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Data X:
99 13 12 14 12 53 32 41 38 9 16 11 18 11 86 51 39 32 9 19 15 11 14 66 42 30 35 9 15 6 12 12 67 41 31 33 9 14 13 16 21 76 46 34 37 9 13 10 18 12 78 47 35 29 9 19 12 14 22 53 37 39 31 9 15 14 14 11 80 49 34 36 9 14 12 15 10 74 45 36 35 9 15 6 15 13 76 47 37 38 9 16 10 17 10 79 49 38 31 9 16 12 19 8 54 33 36 34 9 16 12 10 15 67 42 38 35 9 16 11 16 14 54 33 39 38 9 17 15 18 10 87 53 33 37 9 15 12 14 14 58 36 32 33 9 15 10 14 14 75 45 36 32 9 20 12 17 11 88 54 38 38 9 18 11 14 10 64 41 39 38 9 16 12 16 13 57 36 32 32 9 16 11 18 7 66 41 32 33 9 16 12 11 14 68 44 31 31 9 19 13 14 12 54 33 39 38 9 16 11 12 14 56 37 37 39 9 17 9 17 11 86 52 39 32 9 17 13 9 9 80 47 41 32 9 16 10 16 11 76 43 36 35 9 15 14 14 15 69 44 33 37 9 16 12 15 14 78 45 33 33 9 14 10 11 13 67 44 34 33 9 15 12 16 9 80 49 31 28 9 12 8 13 15 54 33 27 32 9 14 10 17 10 71 43 37 31 9 16 12 15 11 84 54 34 37 9 14 12 14 13 74 42 34 30 9 7 7 16 8 71 44 32 33 9 10 6 9 20 63 37 29 31 9 14 12 15 12 71 43 36 33 9 16 10 17 10 76 46 29 31 9 16 10 13 10 69 42 35 33 9 16 10 15 9 74 45 37 32 9 14 12 16 14 75 44 34 33 9 20 15 16 8 54 33 38 32 9 14 10 12 14 52 31 35 33 9 14 10 12 11 69 42 38 28 9 11 12 11 13 68 40 37 35 9 14 13 15 9 65 43 38 39 9 15 11 15 11 75 46 33 34 9 16 11 17 15 74 42 36 38 9 14 12 13 11 75 45 38 32 9 16 14 16 10 72 44 32 38 9 14 10 14 14 67 40 32 30 9 12 12 11 18 63 37 32 33 9 16 13 12 14 62 46 34 38 9 9 5 12 11 63 36 32 32 9 14 6 15 12 76 47 37 32 9 16 12 16 13 74 45 39 34 9 16 12 15 9 67 42 29 34 9 15 11 12 10 73 43 37 36 9 16 10 12 15 70 43 35 34 9 12 7 8 20 53 32 30 28 9 16 12 13 12 77 45 38 34 9 16 14 11 12 77 45 34 35 9 14 11 14 14 52 31 31 35 9 16 12 15 13 54 33 34 31 10 17 13 10 11 80 49 35 37 10 18 14 11 17 66 42 36 35 10 18 11 12 12 73 41 30 27 10 12 12 15 13 63 38 39 40 10 16 12 15 14 69 42 35 37 10 10 8 14 13 67 44 38 36 10 14 11 16 15 54 33 31 38 10 18 14 15 13 81 48 34 39 10 18 14 15 10 69 40 38 41 10 16 12 13 11 84 50 34 27 10 17 9 12 19 80 49 39 30 10 16 13 17 13 70 43 37 37 10 16 11 13 17 69 44 34 31 10 13 12 15 13 77 47 28 31 10 16 12 13 9 54 33 37 27 10 16 12 15 11 79 46 33 36 10 20 12 16 10 30 0 37 38 10 16 12 15 9 71 45 35 37 10 15 12 16 12 73 43 37 33 10 15 11 15 12 72 44 32 34 10 16 10 14 13 77 47 33 31 10 14 9 15 13 75 45 38 39 10 16 12 14 12 69 42 33 34 10 16 12 13 15 54 33 29 32 10 15 12 7 22 70 43 33 33 10 12 9 17 13 73 46 31 36 10 17 15 13 15 54 33 36 32 10 16 12 15 13 77 46 35 41 10 15 12 14 15 82 48 32 28 10 13 12 13 10 80 47 29 30 10 16 10 16 11 80 47 39 36 10 16 13 12 16 69 43 37 35 10 16 9 14 11 78 46 35 31 10 16 12 17 11 81 48 37 34 10 14 10 15 10 76 46 32 36 10 16 14 17 10 76 45 38 36 10 16 11 12 16 73 45 37 35 10 20 15 16 12 85 52 36 37 10 15 11 11 11 66 42 32 28 10 16 11 15 16 79 47 33 39 10 13 12 9 19 68 41 40 32 10 17 12 16 11 76 47 38 35 10 16 12 15 16 71 43 41 39 10 16 11 10 15 54 33 36 35 10 12 7 10 24 46 30 43 42 10 16 12 15 14 82 49 30 34 10 16 14 11 15 74 44 31 33 10 17 11 13 11 88 55 32 41 10 13 11 14 15 38 11 32 33 10 12 10 18 12 76 47 37 34 10 18 13 16 10 86 53 37 32 10 14 13 14 14 54 33 33 40 10 14 8 14 13 70 44 34 40 10 13 11 14 9 69 42 33 35 10 16 