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paper

*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 17:13:23 +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/t1293038134qqam1kjq66o6pf0.htm/, Retrieved Wed, 22 Dec 2010 18:15:38 +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/t1293038134qqam1kjq66o6pf0.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 «
10554,27 2,08 83,9 61,2 11451 63,96 2,17 69 10532,54 2,09 85,6 62 11964 63,77 2,23 67 10324,31 2,07 87,5 65,1 12574 59,15 2,17 69 10695,25 2,04 88,5 63,2 13031 56,12 2,39 79 10827,81 2,35 91 66,3 13812 57,42 2,6 104 10872,48 2,33 90,6 61,9 14544 63,52 2,67 117 10971,19 2,37 91,2 62,1 14931 61,71 2,63 73 11145,65 2,59 93,2 66,3 14886 63,01 2,85 97 11234,68 2,62 90,1 72 16005 68,18 3,1 124 11333,88 2,6 95 65,3 17064 72,03 3,2 129 10997,97 2,83 95,4 67,6 15168 69,75 3,21 122 11036,89 2,78 93,7 70,5 16050 74,41 3,36 113 11257,35 3,01 93,9 74,2 15839 74,33 3,41 131 11533,59 3,06 92,5 77,8 15137 64,24 3,32 155 11963,12 3,33 89,2 78,5 14954 60,03 3,24 161 12185,15 3,32 93,3 77,8 15648 59,44 3,24 141 12377,62 3,6 93 81,4 15305 62,5 3,26 116 12512,89 3,57 96,1 84,5 15579 55,04 3,48 197 12631,48 3,57 96,7 88 16348 58,34 3,61 163 12268,53 3,83 97,6 93,9 15928 61,92 3,68 154 12754,8 3,84 102,6 98,9 16171 67,65 3,67 143 13407,75 3,8 107,6 96,7 15937 67,68 3,71 165 13480,21 4,07 103,5 98,9 15713 70,3 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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Multiple Linear Regression - Estimated Regression Equation
DowJones[t] = + 3294.69304179701 + 1902.00849170329Eonia[t] + 75.8419583730737deposits[t] -40.7278509972586`2JAAR`[t] -0.0314633513384605Goudkoers[t] + 19.7117420762761Brent[t] -859.836876632501gewrentevoet[t] + 5.39966362404075kasbons[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)3294.693041797011695.7496281.94290.0573470.028673
Eonia1902.00849170329156.88105612.123900
deposits75.841958373073725.3995942.9860.0042720.002136
`2JAAR`-40.727850997258610.002704-4.07170.0001567.8e-05
Goudkoers-0.03146335133846050.056027-0.56160.5767720.288386
Brent19.71174207627615.8863053.34870.00150.00075
gewrentevoet-859.836876632501345.900734-2.48580.0161150.008057
kasbons5.399663624040751.2803364.21749.7e-054.8e-05


Multiple Linear Regression - Regression Statistics
Multiple R0.921944437296067
R-squared0.849981545461161
Adjusted R-squared0.830167787314522
F-TEST (value)42.8985525699141
F-TEST (DF numerator)7
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation676.704973303932
Sum Squared Residuals24270269.9073966


