Wessa.net

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

Sample Range: (leave blank to include all observations)
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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)	18.2629853555222	4.192248	4.3564	2.4e-05	1.2e-05
Separate	0.336031993936644	0.07061	4.759	4e-06	2e-06
Learning	0.324387357179273	0.133361	2.4324	0.016145	0.008073
Software	-0.139120947054737	0.137233	-1.0138	0.312289	0.156145
Happiness	0.0353030642697499	0.129038	0.2736	0.784768	0.392384
Depression	-0.022863659926687	0.095489	-0.2394	0.811084	0.405542
Belonging	0.0349149616469285	0.075203	0.4643	0.643107	0.321554
Belonging_Final	-0.0249943108224562	0.108077	-0.2313	0.817417	0.408708

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	41	34.8503394054135	6.14966059458646
2	39	34.7878082061136	4.21219179388636
3	30	35.4235196060377	-5.42351960603767
4	31	34.8469343695324	-3.84693436953244
5	34	35.0175307771655	-1.01753077716548
6	35	32.7434649900083	2.25653500999165
7	39	34.0908314375533	4.90916856244673
8	34	35.0894725782011	-1.08947257820108
9	36	34.6559493187993	1.34405068120067
10	37	36.7744086619858	0.225591338014155
11	38	34.3840416450051	3.61595835499491
12	36	34.7572641130845	1.24273588691549
13	38	34.8444686131146	3.15553138688544
14	39	35.9974218835165	3.00257811648355
15	33	36.2436617446859	-3.2436617446859
16	32	33.8478243951801	-1.84782439518007
17	36	34.1586398459486	1.84136015405142
18	38	37.9219725779526	0.0780274220473926
19	39	36.8162402389319	2.18375976106812
20	32	33.894734585242	-1.89473458524195
21	32	34.766938715043	-2.76693871504303
22	31	33.5434337015664	-2.54343370156644
23	39	36.6674632522587	2.33253674774133
24	37	36.1620943003781	0.837905699621869
25	39	35.3301400823102	3.66985991768981
26	41	34.4524408840173	6.54755911598267
27	36	35.7152238765492	0.284776123450816
28	33	35.074956908427	-2.07495690842705
29	33	34.6808652521656	-1.68086525216555
30	34	33.8329135674704	0.167086432529586
31	31	32.7957919692103	-1.79579196921028
32	27	32.9722704796078	-5.97227047960777
33	37	33.6059131024059	3.39408689759414
34	34	36.0781251801717	-2.07812518017166
35	34	32.9468782375337	1.05312176246633
36	32	32.4100583759496	-0.410058375949616
37	29	32.2244325202422	-3.22443252024216
38	36	33.8834017477768	2.1165982522232
39	29	34.3542796925317	-5.35427969253167
40	35	34.7407039350873	0.259296064912711
41	37	34.5977336053841	2.40226639461589
42	34	33.9876430279584	0.0123569720415706
43	38	34.8594775199549	3.14052248004515
44	35	33.6465545878015	1.35344541219852
45	38	32.3536025268491	5.64639747315091
46	37	33.3884657946332	3.61153420536684
47	38	35.6195739742404	2.38042602575958
48	33	34.7704826199945	-1.77048261999448
49	36	36.483211723396	-0.483211723395981
50	38	33.5892985101701	4.41070148982986
51	32	36.0250455726567	-4.02504557265668
52	32	33.0078403618329	-1.00784036183287
53	32	32.8268789885384	-0.826878988538438
54	34	35.5323613848115	-1.53236138481148
55	32	32.7118745470261	-0.711874547026152
56	37	34.4566930011134	2.54330699888661
57	39	34.9354041237109	4.06459587628908
58	29	34.8221339000868	-5.82213390008679
59	37	35.3646540841587	1.63534591584128
60	35	34.9370352159452	0.0629647840547789
61	30	31.4665091791093	-1.46650917910928
62	38	34.9571034757691	3.04289652423085
63	34	34.9442874470568	-0.944287447056815
64	31	34.2501037571595	-3.25010375715953
65	34	33.4936375745621	0.506362425437858
66	35	36.072187976472	-1.07218797647201
67	36	35.1696622161331	0.83033778386692
68	30	33.3177895120583	-3.31778951205828
69	39	35.4096391919849	3.59036080801507
70	35	35.7857415055571	-0.785741505557144
71	38	33.927611207482	4.07238879251802
72	31	35.3257835092312	-4.32578350923122
73	34	37.1202156484346	-3.1202156484346
74	38	37.6418455629045	0.358154437095548
75	34	32.7471763555555	1.25282364444446
76	39	34.1641446562605	4.83585534373955
77	37	35.7500109977931	1.24998900220693
78	34	33.7194847590275	0.280515240972458
79	28	32.9735992693893	-4.97359926938929
