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Data X:
12 41 38 13 12 53 32 11 39 32 16 11 86 51 14 30 35 19 15 66 42 12 31 33 15 6 67 41 21 34 37 14 13 76 46 12 35 29 13 10 78 47 22 39 31 19 12 53 37 11 34 36 15 14 80 49 10 36 35 14 12 74 45 13 37 38 15 6 76 47 10 38 31 16 10 79 49 8 36 34 16 12 54 33 15 38 35 16 12 67 42 14 39 38 16 11 54 33 10 33 37 17 15 87 53 14 32 33 15 12 58 36 14 36 32 15 10 75 45 11 38 38 20 12 88 54 10 39 38 18 11 64 41 13 32 32 16 12 57 36 7 32 33 16 11 66 41 14 31 31 16 12 68 44 12 39 38 19 13 54 33 14 37 39 16 11 56 37 11 39 32 17 9 86 52 9 41 32 17 13 80 47 11 36 35 16 10 76 43 15 33 37 15 14 69 44 14 33 33 16 12 78 45 13 34 33 14 10 67 44 9 31 28 15 12 80 49 15 27 32 12 8 54 33 10 37 31 14 10 71 43 11 34 37 16 12 84 54 13 34 30 14 12 74 42 8 32 33 7 7 71 44 20 29 31 10 6 63 37 12 36 33 14 12 71 43 10 29 31 16 10 76 46 10 35 33 16 10 69 42 9 37 32 16 10 74 45 14 34 33 14 12 75 44 8 38 32 20 15 54 33 14 35 33 14 10 52 31 11 38 28 14 10 69 42 13 37 35 11 12 68 40 9 38 39 14 13 65 43 11 33 34 15 11 75 46 15 36 38 16 11 74 42 11 38 32 14 12 75 45 10 32 38 16 14 72 44 14 32 30 14 10 67 40 18 32 33 12 12 63 37 14 34 38 16 13 62 46 11 32 32 9 5 63 36 12 37 32 14 6 76 47 13 39 34 16 12 74 45 9 29 34 16 12 67 42 10 37 36 15 11 73 43 15 35 34 16 10 70 43 20 30 28 12 7 53 32 12 38 34 16 12 77 45 12 34 35 16 14 77 45 14 31 35 14 11 52 31 13 34 31 16 12 54 33 11 35 37 17 13 80 49 17 36 35 18 14 66 42 12 30 27 18 11 73 41 13 39 40 12 12 63 38 14 35 37 16 12 69 42 13 38 36 10 8 67 44 15 31 38 14 11 54 33 13 34 39 18 14 81 48 10 38 41 18 14 69 40 11 34 27 16 12 84 50 19 39 30 17 9 80 49 13 37 37 16 13 70 43 17 34 31 16 11 69 44 13 28 31 13 12 77 47 9 37 27 16 12 54 33 11 33 36 16 12 79 46 10 37 38 20 12 30 0 9 35 37 16 12 71 45 12 37 33 15 12 73 43 12 32 34 15 11 72 44 13 33 31 16 10 77 47 13 38 39 14 9 75 45 12 33 34 16 12 69 42 15 29 32 16 12 54 33 22 33 33 15 12 70 43 13 31 36 12 9 73 46 15 36 32 17 15 54 33 13 35 41 16 12 77 46 15 32 28 15 12 82 48 10 29 30 13 12 80 47 11 39 36 16 10 80 47 16 37 35 16 13 69 43 11 35 31 16 9 78 46 11 37 34 16 12 81 48 10 32 36 14 10 76 46 10 38 36 16 14 76 45 16 37 35 16 11 73 45 12 36 37 20 15 85 52 11 32 28 15 11 66 42 16 33 39 16 11 79 47 19 40 32 13 12 68 41 11 38 35 17 12 76 47 16 41 39 16 12 71 43 15 36 35 16 11 54 33 24 43 42 12 7 46 30 14 30 34 16 12 82 49 15 31 33 16 14 74 44 11 32 41 17 11 88 55 15 32 33 13 11 38 11 12 37 34 12 10 76 47 10 37 32 18 13 86 53 14 33 40 14 13 54 33 13 34 40 14 8 70 44 9 33 35 13 11 69 42 15 38 36 16 12 90 55 15 33 37 13 11 54 33 14 31 27 16 13 76 46 11 38 39 13 12 89 54 8 37 38 16 14 76 47 11 33 31 15 13 73 45 11 31 33 16 15 79 47 8 39 32 15 10 90 55 10 44 39 17 11 74 44 11 33 36 15 9 81 53 13 35 33 12 11 72 44 11 32 33 16 10 71 42 20 28 32 10 11 66 40 10 40 37 16 8 77 46 15 27 30 12 11 65 40 12 37 38 14 12 74 46 14 32 29 15 12 82 53 23 28 22 13 9 54 33 14 34 35 15 11 63 42 16 30 35 11 10 54 35 11 35 34 12 8 64 40 12 31 35 8 9 69 41 10 32 34 16 8 54 33 14 30 34 15 9 84 51 12 30 35 17 15 86 53 12 31 23 16 11 77 46 11 40 31 10 8 89 55 12 32 27 18 13 76 47 13 36 36 13 12 60 38 11 32 31 16 12 75 46 19 35 32 13 9 73 46 12 38 39 10 7 85 53 17 42 37 15 13 79 47 9 34 38 16 9 71 41 12 35 39 16 6 72 44 19 35 34 14 8 69 43 18 33 31 10 8 78 51 15 36 32 17 15 54 33 14 32 37 13 6 69 43 11 33 36 15 9 81 53 9 34 32 16 11 84 51 18 32 35 12 8 84 50 16 34 36 13 8 69 46
