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Data X:
41 38 13 12 14 12 53 39 32 16 11 18 11 83 30 35 19 15 11 14 66 31 33 15 6 12 12 67 34 37 14 13 16 21 76 35 29 13 10 18 12 78 39 31 19 12 14 22 53 34 36 15 14 14 11 80 36 35 14 12 15 10 74 37 38 15 9 15 13 76 38 31 16 10 17 10 79 36 34 16 12 19 8 54 38 35 16 12 10 15 67 39 38 16 11 16 14 54 33 37 17 15 18 10 87 32 33 15 12 14 14 58 36 32 15 10 14 14 75 38 38 20 12 17 11 88 39 38 18 11 14 10 64 32 32 16 12 16 13 57 32 33 16 11 18 9.5 66 31 31 16 12 11 14 68 39 38 19 13 14 12 54 37 39 16 11 12 14 56 39 32 17 12 17 11 86 41 32 17 13 9 9 80 36 35 16 10 16 11 76 33 37 15 14 14 15 69 33 33 16 12 15 14 78 34 33 14 10 11 13 67 31 31 15 12 16 9 80 27 32 12 8 13 15 54 37 31 14 10 17 10 71 34 37 16 12 15 11 84 34 30 14 12 14 13 74 32 33 10 7 16 8 71 29 31 10 9 9 20 63 36 33 14 12 15 12 71 29 31 16 10 17 10 76 35 33 16 10 13 10 69 37 32 16 10 15 9 74 34 33 14 12 16 14 75 38 32 20 15 16 8 54 35 33 14 10 12 14 52 38 28 14 10 15 11 69 37 35 11 12 11 13 68 38 39 14 13 15 9 65 33 34 15 11 15 11 75 36 38 16 11 17 15 74 38 32 14 12 13 11 75 32 38 16 14 16 10 72 32 30 14 10 14 14 67 32 33 12 12 11 18 63 34 38 16 13 12 14 62 32 32 9 5 12 11 63 37 35 14 6 15 14.5 76 39 34 16 12 16 13 74 29 34 16 12 15 9 67 37 36 15 11 12 10 73 35 34 16 10 12 15 70 30 28 12 7 8 20 53 38 34 16 12 13 12 77 34 35 16 14 11 12 80 31 35 14 11 14 14 52 34 31 16 12 15 13 54 35 37 17 13 10 11 80 36 35 18 14 11 17 66 30 27 18 11 12 12 73 39 40 12 12 15 13 63 35 37 16 12 15 14 69 38 36 10 8 14 13 67 31 38 14 11 16 15 54 34 39 18 14 15 13 81 38 41 18 14 15 10 69 34 27 16 12 13 11 84 39 30 17 9 12 19 80 37 37 16 13 17 13 70 34 31 16 11 13 17 69 28 31 13 12 15 13 77 37 27 16 12 13 9 54 33 36 16 12 15 11 79 35 37 16 12 15 9 71 37 33 15 12 16 12 73 32 34 15 11 15 12 72 33 31 16 10 14 13 77 38 39 14 9 15 13 75 33 34 16 12 14 12 69 29 32 16 12 13 15 54 33 33 15 12 7 22 70 31 36 12 9 17 13 73 36 32 17 15 13 15 54 35 41 16 12 15 13 77 32 28 15 12 14 15 82 29 30 13 12 13 12.5 80 39 36 16 10 16 11 80 37 35 16 13 12 16 69 35 31 16 9 14 11 78 37 34 16 12 17 11 81 32 36 14 10 15 10 76 38 36 16 14 17 10 76 37 35 16 11 12 16 73 36 37 20 15 16 12 85 32 28 15 11 11 11 66 33 39 16 11 15 16 79 40 32 13 12 9 19 68 38 35 17 12 16 11 76 41 39 16 12 15 16 71 36 35 16 11 10 15 54 43 42 12 7 10 24 46 30 34 16 12 15 14 85 31 33 16 14 11 15 74 32 41 17 11 13 11 88 32 33 13 11 14 15 38 37 34 12 10 18 12 76 37 32 18 13 16 10 86 33 40 14 13 14 14 54 34 40 14 8 14 13 67 33 35 13 11 14 9 69 38 36 16 12 14 15 90 33 37 13 11 12 15 54 31 27 16 13 14 14 76 38 39 13 12 15 11 89 37 38 16 14 15 8 76 36 31 15 13 15 11 73 31 33 16 15 13 11 79 39 32 15 10 17 8 90 44 39 17 11 17 10 74 33 36 15 9 19 11 81 35 33 12 11 15 13 72 32 33 16 10 13 11 71 28 32 10 11 9 20 66 40 37 16 8 15 10 77 27 30 12 11 15 15 65 37 38 14 12 15 12 74 32 29 15 12 16 14 85 28 22 13 9 11 23 54 34 35 15 11 14 14 63 30 35 11 10 11 16 54 35 34 12 8 15 11 64 31 35 11 9 13 12 69 32 34 16 8 15 10 54 30 37 15 9 16 14 84 30 35 17 15 14 12 86 31 23 16 11 15 12 77 40 31 10 8 16 11 89 32 27 18 13 16 12 76 36 36 13 12 11 13 60 32 31 16 12 12 11 75 35 32 13 9 9 19 73 38 39 10 7 16 12 85 42 37 15 13 13 17 79 34 38 16 9 16 9 71 35 39 16 6 12 12 72 38 34 14 8 9 19 69 33 31 10 8 13 18 78 36 32 17 15 13 15 54 32 37 13 6 14 14 69 33 36 15 9 19 11 81 34 32 16 11 13 9 84 32 38 12 8 12 18 84 34 36 13 8 13 16 69 27 26 13 10 10 24 66 31 26 12 8 14 14 81 38 33 17 14 16 20 82 34 39 15 10 10 18 72 24 30 10 8 11 23 54 30 33 14 11 14 12 78 26 25 11 12 12 14 74 34 38 13 12 9 16 82 27 37 16 12 9 18 73 37 31 12 5 11 20 55 36 37 16 12 16 12 72 41 35 12 10 9 12 78 29 25 9 7 13 17 59 36 28 12 12 16 13 72 32 35 15 11 13 9 78 37 33 12 8 9 16 68 30 30 12 9 12 18 69 31 31 14 10 16 10 67 38 37 12 9 11 14 74 36 36 16 12 14 11 54 35 30 11 6 13 9 67 31 36 19 15 15 11 70 38 32 15 12 14 10 80 22 28 8 12 16 11 89 32 36 16 12 13 19 76 36 34 17 11 14 14 74 39 31 12 7 15 12 87 28 28 11 7 13 14 54 32 36 11 5 11 21 61 32 36 