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Data X:
2.5 4 3.5 0.2 2.3 1.4 0.7 0.8 1.4 0.3 5.2 1.2 0.5 -1 2.9 4.5 -2.3 -1.9 1 0.4 -3.7 -1.5 -5.5 5.9 -3.5 -0.2 -7.1 6.5 -0.2 3.4 -7.9 12.8 0.2 3 -8.9 4.2 -0.1 4.1 -7 -3.3 -1 3.4 -5.6 -12.5 -0.9 3.2 -7.3 -16.3 2.2 6.1 -7.5 -10.5 1.9 5.8 -3.5 -11.8 2.4 6.2 2.3 -11.4 2.3 5.8 3.1 -17.7 2.3 5.9 4.1 -17.3 3.8 6.7 4.2 -18.6 3 5.9 6 -17.9 2.4 3.8 5.5 -21.4 0.7 1.7 5.1 -19.4 1.4 1.4 5.6 -15.5 2.5 1.8 1 -7.7 2.9 3 1.6 -0.7 3.8 3.6 1.3 -1.6 2.9 4.8 1.7 1.4 3 4.3 -8.6 0.7 5.1 4.2 -11.2 9.5 3.4 2.9 -12.7 1.4 3.8 4.9 -3.1 4.1 2.7 7.2 -1 6.6 4.7 8.7 0.3 18.4 4.8 9.1 -2.9 16.9 5.5 8.9 -2.3 9.2 5.1 9 3.2 -4.3 7.7 11.6 5.9 -5.9 5.4 9.6 13.2 -7.7 4.8 9.1 7.2 -5.4 4.7 9.2 6.9 -2.3 5.3 10.8 8.2 -4.8 7.5 11 11.8 2.3 5.7 8.5 9.3 -5.2 3.6 6.5 5.8 -10 2.8 7.2 8.8 -17.1 3.4 7.8 14.6 -14.4 3.8 8.7 28.2 -3.9 1.5 7.8 19.8 3.7 0.3 7.5 16.5 6.5 0.4 7.7 -5.3 0.9 0.3 7.5 -2.4 -4.1 1.2 8.3 1.9 -7 0.9 7.9 1.6 -12.2 2.8 10.4 -0.1 -2.5 2.9 11.5 -2 4.4 4.9 14 3.4 13.7 2.3 11.9 3.3 12.3 4 11.9 3.3 13.4 2.3 10.3 -9.8 2.2 5 11.3 -4.6 1.7 2.6 9.9 -6.1 -7.2 1.7 8.9 10.6 -4.8 4.3 9.2 8.9 -2.9 4 8.8 10.7 -2.4 3.8 6.7 11.7 -2.5 2.5 7.1 13.5 -5.3 3.2 6.6 14.6 -7.1 4 7.2 14.1 -8 4.1 5 11.1 -8.9 3.3 5.3 9.2 -7.7 4.3 6.3 13 -1.1 5.8 8 14.4 4 8.1 7.6 16.5 9.6 6.8 7 11.7 10.9 5.3 6.9 11.8 13 4.8 6.8 10.4 14.9 5.5 7.5 12.2 20.1 5.2 6.4 14.7 10.8 6 8 15 11 4 6.4 10.3 3.8 6.2 9.6 11.9 10.8 3.7 7.5 13.1 7.6 5.2 9 15.5 10.2 2.7 7.8 10.3 2.2 0.8 7.8 5.2 -0.1 2.9 8.7 5.4 -1.7 0.2 4.3 4.3 -4.8 -2.6 -0.4 6.6 -9.9 -6.7 -4.9 4.2 -13.5 -12.5 -10.1 -3.3 -18.1 -14.4 -13.4 -6.6 -18 -16 -15.8 -8 -15.7
Names of X columns:
IndProd TotOmzet Invest RegWag

Sample Range: (leave blank to include all observations)
From:
To:

Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


Chart options



R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-1.48358856264544	0.345152	-4.2984	4.5e-05	2.2e-05
TotOmzet	0.618539905255023	0.048218	12.8279	0	0
Invest	0.0730223253351927	0.027345	2.6704	0.009041	0.004521
RegWag	0.0459292845305722	0.02241	2.0495	0.043429	0.021715