Names of X columns:
depression connected separate learning software 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	2 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)	25.7053808259285	3.365035	7.639	0	0
connected	-0.0324004125035537	0.076932	-0.4212	0.674223	0.337111
separate	-0.0572463805845195	0.071532	-0.8003	0.424768	0.212384
learning	-0.240771499295297	0.12814	-1.879	0.062125	0.031062
software	-0.0219056272594498	0.131424	-0.1667	0.86784	0.43392
belonging	-0.205751622268116	0.069607	-2.9559	0.003605	0.001803
`belonging_final\r\r`	0.20150499680328	0.101891	1.9776	0.049742	0.024871

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	12	14.3520283506136	-2.3520283506136
2	11	11.0986899929158	-0.0986899929157768
3	14	12.7101050309033	1.28989496909667
4	12	13.5451774030137	-1.54517740301366
5	21	12.4621831352474	8.53781686475259
6	12	12.9842439007607	-0.984243900760714
7	22	14.3804498279569	7.6195501720431
8	11	12.0382608906146	-1.03826089061456
9	10	12.7437789464017	-2.74377894640175
10	13	12.4218084054764	0.578191594523632
11	10	12.2474937755336	-2.24749377553356
12	8	14.0164548121186	-6.01645481211865
13	15	13.033181488271	1.96681851172898
14	14	13.7121736795294	0.287826320470641
15	10	10.8757249280199	-0.875724928019854
16	14	14.2255828433501	-0.225582843350053
17	14	12.5128062211108	1.48719377888921
18	11	9.99563224334519	1.00436775665481
19	10	12.7851544326838	-2.78515443268384
20	13	14.2478093469074	-1.24780934690739
21	7	13.3682289771857	-6.36822897718567
22	14	13.6862282694724	0.313771730527577
23	12	12.9460479271246	-0.946047927124569
24	14	14.1142448666288	-0.114244866628834
25	11	11.1032347449427	-0.103234744942666
26	9	11.1777961604901	-2.17779616049006
27	11	11.4915339641873	-0.491533964187262
28	15	13.2691577834665	1.73084221653352
29	14	11.6509234574184	2.34907654258161
30	13	14.2056401461703	-1.20564014617033
31	9	12.6372444273203	-3.63724442732028
32	15	15.4732597920387	-0.473259792038659
33	10	13.198420183953	-3.19842018395296
34	11	11.9685727601976	-0.968572760197578
35	13	12.4902966839216	0.509703316078386
36	8	15.1985518589504	-7.1985518589504
37	20	14.9453149875256	5.05468501247436
38	12	13.0725165807686	-1.07251658076858
39	10	12.5518373644601	-2.55183736446006
40	10	12.8771834969334	-2.87718349693339
41	9	12.4453859315801	-3.44538593158006
42	14	12.5158159135065	1.4841840864935
43	8	13.037343869321	-5.03734386932105
44	14	14.6399491092459	-0.63994910924589
45	11	13.5477571609359	-2.54775716093592
46	13	13.6606777813764	-0.660677781376385
47	9	13.8768415786036	-4.8768415786036
48	11	12.6751140669962	-1.67511406699625
49	15	11.5078874429072	3.49211255709279
50	11	12.6449656408801	-1.64496564088008
51	10	12.3862854492859	-2.38628544928587
52	14	13.6361601257179	0.363839874282122
53	18	14.1206442266986	3.87935577330136
54	14	14.8039164678259	-0.803916467825935
55	11	14.8520394991819	-3.85203949918192
56	12	13.0060581882788	-1.00605818827878
57	13	12.222281090885	0.777718909114987
58	9	13.3820315813875	-4.38203158138753
59	10	12.2380079099394	-2.23800790993939
