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Data X:
1 41 12 12 13 2 39 11 11 16 3 30 15 14 19 4 31 6 12 15 5 34 13 21 14 6 35 10 12 13 7 39 12 22 19 8 34 14 11 15 9 36 12 10 14 10 37 6 13 15 11 38 10 10 16 12 36 12 8 16 13 38 12 15 16 14 39 11 14 16 15 33 15 10 17 16 32 12 14 15 17 36 10 14 15 18 38 12 11 20 19 39 11 10 18 20 32 12 13 16 21 32 11 7 16 22 31 12 14 16 23 39 13 12 19 24 37 11 14 16 25 39 9 11 17 26 41 13 9 17 27 36 10 11 16 28 33 14 15 15 29 33 12 14 16 30 34 10 13 14 31 31 12 9 15 32 27 8 15 12 33 37 10 10 14 34 34 12 11 16 35 34 12 13 14 36 32 7 8 7 37 29 6 20 10 38 36 12 12 14 39 29 10 10 16 40 35 10 10 16 41 37 10 9 16 42 34 12 14 14 43 38 15 8 20 44 35 10 14 14 45 38 10 11 14 46 37 12 13 11 47 38 13 9 14 48 33 11 11 15 49 36 11 15 16 50 38 12 11 14 51 32 14 10 16 52 32 10 14 14 53 32 12 18 12 54 34 13 14 16 55 32 5 11 9 56 37 6 12 14 57 39 12 13 16 58 29 12 9 16 59 37 11 10 15 60 35 10 15 16 61 30 7 20 12 62 38 12 12 16 63 34 14 12 16 64 31 11 14 14 65 34 12 13 16 66 35 13 11 17 67 36 14 17 18 68 30 11 12 18 69 39 12 13 12 70 35 12 14 16 71 38 8 13 10 72 31 11 15 14 73 34 14 13 18 74 38 14 10 18 75 34 12 11 16 76 39 9 19 17 77 37 13 13 16 78 34 11 17 16 79 28 12 13 13 80 37 12 9 16 81 33 12 11 16 82 37 12 10 20 83 35 12 9 16 84 37 12 12 15 85 32 11 12 15 86 33 10 13 16 87 38 9 13 14 88 33 12 12 16 89 29 12 15 16 90 33 12 22 15 91 31 9 13 12 92 36 15 15 17 93 35 12 13 16 94 32 12 15 15 95 29 12 10 13 96 39 10 11 16 97 37 13 16 16 98 35 9 11 16 99 37 12 11 16 100 32 10 10 14 101 38 14 10 16 102 37 11 16 16 103 36 15 12 20 104 32 11 11 15 105 33 11 16 16 106 40 12 19 13 107 38 12 11 17 108 41 12 16 16 109 36 11 15 16 110 43 7 24 12 111 30 12 14 16 112 31 14 15 16 113 32 11 11 17 114 32 11 15 13 115 37 10 12 12 116 37 13 10 18 117 33 13 14 14 118 34 8 13 14 119 33 11 9 13 120 38 12 15 16 121 33 11 15 13 122 31 13 14 16 123 38 12 11 13 124 37 14 8 16 125 33 13 11 15 126 31 15 11 16 127 39 10 8 15 128 44 11 10 17 129 33 9 11 15 130 35 11 13 12 131 32 10 11 16 132 28 11 20 10 133 40 8 10 16 134 27 11 15 12 135 37 12 12 14 136 32 12 14 15 137 28 9 23 13 138 34 11 14 15 139 30 10 16 11 140 35 8 11 12 141 31 9 12 8 142 32 8 10 16 143 30 9 14 15 144 30 15 12 17 145 31 11 12 16 146 40 8 11 10 147 32 13 12 18 148 36 12 13 13 149 32 12 11 16 150 35 9 19 13 151 38 7 12 10 152 42 13 17 15 153 34 9 9 16 154 35 6 12 16 155 35 8 19 14 156 33 8 18 10 157 36 15 15 17 158 32 6 14 13 159 33 9 11 15 160 34 11 9 16 161 32 8 18 12 162 34 8 16 13
Names of X columns:
t Connected Software Depression Learning

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

Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


Chart options



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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	7.02339996521226	1.893738	3.7088	0.000288	0.000144
t	-0.00404340643189373	0.003147	-1.2848	0.200765	0.100383
Connected	0.104338976442402	0.043364	2.4061	0.017285	0.008642
Software	0.532415969150615	0.068683	7.7518	0	0
Depression	-0.0961299660096881	0.046568	-2.0643	0.040634	0.020317

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	13	16.5326866306101	-3.53268663061012
2	16	15.8836792681523	0.116320731847662
3	19	16.7818590523122	2.21814094768777
4	15	12.2826708319866	2.71732916801343
5	14	15.453386444849	-1.453386444849
6	13	14.8216038014949	-1.82160380149486
7	19	15.3384485790369	3.66155142096308
8	15	16.9349718548008	-1.93497185480082
9	14	16.1709044289622	-2.17090442896218
10	15	12.7883142860399	2.21168571396006
11	16	15.306663630682	0.69333636931803
12	16	16.3510341416859	-0.351034141685879
13	16	15.882758926071	0.117241073929028
14	16	15.5467684929406	0.453231507059447
15	17	17.4308749684955	-0.430874968495462
