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Data X:
41 12 12 13 39 11 11 16 30 15 14 19 31 6 12 15 34 13 21 14 35 10 12 13 39 12 22 19 34 14 11 15 36 12 10 14 37 6 13 15 38 10 10 16 36 12 8 16 38 12 15 16 39 11 14 16 33 15 10 17 32 12 14 15 36 10 14 15 38 12 11 20 39 11 10 18 32 12 13 16 32 11 7 16 31 12 14 16 39 13 12 19 37 11 14 16 39 9 11 17 41 13 9 17 36 10 11 16 33 14 15 15 33 12 14 16 34 10 13 14 31 12 9 15 27 8 15 12 37 10 10 14 34 12 11 16 34 12 13 14 32 7 8 7 29 6 20 10 36 12 12 14 29 10 10 16 35 10 10 16 37 10 9 16 34 12 14 14 38 15 8 20 35 10 14 14 38 10 11 14 37 12 13 11 38 13 9 14 33 11 11 15 36 11 15 16 38 12 11 14 32 14 10 16 32 10 14 14 32 12 18 12 34 13 14 16 32 5 11 9 37 6 12 14 39 12 13 16 29 12 9 16 37 11 10 15 35 10 15 16 30 7 20 12 38 12 12 16 34 14 12 16 31 11 14 14 34 12 13 16 35 13 11 17 36 14 17 18 30 11 12 18 39 12 13 12 35 12 14 16 38 8 13 10 31 11 15 14 34 14 13 18 38 14 10 18 34 12 11 16 39 9 19 17 37 13 13 16 34 11 17 16 28 12 13 13 37 12 9 16 33 12 11 16 37 12 10 20 35 12 9 16 37 12 12 15 32 11 12 15 33 10 13 16 38 9 13 14 33 12 12 16 29 12 15 16 33 12 22 15 31 9 13 12 36 15 15 17 35 12 13 16 32 12 15 15 29 12 10 13 39 10 11 16 37 13 16 16 35 9 11 16 37 12 11 16 32 10 10 14 38 14 10 16 37 11 16 16 36 15 12 20 32 11 11 15 33 11 16 16 40 12 19 13 38 12 11 17 41 12 16 16 36 11 15 16 43 7 24 12 30 12 14 16 31 14 15 16 32 11 11 17 32 11 15 13 37 10 12 12 37 13 10 18 33 13 14 14 34 8 13 14 33 11 9 13 38 12 15 16 33 11 15 13 31 13 14 16 38 12 11 13 37 14 8 16 33 13 11 15 31 15 11 16 39 10 8 15 44 11 10 17 33 9 11 15 35 11 13 12 32 10 11 16 28 11 20 10 40 8 10 16 27 11 15 12 37 12 12 14 32 12 14 15 28 9 23 13 34 11 14 15 30 10 16 11 35 8 11 12 31 9 12 8 32 8 10 16 30 9 14 15 30 15 12 17 31 11 12 16 40 8 11 10 32 13 12 18 36 12 13 13 32 12 11 16 35 9 19 13 38 7 12 10 42 13 17 15 34 9 9 16 35 6 12 16 35 8 19 14 33 8 18 10 36 15 15 17 32 6 14 13 33 9 11 15 34 11 9 16 32 8 18 12 34 8 16 13
Names of X columns:
Connected Software Depression Learning

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)	6.45638046344026	1.84538	3.4987	0.000608	0.000304
Connected	0.109869875550479	0.043238	2.541	0.012017	0.006008
Software	0.543599269953389	0.068269	7.9626	0	0
Depression	-0.102140496668171	0.046427	-2.2	0.029258	0.014629

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	13	16.2585506404327	-3.25855064043269
2	16	15.5973521160464	0.402647883953642
3	19	16.4764988259011	2.52350117409892
4	15	11.8982562652074	3.10174373479259
5	14	15.113796311519	-1.11379631151903
6	13	14.5121328472229	-1.51213284722288
7	19	15.0174059226499	3.98259407735013
8	15	16.6788005481541	-1.67880054815413
9	14	15.9134822560165	-1.91348225601648
10	15	12.4553350218421	2.54466497815788
11	16	15.0460234672107	0.953976532789337
12	16	16.1177632493528	-0.117763249352824
13	16	15.6225195237766	0.377480476223414
14	16	15.2909306260418	0.709069373958152
15	17	17.2146704392252	-0.21467043922521
16	15	15.0654407671419	-0.0654407671418807
17	15	14.417721729437	0.582278270562979
18	20	16.0310815104493	3.96891848955073
19	18	15.6994926127145	2.30050738728547
20	16	15.1675812638101	0.832418736189948
21	16	15.2368249738657	0.763175026134312
22	16	14.9555708915914	1.0444291084086
23	19	16.582410159285	2.41758984071503
24	16	15.0711908749409	0.928809125059111
25	17	14.5101535761396	2.48984642386042
26	17	17.1085714003904	-0.108571400390439
27	16	14.7241432194415	1.27585678055847
28	15	16.160368685931	-1.16036868593097
29	16	15.1753106426924	0.82468935730764
30	14	14.3001224750042	-0.300122475004233
31	15	15.4662733749323	-0.466273374932256