14 12 11 13 38 38 40 16 12 14 10 75 32 33 12 3 15 15 69 35 37 16 11 11 16 62 32 32 13 10 15 14 72 37 38 15 12 12 12 70 34 31 16 9 14 19 79 33 37 16 12 14 15 87 33 33 14 9 8 19 62 26 32 16 12 13 13 77 30 30 16 12 9 17 69 24 30 14 10 15 12 69 34 31 11 9 17 11 75 34 32 12 12 13 14 54 33 34 15 8 15 11 72 34 36 15 11 15 13 74 35 37 16 11 14 12 85 35 36 16 12 16 15 52 36 33 11 10 13 14 70 34 33 15 10 16 12 84 34 33 12 12 9 17 64 41 44 12 12 16 11 84 32 39 15 11 11 18 87 30 32 15 8 10 13 79 35 35 16 12 11 17 67 28 25 14 10 15 13 65 33 35 17 11 17 11 85 39 34 14 10 14 12 83 36 35 13 8 8 22 61 36 39 15 12 15 14 82 35 33 13 12 11 12 76 38 36 14 10 16 12 58 33 32 15 12 10 17 72 31 32 12 9 15 9 72 34 36 13 9 9 21 38 32 36 8 6 16 10 78 31 32 14 10 19 11 54 33 34 14 9 12 12 63 34 33 11 9 8 23 66 34 35 12 9 11 13 70 34 30 13 6 14 12 71 33 38 10 10 9 16 67 32 34 16 6 15 9 58 41 33 18 14 13 17 72 34 32 13 10 16 9 72 36 31 11 10 11 14 70 37 30 4 6 12 17 76 36 27 13 12 13 13 50 29 31 16 12 10 11 72 37 30 10 7 11 12 72 27 32 12 8 12 10 88 35 35 12 11 8 19 53 28 28 10 3 12 16 58 35 33 13 6 12 16 66 37 31 15 10 15 14 82 29 35 12 8 11 20 69 32 35 14 9 13 15 68 36 32 10 9 14 23 44 19 21 12 8 10 20 56 21 20 12 9 12 16 53 31 34 11 7 15 14 70 33 32 10 7 13 17 78 36 34 12 6 13 11 71 33 32 16 9 13 13 72 37 33 12 10 12 17 68 34 33 14 11 12 15 67 35 37 16 12 9 21 75 31 32 14 8 9 18 62 37 34 13 11 15 15 67 35 30 4 3 10 8 83 27 30 15 11 14 12 64 34 38 11 12 15 12 68 40 36 11 7 7 22 62 29 32 14 9 14 12 72
Names of X columns:
Connected Separate Learning Software Happiness Depression Belonging

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	3 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	5.45153304933195	1.942794	2.806	0.005401	0.0027
Connected	0.0321668940555692	0.034424	0.9344	0.350967	0.175483
Separate	0.0427653888947307	0.035076	1.2192	0.223886	0.111943
Software	0.558489671132888	0.053726	10.3952	0	0
Happiness	0.0702334998707389	0.057809	1.2149	0.225518	0.112759
Depression	-0.0311264244363035	0.041759	-0.7454	0.456725	0.228362
Belonging	0.00747850801958737	0.011869	0.6301	0.529191	0.264595
t	-0.00491058171792585	0.00168	-2.9227	0.00378	0.00189

Multiple Linear Regression - Regression Statistics
Multiple R	0.667212929789438
R-squared	0.445173093678205
Adjusted R-squared	0.430002045458468
F-TEST (value)	29.3435949336093
F-TEST (DF numerator)	7
F-TEST (DF denominator)	256
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.85421524491434
Sum Squared Residuals	880.157228665026

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	13	16.098538785479	-3.09853878547897
2	16	15.7506280756561	0.249371924343889
3	19	17.1063218899167	1.89367811008325
4	15	12.1616052410319	2.83839475896812
5	14	16.4017873467223	-2.40178734672229
6	13	14.8470133702108	-1.84701337021082
7	19	15.3941195404347	3.60588045956527
8	15	17.1034911605066	-2.10349116050658
9	14	16.0596585119288	-2.05965851192881
10	15	14.4613197202822	0.538680279717755
11	16	15.0039897785988	0.996010221401189
12	16	16.1957780658441	-0.195778065844116
13	16	15.5452007954959	0.45479920450408
14	16	15.497570422791	0.502429577209031
15	17	18.0026152345095	-1.00261523450952
16	15	15.4966907599622	-0.496690759962239
17	15	14.5878376596391	0.412162340360932
18	20	16.4221329188422	3.5778670811578
19	18	15.5318412924009	2.46815870759905
20	16	15.598397960354	0.401602039646002
21	16	15.3944791538427	0.605520846157257
22	16	15.2136141783933	0.78638582160669
23	19	16.4921403787265	2.50785962127346
24	16	15.1609192229515	0.839080777048478
25	17	16.1483763914647	0.851623608535269
26	17	16.22180307078	0.778196929219997
27	16	14.908352790214	1.09164720978602
28	15	16.8091087350266	-1.80910873502655
29	16	15.6848237519473	0.315176248052743
30	14	14.263029558757	-0.263029558757013
31	15	15.7659606607022	-0.765960660702237
32	12	12.8492889523859	-0.849288952385907
33	14	14.8039620225922	-0.803962022592174
34	16	16.0017496144186	-0.0017496144185554
35	14	15.4902098814983	-1.49020988149829
