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Data X:
1910 61 56 51 2598 74 73 48 2144 57 62 46 1331 50 42 42 1431 48 59 38 7334 2 27 38 1133 41 59 36 1535 61 56 36 1196 31 78 35 1551 12 47 35 2108 46 51 34 1335 31 47 34 1532 60 55 32 842 49 35 31 1539 15 47 31 1065 33 48 31 1474 36 47 31 1226 55 55 30 1598 28 42 30 1546 44 54 30 914 41 60 30 1371 26 51 28 1318 28 47 27 1313 40 52 27 1743 28 38 27 1060 57 46 26 1102 67 12 26 1275 56 48 26 1253 54 48 26 1487 25 32 26 1098 19 27 26 930 28 60 25 1176 36 47 25 1290 19 47 25 903 42 58 25 1240 30 47 24 1402 28 45 24 1495 41 48 24 1493 35 42 24 826 10 41 24 1469 57 48 24 1064 48 60 24 821 32 56 24 1317 39 41 23 873 49 52 23 982 22 50 23 708 17 49 23 1174 30 42 23 853 55 39 23 872 33 39 23 1202 42 41 23 793 24 46 22 1000 13 36 22 1205 35 49 22 1671 37 48 22 1106 3 55 22 1131 15 45 22 775 19 48 21 1224 29 52 21 1375 28 39 21 804 38 45 21 923 23 32 20 1233 38 51 20 1170 35 41 20 613 27 52 20 987 23 22 20 1204 32 54 19 933 7 27 18 861 57 41 18 932 39 45 18 705 18 52 18 828 18 57 17 1083 41 47 17 779 33 41 16 792 0 46 16 587 35 43 16 918 37 31 16 649 16 40 16 843 34 24 16 1060 35 30 16 575 25 45 15 548 26 32 15 503 13 46 15 743 30 9 15 846 17 44 15 861 54 32 15 486 40 37 15 634 9 64 15 871 25 21 14 715 29 20 14 812 40 33 14 970 32 26 14 959 17 36 13 960 18 33 13 646 17 20 13 562 15 31 13 636 28 13 13 646 18 35 13 428 10 24 12 830 10 40 12 460 4 19 12 781 10 15 12 567 2 34 12 694 25 32 12 475 16 58 12 485 28 21 12 613 25 31 11 480 7 21 11 582 27 26 11 569 16 47 11 559 7 37 11 508 16 28 10 488 0 9 10 475 36 45 9 630 15 32 9 386 5 35 9 511 14 29 9 585 43 1 9 580 10 20 9 516 0 15 9 413 8 11 8 495 12 33 8 478 10 18 8 350 39 10 7 427 0 41 7 349 10 10 6 335 7 0 6 470 8 28 5 250 0 31 5 308 3 24 5 229 0 38 5 244 8 0 5 242 1 25 5 352 0 40 5 428 8 4 5 270 3 23 5 242 0 13 4 291 0 6 4 135 0 0 3 210 3 3 3 231 0 0 2 268 0 7 2 126 0 2 2 340 0 0 2 44 0 0 2 25 0 0 1 104 0 0 1 142 2 5 1 11 0 0 0
Names of X columns:
pg blogs LFM hours

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)	13.2170503807952	1.938188	6.8193	0	0
pg	-0.00496323024730048	0.001994	-2.4888	0.013948	0.006974
blogs	0.00661990743002855	0.078269	0.0846	0.932713	0.466357
hours	1.48120267528336	0.176157	8.4084	0	0

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	56	79.682431401135	-23.682431401135
2	73	71.9101797617319	1.08982023826807
3	62	71.0885425171292	-9.08854251712917
4	42	69.1524986550408	-27.1524986550408
5	59	62.7181251143173	-3.71812511431729
6	27	33.1156612227213	-6.11566122272125
7	59	61.1884230254359	-2.18842302543592
8	56	59.3256026146217	-3.32560261462169
9	78	59.3283377702723	18.6716622297277
10	47	57.4406127913101	-10.4406127913101
11	51	53.4199677209014	-2.41996772090137
12	47	57.1572460906142	-10.1572460906142
13	55	53.4090616968001	1.59093830319987
14	35	55.2796689104238	-20.2796689104238
15	47	51.5952205754344	-4.59522057543438
16	48	54.0669500463953	-6.06695004639533
17	47	52.0568485975395	-5.05684859753951
18	55	51.9323052647572	3.06769473524278
19	42	49.9072461121507	-7.90724611215067
20	54	50.2712526038907	3.72874739610925
21	60	53.3881543978946	6.61184560210543
22	51	48.0582542128611	2.9417457871389
23	47	46.8533425555447	0.146657444455277
24	52	46.9575975959416	5.04240240405843
25	38	44.743969700442	-6.74396970044202
26	46	46.8446305995357	-0.844630599535713
