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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: Tue, 21 Dec 2010 12:12:10 +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/t1292933504oel4ubckd1jyfp5.htm/, Retrieved Tue, 21 Dec 2010 13:11:59 +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/t1292933504oel4ubckd1jyfp5.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 «
-999.00 -999.00 38.60 6654.00 5712.00 645.00 3.00 5.00 3.00 6.30 2.00 4.50 1.00 6600.00 42.00 3.00 1.00 3.00 -999.00 -999.00 14.00 3.39 44.50 60.00 1.00 1.00 1.00 -999.00 -999.00 -999.00 0.92 5.70 25.00 5.00 2.00 3.00 2.10 1.80 69.00 2547.00 4603.00 624.00 3.00 5.00 4.00 9.10 0.70 27.00 10.55 179.50 180.00 4.00 4.00 4.00 15.80 3.90 19.00 0.02 0.30 35.00 1.00 1.00 1.00 5.20 1.00 30.40 160.00 169.00 392.00 4.00 5.00 4.00 10.90 3.60 28.00 3.30 25.60 63.00 1.00 2.00 1.00 8.30 1.40 50.00 52.16 440.00 230.00 1.00 1.00 1.00 11.00 1.50 7.00 0.43 6.40 112.00 5.00 4.00 4.00 3.20 0.70 30.00 465.00 423.00 281.00 5.00 5.00 5.00 7.60 2.70 -999.00 0.55 2.40 -999.00 2.00 1.00 2.00 -999.00 -999.00 40.00 187.10 419.00 365.00 5.00 5.00 5.00 6.30 2.10 3.50 0.08 1.20 42.00 1.00 1.00 1.00 8.60 0.00 50.00 3.00 25.00 28.00 2.00 2.00 2.00 6.60 4.10 6.00 0.79 3500.00 42.00 2.00 2.00 2.00 9.50 1.20 10.40 0.20 5.00 120.00 2.00 2.00 2.00 4.80 1.30 34.00 1.41 17.50 -999.00 1.00 2.00 1.00 12.00 6.10 7.00 60.00 81.00 -999 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 time14 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework
error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.


Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 58.5962033954784 + 0.955258558887444PS[t] + 0.0198987966065785L[t] + 0.00797442355260723Wb[t] + 0.0026217940522085Wbr[t] -0.067620433809013Tg[t] -2.79466247616151P[t] -22.0902142737359S[t] -11.4834724299415`D `[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)58.596203395478455.6857071.05230.2974510.148726
PS0.9552585588874440.06223515.349300
L0.01989879660657850.0985190.2020.8407060.420353
Wb0.007974423552607230.0375020.21260.8324230.416211
Wbr0.00262179405220850.0246410.10640.9156660.457833
Tg-0.0676204338090130.086431-0.78240.4374810.21874
P-2.7946624761615144.714931-0.06250.95040.4752
S-22.090214273735929.839333-0.74030.4623820.231191
`D `-11.483472429941559.599358-0.19270.8479480.423974


Multiple Linear Regression - Regression Statistics
Multiple R0.918797752642164
R-squared0.844189310260292
Adjusted R-squared0.8206707155826
F-TEST (value)35.8945473498481
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation179.879442882999
Sum Squared Residuals1714900.54051059


