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Data X:
9 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	2 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)	7.13849143549929	3.614334	1.9751	0.050062	0.025031
month	-0.195411192119583	0.301057	-0.6491	0.517257	0.258629
Software	0.543562867871673	0.069099	7.8665	0	0
Happiness	0.0595549258757391	0.076525	0.7782	0.437631	0.218816
Depression	-0.064617043703798	0.05734	-1.1269	0.261545	0.130772
Belonging	0.0400975540319466	0.045112	0.8888	0.375481	0.187741
Belonging_Final	-0.0562249832541975	0.064373	-0.8734	0.383799	0.191899
Connected	0.111120048323479	0.047195	2.3545	0.019819	0.009909
Separate	-0.017125630717054	0.045226	-0.3787	0.705457	0.352728

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	13	16.1920284722715	-3.19202847227153
2	16	16.0867606404863	-0.086760640486274
3	19	16.3028929413178	2.69710705868222
4	15	11.8413099907998	3.15869000920022
5	14	15.647527068189	-1.64752706818901
6	13	14.9895969285293	-1.98959692852926
7	19	15.1623724373348	3.83762756266521
8	15	16.7269914184294	-1.72699141842944
9	14	15.9877179884548	-1.98771798845475
10	15	12.5599779478411	2.44002205215887
11	16	15.286032561121	0.713967438879
12	16	16.2450461284938	-0.245046128493797
13	16	15.477090308743	0.52290969125701
14	16	15.3999738428743	0.600026157125661
15	17	17.5009282996742	-0.500928299674168
16	15	15.1239299406806	-0.123929940680644
17	15	14.6740435982036	0.325956401796418
18	20	16.2684149081577	3.7315850918423
19	18	15.4905078402239	2.50949215977606
20	16	15.284684912821	0.715315087178972
21	16	15.3105615982231	0.6894384017769
22	16	14.8195720504499	1.18042794955006
23	19	16.4972238142738	2.5027761857262
24	16	14.7776835870545	1.22231641294553
25	17	14.883854995613	2.11614500438701
26	17	16.9736808362276	0.026319163772371
27	16	15.0881752094556	0.911824790544366
28	15	16.1803293864933	-1.18032938649329
29	16	15.590531146231	0.409468853768984
30	14	14.0560746879148	-0.056074687914776
31	15	15.6918545226112	-0.691854522611161
32	12	12.2953166223489	-0.295316622348879
33	14	15.1914819800671	-1.19148198006713
34	16	15.5615602777016	0.438439722298425
35	14	15.7663749381685	-1.76637493816852
36	7	12.9843960516783	-5.98439605167827
37	10	11.0222297452183	-1.02222974521833
38	14	15.8848924668938	-1.88489246689382
39	16	14.3343344138764	1.66566558612356
40	16	14.6728007936734	1.32719920632658
41	16	15.1277062368898	0.872293763110155
42	14	15.6971384415886	-1.69713844158859
43	20	17.9535613125627	2.04643868743733
44	14	14.2915940902356	-0.291594090235565
45	14	14.9676171226496	-0.967617122649585
46	11	15.7073067942432	-4.70730679424319
47	14	16.5012074540298	-2.50120745402984
48	15	15.0471761334036	-0.047176133403646
49	16	15.357477811427	0.642522188573004
50	14	16.1177056358295	-2.11770563582954
51	16	16.6145714398191	-0.614571439819063
52	14	14.2241591503592	-0.224159150359189
53	12	14.8310597751438	-2.83105977514377
54	16	15.2831352834483	0.716864716551661
55	9	11.6113445458156	-2.61134454581563
56	14	12.7273487758473	1.27265122415274
57	16	16.2039075589066	-0.203907558906587
58	16	15.1796123961502	0.820387603849783
59	15	15.4318371730387	-0.431837173038734
60	16	14.2569075893394	1.74309241066062
61	12	11.5488840036404	0.451115996359575
62	16	16.0990324387555	-0.0990324387555284
63	16	16.6054424987364	-0.605442498736426
64	14	14.4755353551307	-0.475535355130693
65	16	15.5128780019761	0.487121998023946
66	17	15.8437920725416	1.15620792745835
67	18	16.036788040156	1.96321195984401
68	18	14.5959321982091	3.40406780179092
69	12	15.7986894450369	-3.79868944503691
70	16	15.3566544913652	0.643345508634752
71	10	13.3453058388218	-3.34530583882182
72	14	14.2509852204631	-0.250985220463125
73	18	16.506846709913	1.49315329008702
74	18	17.0795559905344	0.920444009465598
75	16	15.6431954740178	0.356804525982156
76	17	13.8360737114893	3.16392628851066
77	16	16.2900569221169	-0.290056922116905
78	16	14.3793144101012	1.62068558989885
79	13	14.7858404970916	-1.7858404970916