12 14 15 90 55 38 36 10 13 11 12 15 54 33 33 37 10 16 13 14 14 76 46 31 27 10 13 12 15 11 89 54 38 39 10 16 14 15 8 76 47 37 38 10 15 13 15 11 73 45 33 31 10 16 15 13 11 79 47 31 33 10 15 10 17 8 90 55 39 32 10 17 11 17 10 74 44 44 39 10 15 9 19 11 81 53 33 36 10 12 11 15 13 72 44 35 33 10 16 10 13 11 71 42 32 33 10 10 11 9 20 66 40 28 32 10 16 8 15 10 77 46 40 37 10 12 11 15 15 65 40 27 30 10 14 12 15 12 74 46 37 38 10 15 12 16 14 82 53 32 29 10 13 9 11 23 54 33 28 22 10 15 11 14 14 63 42 34 35 10 11 10 11 16 54 35 30 35 10 12 8 15 11 64 40 35 34 10 8 9 13 12 69 41 31 35 10 16 8 15 10 54 33 32 34 10 15 9 16 14 84 51 30 34 10 17 15 14 12 86 53 30 35 10 16 11 15 12 77 46 31 23 10 10 8 16 11 89 55 40 31 10 18 13 16 12 76 47 32 27 10 13 12 11 13 60 38 36 36 10 16 12 12 11 75 46 32 31 10 13 9 9 19 73 46 35 32 10 10 7 16 12 85 53 38 39 10 15 13 13 17 79 47 42 37 10 16 9 16 9 71 41 34 38 9 16 6 12 12 72 44 35 39 10 14 8 9 19 69 43 35 34 10 10 8 13 18 78 51 33 31 10 17 15 13 15 54 33 36 32 10 13 6 14 14 69 43 32 37 10 15 9 19 11 81 53 33 36 10 16 11 13 9 84 51 34 32 10 12 8 12 18 84 50 32 35 11 13 8 13 16 69 46 34 36
Names of X columns:
month Learning Software Happiness Depression Belonging Belonging_Final Connected Separate

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)	7.90052642188886	9.960487	0.7932	0.428897	0.214449
Learning	-0.546224610912348	0.304659	-1.7929	0.074963	0.037482
Software	0.385220587964974	0.308675	1.248	0.213945	0.106972
Happiness	0.00580595619599072	0.289348	0.0201	0.984017	0.492009
Depression	-0.119114624846749	0.214107	-0.5563	0.578796	0.289398
Belonging	-0.106498863231622	0.168709	-0.6313	0.528815	0.264408
Belonging_Final	0.023086917834615	0.24233	0.0953	0.924225	0.462112
Connected	0.355674279225881	0.18065	1.9689	0.050776	0.025388
Separate	0.0539805975124328	0.169535	0.3184	0.750612	0.375306

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	99	15.8024111973558	83.1975888026442
2	9	9.80981203497265	-0.809812034972646
3	9	8.1971032698086	0.802896730191397
4	9	7.27717893079748	1.72282106920252
5	9	9.91102990644372	-0.911029906443725
6	9	10.1191549799716	-1.11915497997165
7	9	10.3601391315489	-1.36013913154888
8	9	10.5188449226368	-1.51884492263679
9	9	11.6235624076525	-2.62356240765252
10	9	8.75946257537925	0.240537424620753
11	9	9.82756344587224	-0.827563445872239
12	9	12.5915199134099	-3.59151991340993
13	9	11.2940901281834	-2.2940901281834
14	9	12.7571389355039	-3.75713893550386
15	9	8.99911668541143	0.000883314588570272
16	9	10.5606145804232	-1.56061458042322
17	9	9.55619150945832	-0.556191509458319
18	9	7.82880055598155	1.17119944401845
19	9	11.2492430110349	-2.24924301103487
20	9	10.1976347724688	-1.1976347724688
21	9	9.7496392635772	-0.749639263577206
22	9	8.65304333703285	0.346956662967148
23	9	12.1155236159983	-3.11552361599828
24	9	11.9558970946558	-2.95589709465579
25	9	8.51042720976897	0.489572790231032
26	9	11.4779983104229	-2.47799831042286
27	9	9.38819177911465	-0.388191779114647
28	9	10.7967456479111	-1.79674564791111
29	9	8.45367520081183	0.546324799188166
30	9	10.3756489037085	-1.37564890370848
31	9	8.4993772910152	0.500622708984795
32	9	9.05786818603843	-0.0578681860384345