32	12	12.2395538129078	-0.239553812907758
33	14	14.9361535916602	-0.936153591660184
34	16	15.5916020082474	0.408397991752648
35	14	15.387321014911	-1.38732101491101
36	7	12.960287397384	-5.96028739738396
37	10	10.8613925407611	-0.861392540761085
38	14	15.7092012626801	-1.70920126268014
39	16	14.0571945872563	1.94280541274365
40	16	14.7164138405592	1.28358615944077
41	16	15.0382940883284	0.961705911671645
42	14	15.2851805182428	-1.28518051824284
43	20	17.9683008103139	2.03169918968605
44	14	14.3078518538865	-0.307851853886541
45	14	14.9438829705425	-0.943882970542492
46	11	15.7169306415624	-4.71693064156245
47	14	16.778961773739	-2.778961773739
48	15	14.9381328627435	0.0618671372565159
49	16	14.8591805027222	1.14081949727776
50	14	16.0310815104493	-2.03108151044927
51	16	16.5612012937213	-0.561201293721342
52	14	13.9782422272351	0.0217577727648967
53	12	14.6568787804692	-2.6568787804692
54	16	15.8287797881962	0.171220211803772
55	9	11.5666673674727	-2.56666736747267
56	14	12.5574755185103	1.44252448148971
57	16	15.9366703926634	0.0633296073365927
58	16	15.2465336238313	0.753466376168703
59	15	15.4797528616136	-0.479752861613573
60	16	14.2057113572184	1.79428864278163
61	12	11.514861686265	0.485138313735047
62	16	15.9289410137811	0.0710589862189011
63	16	16.576660051486	-0.576660051485959
64	14	14.411971621638	-0.411971621638013
65	16	15.387321014911	0.61267898508899
66	17	16.2450711537512	0.75492884624878
67	18	16.2856973192461	1.71430268075394
68	18	14.5063827394239	3.49361726057613
69	12	15.9366703926634	-3.93667039266341
70	16	15.3950503937933	0.604949606206681
71	10	13.6524034372994	-3.65240343729937
72	14	14.3098311249698	-0.309831124969842
73	18	16.4745195548178	1.52548044518221
74	18	17.2204205470242	0.779579452975782
75	16	15.5916020082474	0.408397991752648
76	17	13.6930296027942	3.30697039720578
77	16	16.2605299115158	-0.260529911515837
78	16	14.4351597582849	1.56484024171506
79	13	14.7281017616081	-1.72810176160813
80	16	16.1254926282351	-0.125492628235132
81	16	15.4817321326969	0.518267867303127
82	20	16.023352131567	3.97664786843304
83	16	15.9057528771342	0.0942471228658264
84	15	15.8190711382306	-0.819071138230619
85	15	14.7261224905248	0.273877509475166
86	16	14.1902525994538	1.80974740054625
87	14	14.1960027072528	-0.196002707252762
88	16	15.3795916360287	0.620408363971298
89	16	14.6336906438223	1.36630935617773
90	15	14.358186669347	0.641813330653007
91	12	13.4269135783994	-1.42691357839941
92	17	17.0335775825358	-0.0335775825357936
93	16	15.4971908904615	0.50280910953851
94	15	14.9633002704737	0.0366997295262901
95	13	15.1443931271631	-2.14439312716313
96	16	15.053752846093	0.946247153907028
97	16	15.9541084215113	0.0458915784886756
98	16	14.0706740739377	1.92932592606233
99	16	15.9212116348988	0.0787883651012096
100	14	14.3868042139078	-0.386804213907787
101	16	17.2204205470242	-1.22042054702422
102	16	14.8669098816045	1.13309011839545
103	20	17.3399990725403	2.66000092745969
104	15	14.828262987193	0.171737012806995
105	16	14.4274303794026	1.57256962059737
106	13	15.4336972882049	-2.43369728820486
107	17	16.0310815104493	0.96891848955073
108	16	15.8499886537599	0.150011346240147
109	16	14.8591805027222	1.14081949727776
110	12	12.5346080817485	-0.534608081748501
111	16	14.8457010160409	1.15429898395908
112	16	15.94062893483	0.0593710651699924
113	17	14.828262987193	2.171737012807
114	13	14.4197010005203	-1.41970100052032
115	12	14.7318725983238	-2.73187259832384
116	18	16.5669514015203	1.43304859847965
117	14	15.7189099126457	-1.71890991264575
118	14	13.2129239350975	0.787076064902544
119	13	15.1424138560798	-2.14241385607983
