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Regressiemodel 4 opschuivingen

*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, 20 Nov 2009 14:32:43 -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/t125875335387jwjzq7ccxtskr.htm/, Retrieved Fri, 20 Nov 2009 22:42:46 +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/t125875335387jwjzq7ccxtskr.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 documet
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
96.38 108.3 98.30 95.62 93.11 96.96 100.82 113.2 96.38 98.30 95.62 93.11 99.06 105 100.82 96.38 98.30 95.62 94.03 104 99.06 100.82 96.38 98.30 102.07 109.8 94.03 99.06 100.82 96.38 99.31 98.6 102.07 94.03 99.06 100.82 98.64 93.5 99.31 102.07 94.03 99.06 101.82 98.2 98.64 99.31 102.07 94.03 99.14 88 101.82 98.64 99.31 102.07 97.63 85.3 99.14 101.82 98.64 99.31 100.06 96.8 97.63 99.14 101.82 98.64 101.32 98.8 100.06 97.63 99.14 101.82 101.49 110.3 101.32 100.06 97.63 99.14 105.43 111.6 101.49 101.32 100.06 97.63 105.09 111.2 105.43 101.49 101.32 100.06 99.48 106.9 105.09 105.43 101.49 101.32 108.53 117.6 99.48 105.09 105.43 101.49 104.34 97 108.53 99.48 105.09 105.43 106.10 97.3 104.34 108.53 99.48 105.09 107.35 98.4 106.10 104.34 108.53 99.48 103.00 87.6 107.35 106.10 104.34 108.53 104.50 87.4 103.00 107.35 106.10 104.34 105.17 94.7 104.50 103.00 107.35 106.10 104.84 101.5 105.17 104.50 103.00 107.35 106.18 110.4 104.84 105.17 104.50 103.00 108.86 108.4 106.18 104.84 105.17 104.50 107 etc...
 
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
BESTC[t] = -6.92605201264424 + 0.259801879535493INDUSTR[t] + 0.155336451654076Y1[t] + 0.0649152601439141Y2[t] + 0.580833848588579Y3[t] + 0.0285751652133791Y4[t] -2.62125269928172M1[t] + 0.187869465600903M2[t] -2.89674579770143M3[t] -6.45300354254062M4[t] -2.02482601983913M5[t] -2.17130347109709M6[t] + 3.40503068660951M7[t] -0.58488239993721M8[t] + 0.271970079284188M9[t] + 0.552450778154068M10[t] -1.88858950952297M11[t] + 0.00827710547737372t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)-6.9260520126442412.419377-0.55770.5803330.290166
INDUSTR0.2598018795354930.0504565.1498e-064e-06
Y10.1553364516540760.1277931.21550.231660.11583
Y20.06491526014391410.1342930.48340.6315960.315798
Y30.5808338485885790.1219494.76292.8e-051.4e-05
Y40.02857516521337910.1574210.18150.8569240.428462
M1-2.621252699281720.959351-2.73230.009490.004745
M20.1878694656009031.0284260.18270.8560230.428011
M3-2.896745797701431.065543-2.71860.0098250.004913
M4-6.453003542540621.00255-6.436600
M5-2.024826019839131.086113-1.86430.070020.03501
M6-2.171303471097090.954236-2.27540.0286050.014303
M73.405030686609511.3568392.50950.016470.008235
M8-0.584882399937211.429526-0.40910.6847310.342365
M90.2719700792841881.2737980.21350.832070.416035
M100.5524507781540681.559290.35430.7250750.362538
M11-1.888589509522970.950698-1.98650.0542210.02711
t0.008277105477373720.046890.17650.860820.43041


Multiple Linear Regression - Regression Statistics
Multiple R0.985914410283178
R-squared0.972027224404027
Adjusted R-squared0.959513087953198
F-TEST (value)77.6743347991521
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.07109106142283
Sum Squared Residuals43.5949703506755