80	16	15.8586878007588	0.141312199241162
81	16	15.5214667638494	0.478533236150586
82	20	16.6774367474165	3.32256325258354
83	16	15.5912598681855	0.40874013181446
84	15	15.9403513570373	-0.940351357037346
85	15	14.6681851536693	0.331814846330659
86	16	14.1947600770899	1.80523992291009
87	14	14.1616021894194	-0.161602189419443
88	16	15.2554704484013	0.744529551598691
89	16	14.4963969983629	1.50360300163705
90	15	14.193413731728	0.806586268272043
91	12	13.4178285037396	-1.41782850373964
92	17	16.9049259402423	0.0950740597576807
93	16	15.4486495114396	0.551350488560387
94	15	15.2371713561589	-0.23717135615888
95	13	15.1091201175879	-2.10912011758789
96	16	15.1444888147004	0.85551118529957
97	16	15.792584869029	0.207415130971023
98	16	14.0989939305589	1.90100606944106
99	16	16.0870532118844	-0.0870532118844143
100	14	14.2675453613905	-0.267545361390547
101	16	17.2838519578238	-1.28385195782379
102	16	14.753399382905	1.24660061709498
103	20	17.3665631885563	2.63343681144375
104	15	14.4692009204892	0.530799079510777
105	16	14.5472168020534	1.45278319794661
106	13	15.3335955420167	-2.33359554201674
107	17	15.9772299167096	1.02277008329043
108	16	15.8838595572741	0.116140442725887
109	16	14.5006327989772	1.49936720102275
110	12	12.2506833749084	-0.250683374908368
111	16	14.9801244615349	1.01987553846506
112	16	15.8530036131275	0.146996386872522
113	17	14.5168989793173	2.48310102068274
114	13	14.9240123377016	-1.9240123377016
115	12	14.8506025714075	-2.85060257140747
116	18	16.589292312907	1.410707687093
117	14	15.4716069833716	-1.47160698337164
118	14	12.9526157847555	1.04738421524447
119	13	14.888633080924	-1.88863308092398
120	16	15.6940921498395	0.305907850160488
121	13	14.2521312443242	-1.25213124432418
122	16	15.6232214924446	0.376778507555358
123	13	15.9768657876015	-2.97686578760153
124	16	17.0361549172139	-1.03615491721393
125	15	15.9662974443689	-0.966297444368876
126	16	16.805957327963	-0.805957327962961
127	15	15.4175730688417	-0.417573068841665
128	17	16.2445366271888	0.755463372811213
129	15	13.8156180890219	1.18438191097814
130	12	14.954053885653	-2.95405388565303
131	16	14.1596075209435	1.84039247905651
132	10	13.3680049257498	-3.36800492574982
133	16	14.0923519355499	1.90764806445007
134	12	13.9314533586019	-1.93145335860193
135	14	15.6665908818457	-1.66659088184569
136	15	15.1226477046261	-0.122647704626115
137	13	12.2897984522131	0.710201547786874
138	15	14.4360825865368	0.563917413463214
139	11	13.1728375568282	-2.17283755682825
140	12	13.3395932394898	-1.33959323948977
141	8	13.3820861748007	-5.38208617480074
142	16	13.0634494806831	2.93655051931695
143	15	13.3767359253512	1.62326407464884
144	17	16.5988568790758	0.401143120924244
145	16	14.8334848468848	1.16651515311521
146	10	14.1651894011198	-4.16518940111977
147	18	15.926460496673	2.073539503327
148	13	15.1753194573359	-2.17531945733589
149	16	15.1569198753562	0.843080124643798
150	13	13.0666695506731	-0.06666955067307
151	10	13.1498240975415	-3.14982409754152
152	15	16.4849473386869	-1.48494733868688
153	16	14.1167804444225	1.88321955557748
154	16	12.2148492201885	3.78515077981149
155	14	12.4971401550021	1.5028598449979
156	10	12.540191817967	-2.54019181796699
157	17	16.9049259402423	0.0950740597576807
158	13	11.6461372300348	1.35386276996516
159	15	13.8156180890219	1.18438191097814
160	16	15.0870135567143	0.9129864432857
161	12	12.5978246283454	-0.597824628345435
162	13	12.4197535379767	0.580246462023317

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.759669005338186	0.480661989323629	0.240330994661814
13	0.622579219163299	0.754841561673402	0.377420780836701
14	0.582435660136412	0.835128679727177	0.417564339863588
15	0.46254000293873	0.92508000587746	0.53745999706127
16	0.362240895874605	0.72448179174921	0.637759104125395
17	0.299077928194038	0.598155856388075	0.700922071805962
18	0.50597205838619	0.988055883227621	0.49402794161381
19	0.421532914646167	0.843065829292334	0.578467085353833
20	0.332277216301394	0.664554432602788	0.667722783698606
