Wessa.net

Send output to:

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)
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)	5.5100460265441	2.596867	2.1218	0.035454	0.017727
Connected	0.114054780757305	0.04689	2.4324	0.016145	0.008073
Separate	-0.0206657003468084	0.044811	-0.4612	0.645324	0.322662
Software	0.54256426481915	0.068952	7.8688	0	0
Happiness	0.0597091397316791	0.076381	0.7817	0.435576	0.217788
Depression	-0.0711620165288007	0.05634	-1.2631	0.208471	0.104236
Belonging	0.0357610911025548	0.044531	0.8031	0.423173	0.211586
Belonging_Final	-0.0522339742838427	0.063958	-0.8167	0.415366	0.207683

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	13	16.1176010114952	-3.11760101149523
2	16	15.968590457689	0.0314095423110496
3	19	16.1737913079047	2.82620869209529
4	15	11.736127344159	3.26387265584103
5	14	15.452637097449	-1.45263709744898
6	13	14.8834893226671	-1.88348932266713
7	19	15.0613613157009	3.93863868429912
8	15	16.5944113899983	-1.59441138999828
9	14	15.8832986290019	-1.88329862900192
10	15	12.4335389038549	2.56646109614508
11	16	15.1982303001062	0.801769699893772
12	16	16.1967107906881	-0.196710790688067
13	16	15.3634266943478	0.63657330565222
14	16	15.3075485483858	0.692451451614243
15	17	17.3536454897514	-0.353645489751349
16	15	15.0219820117331	-0.0219820117331462
17	15	14.5515710856597	0.448428914340279
18	20	16.1282168592919	3.87178314070815
19	18	15.4125174517924	2.58748254820759
20	16	15.1974669169696	0.802533083030441
21	16	15.2413072789435	0.758692721056458
22	16	14.7098703292292	1.29012967077077
23	19	16.4155828316183	2.5844171683817
24	16	14.6825230126674	1.31747698733264
25	17	14.7715188140351	2.22848118596487
26	17	16.8811396748344	0.118860325165605
27	16	14.9627073533393	1.0372926466607
28	15	16.0428407120701	-1.04284071207008
29	16	15.4408619857186	0.55913801428136
30	14	13.9609756665955	0.0390243334045292
31	15	15.5941864333835	-0.594186433383494
32	12	12.1849031511977	-0.184903151197657
33	14	15.1114906362315	-1.11149063623154
34	16	15.4302007927359	0.569799207264141
35	14	15.6420243027548	-1.6420243027548
36	7	12.902573456336	-5.90257345633603
37	10	10.8668171646377	-0.866817164637678
38	14	15.779490671898	-1.77949067189796
39	16	14.2211559228343	1.77884407716566
40	16	14.5839249071753	1.41607509282467
41	16	15.0453839976902	0.954616002309846
42	14	15.5595766071838	-1.5595766071838
43	20	17.9147171281587	2.08528287184129
44	14	14.2062028697073	-0.206202869707288
45	14	14.8985465949208	-0.898546594920809
46	11	15.59163412605	-4.59163412604999
47	14	16.4250897991219	-2.42508979912194
48	15	14.9316008225476	0.0683991774524223
49	16	15.1990473828132	0.800952617186769
50	14	16.0185860866674	-2.01858608666736
51	16	16.490631866381	-0.490631866380991
52	14	14.1117645059229	-0.111764505922893
53	12	14.6847780076518	-2.68477800765184
54	16	15.1906136784413	0.809386321558702
55	9	11.5175710839817	-2.51757108398172
56	14	12.6286951224646	1.37130487753542
57	16	16.0923517617659	-0.0923517617659242
58	16	15.08311716571	0.916882834289958
59	15	15.3237028828634	-0.323702882863364
60	16	14.1312671012716	1.86873289872845
61	12	11.4292831319165	0.570716868083456
62	16	15.97761485165	0.0223851483499527
63	16	16.466440278449	-0.466440278448961
64	14	14.370634890267	-0.370634890266959
65	16	15.4359516886432	0.564048311356838
66	17	15.9063996466629	1.09360035333709
67	18	16.102069678043	1.89793032195701
68	18	14.6734546361936	3.32654536380641
69	12	15.8809142378122	-3.88091423781219
70	16	15.4211608487746	0.578839151225446
71	10	13.449196578141	-3.449196578141
72	14	14.3239482857985	-0.323948285798515
73	18	16.5377944610183	1.46220553898167
74	18	17.1549069339804	0.845093066019575
75	16	15.726375413876	0.273624586124044
76	17	13.8871437600761	3.11285623992386
77	16	16.3659420879192	-0.365942087919184
78	16	14.4511637276615	1.5488362723385
79	13	14.8428524594843	-1.84285245948429
80	16	16.0260086189542	-0.0260086189541552
81	16	15.5758780510833	0.42412194891667
82	20	16.772106282711	3.22789371728901