36	10	13.0304769205532	-3.03047692055322
37	10	13.0355345646574	-3.03553456465739
38	14	15.7470324913881	-1.74703249138813
39	16	14.554555919938	1.445444080062
40	16	14.4948939247229	1.50510607527711
41	16	14.7205377064971	1.27946229350291
42	14	15.7009510594818	-1.70095105948177
43	20	17.4871215566965	2.51287844330346
44	14	14.1533777639022	-0.153377763902245
45	14	14.4623553291315	-0.462355329131485
46	11	15.4909495615117	-4.49094956151173
47	14	16.6307612737306	-2.63076127373059
48	15	15.1467421663187	-0.146742166318659
49	16	15.417876616323	0.58212338367696
50	14	15.6302473667626	-1.6302473667626
51	16	17.0252984963352	-1.02529849633518
52	14	14.1419408813432	-0.141940881343227
53	12	15.0171855791395	-3.01718557913949
54	16	16.0361860907356	-0.0361860907356081
55	9	11.3432897998036	-2.34328979980356
56	14	12.3849781445203	1.61502185547966
57	16	15.8545401093022	0.145459890697828
58	16	15.5298832287659	0.47011677123408
59	15	15.1123930302181	-0.112393030218124
60	16	14.2210605652264	1.77893943477357
61	12	11.5595534084662	0.440446591533846
62	16	15.6406817555398	0.359318244460177
63	16	16.5488168530774	-0.548816853077411
64	14	14.7109860019853	-0.710986001985279
65	16	15.3063211583342	0.693678841665758
66	17	16.0541860332013	0.945813966798659
67	18	16.3331770798611	1.66682292013889
68	18	14.3958881874426	3.60411181255738
69	12	15.8997083739692	-3.89970837396925
70	16	15.6515786730261	0.348421326973927
71	10	13.412380608575	-3.41238060857496
72	14	14.9242951062704	-0.924295106270414
73	18	16.9280586745433	1.07194132545669
74	18	17.140983623911	0.85901637608901
75	16	15.2322948752948	0.76770512470519
76	17	13.4918869897007	3.50811301029926
77	16	16.419099992442	-0.419099992441987
78	16	14.5311988476754	1.46880115232456
79	13	15.2165773344004	-2.21657733440037
80	16	15.1421402571569	0.857859742843126
81	16	15.6586274506278	0.341372549372196
82	16	15.7632408306317	0.236759169368344
83	15	15.6434137240469	-0.643413724046948
84	15	14.8842323819227	0.115767618077307
85	16	14.1607354722341	1.83926452776585
86	14	14.1555692846506	-0.155569284650594
87	16	15.3674881780279	0.632511821972129
88	16	14.8725888488247	1.12741115117525
89	15	14.5194813902587	0.480518609741331
90	12	13.907972516408	-1.90797251640802
91	17	16.7584943754586	0.241505624541383
92	16	15.8055619194036	0.19443808059637
93	15	15.0531067912421	-0.0531067912420874
94	13	15.0298518503278	-2.02985185032776
95	16	14.7436133365348	1.25638666346519
96	16	15.7882428813297	0.211757118670259
97	16	13.6773839654895	2.32261603451051
98	16	15.7737083756365	0.226291624363468
99	14	14.4297816437613	-0.429781643761336
100	16	16.9922981106499	-0.992298110649853
101	16	14.6766246625527	1.32337533744731
102	20	17.4542184425634	2.54578155743658
103	15	14.2396603727495	0.760339627250459
104	16	14.9598584444853	1.0401415555147
105	13	14.8422042092773	-1.84220420927732
106	17	15.7017299648747	1.29827003512525
107	16	15.7011234587523	0.298876541247739
108	16	14.3586514687943	1.6413485312057
109	12	12.3043422991135	-0.304342299113495
110	16	15.2856808956604	0.714319104339601
111	16	15.9928271492344	0.00717285076563326
112	17	15.0564093690921	1.94359063090789
113	13	14.2811780773625	-1.28117807736249
114	12	14.5798742612204	-2.57987426122044
115	18	16.1614728444387	1.83852715556128
116	14	15.8657328435429	-1.86573284354288
117	14	13.228887828907	0.771112171092978
118	13	14.7929151358429	-1.79291513584292
119	16	15.520384206224	0.479615793776025
120	13	14.4292215835434	-1.42922158354343
121	16	15.3854232676415	0.614576732358469
122	13	15.8212093173508	-2.82120931735076
123	16	16.8545044640026	-0.85450446400258
124	15	15.8437647974654	-0.843764797465409
125	16	16.7849339139009	-0.784933913900921
126	15	14.6587216010757	0.341278398924294
127	17	15.4905839058456	1.50941609415438
128	15	14.0482521120088	0.951747887991243
129	12	14.6858650734517	-2.6858650734517
130	16	13.9402714795457	2.05972852045428
131	10	13.7239532443361	-3.72395324433606
132	16	13.4583321541629	2.54166784583712