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	2.5	1.25533505395394	1.24466494604606
2	2.3	-0.529773639929307	2.82977363992931
3	1.4	-0.863195357889246	2.26319535788925
4	0.5	-1.68368194404083	2.18368194404083
5	-2.3	-2.56742034348256	0.267420343482558
6	-3.7	-2.54203843114115	-1.15796156885885
7	-3.5	-1.82721470412759	-1.67278529587241
8	-0.2	0.630465587064943	-0.830465587064943
9	0.2	-0.0849645473351802	0.28496454733518
10	-0.1	0.389702132602919	-0.489702132602919
11	-1	-0.363593963287592	-0.636406036712408
12	-0.9	-0.785971178624598	-0.114028821375402
13	2.2	1.25957993182525	0.940420068174753
14	1.9	1.30639919169977	0.593600808300233
15	2.4	1.99571635455812	0.404283645441877
16	2.3	1.51736376018166	0.782636239818337
17	2.3	1.67061178985459	0.629388210145413
18	3.8	2.11303787670238	1.68696212329762
19	3	1.78179663727311	1.21820336272689
20	2.4	0.285599177712963	2.11440082228704
21	0.7	-0.950684984395518	1.65068498439552
22	1.4	-0.920611583635197	2.3206115836352
23	2.5	-0.65084989873661	3.15084989873661
24	2.9	0.456716374484539	2.44328362551546
25	3.8	0.76459726395948	3.03540273604052
26	2.9	1.6738419339913	1.2261580660087
27	3	0.580291531239904	2.4197084687601
28	5.1	0.732757198711936	4.36724280128806
29	3.4	-0.552905370820016	3.95290537082002
30	3.8	1.50919783114042	2.29080216885958
31	2.7	3.20000970775731	-0.50000970775731
32	4.7	4.76471414603635	-0.0647141460363465
33	4.8	4.70956474026988	0.0904352597301184
34	5.5	4.27601466353459	1.22398533646541
35	5.1	4.11944610224092	0.980553897759077
36	7.7	5.85132327906009	1.84867672093991
37	5.4	5.06463373134192	0.335366268658084
38	4.8	4.42286718112357	0.377132818876434
39	4.7	4.60519525609328	0.0948047439067165
40	5.3	5.57496491611064	-0.274964916110641
41	7.5	6.2876511885354	1.2123488114646
42	5.7	4.21427597808057	1.48572402191943
43	3.6	2.5011574631506	1.0988425368494
44	2.8	2.82710445266764	-0.0271044526676358
45	3.4	3.74576695099731	-0.345766950997311
46	3.8	5.77781397785646	-1.97781397785646
47	1.5	4.95680309274367	-3.45680309274367
48	0.3	4.65886944424663	-4.35886944424663
49	0.4	2.93348673961923	-2.53348673961923
50	0.3	2.79189707938742	-2.49189707938742
51	1.2	3.46753007739411	-2.26753007739411
52	0.9	2.95937513813257	-2.05937513813257
53	2.8	4.82710100814685	-2.02710100814685
54	2.9	5.68566454905146	-2.78566454905146
55	4.9	8.05347721513338	-3.15347721513338
56	2.3	6.68294018322151	-4.38294018322151
57	4	6.73346239620514	-2.73346239620514
58	2.3	4.27279809916367	-1.97279809916367
59	5	5.24808945389641	-0.248089453896406
60	2.6	3.86382946621449	-1.26382946621449
61	1.7	4.57499267693056	-2.87499267693056
62	4.3	4.72368233604533	-0.423682336045326
63	4	4.63067120181195	-0.63067120181195
64	3.8	3.40016679765854	0.399833202341462
65	2.5	3.65042094867829	-1.15042094867829
66	3.2	3.33880284176446	-0.138802841764461
67	4	3.63207926617236	0.367920733827636
68	4.1	2.01088814252822	2.08911185747178
69	3.3	2.11282283740455	1.18717716259545
70	4.3	3.31198085683508	0.98801914316492
71	5.8	4.69996930234381	1.10003069765619
72	8.1	4.86310421681691	3.23689578318309
73	6.8	4.20118118194471	2.59881881805529
74	5.3	4.24308092146693	1.05691907853307
75	4.8	4.16626131608025	0.633738683919753
76	5.5	4.96951171492108	0.530488285078915
77	5.2	4.04453128634422	1.15546871365578
78	6	5.06528768925893	0.934712310741072
79	4	3.40172806315537	0.598271936844633
80	6.2	5.81939647222175	0.380603527778247
81	3.7	4.46111575109061	-0.761115751090605
82	5.2	5.68359532955709	-0.483595329557089
83	2.7	4.19419707526348	-1.49419707526348
84	0.8	3.71614586163368	-2.91614586163368
85	2.9	4.21394938618133	-1.31394938618133
86	0.2	1.26966846314574	-1.06966846314574
87	-2.6	-1.70375709438784	-0.89624290561216
88	-6.7	-4.82778567314997	-1.87221432685003
89	-12.5	-8.80313532933066	-3.69686467066934
90	-14.4	-11.0806977618253	-3.31930223817469
91	-16	-12.5617874354863	-3.43821256451368

