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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 computationThu, 02 Dec 2010 17:48:16 +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/2010/Dec/02/t1291312010zf6sua4mmw7yh1l.htm/, Retrieved Sun, 05 May 2024 14:38:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104387, Retrieved Sun, 05 May 2024 14:38:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-12-02 17:48:16] [3213ed9efdb724e3c847d204cd8135dd] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
321.61	26.75	  109.3
345.85	22.33	  109.6 
338.60	16.38	  109.3 
345.64	12.77	  108.8 
340.71	11.89	108.60
342.49	13.49	  108.9 
342.65	11.95	  109.5 
348.68	9.88	  109.5 
377.36	13.42	  109.7 
418.05	14.03	  110.2 
423.13	14.01	  110.3 
397.69	14.47	  110.4 
390.80	15.44	  110.5 
408.29	18.1	  111.2 
401.02	17.28	  111.6 
409.24	17.74	  112.1 
439.28	18.05	112.70
459.95	18.41	  113.1 
449.66	18.71	  113.5 
451.14	19.62	  113.8 
460.66	18.88	  114.4 
460.23	18.32	  115 
465.69	18.63	  115.3 
468.01	17.87	  115.4 
486.74	16.77	  115.4 
475.89	16.5	  115.7 
441.52	15.9	  116 
443.63	14.86	  116.5 
451.62	16.42	117.10
451.14	16.36	  117.5 
450.88	15.49	  118 
437.56	14.47	  118.5 
431.18	14.57	  119 
412.02	13.22	  119.8 
407.14	12.23	  120.2 
420.48	12.53	  120.3 
418.92	14.68	  120.5 
403.57	16.45	  121.1 
387.55	16.52	  121.6 
390.02	18.1	  122.3 
384.37	19.39	123.10
370.62	18.22	  123.8 
367.90	17.8	  124.1 
374.91	17.67	  124.4 
365.15	16.87	  124.6 
361.91	17.69	  125 
367.03	18.41	  125.6 
395.19	18.38	  125.9 
409.25	19.37	  126.1 
410.49	20.59	  127.4 
416.58	19.68	  128 
392.21	18.12	  128.7 
374.29	16.32	128.90
369.05	16.21	  129.2 
352.19	14.93	  129.9 
362.85	16.81	  130.4 
395.47	26.54	  131.6 
388.82	33.62	  132.7 
380.39	34.85	  133.5 
381.73	31.54	  133.8 
377.69	26.61	  133.8 
383.04	22.81	  134.6 
363.89	18.53	  134.8 
363.23	18.21	  135 
358.37	18.49	135.20
356.97	18.72	  135.6 
366.87	17.78	  136 
367.57	19.02	  136.2 
355.88	19.3	  136.6 
348.88	19.95	  137.2 
358.77	21.56	  137.4 
360.42	20.41	  137.8 
361.08	17.63	  137.9 
354.57	17.52	  138.1 
353.73	17.65	  138.6 
344.20	17.35	  139.3 
338.34	18.65	139.50
337.21	19.52	  139.7 
340.96	20.88	  140.2 
353.29	20.18	  140.5 
342.67	19.62	  140.9 
345.71	20.19	  141.3 
344.17	20.04	  141.8 
334.92	18.9	  142 
334.81	17.93	  141.9 
329.05	17.24	  142.6 
329.31	18.23	  143.1 
330.25	18.5	  143.6 
341.89	18.44	144.00
367.74	18.17	  144.2 
371.93	17.37	  144.4 
392.79	16.37	  144.4 
377.97	16.43	  144.8 
354.93	15.8	  145.1 
364.40	16.44	  145.7 
374.05	15.09	  145.8 
383.63	13.36	  145.8 
386.56	14.17	  146.2 
381.90	13.75	  146.7 
384.08	13.69	  147.2 
377.29	15.15	147.40
381.54	16.43	  147.5 
385.60	17.23	  148 
385.47	18.04	  148.4 
380.40	16.98	  149 
391.74	16.13	  149.4 
389.57	16.48	  149.5 
384.29	17.2	  149.7 
379.26	16.13	  149.7 
378.44	16.88	  150.3 
376.63	17.44	  150.9 
382.48	17.35	  151.4 
390.89	18.77	151.90
385.04	18.43	  152.2 
387.58	17.33	  152.5 
386.19	16.06	  152.5 
383.78	16.49	  152.9 
383.10	16.77	  153.2 
383.25	16.18	  153.7 
385.19	16.82	  153.6 
387.35	17.93	  153.5 
400.49	17.79	  154.4 
404.53	17.69	  154.9 
396.15	19.46	  155.7 
392.79	20.78	156.30
391.96	19.12	  156.6 
385.04	18.56	  156.7 
383.58	19.56	  157 
387.46	20.19	  157.3 
382.90	22.14	  157.8 
381.04	23.43	  158.3 
377.69	22.25	  158.6 
368.95	23.51	  158.6 
353.87	23.29	  159.1 
347.03	20.54	  159.6 
351.49	19.42	  160 
344.23	17.98	160.20
344.09	19.47	  160.1 
340.51	18.02	  160.3 
323.90	18.45	  160.5 
324.02	18.79	  160.8 
323.11	18.73	  161.2 
324.36	20.12	  161.6 
305.55	19.16	  161.5 
288.59	17.24	  161.3 
289.15	15.07	  161.6 
297.49	14.18	  161.9 
295.94	13.24	  162.2 
308.29	13.39	162.50