80	37	32.1703581099828	4.82964189001719
81	33	35.76747286458	-2.76747286458002
82	37	37.2341581825003	-0.234158182500322
83	35	35.8949067960171	-0.894906796017068
84	37	34.3129220925197	2.68707790748032
85	32	34.6928626967719	-2.69286269677192
86	33	34.1897001707668	-1.18970017076684
87	38	36.383764117577	1.61623588242301
88	33	34.7880697793308	-1.78806977933083
89	29	33.7133361201059	-4.71333612010591
90	33	33.6618130298843	-0.661813029884271
91	31	34.7026753158317	-3.7026753158317
92	36	33.620360636121	2.37963936387903
93	35	37.332075591116	-2.33207559111601
94	32	32.682828115227	-0.682828115226969
95	29	32.740297011634	-3.740297011634
96	39	36.0909384737837	2.90906152621628
97	37	34.797925747144	2.20207425285596
98	35	34.4344577101443	0.565542289855667
99	37	35.1858563068952	1.81414369310482
100	32	35.3150588193169	-3.31505881931685
101	38	35.5029501848184	2.4970498151816
102	37	35.1658388661963	1.83416113380369
103	36	37.0556547553594	-1.05565475535944
104	32	32.3988209877631	-0.398820987763085
105	33	36.7753771829888	-3.7753771829888
106	40	32.7963621282597	7.2036378717403
107	38	35.6613920963292	2.33860790367084
108	41	36.4569137860485	4.54308621395154
109	36	34.7546438561613	1.24535614383867
110	43	35.9556924731715	7.04430752682854
111	30	35.0565798494001	-5.05657984940009
112	31	34.1238819052851	-3.12388190528514
113	32	37.929820867378	-5.929820867378
114	32	33.2418655055724	-1.24186550557241
115	37	34.0294076792184	2.97059232078156
116	37	35.060609936105	1.939390063895
117	33	35.6718631343822	-2.67186313438222
118	34	36.6740334968864	-2.67403349688644
119	33	34.3586516285645	-1.35865162856446
120	38	35.7998309413176	2.20016905868237
121	33	34.5241519010363	-1.5241519010363
122	31	32.3954250431049	-1.39542504310489
123	38	35.9516019047418	2.04839809525823
124	37	36.1001467423607	0.899853257639345
125	33	33.4393091316037	-0.439309131603674
126	31	34.2464136022439	-3.24641360224392
127	39	34.6755123147973	4.32448768520268
128	44	37.2079607525004	6.79203924749958
129	33	35.8965303531806	-2.89653035318062
130	35	33.3608049713708	1.63919502862923
131	32	34.7876701984545	-2.78767019845446
132	28	31.8946217313785	-3.89462173137853
133	40	36.6130223833684	3.38697761663155
134	27	32.1625541814688	-5.16255418146879
135	37	35.5933236699334	1.40667633006657
136	32	32.9873583435175	-0.987358343517519
137	28	29.5437015424129	-1.54370154241287
138	34	34.683618273408	-0.683618273407999
139	30	33.2342788000179	-3.23427880001786
140	35	33.9805846764394	1.0194153235606
141	31	32.9360570035502	-1.93605700355022
142	32	35.1268083243711	-3.12680832437109
143	30	35.2046996993037	-5.20469969930372
144	30	35.3497432182333	-5.34974321823331
145	31	31.4454843072378	-0.445484307237837
146	40	32.856976423377	7.14302357662296
147	32	33.1355388950339	-1.13553889503385
148	36	34.1439414313978	1.85605856860216
149	32	33.8417438554998	-1.84174385549984
150	35	33.2633282235463	1.73667177645373
151	38	35.5718184370554	2.42818156294455
152	42	35.4072141553606	6.59278584463942
153	34	36.7835819386775	-2.7835819386775
154	35	37.2871055660989	-2.28710556609885
155	35	34.3342236015332	0.665776398466775
156	33	32.3069342762546	0.693065723745405
157	36	33.620360636121	2.37963936387903
158	32	35.5870077412555	-3.58700774125554
159	33	35.8965303531806	-2.89653035318062
160	34	34.5871902813244	-0.587190281324417
161	32	34.499017982794	-2.49901798279399
162	34	34.8167205366189	-0.816720536618932

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.1858689979561	0.371737995912201	0.8141310020439
12	0.871126519820884	0.257746960358232	0.128873480179116
13	0.91778560736336	0.16442878527328	0.08221439263664
14	0.878254971065548	0.243490057868904	0.121745028934452
15	0.840974874562455	0.31805025087509	0.159025125437545
16	0.865021290690045	0.26995741861991	0.134978709309955
17	0.821693889765805	0.35661222046839	0.178306110234195
18	0.789006256280516	0.421987487438967	0.210993743719484
19	0.742707446720567	0.514585106558867	0.257292553279433
20	0.770547434122858	0.458905131754283	0.229452565877142