60	15	12.815690490884	2.18430950911596
61	20	15.6311963295904	4.36880367040964
62	12	11.6374266365842	0.362573363415783
63	12	11.665970651495	0.334029348504987
64	14	14.6331523708316	-0.633152370831616
65	13	14.2529947788793	-1.25299477887931
66	11	11.4889767261953	-0.488976726195344
67	17	12.7783796824368	4.22162031756324
68	12	11.8547037312325	0.145296268767512
69	13	13.6946216718854	-0.694621671885407
70	14	12.6043867200764	1.39561327992359
71	13	14.9111966061026	-1.91119660610265
72	15	14.4529199781484	0.547080021851616
73	13	10.7369506319037	2.26304936809629
74	10	11.3498357135116	-1.34983571351162
75	11	11.7350165788297	-0.735016578829651
76	19	11.8477222493106	7.15227775068944
77	13	12.513433642345	0.486566357654981
78	17	13.4051810369531	3.5948189630469
79	13	13.2584943948658	-0.258494394865764
80	9	14.3847790637067	-5.38477906370673
81	11	11.4749376902	-0.474937690199994
82	10	11.0803569200224	-1.08035692002239
83	9	12.79739846595	-3.79739846595002
84	12	12.3878414244335	-0.387841424433494
85	12	12.9217593526976	-0.921759352697591
86	13	12.417989088981	0.582010911018994
87	13	12.3099578585668	0.690042141433203
88	12	12.8409266868371	-0.84092668683708
89	15	14.3577504608126	0.642249539187436
90	22	13.1346979412521	8.86530205874794
91	13	13.8030511277753	-0.803051127775339
92	15	13.824459192214	1.17554080778596
93	13	11.5354082068065	1.46459179319348
94	15	11.9918357734772	3.00816422652279
95	10	12.6660854961424	-2.66608549614238
96	11	11.3200998442327	-0.320099844232735
97	16	12.8336780257822	3.16632197421782
98	11	11.967837272162	-0.96783727216195
99	11	11.4513355504251	-0.451335550425144
100	10	12.6499472226174	-2.64994722261739
101	10	11.6848742431644	-1.68487424316439
102	16	12.4574927848352	3.54250721516483
103	12	10.2662074403563	1.73379255964374
104	11	14.0967373762068	-3.09673737620685
105	16	11.5266091725092	4.47339082749083
106	19	13.455177683832	5.54482231616803
107	11	11.9481703725791	-0.948170372579078
108	16	12.0854932361531	3.9145067638469
109	15	13.9811140587936	1.01888594120642
110	24	15.4457930011311	8.55420699886885
111	14	11.6738918124852	2.32610818751482
112	15	12.2934145201758	2.70658547982422
113	11	10.9640206985616	0.0359793014384249
114	15	13.8064389944804	1.19356100551957
115	12	13.2854859166625	-1.28548591666253
116	10	11.04114655842	-1.04114655841996
117	14	14.2298151374533	-0.229815137453337
118	13	13.2314718697932	-0.231471869793246
119	9	13.5279004313979	-4.5279004313979
120	15	10.8632127539625	4.13678724603754
121	15	14.6861370330211	0.313862966978908
122	14	12.6503051800127	1.34969481998732
123	11	11.4180347355596	-0.418034735559631
124	8	12.0057918881055	-4.00579188810547
125	11	13.0130402019639	-2.01304020196386
126	11	11.8472657719856	-0.84726577198559
127	8	11.3443806176112	-3.34438061761118
128	10	11.3536762566055	-1.35367625660552
129	11	12.7804578043604	-1.78045780436037
130	13	13.6041189936573	-0.604118993657339
131	11	12.5628814899078	-1.56288148990782
132	20	14.7982010067529	5.20179899324709
133	10	11.6900141756646	-1.69001417566463
134	15	14.669302804103	0.330697195896975
135	12	12.7411443889479	-0.741144388947917
136	14	12.9421143769091	1.05788562309091
137	23	15.7506460588256	7.24935394117444
138	14	14.2484667539125	-0.248466753912453