299.10	13.97	  162.8 
292.32	12.48	  163 
292.87	12.72	  163.2 
284.11	12.49	  163.4 
288.98	13.8	  163.6 
295.93	13.26	  164 
294.17	11.88	  164 
291.68	10.41	  163.9 
287.07	11.32	  164.3 
287.33	10.75	  164.5 
285.96	12.86	  165 
282.62	15.73	166.20
276.44	16.12	  166.2 
261.31	16.24	  166.2 
256.08	18.75	  166.7 
256.69	20.21	  167.1 
264.74	22.37	  167.9 
310.72	22.19	  168.2 
293.18	24.22	  168.3 
283.07	25.01	  168.3 
284.32	25.21	  168.8 
299.86	27.15	  169.8 
286.39	27.49	  171.2 
279.69	23.45	171.30
275.19	27.23	  171.5 
285.73	29.62	  172.4 
281.59	28.16	  172.8 
274.47	29.41	  172.8 
273.68	32.08	  173.7 
270.00	31.4	  174 
266.01	32.33	  174.1 
271.45	25.28	  174 
265.49	25.95	  175.1 
261.87	27.24	  175.8 
263.03	25.02	  176.2 
260.48	25.66	176.90
272.36	27.55	  177.7 
269.82	26.97	  178 
267.53	24.8	  177.5 
272.39	25.81	  177.5 
283.42	25.03	  178.3 
283.06	20.73	  177.7 
276.16	18.69	  177.4 
275.85	18.52	  176.7 
281.51	19.15	  177.1 
295.50	19.98	  177.8 
294.06	23.64	  178.8 
302.68	25.43	179.80
314.58	25.69	  179.8 
321.18	24.49	  179.9 
313.29	25.75	  180.1 
310.25	26.78	  180.7 
319.14	28.28	  181 
316.56	27.53	  181.3 
319.07	24.79	  181.3 
331.92	27.89	  180.9 
356.86	30.77	  181.7 
358.97	32.88	  183.1 
340.55	30.36	  184.2 
328.18	25.49	183.80
355.68	26.06	  183.5 
356.35	27.91	  183.7 
350.99	28.59	  183.9 
359.77	29.68	  184.6 
378.95	26.88	  185.2 
378.92	29.01	  185 
389.91	29.12	  184.5 
406.11	29.95	  184.3 
413.79	31.4	  185.2 
404.95	31.32	  186.2 
406.67	33.67	  187.4 
403.26	33.71	188.00
383.78	37.63	  189.1 
392.48	35.54	  189.7 
398.09	37.93	  189.4 
400.51	42.08	  189.5 
405.28	41.65	  189.9 
420.46	46.87	  190.9 
439.38	42.23	  191 
442.08	39.09	  190.3 
424.03	42.89	  190.7 
423.35	44.56	  191.8 
434.32	50.93	  193.3 
429.23	50.64	194.60
421.87	47.81	  194.4 
430.66	53.89	  194.5 
424.48	56.37	  195.4 
437.93	61.87	  196.4 
456.05	61.65	  198.8 
469.90	58.19	  199.2 
476.67	54.98	  197.6 
510.10	56.47	  196.8 
549.86	62.36	  198.3 
555.00	59.71	  198.7 
557.09	60.93	  199.8 
610.65	68	201.50
675.39	68.61	  202.5 
596.15	68.29	  202.9 
633.71	72.51	  203.5 
632.33	71.81	  203.9 
598.06	61.97	  202.9 
585.78	57.95	  201.8 
627.83	58.13	  201.5 
629.42	61	  201.8 
631.17	53.4	  202.416 
664.75	57.58	  203.499 
654.90	60.6	  205.352 
679.37	65.1	206.69
666.92	65.1	  207.949 
655.49	68.19	  208.352 
665.30	73.67	  208.299 
665.41	70.13	  207.917 
712.65	76.91	  208.49 
754.60	82.15	  208.936 
806.25	91.27	  210.177 
803.20	89.43	  210.036 
889.60	90.82	  211.08 
922.30	93.75	  211.693 
968.43	101.84	  213.528 
909.70	109.05	214.82
890.51	122.77	  216.632 
889.49	131.52	  218.815 
939.77	132.55	  219.964 
838.31	114.57	  219.086 
829.93	99.29	  218.783 
806.62	72.69	  216.573 
760.86	54.04	  212.425 
822.00	41.53	  210.228 
859.19	43.91	  211.143 
943.16	41.76	  212.193 
924.27	46.95	  212.709 
889.50	50.28	213.24
930.20	58.1	  213.856 
945.67	69.13	  215.693 
934.23	64.65	  215.351 
949.67	71.63	  215.834 
996.59	68.38	  215.969 
1043.16	74.08	  216.177 
1127.04	77.56	  216.33 
1126.22	74.88	  215.949 
1116.51	77.09	  216.687 
1095.41	74.7	  216.741 
1113.34	79.3	  217.631 
1148.69	84.19	218.01
1205.43	75.56	  218.178 
1232.92	74.73	  217.965 
1192.97	74.49	  218.011 
1215.81	75.93	  218.312 
1270.98	76.14	  218.439 
1342.02	81.72	  218.711

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104387&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104387&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104387&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,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$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$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')
}