133	12	14.1659890224415	-2.16598902244154
134	14	15.5440460090552	-1.54404600905523
135	15	15.0836566962205	-0.0836566962204762
136	13	12.1121127347309	0.887887265269139
137	15	14.5312778069589	0.46872219304114
138	11	13.4989500572247	-2.49895005722466
139	12	13.0064804164844	-1.00648041648442
140	11	13.339956434492	-2.33995643449199
141	16	12.8564999151223	3.14350008487771
142	15	13.6441244258235	1.35587557417655
143	17	16.8413639582837	0.158636041716305
144	16	14.1244038470475	1.87559615295253
145	10	13.2667514301309	-3.26675143013094
146	18	15.497545467363	2.50245453263696
147	13	14.9457512386837	-1.94575123868368
148	16	14.843010105307	1.15698989469302
149	13	12.82722767011	0.172772329889987
150	10	12.9004577169405	-2.90045771694048
151	15	15.8784182905414	-0.878418290541436
152	16	13.8248630916881	2.17513690831192
153	16	11.8525810147495	4.14741898525048
154	14	12.3963025182653	1.60369748173468
155	10	12.4816282956809	-2.4816282956809
156	17	16.4393065637934	0.560693436206563
157	13	11.7066858547318	1.29331414526825
158	15	13.900934660471	1.09906533952902
159	16	14.5373961332024	1.46260386679759
160	12	12.6989037635456	-0.6989037635456
161	13	12.6931049205989	0.306895079401114
162	13	12.670204114649	0.329795885350963
163	12	12.3813576310274	-0.381357631027413
164	17	16.2130980179022	0.786901982097844
165	15	13.6682202782511	1.33177972174891
166	10	11.6197611469958	-1.61976114699577
167	14	14.3441924705758	-0.344192470575764
168	11	14.1943469919182	-3.19434699191817
169	13	14.7895963339482	-1.78959633394817
170	16	14.3871926838976	1.61280731610236
171	12	10.4815320179531	1.5184679820469
172	16	15.3377881046553	0.662211895344686
173	12	13.8444384221823	-1.84443842218235
174	9	11.3336124343809	-2.3336124343809
175	12	14.9070414350127	-2.90704143501266
176	15	14.4730075744532	0.526992425546797
177	12	12.294327621092	-0.294327621092045
178	12	12.650368444193	-0.650368444193027
179	14	13.7938681954925	0.206131804507502
180	12	13.2889048934373	-1.28890489343726
181	16	15.0068737606415	0.993126239358496
182	11	11.4515058779588	-0.451505877958803
183	19	16.7015767685106	2.29842323148939
184	15	15.1109818809655	-0.110981880965516
185	8	14.596986586261	-6.59698658626102
186	16	14.6989355568994	1.30106444310065
187	17	14.3895807084944	2.61041929150556
188	12	12.3486229107255	-0.348622910725456
189	11	11.4120697144516	-0.412069714451611
190	11	10.4549680631895	0.545031936810458
191	14	14.4364908904417	-0.436490890441748
192	16	15.376427798282	0.623572201717981
193	12	9.72248141934327	2.27751858065673
194	16	14.0886404643777	1.91135953562229
195	13	13.632884513438	-0.632884513437968
196	15	14.9989754108533	0.00102458914673653
197	16	12.9126260121705	3.08737398782951
198	16	14.991943645066	1.00805635493404
199	14	12.4076330969111	1.59236690308889
200	16	14.4603615475735	1.53963845242654
201	16	14.0333200029035	1.96667999709652
202	14	13.2954618359923	0.704538164007682
203	11	13.3129603848872	-2.31296038488723
204	12	14.4949222642595	-2.4949222642595
205	15	12.6778762991469	2.32212370085312
206	15	14.4188365698392	0.581163430160789
207	16	14.5320147838526	1.46798521614739
208	16	14.843125446159	1.15687455384097
209	11	13.5801453187234	-2.58014531872336
210	15	13.8885534096533	1.11144659034666
211	12	14.2037853885328	-2.20378538853276
212	12	15.7224255491506	-3.72242554915059
213	15	14.1090793589769	0.890920641023053
214	15	12.0905788116402	2.90942118835982
215	16	14.4647432573063	1.53525674269368
216	14	13.0805138671753	0.919486132824675
217	17	14.5748713248213	2.42512867517873
218	14	13.9049231073214	0.0950768926785521
219	13	11.8321054700474	1.16789452995262
220	15	15.1299096914369	-0.129909691436863
221	13	14.5726876835671	-1.57268768356711
222	14	13.8921489634354	0.107851036564569
223	15	14.1999876889948	0.800012311005216
224	12	13.0554532006112	-1.05545320061118
225	13	12.2689174915128	0.731082508487161
226	8	11.6573695969631	-3.65736959696312
227	14	13.6832791328481	0.316720867151934
228	14	12.8142890945427	1.18571090545734
