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*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: Tue, 15 Dec 2009 07:33:24 -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/Dec/15/t1260887700ram9tcagt02mrf0.htm/, Retrieved Tue, 15 Dec 2009 15:35:12 +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/Dec/15/t1260887700ram9tcagt02mrf0.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 «
27 0 29 0 27 0 26 0 24 0 30 0 26 0 28 0 28 0 24 0 23 0 24 0 24 0 27 0 28 0 25 0 19 0 19 0 19 0 20 0 16 0 22 0 21 0 25 0 29 0 28 0 25 0 26 0 24 0 28 0 28 0 28 0 28 0 32 0 31 0 22 0 29 0 31 0 29 0 32 0 32 0 31 0 29 0 28 0 28 0 29 0 22 0 26 0 24 0 27 0 27 0 23 0 21 0 19 0 17 0 19 0 21 1 13 1 8 1 5 1 10 1 6 1 6 1 8 1 11 1 12 1 13 1 19 1 19 1 18 1 20 1
 
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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 25.5892857142857 -12.9892857142857X[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)25.58928571428570.5860643.663300
X-12.98928571428571.275045-10.187300


Multiple Linear Regression - Regression Statistics
Multiple R0.775017021114863
R-squared0.600651383017755
Adjusted R-squared0.59486372190207
F-TEST (value)103.781367120818
F-TEST (DF numerator)1
F-TEST (DF denominator)69
p-value2.22044604925031e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.38567095560632
Sum Squared Residuals1327.15357142857


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
12725.58928571428571.41071428571430
22925.58928571428573.41071428571429
32725.58928571428571.41071428571429
42625.58928571428570.410714285714286
52425.5892857142857-1.58928571428571
63025.58928571428574.41071428571428
72625.58928571428570.410714285714286
82825.58928571428572.41071428571429
92825.58928571428572.41071428571429
102425.5892857142857-1.58928571428571
112325.5892857142857-2.58928571428571
122425.5892857142857-1.58928571428571
132425.5892857142857-1.58928571428571
142725.58928571428571.41071428571429
152825.58928571428572.41071428571429
162525.5892857142857-0.589285714285714
171925.5892857142857-6.58928571428572
181925.5892857142857-6.58928571428572
191925.5892857142857-6.58928571428572
202025.5892857142857-5.58928571428572
211625.5892857142857-9.58928571428571
222225.5892857142857-3.58928571428571
232125.5892857142857-4.58928571428572
242525.5892857142857-0.589285714285714
252925.58928571428573.41071428571429
262825.58928571428572.41071428571429
272525.5892857142857-0.589285714285714
282625.58928571428570.410714285714286
292425.5892857142857-1.58928571428571
302825.58928571428572.41071428571429
312825.58928571428572.41071428571429
322825.58928571428572.41071428571429
332825.58928571428572.41071428571429
343225.58928571428576.41071428571428
353125.58928571428575.41071428571428
362225.5892857142857-3.58928571428571
372925.58928571428573.41071428571429
383125.58928571428575.41071428571428
392925.58928571428573.41071428571429
403225.58928571428576.41071428571428
413225.58928571428576.41071428571428
423125.58928571428575.41071428571428
432925.58928571428573.41071428571429
442825.58928571428572.41071428571429
452825.58928571428572.41071428571429
462925.58928571428573.41071428571429
472225.5892857142857-3.58928571428571
482625.58928571428570.410714285714286
492425.5892857142857-1.58928571428571
502725.58928571428571.41071428571429
512725.58928571428571.41071428571429
522325.5892857142857-2.58928571428571
532125.5892857142857-4.58928571428572
541925.5892857142857-6.58928571428572
551725.5892857142857-8.58928571428571
561925.5892857142857-6.58928571428572
572112.68.4
581312.60.4
59812.6-4.6
60512.6-7.6
611012.6-2.6
62612.6-6.6
63612.6-6.6
64812.6-4.6
651112.6-1.6
661212.6-0.6
671312.60.4
681912.66.4
691912.66.4
701812.65.4
712012.67.4


