Home » date » 2009 » Nov » 19 »

ws 7

*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: Thu, 19 Nov 2009 11:07:30 -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/19/t12586541506cjqdoxspnvq363.htm/, Retrieved Mon, 20 May 2013 03:21:22 +0000

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

System-generated keywords (parent):

t1258035301gehccu34anmb0m8 (pk = 56008)

Estimated Impact

Dataseries X:

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

8,3	98,6	8,2	8,7
8,5	96,5	8,3	8,2
8,6	95,9	8,5	8,3
8,5	103,7	8,6	8,5
8,2	103,1	8,5	8,6
8,1	103,7	8,2	8,5
7,9	112,1	8,1	8,2
8,6	86,9	7,9	8,1
8,7	95	8,6	7,9
8,7	111,8	8,7	8,6
8,5	108,8	8,7	8,7
8,4	109,3	8,5	8,7
8,5	101,4	8,4	8,5
8,7	100,5	8,5	8,4
8,7	100,7	8,7	8,5
8,6	113,5	8,7	8,7
8,5	106,1	8,6	8,7
8,3	111,6	8,5	8,6
8	114,9	8,3	8,5
8,2	88,6	8	8,3
8,1	99,5	8,2	8
8,1	115,1	8,1	8,2
8	118	8,1	8,1
7,9	111,4	8	8,1
7,9	107,3	7,9	8
8	105,3	7,9	7,9
8	105,3	8	7,9
7,9	117,9	8	8
8	110,2	7,9	8
7,7	112,4	8	7,9
7,2	117,5	7,7	8
7,5	93	7,2	7,7
7,3	103,5	7,5	7,2
7	116,3	7,3	7,5
7	120	7	7,3
7	114,3	7	7
7,2	104,7	7	7
7,3	109,8	7,2	7
7,1	112,6	7,3	7,2
6,8	114,4	7,1	7,3
6,4	115,7	6,8	7,1
6,1	114,7	6,4	6,8
6,5	118,4	6,1	6,4
7,7	94,9	6,5	6,1
7,9	103,8	7,7	6,5
7,5	115,1	7,9	7,7
6,9	113,7	7,5	7,9
6,6	104	6,9	7,5
6,9	94,3	6,6	6,9
7,7	92,5	6,9	6,6
8	93,2	7,7	6,9
8	104,7	8	7,7
7,7	94	8	8
7,3	98,1	7,7	8
7,4	102,7	7,3	7,7
8,1	82,4	7,4	7,3

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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 4.14999041251723 -0.0152438597880977X[t] + 1.34171092585125Y1[t] -0.632193949965465Y2[t] + 0.0835740790049867M1[t] + 0.0525149196902091M2[t] -0.176803720858912M3[t] -0.0232878832478820M4[t] -0.105116917657922M5[t] -0.129479933541223M6[t] + 0.0778523659487016M7[t] + 0.310812223645426M8[t] -0.447340912981457M9[t] -0.0193029508698561M10[t] + 0.00728265666300871M11[t] -0.00840207262544193t + 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)	4.14999041251723	0.990631	4.1892	0.00015	7.5e-05
X	-0.0152438597880977	0.004999	-3.0491	0.004058	0.002029
Y1	1.34171092585125	0.116031	11.5634	0	0
Y2	-0.632193949965465	0.118245	-5.3465	4e-06	2e-06
M1	0.0835740790049867	0.126953	0.6583	0.514113	0.257056
M2	0.0525149196902091	0.13346	0.3935	0.696049	0.348025
M3	-0.176803720858912	0.134934	-1.3103	0.197569	0.098784
M4	-0.0232878832478820	0.123139	-0.1891	0.850957	0.425478
M5	-0.105116917657922	0.11934	-0.8808	0.38368	0.19184
M6	-0.129479933541223	0.117649	-1.1006	0.277666	0.138833
M7	0.0778523659487016	0.118305	0.6581	0.514266	0.257133
M8	0.310812223645426	0.161086	1.9295	0.060786	0.030393
M9	-0.447340912981457	0.161306	-2.7732	0.00839	0.004195
M10	-0.0193029508698561	0.13103	-0.1473	0.883622	0.441811
M11	0.00728265666300871	0.128388	0.0567	0.955048	0.477524
t	-0.00840207262544193	0.002576	-3.2615	0.002269	0.001135

Multiple Linear Regression - Regression Statistics
Multiple R	0.974568997085734
R-squared	0.949784730080693
Adjusted R-squared	0.930954003860953
F-TEST (value)	50.4380297922358
F-TEST (DF numerator)	15
F-TEST (DF denominator)	40
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.174779636784900
Sum Squared Residuals	1.22191685738646