229	11	12.197890873762	-1.19789087376204
230	12	12.8303898458872	-0.830389845887179
231	13	11.185488738365	1.81451126163496
232	10	13.2189058291037	-3.21890582910369
233	16	11.348787511322	4.65121248867801
234	18	15.7737516733149	2.22624832668513
235	13	13.7266606548843	-0.726660654884329
236	11	13.2215618348084	-2.22156183480842
237	4	10.9938193483991	-6.99381934839914
238	13	14.1796817218455	-1.17968172184546
239	16	14.1367439630088	1.86325603699122
240	10	11.5930618646107	-1.59306186461068
241	12	12.1626452683162	-0.162645268316154
242	12	13.3960154190304	-1.39601541903039
243	10	8.81036730048707	1.18963269951293
244	13	10.9797489991871	2.02025100081286
245	15	13.5802095891207	1.41979041087934
246	12	11.8071329179159	0.192867082084079
247	14	12.745833303401	1.254166696599
248	10	12.3830320431314	-2.38303204313137
249	12	10.7045626835548	1.29543731644515
250	12	11.5222473456141	0.477752654385854
251	11	11.7208297915302	-0.720829791530176
252	10	11.5207040112402	-1.52070401124024
253	12	11.2737442088263	0.726255791173698
254	16	12.7074968396978	3.29250316030215
255	12	13.2078556645355	-1.20785566453552
256	14	13.7197084126368	0.280291587363209
257	16	14.1388849696129	1.86111503038709
258	14	11.5536798517218	2.44632014827823
259	13	14.0549432381567	-1.05494323815667
260	4	9.33309354369941	-5.33309354369941
261	15	13.5531018279656	1.44689817203439
262	11	14.7741198188765	-3.77411981887649
263	11	11.1662281765916	-0.166228176591611
264	14	12.6310833706034	1.36891662939664

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.402475229471653	0.804950458943307	0.597524770528347
12	0.799813019552283	0.400373960895434	0.200186980447717
13	0.760826664987249	0.478346670025502	0.239173335012751
14	0.673552621178249	0.652894757643502	0.326447378821751
15	0.618114699975933	0.763770600048135	0.381885300024067
16	0.680833200974806	0.638333598050388	0.319166799025194
17	0.608142628027366	0.783714743945269	0.391857371972634
18	0.857694380421813	0.284611239156373	0.142305619578187
19	0.830980289213744	0.338039421572512	0.169019710786256
20	0.780467388615392	0.439065222769216	0.219532611384608
21	0.716435090676601	0.567129818646798	0.283564909323399
22	0.677924859700136	0.644150280599728	0.322075140299864
23	0.64008062396809	0.719838752063819	0.35991937603191
24	0.608002819411591	0.783994361176817	0.391997180588409
25	0.544602520253449	0.910794959493103	0.455397479746551
26	0.495118873916556	0.990237747833111	0.504881126083444
27	0.435695066526812	0.871390133053624	0.564304933473188
28	0.489428727652138	0.978857455304276	0.510571272347862
29	0.429531193251687	0.859062386503374	0.570468806748313
30	0.43911069134142	0.87822138268284	0.56088930865858
31	0.390777631300563	0.781555262601126	0.609222368699437
32	0.386536250945879	0.773072501891758	0.613463749054121
33	0.367573743538395	0.735147487076789	0.632426256461605
34	0.311946111516887	0.623892223033773	0.688053888483113
35	0.300790075632003	0.601580151264006	0.699209924367997
36	0.399446956221477	0.798893912442955	0.600553043778523
37	0.480233640547253	0.960467281094507	0.519766359452747
38	0.456414493869952	0.912828987739904	0.543585506130048
39	0.504036781673619	0.991926436652762	0.495963218326381
40	0.484662564347358	0.969325128694716	0.515337435652642
41	0.448981538814127	0.897963077628253	0.551018461185873
42	0.419330929144973	0.838661858289946	0.580669070855027
43	0.435488787174067	0.870977574348133	0.564511212825933
44	0.388357018766528	0.776714037533056	0.611642981233472
45	0.349784441710685	0.699568883421369	0.650215558289315
46	0.574490258949647	0.851019482100707	0.425509741050353
47	0.592513782373666	0.814972435252669	0.407486217626334
48	0.554065143583589	0.891869712832823	0.445934856416411
49	0.541344029892326	0.917311940215348	0.458655970107674
50	0.514186099361112	0.971627801277775	0.485813900638888
51	0.469970781504948	0.939941563009895	0.530029218495052
52	0.428427599419145	0.856855198838291	0.571572400580855
53	0.43566129044454	0.871322580889079	0.56433870955546