27	12	46.7023740034494	-34.7023740034494
28	48	45.7709161889361	2.22908381106392
29	48	45.8668674395166	2.13313256048336
30	32	44.5134942461775	-12.5134942461775
31	27	46.4044713677972	-19.4044713677972
32	60	45.8166705409306	14.1833294590694
33	47	44.6486751595349	2.3513248404651
34	47	43.9703284850322	3.02967151496784
35	58	46.0433564616281	11.9566435383719
36	47	42.8101063038441	4.18989369615586
37	45	41.9928231889214	3.00717681107859
38	48	41.6173015725128	6.38269842748717
39	42	41.5875085884273	0.41249141157274
40	41	44.732485477626	-3.73248547762597
41	48	41.8522640778231	6.1477359221769
42	60	43.8027931611095	16.1972068388905
43	56	44.9029395923231	11.0970604076769
44	41	41.0063140663889	-0.00631406638889986
45	52	43.2761873704906	8.7238126295094
46	50	42.5564577729241	7.44354222707593
47	49	43.8832833235343	5.11671667646573
48	42	41.6564768248826	0.343523175117388
49	39	43.4151714200168	-4.41517142001678
50	39	43.1752320818574	-4.17523208185744
51	41	41.5969452671185	-0.596945267118539
52	46	42.0265454292406	3.97345457075943
53	36	40.9263377863191	-4.92633778631905
54	49	40.0545135490831	8.94548645091692
55	48	37.7548880687011	10.2451119312989
56	55	40.3340363058049	14.6659636941951
57	45	40.2893944387827	4.71060556121725
58	48	40.6015813612585	7.39841863874153
59	52	38.4392900545208	13.5607099454792
60	39	37.6832223797484	1.31677762025156
61	45	40.5834259252573	4.4165740747427
62	32	38.4123002390948	-6.41230023909475
63	51	36.972997473882	14.027002526118
64	41	37.2658212571719	3.73417874282812
65	52	39.977381245478	12.022618754522
66	22	38.0946535032675	-16.0946535032675
67	54	35.5960090311902	18.4039909688098
68	27	35.2943440671746	-8.29434406717458
69	41	35.9826920164816	5.01730798351837
70	45	35.5111443351828	9.48885566481721
71	52	36.4987795452894	15.5012204547106
72	57	34.4070995495881	22.5929004504119
73	47	33.2937337074171	13.7062662925829
74	41	33.2683937678729	7.73160623212713
75	46	32.985414829467	13.014585170533
76	43	34.2345737902146	8.76542620978538
77	31	32.6049843932182	-1.60498439321822
78	40	33.8010752737114	6.19892472628855
79	24	32.9573669394757	-8.95736693947567
80	30	31.8869658832415	-1.88696588324149
81	45	32.7467308035986	12.2532691964014
82	32	32.8873579277057	-0.887357927705723
83	46	33.0246444922439	12.9753555077561
84	9	31.9460076592022	-22.9460076592022
85	44	31.3487361471399	12.6512638528601
86	32	31.5192242683415	0.480775731658529
87	37	33.2877569070587	3.71224309294125
88	64	32.3479817001274	31.6520182998726
89	21	29.7964119751143	-8.79641197511428
90	20	30.5971555234133	-10.5971555234133
91	33	30.1885411711554	2.81145882884456
92	26	29.3513915326417	-3.35139153264173
93	36	27.8254857786283	8.17451422137175
94	33	27.827142455811	5.17285754418902
95	20	29.3789768460333	-9.3789768460333
96	31	29.7826483719465	1.21735162805351
97	13	29.5014281302366	-16.5014281302366
98	35	29.3855967534633	5.61440324653667
99	24	28.9334190126512	-4.93341901265125
100	40	26.9382004532365	13.0617995467635
101	19	28.7348762001575	-9.73487620015746
102	15	27.1813987353542	-12.1813987353542
103	34	28.1905707488363	5.80942925116375
104	32	27.7124983783197	4.28750162168025
105	58	28.7398666356083	29.2601333643917
106	21	28.7696732222956	-7.76967322229563
107	31	26.6333173530677	4.36668264693227
108	21	27.1742686422182	-6.17426864221818
109	26	26.8004173055941	-0.800417305594097
