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Data X:
1 87.28 255 2 87.28 280.2 3 87.09 299.9 4 86.92 339.2 5 87.59 374.2 6 90.72 393.5 7 90.69 389.2 8 90.3 381.7 9 89.55 375.2 10 88.94 369 11 88.41 357.4 12 87.82 352.1 1 87.07 346.5 2 86.82 342.9 3 86.4 340.3 4 86.02 328.3 5 85.66 322.9 6 85.32 314.3 7 85 308.9 8 84.67 294 9 83.94 285.6 10 82.83 281.2 11 81.95 280.3 12 81.19 278.8 1 80.48 274.5 2 78.86 270.4 3 69.47 263.4 4 68.77 259.9 5 70.06 258 6 73.95 262.7 7 75.8 284.7 8 77.79 311.3 9 81.57 322.1 10 83.07 327 11 84.34 331.3 12 85.1 333.3 1 85.25 321.4 2 84.26 327 3 83.63 320 4 86.44 314.7 5 85.3 316.7 6 84.1 314.4 7 83.36 321.3 8 82.48 318.2 9 81.58 307.2 10 80.47 301.3 11 79.34 287.5 12 82.13 277.7 1 81.69 274.4 2 80.7 258.8 3 79.88 253.3 4 79.16 251 5 78.38 248.4 6 77.42 249.5 7 76.47 246.1 8 75.46 244.5 9 74.48 243.6 10 78.27 244 11 80.7 240.8 12 79.91 249.8 1 78.75 248 2 77.78 259.4 3 81.14 260.5 4 81.08 260.8 5 80.03 261.3 6 78.91 259.5 7 78.01 256.6 8 76.9 257.9 9 75.97 256.5 10 81.93 254.2 11 80.27 253.3 12 78.67 253.8 1 77.42 255.5 2 76.16 257.1 3 74.7 257.3 4 76.39 253.2 5 76.04 252.8 6 74.65 252 7 73.29 250.7 8 71.79 252.2 9 74.39 250 10 74.91 251 11 74.54 253.4 12 73.08 251.2 1 72.75 255.6 2 71.32 261.1 3 70.38 258.9 4 70.35 259.9 5 70.01 261.2 6 69.36 264.7 7 67.77 267.1 8 69.26 266.4 9 69.8 267.7 10 68.38 268.6 11 67.62 267.5 12 68.39 268.5 1 66.95 268.5 2 65.21 270.5 3 66.64 270.9 4 63.45 270.1 5 60.66 269.3 6 62.34 269.8 7 60.32 270.1 8 58.64 264.9 9 60.46 263.7 10 58.59 264.8 11 61.87 263.7 12 61.85 255.9 1 67.44 276.2 2 77.06 360.1 3 91.74 380.5 4 93.15 373.7 5 94.15 369.8 6 93.11 366.6 7 91.51 359.3 8 89.96 345.8 9 88.16 326.2 10 86.98 324.5 11 88.03 328.1 12 86.24 327.5 1 84.65 324.4 2 83.23 316.5 3 81.7 310.9 4 80.25 301.5 5 78.8 291.7 6 77.51 290.4 7 76.2 287.4 8 75.04 277.7 9 74 281.6 10 75.49 288 11 77.14 276 12 76.15 272.9 1 76.27 283 2 78.19 283.3 3 76.49 276.8 4 77.31 284.5 5 76.65 282.7 6 74.99 281.2 7 73.51 287.4 8 72.07 283.1 9 70.59 284 10 71.96 285.5 11 76.29 289.2
Names of X columns:
month col usa

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)	8.33938962516957	2.904272	2.8714	0.004722	0.002361
col	-0.0391199022598999	0.0513	-0.7626	0.447004	0.223502
usa	0.00408947676083809	0.01064	0.3843	0.701314	0.350657

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1	5.9678211299392	-4.9678211299392
2	2	6.07087594431233	-4.07087594431233
3	3	6.15887141793023	-3.15887141793023
4	4	6.32623823801535	-2.32623823801535
5	5	6.44315959013055	-1.44315959013055
6	6	6.39964119754123	-0.399641197541234
7	7	6.38323004453743	0.616769955462573
8	8	6.3678157307125	1.6321842692875
9	9	6.37057405846198	2.62942594153802
10	10	6.36908244292332	3.63091755707668
11	11	6.34237806069535	4.65762193930465
12	12	6.34378457619625	5.65621542380375
13	1	6.35022343303048	-5.35022343303048
14	2	6.34528129225644	-4.34528129225644
15	3	6.35107901162742	-3.35107901162742
16	4	6.31687085335612	-2.31687085335612
17	5	6.30887084366116	-1.30887084366116
18	6	6.28700211028632	-0.287002110286318
19	7	6.27743730450096	0.72256269549904
20	8	6.22941366851024	1.77058633148976
21	9	6.22361959236893	2.77638040763107
22	10	6.24904898612973	3.75095101387027
23	11	6.27979397103369	4.72020602896631