54	0.406008878029808	0.812017756059616	0.593991121970192
55	0.404277688955219	0.808555377910438	0.595722311044781
56	0.420734697246853	0.841469394493707	0.579265302753147
57	0.380141520587537	0.760283041175074	0.619858479412463
58	0.365628947608547	0.731257895217094	0.634371052391453
59	0.32650800556785	0.653016011135699	0.67349199443215
60	0.353372037232726	0.706744074465452	0.646627962767274
61	0.327220430673944	0.654440861347888	0.672779569326056
62	0.293131978019975	0.58626395603995	0.706868021980025
63	0.259117537401109	0.518235074802219	0.740882462598891
64	0.226514476962293	0.453028953924587	0.773485523037706
65	0.20290948858784	0.40581897717568	0.79709051141216
66	0.193131794747019	0.386263589494038	0.806868205252981
67	0.19583124519953	0.39166249039906	0.80416875480047
68	0.309302990917946	0.618605981835892	0.690697009082054
69	0.422218465126527	0.844436930253053	0.577781534873473
70	0.390197657422745	0.780395314845489	0.609802342577255
71	0.462670997051231	0.925341994102462	0.537329002948769
72	0.427799440490201	0.855598880980403	0.572200559509799
73	0.421024395746238	0.842048791492476	0.578975604253762
74	0.399631793508307	0.799263587016614	0.600368206491693
75	0.363591795361298	0.727183590722596	0.636408204638702
76	0.441561638540613	0.883123277081226	0.558438361459387
77	0.403416720425512	0.806833440851025	0.596583279574488
78	0.382623079741808	0.765246159483616	0.617376920258192
79	0.392051967190838	0.784103934381676	0.607948032809162
80	0.357774230719215	0.715548461438429	0.642225769280785
81	0.326049221426077	0.652098442852154	0.673950778573923
82	0.294041160294514	0.588082320589028	0.705958839705486
83	0.267059646529438	0.534119293058876	0.732940353470562
84	0.23709403574818	0.47418807149636	0.76290596425182
85	0.234219607694564	0.468439215389128	0.765780392305436
86	0.205457331994003	0.410914663988006	0.794542668005997
87	0.182360085247981	0.364720170495963	0.817639914752019
88	0.16866724698052	0.337334493961039	0.83133275301948
89	0.146035404763234	0.292070809526467	0.853964595236766
90	0.141401398675104	0.282802797350207	0.858598601324896
91	0.121453403152488	0.242906806304977	0.878546596847512
92	0.105538335362566	0.211076670725132	0.894461664637434
93	0.09047315031227	0.18094630062454	0.90952684968773
94	0.0944517045305446	0.188903409061089	0.905548295469455
95	0.0851452882570969	0.170290576514194	0.914854711742903
96	0.0713437597109419	0.142687519421884	0.928656240289058
97	0.0765390419752528	0.153078083950506	0.923460958024747
98	0.0639172957190728	0.127834591438146	0.936082704280927
99	0.0532560540094882	0.106512108018976	0.946743945990512
100	0.047196464347765	0.09439292869553	0.952803535652235
101	0.0418237708668177	0.0836475417336354	0.958176229133182
102	0.0495646640737906	0.0991293281475813	0.950435335926209
103	0.0418159325305985	0.083631865061197	0.958184067469402
104	0.0371546247444248	0.0743092494888495	0.962845375255575
105	0.0443799645112816	0.0887599290225631	0.955620035488718
106	0.0392467240758448	0.0784934481516896	0.960753275924155
107	0.0321015407475388	0.0642030814950776	0.967898459252461
108	0.0306389619503833	0.0612779239007667	0.969361038049617
109	0.024976650927647	0.0499533018552939	0.975023349072353
110	0.0206802880721734	0.0413605761443469	0.979319711927827
111	0.0165367018611439	0.0330734037222877	0.983463298138856
112	0.0174610968140098	0.0349221936280197	0.98253890318599
113	0.0151868048729551	0.0303736097459102	0.984813195127045
114	0.0203427840188146	0.0406855680376292	0.979657215981185
115	0.0195545789159241	0.0391091578318481	0.980445421084076
116	0.0191565828176053	0.0383131656352107	0.980843417182395
117	0.0161895415427554	0.0323790830855109	0.983810458457245
118	0.0161428798213071	0.0322857596426143	0.983857120178693
119	0.0131115966255751	0.0262231932511502	0.986888403374425
120	0.0118512862501971	0.0237025725003941	0.988148713749803
121	0.00961138913138503	0.0192227782627701	0.990388610868615
122	0.0142976132082602	0.0285952264165205	0.98570238679174
