Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2010 » Dec » 08 »

*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: Wed, 08 Dec 2010 18:44:02 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u.htm/, Retrieved Sat, 25 May 2013 05:16:15 +0000

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

System-generated keywords (parent):

t1291115218x381nld3ap8ize7 (pk = 103330)

Estimated Impact

Dataseries X:

» Textfile « » CSV « » Correlation Matrix « » Notched Boxplots «

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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

HPC[t] = + 9330.58695652173 + 107.620600414074M1[t] -635.532091097308M2[t] -287.827639751553M3[t] + 8.74534161490709M4[t] -879.764492753623M5[t] + 75.725672877847M6[t] -309.617494824016M7[t] -141.293995859213M8[t] -196.970496894410M9[t] + 367.186335403727M10[t] + 234.50983436853M11[t] + 11.0098343685301t + 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)	9330.58695652173	136.432397	68.3898	0	0
M1	107.620600414074	162.984839	0.6603	0.5115	0.25575
M2	-635.532091097308	162.916845	-3.901	0.000238	0.000119
M3	-287.827639751553	162.863941	-1.7673	0.082101	0.04105
M4	8.74534161490709	169.407011	0.0516	0.958995	0.479497
M5	-879.764492753623	169.297972	-5.1965	2e-06	1e-06
M6	75.725672877847	169.203414	0.4475	0.656043	0.328022
M7	-309.617494824016	169.123363	-1.8307	0.071949	0.035975
M8	-141.293995859213	169.057838	-0.8358	0.406492	0.203246
M9	-196.970496894410	169.006856	-1.1655	0.248298	0.124149
M10	367.186335403727	168.970432	2.1731	0.033604	0.016802
M11	234.50983436853	168.948573	1.3881	0.170088	0.085044
t	11.0098343685301	1.569122	7.0166	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.846483642463992
R-squared	0.716534556959107
Adjusted R-squared	0.66167027766087
F-TEST (value)	13.0601288511253
F-TEST (DF numerator)	12
F-TEST (DF denominator)	62
p-value	8.07798272717264e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	292.614891203775
Sum Squared Residuals	5308655.42236023

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9700	9449.21739130438	250.782608695625
2	9081	8717.07453416149	363.925465838511
3	9084	9075.78881987578	8.21118012422474
4	9743	9383.37163561077	359.628364389235
5	8587	8505.87163561077	81.128364389235
6	9731	9472.37163561077	258.628364389235
7	9563	9098.03830227743	464.961697722568
8	9998	9277.37163561077	720.628364389235
9	9437	9232.7049689441	204.295031055902
10	10038	9807.87163561077	230.128364389235
11	9918	9686.2049689441	231.795031055901
12	9252	9462.7049689441	-210.704968944098
13	9737	9581.3354037267	155.664596273298
14	9035	8849.19254658385	185.80745341615
15	9133	9207.90683229814	-74.9068322981358
16	9487	9515.48964803313	-28.4896480331258
17	8700	8637.98964803313	62.0103519668743
18	9627	9604.48964803313	22.5103519668743
19	8947	9230.1563146998	-283.156314699792
20	9283	9409.48964803313	-126.489648033126
21	8829	9364.82298136646	-535.822981366459
22	9947	9939.98964803313	7.01035196687427
23	9628	9818.32298136646	-190.322981366459
24	9318	9594.82298136646	-276.822981366459
25	9605	9713.45341614906	-108.453416149063
26	8640	8981.31055900621	-341.310559006211
27	9214	9340.0248447205	-126.024844720497
28	9567	9647.60766045549	-80.6076604554864
29	8547	8770.10766045549	-223.107660455487
30	9185	9736.60766045549	-551.607660455486
31	9470	9362.27432712215	107.725672877847
32	9123	9541.60766045549	-418.607660455486
33	9278	9496.94099378882	-218.940993788820
34	10170	10072.1076604555	97.8923395445137
35	9434	9950.44099378882	-516.44099378882
36	9655	9726.94099378882	-71.9409937888196
37	9429	9845.57142857142	-416.571428571424
38	8739	9113.42857142857	-374.428571428572
39	9552	9472.14285714286	79.8571428571428
40	9687	9779.72567287785	-92.725672877847
41	9019	8902.22567287785	116.774327122153
42	9672	9868.72567287785	-196.725672877847
43	9206	9494.39233954451	-288.392339544514
44	9069	9673.72567287785	-604.725672877847
45	9788	9629.05900621118	158.940993788820
46	10312	10204.2256728778	107.774327122153
47	10105	10082.5590062112	22.4409937888197
48	9863	9859.05900621118	3.94099378881966
49	9656	9977.68944099378	-321.689440993785
50	9295	9245.54658385093	49.4534161490679
51	9946	9604.26086956522	341.739130434782
52	9701	9911.8436853002	-210.843685300208
53	9049	9034.3436853002	14.6563146997924
54	10190	10000.8436853002	189.156314699792
55	9706	9626.51035196687	79.4896480331256
56	9765	9805.8436853002	-40.8436853002078
57	9893	9761.17701863354	131.822981366459
58	9994	10336.3436853002	-342.343685300208
59	10433	10214.6770186335	218.322981366459
60	10073	9991.17701863354	81.8229813664592
61	10112	10109.8074534161	2.19254658385478
62	9266	9377.6645962733	-111.664596273293
63	9820	9736.37888198758	83.6211180124216
64	10097	10043.9616977226	53.0383022774317
65	9115	9166.46169772257	-51.4616977225683
66	10411	10132.9616977226	278.038302277432
67	9678	9758.62836438923	-80.628364389235
68	10408	9937.96169772257	470.038302277432
69	10153	9893.2950310559	259.704968944098
70	10368	10468.4616977226	-100.461697722568
71	10581	10346.7950310559	234.204968944098
72	10597	10123.2950310559	473.704968944098
73	10680	10241.9254658385	438.074534161494
74	9738	9509.78260869565	228.217391304347
75	9556	9868.49689440994	-312.496894409939