24	12	6.30339088160995	5.69660911839005
25	1	6.31358126214288	-5.31358126214288
26	2	6.36018864908448	-4.36018864908448
27	3	6.69889819397907	-3.69889819397907
28	4	6.71196895689807	-2.71196895689807
29	5	6.65373427713721	-1.65373427713721
30	6	6.52077839812213	-0.520778398122135
31	7	6.53837506767976	0.461624932320242
32	8	6.56930654402085	1.43069345597915
33	9	6.46559966249548	2.53440033750452
34	10	6.42695824523374	3.57304175476626
35	11	6.39486071943527	4.60513928056473
36	12	6.37330854723942	5.62669145276058
37	1	6.31877578844646	-5.31877578844646
38	2	6.38040556154446	-4.38040556154446
39	3	6.37642476264233	-3.37642476264233
40	4	6.24482361045956	-2.24482361045956
41	5	6.29759925255753	-1.29759925255753
42	6	6.33513733871948	-0.335137338719479
43	7	6.39230345604159	0.607696543958412
44	8	6.4140515920717	1.5859484079283
45	9	6.40427525973639	2.59572474026361
46	10	6.42357043835594	3.57642956164406
47	11	6.41134114861006	4.58865885138994
48	12	6.26211974904872	5.73788025095128
49	1	6.26583723273231	-5.26583723273231
50	2	6.24077009850054	-4.24077009850054
51	3	6.25035629616905	-3.25035629616905
52	4	6.26911682924625	-2.26911682924625
53	5	6.28899771343079	-1.28899771343079
54	6	6.33105124403722	-0.331051244037219
55	7	6.35431093019727	0.645689069802725
56	8	6.38727886866243	1.61272113133757
57	9	6.42193584379238	2.57806415620762
58	10	6.27530720493169	3.72469279506831
59	11	6.16715951680546	4.83284048319454
60	12	6.23486953043832	5.76513046956168
61	1	6.27288755889029	-5.27288755889029
62	2	6.35745389915595	-4.35745389915595
63	3	6.23050945199961	-3.23050945199961
64	4	6.23408348916346	-2.23408348916346
65	5	6.27720412491677	-1.27720412491677
66	6	6.31365735727835	-0.313657357278349
67	7	6.33700578670583	0.662994213294171
68	8	6.38574519800341	1.61425480199659
69	9	6.41640143963994	2.58359856036006
70	10	6.17384102562101	3.82615897437899
71	11	6.23509953428769	4.76490046571231
72	12	6.29973611628395	5.70026388371605
73	1	6.35558810460225	-5.35558810460225
74	2	6.41142234426706	-4.41142234426706
75	3	6.46935529691868	-3.46935529691868
76	4	6.38647580738002	-2.38647580738002
77	5	6.39853198246665	-1.39853198246665
78	6	6.44963706519924	-0.449637065199237
79	7	6.49752381248361	0.502476187516389
80	8	6.56233788101472	1.43766211898528
81	9	6.45162928626514	2.54837071373486
82	10	6.43537641385083	3.56462358614917
83	11	6.459665521913	4.540334478087
84	12	6.50778373033861	5.49221626966139
85	1	6.53868699583206	-5.53868699583206
86	2	6.61712057824833	-4.61712057824833
87	3	6.64489643749879	-3.64489643749879
88	4	6.65015951132743	-2.65015951132743
89	5	6.66877659788488	-1.66877659788488
90	6	6.70851770301675	-0.708517703016752
91	7	6.780533091836	0.219466908163996
92	8	6.71938180373617	1.28061819626383
93	9	6.70357337630491	2.29642662369509
94	10	6.76280416659872	3.23719583340128
95	11	6.78803686787932	4.21196313212068
96	12	6.76200401990004	5.23799598009996
97	1	6.81833667915429	-5.81833667915429
98	2	6.8945842626082	-4.8945842626082
99	3	6.84027859308087	-3.84027859308088
100	4	6.96179949988129	-2.96179949988129
101	5	7.06767244577774	-2.06767244577774
102	6	7.00399574836152	-1.00399574836152
103	7	7.08424479395477	-0.0842447939547727
104	8	7.12870095059505	0.871299049404954