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1145106805661580.2290213611323150.885489319433842
60.1096853918897390.2193707837794790.89031460811026
70.05253393565800430.1050678713160090.947466064341996
80.02370477209250810.04740954418501610.976295227907492
90.01006220692808430.02012441385616860.989937793071916
100.01065152045406710.02130304090813420.989348479545933
110.0146708177578640.0293416355157280.985329182242136
120.01033077100449950.02066154200899900.9896692289955
130.006794186899886190.01358837379977240.993205813100114
140.003217969454296190.006435938908592380.996782030545704
150.001819271094464620.003638542188929240.998180728905535
160.0008864352228931640.001772870445786330.999113564777107
170.01035125082965950.02070250165931910.98964874917034
180.03396817830409580.06793635660819160.966031821695904
190.06895906001508130.1379181200301630.931040939984919
200.08733001137076420.1746600227415280.912669988629236
210.2635482982785410.5270965965570820.736451701721459
220.2312849778543620.4625699557087230.768715022145638
230.2229717259911170.4459434519822350.777028274008883
240.1731161779157770.3462323558315540.826883822084223
250.1736098432436040.3472196864872090.826390156756396
260.1524465896909360.3048931793818730.847553410309064
270.1143473711222070.2286947422444130.885652628877794
280.08500345011589890.1700069002317980.914996549884101
290.06263835309002460.1252767061800490.937361646909975
300.05207104721646370.1041420944329270.947928952783536
310.04246441820669140.08492883641338280.957535581793309
320.03398399143859160.06796798287718320.966016008561408
330.02669646376386380.05339292752772770.973303536236136
340.04523434564130800.09046869128261610.954765654358692
350.05585426178762650.1117085235752530.944145738212374
360.04981568769971450.0996313753994290.950184312300286
370.04313092169450510.08626184338901030.956869078305495
380.05177310613890540.1035462122778110.948226893861095
390.04460986562172100.08921973124344190.955390134378279
400.0661225378580170.1322450757160340.933877462141983
410.09709397576000160.1941879515200030.902906024239998
420.1196690676541630.2393381353083260.880330932345837
430.1140343117012420.2280686234024840.885965688298758
440.1000162010658660.2000324021317320.899983798934134
450.09006205630266470.1801241126053290.909937943697335
460.09765315984836960.1953063196967390.90234684015163
470.08063730362587510.1612746072517500.919362696374125
480.06499578129364990.1299915625873000.93500421870635
490.04858819878251750.0971763975650350.951411801217482
500.04699058836988390.09398117673976770.953009411630116
510.0536227549519520.1072455099039040.946377245048048
520.04588825473772770.09177650947545540.954111745262272
530.03996959177753190.07993918355506370.960030408222468
540.03810804107285340.07621608214570680.961891958927147
550.04341643101601520.08683286203203040.956583568983985
560.03690453033693360.07380906067386720.963095469663066
570.06455629083123340.1291125816624670.935443709168767
580.05013263580403310.1002652716080660.949867364195967
590.05268889311380380.1053777862276080.947311106886196
600.09761404432914450.1952280886582890.902385955670856
610.07138312307722890.1427662461544580.928616876922771
620.1179159250796540.2358318501593090.882084074920346
630.2493080322198230.4986160644396460.750691967780177
640.4233018956165620.8466037912331240.576698104383438
650.4984699371649770.9969398743299540.501530062835023
660.6168055906628350.766388818674330.383194409337165


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.0483870967741935NOK
5% type I error level100.161290322580645NOK
10% type I error level250.403225806451613NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/10w3v81260887600.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/10w3v81260887600.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/1eu3h1260887600.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/1eu3h1260887600.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/24jaj1260887600.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/24jaj1260887600.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/3c6cb1260887600.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/3c6cb1260887600.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/4z5yn1260887600.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/4z5yn1260887600.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/5d6cy1260887600.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/5d6cy1260887600.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/6d8zl1260887600.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/6d8zl1260887600.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/7m43a1260887600.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/7m43a1260887600.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/8hwqx1260887600.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/8hwqx1260887600.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/99c661260887600.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/15/t1260887700ram9tcagt02mrf0/99c661260887600.ps (open in new window)


 
Parameters (Session):
 
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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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