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Data X:
72772 26073 22274 45104 18103 14819 44525 15100 15136 41169 14738 13704 31118 22259 19638 28435 10277 7551 22162 6225 8019 20202 7663 6509 17773 6618 6634 17094 9945 11166 15153 7590 7508 11218 4293 4275 10796 4656 4944 9594 5145 5441 9309 2001 1689 8556 1779 1522 8041 1609 1416 7639 2191 1594 6884 1617 1909 6642 2554 2599 6321 2198 1262 6216 1578 1199 5865 3446 4404 5799 1380 1166 5695 1249 1122 5644 1223 886 5446 834 778 5395 3754 4436 5363 2283 1890 5338 3028 3107 5160 1100 1038 5091 457 300 5057 1201 988 5039 2192 2008 4880 1508 1522 4735 1393 1336 4693 952 976 4653 1032 798 4586 1279 869 4398 1370 1260 3974 649 578 3858 1900 2359 3826 666 736 3819 1313 1690 3556 1353 1201 3372 1500 813 3193 877 778 3126 874 687 3104 1133 1270 2967 754 671 2848 695 1559 2748 609 489 2649 696 773 2625 756 629 2572 670 637 2548 301 277 2477 630 776 2442 798 1651 2392 436 377 2372 388 222 2346 864 1068 2251 497 399 2230 449 547 2225 919 668 2220 536 451 2205 673 724 2193 837 853 2116 534 434 2102 845 730 2099 626 612 2096 871 558 2064 740 859 2036 391 311 1920 435 318 1813 424 312 1776 338 343 1752 744 710 1738 368 273 1729 393 259 1685 938 1274 1684 804 625 1549 456 245 1533 267 235 1528 338 250 1489 NA NA 1456 635 303 1393 402 250 1370 462 403 1357 525 441 1292 1069 1507 1278 487 388 1256 756 530 1219 360 107 1217 481 868 1187 NA NA 1187 480 753 1182 68 62 1157 262 346 1156 520 478 1155 298 292
Names of X columns:
weekdag zaterdag zondag

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)	136.517245101428	66.748939	2.0452	0.043597	0.021799
weekdag	-0.0172470849038201	0.017717	-0.9735	0.332788	0.166394
zaterdag	0.932853142070083	0.043232	21.5779	0	0