110	47	26.7921203170787	20.2078796829213
111	37	26.7821734526814	10.2178265473186
112	28	25.6136746868807	2.38632531311934
113	9	25.6070207729462	-16.6070207729462
114	45	24.4286567583588	20.5713432416412
115	32	23.5203380139966	8.47966198600338
116	35	24.6651671200377	10.3348328799624
117	29	24.1043425059953	4.89565749400466
118	1	23.9290407831659	-22.9290407831659
119	20	23.7353999892115	-3.7353999892115
120	15	23.9868476507384	-8.98684765073845
121	11	23.0698169503673	-12.0698169503673
122	33	22.6893116998087	10.3106883001913
123	18	22.7604467991528	-4.76044679915279
124	10	22.1065149109947	-12.1065149109947
125	41	21.4661697921815	19.5338302078185
126	10	20.4382981504878	-10.4382981504878
127	0	20.4879236516599	-20.4879236516599
128	28	18.3433048004211	9.65669519957895
129	31	19.3822561953869	11.6177438046131
130	24	19.1142485633336	4.88575143666641
131	38	19.4864840305802	18.5135159694198
132	0	19.464994836311	-19.464994836311
133	25	19.4285819447954	5.57141805520464
134	40	18.8760067101623	21.1239932898377
135	4	18.5517604708077	-14.5517604708077
136	23	19.302851312731	3.69714868726899
137	13	17.940759362082	-4.94075936208198
138	6	17.6975610799643	-11.6975610799643
139	0	16.9906223232598	-16.9906223232598
140	3	16.6382397770023	-13.6382397770023
141	0	15.0329495442356	-15.0329495442356
142	7	14.8493100250854	-7.84931002508544
143	2	15.5540887202021	-13.5540887202021
144	0	14.4919574472798	-14.4919574472798
145	0	15.9610736004808	-15.9610736004808
146	0	14.5741722998961	-14.5741722998961
147	0	14.1820771103594	-14.1820771103594
148	5	14.006714175822	-9.00671417582201
149	0	13.162454848075	-13.162454848075

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.325074635781798	0.650149271563597	0.674925364218202
8	0.371607782438908	0.743215564877815	0.628392217561092
9	0.800176432136307	0.399647135727386	0.199823567863693
10	0.725402437146915	0.549195125706171	0.274597562853085
11	0.652143204112455	0.69571359177509	0.347856795887545
12	0.598803303294896	0.802393393410208	0.401196696705104
13	0.508737037975465	0.98252592404907	0.491262962024535
14	0.694078793449163	0.611842413101674	0.305921206550837
15	0.616519759537461	0.766960480925079	0.38348024046254
16	0.540732068720033	0.918535862559935	0.459267931279967
17	0.464314229286967	0.928628458573934	0.535685770713033
18	0.389628387233072	0.779256774466144	0.610371612766928
19	0.338410504449102	0.676821008898204	0.661589495550898
20	0.280682943194649	0.561365886389297	0.719317056805352
21	0.252516004924874	0.505032009849748	0.747483995075126
22	0.202569006473999	0.405138012947998	0.797430993526001
23	0.155485327623611	0.310970655247222	0.844514672376389
24	0.117554225307359	0.235108450614718	0.882445774692641
25	0.107044529356052	0.214089058712104	0.892955470643948
26	0.0841937336186082	0.168387467237216	0.915806266381392
27	0.571059803143079	0.857880393713842	0.428940196856921
28	0.524273194643751	0.951453610712498	0.475726805356249
29	0.473228673783394	0.946457347566788	0.526771326216606
30	0.50060431403813	0.99879137192374	0.49939568596187
31	0.628364232098118	0.743271535803763	0.371635767901882
32	0.675395745683015	0.649208508633971	0.324604254316985
33	0.629648517996606	0.740702964006788	0.370351482003394
34	0.583998544355801	0.832002911288398	0.416001455644199
35	0.591549766496426	0.816900467007148	0.408450233503574
