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

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
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	20.8206973025298	4.122094	5.051	1e-06	1e-06
Separate	0.341980886543588	0.071678	4.7711	4e-06	2e-06
Software	0.0382969486948975	0.118074	0.3243	0.746112	0.373056
Happiness	0.0567724161221245	0.130761	0.4342	0.664769	0.332385
Depression	-0.0477130006041383	0.096434	-0.4948	0.621462	0.310731
Belonging	0.0483024999381058	0.076182	0.634	0.526988	0.263494
`Belonging_Final\r`	-0.0435495965731524	0.109504	-0.3977	0.691399	0.345699

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	41	35.6642376003641	5.33576239963592
2	39	34.6153981605676	4.38460183943243
3	30	34.6798790707069	-4.6798790707069
4	31	33.8952952732073	-2.8952952732073
5	34	35.5459446358744	-1.54594463587439
6	35	33.2912239384255	1.70877606157446
7	39	32.5754934056515	6.42450659434849
8	34	35.6684070818558	-1.66840708185579
9	36	35.2387011013126	0.761298898687352
10	37	35.9012288736915	1.09877112630848
11	38	33.9750426033907	4.02495739660933
12	36	34.7757810405815	1.22421895941845
13	38	34.5088053078341	3.49119469216595
14	39	35.6488123860698	3.3511876139302
15	33	36.487406695461	-3.48740669546105
16	32	33.9262212798355	-1.92622127983547
17	36	33.9368426256915	2.06315737430848
18	38	36.6147642225586	1.38523577744137
19	39	35.8607477830379	3.13925221696206
20	32	33.6971957262022	-1.69719572620217
21	32	34.6176770164971	-2.61767701649713
22	31	33.2065704851778	-2.20657048517776
23	39	35.7072874524236	3.29271254757638
24	37	35.6861102217085	1.31388977829151
25	39	34.4384822504825	4.5615177495175
26	41	34.1608497007305	6.8391502992695
27	36	35.3548708124634	0.645129187536644
28	33	35.5059564495294	-2.50595644952943
29	33	34.5570973255613	-1.55709732556134
30	34	33.8133488615412	0.186651138458825
31	31	33.0649369255698	-2.06493692556982
32	27	33.2640059717531	-6.26400597175307
33	37	33.8499201833247	3.15007981667526
34	34	35.9660285040184	-1.96602850401837
35	34	33.4595340403796	0.540465959620374
36	32	34.4140950988402	-2.41409509884022
37	29	32.6403006334608	-3.64030063346084
38	36	34.4015050203492	1.59849497965082
39	29	33.9607838932958	-4.96078389329581
40	35	34.2537368886204	0.746263111379644
41	37	34.1838775448962	2.81612245510377
42	34	34.5125118384423	-0.512511838442302
43	38	34.0363928652127	3.96360713478731
44	35	33.6640155334386	1.33598446656144
45	38	32.4393470391762	5.56065296082385
46	37	34.7964054182489	2.20359458175114
47	38	36.3450112904894	1.65498870951061
48	33	34.815463168835	-1.81546316883498
49	36	36.2319754311915	-0.231975431191531
50	38	34.0998031087716	3.9001968912284
51	32	36.3449546711523	-4.34495467115227
52	32	33.0842088359653	-1.08420883596526
53	32	33.7630149321699	-1.76301493216992
54	34	35.318591863025	-1.31859186302496
55	32	33.5872684216863	-1.5872684216863
56	37	33.8970565550341	3.10294344496587
57	39	34.8103536290788	4.18964637092123
58	29	34.7369645055259	-5.73696450552591
59	37	35.4108644840032	1.58913551599684
60	35	34.3051332593861	0.694866740613918
61	30	31.3306054898875	-1.33060548988755
62	38	34.8326568811309	3.16734311886915
63	34	35.13768683282	-1.13768683281999
64	31	34.4998190874649	-3.49981908746488
65	34	33.2841837134416	0.715816286558406
66	35	35.745001355216	-0.745001355215986
67	36	34.4984431201996	1.50155687980041
68	30	32.3247096970489	-2.3247096970489
69	39	36.5789862089111	2.42101379108892
70	35	35.6209471620122	-0.6209471620122
71	38	34.9330148721485	3.06698512785148
72	31	35.6010993854657	-4.60109938546566
73	34	36.7475482529117	-2.74754825291166
74	38	37.3434158011392	0.656584198860795
75	34	32.6068731926309	1.39312680736915
76	39	32.9297881820424	6.07021181795758
77	37	35.8252548469204	1.17474515307956
78	34	33.1869818668528	0.813018133147195
79	28	33.7854468599939	-5.78544685999389
80	37	31.9935673374395	5.00643266256046
81	33	35.7309318903695	-2.73093189036947
82	37	36.1558380255807	0.84416197441926
83	35	35.8254683751896	-0.825468375189645