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.999999997905836	4.18832781277299e-09	2.0941639063865e-09
7	1	8.24074931462317e-16	4.12037465731158e-16
8	1	6.93299534810111e-18	3.46649767405055e-18
9	1	4.0901178892648e-17	2.0450589446324e-17
10	1	1.44801722683878e-19	7.24008613419388e-20
11	1	3.73406209947111e-19	1.86703104973556e-19
12	1	7.82340479512109e-19	3.91170239756055e-19
13	1	3.08929570405547e-18	1.54464785202773e-18
14	1	1.41780106663248e-17	7.08900533316239e-18
15	1	6.86938945168096e-18	3.43469472584048e-18
16	1	7.37159741430084e-18	3.68579870715042e-18
17	1	1.2063706216434e-17	6.031853108217e-18
18	1	4.94858287303823e-18	2.47429143651911e-18
19	1	6.2079155263897e-18	3.10395776319485e-18
20	1	2.32417564133121e-17	1.16208782066561e-17
21	1	1.70291735234368e-19	8.5145867617184e-20
22	1	2.84295437120724e-19	1.42147718560362e-19
23	1	2.67311752235706e-20	1.33655876117853e-20
24	1	8.01947429851139e-20	4.00973714925569e-20
25	1	2.78615146312717e-19	1.39307573156359e-19
26	1	5.91806478815743e-19	2.95903239407872e-19
27	1	1.9624175503733e-18	9.81208775186652e-19
28	1	8.80631355193432e-19	4.40315677596716e-19
29	1	1.05956326300775e-18	5.29781631503876e-19
30	1	4.21089194052843e-18	2.10544597026422e-18
31	1	1.48730193704781e-17	7.43650968523903e-18
32	1	4.7273504496445e-17	2.36367522482225e-17
33	1	1.34001548897055e-16	6.70007744485273e-17
34	1	3.37492125747117e-16	1.68746062873559e-16
35	0.999999999999999	1.1813456656315e-15	5.9067283281575e-16
36	0.999999999999998	3.93928394961791e-15	1.96964197480895e-15
37	0.999999999999994	1.23861398577142e-14	6.19306992885712e-15
38	0.999999999999985	3.03027907143346e-14	1.51513953571673e-14
39	0.999999999999987	2.68127513819576e-14	1.34063756909788e-14
40	0.999999999999965	6.9820542094855e-14	3.49102710474275e-14
41	0.999999999999896	2.08732193791385e-13	1.04366096895692e-13
42	0.999999999999869	2.62188798514867e-13	1.31094399257433e-13
43	0.999999999999595	8.09695829451735e-13	4.04847914725868e-13
44	0.999999999999524	9.51432283034026e-13	4.75716141517013e-13
45	0.999999999998708	2.58340280488202e-12	1.29170140244101e-12
46	0.999999999999948	1.03938740240477e-13	5.19693701202387e-14
47	0.999999999999866	2.67935577703447e-13	1.33967788851724e-13
48	0.999999999999773	4.53845221120395e-13	2.26922610560197e-13
49	0.999999999999267	1.46515025383124e-12	7.32575126915618e-13
50	0.999999999998189	3.62256883284575e-12	1.81128441642287e-12
51	0.999999999999985	2.93825308548434e-14	1.46912654274217e-14
52	0.999999999999955	9.02810222774421e-14	4.5140511138721e-14
53	0.999999999999847	3.06360690376857e-13	1.53180345188428e-13
54	0.999999999999594	8.12293449450341e-13	4.0614672472517e-13
55	0.999999999998664	2.67215027857415e-12	1.33607513928707e-12
56	0.999999999995695	8.6108332430671e-12	4.30541662153355e-12
57	0.999999999988264	2.34728014035971e-11	1.17364007017986e-11
58	0.999999999999994	1.29562465390506e-14	6.47812326952529e-15
59	0.999999999999976	4.87533934289e-14	2.437669671445e-14
60	0.999999999999909	1.82611054978865e-13	9.13055274894324e-14
61	0.999999999999857	2.86933001456647e-13	1.43466500728323e-13
62	0.999999999999449	1.10178272725631e-12	5.50891363628157e-13
63	0.999999999999043	1.91425913627918e-12	9.57129568139589e-13
64	0.999999999998156	3.6882727615621e-12	1.84413638078105e-12
65	0.999999999993058	1.38844927564582e-11	6.94224637822912e-12
66	0.99999999998046	3.90803099624822e-11	1.95401549812411e-11
67	0.999999999933263	1.33474459986796e-10	6.67372299933981e-11
68	0.999999999762057	4.7588646862299e-10	2.37943234311495e-10
69	0.999999999188991	1.62201867438573e-09	8.11009337192867e-10
70	0.999999997479075	5.04185011972293e-09	2.52092505986146e-09
71	0.999999997064418	5.87116343251125e-09	2.93558171625562e-09
72	0.999999992657942	1.4684116037449e-08	7.34205801872451e-09
73	0.999999977127088	4.57458233637337e-08	2.28729116818668e-08
74	0.999999923769325	1.52461350195308e-07	7.6230675097654e-08
75	0.999999750600552	4.98798895329521e-07	2.49399447664761e-07
76	0.999999436211667	1.12757666572881e-06	5.63788332864403e-07
77	0.99999819882418	3.60235163997309e-06	1.80117581998654e-06
78	0.999994932617561	1.01347648773718e-05	5.06738243868588e-06
79	0.999985503366364	2.89932672717987e-05	1.44966336358994e-05
80	0.999991292565259	1.74148694828513e-05	8.70743474142564e-06
81	0.999972636533441	5.47269331185962e-05	2.73634665592981e-05
82	0.999918959950274	0.000162080099452923	8.10400497264617e-05
83	0.99984408086261	0.000311838274780349	0.000155919137390174
84	0.999743458166359	0.000513083667281154	0.000256541833640577
85	0.999442034266567	0.00111593146686517	0.000557965733432585
86	0.998370067428235	0.00325986514353062	0.00162993257176531
87	0.995604110225983	0.00879177954803324	0.00439588977401662
88	0.988453366885279	0.0230932662294413	0.0115466331147207
89	0.986752786983639	0.0264944260327223	0.0132472130163612
90	0.964700579355514	0.0705988412889727	0.0352994206444863
91	0.948901796303883	0.102196407392233	0.0510982036961165
92	0.993537193096326	0.012925613807347	0.0064628069036735
93	0.958129386537871	0.083741226924258	0.041870613462129
94	0.914465885974617	0.171068228050766	0.0855341140253828

Multiple Linear Regression - Regression Statistics
Multiple R	0.990648729993195
R-squared	0.981384906237129
Adjusted R-squared	0.980993009526332
F-TEST (value)	2504.19276099711
F-TEST (DF numerator)	2
F-TEST (DF denominator)	95
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	556.463791154703
Sum Squared Residuals	29416935.3322951