83	16	15.6917911907721	0.308208809227861
84	15	16.0247767745921	-1.02477677459205
85	15	14.7435687005215	0.256431299478507
86	16	14.2682886939008	1.73171130609916
87	14	14.223327636188	-0.223327636188003
88	16	15.3376632816263	0.66233671837371
89	16	14.5832697719889	1.41673022801113
90	15	14.2122719553821	0.78772804461795
91	12	13.4826033949017	-1.4826033949017
92	17	17.0093460317475	-0.00934603174745505
93	16	15.4868128956012	0.513187104398812
94	15	15.2856069919936	-0.285606991993589
95	13	15.1789239840191	-2.17892398401911
96	16	15.2183144625393	0.781685537460748
97	16	15.8594806492654	0.140519350734551
98	16	14.1841530890403	1.8158469109597
99	16	16.1599010879069	-0.15990108790695
100	14	14.3405734839089	-0.340573483908861
101	16	17.3668114814765	-1.36681148147649
102	16	14.8129285354697	1.18707146453032
103	20	17.414779311581	2.58522068841896
104	15	14.5897897621568	0.410210237843215
105	16	14.5632726282959	1.43672737170409
106	13	15.3971712164423	-2.39717121644233
107	17	16.0670095473568	0.932990452643164
108	16	15.9411223074884	0.0588776925115675
109	16	14.5979644522354	1.40203554776461
110	12	12.3115860011042	-0.311586001104153
111	16	15.0121404103748	0.987859589625235
112	16	15.8970719882604	0.102928011739562
113	17	14.5482562756779	2.45174372432212
114	13	14.9988832654302	-1.99888326543017
115	12	14.9367482002426	-2.9367482002426
116	18	16.6728852143104	1.3271147856896
117	14	15.5475987133233	-1.54759871332325
118	14	13.0175979270322	0.982402072967786
119	13	14.9879193660467	-1.98791936604673
120	16	15.7250609825965	0.274939017403509
121	13	14.3338869887332	-1.33388698873322
122	16	15.6958455948834	0.3041544051166
123	13	16.0238939705842	-3.02389397058424
124	16	17.1298631050521	-1.12986310505213
125	15	16.059438245305	-1.05943824530504
126	16	16.8658061313194	-0.865806131319395
127	15	15.5139115699994	-0.513911569999372
128	17	16.3421620626012	0.657837937398818
129	15	13.8929061777708	1.10709382222919
130	12	15.0352367266114	-3.03523672661142
131	16	14.2421207305797	1.75787926942027
132	10	13.3954993580855	-3.39549935808545
133	16	14.1829785910847	1.81702140891525
134	12	14.0010798079533	-2.00107980795329
135	14	15.7407983013774	-1.74079830137736
136	15	15.1943517162197	-0.194351716219725
137	13	12.3594647885484	0.640535211451569
138	15	14.5315975175447	0.468402482455293
139	11	13.2551506775076	-2.2551506775076
140	12	13.4520494331797	-1.45204943317969
141	8	13.4537200598596	-5.45372005985959
142	16	13.1890740163979	2.81092598360207
143	15	13.4112109892864	1.58878901071358
144	17	16.6358908650862	0.364109134913822
145	16	14.9311741305242	1.06882586947583
146	10	14.2545472410445	-4.25454724104455
147	18	16.0194087138778	1.98059128612218
148	13	15.2752928646931	-2.27529286469313
149	16	15.2429799884548	0.757020011545189
150	13	13.1168401022919	-0.116840102291915
151	10	13.208809379565	-3.20880937956499
152	15	16.5256452894514	-1.52564528945141
153	16	14.1980229520776	1.80197704792238
154	16	12.0904557977686	3.90954420223142
155	14	12.5466019952205	1.45339800477954
156	10	12.594466135854	-2.59446613585404
157	17	17.0093460317475	-0.00934603174745505
158	13	11.7116678035722	1.28833219642779
159	15	13.8929061777708	1.10709382222919
160	16	15.1705727060965	0.829427293903478
161	12	12.604839934877	-0.604839934876993
162	13	12.6868364994311	0.313163500568875

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.391592737316464	0.783185474632928	0.608407262683536
12	0.6254943243076	0.749011351384799	0.3745056756924
13	0.486709110966603	0.973418221933206	0.513290889033397
14	0.460379032502385	0.920758065004771	0.539620967497614
15	0.351640382634584	0.703280765269167	0.648359617365416
16	0.267145938881103	0.534291877762205	0.732854061118897
17	0.217971969276914	0.435943938553829	0.782028030723086
18	0.419231086271127	0.838462172542255	0.580768913728873
19	0.342077757020335	0.684155514040669	0.657922242979665
20	0.262867097830822	0.525734195661644	0.737132902169178
21	0.203320554940341	0.406641109880683	0.796679445059659