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0857950324780305	0.171590064956061	0.91420496752197
17	0.0524991423628487	0.104998284725697	0.947500857637151
18	0.0231043965667466	0.0462087931334933	0.976895603433253
19	0.231495210219770	0.462990420439541	0.76850478978023
20	0.455792536295224	0.911585072590449	0.544207463704775
21	0.504624854768491	0.990750290463018	0.495375145231509
22	0.437677666203558	0.875355332407117	0.562322333796441
23	0.340351871668433	0.680703743336866	0.659648128331567
24	0.310855860227999	0.621711720455999	0.689144139772
25	0.267236336881097	0.534472673762193	0.732763663118903
26	0.206849883256790	0.413699766513581	0.79315011674321
27	0.239916252646907	0.479832505293814	0.760083747353093
28	0.205631454710160	0.411262909420319	0.79436854528984
29	0.153268958573314	0.306537917146629	0.846731041426686
30	0.173008461432075	0.346016922864150	0.826991538567925
31	0.268002998181082	0.536005996362165	0.731997001818918
32	0.261416420975539	0.522832841951078	0.738583579024461
33	0.268458916033421	0.536917832066843	0.731541083966579
34	0.342995129165929	0.685990258331858	0.657004870834071
35	0.35877367472747	0.71754734945494	0.64122632527253
36	0.431905718587352	0.863811437174704	0.568094281412648
37	0.380362945006806	0.760725890013612	0.619637054993194
38	0.329100876838706	0.658201753677411	0.670899123161294
39	0.450505003315907	0.901010006631814	0.549494996684093
40	0.403190199995985	0.80638039999197	0.596809800004015
41	0.485094127735026	0.970188255470051	0.514905872264974
42	0.454317102071593	0.908634204143186	0.545682897928407
43	0.380756271864448	0.761512543728896	0.619243728135552
44	0.606126854145476	0.787746291709047	0.393873145854524
45	0.674996769060583	0.650006461878833	0.325003230939417
46	0.752290768263355	0.49541846347329	0.247709231736645
47	0.728811920081672	0.542376159836656	0.271188079918328
48	0.704138728110715	0.59172254377857	0.295861271889285
49	0.74712682558055	0.5057463488389	0.25287317441945
50	0.699421991351506	0.601156017296988	0.300578008648494
51	0.916224561685615	0.167550876628770	0.0837754383143848
52	0.872687059430543	0.254625881138913	0.127312940569457
53	0.8375866932	0.324826613599999	0.162413306800000
54	0.791320712635809	0.417358574728382	0.208679287364191
55	0.7894838579563	0.421032284087401	0.210516142043701
56	0.775925335602774	0.448149328794452	0.224074664397226
57	0.673058641197513	0.653882717604975	0.326941358802487
58	0.53945230881435	0.921095382371301	0.460547691185650
59	0.416110442244624	0.832220884489249	0.583889557755376

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	1	0.0227272727272727	OK
10% type I error level	1	0.0227272727272727	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/10k1kg1291833833.png (opens in new window)

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/10k1kg1291833833.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/1di5m1291833833.png (opens in new window)

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/1di5m1291833833.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/2di5m1291833833.png (opens in new window)

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/2di5m1291833833.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/3oa5p1291833833.png (opens in new window)

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/3oa5p1291833833.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/4oa5p1291833833.png (opens in new window)

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/4oa5p1291833833.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/5oa5p1291833833.png (opens in new window)

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/5oa5p1291833833.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/6gjma1291833833.png (opens in new window)

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/6gjma1291833833.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/79a3d1291833833.png (opens in new window)

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/79a3d1291833833.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/89a3d1291833833.png (opens in new window)

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/89a3d1291833833.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/99a3d1291833833.png (opens in new window)

http://www.freestatistics.org/blog/date/2010/Dec/08/t12918337307wuvleaeocq2w7u/99a3d1291833833.ps (opens in new window)

Click here to open pdf file.

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}