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WS 7.3

*Unverified author*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Fri, 20 Nov 2009 08:50:20 -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/20/t1258732264ut062q0m9xjbesn.htm/, Retrieved Fri, 20 Nov 2009 16:51:16 +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/2009/Nov/20/t1258732264ut062q0m9xjbesn.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
9.9 8.2 9.8 8 9.3 7.5 8.3 6.8 8 6.5 8.5 6.6 10.4 7.6 11.1 8 10.9 8.1 10 7.7 9.2 7.5 9.2 7.6 9.5 7.8 9.6 7.8 9.5 7.8 9.1 7.5 8.9 7.5 9 7.1 10.1 7.5 10.3 7.5 10.2 7.6 9.6 7.7 9.2 7.7 9.3 7.9 9.4 8.1 9.4 8.2 9.2 8.2 9 8.2 9 7.9 9 7.3 9.8 6.9 10 6.6 9.8 6.7 9.3 6.9 9 7 9 7.1 9.1 7.2 9.1 7.1 9.1 6.9 9.2 7 8.8 6.8 8.3 6.4 8.4 6.7 8.1 6.6 7.7 6.4 7.9 6.3 7.9 6.2 8 6.5 7.9 6.8 7.6 6.8 7.1 6.4 6.8 6.1 6.5 5.8 6.9 6.1 8.2 7.2 8.7 7.3 8.3 6.9 7.9 6.1 7.5 5.8 7.8 6.2
 
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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
WLMan[t] = + 3.06136906151439 + 0.483974470410013WLVrouw[t] + 0.259166439019543M1[t] + 0.253554573223732M2[t] + 0.164737601509922M3[t] + 0.104318076837112M4[t] + 0.00582161571510191M5[t] -0.237226165346313M6[t] -0.255209948593140M7[t] -0.355693644920158M8[t] -0.284510616633968M9[t] -0.266212183673976M10[t] -0.176952218938585M11[t] -0.00534966597958625t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)3.061369061514390.9948523.07720.0035150.001757
WLVrouw0.4839744704100130.0959145.04598e-064e-06
M10.2591664390195430.2506771.03390.3066040.153302
M20.2535545732237320.2502781.01310.3163150.158158
M30.1647376015099220.2506020.65740.5142210.25711
M40.1043180768371120.2543790.41010.6836450.341822
M50.005821615715101910.2584650.02250.9821280.491064
M6-0.2372261653463130.255006-0.93030.3570830.178542
M7-0.2552099485931400.253746-1.00580.3197890.159895
M8-0.3556936449201580.260746-1.36410.1791610.089581
M9-0.2845106166339680.255123-1.11520.2705620.135281
M10-0.2662121836739760.249265-1.0680.2910990.14555
M11-0.1769522189385850.248861-0.7110.4806440.240322
t-0.005349665979586250.004964-1.07780.2867620.143381


Multiple Linear Regression - Regression Statistics
Multiple R0.855373338223595
R-squared0.731663547743777
Adjusted R-squared0.655829332975714
F-TEST (value)9.64819837564813
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value3.1079736562134e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.392871359511733
Sum Squared Residuals7.10000363573149


