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Paper - Multiple 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: Tue, 21 Dec 2010 11:10:31 +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/21/t1292929727bnwqn6dvmwqy2jx.htm/, Retrieved Tue, 21 Dec 2010 12:09:01 +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/21/t1292929727bnwqn6dvmwqy2jx.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 «
105.31 1576.23 29.29 710.45 105.63 1546.37 28.99 720 106.02 1545.05 28.91 720 105.85 1552.34 29.29 720 106.57 1594.3 30.96 754.78 106.48 1605.78 30.57 802.73 106.60 1673.21 30.59 845.24 106.75 1612.94 31.39 893.91 106.69 1566.34 31.28 931.43 106.69 1530.17 31.1 940 106.93 1582.54 31.7 947.73 107.21 1702.16 32.57 960 107.88 1701.93 32.49 996.96 108.84 1811.15 32.46 1000 108.96 1924.2 32.3 1000 109.52 2034.25 32.97 1000 108.45 2011.13 32.9 1013.04 108.67 2013.04 32.93 1095.24 108.96 2151.67 33.72 1159.09 108.76 1902.09 33.33 1200 107.85 1944.01 33.44 1200 108.78 1916.67 33.89 1282.61 107.51 1967.31 34.34 1513.64 108.83 2119.88 33.56 1669.05 111.54 2216.38 32.67 1700 111.74 2522.83 32.57 1700 112.04 2647.64 33.23 1700 111.74 2631.23 32.85 1665.91 111.81 2693.41 32.61 1650 111.86 3021.76 32.57 1650 114.23 2953.67 32.98 1619.57 114.80 2796.8 31.33 1599.05 115.17 2672.05 29.8 1572.73 115.11 2251.23 28.06 1470 114.43 2046.08 25.47 1268 114.66 2420.04 24.65 1217.39 115.11 2608.89 2 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 time18 seconds
R Server'George Udny Yule' @ 72.249.76.132


Multiple Linear Regression - Estimated Regression Equation
PC&S[t] = + 125.283502689994 + 0.00463126562262942PCacao[t] -0.933123751644972PSuiker[t] + 0.00360428981886122PNoten[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)125.2835026899944.88516525.645700
PCacao0.004631265622629420.0008335.55881e-060
PSuiker-0.9331237516449720.147239-6.337500
PNoten0.003604289818861220.0015742.28920.0260820.013041


Multiple Linear Regression - Regression Statistics
Multiple R0.921148485925745
R-squared0.848514533123293
Adjusted R-squared0.8399398840548
F-TEST (value)98.9561819201584
F-TEST (DF numerator)3
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.38745356518327
Sum Squared Residuals302.096529873035


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1105.31107.812915518479-2.50291551847936
2105.63107.988984020252-2.35898402025168
3106.02108.057520649761-2.03752064976142
4105.85107.736695550525-1.88669555052529
5106.57106.4980639907040.0719360092962835
6106.48107.087974880007-0.607974880007427
7106.6107.534817006108-0.93481700610823
8106.75106.6846124112000.0653875887996543
9106.69106.706671999870-0.0166719998704360
10106.69106.738010161344-0.0480101613436659
11106.93106.4485364513140.481463548686425
12107.21106.2349354172390.975064582761179
13107.88106.4417346779821.43826532201768
14108.84106.9865122628851.85348773711541
15108.96107.6593766417861.30062335821395
16109.52107.5438545099541.97614549004572
17108.45107.5490982506120.900901749387815
18108.67107.8262228785120.84377712148755
19108.96107.9612213729120.998778627087663
20108.76107.3167198584481.44328014155237
21107.85107.4082189006670.441781099332684
22108.78107.1594447922411.62055520775949
23107.51107.806765471982-0.296765471981723
24108.83109.801336875059-0.971336875058603
25111.54111.19028691650.349713083499884
26111.74112.702850641719-0.962850641719412
27112.04112.665017227994-0.625017227994101
28111.74112.820734944827-1.08073494482687
29111.81113.275312490619-1.46531249061867
30111.86114.833313507875-2.97331350787485
31114.23114.0257113542680.204288645732377
32114.8114.7648988791770.0351011208230774
33115.17115.519962924738-0.349962924738275
34115.11114.8244003601940.285599639805998
35114.43115.563020191062-1.13302019106208
36114.66117.877676651917-3.2176766519169
37115.11119.186657482884-4.07665748288448
38117.74118.859140695992-1.11914069599185
39118.18118.1119142507720.0680857492283319
40118.56117.6424066700660.917593329933734
41117.63116.6144912933551.01550870664471
42117.71116.0157151587551.69428484124513
43117.46116.4801716880100.979828311990368
44117.37117.0345061083740.335493891626348
45117.34118.144261998434-0.804261998434062
46117.09119.884879918997-2.79487991899719
47116.65118.992889047596-2.34288904759620
48116.71120.638089626479-3.92808962647932
49116.82120.969825236346-4.14982523634641
50117.33120.701082905547-3.37108290554658
51117.95120.636278658953-2.68627865895307
52123.53120.8327647788302.69723522116974
53124.91121.6235434251433.28645657485687
54125.99121.4880004856444.50199951435591
55126.29120.7640852508255.52591474917489
56125.68119.5754746453726.10452535462825
57125.52119.1903900328916.32960996710867