120	16	15.6225195237766	0.377480476223414
121	13	14.5295708760708	-1.5295708760708
122	16	15.4991701615448	0.50082983845521
123	13	16.0310815104493	-3.03108151044927
124	16	17.3148316648101	-1.31483166481008
125	15	16.0253314026503	-1.02533140265026
126	16	16.8927901914561	-0.89279019145608
127	15	15.3601743360975	-0.360174336097485
128	17	16.2488419904669	0.751158009533071
129	15	13.8509343228367	1.14906567716329
130	12	14.9535916205081	-2.9535916205081
131	16	14.2846637172396	1.71533628276038
132	10	13.4695190149775	-3.46951901497755
133	16	14.1785646784048	1.82143532159516
134	12	13.8703516227679	-1.87035162276792
135	14	15.8190711382306	-1.81907113823062
136	15	15.0654407671419	-0.0654407671418807
137	13	12.0758989850663	0.924101014933741
138	15	14.7415812482895	0.258418751710549
139	11	13.5542214827978	-2.5542214827978
140	12	13.5270748039843	-1.52707480398428
141	8	13.5290540750676	-5.52905407506758
142	16	13.299605674001	2.70039432599899
143	15	13.2149032061808	1.78509679381924
144	17	16.6807798192374	0.31922018076257
145	16	14.6162526149744	1.38374738502565
146	10	14.0764241817367	-4.07642418173667
147	18	15.8133210304316	2.18667896956839
148	13	15.607060766012	-2.60706076601197
149	16	15.3718622571464	0.628137742853607
150	13	13.2535501005923	-0.253550100592298
151	10	13.2109446640142	-3.21094466401416
152	15	16.4013173025956	-1.40131730259555
153	16	14.1650851917235	1.83491480827647
154	16	12.3377357674093	3.66226423259067
155	14	12.7099508306389	1.29004916936109
156	10	12.5923515762061	-2.59235157620612
157	17	17.0335775825358	-0.0335775825357936
158	13	11.8038451474215	1.19615485257845
159	15	13.8509343228367	1.14906567716329
160	16	15.2522837316303	0.747716268369695
161	12	12.4824817006556	-0.482481700655642
162	13	12.9065024450929	0.0934975549070569

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.968060750114	0.0638784997720003	0.0319392498860001
8	0.939350554124971	0.121298891750057	0.0606494458750287
9	0.897887369295708	0.204225261408584	0.102112630704292
10	0.855586071913159	0.288827856173682	0.144413928086841
11	0.834434594824157	0.331130810351686	0.165565405175843
12	0.789786679116172	0.420426641767656	0.210213320883828
13	0.71439409157258	0.57121181685484	0.28560590842742
14	0.63941320600377	0.72117358799246	0.36058679399623
15	0.571298042864068	0.857403914271864	0.428701957135932
16	0.511495161457485	0.97700967708503	0.488504838542515
17	0.42970753031551	0.859415060631019	0.57029246968449
18	0.77035162305598	0.45929675388804	0.22964837694402
19	0.793275472145621	0.413449055708758	0.206724527854379
20	0.737727536059207	0.524544927881585	0.262272463940793
21	0.679932019651359	0.640135960697282	0.320067980348641
22	0.616180020374755	0.767639959250491	0.383819979625246
23	0.660831585960642	0.678336828078716	0.339168414039358
24	0.599081353214565	0.80183729357087	0.400918646785435
25	0.583258757928774	0.833482484142453	0.416741242071226
26	0.517547627244563	0.964904745510874	0.482452372755437
27	0.459602912074603	0.919205824149206	0.540397087925397
28	0.43388646389443	0.86777292778886	0.56611353610557
29	0.37475074534372	0.74950149068744	0.62524925465628
30	0.350361771290275	0.70072354258055	0.649638228709725
31	0.300294243968168	0.600588487936336	0.699705756031832
32	0.287170072949583	0.574340145899167	0.712829927050417
33	0.277503054346901	0.555006108693802	0.722496945653099
34	0.230738746363258	0.461477492726516	0.769261253636742
35	0.227480541834956	0.454961083669911	0.772519458165044
36	0.763003397646507	0.473993204706986	0.236996602353493
37	0.750249416865135	0.499501166269729	0.249750583134865
