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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: Sat, 12 Dec 2009 10:09:31 -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/12/t1260637914pa86nl1hgynwiob.htm/, Retrieved Sat, 12 Dec 2009 18:12:06 +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/12/t1260637914pa86nl1hgynwiob.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 «
99.9 98.8 98.6 100.5 107.2 110.4 95.7 96.4 93.7 101.9 106.7 106.2 86.7 81 95.3 94.7 99.3 101 101.8 109.4 96 102.3 91.7 90.7 95.3 96.2 96.6 96.1 107.2 106 108 103.1 98.4 102 103.1 104.7 81.1 86 96.6 92.1 103.7 106.9 106.6 112.6 97.6 101.7 87.6 92 99.4 97.4 98.5 97 105.2 105.4 104.6 102.7 97.5 98.1 108.9 104.5 86.8 87.4 88.9 89.9 110.3 109.8 114.8 111.7 94.6 98.6 92 96.9 93.8 95.1 93.8 97 107.6 112.7 101 102.9 95.4 97.4 96.5 111.4 89.2 87.4 87.1 96.8 110.5 114.1 110.8 110.3 104.2 103.9 88.9 101.6 89.8 94.6 90 95.9 93.9 104.7 91.3 102.8 87.8 98.1 99.7 113.9 73.5 80.9 79.2 95.7 96.9 113.2 95.2 105.9 95.6 108.8 89.7 102.3
 
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
IndProd[t] = + 29.9537315632205 + 0.729251732970066ProdMetal[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)29.95373156322057.8629573.80950.0003390.000169
ProdMetal0.7292517329700660.0808089.024500


Multiple Linear Regression - Regression Statistics
Multiple R0.764233944431027
R-squared0.584053521820605
Adjusted R-squared0.576882030817512
F-TEST (value)81.4410171565031
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value1.20836674000202e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.15279922314939
Sum Squared Residuals1539.97771037716


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
198.8102.805979686931-4.00597968693054
2100.5101.857952434069-1.35795243406910
3110.4108.1295173376122.27048266238835
496.499.743122408456-3.34312240845589
5101.998.28461894251583.61538105748424
6106.2107.764891471127-1.56489147112662
78193.1798568117253-12.1798568117253
894.799.4514217152679-4.75142171526786
9101102.368428647148-1.36842864714813
10109.4104.1915579795735.20844202042671
11102.399.96189792834692.33810207165308
1290.796.8261154765756-6.12611547657563
1396.299.4514217152679-3.25142171526786
1496.1100.399448968129-4.29944896812896
15106108.129517337612-2.12951733761166
16103.1108.712918723988-5.61291872398772
17102101.7121020874750.287897912524922
18104.7105.139585232434-0.439585232434379
198689.0960471070929-3.09604710709292
2092.1100.399448968129-8.29944896812896
21106.9105.5771362722161.32286372778358
22112.6107.6919662978304.90803370217038
23101.7101.1287007010990.571299298900986
249293.8361833713983-1.83618337139835
2597.4102.441353820445-5.04135382044514
2697101.785027260772-4.78502726077208
27105.4106.671013871672-1.27101387167152
28102.7106.233462831889-3.53346283188948
2998.1101.055775527802-2.95577552780202
30104.5109.369245283661-4.86924528366077
3187.493.2527819850223-5.8527819850223
3289.994.7842106242594-4.88421062425944
33109.8110.390197709819-0.590197709818864
34111.7113.671830508184-1.97183050818416
3598.698.9409455021888-0.340945502188824
3696.997.0448909964666-0.144890996466644
3795.198.3575441158128-3.25754411581277
389798.3575441158128-1.35754411581277
39112.7108.4212180308004.27878196920032
40102.9103.608156593197-0.708156593197241
4197.499.5243468885649-2.12434688856487
42111.4100.32652379483211.0734762051681
4387.495.0029861441505-7.60298614415046
4496.893.47155750491333.32844249508668
45114.1110.5360480564133.56395194358712
46110.3110.754823576304-0.454823576303898
47103.9105.941762138701-2.04176213870145
48101.694.78421062425946.81578937574055
4994.695.4405371839325-0.840537183932507
5095.995.58638753052650.313612469473489
51104.798.43046928910986.26953071089022
52102.896.53441478338766.2655852166124
5398.193.98203371799244.11796628200763
54113.9102.66012934033611.2398706596638
5580.983.5537339365204-2.65373393652042
5695.787.71046881444987.9895311855502
57113.2100.6182244880212.5817755119800
58105.999.37849654197096.52150345802914
59108.899.67019723515899.12980276484111
60102.395.36761201063556.9323879893645


