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Regressiemodel - no seasonal dummies

*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: Thu, 19 Nov 2009 09:32:08 -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/19/t1258649269rayr01a4fip381c.htm/, Retrieved Thu, 19 Nov 2009 17:48:01 +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/19/t1258649269rayr01a4fip381c.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:
Uitleg in Word document
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
96.96 89.1 93.11 83.3 95.62 97.7 98.30 100.9 96.38 108.3 100.82 113.2 99.06 105 94.03 104 102.07 109.8 99.31 98.6 98.64 93.5 101.82 98.2 99.14 88 97.63 85.3 100.06 96.8 101.32 98.8 101.49 110.3 105.43 111.6 105.09 111.2 99.48 106.9 108.53 117.6 104.34 97 106.10 97.3 107.35 98.4 103.00 87.6 104.50 87.4 105.17 94.7 104.84 101.5 106.18 110.4 108.86 108.4 107.77 109.7 102.74 105.2 112.63 111.1 106.26 96.2 108.86 97.3 111.38 98.9 106.85 91.7 107.86 90.9 107.94 98.8 111.38 111.5 111.29 119 113.72 115.3 111.88 116.3 109.87 113.6 113.72 115.1 111.71 109.7 114.81 97.6 112.05 100.8 111.54 94 110.87 87.2 110.87 102.9 115.48 111.3 111.63 106.6 116.24 108.9 113.56 108.3 106.01 100.5 110.45 104 107.77 89.9 108.61 86.8 108.19 91.2
 
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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
Bestc[t] = + 82.8947144033536 + 0.226989745422757Industr[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)82.89471440335367.8059610.619400
Industr0.2269897454227570.0764512.96910.0043370.002169


Multiple Linear Regression - Regression Statistics
Multiple R0.363231545188491
R-squared0.131937155420019
Adjusted R-squared0.116970554651398
F-TEST (value)8.81543895368974
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.00433737723847472
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.4710305338954
Sum Squared Residuals1736.06615596331


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
196.96103.119500720522-6.15950072052163
293.11101.802960197069-8.69296019706928
395.62105.071612531157-9.45161253115697
498.3105.797979716510-7.4979797165098
596.38107.477703832638-11.0977038326382
6100.82108.589953585210-7.76995358520971
799.06106.728637672743-7.6686376727431
894.03106.501647927320-12.4716479273203
9102.07107.818188450772-5.74818845077234
1099.31105.275903302037-5.96590330203745
1198.64104.118255600381-5.4782556003814
12101.82105.185107403868-3.36510740386836
1399.14102.869812000556-3.72981200055623
1497.63102.256939687915-4.6269396879148
15100.06104.867321760276-4.80732176027649
16101.32105.321301251122-4.00130125112201
17101.49107.931683323484-6.44168332348371
18105.43108.226769992533-2.79676999253328
19105.09108.135974094364-3.04597409436419
2099.48107.159918189046-7.67991818904633
21108.53109.588708465070-1.05870846506983
22104.34104.912719709361-0.572719709361042
23106.1104.9808166329881.11918336701212
24107.35105.2305053529532.11949464704709
25103102.7790161023870.220983897612869
26104.5102.7336181533031.76638184669742
27105.17104.3906432948890.779356705111296
28104.84105.934173563763-1.09417356376345
29106.18107.954382298026-1.77438229802598
30108.86107.5004028071801.35959719281953
31107.77107.79548947623-0.0254894762300594
32102.74106.774035621828-4.03403562182766
33112.63108.1132751198224.51672488017808
34106.26104.7311279130231.52887208697716
35108.86104.9808166329883.87918336701213
36111.38105.3440002256646.03599977433571
37106.85103.7096740586203.14032594137956
38107.86103.5280822622824.33191773771777
39107.94105.3213012511222.61869874887799
40111.38108.2040710179913.17592898200898
41111.29109.9064941086621.38350589133831
42113.72109.0666320505974.65336794940251
43111.88109.2936217960202.58637820397975
44109.87108.6807494833791.1892505166212
45113.72109.0212341015134.69876589848706
46111.71107.795489476233.91451052376994
47114.81105.0489135566159.7610864433853
48112.05105.7752807419686.27471925803248
49111.54104.2317504730937.30824952690723
50110.87102.6882202042188.18177979578197
51110.87106.2519592073554.61804079264469
52115.48108.1586730689067.32132693109354
53111.63107.0918212654204.53817873458049
54116.24107.6138976798928.62610232010815
55113.56107.4777038326386.08229616736181
56106.01105.7071838183410.302816181659311
57110.45106.5016479273203.94835207267966
58107.77103.3010925168594.46890748314052
59108.61102.5974243060496.01257569395107
60108.19103.5961791859094.59382081409094