105	9	7.05259535636902	1.94740464363098
106	10	7.13024799803196	2.86975200196804
107	11	6.99743629418256	4.00256370581744
108	12	6.96632077349322	5.03367922650678
109	1	6.8306568981054	-5.8306568981054
110	2	6.79743053859947	-4.79743053859947
111	3	6.30657569934524	-3.30657569934524
112	4	6.22360819518508	-2.22360819518508
113	5	6.16853933355791	-1.16853933355791
114	6	6.19613770627353	-0.196137706273529
115	7	6.22887636953525	0.771123630464749
116	8	6.23430428176678	1.76569571823322
117	9	6.22456636132218	2.77543363867782
118	10	6.26377573549543	3.73622426450457
119	11	6.23742195446155	4.76257804553845
120	12	6.30499289345027	5.69500710654973
121	1	6.35451616008492	-5.35451616008492
122	2	6.37775955488335	-4.37775955488335
123	3	6.41471193548031	-3.41471193548031
124	4	6.43299471220528	-2.43299471220528
125	5	6.44964169822592	-1.44964169822592
126	6	6.49479005235211	-0.494790052352106
127	7	6.53376869403006	0.46623130596994
128	8	6.53947985607141	1.46052014392859
129	9	6.59611351378898	2.40388648621102
130	10	6.56399751069109	3.43600248930891
131	11	6.4503759508322	4.5496240491678
132	12	6.4764272761109	5.5235727238891
133	1	6.51303660312418	-5.51303660312418
134	2	6.43915323381342	-4.43915323381342
135	3	6.47907546870981	-3.47907546870981
136	4	6.47848611991514	-2.47848611991514
137	5	6.49694419723717	-1.49694419723717
138	6	6.55574901984734	-0.555749019847344
139	7	6.63900123110919	0.360998768890809
140	8	6.67774914029184	1.32225085970816
141	9	6.73932712472125	2.26067287527875
142	10	6.69186707376644	3.30813292623356
143	11	6.53760896099618	4.46239103900382

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.000462118325426455	0.00092423665085291	0.999537881674573
7	0.00065691920948121	0.00131383841896242	0.999343080790519
8	0.002286938304982	0.00457387660996399	0.997713061695018
9	0.0101997115864828	0.0203994231729655	0.989800288413517
10	0.0393389111533316	0.0786778223066632	0.960661088846668
11	0.132210074560125	0.264420149120249	0.867789925439875
12	0.290325632929142	0.580651265858284	0.709674367070858
13	0.440507428896734	0.881014857793468	0.559492571103266
14	0.427576239678783	0.855152479357565	0.572423760321217
15	0.355863691548561	0.711727383097121	0.644136308451439
16	0.282116661250302	0.564233322500604	0.717883338749698
17	0.233288586717557	0.466577173435114	0.766711413282443
18	0.215522585629838	0.431045171259677	0.784477414370162
19	0.221018316217907	0.442036632435814	0.778981683782093
20	0.263438839189588	0.526877678379177	0.736561160810412
21	0.321607387182154	0.643214774364307	0.678392612817846
22	0.364259639266017	0.728519278532035	0.635740360733983
23	0.382160817962472	0.764321635924944	0.617839182037528
24	0.387623512048816	0.775247024097632	0.612376487951184
25	0.61975047122839	0.76049905754322	0.38024952877161
26	0.691637948212783	0.616724103574434	0.308362051787217
27	0.694226356990435	0.611547286019131	0.305773643009565
28	0.646297393282141	0.707405213435718	0.353702606717859
29	0.590629107216442	0.818741785567117	0.409370892783558
30	0.53558178650871	0.92883642698258	0.46441821349129
31	0.484765662064274	0.969531324128548	0.515234337935726
32	0.44166781068257	0.88333562136514	0.55833218931743
33	0.418469532967675	0.83693906593535	0.581530467032325