123	0.01191824859868	0.02383649719736	0.98808175140132
124	0.0100417231243228	0.0200834462486456	0.989958276875677
125	0.00820386346908054	0.0164077269381611	0.991796136530919
126	0.00652402141000318	0.0130480428200064	0.993475978589997
127	0.00592499471708863	0.0118499894341773	0.994075005282911
128	0.00498958031437574	0.00997916062875147	0.995010419685624
129	0.00679676778773438	0.0135935355754688	0.993203232212266
130	0.00737896470885787	0.0147579294177157	0.992621035291142
131	0.0150380035701471	0.0300760071402942	0.984961996429853
132	0.0179020011010291	0.0358040022020581	0.982097998898971
133	0.0190014277786268	0.0380028555572536	0.980998572221373
134	0.0179399512149193	0.0358799024298386	0.982060048785081
135	0.0143280831078872	0.0286561662157743	0.985671916892113
136	0.0122309426263336	0.0244618852526672	0.987769057373666
137	0.00990648741823738	0.0198129748364748	0.990093512581763
138	0.0125384210857651	0.0250768421715302	0.987461578914235
139	0.0107618216212594	0.0215236432425187	0.989238178378741
140	0.0129052955180907	0.0258105910361813	0.987094704481909
141	0.0201560166619607	0.0403120333239213	0.979843983338039
142	0.0185242814551175	0.0370485629102349	0.981475718544883
143	0.0150205312093494	0.0300410624186988	0.984979468790651
144	0.0157236012587871	0.0314472025175742	0.984276398741213
145	0.0266974551191017	0.0533949102382034	0.973302544880898
146	0.0328992839312784	0.0657985678625569	0.967100716068721
147	0.0347106625346168	0.0694213250692335	0.965289337465383
148	0.030826815791158	0.0616536315823159	0.969173184208842
149	0.0251694491161798	0.0503388982323596	0.97483055088382
150	0.0347431669180131	0.0694863338360262	0.965256833081987
151	0.0295649645330981	0.0591299290661961	0.970435035466902
152	0.0312118999658567	0.0624237999317135	0.968788100034143
153	0.0614921647203982	0.122984329440796	0.938507835279602
154	0.0592693484311593	0.118538696862319	0.940730651568841
155	0.066851467294983	0.133702934589966	0.933148532705017
156	0.0568828372036414	0.113765674407283	0.943117162796359
157	0.0507263507797272	0.101452701559454	0.949273649220273
158	0.0443069457737607	0.0886138915475213	0.955693054226239
159	0.0428963705597263	0.0857927411194525	0.957103629440274
160	0.0368289288531124	0.0736578577062248	0.963171071146888
161	0.0301494047603984	0.0602988095207967	0.969850595239602
162	0.024688251117222	0.0493765022344441	0.975311748882778
163	0.0204556922545628	0.0409113845091255	0.979544307745437
164	0.0174663342278843	0.0349326684557686	0.982533665772116
165	0.015289971229587	0.0305799424591739	0.984710028770413
166	0.0162158571129129	0.0324317142258259	0.983784142887087
167	0.0129519190795866	0.0259038381591732	0.987048080920413
168	0.0190097943455472	0.0380195886910944	0.980990205654453
169	0.0193145060535542	0.0386290121071084	0.980685493946446
170	0.0178019312531851	0.0356038625063702	0.982198068746815
171	0.016327213558843	0.0326544271176859	0.983672786441157
172	0.0131776500985954	0.0263553001971908	0.986822349901405
173	0.0128128065802384	0.0256256131604767	0.987187193419762
174	0.0146859512169986	0.0293719024339971	0.985314048783001
175	0.0188929908902687	0.0377859817805374	0.981107009109731
176	0.0151454063778122	0.0302908127556244	0.984854593622188
177	0.0119808719881983	0.0239617439763966	0.988019128011802
178	0.0100781507238406	0.0201563014476812	0.989921849276159
179	0.00783798740929301	0.015675974818586	0.992162012590707
180	0.00689228384446787	0.0137845676889357	0.993107716155532
181	0.00557929233420559	0.0111585846684112	0.994420707665794
182	0.00430170227341235	0.00860340454682471	0.995698297726588
183	0.00454272486774686	0.00908544973549373	0.995457275132253
184	0.00346340780470004	0.00692681560940007	0.9965365921953
185	0.0945485528934925	0.189097105786985	0.905451447106507
186	0.0837673286341582	0.167534657268316	0.916232671365842
187	0.0937319873577427	0.187463974715485	0.906268012642257
188	0.0791422429758007	0.158284485951601	0.920857757024199
189	0.0705418654748856	0.141083730949771	0.929458134525114