21	0.262251467129041	0.524502934258082	0.737748532870959
22	0.238943387702369	0.477886775404738	0.761056612297631
23	0.435115487867794	0.870230975735588	0.564884512132206
24	0.469907759946192	0.939815519892384	0.530092240053808
25	0.45775482696869	0.91550965393738	0.54224517303131
26	0.416942696980978	0.833885393961956	0.583057303019022
27	0.476615581918384	0.953231163836768	0.523384418081616
28	0.479845334345388	0.959690668690776	0.520154665654612
29	0.459659913655992	0.919319827311984	0.540340086344008
30	0.500482361582846	0.999035276834307	0.499517638417154
31	0.439749298400252	0.879498596800503	0.560250701599748
32	0.390507946283894	0.781015892567788	0.609492053716106
33	0.364115235936932	0.728230471873863	0.635884764063068
34	0.329150473722932	0.658300947445865	0.670849526277068
35	0.282675334072612	0.565350668145224	0.717324665927388
36	0.850419973215056	0.299160053569888	0.149580026784944
37	0.825286663658169	0.349426672683661	0.174713336341831
38	0.818698394586819	0.362603210826362	0.181301605413181
39	0.837988822550587	0.324022354898826	0.162011177449413
40	0.820657708821078	0.358684582357843	0.179342291178922
41	0.789885892996277	0.420228214007446	0.210114107003723
42	0.764469438827109	0.471061122345783	0.235530561172891
43	0.788600402152443	0.422799195695113	0.211399597847557
44	0.74763295013454	0.50473409973092	0.25236704986546
45	0.719207433467021	0.561585133065959	0.280792566532979
46	0.873892109305339	0.252215781389323	0.126107890694661
47	0.918290149217736	0.163419701564528	0.0817098507822641
48	0.896685592967334	0.206628814065332	0.103314407032666
49	0.881026426741798	0.237947146516404	0.118973573258202
50	0.880795596922432	0.238408806155135	0.119204403077568
51	0.854530781453067	0.290938437093866	0.145469218546933
52	0.823740225439621	0.352519549120758	0.176259774560379
53	0.845552854099048	0.308894291801904	0.154447145900952
54	0.829759610437591	0.340480779124819	0.170240389562409
55	0.848672051547674	0.302655896904652	0.151327948452326
56	0.821625857518666	0.356748284962669	0.178374142481334
57	0.789412465621842	0.421175068756317	0.210587534378158
58	0.764071910243078	0.471856179513844	0.235928089756922
59	0.731147703611897	0.537704592776206	0.268852296388103
60	0.719798752556881	0.560402494886239	0.280201247443119
61	0.681620375814976	0.636759248370048	0.318379624185024
62	0.64162403252482	0.716751934950359	0.35837596747518
63	0.611743762178633	0.776512475642733	0.388256237821366
64	0.572829435324304	0.854341129351392	0.427170564675696
65	0.529722397709642	0.940555204580717	0.470277602290358
66	0.485151606712435	0.970303213424869	0.514848393287565
67	0.467602264983995	0.93520452996799	0.532397735016005
68	0.520603587739928	0.958792824520144	0.479396412260072
69	0.743816488373516	0.512367023252967	0.256183511626484
70	0.706441434148368	0.587117131703264	0.293558565851632
71	0.825074412880332	0.349851174239337	0.174925587119668
72	0.792959734714184	0.414080530571631	0.207040265285816
73	0.783452839082717	0.433094321834565	0.216547160917283
74	0.762914416923587	0.474171166152825	0.237085583076413
75	0.7257852152687	0.5484295694626	0.2742147847313
76	0.769231148572828	0.461537702854343	0.230768851427172
77	0.73543407699416	0.529131846011681	0.26456592300584
78	0.719713950811243	0.560572098377514	0.280286049188757
79	0.731082967782323	0.537834064435353	0.268917032217677
80	0.690950759288039	0.618098481423922	0.309049240711961
81	0.65146189825327	0.697076203493461	0.34853810174673
82	0.776489644643655	0.447020710712691	0.223510355356345
83	0.740072589345872	0.519854821308256	0.259927410654128
84	0.714558361288209	0.570883277423582	0.285441638711791
85	0.673462073514429	0.653075852971143	0.326537926485571
86	0.663789355981807	0.672421288036386	0.336210644018193
87	0.620735744174413	0.758528511651173	0.379264255825587
88	0.580045174619223	0.839909650761554	0.419954825380777
89	0.559029467576591	0.881941064846819	0.440970532423409
90	0.525146046496619	0.949707907006762	0.474853953503381