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8.3	8.22406007107107	0.0759399289289281
2	8.5	8.6668790122537	-0.166879012253704
3	8.6	8.6434274051257	-0.0434274051257002
4	8.5	8.67737136635616	-0.177371366356160
5	8.2	8.39889608761187	-0.198896087611866
6	8.1	8.01769080047143	0.0823091995285654
7	7.9	8.14405969752041	-0.244059697520411
8	8.6	8.54763995907805	0.0523600409219463
9	8.7	8.7232459236311	-0.0232459236311051
10	8.7	8.57842029628652	0.121579703713478
11	8.5	8.5791160155617	-0.0791160155616911
12	8.4	8.28746717120894	0.112532828791058
13	8.5	8.47533336732243	0.0246666326775733
14	8.7	8.64698209677317	0.0530179032268333
15	8.7	8.61133540181469	0.0886645981853136
16	8.6	8.43488897151953	0.165111028480468
17	8.5	8.32329133433085	0.176708665669152
18	8.3	8.13573331939899	0.164266680601011
19	8	8.07923601878905	-0.0792360187890467
20	8.2	8.42863282852502	-0.228632828525016
21	8.1	7.95391991774231	0.146080082257685
22	8.1	7.87514171195593	0.224858288044067
23	8	7.91233744847442	0.0876625515255819
24	7.9	7.86309110120229	0.0369088987977128
25	7.9	7.92981123512445	-0.0298112351244543
26	8	7.98405711775698	0.0159428822430233
27	8	7.88050749716754	0.119492502832462
28	7.9	7.77032923382655	0.129670766173451
29	8	7.6633047545743	0.336695245425706
30	7.7	7.7943936621134	-0.0943936621134074
31	7.2	7.44984753130667	-0.249847531306671
32	7.5	7.56668260325036	-0.0666826032503617
33	7.3	7.35867711896112	-0.0586771189611185
34	7	7.12519123299974	-0.125191232999737
35	7	6.81089799892892	0.189102001071084
36	7	7.07176145542226	-0.071761455422262
37	7.2	7.29327451576754	-0.0932745157675443
38	7.3	7.44441178407828	-0.144411784078277
39	7.1	7.17174056608907	-0.0717405660890719
40	6.8	6.95785380328929	-0.157853803289287
41	6.4	6.571731190767	-0.171731190766996
42	6.1	6.20718377669549	-0.107183776695492
43	6.5	6.20007602457482	0.299923975425178
44	7.7	7.50920706999654	0.190792930003459
45	7.9	7.96415703966546	-0.0641570396654613
46	7.5	7.72124675875781	-0.221246758757809
47	6.9	7.09764853703497	-0.197648537034974
48	6.6	6.67768027216651	-0.0776802721665084
49	6.9	6.8775208107145	0.0224791892854972
50	7.7	7.45766998913787	0.242330010862125
51	8	8.092989129803	-0.0929891298030035
52	8	7.95955662500847	0.0404433749915284
53	7.7	7.842776632716	-0.142776632715995
54	7.3	7.34499844132068	-0.0449984413206777
55	7.4	7.12678072780905	0.273219272190951
56	8.1	8.04783753915003	0.0521624608499723

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.0579517813761658	0.115903562752332	0.942048218623834
20	0.633095328491959	0.733809343016082	0.366904671508041
21	0.489142159225657	0.978284318451313	0.510857840774343
22	0.378595869534654	0.757191739069307	0.621404130465346
23	0.26144365379909	0.52288730759818	0.73855634620091
24	0.166170751466174	0.332341502932348	0.833829248533826
25	0.123668340175387	0.247336680350774	0.876331659824613
26	0.0702743195039103	0.140548639007821	0.92972568049609
27	0.04308655788192	0.08617311576384	0.95691344211808
28	0.0300358848603494	0.0600717697206987	0.96996411513965
29	0.370231502233012	0.740463004466024	0.629768497766988
30	0.407682545058558	0.815365090117116	0.592317454941442
31	0.532341696441734	0.935316607116532	0.467658303558266
32	0.443387063382272	0.886774126764544	0.556612936617728
33	0.361829756297596	0.723659512595192	0.638170243702404
34	0.283028299628391	0.566056599256783	0.716971700371609
35	0.579298503490978	0.841402993018044	0.420701496509022
36	0.451588126053134	0.903176252106268	0.548411873946866
37	0.362616537448729	0.725233074897457	0.637383462551271

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	2	0.105263157894737	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/10ud4l1258654045.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/10ud4l1258654045.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/1ru1m1258654045.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/1ru1m1258654045.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/2il5v1258654045.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/2il5v1258654045.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/3acka1258654045.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/3acka1258654045.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/4z30b1258654045.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/4z30b1258654045.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/5ad9t1258654045.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/5ad9t1258654045.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/6uip91258654045.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/6uip91258654045.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/7qp1o1258654045.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/7qp1o1258654045.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/8uw9z1258654045.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/8uw9z1258654045.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/9lzwf1258654045.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/9lzwf1258654045.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')
}