16	15	15.3407248141306	-0.340724814130569
17	15	14.6892053751671	0.310794624832948
18	20	16.2470617579503	3.75293824204974
19	18	15.9110713248198	2.08892867518016
20	16	15.4206811544127	0.579318845587318
21	16	15.4610015748883	0.538998425111699
22	16	15.2121253990968	0.787874600903195
23	19	16.7674697053741	2.23253029462588
24	16	15.2976564757368	0.702343524263188
25	17	14.7258489819176	2.27415101808244
26	17	17.2524073369923	-0.252407336992302
27	16	14.9371612088772	1.06283879112282
28	15	16.3652448856818	-1.36524488568179
29	16	15.3924995069584	0.607500493041648
30	14	14.5240931046773	-0.524093104677318
31	15	15.6563845712582	-0.656384571258201
32	12	12.5285415863961	-0.528541586396111
33	14	15.1133697127379	-1.11336971273791
34	16	15.7650113492703	0.234988650729651
35	14	15.5687080108191	-1.56870801081908
36	7	13.1745566357977	-6.17455663579775
37	10	11.1715207387718	-1.17152073877178
38	14	15.8613857104179	-1.86138571041789
39	16	14.2543974626073	1.74560253739267
40	16	14.8763879148298	1.12361208517015
41	16	15.1771524272924	0.822847572707555
42	14	15.4442741997861	-1.44427419978613
43	20	18.0316144026338	1.96838559736618
44	14	14.4756944250635	-0.475694425063519
45	14	15.0730578459879	-1.0730578459879
46	11	15.8372474693955	-4.83724746939545
47	14	16.8544788725953	-2.85447887259533
48	15	15.0716487136308	-0.0716487136308204
49	16	14.9961023724874	1.00389762751262
50	14	16.1176727521297	-2.11767275212966
51	16	16.6485573913543	-0.648557391354271
52	14	14.1303302442812	-0.130330244281165
53	12	14.8065989121117	-2.80659891211175
54	16	15.928169291754	0.0718307082459743
55	9	11.7445100772615	-2.74451007726147
56	14	12.6984475561825	1.30155244381749
57	16	16.0014479515294	-0.00144795152942617
58	16	15.3385346447123	0.661465355287733
59	15	15.5406571146593	-0.540657114659284
60	16	14.3148699561435	1.68513004385647
61	12	11.7112339299993	0.288766070000656
62	16	15.9730219089372	0.0269780910627562
63	16	16.616454535037	-0.616454535036973
64	14	14.5098863598067	-0.509886359806653
65	16	15.4474058178623	0.552594182137732
66	17	16.2723772890428	0.727622710957233
67	18	16.3283090321458	1.67169096785424
68	18	14.5816336896561	3.41836631034395
69	12	15.9529270743467	-3.9529270743467
70	16	15.4353977961355	0.564602203864487
71	10	13.7108374084381	-3.71083740843805
72	14	14.3814091423418	-0.381409142341815
73	18	16.4798905047083	1.52010949529165
74	18	17.1815929020751	0.818407097924875
75	16	15.5992316855627	0.400768314437293
76	17	13.7505955258135	3.24940447418653
77	16	16.2443178391574	-0.244317839157363
78	16	14.4779057010583	1.52209429894172
79	13	14.7647642691613	-1.76476426916135
80	16	16.0842915147498	-0.0842915147498195
81	16	15.4706322705289	0.529367729471057
82	20	15.9800747358763	4.01992526412366
83	16	15.8634833425693	0.136516657430665
84	15	15.7797279909932	-0.77972799099318
85	15	14.7215737331987	0.278426266801337
86	16	14.1933233680489	1.80667663195113
87	14	14.1785588746784	-0.178558874678368
88	16	15.346198459496	0.653801540504001
89	16	14.6364092492654	1.36359075073457
90	15	14.3768119865353	0.62318801346467
91	12	13.432012413854	-1.43201241385398
92	17	16.9518997725184	0.0481002274815907
93	16	15.4385294142116	0.561470585788355
94	15	14.9292091464332	0.0707908535668299
95	13	15.0927986407225	-2.09279864072251
96	16	14.9711830944037	1.02881690559628
97	16	15.8750598124904	0.124940187509576
98	16	14.0133244066197	1.98667559338029
99	16	15.8152068605245	0.184793139475537
100	14	14.320766599589	-0.320766599589018
101	16	17.072420928414	-1.07242092841399
102	16	14.7900108420297	1.20998915797027
103	20	17.1958121997966	2.80418780020336
104	15	14.7408789770024	0.25912102299763
105	16	14.3605247169644	1.63947528303556
106	13	15.3308802167509	-2.33088021675091
