FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 11 Nov 2014 20:29:51 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Nov/11/t14157378051cgtzmi5pvk0tng.htm/, Retrieved Sun, 19 May 2024 19:56:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253727, Retrieved Sun, 19 May 2024 19:56:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-11-11 20:29:51] [92b9176a7d614ba60c8f41dcecd4e71d] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12 41 38 13 12 14 53
11 39 32 16 11 18 83
14 30 35 19 15 11 66
12 31 33 15 6 12 67
21 34 37 14 13 16 76
12 35 29 13 10 18 78
22 39 31 19 12 14 53
11 34 36 15 14 14 80
10 36 35 14 12 15 74
13 37 38 15 9 15 76
10 38 31 16 10 17 79
8 36 34 16 12 19 54
15 38 35 16 12 10 67
14 39 38 16 11 16 54
10 33 37 17 15 18 87
14 32 33 15 12 14 58
14 36 32 15 10 14 75
11 38 38 20 12 17 88
10 39 38 18 11 14 64
13 32 32 16 12 16 57
9.5 32 33 16 11 18 66
14 31 31 16 12 11 68
12 39 38 19 13 14 54
14 37 39 16 11 12 56
11 39 32 17 12 17 86
9 41 32 17 13 9 80
11 36 35 16 10 16 76
15 33 37 15 14 14 69
14 33 33 16 12 15 78
13 34 33 14 10 11 67
9 31 31 15 12 16 80
15 27 32 12 8 13 54
10 37 31 14 10 17 71
11 34 37 16 12 15 84
13 34 30 14 12 14 74
8 32 33 10 7 16 71
20 29 31 10 9 9 63
12 36 33 14 12 15 71
10 29 31 16 10 17 76
10 35 33 16 10 13 69
9 37 32 16 10 15 74
14 34 33 14 12 16 75
8 38 32 20 15 16 54
14 35 33 14 10 12 52
11 38 28 14 10 15 69
13 37 35 11 12 11 68
9 38 39 14 13 15 65
11 33 34 15 11 15 75
15 36 38 16 11 17 74
11 38 32 14 12 13 75
10 32 38 16 14 16 72
14 32 30 14 10 14 67
18 32 33 12 12 11 63
14 34 38 16 13 12 62
11 32 32 9 5 12 63
14.5 37 35 14 6 15 76
13 39 34 16 12 16 74
9 29 34 16 12 15 67
10 37 36 15 11 12 73
15 35 34 16 10 12 70
20 30 28 12 7 8 53
12 38 34 16 12 13 77
12 34 35 16 14 11 80
14 31 35 14 11 14 52
13 34 31 16 12 15 54
11 35 37 17 13 10 80
17 36 35 18 14 11 66
12 30 27 18 11 12 73
13 39 40 12 12 15 63
14 35 37 16 12 15 69
13 38 36 10 8 14 67
15 31 38 14 11 16 54
13 34 39 18 14 15 81
10 38 41 18 14 15 69
11 34 27 16 12 13 84
19 39 30 17 9 12 80
13 37 37 16 13 17 70
17 34 31 16 11 13 69
13 28 31 13 12 15 77
9 37 27 16 12 13 54
11 33 36 16 12 15 79
9 35 37 16 12 15 71
12 37 33 15 12 16 73
12 32 34 15 11 15 72
13 33 31 16 10 14 77
13 38 39 14 9 15 75
12 33 34 16 12 14 69
15 29 32 16 12 13 54
22 33 33 15 12 7 70
13 31 36 12 9 17 73
15 36 32 17 15 13 54
13 35 41 16 12 15 77
15 32 28 15 12 14 82
12.5 29 30 13 12 13 80
11 39 36 16 10 16 80
16 37 35 16 13 12 69
11 35 31 16 9 14 78
11 37 34 16 12 17 81
10 32 36 14 10 15 76
10 38 36 16 14 17 76
16 37 35 16 11 12 73
12 36 37 20 15 16 85
11 32 28 15 11 11 66
16 33 39 16 11 15 79
19 40 32 13 12 9 68
11 38 35 17 12 16 76
16 41 39 16 12 15 71
15 36 35 16 11 10 54
24 43 42 12 7 10 46
14 30 34 16 12 15 85
15 31 33 16 14 11 74
11 32 41 17 11 13 88
15 32 33 13 11 14 38
12 37 34 12 10 18 76
10 37 32 18 13 16 86
14 33 40 14 13 14 54
13 34 40 14 8 14 67
9 33 35 13 11 14 69
15 38 36 16 12 14 90
15 33 37 13 11 12 54
14 31 27 16 13 14 76
11 38 39 13 12 15 89
8 37 38 16 14 15 76
11 36 31 15 13 15 73
11 31 33 16 15 13 79
8 39 32 15 10 17 90
10 44 39 17 11 17 74
11 33 36 15 9 19 81
13 35 33 12 11 15 72
11 32 33 16 10 13 71
20 28 32 10 11 9 66
10 40 37 16 8 15 77
15 27 30 12 11 15 65
12 37 38 14 12 15 74
14 32 29 15 12 16 85
23 28 22 13 9 11 54
14 34 35 15 11 14 63
16 30 35 11 10 11 54
11 35 34 12 8 15 64
12 31 35 11 9 13 69
10 32 34 16 8 15 54
14 30 37 15 9 16 84
12 30 35 17 15 14 86
12 31 23 16 11 15 77
11 40 31 10 8 16 89
12 32 27 18 13 16 76
13 36 36 13 12 11 60
11 32 31 16 12 12 75