33	9	11.2778077873163	-2.27780778731634
34	9	8.95140482574949	0.0485951742505071
35	9	10.2099002773985	-1.20990027739851
36	9	12.5308183100354	-3.53081831003543
37	9	8.55230514610866	0.447694853891343
38	9	11.5506947169598	-2.5506947169598
39	9	6.87673076903033	2.12326923096967
40	9	9.74865818590942	-0.748658185909417
41	9	10.0735191214332	-1.07351912143321
42	9	10.2040143299187	-1.20401432991865
43	9	12.1482527284944	-3.14825272849438
44	9	11.9153675309079	-2.91536753090794
45	9	11.5133066768069	-2.51330667680685
46	9	13.7609014105079	-4.76090141050789
47	9	13.9674845023811	-4.96748450238111
48	9	9.36858720334137	-0.368587203341368
49	9	9.65461242505454	-0.654612425054545
50	9	11.9357437730966	-2.93574377309663
51	9	10.2365158022162	-1.23651580221617
52	9	9.30831412512218	-0.308314125122181
53	9	11.1960046468618	-2.1960046468618
54	9	11.1741239165175	-2.17412391651746
55	9	10.9006751786202	-1.9006751786202
56	9	9.10091822606374	-0.100918226063744
57	9	11.1926175076488	-2.19261750764879
58	9	8.78275854769849	0.21724145230151
59	9	11.1446792444879	-2.14467924448793
60	9	9.11784775759511	-0.117847757595107
61	9	8.98255708588468	0.0174429141153176
62	9	10.6191433949868	-1.61914339498681
63	9	10.0092561391337	-1.00925613913369
64	9	10.9974641093862	-1.99746410938624
65	9	11.0994326134032	-2.09943261340316
66	10	9.42760616480146	0.572393835198538
67	10	10.134809093571	-0.134809093571012
68	10	6.2460569941951	3.7539430058049
69	10	14.7054726508823	-4.70547265088232
70	10	10.2701751648941	-0.270175164894087
71	10	13.3921629494567	-3.39216294945671
72	10	10.8850792986748	-0.885079298674756
73	10	8.68010380787329	1.31989619212671
74	10	11.6613970104445	-1.66139701044448
75	10	8.30763926689472	1.69236073310528
76	10	7.99025166087605	2.00974833912395
77	10	11.4140589031525	-1.41405890315254
78	10	8.88261476136563	1.11738523863437
79	10	8.47780376614221	1.52219623385779
80	10	12.4153796480261	-2.41537964802609
81	10	8.88954892249237	1.11045107750763
82	10	12.5166754497713	-2.51667544977132
83	10	10.7220113161684	-0.722011316168429
84	10	11.1529526150061	-1.15295261500608
85	10	9.16712105329438	0.832878946705623
86	10	7.84125419740863	2.15874580259137
87	10	10.9313288544872	-0.931328854487222
88	10	9.62930810740253	0.370691892597469
89	10	9.12520065270071	0.874799347299295
90	10	8.80635223356601	1.19364776643399
91	10	9.6198128858833	0.380187114116705
92	10	12.2243577602644	-2.22435776026445
93	10	9.84556894527604	0.154431054723956
94	10	7.89266726447075	2.10733273552925
95	10	8.80573282030905	1.19426717969095
96	10	10.1755474327167	-0.175547432716691
97	10	11.0262229158391	-1.0262229158391
98	10	8.27602563652268	1.72397436347732
99	10	10.049072865969	-0.0490728659689967
100	10	9.29449390370285	0.705506096297149
101	10	11.8654977036507	-1.86549770365071
102	10	9.87596012265189	0.124039877348109
103	10	8.36753533689518	1.63246466310482
104	10	9.53194761200307	0.468052387996929
105	10	8.09378392066557	1.90621607933443
106	10	12.8703204901857	-2.87032049018569
107	10	10.4161045739225	-0.416104573922471
108	10	12.0840419769521	-2.08404197695211
109	10	11.3742239432662	-1.37422394326622
110	10	14.5965227009523	-4.59652270095231
