18 17 17 1 24 24 9 9 13 13 10 10 19 15 15 1 25 25 14 14 15 15 11 11 22 21 21 1 13 13 8 8 12 12 7 7 14 19 19 1 28 28 8 8 16 16 12 12 18 24 24 1 25 25 7 7 18 18 11 11 35 17 17 1 9 9 6 6 8 8 11 11 29 23 23 1 16 16 8 8 13 13 5 5 21 24 24 1 19 19 6 6 17 17 8 8 25 14 14 1 29 29 11 11 7 7 4 4 26 22 22 1 14 14 11 11 12 12 7 7 17 16 16 1 22 22 14 14 14 14 11 11 25 19 19 1 15 15 8 8 6 6 6 6 20 25 25 1 15 15 8 8 10 10 4 4 22 24 24 1 20 20 11 11 11 11 8 8 24 26 26 1 18 18 10 10 14 14 9 9 21 26 26 1 33 33 14 14 11 11 8 8 26 25 25 1 22 22 11 11 13 13 11 11 24 18 18 1 16 16 9 9 12 12 8 8 16 21 21 1 16 16 8 8 9 9 4 4 18 23 23 1 18 18 13 13 12 12 6 6 19 20 20 1 18 18 12 12 13 13 9 9 21 13 13 1 22 22 13 13 12 12 13 13 22 15 15 1 30 30 14 14 9 9 9 9 23 14 14 1 30 30 12 12 15 15 10 10 29 22 22 1 24 24 14 14 24 24 20 20 21 10 10 1 21 21 13 13 17 17 11 11 23 22 22 1 29 29 16 16 11 11 6 6 27 24 24 1 31 31 9 9 17 17 9 9 25 19 19 1 20 20 9 9 11 11 7 7 21 20 20 1 16 16 9 9 12 12 9 9 10 13 13 1 22 22 8 8 14 14 10 10 20 20 20 1 20 20 7 7 11 11 9 9 26 22 22 1 28 28 16 16 16 16 8 8 24 24 24 1 38 38 11 11 21 21 7 7 29 29 29 1 22 22 9 9 14 14 6 6 19 12 12 1 20 20 11 11 20 20 13 13 24 20 20 1 17 17 9 9 13 13 6 6 19 21 21 1 22 22 13 13 15 15 10 10 22 22 22 1 31 31 16 16 19 19 16 16 17 20 20 2 24 48 14 28 11 22 12 24 24 26 52 2 18 36 12 24 10 20 8 16 19 23 46 2 23 46 13 26 14 28 12 24 19 24 48 2 15 30 11 22 11 22 8 16 23 22 44 2 12 24 4 8 15 30 4 8 27 28 56 2 15 30 8 16 11 22 8 16 14 12 24 2 20 40 8 16 17 34 7 14 22 24 48 2 34 68 16 32 18 36 11 22 21 20 40 2 31 62 14 28 10 20 8 16 18 23 46 2 19 38 11 22 11 22 8 16 20 28 56 2 21 42 9 18 13 26 9 18 19 24 48 2 22 44 9 18 16 32 9 18 24 23 46 2 24 48 10 20 9 18 6 12 25 29 58 2 32 64 16 32 9 18 6 12 29 26 52 2 33 66 11 22 9 18 6 12 28 22 44 2 13 26 16 32 12 24 5 10 17 22 44 2 25 50 12 24 12 24 7 14 29 23 46 2 29 58 14 28 18 36 10 20 26 30 60 2 18 36 10 20 15 30 8 16 14 17 34 2 20 40 10 20 10 20 8 16 26 23 46 2 15 30 12 24 11 22 8 16 20 25 50 2 33 66 14 28 9 18 6 12 32 24 48 2 26 52 16 32 5 10 4 8 23 24 48 2 18 36 9 18 12 24 8 16 21 24 48 2 28 56 8 16 24 48 20 40 30 20 40 2 17 34 8 16 14 28 6 12 24 22 44 2 12 24 7 14 7 14 4 8 22 28 56 2 17 34 9 18 12 24 9 18 24 25 50 2 21 42 10 20 13 26 6 12 24 24 48 2 18 36 13 26 8 16 9 18 24 24 48 2 10 20 10 20 11 22 5 10 19 23 46 2 29 58 11 22 9 18 5 10 31 30 60 2 31 62 8 16 11 22 8 16 22 24 48 2 19 38 9 18 13 26 8 16 27 21 42 2 9 18 13 26 10 20 6 12 19 25 50 2 13 26 14 28 13 26 6 12 21 25 50 2 19 38 12 24 10 20 8 16 23 29 58 2 21 42 12 24 13 26 8 16 19 22 44 2 23 46 14 28 8 16 5 10 19 27 54 2 21 42 11 22 16 32 7 14 20 24 48 2 15 30 14 28 9 18 8 16 23 29 58 2 19 38 10 20 12 24 7 14 17 21 42 2 26 52 14 28 14 28 8 16 17 24 48 2 16 32 11 22 9 18 5 10 17 23 46 2 19 38 9 18 11 22 10 20 21 27 54 2 31 62 16 32 14 28 9 18 21 25 50 2 19 38 9 18 12 24 7 14 18 21 42 2 15 30 7 14 12 24 6 12 19 21 42 2 23 46 14 28 11 22 10 20 20 29 58 2 17 34 14 28 12 24 6 12 15 21 42 2 21 42 8 16 9 18 11 22 24 20 40 2 17 34 11 22 9 18 6 12 20 19 38 2 25 50 14 28 15 30 9 18 22 24 48 2 20 40 11 22 8 16 4 8 13 13 26 2 19 38 20 40 8 16 7 14 19 25 50 2 20 40 11 22 17 34 8 16 21 23 46 2 17 34 9 18 11 22 5 10 23 26 52 2 21 42 10 20 12 24 8 16 16 23 46 2 26 52 13 26 20 40 10 20 26 22 44 2 17 34 8 16 12 24 9 18 21 24 48 2 21 42 15 30 7 14 5 10 21 24 48 2 28 56 14 28 11 22 8 16 24 24 48
Names of X columns:
Gender CM CM_G D D_G PE PE_G PC PC_G PS O O_G

Sample Range: (leave blank to include all observations)
From:
To:

Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


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') }

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center