22	0.185523847760744	0.371047695521488	0.814476152239256
23	0.370009803174081	0.740019606348163	0.629990196825919
24	0.406334223437289	0.812668446874578	0.593665776562711
25	0.395346365960103	0.790692731920206	0.604653634039897
26	0.357806583905505	0.715613167811011	0.642193416094495
27	0.417480679975861	0.834961359951723	0.582519320024139
28	0.4237379098368	0.8474758196736	0.5762620901632
29	0.404994581979501	0.809989163959002	0.595005418020499
30	0.446979809793499	0.893959619586998	0.553020190206501
31	0.388960387822835	0.777920775645669	0.611039612177165
32	0.342451535732888	0.684903071465777	0.657548464267112
33	0.319171912792347	0.638343825584694	0.680828087207653
34	0.286039861582081	0.572079723164162	0.713960138417919
35	0.245073281748215	0.490146563496429	0.754926718251785
36	0.829140931634887	0.341718136730226	0.170859068365113
37	0.802778452692216	0.394443094615567	0.197221547307784
38	0.796786492944589	0.406427014110823	0.203213507055411
39	0.818235582728634	0.363528834542731	0.181764417271366
40	0.799793749367704	0.400412501264592	0.200206250632296
41	0.766969363362433	0.466061273275135	0.233030636637567
42	0.741892014655353	0.516215970689294	0.258107985344647
43	0.767213510937845	0.46557297812431	0.232786489062155
44	0.72447243346754	0.551055133064921	0.27552756653246
45	0.695845745479515	0.608308509040969	0.304154254520484
46	0.86170784745569	0.276584305088621	0.13829215254431
47	0.909597950954779	0.180804098090442	0.0904020490452208
48	0.886622330335454	0.226755339329092	0.113377669664546
49	0.87065953551273	0.25868092897454	0.12934046448727
50	0.869685546593415	0.260628906813169	0.130314453406585
51	0.841428778594039	0.317142442811921	0.158571221405961
52	0.808924528327295	0.38215094334541	0.191075471672705
53	0.826100561355738	0.347798877288523	0.173899438644262
54	0.813114570823474	0.373770858353051	0.186885429176526
55	0.827013016996457	0.345973966007085	0.172986983003543
56	0.801706626075066	0.396586747849868	0.198293373924934
57	0.767472482586961	0.465055034826079	0.232527517413039
58	0.744056101505622	0.511887796988757	0.255943898494378
59	0.706276867635618	0.587446264728764	0.293723132364382
60	0.702598452223427	0.594803095553147	0.297401547776573
61	0.664522974947351	0.670954050105298	0.335477025052649
62	0.621918236819518	0.756163526360964	0.378081763180482
63	0.582183275094973	0.835633449810053	0.417816724905027
64	0.534725232148108	0.930549535703783	0.465274767851892
65	0.495843302829513	0.991686605659026	0.504156697170487
66	0.466674057559934	0.933348115119868	0.533325942440066
67	0.463721146315533	0.927442292631066	0.536278853684467
68	0.613478939748884	0.773042120502232	0.386521060251116
69	0.729358335569008	0.541283328861984	0.270641664430992
70	0.694403882039751	0.611192235920499	0.305596117960249
71	0.802629523256329	0.394740953487341	0.197370476743671
72	0.76821289337698	0.463574213246041	0.23178710662302
73	0.758667373560068	0.482665252879865	0.241332626439933
74	0.739015604249319	0.521968791501361	0.260984395750681
75	0.699570990713304	0.600858018573392	0.300429009286696
76	0.750856404926373	0.498287190147254	0.249143595073627
77	0.713685952350374	0.572628095299252	0.286314047649626
78	0.700496476174034	0.599007047651932	0.299503523825966
79	0.706346825923364	0.587306348153271	0.293653174076636
80	0.664780080930311	0.670439838139379	0.335219919069689
81	0.625472431732702	0.749055136534596	0.374527568267298
82	0.749640195870538	0.500719608258924	0.250359804129462
83	0.712578525224239	0.574842949551522	0.287421474775761
84	0.682801845209803	0.634396309580394	0.317198154790197
85	0.640736724081723	0.718526551836553	0.359263275918277
86	0.63433147093822	0.731337058123561	0.36566852906178
87	0.589500320931649	0.820999358136702	0.410499679068351
88	0.549615687265226	0.900768625469548	0.450384312734774
89	0.532961058951719	0.934077882096562	0.467038941048281
90	0.49894953919385	0.9978990783877	0.50105046080615
91	0.480027937635371	0.960055875270743	0.519972062364629
