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*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 20 Nov 2009 05:55:29 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u.htm/, Retrieved Fri, 20 Nov 2009 14:09:00 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u.htm/},
    year = {2009},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

10144 112 10751 304 11752 794 13808 901 16203 1232 17432 1240 18014 1032 16956 1145 17982 1588 19435 2264 19990 2209 20154 2917 10327 243 9807 558 10862 1238 13743 1502 16458 2000 18466 2146 18810 2066 17361 2046 17411 1952 18517 2771 18525 3278 17859 4000 9499 410 9490 1107 9255 1622 10758 1986 12375 2036 14617 2400 15427 2736 14136 2901 14308 2883 15293 3747 15679 4075 16319 4996 11196 575 11169 999 12158 1411 14251 1493 16237 1846 19706 2899 18960 2372 18537 2856 19103 3468 19691 4193 19464 4440 17264 4186 8957 655 9703 1453 9166 1989 9519 2209 10535 2667 11526 3005 9630 2195 7061 2236 6021 2489 4728 2651 2657 2636 1264 2819

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 13159.5925557422 + 0.373297241848464X[t] -3283.9381552397M1[t] -3305.66197698457M2[t] -3047.64030454197M3[t] -1347.86215250134M4[t] + 471.763379753883M5[t] + 2317.03849281614M6[t] + 2232.07452176467M7[t] + 815.616173691201M8[t] + 881.123473441051M9[t] + 1206.57890403303M10[t] + 861.223542282898M11[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	13159.5925557422	4215.589279	3.1216	0.003074	0.001537
X	0.373297241848464	0.978075	0.3817	0.70443	0.352215
M1	-3283.9381552397	4371.676915	-0.7512	0.456286	0.228143
M2	-3305.66197698457	4024.276034	-0.8214	0.415549	0.207774
M3	-3047.64030454197	3679.519981	-0.8283	0.411703	0.205852
M4	-1347.86215250134	3555.061961	-0.3791	0.706292	0.353146
M5	471.763379753883	3368.594662	0.14	0.889221	0.44461
M6	2317.03849281614	3186.219587	0.7272	0.470707	0.235353
M7	2232.07452176467	3305.84317	0.6752	0.502863	0.251431
M8	815.616173691201	3231.360754	0.2524	0.801828	0.400914
M9	881.123473441051	3128.662936	0.2816	0.779464	0.389732
M10	1206.57890403303	2927.039294	0.4122	0.682054	0.341027
M11	861.223542282898	2889.945811	0.298	0.767011	0.383506

Multiple Linear Regression - Regression Statistics
Multiple R	0.489473303827329
R-squared	0.239584115159641
Adjusted R-squared	0.0454353786046553
F-TEST (value)	1.23402356054883
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.289347308080491
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4514.66194796345
Sum Squared Residuals	957962107.70629

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	10144	9917.46369158953	226.536308410474
2	10751	9967.41294027952	783.587059720477
3	11752	10408.3502612279	1343.64973877213
4	13808	12148.0712181463	1659.92878185372
5	16203	14091.2581374533	2111.74186254666
6	17432	15939.5196284504	1492.48037154961
7	18014	15776.9098310944	2237.09016890555
8	16956	14402.6340713498	2553.36592865015
9	17982	14633.5120492386	3348.48795076144
10	19435	15211.3164153201	4223.6835846799
11	19990	14845.4297052683	5144.57029473169
12	20154	14248.5006102141	5905.49938978588
13	10327	9966.36563027163	360.634369728369
14	9807	10062.2304397090	-255.230439709031
15	10862	10574.0942366086	287.905763391415
16	13743	12372.4228604972	1370.57713950279
17	16458	14377.9504191930	2080.04958080704
18	18466	16277.7269295651	2188.27307043490
19	18810	16162.8991791658	2647.10082083425
20	17361	14738.9748862553	2622.02511374469
21	17411	14769.3922452714	2641.60775472859
22	18517	15400.5781169373	3116.42188306272
23	18525	15244.4844568043	3280.51554319568
24	17859	14652.781523136	3206.21847686399
25	9499	10028.7062696603	-529.706269660324
26	9490	10267.1706254838	-777.170625483837
27	9255	10717.4403774784	-1462.44037747840
28	10758	12553.0987255519	-1795.09872555186
29	12375	14391.3891198995	-2016.38911989951
30	14617	16372.5444289946	-1755.54442899461
31	15427	16413.0083312042	-986.008331204222
32	14136	15058.1440280357	-922.144028035748
33	14308	15116.9319774323	-808.931977432325
34	15293	15764.9162249814	-471.916224981375
35	15679	15542.0023585575	136.997641442459
36	16319	15024.5855760171	1294.41442398292
37	11196	10090.3003145653	1105.69968543468
38	11169	10226.8545233642	942.145476635797
39	12158	10638.6746594484	1519.32534055163
40	14251	12369.0631853206	1881.93681467943
41	16237	14320.4626439483	1916.5373560517
42	19706	16558.819752677	3147.18024732301
43	18960	16277.1281351714	2682.87186482862
44	18537	15041.3456521526	3495.65434784743
45	19103	15335.3108639137	3767.68913608632
46	19691	15931.4067948458	3759.59320515421
47	19464	15678.2558518322	3785.74414816777
48	17264	14722.2148101198	2541.78518988018
49	8957	10120.1640939132	-1163.16409391320
50	9703	10396.3314711634	-693.331471163406
51	9166	10854.4404652368	-1688.44046523678
52	9519	12636.3440104841	-3117.34401048407
53	10535	14626.9396795059	-4091.93967950589
54	11526	16598.3892603129	-5072.38926031292
55	9630	16211.0545233642	-6581.0545233642
56	7061	14809.9013622065	-7748.90136220652
57	6021	14969.8528641440	-8948.85286414403
58	4728	15355.7824479155	-10627.7824479155
59	2657	15004.8276275376	-12347.8276275376
60	1264	14211.9174805130	-12947.9174805130