190	0.0596705613214319	0.119341122642864	0.940329438678568
191	0.0516888003813333	0.103377600762667	0.948311199618667
192	0.0430109766894643	0.0860219533789286	0.956989023310536
193	0.0433120762544696	0.0866241525089393	0.95668792374553
194	0.0410696683806253	0.0821393367612507	0.958930331619375
195	0.035702773898195	0.0714055477963901	0.964297226101805
196	0.0284550140215669	0.0569100280431338	0.971544985978433
197	0.0373222402395989	0.0746444804791978	0.962677759760401
198	0.0305194995352497	0.0610389990704994	0.96948050046475
199	0.0274057819364692	0.0548115638729385	0.972594218063531
200	0.0232765389624399	0.0465530779248797	0.97672346103756
201	0.0213113555866387	0.0426227111732773	0.978688644413361
202	0.017816554441518	0.035633108883036	0.982183445558482
203	0.0198469109119102	0.0396938218238205	0.98015308908809
204	0.0263675080730801	0.0527350161461602	0.97363249192692
205	0.0278237067069442	0.0556474134138884	0.972176293293056
206	0.0217503282101579	0.0435006564203158	0.978249671789842
207	0.0193869270812976	0.0387738541625953	0.980613072918702
208	0.0154903722401228	0.0309807444802457	0.984509627759877
209	0.0180778994705218	0.0361557989410435	0.981922100529478
210	0.0149636941222892	0.0299273882445784	0.985036305877711
211	0.0183958392351673	0.0367916784703345	0.981604160764833
212	0.0324717611190881	0.0649435222381763	0.967528238880912
213	0.0253710945551448	0.0507421891102895	0.974628905444855
214	0.0314791169033977	0.0629582338067955	0.968520883096602
215	0.0267612504796719	0.0535225009593438	0.973238749520328
216	0.0207293671329075	0.0414587342658149	0.979270632867093
217	0.0231370663002716	0.0462741326005433	0.976862933699728
218	0.0196067553481166	0.0392135106962332	0.980393244651883
219	0.018294390391927	0.036588780783854	0.981705609608073
220	0.0137419038079818	0.0274838076159636	0.986258096192018
221	0.0114025137241161	0.0228050274482322	0.988597486275884
222	0.00831154801890274	0.0166230960378055	0.991688451981097
223	0.00641631381835698	0.012832627636714	0.993583686181643
224	0.00491691159901379	0.00983382319802759	0.995083088400986
225	0.00343960111377309	0.00687920222754618	0.996560398886227
226	0.00710199123298353	0.0142039824659671	0.992898008767017
227	0.00565156735869477	0.0113031347173895	0.994348432641305
228	0.00412261775956938	0.00824523551913875	0.995877382240431
229	0.00293161983497722	0.00586323966995445	0.997068380165023
230	0.00207599045903008	0.00415198091806016	0.99792400954097
231	0.00226850989205011	0.00453701978410022	0.99773149010795
232	0.0077985191670701	0.0155970383341402	0.99220148083293
233	0.029369840495763	0.0587396809915259	0.970630159504237
234	0.0449507472439715	0.089901494487943	0.955049252756029
235	0.033091410580894	0.0661828211617879	0.966908589419106
236	0.0271011514226525	0.054202302845305	0.972898848577348
237	0.238959015911461	0.477918031822922	0.761040984088539
238	0.19294927201925	0.3858985440385	0.80705072798075
239	0.1632899251623	0.326579850324601	0.8367100748377
240	0.130447620662191	0.260895241324381	0.869552379337809
241	0.116817470818194	0.233634941636387	0.883182529181806
242	0.177907984064825	0.35581596812965	0.822092015935175
243	0.150453950204245	0.30090790040849	0.849546049795755
244	0.151452822534991	0.302905645069982	0.848547177465009
245	0.132852249443852	0.265704498887705	0.867147750556148
246	0.116475820843447	0.232951641686895	0.883524179156553
247	0.0792562973088716	0.158512594617743	0.920743702691128
248	0.0704290884946226	0.140858176989245	0.929570911505377
249	0.0534977901152154	0.106995580230431	0.946502209884785
250	0.130779813206018	0.261559626412036	0.869220186793982
251	0.107472861596092	0.214945723192184	0.892527138403908
252	0.54397838950358	0.912043220992839	0.45602161049642
253	0.382288727481096	0.764577454962192	0.617711272518904

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	10	0.0411522633744856	NOK
5% type I error level	86	0.353909465020576	NOK
10% type I error level	125	0.51440329218107	NOK