21	0.790254852576263	0.419490294847474	0.209745147423737
22	0.749217757834835	0.501564484330329	0.250782242165165
23	0.705947260246019	0.588105479507961	0.294052739753981
24	0.638800859989382	0.722398280021236	0.361199140010618
25	0.633268371280512	0.733463257438976	0.366731628719488
26	0.779503496322745	0.440993007354509	0.220496503677255
27	0.773102592418726	0.453794815162549	0.226897407581274
28	0.729322465835067	0.541355068329865	0.270677534164933
29	0.738390077148154	0.523219845703692	0.261609922851846
30	0.682610292386294	0.634779415227411	0.317389707613705
31	0.645134194960433	0.709731610079135	0.354865805039567
32	0.803653205619378	0.392693588761245	0.196346794380622
33	0.801253682643225	0.39749263471355	0.198746317356775
34	0.765074623665268	0.469850752669464	0.234925376334732
35	0.717559050892448	0.564881898215104	0.282440949107552
36	0.671110324094212	0.657779351811575	0.328889675905787
37	0.659073000016092	0.681853999967817	0.340926999983908
38	0.626294877302242	0.747410245395516	0.373705122697758
39	0.744233054141723	0.511533891716555	0.255766945858277
40	0.697111287933566	0.605777424132868	0.302888712066434
41	0.667212852233554	0.665574295532892	0.332787147766446
42	0.615448643113681	0.769102713772638	0.384551356886319
43	0.586125123901757	0.827749752196486	0.413874876098243
44	0.537555411124531	0.924889177750938	0.462444588875469
45	0.634730088415484	0.730539823169031	0.365269911584516
46	0.641040547000798	0.717918905998403	0.358959452999201
47	0.623760500467487	0.752478999065027	0.376239499532513
48	0.593050052289298	0.813899895421404	0.406949947710702
49	0.545345436763092	0.909309126473817	0.454654563236908
50	0.57238704712963	0.855225905740741	0.42761295287037
51	0.618862868054639	0.762274263890722	0.381137131945361
52	0.579867418097311	0.840265163805378	0.420132581902689
53	0.534978066211187	0.930043867577625	0.465021933788813
54	0.486234191594762	0.972468383189523	0.513765808405238
55	0.441307165698587	0.882614331397174	0.558692834301413
56	0.430486646517829	0.860973293035658	0.569513353482171
57	0.457975307511579	0.915950615023159	0.54202469248842
58	0.593083681731844	0.813832636536313	0.406916318268156
59	0.553952918186372	0.892094163627255	0.446047081813627
60	0.505559403401576	0.988881193196848	0.494440596598424
61	0.469026359389674	0.938052718779347	0.530973640610326
62	0.453523263933423	0.907046527866846	0.546476736066577
63	0.41981015165876	0.83962030331752	0.58018984834124
64	0.426247206904205	0.852494413808409	0.573752793095795
65	0.38074419047387	0.76148838094774	0.61925580952613
66	0.344715055638467	0.689430111276935	0.655284944361533
67	0.304223928374071	0.608447856748142	0.695776071625929
68	0.334892545079035	0.66978509015807	0.665107454920965
69	0.34548636528906	0.69097273057812	0.65451363471094
70	0.306021113937708	0.612042227875417	0.693978886062292
71	0.33907245226997	0.67814490453994	0.66092754773003
72	0.377914572185564	0.755829144371129	0.622085427814436
73	0.378925146067662	0.757850292135323	0.621074853932338
74	0.335406821220179	0.670813642440357	0.664593178779821
75	0.300771858826299	0.601543717652599	0.699228141173701
76	0.356308717806684	0.712617435613368	0.643691282193316
77	0.318767445274823	0.637534890549646	0.681232554725177
78	0.278253678827505	0.556507357655011	0.721746321172495
79	0.342165867749573	0.684331735499146	0.657834132250427
80	0.417807349655203	0.835614699310407	0.582192650344797
81	0.40756816146248	0.815136322924961	0.59243183853752
82	0.362945192875971	0.725890385751942	0.637054807124029
83	0.323405064772603	0.646810129545206	0.676594935227397
84	0.311794695154201	0.623589390308402	0.688205304845799
85	0.298876532721292	0.597753065442585	0.701123467278708
86	0.265082865905621	0.530165731811242	0.734917134094379
87	0.238293481019031	0.476586962038061	0.76170651898097
88	0.213007993583084	0.426015987166168	0.786992006416916
89	0.250384017614447	0.500768035228894	0.749615982385553
90	0.216434526098504	0.432869052197008	0.783565473901496