139	16	15.8042896511574	0.195710348842604
140	11	14.452582485783	-3.45258248578298
141	12	14.6388650105971	-2.63886501059711
142	10	14.2336789711707	-4.23367897117066
143	14	11.9718869426292	2.02811305737084
144	12	11.2931705489677	0.706829451032328
145	12	12.7173503346015	-0.717350334601539
146	11	12.8226469589556	-1.82264695895565
147	12	12.3378667657218	-0.337866765721809
148	13	14.3972917992432	-1.3972917992432
149	11	12.6165764946986	-1.61657649469861
150	19	13.6616635008039	5.3383364991961
151	12	12.871378862012	-0.871378862011957
152	17	11.5464584659226	5.45354153407738
153	9	12.0322493924343	-3.03224939243428
154	12	12.4070828492663	-0.407082849266286
155	19	13.5467963662616	5.45320363373835
156	18	14.5066977042167	3.49330229578331
157	15	13.824459192214	1.17554080778596
158	14	13.7568412158329	0.243158784167054
159	11	12.7804578043604	-1.78045780436037
160	9	11.6721952999698	-2.67219529996978
161	18	12.3925548653796	5.60744513462041
162	16	14.3099905073013	1.6900094926987

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.983315245310604	0.0333695093787915	0.0166847546893957
11	0.980116358623106	0.0397672827537889	0.0198836413768945
12	0.977204462258499	0.0455910754830016	0.0227955377415008
13	0.958490869627	0.0830182607460004	0.0415091303730002
14	0.944297879236346	0.111404241527307	0.0557021207636536
15	0.931326905705723	0.137346188588554	0.0686730942942769
16	0.895506663549737	0.208986672900526	0.104493336450263
17	0.897858980367318	0.204282039265364	0.102141019632682
18	0.854210030506237	0.291579938987527	0.145789969493763
19	0.888301559855282	0.223396880289436	0.111698440144718
20	0.851284990542375	0.297430018915249	0.148715009457625
21	0.927510546024248	0.144978907951504	0.0724894539757522
22	0.905173484329596	0.189653031340809	0.0948265156704043
23	0.877491923154617	0.245016153690766	0.122508076845383
24	0.851009383822574	0.297981232354852	0.148990616177426
25	0.806532866682835	0.38693426663433	0.193467133317165
26	0.762702815086459	0.474594369827082	0.237297184913541
27	0.765369859763161	0.469260280473678	0.234630140236839
28	0.715357941948737	0.569284116102526	0.284642058051263
29	0.753635894334097	0.492728211331806	0.246364105665903
30	0.719741234793061	0.560517530413879	0.28025876520694
31	0.724173274592764	0.551653450814473	0.275826725407236
32	0.688572550595289	0.622854898809422	0.311427449404711
33	0.663917298133013	0.672165403733975	0.336082701866987
34	0.636707601754667	0.726584796490666	0.363292398245333
35	0.605381370349728	0.789237259300543	0.394618629650272
36	0.698869428321158	0.602261143357684	0.301130571678842
37	0.865649360501959	0.268701278996081	0.134350639498041
38	0.834307437833545	0.33138512433291	0.165692562166455
39	0.822738250883262	0.354523498233477	0.177261749116738
40	0.811070951956236	0.377858096087528	0.188929048043764
41	0.807646345863619	0.384707308272762	0.192353654136381
42	0.78668053513366	0.42663892973268	0.21331946486634
43	0.838121172128554	0.323757655742892	0.161878827871446
44	0.806270872699703	0.387458254600593	0.193729127300297
45	0.783269786952081	0.433460426095838	0.216730213047919
46	0.745178132226191	0.509643735547617	0.254821867773809
47	0.794183073538141	0.411633852923718	0.205816926461859