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
110554.2710528.673485796725.5965142033081
210532.5410561.767149073-29.2271490730023
310324.3110493.6990091934-169.389009193449
410695.2510380.5908230387314.65917696132
510827.8110989.0402471199-161.230247119897
610872.4811207.0833375583-334.603337558325
710971.1911069.4769875323-98.2869875323122
811145.6511436.0147278919-290.364727891915
911234.6811023.3500760797211.329923920305
1011333.8811613.3972523385-279.517252338512
1110997.9711955.837659555-957.867659555005
1211036.8911500.2286759272-463.338675927247
1311257.3511861.4299011712-604.079901171181
1411533.5911733.9043614078-200.314361407788
1511963.1211992.6149868671-29.494986867111
1612185.1512171.597660843113.5523391569473
1712377.6212453.7087195467-76.0887195467481
1812512.8912598.0402841931-85.1502841930693
1912631.4812246.4840552193384.995944780666
2012268.5312543.9667949287-275.436794928708
2112754.812793.0621733485-38.2621733485098
2213407.7513278.1457988788129.6042011212
2313480.2113187.6798302622292.530169737786
2413673.2812909.3965343773763.883465622682
2513239.7112631.2063680423608.503631957706
2613557.6912347.91905631411209.77094368588
2713901.2812006.87551828371894.4044817163
2813200.5812588.5994010953611.980598904663
2913406.9712237.82197639131169.14802360868
3012538.1212587.9431892655-49.82318926547
3112419.5712397.630573010721.9394269892975
3212193.8812969.1044047922-775.224404792186
3312656.6312642.936230189413.6937698105921
3412812.4812886.1446702486-73.6646702485682
3512056.6712340.5011832486-283.831183248593
3611322.3811938.3219296858-615.94192968583
3711530.7511380.0514826364150.698517363592
3811114.0812164.0521395884-1049.97213958838
399181.7310593.2814057205-1411.55140572055
408614.559410.4667686929-795.916768692905
418595.568174.04321224578421.516787754221
428396.27274.743101895321121.45689810468
437690.58028.02975046156-337.529750461559
447235.478290.09460984531-1054.62460984531
457992.127896.3040277906395.8159722093657
468398.3710020.8744525515-1622.5044525515
4785939430.3145697153-837.314569715294
488679.758391.92749340474287.822506595261
499374.638677.5817127196697.048287280401
509634.979520.5760951533114.393904846689
519857.349742.59832944376114.741670556242
5210238.8310375.3727019946-136.542701994567
5310433.449878.14278372166555.29721627834
5410471.249867.4113426894603.828657310608
5510214.519723.24523136488491.264768635117
5610677.529975.25099015872702.269009841283
5711052.1510176.0831431029876.06685689712
5810500.1910573.1096162135-72.9196162134961
5910159.2710893.3075441308-734.03754413081
6010222.2410178.386432934843.8535670651953
6110350.410348.01602910762.38397089234955


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.0086953359219630.0173906718439260.991304664078037
120.002509473332932140.005018946665864280.997490526667068
130.0005704009373347470.001140801874669490.999429599062665
140.0001316016005831730.0002632032011663470.999868398399417
152.61510263608374e-055.23020527216747e-050.999973848973639
164.60673042409094e-059.21346084818188e-050.99995393269576
172.38337896278801e-054.76675792557602e-050.999976166210372
182.40889505652024e-054.81779011304049e-050.999975911049435
192.49538186387101e-054.99076372774203e-050.999975046181361
203.77314338137755e-057.5462867627551e-050.999962268566186
217.84173641128795e-050.0001568347282257590.999921582635887
220.0005213395213629670.001042679042725930.999478660478637
230.001546103563624280.003092207127248550.998453896436376
240.002937895803587490.005875791607174970.997062104196413
250.003098318612833020.006196637225666040.996901681387167
260.003083564652338210.006167129304676420.996916435347662
270.008214432417240020.016428864834480.99178556758276
280.01341153585758210.02682307171516410.986588464142418
290.02903977081731990.05807954163463980.97096022918268
300.02889195570131290.05778391140262580.971108044298687
310.02498254323775080.04996508647550170.97501745676225
320.02035455695224860.04070911390449710.979645443047751
330.01622011046096350.0324402209219270.983779889539037
340.0789673566848020.1579347133696040.921032643315198
350.109397235845150.21879447169030.89060276415485
360.186916858148460.3738337162969190.81308314185154
370.1782265767193670.3564531534387340.821773423280633
380.51114064824050.9777187035190.4888593517595
390.8329499638049070.3341000723901860.167050036195093
400.7914775779995910.4170448440008170.208522422000409
410.7385713748744370.5228572502511260.261428625125563
420.9432107733805580.1135784532388850.0567892266194424
430.9857287290211360.02854254195772820.0142712709788641
440.9749714073927740.05005718521445220.0250285926072261
450.9978777073892020.004244585221596170.00212229261079808
460.9996273695689260.0007452608621477770.000372630431073889
470.9996610698806920.0006778602386162650.000338930119308133
480.9987465796980730.002506840603853770.00125342030192689
490.9994271851588870.001145629682225980.000572814841112992
500.9959729167976430.008054166404713740.00402708320235687


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.525NOK
5% type I error level280.7NOK
10% type I error level310.775NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/10ge5w1293037994.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/10ge5w1293037994.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/1247n1293037994.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/1247n1293037994.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/2247n1293037994.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/2247n1293037994.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/3247n1293037994.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/3247n1293037994.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/4vd7q1293037994.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/4vd7q1293037994.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/5vd7q1293037994.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/5vd7q1293037994.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/6vd7q1293037994.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/6vd7q1293037994.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/7n4ot1293037994.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/7n4ot1293037994.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/8ge5w1293037994.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/8ge5w1293037994.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/9ge5w1293037994.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038134qqam1kjq66o6pf0/9ge5w1293037994.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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