36	0.542548374515555	0.914903250968891	0.457451625484445
37	0.491809020290902	0.983618040581805	0.508190979709098
38	0.448020031090492	0.896040062180983	0.551979968909508
39	0.400768071704559	0.801536143409118	0.599231928295441
40	0.374643395202959	0.749286790405917	0.625356604797041
41	0.331825755797133	0.663651511594265	0.668174244202867
42	0.361951845290771	0.723903690581542	0.638048154709229
43	0.34348078455538	0.686961569110761	0.65651921544462
44	0.303291134127654	0.606582268255307	0.696708865872346
45	0.269023446109985	0.538046892219971	0.730976553890015
46	0.234252427820649	0.468504855641297	0.765747572179351
47	0.198813903356432	0.397627806712864	0.801186096643568
48	0.170648518998045	0.34129703799609	0.829351481001955
49	0.155709819657838	0.311419639315677	0.844290180342162
50	0.144812310106823	0.289624620213647	0.855187689893177
51	0.123598614879691	0.247197229759382	0.876401385120309
52	0.10122753743585	0.202455074871699	0.89877246256415
53	0.102479833206098	0.204959666412196	0.897520166793902
54	0.0861469968215323	0.172293993643065	0.913853003178468
55	0.072919640096291	0.145839280192582	0.927080359903709
56	0.0714715248335709	0.142943049667142	0.928528475166429
57	0.0582703307783967	0.116540661556793	0.941729669221603
58	0.0464018723139837	0.0928037446279674	0.953598127686016
59	0.0412873191563689	0.0825746383127378	0.958712680843631
60	0.0337444964169958	0.0674889928339917	0.966255503583004
61	0.0257034068843466	0.0514068137686931	0.974296593115653
62	0.0305692222458492	0.0611384444916983	0.969430777754151
63	0.0273926656742684	0.0547853313485368	0.972607334325732
64	0.0208883990308859	0.0417767980617719	0.979111600969114
65	0.0170636915658436	0.0341273831316872	0.982936308434156
66	0.0515291045885312	0.103058209177062	0.948470895411469
67	0.0547302302500732	0.109460460500146	0.945269769749927
68	0.0823046266407343	0.164609253281469	0.917695373359266
69	0.0669507033977617	0.133901406795523	0.933049296602238
70	0.0540163438899133	0.108032687779827	0.945983656110087
71	0.048939511973047	0.0978790239460941	0.951060488026953
72	0.0609179372431449	0.12183587448629	0.939082062756855
73	0.0556408922501357	0.111281784500271	0.944359107749864
74	0.0443046390028651	0.0886092780057301	0.955695360997135
75	0.035506571870411	0.071013143740822	0.964493428129589
76	0.0281694022493521	0.0563388044987041	0.971830597750648
77	0.0253002106816925	0.050600421363385	0.974699789318308
78	0.0191839763307441	0.0383679526614883	0.980816023669256
79	0.0256225546440931	0.0512451092881861	0.974377445355907
80	0.0225647198562597	0.0451294397125195	0.97743528014374
81	0.0186109877985662	0.0372219755971323	0.981389012201434
82	0.0158524892293885	0.0317049784587771	0.984147510770611
83	0.0125858992856305	0.0251717985712611	0.98741410071437
84	0.0643083862391632	0.128616772478326	0.935691613760837
85	0.0534339806259323	0.106867961251865	0.946566019374068
86	0.043753313506001	0.0875066270120021	0.956246686493999
87	0.0344280421130555	0.068856084226111	0.965571957886944
88	0.0850148912028988	0.170029782405798	0.914985108797101
89	0.100288595261804	0.200577190523607	0.899711404738196
90	0.120596647607842	0.241193295215685	0.879403352392158
91	0.0991034473324646	0.198206894664929	0.900896552667535
92	0.0909448692218073	0.181889738443615	0.909055130778193
93	0.0726022684357556	0.145204536871511	0.927397731564244
94	0.0576134326810375	0.115226865362075	0.942386567318962