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.00103366275599287	0.00206732551198574	0.998966337244007
17	0.000178397686323817	0.000356795372647633	0.999821602313676
18	5.79883848532718e-05	0.000115976769706544	0.999942011615147
19	7.71903337609848e-06	1.54380667521970e-05	0.999992280966624
20	8.93142023945345e-07	1.78628404789069e-06	0.999999106857976
21	2.50123982539330e-07	5.00247965078659e-07	0.999999749876017
22	1.73845984559699e-07	3.47691969119398e-07	0.999999826154016
23	2.98466520761952e-07	5.96933041523904e-07	0.99999970153348
24	6.95049547053567e-07	1.39009909410713e-06	0.999999304950453
25	1.53168588365488e-07	3.06337176730976e-07	0.999999846831412
26	2.82517266609663e-08	5.65034533219326e-08	0.999999971748273
27	2.78995018220803e-08	5.57990036441606e-08	0.999999972100498
28	7.63208124319172e-08	1.52641624863834e-07	0.999999923679188
29	7.68390189708954e-07	1.53678037941791e-06	0.99999923160981
30	7.81644607664256e-07	1.56328921532851e-06	0.999999218355392
31	2.71394374472201e-07	5.42788748944403e-07	0.999999728605625
32	7.54328461012669e-08	1.50865692202534e-07	0.999999924567154
33	2.8857189387646e-08	5.7714378775292e-08	0.99999997114281
34	9.19055057678535e-09	1.83811011535707e-08	0.99999999080945
35	2.25882142378441e-09	4.51764284756882e-09	0.999999997741179
36	7.18331622083155e-10	1.43666324416631e-09	0.999999999281668
37	4.07623712871066e-10	8.15247425742133e-10	0.999999999592376
38	3.00874112913403e-10	6.01748225826806e-10	0.999999999699126
39	4.96748352579322e-10	9.93496705158644e-10	0.999999999503252
40	4.02797482465117e-09	8.05594964930234e-09	0.999999995972025
41	2.45484616495969e-06	4.90969232991938e-06	0.999997545153835
42	0.00177377321618241	0.00354754643236481	0.998226226783818
43	0.0541556301945603	0.108311260389121	0.94584436980544
44	0.305739012063059	0.611478024126118	0.694260987936941

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	27	0.93103448275862	NOK
5% type I error level	27	0.93103448275862	NOK
10% type I error level	27	0.93103448275862	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/1021nm1258721721.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/1021nm1258721721.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/1umwh1258721721.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/1umwh1258721721.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/21x6p1258721721.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/21x6p1258721721.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/34c8t1258721721.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/34c8t1258721721.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/4qtrd1258721721.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/4qtrd1258721721.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/57x7w1258721721.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/57x7w1258721721.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/66y9r1258721721.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/66y9r1258721721.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/7yxe71258721721.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/7yxe71258721721.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/812bl1258721721.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/812bl1258721721.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/9mb391258721721.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722528vw0d60g7wqdvs9u/9mb391258721721.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

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')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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