107	17	15.8871985855117	1.11280141448829
108	16	15.7155222783586	0.284477721641414
109	16	14.7534979865738	1.24650201342624
110	12	12.484993844549	-0.484993844549022
111	16	14.7479232502159	1.25207674978414
112	16	15.8169207925179	0.183079207482089
113	17	14.7044883191153	2.29551168088467
114	13	14.3159250486447	-1.31592504864468
115	12	14.5895504533032	-2.58955045330324
116	18	16.3750148863426	1.62498511365743
117	14	15.5690957101023	-1.56909571010232
118	14	13.1034414003694	0.89655859963056
119	13	14.9768267889857	-1.97682678898574
120	16	15.4501144378583	0.549885562141656
121	13	14.3919601800638	-1.39196018006383
122	16	15.340200725058	0.659799274941953
123	13	15.8225040826014	-2.82250408260142
124	16	17.0673435360574	-1.06734353605741
125	15	15.8251383566762	-0.825138356676234
126	16	16.6772489356608	-0.677248935660766
127	15	15.1342273930441	-0.134227393044076
128	17	15.9920349059554	1.00796509404457
129	15	13.6793008543462	1.3206991456538
130	12	14.756507407081	-2.75650740708096
131	16	14.0992910341906	1.90070896580938
132	10	13.3451379970525	-3.34513799705255
133	16	13.9572140605745	2.04278593942549
134	12	13.7133620377948	-1.7133620377948
135	14	15.5735142629666	-1.5735142629666
136	15	14.8555160423033	0.144483957696678
137	13	11.9716991285628	1.02830087143722
138	15	14.5236912131737	0.476308786826277
139	11	13.3776159998022	-2.37761599980223
140	12	13.3110853673296	-1.31108536732956
141	8	13.325972058269	-5.32597205826898
142	16	13.0861115911483	2.91388840885175
143	15	13.0212863369434	1.97871366305658
144	17	16.4039986774346	0.59600132256541
145	16	14.3746303708426	1.62536962915736
146	10	13.8085198109502	-3.8085198109502
147	18	15.5357144727225	2.46428552727752
148	13	15.3204810368999	-2.32048103689989
149	16	15.0913416567178	0.908658343282232
150	13	13.0340275440837	-0.0340275440837293
151	10	12.9510788907456	-2.95107889074563
152	15	16.0782373749386	-1.07823737493859
153	16	13.8788580084425	2.12114199155747
154	16	12.0935157729721	3.90648422702787
155	14	12.4813945427736	1.51860545722635
156	10	12.3648031494666	-2.36480314946664
157	17	16.6890783544453	0.310921645554682
158	13	11.572065285898	1.42793471410203
159	15	13.5579986613894	1.44200133861061
160	16	14.9153861017205	1.0846138982795
161	12	12.2402471408648	-0.240247140864766
162	13	12.6371416193371	0.362858380662948

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.969709844322742	0.0605803113545167	0.0302901556772583
9	0.938858422589601	0.122283154820799	0.0611415774103994
10	0.909977402687418	0.180045194625164	0.0900225973125822
11	0.885154630376736	0.229690739246528	0.114845369623264
12	0.83250470126517	0.334990597469661	0.16749529873483
13	0.760481499441257	0.479037001117486	0.239518500558743
14	0.677028540182451	0.645942919635099	0.322971459817549
15	0.588281316683998	0.823437366632004	0.411718683316002
16	0.568759106340393	0.862481787319214	0.431240893659607
17	0.494040089661687	0.988080179323373	0.505959910338313
18	0.739218762177385	0.521562475645231	0.260781237822615
19	0.710869725701993	0.578260548596014	0.289130274298007
20	0.662800102389241	0.674399795221518	0.337199897610759
21	0.593679204855879	0.812641590288241	0.406320795144121
22	0.539389069930896	0.921221860138208	0.460610930069104
23	0.510239223430524	0.979521553138953	0.489760776569476
24	0.476113454486542	0.952226908973083	0.523886545513458
25	0.427514025902672	0.855028051805344	0.572485974097328
26	0.376116352046438	0.752232704092876	0.623883647953562
27	0.331354018126028	0.662708036252057	0.668645981873972
28	0.379162652472481	0.758325304944963	0.620837347527519
29	0.329476330922245	0.65895266184449	0.670523669077755
30	0.345482787159732	0.690965574319464	0.654517212840268
31	0.304249433962414	0.608498867924827	0.695750566037586