48	0.764251578196959	0.471496843606081	0.235748421803041
49	0.768987082581156	0.462025834837688	0.231012917418844
50	0.737006494029652	0.525987011940695	0.262993505970348
51	0.724830918561338	0.550338162877324	0.275169081438662
52	0.687050876300438	0.625898247399124	0.312949123699562
53	0.730915163866884	0.538169672266232	0.269084836133116
54	0.695074893783552	0.609850212432895	0.304925106216448
55	0.705871872748098	0.588256254503803	0.294128127251902
56	0.669674298587083	0.660651402825834	0.330325701412917
57	0.633647146375068	0.732705707249864	0.366352853624932
58	0.681164712453287	0.637670575093426	0.318835287546713
59	0.660127824185806	0.679744351628388	0.339872175814194
60	0.645641163403901	0.708717673192199	0.354358836596099
61	0.712527232164621	0.574945535670757	0.287472767835379
62	0.672439579130978	0.655120841738045	0.327560420869022
63	0.628688290591482	0.742623418817036	0.371311709408518
64	0.585546163772202	0.828907672455596	0.414453836227798
65	0.549433088903648	0.901133822192705	0.450566911096352
66	0.50289063452077	0.994218730958461	0.49710936547923
67	0.552869592082539	0.894260815834922	0.447130407917461
68	0.505960113043887	0.988079773912226	0.494039886956113
69	0.463069318825382	0.926138637650763	0.536930681174618
70	0.426691536123394	0.853383072246789	0.573308463876606
71	0.402297476406332	0.804594952812664	0.597702523593668
72	0.358241667835372	0.716483335670745	0.641758332164628
73	0.338150525289076	0.676301050578151	0.661849474710924
74	0.309208415585891	0.618416831171781	0.690791584414109
75	0.271665211011754	0.543330422023509	0.728334788988246
76	0.48616043125221	0.972320862504419	0.51383956874779
77	0.441937332818936	0.883874665637872	0.558062667181064
78	0.46435295079909	0.928705901598179	0.53564704920091
79	0.419365928056144	0.838731856112289	0.580634071943856
80	0.53745867355949	0.92508265288102	0.46254132644051
81	0.492507296832163	0.985014593664327	0.507492703167837
82	0.455215839333166	0.910431678666332	0.544784160666834
83	0.48821752190449	0.976435043808981	0.51178247809551
84	0.445854256912044	0.891708513824088	0.554145743087956
85	0.405072782360741	0.810145564721481	0.594927217639259
86	0.361642870175338	0.723285740350676	0.638357129824662
87	0.321354166590338	0.642708333180676	0.678645833409662
88	0.285196195666128	0.570392391332256	0.714803804333872
89	0.249084498819205	0.49816899763841	0.750915501180795
90	0.586293826194166	0.827412347611668	0.413706173805834
91	0.543612604146612	0.912774791706775	0.456387395853388
92	0.504724853338233	0.990550293323534	0.495275146661767
93	0.47019561488768	0.94039122977536	0.52980438511232
94	0.469206810167849	0.938413620335698	0.530793189832151
95	0.458205272245504	0.916410544491007	0.541794727754496
96	0.412154299561283	0.824308599122566	0.587845700438717
97	0.411316168068071	0.822632336136142	0.588683831931929
98	0.371324230585759	0.742648461171517	0.628675769414241
99	0.328472600828306	0.656945201656612	0.671527399171694
100	0.318567674261558	0.637135348523116	0.681432325738442
101	0.293297359901634	0.586594719803269	0.706702640098366
102	0.303875475347611	0.607750950695223	0.696124524652389
103	0.281837462784551	0.563674925569102	0.718162537215449
104	0.2961038002228	0.5922076004456	0.7038961997772
