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geknakte trend

*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: Wed, 22 Dec 2010 15:26:21 +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/22/t1293031565slo7i89l4s40yut.htm/, Retrieved Wed, 22 Dec 2010 16:26:08 +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/22/t1293031565slo7i89l4s40yut.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 «
94.6 0 1 0 95.9 0 2 0 104.7 0 3 0 102.8 0 4 0 98.1 0 5 0 113.9 0 6 0 80.9 0 7 0 95.7 0 8 0 113.2 0 9 0 105.9 0 10 0 108.8 0 11 0 102.3 0 12 0 99 0 13 0 100.7 0 14 0 115.5 0 15 0 100.7 0 16 0 109.9 0 17 0 114.6 0 18 0 85.4 0 19 0 100.5 0 20 0 114.8 0 21 0 116.5 0 22 0 112.9 0 23 0 102 0 24 0 106 0 25 0 105.3 0 26 0 118.8 0 27 0 106.1 0 28 0 109.3 0 29 0 117.2 0 30 0 92.5 0 31 0 104.2 0 32 0 112.5 0 33 0 122.4 0 34 0 113.3 0 35 0 100 0 36 0 110.7 0 37 0 112.8 0 38 0 109.8 0 39 0 117.3 0 40 0 109.1 0 41 0 115.9 0 42 0 96 0 43 0 99.8 0 44 0 116.8 0 45 0 115.7 0 46 0 99.4 1 47 47 94.3 1 48 48 91 1 49 49 93.2 1 50 50 103.1 1 51 51 94.1 1 52 52 91.8 1 53 53 102.7 1 54 54 82.6 1 55 55 89.1 1 56 56 104.5 1 57 57 105.1 1 58 58 95.1 1 59 59 88.7 1 60 60 86.3 1 61 61 91.8 1 62 62 111.5 1 63 63 99.7 1 64 64 97.5 1 65 65 111.7 1 66 66 86.2 1 67 67 95.4 1 68 68
 
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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Multiple Linear Regression - Estimated Regression Equation
productie[t] = + 95.7114276882826 -19.4097565919733d[t] + 0.252831229919121t + 0.0190069755286254dt[t] + 0.505595163524223M1[t] + 2.26309494176223M2[t] + 12.6205947200002M3[t] + 5.24476116490491M4[t] + 4.15226094314291M5[t] + 13.9430940547142M6[t] -11.7160728337144M7[t] -1.79190638880974M8[t] + 11.9704135217547M9[t] + 12.4737808967298M10[t] + 8.70043402013057M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)95.71142768828262.05360946.606500
d-19.40975659197337.830032-2.47890.0163960.008198
t0.2528312299191210.0434355.820900
dt0.01900697552862540.1401580.13560.8926420.446321
M10.5055951635242232.3390410.21620.8296970.414848
M22.263094941762232.3393250.96740.3377310.168866
M312.62059472000022.340845.39152e-061e-06
M45.244761164904912.3435842.23790.0294520.014726
M54.152260943142912.3475541.76880.0826880.041344
M613.94309405471422.3527425.926300
M7-11.71607283371442.359141-4.96627e-064e-06
M8-1.791906388809742.366741-0.75710.4523310.226165
M911.97041352175472.4535794.87881e-055e-06
M1012.47378089672982.4575435.07575e-063e-06
M118.700434020130572.4370213.57010.0007680.000384


Multiple Linear Regression - Regression Statistics
Multiple R0.938849191755345
R-squared0.881437804859664
Adjusted R-squared0.850119489162216
F-TEST (value)28.1444830358967
F-TEST (DF numerator)14
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.85209882397801
Sum Squared Residuals786.449263533717