38	0.752422612437683	0.495154775124634	0.247577387562317
39	0.778151555422134	0.443696889155733	0.221848444577866
40	0.754355148909778	0.491289702180444	0.245644851090222
41	0.719486133300559	0.561027733398883	0.280513866699441
42	0.703953193343362	0.592093613313276	0.296046806656638
43	0.709407001615933	0.581185996768134	0.290592998384067
44	0.669306419270546	0.661387161458907	0.330693580729454
45	0.644503820447452	0.710992359105095	0.355496179552548
46	0.870794576574654	0.258410846850692	0.129205423425346
47	0.898752995105781	0.202494009788438	0.101247004894219
48	0.874958632404637	0.250082735190725	0.125041367595363
49	0.854740140997487	0.290519718005026	0.145259859002513
50	0.861125743692704	0.277748512614593	0.138874256307296
51	0.833672269754125	0.33265546049175	0.166327730245875
52	0.801356677110809	0.397286645778381	0.198643322889191
53	0.83879797836025	0.322404043279499	0.161202021639749
54	0.807741251765948	0.384517496468103	0.192258748234052
55	0.833962918629596	0.332074162740807	0.166037081370404
56	0.81818606695448	0.36362786609104	0.18181393304552
57	0.785553269902039	0.428893460195922	0.214446730097961
58	0.76252927120443	0.474941457591141	0.23747072879557
59	0.726952952219162	0.546094095561676	0.273047047780838
60	0.719852762546926	0.560294474906148	0.280147237453074
61	0.680598706104259	0.638802587791481	0.319401293895741
62	0.63757714768641	0.724845704627179	0.36242285231359
63	0.596267390205908	0.807465219588184	0.403732609794092
64	0.552130056718855	0.895739886562291	0.447869943281146
65	0.510589332149096	0.978821335701807	0.489410667850904
66	0.472456847961405	0.944913695922811	0.527543152038594
67	0.460386052593589	0.920772105187179	0.539613947406411
68	0.584434086188546	0.831131827622909	0.415565913811454
69	0.737250906407679	0.525498187184642	0.262749093592321
70	0.701978829685091	0.596042340629819	0.298021170314909
71	0.806513398897813	0.386973202204374	0.193486601102187
72	0.774848704226083	0.450302591547834	0.225151295773917
73	0.762743130868108	0.474513738263784	0.237256869131892
74	0.732756831665826	0.534486336668347	0.267243168334174
75	0.695624375850421	0.608751248299157	0.304375624149578
76	0.772055475811243	0.455889048377513	0.227944524188757
77	0.737213000939063	0.525573998121874	0.262786999060937
78	0.726189075310673	0.547621849378654	0.273810924689327
79	0.72159354149565	0.556812917008699	0.27840645850435
80	0.682203274000996	0.635593451998009	0.317796725999004
81	0.643900207280977	0.712199585438046	0.356099792719023
82	0.786474429542808	0.427051140914384	0.213525570457192
83	0.751822643524874	0.496354712950251	0.248177356475126
84	0.72168162773248	0.556636744535041	0.27831837226752
85	0.682825373329269	0.634349253341461	0.317174626670731
86	0.681120258692789	0.637759482614422	0.318879741307211
87	0.640007555843154	0.719984888313692	0.359992444156846
88	0.601710607842842	0.796578784314317	0.398289392157158
89	0.582321608171768	0.835356783656464	0.417678391828232
90	0.552182571469791	0.895634857060417	0.447817428530209
91	0.534333805304078	0.931332389391843	0.465666194695922
92	0.492467780704588	0.984935561409175	0.507532219295412
93	0.451658105744368	0.903316211488736	0.548341894255632
94	0.407705492044347	0.815410984088694	0.592294507955653
95	0.423508969673341	0.847017939346681	0.576491030326659
96	0.39100992908942	0.782019858178841	0.60899007091058
97	0.352261298823725	0.70452259764745	0.647738701176275
98	0.35498751962517	0.70997503925034	0.64501248037483
99	0.313175976806382	0.626351953612764	0.686824023193618
100	0.275209033066804	0.550418066133608	0.724790966933196
101	0.250091301257767	0.500182602515535	0.749908698742233
