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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, 18 Dec 2010 12:29:41 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9.htm/, Retrieved Sat, 18 Dec 2010 13:27:44 +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/2010/Dec/18/t1292675261gz8vlo0mvr909f9.htm/},
    year = {2010},
}
@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 = {2010},
    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 «
102.8 112.5 116.7 116.1 98.1 113 112.5 107.5 113.9 126.4 113 116.7 80.9 114.1 126.4 112.5 95.7 112.5 114.1 113 113.2 112.4 112.5 126.4 105.9 113.1 112.4 114.1 108.8 116.3 113.1 112.5 102.3 111.7 116.3 112.4 99 118.8 111.7 113.1 100.7 116.5 118.8 116.3 115.5 125.1 116.5 111.7 100.7 113.1 125.1 118.8 109.9 119.6 113.1 116.5 114.6 114.4 119.6 125.1 85.4 114 114.4 113.1 100.5 117.8 114 119.6 114.8 117 117.8 114.4 116.5 120.9 117 114 112.9 115 120.9 117.8 102 117.3 115 117 106 119.4 117.3 120.9 105.3 114.9 119.4 115 118.8 125.8 114.9 117.3 106.1 117.6 125.8 119.4 109.3 117.6 117.6 114.9 117.2 114.9 117.6 125.8 92.5 121.9 114.9 117.6 104.2 117 121.9 117.6 112.5 106.4 117 114.9 122.4 110.5 106.4 121.9 113.3 113.6 110.5 117 100 114.2 113.6 106.4 110.7 125.4 114.2 110.5 112.8 124.6 125.4 113.6 109.8 120.2 124.6 114.2 117.3 120.8 120.2 125.4 109.1 111.4 120.8 124.6 115.9 124.1 111.4 120.2 96 120.2 124.1 120.8 99.8 125.5 120.2 111.4 116.8 116 125.5 124.1 115.7 117 116 120.2 99. 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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Multiple Linear Regression - Estimated Regression Equation
Y(t)[t] = + 27.5646541936156 + 0.719237622037051T.I.P.[t] + 0.351615816868488`Y(t-1)`[t] -0.225481858966781`Y(t-3)`[t] -2.87531712508168M1[t] -1.25845267587132M2[t] -1.64388521197177M3[t] + 11.4973887999212M4[t] + 5.12018350407216M5[t] -8.78441952464868M6[t] -5.16373479336386M7[t] -4.47697332405873M8[t] + 1.16759647044438M9[t] + 6.50191392593397M10[t] -1.4199990435579M11[t] -0.100533009976323t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)27.564654193615611.1542382.47120.017710.008855
T.I.P.0.7192376220370510.1105766.504400
`Y(t-1)`0.3516158168684880.1115723.15150.0030330.001516
`Y(t-3)`-0.2254818589667810.111906-2.01490.0505010.025251
M1-2.875317125081682.810467-1.02310.3122710.156136
M2-1.258452675871322.606872-0.48270.6318450.315922
M3-1.643885211971772.585307-0.63590.5284030.264202
M411.49738879992123.7932593.0310.004210.002105
M55.120183504072162.9508221.73520.0902230.045111
M6-8.784419524648682.498781-3.51550.0010870.000543
M7-5.163734793363862.503896-2.06230.0455560.022778
M8-4.476973324058732.606334-1.71770.0933920.046696
M91.167596470444382.8413130.41090.683260.34163
M106.501913925933972.9025032.24010.0305670.015283
M11-1.41999904355792.854385-0.49750.6215080.310754
t-0.1005330099763230.034392-2.92310.0056180.002809


Multiple Linear Regression - Regression Statistics
Multiple R0.913268637606618
R-squared0.834059604435848
Adjusted R-squared0.773349703619695
F-TEST (value)13.7384445242568
F-TEST (DF numerator)15
F-TEST (DF denominator)41
p-value1.75230940868687e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.62948298030304
Sum Squared Residuals540.099014876688