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	22274	23203.6923556739	-929.692355673885
2	14819	16246.0451584943	-1427.04515849426
3	15136	13454.6732350171	1681.3267649829
4	13704	13174.8616145249	529.138385475059
5	19638	20364.2005464023	-726.200546402323
6	7551	9233.02812691554	-1682.02812691554
7	8019	5561.29815884923	2457.70184115077
8	6509	6936.5452635575	-427.545263557497
9	6634	6003.60689932564	630.39310067436
10	11166	9118.9200736425	2047.0799263575
11	7508	6955.52751586577	552.472484134232
12	4275	3947.77798555724	327.222014442763
13	4944	4293.68194595809	650.318054041911
14	5441	4770.57812848475	670.421871515249
15	1689	1842.603269014	-153.603269014
16	1522	1648.49692640702	-126.496926407018
17	1416	1498.79414098057	-82.7941409805717
18	1594	2048.6479977967	-454.647997796695
19	1909	1526.21184335085	382.788156649148
20	2599	2404.46903201724	194.530967982757
21	1262	2077.90962769442	-815.90962769442
22	1199	1501.35162352587	-302.35162352587
23	4404	3249.97501971402	1154.02498028598
24	1166	1323.83873580089	-157.838735800887
25	1122	1203.4286710197	-81.4286710197032
26	886	1180.05409065598	-294.054090655976
27	778	820.58914120167	-42.5891412016704
28	4436	3545.39991737641	890.600082623595
29	1890	2173.72485210824	-283.724852108236
30	3107	2869.13162007304	237.868379926957
31	1038	1073.6607432748	-35.6607432748044
32	300	475.026221782105	-175.026221782105
33	988	1169.65536036898	-181.655360368976
34	2008	2094.4232716887	-86.4232716886968
35	1522	1459.09400901247	62.9059909875325
36	1336	1354.31672498546	-18.3167249854621
37	976	943.652866898516	32.3471331014837
38	798	1018.97100166028	-220.971001660275
39	869	1250.54128244014	-381.541282440142
40	1260	1338.67337033044	-78.6733703304372
41	578	673.399018897128	-95.3990188971279
42	2359	1842.39896147564	516.601038524356
43	736	691.810090878085	44.1899091219155
44	1690	1295.48680339175	394.513196608245
45	1201	1337.33691240426	-136.336912404262
46	813	1477.63978791087	-664.639787910868
47	778	899.55950859899	-121.55950859899
48	687	897.916503861336	-210.916503861336
49	1270	1139.90490352537	130.095096474629
50	671	788.716413312633	-117.716413312633
51	1559	735.730481034053	823.269518965947
52	489	657.229819306408	-168.229819306408
53	773	740.095504071983	32.904495928017
54	629	796.48062263388	-167.48062263388
55	637	717.169347915755	-80.1693479157548
56	277	373.360468529586	-96.3604685295861
57	776	681.493695298815	94.5063047011852
58	1651	838.816671138222	812.183328861778
59	377	501.986187954043	-124.986187954043
60	222	457.554178832756	-235.554178832756
61	1068	902.040698665614	165.959301334386
62	399	561.322068591756	-162.322068591756
63	547	516.907306555373	30.0926934446268
64	668	955.434518752831	-287.434518752831
65	451	598.238000764509	-147.238000764509
66	724	726.297587501667	-2.2975875016668
67	853	879.492467820006	-26.4924678200062
68	434	598.165991310365	-164.165991310365
69	730	888.524777682815	-158.524777682815
70	612	684.281680824178	-72.2816808241785
71	558	912.88244188606	-354.88244188606
72	859	791.230586991801	67.7694130081987
73	311	466.147758786649	-155.147758786649
74	318	509.193958886576	-191.193958886576
75	312	500.778012408514	-188.778012408514
76	343	421.190784331928	-78.1907843319284
77	710	800.343090050073	-90.3430900500735
78	273	449.831767820375	-176.831767820375
79	259	473.308320136262	-214.308320136262
80	1274	982.472154300225	291.527845699775
81	625	857.487080347738	-232.487080347738
82	245	535.182543369365	-290.182543369365
83	235	359.14925287658	-124.14925287658
84	250	425.468061388075	-175.468061388075
85	NA	NA	-400.767234695964
86	303	540.499018942576	-237.499018942576
87	250	390.866890419569	-140.866890419569
88	403	564.860850473733	-161.860850473733
89	441	45.4540202786073	395.545979721393
90	1507	1687.77495078247	-180.774950782473
91	388	678.091881867209	-290.091881867209
92	530	874.320179748897	-344.320179748897
93	107	-196.770095890815	303.770095890815
94	868	678.81446351423	189.18553648577
95	NA	NA	-117.565204405875
96	753	767.969891090067	-14.9698910900667
97	62	185.663248829051	-123.663248829051
98	346	448.587098374397	-102.587098374397
99	478	NA	NA
100	292	NA	NA

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	82	0.921348314606742	NOK
5% type I error level	85	0.955056179775281	NOK
10% type I error level	87	0.97752808988764	NOK