111	10	7.1069851790586	2.89301482094141
112	10	8.77333790375118	1.22666209624882
113	10	7.11001301098628	2.88998698901372
114	10	12.7015329082033	-2.70153290820327
115	10	11.8596288633612	-1.85962886336118
116	10	8.93013197875013	1.06986802124987
117	10	12.5823329405361	-2.5823329405361
118	10	9.68099318925866	0.319006810741337
119	10	11.2940858242273	-1.29408582422728
120	10	9.22195062800248	0.778049371997522
121	10	12.0654480457425	-2.06544804574247
122	10	8.03394233335232	1.96605766664768
123	10	11.5882426542568	-1.58824265425681
124	10	11.8905757924204	-1.89057579242044
125	10	10.1669973953626	-0.16699739536265
126	10	9.18339534084087	0.816604659159132
127	10	9.98870619467624	0.011293805323759
128	10	12.6495096053647	-2.64950960536465
129	10	8.25194629267282	1.74805370732718
130	10	11.6997225013499	-1.69972250134988
131	10	8.34952299692178	1.65047700307822
132	10	9.92647856802906	0.0735214319709367
133	10	10.2244794740361	-0.224479474036066
134	10	9.10730159659493	0.892698403405066
135	10	11.9260361475568	-1.92603614755682
136	10	8.19280898839499	1.80719101160501
137	10	7.74820355602664	2.25179644397336
138	10	10.6007309367845	-0.600730936784455
139	10	11.5189459015262	-1.51894590152617
140	10	11.5959138191754	-1.59591381917541
141	10	12.1571823958365	-2.15718239583645
142	10	9.36648737016904	0.633512629830959
143	10	6.33653009147799	3.66346990852201
144	10	7.66917844146308	2.33082155853692
145	10	7.1851151100305	2.8148848899695
146	10	11.9944307875169	-1.99443078751686
147	10	7.5700954706736	2.4299045293264
148	10	13.1725755771535	-3.1725755771535
149	10	8.67254924004284	1.32745075995716
150	10	9.51922760317632	0.480772396823681
151	10	11.5904134136101	-1.5904134136101
152	10	12.3728304882772	-2.3728304882772
153	10	9.17811415541759	0.821885844582413
154	9	8.01430145920899	0.985698540791011
155	10	9.05247829874651	0.947521701253493
156	10	9.73263041462987	0.267369585370132
157	10	12.2243577602644	-2.22435776026445
158	10	8.54778359380226	1.45221640619774
159	10	8.25194629267282	1.74805370732718
160	10	8.45363783402003	1.54636216597997
161	10	7.83254325020868	2.16745674979132
162	11	9.80081827828589	1.19918172171411

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	1	2.76658166308293e-92	1.38329083154146e-92
13	1	1.41454075083925e-125	7.07270375419624e-126
14	1	2.65387852198844e-133	1.32693926099422e-133
15	1	4.63096259034738e-136	2.31548129517369e-136
16	1	3.92875607945596e-135	1.96437803972798e-135
17	1	6.33043430374803e-134	3.16521715187401e-134
18	1	9.84584504475453e-135	4.92292252237727e-135
19	1	5.094137499795e-135	2.5470687498975e-135
20	1	5.85522426526472e-134	2.92761213263236e-134
21	1	6.32953069867927e-133	3.16476534933964e-133
22	1	5.68792249209965e-132	2.84396124604983e-132
23	1	8.1644981001082e-132	4.0822490500541e-132
24	1	7.93834627895488e-133	3.96917313947744e-133
25	1	4.33857694729855e-132	2.16928847364928e-132
26	1	5.23021458840258e-132	2.61510729420129e-132
27	1	4.85585285405673e-131	2.42792642702837e-131
28	1	1.18454375957663e-130	5.92271879788317e-131
29	1	3.61803340724792e-130	1.80901670362396e-130
30	1	3.9294773777741e-129	1.96473868888705e-129
31	1	2.2206165198722e-128	1.1103082599361e-128
