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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: Fri, 18 Dec 2009 07:12:05 -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/18/t1261145594fega8lxqh0jwlmc.htm/, Retrieved Fri, 18 Dec 2009 15:13:25 +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/18/t1261145594fega8lxqh0jwlmc.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 «
100.0 114.1 141.7 93.5 110.3 153.4 88.2 103.9 145 89.2 101.6 137.7 91.4 94.6 148.3 92.5 95.9 152.2 91.4 104.7 169.4 88.2 102.8 168.6 87.1 98.1 161.1 84.9 113.9 174.1 92.5 80.9 179 93.5 95.7 190.6 93.5 113.2 190 91.4 105.9 181.6 90.3 108.8 174.8 91.4 102.3 180.5 93.5 99 196.8 93.5 100.7 193.8 92.5 115.5 197 91.4 100.7 216.3 89.2 109.9 221.4 86.0 114.6 217.9 88.2 85.4 229.7 87.1 100.5 227.4 87.1 114.8 204.2 86.0 116.5 196.6 84.9 112.9 198.8 84.9 102 207.5 86.0 106 190.7 86.0 105.3 201.6 84.9 118.8 210.5 86.0 106.1 223.5 82.8 109.3 223.8 77.4 117.2 231.2 80.6 92.5 244 78.5 104.2 234.7 75.3 112.5 250.2 75.3 122.4 265.7 75.3 113.3 287.6 77.4 100 283.3 78.5 110.7 295.4 76.3 112.8 312.3 73.1 109.8 333.8 68.8 117.3 347.7 65.6 109.1 383.2 69.9 115.9 407.1 82.8 96 413.6 84.9 99.8 362.7 80.6 116.8 321.9 74.2 115.7 239.4 71.0 99.4 191 74.2 94.3 159.7 82.8 91 163.4 86.0 93.2 157.6 86.0 103.1 166.2 82.8 94.1 176.7 78.5 91.8 198.3 79.6 102.7 226.2 87.1 82.6 216.2 89 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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
WRKL(index)[t] = + 92.199660809865 + 0.167067318566415IND[t] -0.0875444282137551GRON[t] -4.59226868346676M1[t] -9.04060415156694M2[t] -10.7862675637739M3[t] -8.53221783711622M4[t] -5.09549250905359M5[t] -4.49506788834226M6[t] -6.2052324845473M7[t] -6.33400974837707M8[t] -8.07746333962857M9[t] -9.49496357315392M10[t] + 1.88043599877322M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)92.19966080986512.904047.14500
IND0.1670673185664150.1463671.14140.25960.1298
GRON-0.08754442821375510.01374-6.371500
M1-4.592268683466764.335642-1.05920.2950430.147522
M2-9.040604151566944.408333-2.05080.0460080.023004
M3-10.78626756377393.933809-2.74190.0086720.004336
M4-8.532217837116223.573799-2.38740.0211320.010566
M5-5.095492509053593.562043-1.43050.1593330.079666
M6-4.495067888342263.590704-1.25190.2169480.108474
M7-6.20523248454734.046778-1.53340.1320330.066016
M8-6.334009748377073.618916-1.75030.0867430.043371
M9-8.077463339628573.563522-2.26670.0281550.014077
M10-9.494963573153924.070397-2.33270.0240940.012047
M111.880435998773223.7746840.49820.6207380.310369


Multiple Linear Regression - Regression Statistics
Multiple R0.758914349095157
R-squared0.575950989262526
Adjusted R-squared0.456111051445414
F-TEST (value)4.80600207037395
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value3.39822078809782e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.42760974112452
Sum Squared Residuals1355.11158508969