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.248658168320258	0.497316336640515	0.751341831679742
8	0.123083124302146	0.246166248604292	0.876916875697854
9	0.103928249934879	0.207856499869758	0.896071750065121
10	0.0562380970541856	0.112476194108371	0.943761902945814
11	0.0279372651069268	0.0558745302138535	0.972062734893073
12	0.0141618572657981	0.0283237145315963	0.985838142734202
13	0.0085651349962888	0.0171302699925776	0.991434865003711
14	0.00407581405092341	0.00815162810184682	0.995924185949077
15	0.00616091483060492	0.0123218296612098	0.993839085169395
16	0.00387486673692363	0.00774973347384726	0.996125133263076
17	0.0025072301124793	0.00501446022495859	0.997492769887521
18	0.00118829630068464	0.00237659260136928	0.998811703699315
19	0.000601958223635988	0.00120391644727198	0.999398041776364
20	0.000347092458243507	0.000694184916487014	0.999652907541757
21	0.000164501597091006	0.000329003194182012	0.999835498402909
22	0.000104257931672478	0.000208515863344955	0.999895742068328
23	0.000462223858838848	0.000924447717677697	0.999537776141161
24	0.000451602461838699	0.000903204923677397	0.999548397538161
25	0.000837421900959967	0.00167484380191993	0.99916257809904
26	0.000466778074396448	0.000933556148792896	0.999533221925604
27	0.00251786433851008	0.00503572867702016	0.99748213566149
28	0.0580140589641358	0.116028117928272	0.941985941035864
29	0.266225893216772	0.532451786433544	0.733774106783228
30	0.287900248150638	0.575800496301276	0.712099751849362
31	0.324677386998502	0.649354773997005	0.675322613001498
32	0.303083775558948	0.606167551117896	0.696916224441052
33	0.266645369609954	0.533290739219909	0.733354630390046
34	0.26186239499032	0.52372478998064	0.73813760500968
35	0.235821193084538	0.471642386169077	0.764178806915462
36	0.235537296227541	0.471074592455083	0.764462703772459
37	0.204940917524681	0.409881835049363	0.795059082475319
38	0.176252934285094	0.352505868570189	0.823747065714906
39	0.15039891242936	0.300797824858719	0.84960108757064
40	0.127646968274192	0.255293936548384	0.872353031725808
41	0.109217366626801	0.218434733253602	0.890782633373199
42	0.103864439743001	0.207728879486003	0.896135560256999
43	0.100714568180706	0.201429136361412	0.899285431819294
44	0.091072622521801	0.182145245043602	0.908927377478199
45	0.0796393074759709	0.159278614951942	0.920360692524029
46	0.135408972359415	0.270817944718829	0.864591027640585
47	0.37370918042993	0.74741836085986	0.62629081957007
48	0.832555089168895	0.33488982166221	0.167444910831105
49	0.879315932782526	0.241368134434949	0.120684067217474
50	0.902176822009872	0.195646355980256	0.0978231779901282
51	0.907102802779481	0.185794394441037	0.0928971972205185
52	0.906618112911843	0.186763774176314	0.093381887088157
53	0.892831583680101	0.214336832639799	0.107168416319899
54	0.884526541839662	0.230946916320676	0.115473458160338
55	0.898673447270168	0.202653105459665	0.101326552729832
56	0.973710901591882	0.0525781968162356	0.0262890984081178
57	0.984306255565872	0.0313874888682558	0.0156937444341279
58	0.978115567440114	0.0437688651197724	0.0218844325598862
59	0.977783070527132	0.0444338589457369	0.0222169294728685
60	0.989513372352234	0.0209732552955314	0.0104866276477657
61	0.995798136526375	0.00840372694724893	0.00420186347362446
62	0.993206651781439	0.0135866964371224	0.00679334821856118
63	0.989028264873956	0.0219434702520877	0.0109717351260438
64	0.982920661722217	0.0341586765555657	0.0170793382777829
65	0.985585075044818	0.0288298499103639	0.0144149249551819
66	0.983462019415455	0.0330759611690906	0.0165379805845453
67	0.976967151280353	0.0460656974392934	0.0230328487196467
68	0.984403847859436	0.0311923042811286	0.0155961521405643
69	0.990581546589415	0.0188369068211703	0.00941845341058517
70	0.986549787439896	0.0269004251202081	0.0134502125601041
71	0.980272334112145	0.0394553317757095	0.0197276658878548
72	0.993158270032733	0.013683459934534	0.00684172996726699
73	0.999166347591723	0.00166730481655443	0.000833652408277214
74	0.998663901629381	0.00267219674123873	0.00133609837061937
75	0.997460418641129	0.00507916271774243	0.00253958135887122
76	0.994316579566273	0.0113668408674532	0.00568342043372658
77	0.990195857481931	0.0196082850361385	0.00980414251806924
78	0.98335107819192	0.0332978436161604	0.0166489218080802
79	0.988459556530618	0.0230808869387631	0.0115404434693815
80	0.991297835994063	0.0174043280118744	0.00870216400593721
81	0.977608448446304	0.0447831031073916	0.0223915515536958
82	0.946071729838695	0.107856540322609	0.0539282701613047
83	0.895780167360215	0.20843966527957	0.104219832639785
84	0.984877423100392	0.0302451537992157	0.0151225768996079

Multiple Linear Regression - Regression Statistics
Multiple R	0.875141180691189
R-squared	0.765872086141568
Adjusted R-squared	0.757798709801622
F-TEST (value)	94.8639149090745
F-TEST (DF numerator)	3
F-TEST (DF denominator)	87
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.96123953070687
Sum Squared Residuals	334.642063222235

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	17	0.217948717948718	NOK
5% type I error level	42	0.538461538461538	NOK
10% type I error level	44	0.564102564102564	NOK