32	0.308598315807024	0.617196631614048	0.691401684192976
33	0.309316341503562	0.618632683007123	0.690683658496438
34	0.258928809531554	0.517857619063108	0.741071190468446
35	0.257341202234098	0.514682404468195	0.742658797765902
36	0.775187328859277	0.449625342281446	0.224812671140723
37	0.75301265085922	0.493974698281561	0.24698734914078
38	0.736593606835855	0.526812786328291	0.263406393164145
39	0.777608728262549	0.444782543474903	0.222391271737451
40	0.762056601817928	0.475886796364144	0.237943398182072
41	0.732125647762962	0.535748704474076	0.267874352237038
42	0.706420839127544	0.587158321744912	0.293579160872456
43	0.724339844691804	0.551320310616392	0.275660155308196
44	0.681370977472685	0.637258045054629	0.318629022527315
45	0.648162026340831	0.703675947318339	0.351837973659169
46	0.842490524234059	0.315018951531882	0.157509475765941
47	0.857949379804438	0.284101240391125	0.142050620195562
48	0.83324250506875	0.3335149898625	0.16675749493125
49	0.820865313502566	0.358269372994869	0.179134686497434
50	0.814046683576484	0.371906632847031	0.185953316423516
51	0.784461215021879	0.431077569956243	0.215538784978121
52	0.750565969008482	0.498868061983036	0.249434030991518
53	0.771392063558649	0.457215872882701	0.228607936441351
54	0.743041772857131	0.513916454285739	0.256958227142869
55	0.769521561090496	0.460956877819008	0.230478438909504
56	0.766453919214755	0.467092161570489	0.233546080785245
57	0.732211182840492	0.535577634319016	0.267788817159508
58	0.722573719660919	0.554852560678161	0.277426280339081
59	0.686278302065888	0.627443395868224	0.313721697934112
60	0.694895103124759	0.610209793750482	0.305104896875241
61	0.65617694977358	0.68764610045284	0.34382305022642
62	0.614765171110959	0.770469657778082	0.385234828889041
63	0.574034651986569	0.851930696026862	0.425965348013431
64	0.533305696313243	0.933388607373514	0.466694303686757
65	0.499134218053415	0.99826843610683	0.500865781946585
66	0.469369665185797	0.938739330371594	0.530630334814203
67	0.464983874169857	0.929967748339715	0.535016125830142
68	0.5934081402869	0.8131837194262	0.4065918597131
69	0.736058081404852	0.527883837190296	0.263941918595148
70	0.702605171117513	0.594789657764975	0.297394828882488
71	0.81284540104739	0.37430919790522	0.18715459895261
72	0.784025103868223	0.431949792263555	0.215974896131777
73	0.776250266549176	0.447499466901647	0.223749733450824
74	0.751071310187779	0.497857379624442	0.248928689812221
75	0.717456945422132	0.565086109155735	0.282543054577868
76	0.785743371975051	0.428513256049898	0.214256628024949
77	0.751678225631109	0.496643548737781	0.248321774368891
78	0.736493071548397	0.527013856903206	0.263506928451603
79	0.737155883723842	0.525688232552315	0.262844116276158
80	0.701593783523994	0.596812432952011	0.298406216476006
81	0.665411625967862	0.669176748064277	0.334588374032138
82	0.798061073089076	0.403877853821848	0.201938926910924
83	0.764855645289275	0.470288709421451	0.235144354710725
84	0.736948438348502	0.526103123302996	0.263051561651498
85	0.698996315721255	0.602007368557489	0.301003684278745
86	0.690582332489124	0.618835335021753	0.309417667510876
87	0.650208660734445	0.699582678531111	0.349791339265555
88	0.610065474114392	0.779869051771215	0.389934525885608
89	0.585561981744618	0.828876036510764	0.414438018255382
90	0.551241636387189	0.897516727225622	0.448758363612811
91	0.539182721988066	0.921634556023868	0.460817278011934
92	0.495251092877066	0.990502185754131	0.504748907122934
93	0.452161991453553	0.904323982907106	0.547838008546447
94	0.406660203279978	0.813320406559956	0.593339796720022
95	0.432195669183446	0.864391338366893	0.567804330816554
96	0.396143500313443	0.792287000626885	0.603856499686557
97	0.354227685237819	0.708455370475638	0.645772314762181