91	0.230629276022685	0.46125855204537	0.769370723977315
92	0.216068074255366	0.432136148510732	0.783931925744634
93	0.202442447920962	0.404884895841925	0.797557552079038
94	0.172236329501923	0.344472659003847	0.827763670498077
95	0.179582142680521	0.359164285361043	0.820417857319479
96	0.175492461448021	0.350984922896043	0.824507538551979
97	0.160289045264962	0.320578090529924	0.839710954735038
98	0.13704864790038	0.27409729580076	0.86295135209962
99	0.120270107977869	0.240540215955739	0.879729892022131
100	0.119163799974446	0.238327599948892	0.880836200025554
101	0.109987033288769	0.219974066577539	0.890012966711231
102	0.0965294643197757	0.193058928639551	0.903470535680224
103	0.0790856421687478	0.158171284337496	0.920914357831252
104	0.0649092095457353	0.129818419091471	0.935090790454265
105	0.076272024920088	0.152544049840176	0.923727975079912
106	0.182706729417193	0.365413458834386	0.817293270582807
107	0.169859135013726	0.339718270027451	0.830140864986274
108	0.193794831674515	0.387589663349029	0.806205168325485
109	0.18004060589312	0.36008121178624	0.81995939410688
110	0.380724337251458	0.761448674502915	0.619275662748543
111	0.456715087555247	0.913430175110495	0.543284912444753
112	0.440209420697277	0.880418841394555	0.559790579302722
113	0.569095535017885	0.861808929964231	0.430904464982115
114	0.522292621459743	0.955414757080514	0.477707378540257
115	0.507719677230698	0.984560645538605	0.492280322769302
116	0.472081698331839	0.944163396663679	0.527918301668161
117	0.442332254328131	0.884664508656262	0.557667745671869
118	0.418308411480952	0.836616822961904	0.581691588519048
119	0.372598547029917	0.745197094059835	0.627401452970083
120	0.336403095660823	0.672806191321645	0.663596904339177
121	0.291972855934125	0.583945711868249	0.708027144065875
122	0.250944068936506	0.501888137873012	0.749055931063494
123	0.21564562263792	0.431291245275839	0.78435437736208
124	0.178444522708875	0.356889045417751	0.821555477291125
125	0.143758678157616	0.287517356315232	0.856241321842384
126	0.153441404692594	0.306882809385187	0.846558595307406
127	0.170795548985603	0.341591097971207	0.829204451014397
128	0.349628553283281	0.699257106566561	0.650371446716719
129	0.320750972885802	0.641501945771604	0.679249027114198
130	0.284001116606468	0.568002233212935	0.715998883393532
131	0.252359479083498	0.504718958166996	0.747640520916502
132	0.294714367728781	0.589428735457562	0.705285632271219
133	0.365841607372554	0.731683214745108	0.634158392627446
134	0.476186391885256	0.952372783770513	0.523813608114744
135	0.420224572277483	0.840449144554966	0.579775427722517
136	0.358131421700582	0.716262843401164	0.641868578299418
137	0.314391932790551	0.628783865581102	0.685608067209449
138	0.254386842827395	0.50877368565479	0.745613157172605
139	0.303572026194397	0.607144052388794	0.696427973805603
140	0.24858864587961	0.497177291759221	0.75141135412039
141	0.476522941384611	0.953045882769222	0.523477058615389
142	0.414987233779991	0.829974467559981	0.585012766220009
143	0.441212214335551	0.882424428671102	0.558787785664449
144	0.702761449898955	0.594477100202089	0.297238550101045
145	0.613255350420534	0.773489299158931	0.386744649579466
146	0.944288864571204	0.111422270857592	0.0557111354287962
147	0.924296562059531	0.151406875880937	0.0757034379404687
148	0.946480751173226	0.107038497653547	0.0535192488267735
149	0.944752966784572	0.110494066430857	0.0552470332154284
150	0.882424288194318	0.235151423611364	0.117575711805682
151	0.769367917204776	0.461264165590448	0.230632082795224

Multiple Linear Regression - Regression Statistics
Multiple R	0.427051855095817
R-squared	0.182373286940778
Adjusted R-squared	0.145208436347178
F-TEST (value)	4.90714435892766
F-TEST (DF numerator)	7
F-TEST (DF denominator)	154
p-value	5.02586788058146e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.1204734771678
Sum Squared Residuals	1499.55262714299

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	0	0	OK
10% type I error level	0	0	OK