32	1	2.59169380430458e-127	1.29584690215229e-127
33	1	9.6415474033865e-127	4.82077370169325e-127
34	1	3.99573918420657e-126	1.99786959210328e-126
35	1	2.22379928193144e-125	1.11189964096572e-125
36	1	5.6020039910956e-126	2.8010019955478e-126
37	1	3.56228265787417e-125	1.78114132893709e-125
38	1	1.09882727906708e-124	5.49413639533539e-125
39	1	7.00683467557159e-125	3.50341733778579e-125
40	1	8.23598126985274e-124	4.11799063492637e-124
41	1	7.65616598539748e-123	3.82808299269874e-123
42	1	1.46901719466414e-122	7.34508597332071e-123
43	1	1.14100325254893e-121	5.70501626274467e-122
44	1	3.86803615268105e-121	1.93401807634053e-121
45	1	3.26833085074425e-120	1.63416542537213e-120
46	1	1.46841474960926e-120	7.34207374804632e-121
47	1	1.7945488720872e-120	8.97274436043602e-121
48	1	5.65105143250536e-120	2.82552571625268e-120
49	1	3.46360611205606e-120	1.73180305602803e-120
50	1	8.88986653869529e-120	4.44493326934764e-120
51	1	1.12379662908035e-119	5.61898314540174e-120
52	1	2.00123243627125e-119	1.00061621813562e-119
53	1	1.30446173092516e-119	6.52230865462578e-120
54	1	1.79761268531535e-119	8.98806342657675e-120
55	1	6.39014186941931e-119	3.19507093470966e-119
56	1	7.54570213268509e-119	3.77285106634255e-119
57	1	1.1968317130652e-119	5.98415856532598e-120
58	1	4.88346013479796e-120	2.44173006739898e-120
59	1	2.36507383671217e-120	1.18253691835608e-120
60	1	4.05110795390468e-121	2.02555397695234e-121
61	1	4.98533531398736e-122	2.49266765699368e-122
62	1	8.65832704499221e-124	4.3291635224961e-124
63	1	2.17843154959342e-126	1.08921577479671e-126
64	1	5.28924061520285e-130	2.64462030760143e-130
65	1	2.36083871160722e-136	1.18041935580361e-136
66	1	1.46874117300385e-134	7.34370586501925e-135
67	1	1.05766420694873e-132	5.28832103474367e-133
68	1	2.9303676992909e-131	1.46518384964545e-131
69	1	6.98392095216358e-130	3.49196047608179e-130
70	1	4.92057349652836e-128	2.46028674826418e-128
71	1	2.78519295275547e-126	1.39259647637773e-126
72	1	1.70618609170132e-124	8.5309304585066e-125
73	1	1.1349495937309e-122	5.67474796865449e-123
74	1	6.1690718863346e-121	3.0845359431673e-121
75	1	3.40821527352244e-119	1.70410763676122e-119
76	1	2.02752790615169e-117	1.01376395307584e-117
77	1	1.19054465518107e-115	5.95272327590533e-116
78	1	7.12389271206163e-114	3.56194635603082e-114
79	1	3.7217521296581e-112	1.86087606482905e-112
80	1	2.10956191981088e-110	1.05478095990544e-110
81	1	1.26684102991299e-108	6.33420514956493e-109
82	1	2.3581933007357e-107	1.17909665036785e-107
83	1	1.45103026224943e-105	7.25515131124713e-106
84	1	8.6213358588538e-104	4.3106679294269e-104
85	1	5.0293937141854e-102	2.5146968570927e-102
86	1	2.63691396191278e-100	1.31845698095639e-100
87	1	1.4738124129395e-98	7.36906206469748e-99
88	1	8.40373188439751e-97	4.20186594219875e-97
89	1	4.64831021143388e-95	2.32415510571694e-95
90	1	2.5901258369945e-93	1.29506291849725e-93
91	1	1.32188813914172e-91	6.60944069570861e-92
92	1	6.41512226317035e-90	3.20756113158518e-90
93	1	3.42951723660285e-88	1.71475861830142e-88
94	1	1.70373731415011e-86	8.51868657075053e-87
95	1	8.54662690463281e-85	4.27331345231641e-85
96	1	4.1603062195729e-83	2.08015310978645e-83
97	1	2.06407864871324e-81	1.03203932435662e-81
