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multiple regression

*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, 01 Dec 2010 14:19:57 +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/01/t1291213279iaqq26rq3h9rjnt.htm/, Retrieved Wed, 01 Dec 2010 15:21:32 +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/2010/Dec/01/t1291213279iaqq26rq3h9rjnt.htm/},
    year = {2010},
}
@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 = {2010},
    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 «
0 8 17 2 6 -2 3 23 3 7 -4 3 24 1 4 -4 7 27 1 3 -7 4 31 0 0 -9 -4 40 1 6 -13 -6 47 -1 3 -8 8 43 2 1 -13 2 60 2 6 -15 -1 64 0 5 -15 -2 65 1 7 -15 0 65 1 4 -10 10 55 3 3 -12 3 57 3 6 -11 6 57 1 6 -11 7 57 1 5 -17 -4 65 -2 2 -18 -5 69 1 3 -19 -7 70 1 -2 -22 -10 71 -1 -4 -24 -21 71 -4 0 -24 -22 73 -2 1 -20 -16 68 -1 4 -25 -25 65 -5 -3 -22 -22 57 -4 -3 -17 -22 41 -5 0 -9 -19 21 0 6 -11 -21 21 -2 -1 -13 -31 17 -4 0 -11 -28 9 -6 -1 -9 -23 11 -2 1 -7 -17 6 -2 -4 -3 -12 -2 -2 -1 -3 -14 0 1 -1 -6 -18 5 -2 0 -4 -16 3 0 3 -8 -22 7 -1 0 -1 -9 4 2 8 -2 -10 8 3 8 -2 -10 9 2 8 -1 0 14 3 8 1 3 12 4 11 2 2 12 5 13 2 4 7 5 5 -1 -3 15 4 12 1 0 14 5 13 -1 -1 19 6 9 -8 -7 39 4 11 1 2 12 6 7 2 3 11 6 12 -2 -3 17 3 11 -2 -5 16 5 10 -2 0 25 5 13 -2 -3 24 5 14 -6 -7 28 3 10 -4 -7 25 5 13 -5 -7 31 5 12 -2 -4 24 6 13 -1 -3 24 6 17 -5 -6 33 5 15
 
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 0.663907484037999 -3.94105757478893indicator[t] + 1.00077195481772economie[t] + 1.03740674497135`finaciën`[t] + 0.888119561734234spaarvermogen[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)0.6639074840379990.46211.43670.1564620.078231
indicator-3.941057574788930.030998-127.138900
economie1.000771954817720.02298943.532600
`finaciën`1.037406744971350.1335967.765200
spaarvermogen0.8881195617342340.05912315.021600


Multiple Linear Regression - Regression Statistics
Multiple R0.998682888644061
R-squared0.997367512070446
Adjusted R-squared0.997176058402843
F-TEST (value)5209.44583905243
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.22777002196627
Sum Squared Residuals82.9080574761475


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
11716.07361398292780.926386017072195
22320.87739566512272.12260433487730
32424.0203386395552-0.0203386395551586
42727.1353068970918-0.135306897091804
53132.2543983268314-1.25439832683137
64038.49646195324421.50353804675578
74747.5199761676191-0.519976167619089
84343.1614767725681-0.161476772568108
96061.3027307262776-1.3027307262776
106463.21959695972540.780403040274638
116565.0324708733475-0.0324708733474597
126564.36965609778020.630343902219804
135555.8587817002212-0.858781700221222
145759.3998518512777-2.39985185127774
155756.38629665099930.61370334900072
165756.49894904408280.501050955917235
176563.36022406970461.63977593029535
186970.3008494863241-1.30084948632414
197067.79976534280652.20023465719354
207172.7695695893089-1.76956958930891
217170.08345124791470.916548752085262
227372.0456123447740.954387655226052
236865.98777920469862.01222079530139
246566.3196555732587-1.31965557325874
255758.5362054583165-1.53620545831646
264140.45786952460320.542130475396827
272122.4474758860071-1.44747588600706
282120.03639670386710.963603296132854
291716.72409837705930.275901622940665
3098.88136604025770.118633959742293
311111.9289767681223-0.928976768122315
3265.61089553877960.389104461220395
33-2-2.48511630108480.485116301084798
340-1.374439975806191.37443997580619
3554.22154425610990.778455743890102
3633.08014519131288-0.0801451913128824
3779.13797833138822-2.13797833138822
3844.77778744928401-0.777787449284012
3988.75547981422656-0.755479814226565
4097.718073069255221.28192693074478
411414.8221417876148-0.822141787614841
421213.6441079326642-1.64410793266420
431211.51592427149740.484075728502638
4476.412511687258930.587488312741073
451516.4097109150700-1.40971091506997
461413.45543793665090.544562063349146
471917.82170962944541.1782903705546
483939.1059065575873-0.105906557587346
491211.16567122085220.834328779147765
501112.6659834095522-1.66598340955220
511718.4252421831533-1.42524218315331
521617.6103922017263-1.61039220172633
532525.2786106610176-0.278610661017638
542423.16441435829870.835585641701289
552829.2982651013039-1.29826510130391
562526.1553221268715-1.15532212687145
573129.20826013992611.79173986007386
582422.31292958671811.68707041328189
592422.92512221368381.07487778631616
603332.87339077994660.126609220053433