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.001662415379140450.003324830758280890.99833758462086
80.0005649959278089710.001129991855617940.99943500407219
97.1135137782968e-050.0001422702755659360.999928864862217
107.35714506815169e-061.47142901363034e-050.999992642854932
116.97563621693086e-071.39512724338617e-060.999999302436378
126.86078948639749e-081.37215789727950e-070.999999931392105
133.13939630109891e-086.27879260219782e-080.999999968606037
144.55372555287114e-089.10745110574227e-080.999999954462744
155.36821179239029e-091.07364235847806e-080.999999994631788
166.33243574274573e-101.26648714854915e-090.999999999366756
171.76259491296416e-093.52518982592833e-090.999999998237405
188.90041852530964e-101.78008370506193e-090.999999999109958
199.43750827635279e-101.88750165527056e-090.99999999905625
201.50693980052910e-103.01387960105821e-100.999999999849306
211.72391456069711e-103.44782912139422e-100.999999999827609
223.38880524818412e-116.77761049636824e-110.999999999966112
232.67308595052285e-115.34617190104571e-110.99999999997327
241.43559717483360e-112.87119434966720e-110.999999999985644
251.71641823075746e-093.43283646151491e-090.999999998283582
263.80291424345624e-107.60582848691247e-100.999999999619709
278.49697167464967e-111.69939433492993e-100.99999999991503
281.88609949904134e-113.77219899808268e-110.999999999981139
294.35474736322384e-128.70949472644768e-120.999999999995645
307.80430854151456e-121.56086170830291e-110.999999999992196
318.26561014076434e-121.65312202815287e-110.999999999991734
321.06322631866251e-102.12645263732502e-100.999999999893677
331.15430202102741e-092.30860404205482e-090.999999998845698
341.82109689903559e-083.64219379807119e-080.999999981789031
352.03606150027954e-084.07212300055907e-080.999999979639385
369.11034397865411e-081.82206879573082e-070.99999990889656
374.8729114376511e-069.7458228753022e-060.999995127088562
381.13156852060907e-052.26313704121815e-050.999988684314794
392.92486560716031e-055.84973121432062e-050.999970751343928
405.92465862641786e-050.0001184931725283570.999940753413736
410.0001110873115842170.0002221746231684350.999888912688416
428.70156477070206e-050.0001740312954140410.999912984352293
434.82396208772871e-059.64792417545743e-050.999951760379123
442.18232340800261e-054.36464681600522e-050.99997817676592
451.17237066870381e-052.34474133740761e-050.999988276293313
461.76564099198493e-053.53128198396986e-050.99998234359008
471.36888120321300e-052.73776240642600e-050.999986311187968
481.81965296429609e-053.63930592859217e-050.999981803470357
491.09645851939741e-052.19291703879482e-050.999989035414806
500.01019407947701540.02038815895403080.989805920522985


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level430.977272727272727NOK
5% type I error level441NOK
10% type I error level441NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/10k4qd1292929812.png (open in new window)
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http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/1oca41292929812.png (open in new window)
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http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/2oca41292929812.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/2oca41292929812.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/3h3s71292929812.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/3h3s71292929812.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/4h3s71292929812.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/4h3s71292929812.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/5h3s71292929812.png (open in new window)
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http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/6sd9s1292929812.png (open in new window)
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http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/7sd9s1292929812.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/7sd9s1292929812.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/8k4qd1292929812.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/8k4qd1292929812.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/9k4qd1292929812.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292929727bnwqn6dvmwqy2jx/9k4qd1292929812.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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