98	1	8.4855103502035e-80	4.24275517510175e-80
99	1	4.22490954907241e-78	2.11245477453621e-78
100	1	2.01197794431316e-76	1.00598897215658e-76
101	1	9.35610178120825e-75	4.67805089060412e-75
102	1	4.43075798031062e-73	2.21537899015531e-73
103	1	1.99823525502934e-71	9.99117627514672e-72
104	1	9.07682401062687e-70	4.53841200531343e-70
105	1	4.02328081276769e-68	2.01164040638385e-68
106	1	1.61515525021548e-66	8.07577625107741e-67
107	1	7.16860574539413e-65	3.58430287269707e-65
108	1	2.9451913050502e-63	1.4725956525251e-63
109	1	1.20526766618896e-61	6.0263383309448e-62
110	1	4.05122870336474e-60	2.02561435168237e-60
111	1	1.60397613790183e-58	8.01988068950916e-59
112	1	6.52800903634638e-57	3.26400451817319e-57
113	1	2.20297816775856e-55	1.10148908387928e-55
114	1	5.21536689735844e-54	2.60768344867922e-54
115	1	1.87778551402067e-52	9.38892757010336e-53
116	1	7.35222539923737e-51	3.67611269961868e-51
117	1	2.70152199633886e-49	1.35076099816943e-49
118	1	1.01233895335146e-47	5.06169476675729e-48
119	1	3.72244435925284e-46	1.86122217962642e-46
120	1	1.36549796729615e-44	6.82748983648075e-45
121	1	4.84695612893496e-43	2.42347806446748e-43
122	1	1.67873229348007e-41	8.39366146740037e-42
123	1	5.73066333371984e-40	2.86533166685992e-40
124	1	1.91622272849918e-38	9.58111364249591e-39
125	1	6.27617004048081e-37	3.1380850202404e-37
126	1	2.0053746329184e-35	1.0026873164592e-35
127	1	6.35734941556111e-34	3.17867470778055e-34
128	1	1.89774341862321e-32	9.48871709311604e-33
129	1	5.18755748851945e-31	2.59377874425972e-31
130	1	1.50193524284929e-29	7.50967621424644e-30
131	1	3.60370146001588e-28	1.80185073000794e-28
132	1	1.03172304979665e-26	5.15861524898326e-27
133	1	2.29701777263963e-25	1.14850888631981e-25
134	1	5.62744590927003e-24	2.81372295463501e-24
135	1	1.47397995436801e-22	7.36989977184007e-23
136	1	2.94310116067341e-21	1.4715505803367e-21
137	1	6.53513252791644e-20	3.26756626395822e-20
138	1	1.35153520690783e-18	6.75767603453913e-19
139	1	2.4624395169397e-17	1.23121975846985e-17
140	1	5.86893850677212e-16	2.93446925338606e-16
141	0.999999999999994	1.21386999926167e-14	6.06934999630833e-15
142	0.999999999999867	2.66831400353887e-13	1.33415700176943e-13
143	0.999999999997158	5.68482922286897e-12	2.84241461143448e-12
144	0.999999999947859	1.04282393540202e-10	5.21411967701008e-11
145	0.99999999901048	1.97904018595712e-09	9.8952009297856e-10
146	0.999999983556906	3.28861875928187e-08	1.64430937964094e-08
147	0.999999709012148	5.81975704192842e-07	2.90987852096421e-07
148	0.999997708453525	4.58309295089723e-06	2.29154647544861e-06
149	0.999969189303386	6.16213932280736e-05	3.08106966140368e-05
150	0.999476561454621	0.00104687709075723	0.000523438545378615

Multiple Linear Regression - Regression Statistics
Multiple R	0.249202565400241
R-squared	0.0621019186020615
Adjusted R-squared	0.0130614960453065
F-TEST (value)	1.26634142538618
F-TEST (DF numerator)	8
F-TEST (DF denominator)	153
p-value	0.265045045761541
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.99550441176068
Sum Squared Residuals	7487.37354216937

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	139	1	NOK
5% type I error level	139	1	NOK
10% type I error level	139	1	NOK