95	0.0699958779778416	0.139991755955683	0.930004122022158
96	0.0570638517490333	0.114127703498067	0.942936148250967
97	0.100240314524869	0.200480629049739	0.899759685475131
98	0.0800275410917498	0.1600550821835	0.91997245890825
99	0.0776106753673612	0.155221350734722	0.922389324632639
100	0.0658729966035024	0.131745993207005	0.934127003396498
101	0.0910600690446127	0.182120138089225	0.908939930955387
102	0.138360285242571	0.276720570485142	0.861639714757429
103	0.116316807949844	0.232633615899688	0.883683192050156
104	0.0932599542495969	0.186519908499194	0.906740045750403
105	0.187385704079372	0.374771408158744	0.812614295920628
106	0.183039887648488	0.366079775296976	0.816960112351512
107	0.150482881639398	0.300965763278796	0.849517118360602
108	0.161034394599772	0.322068789199544	0.838965605400228
109	0.135331600331405	0.27066320066281	0.864668399668595
110	0.153950984027465	0.30790196805493	0.846049015972535
111	0.126107544375487	0.252215088750974	0.873892455624513
112	0.100678231696935	0.201356463393871	0.899321768303065
113	0.232033183350779	0.464066366701559	0.767966816649221
114	0.491334488849475	0.98266897769895	0.508665511150525
115	0.454956441106871	0.909912882213742	0.545043558893129
116	0.40634791066555	0.8126958213311	0.59365208933445
117	0.359062458128659	0.718124916257317	0.640937541871341
118	0.391079513030981	0.782159026061962	0.608920486969019
119	0.36413300644269	0.72826601288538	0.63586699355731
120	0.511038866711547	0.977922266576905	0.488961133288453
121	0.632847745141944	0.734304509716112	0.367152254858056
122	0.583761107703172	0.832477784593656	0.416238892296828
123	0.599235312195103	0.801529375609795	0.400764687804897
124	0.829334095263895	0.34133180947221	0.170665904736105
125	0.790886909607452	0.418226180785096	0.209113090392548
126	0.750542689536607	0.498914620926787	0.249457310463393
127	0.861671369584461	0.276657260831077	0.138328630415539
128	0.940363150944044	0.119273698111913	0.0596368490559563
129	0.917523418555169	0.164953162889662	0.082476581444831
130	0.893904345726043	0.212191308547915	0.106095654273957
131	0.926642504234884	0.146714991530232	0.0733574957651158
132	0.91739130044188	0.165217399116241	0.0826086995581204
133	0.885641404980556	0.228717190038888	0.114358595019444
134	0.995721531353987	0.00855693729202605	0.00427846864601303
135	0.998354979920865	0.00329004015827002	0.00164502007913501
136	0.999505767531126	0.000988464937748066	0.000494232468874033
137	0.999926200487072	0.000147599025855332	7.3799512927666e-05
138	0.999868545866003	0.000262908267992866	0.000131454133996433
139	0.999408328900793	0.00118334219841413	0.000591671099207067
140	0.998190374311402	0.00361925137719512	0.00180962568859756
141	0.993805423316235	0.0123891533675309	0.00619457668376544
142	0.999259532868185	0.00148093426362937	0.000740467131814684

Multiple Linear Regression - Regression Statistics
Multiple R	0.750625603827689
R-squared	0.563438797121683
Adjusted R-squared	0.554406496372477
F-TEST (value)	62.3804291692988
F-TEST (DF numerator)	3
F-TEST (DF denominator)	145
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	11.8229483727053
Sum Squared Residuals	20268.4056924301

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	8	0.0588235294117647	NOK
5% type I error level	16	0.117647058823529	NOK
10% type I error level	30	0.220588235294118	NOK