105	0.368150753272451	0.736301506544902	0.631849246727549
106	0.455638155147183	0.911276310294366	0.544361844852817
107	0.411762384748451	0.823524769496903	0.588237615251549
108	0.447932584636325	0.895865169272651	0.552067415363675
109	0.402739530983166	0.805479061966332	0.597260469016834
110	0.756080534595264	0.487838930809473	0.243919465404736
111	0.737066633372473	0.525866733255054	0.262933366627527
112	0.731207924762229	0.537584150475542	0.268792075237771
113	0.697406527008288	0.605186945983425	0.302593472991712
114	0.656790292906308	0.686419414187383	0.343209707093692
115	0.621399621256557	0.757200757486887	0.378600378743443
116	0.572705633731756	0.854588732536488	0.427294366268244
117	0.523905261746905	0.95218947650619	0.476094738253095
118	0.474060898761062	0.948121797522123	0.525939101238938
119	0.545517518109207	0.908964963781586	0.454482481890793
120	0.635918875951741	0.728162248096517	0.364081124048259
121	0.583294753599378	0.833410492801245	0.416705246400622
122	0.535128905973773	0.929742188052453	0.464871094026227
123	0.481102730383615	0.962205460767231	0.518897269616385
124	0.482931602060547	0.965863204121095	0.517068397939453
125	0.460406086067445	0.92081217213489	0.539593913932555
126	0.408002808449525	0.81600561689905	0.591997191550475
127	0.425680520386893	0.851361040773787	0.574319479613107
128	0.372992659894531	0.745985319789061	0.627007340105469
129	0.332012129920136	0.664024259840273	0.667987870079864
130	0.291423893368002	0.582847786736003	0.708576106631998
131	0.251893974181706	0.503787948363411	0.748106025818294
132	0.297694178366093	0.595388356732187	0.702305821633907
133	0.255678515056007	0.511357030112013	0.744321484943993
134	0.207799272861843	0.415598545723687	0.792200727138157
135	0.164897148861341	0.329794297722681	0.835102851138659
136	0.12866184164578	0.257323683291561	0.87133815835422
137	0.301980694262711	0.603961388525422	0.698019305737289
138	0.245355262380173	0.490710524760346	0.754644737619827
139	0.196827018226577	0.393654036453154	0.803172981773423
140	0.203160669370148	0.406321338740295	0.796839330629852
141	0.222684828870949	0.445369657741897	0.777315171129051
142	0.261814422277468	0.523628844554937	0.738185577722531
143	0.220100935746891	0.440201871493782	0.779899064253109
144	0.175908096191693	0.351816192383387	0.824091903808307
145	0.13670495722724	0.27340991445448	0.86329504277276
146	0.473382850788787	0.946765701577575	0.526617149211213
147	0.389631722385055	0.77926344477011	0.610368277614945
148	0.341781923845403	0.683563847690806	0.658218076154597
149	0.264136503546517	0.528273007093034	0.735863496453483
150	0.193826170363317	0.387652340726635	0.806173829636683
151	0.470868141020884	0.941736282041769	0.529131858979116
152	0.351302629278099	0.702605258556198	0.648697370721901

Multiple Linear Regression - Regression Statistics
Multiple R	0.382456737564587
R-squared	0.146273156108547
Adjusted R-squared	0.113225665377265
F-TEST (value)	4.42615015154806
F-TEST (DF numerator)	6
F-TEST (DF denominator)	155
p-value	0.000368187229363048
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.98153266129728
Sum Squared Residuals	1377.87823660928

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	3	0.020979020979021	OK
10% type I error level	4	0.027972027972028	OK