34	0.423233810618094	0.846467621236188	0.576766189381906
35	0.46365442036181	0.927308840723621	0.53634557963819
36	0.547959427333886	0.904081145332227	0.452040572666114
37	0.619203822088914	0.761592355822173	0.380796177911086
38	0.648723943421862	0.702552113156276	0.351276056578138
39	0.640871978676259	0.718256042647483	0.359128021323741
40	0.604422574723569	0.791154850552861	0.395577425276431
41	0.555769235456354	0.888461529087293	0.444230764543646
42	0.502032123915756	0.995935752168488	0.497967876084244
43	0.450958342779524	0.901916685559048	0.549041657220476
44	0.411598273917393	0.823196547834787	0.588401726082607
45	0.395362303860486	0.790724607720971	0.604637696139514
46	0.407101651864574	0.814203303729149	0.592898348135426
47	0.458886583271614	0.917773166543229	0.541113416728386
48	0.569167561906503	0.861664876186994	0.430832438093497
49	0.616105295495384	0.767789409009232	0.383894704504616
50	0.621715294725104	0.756569410549791	0.378284705274896
51	0.60269911663165	0.794601766736701	0.39730088336835
52	0.568118050020859	0.863763899958282	0.431881949979141
53	0.525245969462743	0.949508061074514	0.474754030537257
54	0.480113483833096	0.960226967666193	0.519886516166904
55	0.440429985171809	0.880859970343619	0.559570014828191
56	0.411283885838246	0.822567771676492	0.588716114161754
57	0.396947673415561	0.793895346831121	0.603052326584439
58	0.417198562000727	0.834397124001454	0.582801437999273
59	0.47594654459935	0.9518930891987	0.52405345540065
60	0.56301986301626	0.87396027396748	0.43698013698374
61	0.623607255447638	0.752785489104724	0.376392744552362
62	0.650393453278838	0.699213093442324	0.349606546721162
63	0.645492104620626	0.709015790758748	0.354507895379374
64	0.621391789851378	0.757216420297245	0.378608210148622
65	0.583917553944107	0.832164892111785	0.416082446055893
66	0.538923569820337	0.922152860359326	0.461076430179663
67	0.493500790811586	0.987001581623171	0.506499209188414
68	0.454353368455475	0.908706736910951	0.545646631544525
69	0.428876951209853	0.857753902419707	0.571123048790147
70	0.435799385773697	0.871598771547395	0.564200614226303
71	0.471217804991727	0.942435609983455	0.528782195008273
72	0.546084154280433	0.907831691439134	0.453915845719567
73	0.614807644722625	0.770384710554749	0.385192355277375
74	0.649715069159808	0.700569861680384	0.350284930840192
75	0.656657473947041	0.686685052105918	0.343342526052959
76	0.642901410937096	0.714197178125808	0.357098589062904
77	0.613547500307929	0.772904999384141	0.386452499692071
78	0.573052136948934	0.853895726102131	0.426947863051066
79	0.52765726969673	0.944685460606539	0.47234273030327
80	0.484550087489975	0.96910017497995	0.515449912510025
81	0.452574931264244	0.905149862528488	0.547425068735756
82	0.439792064461464	0.879584128922927	0.560207935538536
83	0.456259869524072	0.912519739048144	0.543740130475928
84	0.512163604833591	0.975672790332819	0.487836395166409
85	0.601868207902343	0.796263584195314	0.398131792097657
86	0.651273865850524	0.697452268298952	0.348726134149476
87	0.668613967140947	0.662772065718106	0.331386032859053
88	0.660852970689292	0.678294058621415	0.339147029310708
89	0.633474945597239	0.733050108805522	0.366525054402761
90	0.591289734036055	0.817420531927891	0.408710265963945