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
110094.2647276969375.73527230306293
293.588.15726660818365.34273339181641
388.286.07774555414712.12225444585289
489.288.58661477406250.613385225937554
591.489.92589793309441.47410206690563
692.590.40208679790842.09791320209159
791.488.65635043981122.74364956018878
888.288.2801808132763-0.080180813276266
987.186.40809403636580.691905963634222
1084.986.492179869411-1.59217986941096
1192.591.9253902303990.574609769600989
1293.591.50203517912921.99796482087082
1393.589.88597122750293.61402877249707
1491.484.95341753086356.44658246913653
1590.384.28755145435266.01244854564736
1691.484.95666036951026.44333963048978
1793.586.41508936641957.08491063358052
1893.587.5621617133355.93783828666502
1992.588.04445126162894.45554873837114
2091.483.75347021849077.64652978150933
2189.283.100559374166.09944062583996
228682.7746810366453.22531896335501
2388.288.2386906535105-0.0386906535104934
2487.189.0823233499818-1.98232334998178
2587.188.9101480565739-1.81014805657388
268685.41116468446110.588835315538858
2784.982.87146118334482.02853881665519
2884.982.54284061216892.3571593878311
298688.1185816084883-2.11858160848829
308687.6478248386732-1.64782483867319
3184.987.4139236320123-2.51392363201233
328684.02531385561031.97468614438972
3382.882.79021235530720.00978764469281779
3477.482.0447151696747-4.64471516967472
3580.688.1729832918753-7.57298329187535
3678.589.0613981027171-10.5613981027171
3775.384.4988495260384-9.19884952603839
3875.380.3475418744325-5.04754187443252
3975.375.164342885390.135657114610073
4077.475.57283831643341.82716168356657
4178.579.7378963717703-1.23789637177028
4276.379.2096615246586-2.90966152465861
4373.175.1160897661586-2.0160897661586
4468.875.0234498394057-6.22344983940574
4565.668.8022170343213-3.20221703432133
4669.966.42846273273893.47153726726116
4782.873.9101838818058.88981611819508
4884.977.12061508966427.77938491033579
4980.678.94030349294771.65969650705227
5074.281.5306093020593-7.33060930205928
517181.2988989227655-10.2988989227655
5274.285.441045927825-11.241045927825
5382.888.0025347202276-5.20253472022758
548689.4782651254248-3.4782651254248
558688.669184900389-2.66918490038898
5682.886.117585273217-3.31758527321704
5778.582.0989171998457-3.59891719984567
5879.680.0599611915305-0.459961191530488
5987.188.9527519424102-1.85275194241023
6089.286.43362827850772.76637172149227


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.08831765986649870.1766353197329970.911682340133501
180.03406638474421080.06813276948842160.96593361525579
190.02093411866303080.04186823732606160.97906588133697
200.02540841238772030.05081682477544060.97459158761228
210.01316176135101570.02632352270203140.986838238648984
220.005935216538861830.01187043307772370.994064783461138
230.006997613517813130.01399522703562630.993002386482187
240.01315296872299670.02630593744599330.986847031277003
250.05297472520269750.1059494504053950.947025274797302
260.08005563101618450.1601112620323690.919944368983816
270.08171105521065240.1634221104213050.918288944789348
280.08336899590689380.1667379918137880.916631004093106
290.08337822035383250.1667564407076650.916621779646168
300.07535074832043080.1507014966408620.924649251679569
310.06769326562450660.1353865312490130.932306734375493
320.07643641846254580.1528728369250920.923563581537454
330.1597691750975300.3195383501950600.84023082490247
340.1850469134137650.3700938268275300.814953086586235
350.1682966100859010.3365932201718020.831703389914099
360.278650352697390.557300705394780.72134964730261
370.5578668851288630.8842662297422740.442133114871137
380.4728992212548670.9457984425097330.527100778745133
390.6311244633971780.7377510732056450.368875536602822
400.7875817870244530.4248364259510940.212418212975547
410.758088042844230.4838239143115410.241911957155771
420.6185303383648540.7629393232702920.381469661635146
430.8341064602936490.3317870794127020.165893539706351


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.185185185185185NOK
10% type I error level70.259259259259259NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/10c2aq1261145515.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/10c2aq1261145515.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/1wloh1261145515.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/1wloh1261145515.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/2ftr01261145515.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/2ftr01261145515.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/3kz211261145515.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/3kz211261145515.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/47hov1261145515.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/47hov1261145515.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/5ub9l1261145515.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/5ub9l1261145515.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/6pc0g1261145515.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/6pc0g1261145515.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/74o3m1261145515.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/74o3m1261145515.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/8omix1261145515.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/8omix1261145515.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/99ngr1261145515.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261145594fega8lxqh0jwlmc/99ngr1261145515.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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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