84	37	34.5548824363475	2.44511756365248
85	32	34.7099418615628	-2.70994186156283
86	33	33.652080546482	-0.652080546481974
87	38	36.396897299528	1.603102700472
88	33	34.6336580874676	-1.63365808746759
89	29	33.4171937665327	-4.41719376653266
90	33	33.4218931853927	-0.421893185392697
91	31	35.3443448756921	-4.34434487569212
92	36	33.5320846126173	2.46791538738265
93	35	37.2488053220029	-2.24880532200293
94	32	32.8052686861501	-0.805268686150105
95	29	33.6179676428328	-4.61796764283279
96	39	35.7158633124668	3.28413668753324
97	37	34.6659894914721	2.33401050852788
98	35	33.8010616955066	1.19893830449339
99	37	35.1700207562565	1.82997924374355
100	32	35.5571434937695	-3.5571434937695
101	38	35.8674257173665	2.13257428263351
102	37	34.6955064006884	2.30449359931156
103	36	36.2253804587055	-0.225380458705462
104	32	32.2759640719346	-0.275964071934608
105	33	36.4364630017115	-3.4364630017115
106	40	33.3270903261759	6.67290967382413
107	38	35.2572663235605	2.74273367643946
108	41	36.2625383371942	4.73746166280584
109	36	34.2345222291021	1.76547777089785
110	43	35.790012424905	7.20998757509496
111	30	34.9180898255647	-4.91808982556474
112	31	34.2092281546792	-3.20922815467923
113	32	37.3317706724329	-5.33177067243285
114	32	33.9629012461031	-1.96290124610313
115	37	34.9045233712673	2.09547662873273
116	37	34.539061033171	2.46093896682904
117	33	36.2958232243025	-3.29582322430252
118	34	36.4458459181372	-2.44584591813719
119	33	35.0804810271287	-2.08048102712869
120	38	35.6226886019916	2.37731139800841
121	33	35.0320288344336	-2.03202883443357
122	31	32.3465819424232	-1.34658194242322
123	38	36.8915027767961	1.10849722320392
124	37	36.4461694662714	0.553830533728609
125	33	33.813059003291	-0.813059003290949
126	31	34.662785648006	-3.662785648006
127	39	34.6824794110228	4.31752058897722
128	44	36.7254221276095	7.27457787239049
129	33	35.6348885326374	-2.63488853263742
130	35	34.3202479744151	0.679752025584899
131	32	34.3026288878924	-2.30262888789243
132	28	33.1880249735738	-5.18802497357377
133	40	35.8708329828614	4.1291670171386
134	27	33.0349602003019	-6.03496020030193
135	37	36.125668163162	0.874331836838016
136	32	33.0907594226763	-1.09075942267632
137	28	29.3872452179347	-1.38724521793473
138	34	34.5521010244794	-0.552101024479366
139	30	34.1181855027789	-4.11818550277893
140	35	34.43054240287	0.569457597129964
141	31	34.8475253083775	-3.84752530837751
142	32	34.3000775801052	-2.30007758010518
143	30	34.8694772023321	-4.86947720233208
144	30	35.432626756739	-5.43262675673899
145	31	31.1025654161276	-0.102565416127582
146	40	34.0156907092168	5.98430929078324
147	32	32.5120031793026	-0.512003179302596
148	36	34.8390654984339	1.16093450156609
149	32	33.6575002095327	-1.65750020953274
150	35	33.2359639969159	1.76403600308406
151	38	36.5594170456603	1.44058295433969
152	42	35.6678372931657	6.33216270683427
153	34	36.2835292180633	-2.28352921806327
154	35	36.0580443024399	-1.05804430243991
155	35	33.8190676112753	1.18093238872475
156	33	33.1542533435949	-0.154253343594859
157	36	33.5320846126173	2.46791538738265
158	32	35.2908434571475	-3.29084345714754
159	33	35.6348885326374	-2.63488853263742
160	34	34.330357081289	-0.330357081289019
161	32	34.7987690698489	-2.79876906984887
162	34	34.7426092609439	-0.742609260943886

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.329100060731957	0.658200121463913	0.670899939268043
11	0.188082216139747	0.376164432279495	0.811917783860253
12	0.804023197708616	0.391953604582769	0.195976802291384
13	0.877163899659665	0.24567220068067	0.122836100340335
14	0.820976364776169	0.358047270447663	0.179023635223831
15	0.788494306547172	0.423011386905656	0.211505693452828
16	0.810409091486986	0.379181817026027	0.189590908513014
17	0.753291392170708	0.493417215658584	0.246708607829292
18	0.702513451201284	0.594973097597432	0.297486548798716
19	0.65421205456257	0.691575890874861	0.34578794543743
20	0.688802065544733	0.622395868910534	0.311197934455267