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
194.696.469854081726-1.86985408172603
295.998.4801850898831-2.58018508988312
3104.7109.09051609804-4.39051609804025
4102.8101.9675137728640.832486227135958
598.1101.127844781021-3.02784478102117
6113.9111.1715091225122.72849087748838
780.985.7651734640021-4.86517346400209
895.795.9421711388259-0.242171138825872
9113.2109.9573222793093.24267772069058
10105.9110.713520884204-4.8135208842037
11108.8107.1930052375241.60699476247645
12102.398.74540244731213.5545975526879
139999.5038288407554-0.503828840755449
14100.7101.514159848913-0.814159848912575
15115.5112.124490857073.3755091429303
16100.7105.001488531894-4.30148853189349
17109.9104.1618195400515.73818045994939
18114.6114.2054838815410.394516118458917
1985.488.7991482230315-3.39914822303153
20100.598.97614589785531.52385410214467
21114.8112.9912970383391.80870296166112
22116.5113.7474956432332.75250435676685
23112.9110.2269799965532.673020003447
24102101.7793772063420.220622793658446
25106102.5378035997853.4621964002151
26105.3104.5481346079420.751865392057969
27118.8115.1584656160993.64153438390084
28106.1108.035463290923-1.93546329092295
29109.3107.195794299082.10420570091993
30117.2117.239458640571-0.0394586405705309
3192.591.8331229820610.666877017939014
32104.2102.0101206568852.18987934311522
33112.5116.025271797368-3.52527179736833
34122.4116.7814704022635.6185295977374
35113.3113.2609547555820.0390452444175378
36100104.813351965371-4.81335196537101
37110.7105.5717783588145.12822164118565
38112.8107.5821093669715.21789063302851
39109.8118.192440375129-8.3924403751286
40117.3111.0694380499526.2305619500476
41109.1110.22976905811-1.12976905810953
42115.9120.2734333996-4.37343339959998
439694.86709774109041.13290225890956
4499.8105.044095415914-5.24409541591424
45116.8119.059246556398-2.25924655639778
46115.7119.815445161292-4.11544516129205
4799.497.7785007724841.62149922751599
4894.389.34990495780124.95009504219881
499190.12733832677320.872661673226843
5093.292.1566763104591.04332368954109
51103.1102.7860142941450.313985705855333
5294.195.682018944497-1.58201894449708
5391.894.8613569281828-3.06135692818283
54102.7104.924028245202-2.22402824520191
5582.679.5366995622213.063300437779
5689.189.7327042125734-0.632704212573421
57104.5103.7668623285860.733137671414411
58105.1104.5420679090080.557932090991503
5995.1101.040559237857-5.94055923785698
6088.792.6119634231741-3.91196342317414
6186.393.3893967921461-7.08939679214612
6291.895.4187347758319-3.61873477583187
63111.5106.0480727595185.45192724048238
6499.798.944077409870.755922590129967
6597.598.1234153935558-0.623415393555785
66111.7108.1860867105753.51391328942513
6786.282.7987580275943.40124197240605
6895.492.99476267794642.40523732205363


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.796494264094540.4070114718109190.20350573590546
190.7158777805947490.5682444388105020.284122219405251
200.5816740066776810.8366519866446380.418325993322319
210.4828924176664630.9657848353329260.517107582333537
220.4730169595580420.9460339191160840.526983040441958
230.3597370906917430.7194741813834860.640262909308257
240.3333130269427070.6666260538854130.666686973057293
250.2530388050576550.506077610115310.746961194942345
260.1784187460587050.356837492117410.821581253941295
270.1237172359247580.2474344718495160.876282764075242
280.1131355491725110.2262710983450210.88686445082749
290.07613467690205860.1522693538041170.923865323097941
300.06296005805342580.1259201161068520.937039941946574
310.05286500420243130.1057300084048630.947134995797569
320.03163939082278920.06327878164557830.96836060917721
330.09110810880075680.1822162176015140.908891891199243
340.08286321429056320.1657264285811260.917136785709437
350.06476447349790940.1295289469958190.93523552650209
360.1529050319065560.3058100638131120.847094968093444
370.139745595852580.279491191705160.86025440414742
380.1623594264590720.3247188529181440.837640573540928
390.6007603015780970.7984793968438060.399239698421903
400.6616132902694570.6767734194610860.338386709730543
410.630509838415110.7389803231697810.36949016158489
420.5932638969590010.8134722060819980.406736103040999
430.5153173832362830.9693652335274340.484682616763717
440.4818728103373480.9637456206746960.518127189662652
450.3826012087869370.7652024175738750.617398791213063
460.3047782571992370.6095565143984740.695221742800763
470.3023148644580590.6046297289161190.69768513554194
480.4171238765958440.8342477531916880.582876123404156
490.6785897942907330.6428204114185350.321410205709267
500.8836053659750770.2327892680498450.116394634024923


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 level10.0303030303030303OK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/10ajzt1293031571.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/10ajzt1293031571.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/1m02h1293031571.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/1m02h1293031571.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/2erjk1293031571.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/2erjk1293031571.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/3erjk1293031571.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/3erjk1293031571.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/4erjk1293031571.png (open in new window)
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http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/5700n1293031571.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/5700n1293031571.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/6700n1293031571.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/6700n1293031571.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/70s0q1293031571.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/70s0q1293031571.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/80s0q1293031571.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/80s0q1293031571.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/9ajzt1293031571.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031565slo7i89l4s40yut/9ajzt1293031571.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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