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.007008777252563260.01401755450512650.992991222747437
90.0793967049220020.1587934098440040.920603295077998
100.3063079868256580.6126159736513170.693692013174342
110.1973115479250240.3946230958500490.802688452074976
120.1804420343735590.3608840687471180.819557965626441
130.1161414862417640.2322829724835290.883858513758236
140.410780302190070.821560604380140.58921969780993
150.3287565263396960.6575130526793930.671243473660304
160.2531878800158540.5063757600317090.746812119984146
170.3245301081405420.6490602162810840.675469891859458
180.2635618948532150.5271237897064310.736438105146785
190.6501847359386060.6996305281227880.349815264061394
200.7361909563868890.5276180872262220.263809043613111
210.676523929042790.646952141914420.32347607095721
220.6084425618270600.7831148763458790.391557438172940
230.6902813712241750.6194372575516510.309718628775825
240.7460215059857320.5079569880285360.253978494014268
250.7788117264980860.4423765470038280.221188273501914
260.7374266032496540.5251467935006920.262573396750346
270.8225437812640870.3549124374718260.177456218735913
280.8049387153156140.3901225693687710.195061284684386
290.748796924946690.5024061501066210.251203075053311
300.7177678012786060.5644643974427870.282232198721394
310.6773193498511760.6453613002976490.322680650148824
320.6253190227266050.749361954546790.374680977273395
330.6119499800709540.7761000398580930.388050019929046
340.6090863666182120.7818272667635750.390913633381788
350.7063014188003070.5873971623993850.293698581199693
360.6825416969565350.6349166060869310.317458303043465
370.7233266724374180.5533466551251640.276673327562582
380.6663790408750160.6672419182499690.333620959124984
390.6486570760161140.7026858479677720.351342923983886
400.7635192205964420.4729615588071150.236480779403558
410.7263935648566870.5472128702866270.273606435143313
420.7011097205218230.5977805589563540.298890279478177
430.6588022073683120.6823955852633760.341197792631688
440.6376721884624490.7246556230751020.362327811537551
450.5622883293434230.8754233413131540.437711670656577
460.5535524695623100.8928950608753790.446447530437690
470.4914770096986680.9829540193973370.508522990301332
480.4003656784476730.8007313568953460.599634321552327
490.4367420230799810.8734840461599620.563257976920019
500.4310786854540620.8621573709081240.568921314545938
510.3673695026709520.7347390053419040.632630497329048
520.3801067480744010.7602134961488010.6198932519256


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0222222222222222OK
10% type I error level10.0222222222222222OK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/107xc91291213181.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/107xc91291213181.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/1iexf1291213181.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/1iexf1291213181.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/2t5e01291213181.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/2t5e01291213181.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/3t5e01291213181.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/3t5e01291213181.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/4t5e01291213181.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/4t5e01291213181.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/5t5e01291213181.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/5t5e01291213181.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/6mew31291213181.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/6mew31291213181.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/7w5d61291213181.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/7w5d61291213181.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/8w5d61291213181.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/8w5d61291213181.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/9w5d61291213181.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213279iaqq26rq3h9rjnt/9w5d61291213181.ps (open in new window)


 
Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 3 ; 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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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')
}
 





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