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multi lineair 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: Fri, 11 Dec 2009 09:54:58 -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/11/t1260550811c52c7pb254mp88d.htm/, Retrieved Fri, 11 Dec 2009 18:00:22 +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/11/t1260550811c52c7pb254mp88d.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 «
17192,4 0 15386,1 0 14287,1 0 17526,6 0 14497 0 14398,3 0 16629,6 0 16670,7 0 16614,8 0 16869,2 0 15663,9 0 16359,9 0 18447,7 0 16889 0 16505 0 18320,9 0 15052,1 0 15699,8 0 18135,3 0 16768,7 0 18883 0 19021 0 18101,9 0 17776,1 0 21489,9 0 17065,3 0 18690 0 18953,1 0 16398,9 0 16895,6 0 18553 0 19270 0 19422,1 0 17579,4 0 18637,3 0 18076,7 0 20438,6 0 18075,2 0 19563 0 19899,2 0 19227,5 0 17789,6 0 19220,8 0 21968,9 0 21131,5 0 19484,6 0 22168,7 1 20866,8 1 22176,2 1 23533,8 1 21479,6 1 24347,7 1 22751,6 1 20328,3 1 23650,4 1 23335,7 1 19614,9 1 18042,3 1 17282,5 1 16847,2 1 18159,5 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
Invoer[t] = + 17815.7891304348 + 3156.55753623188Dummy[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)17815.7891304348293.19670560.763900
Dummy3156.55753623188591.2598295.33872e-061e-06


Multiple Linear Regression - Regression Statistics
Multiple R0.570725274782864
R-squared0.325727339275976
Adjusted R-squared0.314298989094213
F-TEST (value)28.5016939536695
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value1.56749181168259e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1988.55680493992
Sum Squared Residuals233307131.821898


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
117192.417815.7891304347-623.389130434733
215386.117815.7891304348-2429.68913043478
314287.117815.7891304348-3528.68913043478
417526.617815.7891304348-289.189130434785
51449717815.7891304348-3318.78913043478
614398.317815.7891304348-3417.48913043478
716629.617815.7891304348-1186.18913043479
816670.717815.7891304348-1145.08913043478
916614.817815.7891304348-1200.98913043478
1016869.217815.7891304348-946.589130434783
1115663.917815.7891304348-2151.88913043478
1216359.917815.7891304348-1455.88913043478
1318447.717815.7891304348631.910869565217
141688917815.7891304348-926.789130434783
151650517815.7891304348-1310.78913043478
1618320.917815.7891304348505.110869565218
1715052.117815.7891304348-2763.68913043478
1815699.817815.7891304348-2115.98913043478
1918135.317815.7891304348319.510869565216
2016768.717815.7891304348-1047.08913043478
211888317815.78913043481067.21086956522
221902117815.78913043481205.21086956522
2318101.917815.7891304348286.110869565218
2417776.117815.7891304348-39.689130434785
2521489.917815.78913043483674.11086956522
2617065.317815.7891304348-750.489130434784
271869017815.7891304348874.210869565216
2818953.117815.78913043481137.31086956521
2916398.917815.7891304348-1416.88913043478
3016895.617815.7891304348-920.189130434785
311855317815.7891304348737.210869565217
321927017815.78913043481454.21086956522
3319422.117815.78913043481606.31086956521
3417579.417815.7891304348-236.389130434782
3518637.317815.7891304348821.510869565216
3618076.717815.7891304348260.910869565217
3720438.617815.78913043482622.81086956521
3818075.217815.7891304348259.410869565217
391956317815.78913043481747.21086956522
4019899.217815.78913043482083.41086956522
4119227.517815.78913043481411.71086956522
4217789.617815.7891304348-26.189130434785
4319220.817815.78913043481405.01086956522
4421968.917815.78913043484153.11086956522
4521131.517815.78913043483315.71086956522
4619484.617815.78913043481668.81086956521
4722168.720972.34666666671196.35333333333
4820866.820972.3466666667-105.546666666668
4922176.220972.34666666671203.85333333333
5023533.820972.34666666672561.45333333333
5121479.620972.3466666667507.253333333332
5224347.720972.34666666673375.35333333333
5322751.620972.34666666671779.25333333333
5420328.320972.3466666667-644.046666666668
5523650.420972.34666666672678.05333333333
5623335.720972.34666666672363.35333333333
5719614.920972.3466666667-1357.44666666667
5818042.320972.3466666667-2930.04666666667
5917282.520972.3466666667-3689.84666666667
6016847.220972.3466666667-4125.14666666667
6118159.520972.3466666667-2812.84666666667