19 35 32 13 9 9 73
12 38 39 10 7 16 85
17 42 37 15 13 13 79
9 34 38 16 9 16 71
12 35 39 16 6 12 72
19 38 34 14 8 9 69
18 33 31 10 8 13 78
15 36 32 17 15 13 54
14 32 37 13 6 14 69
11 33 36 15 9 19 81
9 34 32 16 11 13 84
18 32 38 12 8 12 84
16 34 36 13 8 13 69
24 27 26 13 10 10 66
14 31 26 12 8 14 81
20 38 33 17 14 16 82
18 34 39 15 10 10 72
23 24 30 10 8 11 54
12 30 33 14 11 14 78
14 26 25 11 12 12 74
16 34 38 13 12 9 82
18 27 37 16 12 9 73
20 37 31 12 5 11 55
12 36 37 16 12 16 72
12 41 35 12 10 9 78
17 29 25 9 7 13 59
13 36 28 12 12 16 72
9 32 35 15 11 13 78
16 37 33 12 8 9 68
18 30 30 12 9 12 69
10 31 31 14 10 16 67
14 38 37 12 9 11 74
11 36 36 16 12 14 54
9 35 30 11 6 13 67
11 31 36 19 15 15 70
10 38 32 15 12 14 80
11 22 28 8 12 16 89
19 32 36 16 12 13 76
14 36 34 17 11 14 74
12 39 31 12 7 15 87
14 28 28 11 7 13 54
21 32 36 11 5 11 61
13 32 36 14 12 11 38
10 38 40 16 12 14 75
15 32 33 12 3 15 69
16 35 37 16 11 11 62
14 32 32 13 10 15 72
12 37 38 15 12 12 70
19 34 31 16 9 14 79
15 33 37 16 12 14 87
19 33 33 14 9 8 62
13 26 32 16 12 13 77
17 30 30 16 12 9 69
12 24 30 14 10 15 69
11 34 31 11 9 17 75
14 34 32 12 12 13 54
11 33 34 15 8 15 72
13 34 36 15 11 15 74
12 35 37 16 11 14 85
15 35 36 16 12 16 52
14 36 33 11 10 13 70
12 34 33 15 10 16 84
17 34 33 12 12 9 64
11 41 44 12 12 16 84
18 32 39 15 11 11 87
13 30 32 15 8 10 79
17 35 35 16 12 11 67
13 28 25 14 10 15 65
11 33 35 17 11 17 85
12 39 34 14 10 14 83
22 36 35 13 8 8 61
14 36 39 15 12 15 82
12 35 33 13 12 11 76
12 38 36 14 10 16 58
17 33 32 15 12 10 72
9 31 32 12 9 15 72
21 34 36 13 9 9 38
10 32 36 8 6 16 78
11 31 32 14 10 19 54
12 33 34 14 9 12 63
23 34 33 11 9 8 66
13 34 35 12 9 11 70
12 34 30 13 6 14 71
16 33 38 10 10 9 67
9 32 34 16 6 15 58
17 41 33 18 14 13 72
9 34 32 13 10 16 72
14 36 31 11 10 11 70
17 37 30 4 6 12 76
13 36 27 13 12 13 50
11 29 31 16 12 10 72
12 37 30 10 7 11 72
10 27 32 12 8 12 88
19 35 35 12 11 8 53
16 28 28 10 3 12 58
16 35 33 13 6 12 66
14 37 31 15 10 15 82
20 29 35 12 8 11 69
15 32 35 14 9 13 68
23 36 32 10 9 14 44
20 19 21 12 8 10 56
16 21 20 12 9 12 53
14 31 34 11 7 15 70
17 33 32 10 7 13 78
11 36 34 12 6 13 71
13 33 32 16 9 13 72
17 37 33 12 10 12 68
15 34 33 14 11 12 67
21 35 37 16 12 9 75
18 31 32 14 8 9 62
15 37 34 13 11 15 67
8 35 30 4 3 10 83
12 27 30 15 11 14 64
12 34 38 11 12 15 68
22 40 36 11 7 7 62
12 29 32 14 9 14 72

Tables (Output of Computation)





\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Engine error message & 
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  0 (non-NA) cases
Calls: lm -> lm.fit
In addition: Warning messages:
1: In model.matrix.default(mt, mf, contrasts) :
  the response appeared on the right-hand side and was dropped
2: In model.matrix.default(mt, mf, contrasts) :
  problem with term 1 in model.matrix: no columns are assigned
Execution halted

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253727&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Engine error message[/C][C]
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  0 (non-NA) cases
Calls: lm -> lm.fit
In addition: Warning messages:
1: In model.matrix.default(mt, mf, contrasts) :
  the response appeared on the right-hand side and was dropped
2: In model.matrix.default(mt, mf, contrasts) :
  problem with term 1 in model.matrix: no columns are assigned
Execution halted

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253727&T=0


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- ''
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}