98	0.350900951564477	0.701801903128955	0.649099048435523
99	0.308388162422466	0.616776324844932	0.691611837577534
100	0.272821193068456	0.545642386136912	0.727178806931544
101	0.248380243603442	0.496760487206883	0.751619756396558
102	0.228620136835185	0.457240273670371	0.771379863164815
103	0.285619692393137	0.571239384786273	0.714380307606864
104	0.24660132439548	0.49320264879096	0.75339867560452
105	0.243568090370241	0.487136180740482	0.756431909629759
106	0.256979190433123	0.513958380866245	0.743020809566877
107	0.236225650874607	0.472451301749214	0.763774349125393
108	0.211987702736543	0.423975405473085	0.788012297263457
109	0.205167976931751	0.410335953863502	0.794832023068249
110	0.197262701768019	0.394525403536039	0.802737298231981
111	0.187922742849242	0.375845485698485	0.812077257150758
112	0.165684733686497	0.331369467372994	0.834315266313503
113	0.196696095617728	0.393392191235456	0.803303904382272
114	0.172208925422615	0.34441785084523	0.827791074577385
115	0.180141955107255	0.36028391021451	0.819858044892745
116	0.193608651244218	0.387217302488435	0.806391348755782
117	0.171303774855684	0.342607549711367	0.828696225144317
118	0.159465403259338	0.318930806518677	0.840534596740662
119	0.155150913725237	0.310301827450474	0.844849086274763
120	0.157533834335845	0.315067668671689	0.842466165664155
121	0.133487990385618	0.266975980771236	0.866512009614382
122	0.124258282631038	0.248516565262077	0.875741717368962
123	0.130152883890031	0.260305767780062	0.869847116109969
124	0.107148877298838	0.214297754597676	0.892851122701162
125	0.0853497599249836	0.170699519849967	0.914650240075016
126	0.0666632843252521	0.133326568650504	0.933336715674748
127	0.0505379369030177	0.101075873806035	0.949462063096982
128	0.0536962493071629	0.107392498614326	0.946303750692837
129	0.0503629144365309	0.100725828873062	0.949637085563469
130	0.050526427543126	0.101052855086252	0.949473572456874
131	0.0548887226808004	0.109777445361601	0.9451112773192
132	0.0675991678454376	0.135198335690875	0.932400832154562
133	0.134981252515545	0.269962505031091	0.865018747484455
134	0.137101940574922	0.274203881149845	0.862898059425078
135	0.109803251077329	0.219606502154659	0.890196748922671
136	0.0855601643026395	0.171120328605279	0.91443983569736
137	0.0802693352951874	0.160538670590375	0.919730664704813
138	0.077725011144846	0.155450022289692	0.922274988855154
139	0.0734500532418814	0.146900106483763	0.926549946758119
140	0.0534633827254418	0.106926765450884	0.946536617274558
141	0.479251881726552	0.958503763453103	0.520748118273448
142	0.493952958926101	0.987905917852203	0.506047041073899
143	0.444299676414524	0.888599352829048	0.555700323585476
144	0.389128890547721	0.778257781095442	0.610871109452279
145	0.320135706765448	0.640271413530896	0.679864293234552
146	0.387166069004995	0.77433213800999	0.612833930995005
147	0.387600305268914	0.775200610537828	0.612399694731086
148	0.41434424225694	0.828688484513879	0.58565575774306
149	0.327845714282246	0.655691428564491	0.672154285717754
150	0.240878804427137	0.481757608854274	0.759121195572863
151	0.702755214480402	0.594489571039197	0.297244785519598
152	0.851140473992671	0.297719052014658	0.148859526007329
153	0.743793300692781	0.512413398614439	0.256206699307219
154	0.601287626699515	0.79742474660097	0.398712373300485

Multiple Linear Regression - Regression Statistics
Multiple R	0.597283647877421
R-squared	0.356747756021759
Adjusted R-squared	0.340359163818492
F-TEST (value)	21.7680537533075
F-TEST (DF numerator)	4
F-TEST (DF denominator)	157
p-value	2.63122856836162e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.83249632340132
Sum Squared Residuals	527.212715718858

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.00680272108843537	OK