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.5077334671559770.9845330656880450.492266532844023
60.4406527113860610.8813054227721220.559347288613939
70.3421192307491030.6842384614982070.657880769250897
80.2569380373695420.5138760747390850.743061962630458
90.1836304657144230.3672609314288460.816369534285577
100.1342737118999550.2685474237999110.865726288100045
110.09207401985563720.1841480397112740.907925980144363
120.05959622324228880.1191924464845780.940403776757711
130.1023532379278570.2047064758557140.897646762072143
140.07234720933052430.1446944186610490.927652790669476
150.04876092458158380.09752184916316760.951239075418416
160.06006437416978580.1201287483395720.939935625830214
170.0688366749734020.1376733499468040.931163325026598
180.06079028396064230.1215805679212850.939209716039358
190.06527357783033260.1305471556606650.934726422169667
200.0497381401012170.0994762802024340.950261859898783
210.07317057865526880.1463411573105380.926829421344731
220.0972459184131040.1944918368262080.902754081586896
230.08473490300303620.1694698060060720.915265096996964
240.06824655736197680.1364931147239540.931753442638023
250.2825331046446830.5650662092893650.717466895355317
260.2427024630542940.4854049261085890.757297536945706
270.2186095300534260.4372190601068510.781390469946574
280.2017332271303790.4034664542607580.798266772869621
290.1963982141863860.3927964283727730.803601785813614
300.1790057461027400.3580114922054790.82099425389726
310.1542546366289320.3085092732578640.845745363371068
320.1461646360572280.2923292721144560.853835363942772
330.1390903854650780.2781807709301570.860909614534922
340.1171017727460090.2342035454920170.882898227253991
350.0962471140485110.1924942280970220.903752885951489
360.07848109363469680.1569621872693940.921518906365303
370.0938512412688870.1877024825377740.906148758731113
380.07651017019609410.1530203403921880.923489829803906
390.06647701712494170.1329540342498830.933522982875058
400.060102320240220.120204640480440.93989767975978
410.04713995863022010.09427991726044020.95286004136978
420.04306194463058670.08612388926117340.956938055369413
430.03595178758872700.07190357517745410.964048212411273
440.05849474321874890.1169894864374980.941505256781251
450.06292979259678490.1258595851935700.937070207403215
460.04492440229608550.0898488045921710.955075597703914
470.03073118566574850.0614623713314970.969268814334251
480.01883975987454440.03767951974908880.981160240125456
490.01224450226490760.02448900452981520.987755497735092
500.01397636954105090.02795273908210180.98602363045895
510.008117993022951460.01623598604590290.991882006977049
520.02217181551833180.04434363103666360.977828184481668
530.02613427882794990.05226855765589970.97386572117205
540.01548613320195120.03097226640390250.984513866798049
550.06827471304812150.1365494260962430.931725286951878
560.6856887063049150.628622587390170.314311293695085


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level60.115384615384615NOK
10% type I error level140.269230769230769NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/10she11260550494.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/10she11260550494.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/1jk8h1260550494.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/1jk8h1260550494.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/2giqw1260550494.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/2giqw1260550494.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/3an351260550494.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/3an351260550494.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/4cwai1260550494.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/4cwai1260550494.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/5qyl41260550494.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/5qyl41260550494.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/6q36s1260550494.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/6q36s1260550494.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/7rmyq1260550494.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/7rmyq1260550494.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/8hpgn1260550494.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/8hpgn1260550494.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/96ifg1260550494.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/11/t1260550811c52c7pb254mp88d/96ifg1260550494.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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Software written by Ed van Stee & Patrick Wessa


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