91	0.512171390262373	0.975657219475253	0.487828609737626
92	0.473504035557827	0.947008071115654	0.526495964442173
93	0.430147954229183	0.860295908458367	0.569852045770817
94	0.388051967910082	0.776103935820165	0.611948032089918
95	0.403816225956842	0.807632451913684	0.596183774043158
96	0.368160126511548	0.736320253023097	0.631839873488452
97	0.328547897743779	0.657095795487558	0.671452102256221
98	0.328324065378847	0.656648130757695	0.671675934621153
99	0.28683315567803	0.57366631135606	0.71316684432197
100	0.248602153869147	0.497204307738295	0.751397846130853
101	0.227031896972654	0.454063793945308	0.772968103027346
102	0.210541850750981	0.421083701501961	0.789458149249019
103	0.252271041207401	0.504542082414802	0.747728958792599
104	0.215845663502878	0.431691327005756	0.784154336497122
105	0.207958802465844	0.415917604931689	0.792041197534155
106	0.223397247669122	0.446794495338244	0.776602752330878
107	0.200784787344616	0.401569574689233	0.799215212655384
108	0.177990917914054	0.355981835828107	0.822009082085946
109	0.171171653788758	0.342343307577515	0.828828346211242
110	0.158719614962942	0.317439229925885	0.841280385037058
111	0.139619098083404	0.279238196166808	0.860380901916596
112	0.116535753116634	0.233071506233269	0.883464246883366
113	0.137723200342985	0.27544640068597	0.862276799657015
114	0.124209244874226	0.248418489748453	0.875790755125774
115	0.162217422492839	0.324434844985677	0.837782577507161
116	0.153093660177939	0.306187320355879	0.846906339822061
117	0.133779178939297	0.267558357878594	0.866220821060703
118	0.116180459638335	0.232360919276669	0.883819540361665
119	0.117513986612959	0.235027973225919	0.882486013387041
120	0.108308364426013	0.216616728852026	0.891691635573987
121	0.0916509028822687	0.183301805764537	0.908349097117731
122	0.0728044932343419	0.145608986468684	0.927195506765658
123	0.0816667230907184	0.163333446181437	0.918333276909282
124	0.0651888331583375	0.130377666316675	0.934811166841662
125	0.0525748271208091	0.105149654241618	0.947425172879191
126	0.0398532766207215	0.079706553241443	0.960146723379278
127	0.0296317955133558	0.0592635910267117	0.970368204486644
128	0.0234937781425805	0.046987556285161	0.976506221857419
129	0.0180883229873238	0.0361766459746475	0.981911677012676
130	0.0252972199878473	0.0505944399756945	0.974702780012153
131	0.0217525250070475	0.043505050014095	0.978247474992953
132	0.0332924901205396	0.0665849802410792	0.96670750987946
133	0.0370245529366437	0.0740491058732873	0.962975447063356
134	0.0517743789357744	0.103548757871549	0.948225621064225
135	0.0412494597898402	0.0824989195796803	0.95875054021016
136	0.0285182284174242	0.0570364568348485	0.971481771582576
137	0.0193395542236247	0.0386791084472493	0.980660445776375
138	0.0127752841722824	0.0255505683445647	0.987224715827718
139	0.0245127732542166	0.0490255465084332	0.975487226745783
140	0.0203819144764128	0.0407638289528257	0.979618085523587
141	0.614747119241744	0.770505761516512	0.385252880758256
142	0.54895229541725	0.902095409165501	0.45104770458275
143	0.461544891437493	0.923089782874985	0.538455108562507
144	0.385534252991918	0.771068505983836	0.614465747008082
145	0.296104705062299	0.592209410124597	0.703895294937701
146	0.298924121849977	0.597848243699953	0.701075878150023
147	0.255026458422161	0.510052916844323	0.744973541577839
148	0.757402436582745	0.485195126834511	0.242597563417255
149	0.742449312709065	0.51510137458187	0.257550687290935
150	0.582709638539518	0.834580722920964	0.417290361460482

Multiple Linear Regression - Regression Statistics
Multiple R	0.598729202174743
R-squared	0.358476657536805
Adjusted R-squared	0.324932953355723
F-TEST (value)	10.6868536522266
F-TEST (DF numerator)	8
F-TEST (DF denominator)	153
p-value	7.11086745042167e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.85379966537436
Sum Squared Residuals	525.795699499341

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	7	0.0503597122302158	NOK
10% type I error level	14	0.100719424460432	NOK