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.05700432475147590.1140086495029520.942995675248524
60.03266337940487920.06532675880975830.96733662059512
70.01352109024762320.02704218049524630.986478909752377
80.03665134761591420.07330269523182830.963348652384086
90.04475522644696520.08951045289393050.955244773553035
100.03822084076208850.0764416815241770.961779159237911
110.03408129527674080.06816259055348170.96591870472326
120.0586137043924080.1172274087848160.941386295607592
130.05789127492488780.1157825498497760.942108725075112
140.0493273639803590.0986547279607180.95067263601964
150.04998290064427750.0999658012885550.950017099355723
160.06066227091833360.1213245418366670.939337729081666
170.06929592628009670.1385918525601930.930704073719903
180.1252578311719850.2505156623439700.874742168828015
190.1653141256228920.3306282512457840.834685874377108
200.2950464998980270.5900929997960540.704953500101973
210.4255304127617090.8510608255234180.574469587238291
220.5689733349889620.8620533300220770.431026665011038
230.7237497638861450.5525004722277110.276250236113855
240.8350245930036710.3299508139926570.164975406996329
250.8730130326183580.2539739347632840.126986967381642
260.9029444830092360.1941110339815280.0970555169907642
270.9212166200757240.1575667598485520.0787833799242762
280.941533381025050.1169332379499010.0584666189749503
290.9593161931922950.08136761361541020.0406838068077051
300.9673030814992730.06539383700145340.0326969185007267
310.973389262740440.05322147451912170.0266107372595609
320.9971609296214170.005678140757166490.00283907037858325
330.9982463420051320.003507315989735070.00175365799486754
340.998895666827550.002208666344898670.00110433317244933
350.9989944941128640.002011011774271950.00100550588713598
360.9992609211450640.001478157709872470.000739078854936237
370.9992768931608070.001446213678386620.000723106839193309
380.9991516946419430.001696610716114550.000848305358057274
390.999142980936140.001714038127721840.000857019063860918
400.9987697540060020.002460491987996320.00123024599399816
410.9984105020571670.003178995885666970.00158949794283348
420.9976109695594320.004778060881135910.00238903044056795
430.9962576926094230.007484614781154630.00374230739057732
440.996852951668280.006294096663439410.00314704833171970
450.9945707320284230.01085853594315350.00542926797157675
460.9916672174704330.01666556505913320.0083327825295666
470.9964758992735840.007048201452831690.00352410072641585
480.9935702638576730.01285947228465480.00642973614232738
490.9908594132364280.01828117352714360.0091405867635718
500.9930677646446610.01386447071067710.00693223535533857
510.9840687962301590.03186240753968220.0159312037698411
520.9714002549990250.05719949000195050.0285997450009753
530.937176120349570.1256477593008610.0628238796504304
540.9495972860086690.1008054279826620.0504027139913312
550.954889272703450.09022145459310050.0451107272965502


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.274509803921569NOK
5% type I error level210.411764705882353NOK
10% type I error level330.647058823529412NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/10quhy1258648324.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/10quhy1258648324.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/1459p1258648324.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/1459p1258648324.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/2c77l1258648324.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/2c77l1258648324.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/3jxqd1258648324.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/3jxqd1258648324.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/4wuwe1258648324.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/4wuwe1258648324.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/5u4yt1258648324.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/5u4yt1258648324.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/6ib7b1258648324.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/6ib7b1258648324.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/7s2xp1258648324.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/7s2xp1258648324.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/8xg5a1258648324.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/8xg5a1258648324.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/9dl9r1258648324.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/19/t1258649269rayr01a4fip381c/9dl9r1258648324.ps (open in new window)


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