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.3395447523133210.6790895046266410.66045524768668
60.1965357766174080.3930715532348150.803464223382592
70.4281496424621600.8562992849243210.57185035753784
80.3124067696057630.6248135392115250.687593230394237
90.209428695295480.418857390590960.79057130470452
100.2552967765073030.5105935530146060.744703223492697
110.2581704083424280.5163408166848560.741829591657572
120.2025170094432490.4050340188864970.797482990556752
130.1413120060027320.2826240120054640.858687993997268
140.1048797073550260.2097594147100510.895120292644974
150.09765250184596360.1953050036919270.902347498154036
160.1495431151963750.2990862303927510.850456884803624
170.1133033323858350.2266066647716700.886696667614165
180.07613648460650740.1522729692130150.923863515393493
190.06309104559247310.1261820911849460.936908954407527
200.09875445492435040.1975089098487010.90124554507565
210.07280793942269630.1456158788453930.927192060577304
220.07532018437225660.1506403687445130.924679815627743
230.05597919861490980.1119583972298200.94402080138509
240.04346565512417770.08693131024835530.956534344875822
250.04135201633096920.08270403266193830.95864798366903
260.03747489644366730.07494979288733450.962525103556333
270.02508623752905430.05017247505810860.974913762470946
280.02104018723940610.04208037447881220.978959812760594
290.01529203447184310.03058406894368610.984707965528157
300.01939708329758680.03879416659517360.980602916702413
310.02093984242444480.04187968484888960.979060157575555
320.02157890625997980.04315781251995950.97842109374002
330.01460456699016650.02920913398033300.985395433009834
340.01275832562260740.02551665124521480.987241674377393
350.009850867017268760.01970173403453750.99014913298273
360.00783847735490210.01567695470980420.992161522645098
370.007664415679298380.01532883135859680.992335584320702
380.006365543744716970.01273108748943390.993634456255283
390.005664191307523620.01132838261504720.994335808692476
400.004488278302595630.008976556605191260.995511721697404
410.004530758690983150.00906151738196630.995469241309017
420.04542916629491750.0908583325898350.954570833705082
430.1702469368814710.3404938737629420.829753063118529
440.1639045697958400.3278091395916810.83609543020416
450.1276075708330080.2552151416660150.872392429166992
460.1734487334144020.3468974668288030.826551266585598
470.5752482938220090.8495034123559820.424751706177991
480.588437824387610.823124351224780.41156217561239
490.7356571521256050.5286856957487910.264342847874395
500.8689908526905310.2620182946189370.131009147309469
510.8491130937538390.3017738124923230.150886906246161
520.7985153479304850.4029693041390310.201484652069515
530.7393859181157740.5212281637684520.260614081884226
540.6600842004998540.6798315990002920.339915799500146
550.8051446909137470.3897106181725050.194855309086253


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0392156862745098NOK
5% type I error level140.274509803921569NOK
10% type I error level190.372549019607843NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/10qutg1260637767.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/10qutg1260637767.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/1gqww1260637767.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/1gqww1260637767.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/2nxaw1260637767.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/2nxaw1260637767.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/3ln7s1260637767.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/3ln7s1260637767.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/4kzng1260637767.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/4kzng1260637767.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/5y8d21260637767.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/5y8d21260637767.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/67cc81260637767.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/67cc81260637767.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/7ot5y1260637767.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/7ot5y1260637767.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/8klwv1260637767.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/8klwv1260637767.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/9zcat1260637767.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/12/t1260637914pa86nl1hgynwiob/9zcat1260637767.ps (open in new window)


 
Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 2 ; 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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