102	0.234201994558692	0.468403989117383	0.765798005441308
103	0.295118575629192	0.590237151258384	0.704881424370808
104	0.255828392799497	0.511656785598995	0.744171607200503
105	0.254145868372832	0.508291736745664	0.745854131627168
106	0.269401651808432	0.538803303616865	0.730598348191568
107	0.247725570575611	0.495451141151222	0.752274429424389
108	0.2222613535096	0.4445227070192	0.7777386464904
109	0.211421327712655	0.42284265542531	0.788578672287345
110	0.194919354171175	0.389838708342351	0.805080645828825
111	0.177837096835126	0.355674193670251	0.822162903164874
112	0.152028138657148	0.304056277314296	0.847971861342852
113	0.163497996247275	0.32699599249455	0.836502003752725
114	0.146015536959652	0.292031073919304	0.853984463040348
115	0.169374293258131	0.338748586516262	0.830625706741869
116	0.169197033725365	0.338394067450729	0.830802966274635
117	0.15392024922273	0.307840498445459	0.84607975077727
118	0.130422009474703	0.260844018949407	0.869577990525297
119	0.140389777848081	0.280779555696162	0.859610222151919
120	0.12692451064105	0.2538490212821	0.87307548935895
121	0.111459065901757	0.222918131803514	0.888540934098243
122	0.093974631208319	0.187949262416638	0.906025368791681
123	0.114539403243461	0.229078806486922	0.885460596756539
124	0.0978425298563256	0.195685059712651	0.902157470143674
125	0.0806875461217589	0.161375092243518	0.919312453878241
126	0.0648512796808938	0.129702559361788	0.935148720319106
127	0.0501641932443708	0.100328386488742	0.949835806755629
128	0.0433689647224812	0.0867379294449624	0.956631035277519
129	0.0345119740236785	0.0690239480473569	0.965488025976322
130	0.0448015897957337	0.0896031795914674	0.955198410204266
131	0.0396122129035763	0.0792244258071527	0.960387787096424
132	0.0635243149145431	0.127048629829086	0.936475685085457
133	0.0732448071468076	0.146489614293615	0.926755192853192
134	0.0922480703620112	0.184496140724022	0.907751929637989
135	0.0776168704674994	0.155233740934999	0.922383129532501
136	0.0576660603556661	0.115332120711332	0.942333939644334
137	0.0432869093150254	0.0865738186300508	0.956713090684975
138	0.0312458892906885	0.062491778581377	0.968754110709312
139	0.0488873897934151	0.0977747795868303	0.951112610206585
140	0.0404061333524434	0.0808122667048867	0.959593866647557
141	0.558152689253647	0.883694621492707	0.441847310746353
142	0.542107454415652	0.915785091168696	0.457892545584348
143	0.474499763673536	0.948999527347072	0.525500236326464
144	0.441768468060079	0.883536936120159	0.558231531939921
145	0.368893779810475	0.73778755962095	0.631106220189525
146	0.475658290785701	0.951316581571403	0.524341709214299
147	0.447323485922832	0.894646971845665	0.552676514077168
148	0.514819354205015	0.97036129158997	0.485180645794985
149	0.425953601467401	0.851907202934802	0.574046398532599
150	0.333945469095103	0.667890938190206	0.666054530904897
151	0.804096384801681	0.391807230396638	0.195903615198319
152	0.921964383539568	0.156071232920863	0.0780356164604315
153	0.857663644205121	0.284672711589758	0.142336355794879
154	0.760229649848776	0.479540700302449	0.239770350151224
155	0.719350436664675	0.561299126670651	0.280649563335325

Multiple Linear Regression - Regression Statistics
Multiple R	0.591595188540566
R-squared	0.349984867104348
Adjusted R-squared	0.337642807618988
F-TEST (value)	28.3570880143207
F-TEST (DF numerator)	3
F-TEST (DF denominator)	158
p-value	9.99200722162641e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.83626549484212
Sum Squared Residuals	532.75561287255

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	9	0.0604026845637584	OK