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1112.5113.381553606476-0.881553606475701
2113111.9798257784021.02017422159778
3126.4120.9591894664515.44081053354928
4114.1115.923764694843-1.82376469484289
5112.5115.6531277182-3.15312771820009
6112.4110.6506078480071.74939215199312
7113.1111.6585902120491.44140978795053
8116.3114.9375098214411.36249017855949
9111.7116.954220862602-5.2542208626023
10118.8118.0392510965220.760748903478475
11116.5113.0144394255893.48556057441113
12125.1125.207122437768-0.107122437768481
13113.1113.0095303229670.0904696770330307
14119.6117.4420663581442.15793364185637
15114.4120.682876458172-6.28287645817183
16114113.5992589564920.40074104350817
17117.8116.3757303333941.42426966660554
18117115.1643380605551.83566193944535
19120.9119.7160938294181.18390617058195
20115118.227537471127-3.22753747112682
21117.3114.0377363430993.26226365690091
22119.4122.077808405588-2.67780840558763
23114.9115.620632274021-0.720632274021336
24125.8124.5489267535711.25107324642868
25117.6115.7978593186791.80214068132096
26117.6117.747169815461-0.147169815460565
27114.9120.485429220739-5.58542922073857
28121.9116.6605894963235.23941050367726
29117121.05924208641-4.0592420864103
30106.4111.909661827175-5.50966182717538
31110.5117.244765335077-6.74476533507725
32113.6113.832417391967-0.232417391966923
33114.2113.2907105407410.909289459258885
34125.4125.506831410408-0.106831410408123
35124.6122.2338878233482.36611217665222
36120.2120.979059221943-0.779059221943333
37120.8119.3249848375141.47501516248608
38111.4115.334922753339-3.93492275333866
39124.1117.4267045380046.67329546199609
40120.2120.484848620233-0.284848620232935
41125.5117.4884410666498.01155893335099
42116114.7102888231071.28971117689341
43117114.9783081498942.02169185010586
44105.7102.9975253343642.70247466563643
45102103.042269176072-1.04226917607191
46106.4104.3761090874832.02389091251728
4796.9102.031040477042-5.13104047704201
48107.6107.964891586717-0.364891586716859
4998.8101.286071914364-2.48607191436437
50101.1100.1960152946550.903984705345068
51105.7105.945800316635-0.245800316634972
52104.6108.13153823211-3.5315382321096
53103.2105.423458795346-2.22345879534613
54101.6100.9651034411570.634896558843497
55106.7104.6022424735612.09775752643891
5699.5100.105009981102-0.605009981102175
5710198.87506307748562.12493692251442


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.6816132087120210.6367735825759580.318386791287979
200.5310042878508660.9379914242982680.468995712149134
210.6284291597652470.7431416804695070.371570840234753
220.4990385844221850.998077168844370.500961415577815
230.447666255007480.8953325100149610.55233374499252
240.3365006136590020.6730012273180040.663499386340998
250.27071391460650.5414278292130.7292860853935
260.1918915978727080.3837831957454150.808108402127293
270.2457544180302070.4915088360604130.754245581969793
280.422530022808630.845060045617260.57746997719137
290.3666107884563090.7332215769126180.633389211543691
300.38479177421070.7695835484214010.6152082257893
310.6607694539564950.6784610920870090.339230546043505
320.5754092178729040.8491815642541920.424590782127096
330.4810182042250570.9620364084501140.518981795774943
340.7157539490594220.5684921018811560.284246050940578
350.5968694110930250.8062611778139510.403130588906975
360.515315625563610.969368748872780.48468437443639
370.38103557304720.76207114609440.6189644269528
380.2874165261213090.5748330522426180.712583473878691


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/10rz7p1292675371.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/10rz7p1292675371.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/1d7rg1292675371.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/1d7rg1292675371.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/2d7rg1292675371.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/2d7rg1292675371.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/3d7rg1292675371.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/3d7rg1292675371.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/45h8j1292675371.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/45h8j1292675371.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/55h8j1292675371.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/55h8j1292675371.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/65h8j1292675371.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/65h8j1292675371.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/7gqp41292675371.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/7gqp41292675371.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/8rz7p1292675371.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/8rz7p1292675371.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/9rz7p1292675371.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/18/t1292675261gz8vlo0mvr909f9/9rz7p1292675371.ps (open in new window)


 
Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 2 ; 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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