21	0.713210475815256	0.573579048369488	0.286789524184744
22	0.66777272665555	0.664454546688899	0.33222727334445
23	0.643161824425657	0.713676351148686	0.356838175574343
24	0.574733943531149	0.850532112937702	0.425266056468851
25	0.598869359902487	0.802261280195026	0.401130640097513
26	0.807397278281917	0.385205443436166	0.192602721718083
27	0.775220224311455	0.449559551377089	0.224779775688545
28	0.751973208839529	0.496053582320941	0.248026791160471
29	0.741917978421683	0.516164043156633	0.258082021578317
30	0.691896801532266	0.616206396935468	0.308103198467734
31	0.664618818227872	0.670762363544256	0.335381181772128
32	0.862769348997169	0.274461302005661	0.137230651002831
33	0.849863511221022	0.300272977557956	0.150136488778978
34	0.828946631221537	0.342106737556925	0.171053368778463
35	0.789783933727989	0.420432132544022	0.210216066272011
36	0.782335490610903	0.435329018778194	0.217664509389097
37	0.796370836563858	0.407258326872285	0.203629163436142
38	0.760260618121467	0.479478763757067	0.239739381878533
39	0.817278483085441	0.365443033829118	0.182721516914559
40	0.779568726801901	0.440862546396197	0.220431273198099
41	0.764351492370074	0.471297015259853	0.235648507629926
42	0.723217500669832	0.553564998660337	0.276782499330168
43	0.721517426828733	0.556965146342534	0.278482573171267
44	0.679408250914331	0.641183498171338	0.320591749085669
45	0.751531137975558	0.496937724048884	0.248468862024442
46	0.719913338602867	0.560173322794265	0.280086661397133
47	0.684587501297328	0.630824997405344	0.315412498702672
48	0.657451690777083	0.685096618445835	0.342548309222917
49	0.609531059708741	0.780937880582518	0.390468940291259
50	0.616001293278671	0.767997413442658	0.383998706721329
51	0.672334358337326	0.655331283325349	0.327665641662674
52	0.63594758440735	0.7281048311853	0.36405241559265
53	0.607505295711898	0.784989408576204	0.392494704288102
54	0.562951567205695	0.87409686558861	0.437048432794305
55	0.530448727165095	0.93910254566981	0.469551272834905
56	0.533501981057087	0.932996037885826	0.466498018942913
57	0.562356794109882	0.875286411780235	0.437643205890118
58	0.677694694892748	0.644610610214505	0.322305305107252
59	0.641955359026086	0.716089281947827	0.358044640973914
60	0.597550341136279	0.804899317727441	0.402449658863721
61	0.558917759993161	0.882164480013678	0.441082240006839
62	0.549334259274531	0.901331481450938	0.450665740725469
63	0.5139528545807	0.9720942908386	0.4860471454193
64	0.524779422612767	0.950441154774466	0.475220577387233
65	0.479474404538551	0.958948809077102	0.520525595461449
66	0.436223666131373	0.872447332262746	0.563776333868627
67	0.399783051928976	0.799566103857951	0.600216948071024
68	0.386613487389578	0.773226974779156	0.613386512610422
69	0.366729510225752	0.733459020451504	0.633270489774248
70	0.325207467748707	0.650414935497414	0.674792532251293
71	0.323091598092824	0.646183196185648	0.676908401907176
72	0.365377833218195	0.730755666436391	0.634622166781805
73	0.354676551339383	0.709353102678766	0.645323448660617
74	0.314302887424154	0.628605774848307	0.685697112575846
75	0.281663536048449	0.563327072096898	0.718336463951551
76	0.381980934671031	0.763961869342062	0.618019065328969
77	0.342937323730763	0.685874647461526	0.657062676269237
78	0.303166745725034	0.606333491450068	0.696833254274966
79	0.408467808803186	0.816935617606373	0.591532191196814
80	0.493347496676023	0.986694993352046	0.506652503323977
81	0.479677844131608	0.959355688263216	0.520322155868392
82	0.440709403418075	0.881418806836149	0.559290596581925
83	0.397964017692595	0.79592803538519	0.602035982307405
84	0.379915439960897	0.759830879921795	0.620084560039103
85	0.365614725585726	0.731229451171453	0.634385274414274
86	0.32509939106942	0.650198782138839	0.67490060893058
87	0.295441988554107	0.590883977108215	0.704558011445893
88	0.26534747976847	0.53069495953694	0.73465252023153
89	0.29575919547377	0.59151839094754	0.70424080452623
90	0.257664975660281	0.515329951320562	0.742335024339719
91	0.290213951596253	0.580427903192507	0.709786048403747