92	0.441780647044694	0.883561294089388	0.558219352955306
93	0.399737648992296	0.799475297984592	0.600262351007704
94	0.357672069568341	0.715344139136682	0.642327930431659
95	0.373839994920108	0.747679989840216	0.626160005079892
96	0.338949483340494	0.677898966680988	0.661050516659506
97	0.301194309413588	0.602388618827176	0.698805690586412
98	0.299878852263539	0.599757704527079	0.700121147736461
99	0.260020361495589	0.520040722991177	0.739979638504411
100	0.224456210484304	0.448912420968607	0.775543789515696
101	0.202854737340537	0.405709474681074	0.797145262659463
102	0.188122179430181	0.376244358860361	0.811877820569819
103	0.231429393464027	0.462858786928054	0.768570606535973
104	0.197374118907001	0.394748237814001	0.802625881092999
105	0.191072048531604	0.382144097063207	0.808927951468396
106	0.203604934094427	0.407209868188855	0.796395065905573
107	0.183694261991331	0.367388523982661	0.816305738008669
108	0.163405207972349	0.326810415944699	0.836594792027651
109	0.157043647309576	0.314087294619151	0.842956352690424
110	0.147581451752344	0.295162903504688	0.852418548247656
111	0.130037575188794	0.260075150377589	0.869962424811206
112	0.108077816350239	0.216155632700478	0.891922183649761
113	0.126207472920562	0.252414945841123	0.873792527079438
114	0.116111809566636	0.232223619133271	0.883888190433364
115	0.148046379537207	0.296092759074413	0.851953620462793
116	0.141018350437078	0.282036700874156	0.858981649562922
117	0.122198121771529	0.244396243543059	0.877801878228471
118	0.105846938234547	0.211693876469095	0.894153061765453
119	0.108605312623333	0.217210625246667	0.891394687376667
120	0.10073982421656	0.20147964843312	0.89926017578344
121	0.0851309927571701	0.17026198551434	0.91486900724283
122	0.0677193874578021	0.135438774915604	0.932280612542198
123	0.0768877108476784	0.153775421695357	0.923112289152322
124	0.0613855194944305	0.122771038988861	0.93861448050557
125	0.0493717319861978	0.0987434639723956	0.950628268013802
126	0.0376353172064141	0.0752706344128282	0.962364682793586
127	0.0281861879575792	0.0563723759151584	0.971813812042421
128	0.0222052869660712	0.0444105739321425	0.977794713033929
129	0.0176766917512326	0.0353533835024651	0.982323308248767
130	0.024746929558348	0.0494938591166961	0.975253070441652
131	0.0205293694024478	0.0410587388048956	0.979470630597552
132	0.0323139979372074	0.0646279958744147	0.967686002062793
133	0.0334923448476128	0.0669846896952257	0.966507655152387
134	0.0450110794223695	0.090022158844739	0.954988920577631
135	0.035718332411128	0.071436664822256	0.964281667588872
136	0.024520830091543	0.049041660183086	0.975479169908457
137	0.0164419098923992	0.0328838197847985	0.983558090107601
138	0.0114513389057442	0.0229026778114885	0.988548661094256
139	0.0187141566338843	0.0374283132677686	0.981285843366116
140	0.0156735971353232	0.0313471942706463	0.984326402864677
141	0.616099641298336	0.767800717403328	0.383900358701664
142	0.555282969656267	0.889434060687465	0.444717030343733
143	0.471948863858551	0.943897727717102	0.528051136141449
144	0.391835907226904	0.783671814453808	0.608164092773096
145	0.305190253535481	0.610380507070961	0.694809746464519
146	0.350444740839469	0.700889481678938	0.649555259160531
147	0.309883293317788	0.619766586635577	0.690116706682212
148	0.723212344308573	0.553575311382854	0.276787655691427
149	0.682313232972972	0.635373534054056	0.317686767027028
150	0.540947725608553	0.918104548782895	0.459052274391447
151	0.745961346148008	0.508077307703985	0.254038653851992

Multiple Linear Regression - Regression Statistics
Multiple R	0.597252140020401
R-squared	0.356710118758949
Adjusted R-squared	0.327469669611628
F-TEST (value)	12.1992010779915
F-TEST (DF numerator)	7
F-TEST (DF denominator)	154
p-value	2.35689245897674e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.85031335255193
Sum Squared Residuals	527.243563405321

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	9	0.0638297872340425	NOK
10% type I error level	16	0.113475177304965	NOK