91	0.542853927661477	0.914292144677045	0.457146072338523
92	0.49745468397681	0.99490936795362	0.50254531602319
93	0.463228068084544	0.926456136169088	0.536771931915456
94	0.449694699431464	0.899389398862927	0.550305300568537
95	0.46403386912882	0.92806773825764	0.53596613087118
96	0.517065107835147	0.965869784329706	0.482934892164853
97	0.619144066217666	0.761711867564667	0.380855933782334
98	0.669661594804748	0.660676810390504	0.330338405195252
99	0.686713445510251	0.626573108979499	0.313286554489749
100	0.67625194309915	0.6474961138017	0.32374805690085
101	0.646682988498388	0.706634023003223	0.353317011501611
102	0.60394659990254	0.792106800194921	0.39605340009746
103	0.553419831301771	0.893160337396458	0.446580168698229
104	0.502349798861197	0.995300402277607	0.497650201138803
105	0.458792248971454	0.917584497942908	0.541207751028546
106	0.437593601629119	0.875187203258238	0.562406398370881
107	0.461035454015936	0.922070908031872	0.538964545984064
108	0.562770152915056	0.874459694169888	0.437229847084944
109	0.6239849089426	0.7520301821148	0.3760150910574
110	0.610503136360803	0.778993727278394	0.389496863639197
111	0.593380077885403	0.813239844229194	0.406619922114597
112	0.562982679906065	0.87403464018787	0.437017320093935
113	0.517188972426091	0.965622055147818	0.482811027573909
114	0.463060046646947	0.926120093293893	0.536939953353053
115	0.404207591367755	0.80841518273551	0.595792408632245
116	0.349279958006988	0.698559916013976	0.650720041993012
117	0.322522613725919	0.645045227451839	0.677477386274081
118	0.33873741942439	0.677474838848779	0.66126258057561
119	0.489530541518678	0.979061083037355	0.510469458481322
120	0.899994472082116	0.200011055835769	0.100005527917884
121	0.874902317678802	0.250195364642396	0.125097682321198
122	0.84044006704905	0.3191198659019	0.15955993295095
123	0.796303348176893	0.407393303646214	0.203696651823107
124	0.742223458179419	0.515553083641162	0.257776541820581
125	0.676442095992344	0.647115808015312	0.323557904007656
126	0.605184789689321	0.789630420621359	0.394815210310679
127	0.529354550264497	0.941290899471007	0.470645449735503
128	0.445163014565228	0.890326029130456	0.554836985434772
129	0.36876225623123	0.73752451246246	0.63123774376877
130	0.394688810385311	0.789377620770622	0.605311189614689
131	0.478413803041351	0.956827606082703	0.521586196958649
132	0.936122467255876	0.127755065488249	0.0638775327441244
133	0.974377363326937	0.0512452733461266	0.0256226366730633
134	0.974355791689648	0.0512884166207031	0.0256442083103515
135	0.945987740932708	0.108024518134584	0.0540122590672922
136	0.930498724578345	0.13900255084331	0.069501275421655
137	0.85932837170093	0.28134325659814	0.14067162829907

Multiple Linear Regression - Regression Statistics
Multiple R	0.0666125342567079
R-squared	0.00443722972010109
Adjusted R-squared	-0.00978509556961171
F-TEST (value)	0.311990453720713
F-TEST (DF numerator)	2
F-TEST (DF denominator)	140
p-value	0.732496115637196
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.46210025044929
Sum Squared Residuals	1678.05934018255

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.0227272727272727	NOK
5% type I error level	4	0.0303030303030303	OK
10% type I error level	7	0.053030303030303	OK