92	0.274763352648575	0.54952670529715	0.725236647351425
93	0.257229982349742	0.514459964699484	0.742770017650258
94	0.223622771575984	0.447245543151969	0.776377228424016
95	0.25463977612381	0.50927955224762	0.74536022387619
96	0.256972248383189	0.513944496766378	0.743027751616811
97	0.238815647773185	0.47763129554637	0.761184352226815
98	0.212178009898016	0.424356019796031	0.787821990101985
99	0.18991838805087	0.379836776101739	0.81008161194913
100	0.191171151390081	0.382342302780161	0.80882884860992
101	0.174350161824916	0.348700323649831	0.825649838175085
102	0.159746337090455	0.319492674180911	0.840253662909545
103	0.132318757309691	0.264637514619383	0.867681242690309
104	0.111448039858378	0.222896079716757	0.888551960141622
105	0.122222495977733	0.244444991955467	0.877777504022267
106	0.233748390312669	0.467496780625338	0.766251609687331
107	0.2253723837248	0.450744767449599	0.7746276162752
108	0.258531407430624	0.517062814861247	0.741468592569376
109	0.248212139413362	0.496424278826724	0.751787860586638
110	0.476410703813296	0.952821407626591	0.523589296186704
111	0.549677090696721	0.900645818606558	0.450322909303279
112	0.534494693262001	0.931010613475997	0.465505306737999
113	0.637509918903085	0.724980162193831	0.362490081096915
114	0.59857642921889	0.802847141562221	0.40142357078111
115	0.569614305673606	0.860771388652788	0.430385694326394
116	0.542860360880901	0.914279278238199	0.457139639119099
117	0.521375025944794	0.957249948110412	0.478624974055206
118	0.492154652340662	0.984309304681324	0.507845347659338
119	0.452433195861029	0.904866391722059	0.547566804138971
120	0.417834005534705	0.83566801106941	0.582165994465295
121	0.373692680864111	0.747385361728222	0.626307319135889
122	0.327866685055061	0.655733370110123	0.672133314944939
123	0.281589481123399	0.563178962246797	0.718410518876601
124	0.23737307870253	0.474746157405061	0.76262692129747
125	0.19674945053149	0.39349890106298	0.80325054946851
126	0.21338580182781	0.426771603655619	0.78661419817219
127	0.232537321161801	0.465074642323602	0.767462678838199
128	0.450945652743394	0.901891305486787	0.549054347256606
129	0.416226868363479	0.832453736726958	0.583773131636521
130	0.369596337980806	0.739192675961612	0.630403662019194
131	0.329047545585357	0.658095091170715	0.670952454414643
132	0.401113926531735	0.80222785306347	0.598886073468265
133	0.481114021645055	0.962228043290109	0.518885978354945
134	0.582682950843148	0.834634098313704	0.417317049156852
135	0.529262700662355	0.941474598675289	0.470737299337645
136	0.466034441189784	0.932068882379567	0.533965558810216
137	0.419051790852183	0.838103581704365	0.580948209147818
138	0.351182967953201	0.702365935906402	0.648817032046799
139	0.379103956837191	0.758207913674383	0.620896043162809
140	0.329286640259912	0.658573280519823	0.670713359740088
141	0.320833474856232	0.641666949712465	0.679166525143768
142	0.263544872738537	0.527089745477074	0.736455127261463
143	0.334527381469136	0.669054762938271	0.665472618530864
144	0.661582015869994	0.676835968260012	0.338417984130006
145	0.574474021470728	0.851051957058545	0.425525978529272
146	0.974733819799262	0.0505323604014769	0.0252661802007385
147	0.964448027995318	0.0711039440093637	0.0355519720046818
148	0.954820139024404	0.0903597219511914	0.0451798609755957
149	0.942399220435507	0.115201559128985	0.0576007795644926
150	0.887893399584978	0.224213200830044	0.112106600415022
151	0.887920267975188	0.224159464049625	0.112079732024812
152	0.96504327626565	0.0699134474686999	0.0349567237343499

Multiple Linear Regression - Regression Statistics
Multiple R	0.38853646818059
R-squared	0.150960587106246
Adjusted R-squared	0.118094545316811
F-TEST (value)	4.59320863989076
F-TEST (DF numerator)	6
F-TEST (DF denominator)	155
p-value	0.000254585517122985
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.169577653932
Sum Squared Residuals	1557.16448816729

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	4	0.027972027972028	OK