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
18.28.106533091613490.0934669083865133
288.04717411279708-0.0471741127970784
37.57.71102023989868-0.211020239898676
46.87.16127657883627-0.361276578836268
56.56.91223811061167-0.412238110611666
66.66.90582789877567-0.305827898775672
77.67.80204594332828-0.202045943328284
888.03499471030869-0.034994710308689
98.18.004033178533290.0959668214667092
107.77.581404922144680.118595077855316
117.57.278135644572480.221864355427522
127.67.449738197531480.150261802468523
137.87.84874731169444-0.0487473116944376
147.87.88618322696004-0.0861832269600417
157.87.743619142225640.0563808577743558
167.57.484260163409240.0157398365907572
177.57.283619142225640.216380857774356
187.17.083619142225640.0163808577743564
197.57.59265761045024-0.0926576104502444
207.57.58361914222564-0.0836191422256437
217.67.60105505749125-0.00105505749124558
227.77.323619142225640.376380857774357
237.77.213939652817440.486060347182557
247.97.433939652817440.466060347182557
258.17.73615387289840.363846127101598
268.27.7251923411230.474807658876995
278.27.53423080934760.665769190652395
288.27.37166672461320.828333275386793
297.97.267820597511610.63217940248839
307.37.019423150470610.280576849529392
316.97.3832692775722-0.483269277572206
326.67.3742308093476-0.774230809347605
336.77.3432692775722-0.643269277572206
346.97.1142308093476-0.214230809347605
3577.05294876698041-0.052948766980406
367.17.2245513199394-0.124551319939405
377.27.52676554002036-0.326765540020362
387.17.51580400824496-0.415804008244965
396.97.42163737055157-0.521637370551568
4077.40426562694017-0.404265626940174
416.87.10682971167457-0.306829711674573
426.46.61644502942856-0.216445029428564
436.76.641509027243150.0584909727568479
446.66.390483323813540.209516676186455
456.46.262726897956140.137273102043858
466.36.37247055901855-0.0724705590185516
476.26.45638085777436-0.256380857774356
486.56.67638085777436-0.176380857774356
496.86.88180018377331-0.0818001837733118
506.86.725646310874910.0743536891250899
516.46.38949243797650.0105075620234931
526.16.17853090620111-0.0785309062011079
535.85.9294924379765-0.129492437976507
546.15.874684779099510.225315220900489
557.26.480518141406110.719481858593886
567.36.616672014304520.683327985695483
576.96.488915588447120.411084411552884
586.16.30827456726352-0.208274567263517
595.86.19859507785532-0.398595077855316
606.26.51538997193732-0.315389971937318


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.005133560969956040.01026712193991210.994866439030044
180.0006651415262557120.001330283052511420.999334858473744
190.0003434037239602220.0006868074479204440.99965659627604
200.0001445203188261060.0002890406376522120.999855479681174
212.25283597051782e-054.50567194103564e-050.999977471640295
222.96577437613986e-055.93154875227972e-050.999970342256239
237.21257982427057e-061.44251596485411e-050.999992787420176
241.84097440275898e-063.68194880551796e-060.999998159025597
258.53108423484873e-071.70621684696975e-060.999999146891577
263.58514114687643e-067.17028229375285e-060.999996414858853
275.04561291695984e-050.0001009122583391970.99994954387083
280.0007934780659069390.001586956131813880.999206521934093
290.002398208828175060.004796417656350120.997601791171825
300.003234091783073380.006468183566146750.996765908216927
310.02551199740749220.05102399481498450.974488002592508
320.2933389482771320.5866778965542640.706661051722868
330.5785567586154240.8428864827691510.421443241384576
340.5583231250118330.8833537499763330.441676874988167
350.7467214975799590.5065570048400820.253278502420041
360.8561011552735150.287797689452970.143898844726485
370.8092059909939580.3815880180120840.190794009006042
380.7723095834868570.4553808330262850.227690416513143
390.7954536889844190.4090926220311620.204546311015581
400.802295044815930.3954099103681380.197704955184069
410.7181709318704970.5636581362590060.281829068129503
420.7033992194929430.5932015610141140.296600780507057
430.9816050464818120.03678990703637550.0183949535181878


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level130.481481481481481NOK
5% type I error level150.555555555555556NOK
10% type I error level160.592592592592593NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/10dwvd1258732215.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/10dwvd1258732215.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/16xsq1258732215.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/16xsq1258732215.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/2pqop1258732215.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/2pqop1258732215.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/3eu9g1258732215.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/3eu9g1258732215.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/4plx71258732215.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/4plx71258732215.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/54p911258732215.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/54p911258732215.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/66pjj1258732215.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/66pjj1258732215.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/7a25j1258732215.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/7a25j1258732215.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/8ga0a1258732215.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/8ga0a1258732215.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/9eixo1258732215.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258732264ut062q0m9xjbesn/9eixo1258732215.ps (open in new window)


 
Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 2 ; 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')
}
 





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Software written by Ed van Stee & Patrick Wessa


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