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
196.3896.926373980974-0.546373980973961
2100.82102.240407944953-1.42040794495348
399.0699.2271092997082-0.167109299708232
494.0394.3955388344205-0.365538834420504
5102.07101.9672891247560.102710875244458
699.3198.94629520098380.363704799016176
798.64100.327220414353-1.68722041435251
8101.82101.8095827881230.0104172118765759
999.1499.10185279973480.0381472002652051
1097.6398.0112482316177-0.381248231617671
11100.0699.98558200271150.0744179972884663
12101.32101.2157322226460.104267777353873
13101.49100.9903057005930.49969429940682
14105.43105.621925591510-0.191925591509872
15105.09103.8660161963021.22398380369764
1699.4899.5385856687715-0.0585856687714791
17108.53108.1547548672770.375245132722899
18104.34103.6213587235480.718641276452006
19106.1105.952340475710.147659524290009
20107.35107.354123429917-0.00412342991744168
21103103.546725557649-0.546725557648704
22104.5104.0914911278450.40850887215464
23105.17104.2822395636210.887760436378853
24104.84105.656298987443-0.816298987443351
25106.18106.0947411209600.0852588790398619
26108.86109.011286867993-0.151286867992541
27107.77106.6034494832481.16655051675178
28102.74102.6599041034480.0800958965522788
29112.63109.3720152711123.25798472888756
30106.26106.0159932163980.244006783601768
31108.86108.5861640843750.273835915625037
32111.38110.6112893592640.768710640735976
33106.85106.7487697143010.101230285699228
34107.86107.6177425485100.242257451489595
35107.94108.638234630358-0.698234630357818
36111.38111.3534085265680.0265914734319444
37111.29111.685694332439-0.395694332439290
38113.72113.826482487517-0.106482487516764
39111.88113.381925865696-1.50192586569553
40109.87109.0504286846550.819571315344856
41113.72114.853770272849-1.13377027284880
42111.71112.780908813621-1.07090881362101
43114.81113.9395604784990.870439521500768
44112.05113.319128074169-1.26912807416897
45111.54111.1326519283160.40734807168427
46110.87111.139518092027-0.269518092026565
47110.87111.133943803310-0.263943803309501
48115.48114.7945602633420.685439736657536
49111.63111.2728848650330.357115134966568
50116.24114.3698971080271.87010289197265
51113.56114.281499155046-0.72149915504565
52106.01106.485542708705-0.475542708705153
53110.45113.052170464006-2.60217046400612
54107.77108.025444045449-0.255444045448937
55108.61108.2147145470630.395285452936694
56108.19107.6958763485260.494123651473859


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.08656576647860960.1731315329572190.91343423352139
220.04061854673787010.08123709347574030.95938145326213
230.01394936269667910.02789872539335810.98605063730332
240.01319206698206900.02638413396413810.98680793301793
250.01002379512124820.02004759024249630.989976204878752
260.007723423065590930.01544684613118190.99227657693441
270.002898299780920580.005796599561841170.99710170021908
280.001431683150098310.002863366300196620.998568316849902
290.2552517549312510.5105035098625020.744748245068749
300.2062684144456460.4125368288912930.793731585554354
310.1248438043122080.2496876086244160.875156195687792
320.1229958226525910.2459916453051820.87700417734741
330.07939645802145790.1587929160429160.920603541978542
340.06126789770766570.1225357954153310.938732102292334
350.06829487477886990.1365897495577400.93170512522113


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.133333333333333NOK
5% type I error level60.4NOK
10% type I error level70.466666666666667NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/109p0k1258752759.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/109p0k1258752759.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/15dc51258752759.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/15dc51258752759.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/2abtq1258752759.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/2abtq1258752759.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/3u1fy1258752759.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/3u1fy1258752759.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/4eqkj1258752759.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/4eqkj1258752759.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/5ri7q1258752759.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/5ri7q1258752759.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/6avwz1258752759.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/6avwz1258752759.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/7wo091258752759.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/7wo091258752759.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/8aqt31258752759.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/8aqt31258752759.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/95ts11258752759.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t125875335387jwjzq7ccxtskr/95ts11258752759.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 1 ; 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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