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1-999-1023.8021573326024.8021573326012
26.310.1434030540876-3.84340305408762
3-999-935.7103858578-63.2896141422008
4-999-1009.8583831106110.8583831106134
52.1-112.896438635618114.996438635618
69.1-97.288180709049106.388180709049
715.823.9656705741970-8.16567057419695
85.2-132.195444711778137.395444711778
910.9-0.032916745624886410.9329167456249
108.310.5769915678138-2.27699156781375
1111-95.652956488009106.652956488009
123.2-136.164113678583139.364113678583
137.658.213511222444-50.613511222444
14-999-1098.8438029537399.8438029537311
156.321.4672688641947-15.1672688641947
168.6-14.949459158559723.5494591585597
176.6-3.762021335763510.3620213357635
189.5-20.886985410935930.3869854109359
194.869.6659738614078-64.8659738614079
201296.4378671076884-84.4378671076884
21-999-143.448682139840-855.55131786016
223.3-131.855682624405135.155682624406
23117.402193024292373.59780697570763
24-999-1011.8892614867712.8892614867712
254.7-39.136261244485843.8362612444858
26-999-935.410157740953-63.5898422590467
2710.4-10.372859807301220.7728598073012
287.4-71.241680056213778.6416800562137
292.1-138.414506997427140.514506997427
30-999-937.182692428374-61.8173075716263
31-999-1061.9572440897562.9572440897535
327.7-45.555720930438253.2557209304382
3317.921.2356559542704-3.33565595427038
346.111.9332517203540-5.83325172035399
358.2-0.1728762304514248.37287623045142
368.4-26.543506959440134.9435069594401
3711.9-9.1011458333124421.0011458333124
3810.8-9.200112462795420.0001124627954
3913.824.1007622691409-10.3007622691409
4014.314.4656092510884-0.165609251088387
41-999-148.295378247145-850.704621752855
4215.2-21.604639201070236.8046392010702
4310-96.7297281254276106.729728125428
4411.98.821128715016793.07887128498321
456.5-90.298272190151796.7982721901517
467.5-124.072205142965131.572205142965
47-999-972.294284505327-26.7057154946735
4810.6-5.1640338621034715.7640338621035
497.421.3651703817397-13.9651703817397
508.4-45.073557794854553.4735577948545
515.7-28.317839407475834.0178394074758
524.9-42.952439818361247.8524398183612
53-999-1086.977907484187.9779074841008
543.2-131.583702475107134.783702475107
55-999-974.232983000037-24.7670169999626
568.174.8821850857198-66.7821850857198
57116.193126196899644.80687380310036
584.9-19.177435190428524.0774351904285
5913.2-17.508905982787130.7089059827871
609.7-77.7510670064387.45106700643
6112.824.8890440238665-12.0890440238665
62-999-939.898796279822-59.1012037201784


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
124.99658916386013e-069.99317832772025e-060.999995003410836
139.03486512656496e-081.80697302531299e-070.99999990965135
142.45472832420741e-094.90945664841482e-090.999999997545272
151.02082008681588e-102.04164017363175e-100.999999999897918
161.55627668595619e-123.11255337191239e-120.999999999998444
173.09130025553861e-146.18260051107722e-140.99999999999997
184.42630014991552e-168.85260029983105e-161
191.18497578096273e-172.36995156192546e-171
201.75260334599068e-193.50520669198137e-191
210.9838935643266330.03221287134673310.0161064356733665
220.9794727434912820.04105451301743550.0205272565087178
230.966857697766890.06628460446622040.0331423022331102
240.9526235012185850.09475299756282960.0473764987814148
250.9281154695865760.1437690608268480.0718845304134239
260.8943758318889250.2112483362221500.105624168111075
270.8500860067980140.2998279864039710.149913993201986
280.802517568637950.39496486272410.19748243136205
290.9822009599496490.03559808010070260.0177990400503513
300.9800691799111160.03986164017776840.0199308200888842
310.968254968357540.06349006328492060.0317450316424603
320.950285294294840.09942941141031920.0497147057051596
330.9238990147650260.1522019704699490.0761009852349744
340.9946817230535340.01063655389293290.00531827694646645
350.9901487475386950.01970250492261030.00985125246130514
360.9979374628013150.004125074397370780.00206253719868539
370.995946327568560.008107344862878530.00405367243143927
380.9932876052427660.01342478951446740.00671239475723369
390.986793033950910.02641393209818200.0132069660490910
400.9751566371367480.04968672572650450.0248433628632523
4113.45288085588953e-211.72644042794477e-21
4213.81194452559832e-201.90597226279916e-20
4311.76417004321183e-188.82085021605915e-19
4419.63218265915105e-174.81609132957553e-17
450.9999999999999983.42067378438969e-151.71033689219485e-15
460.999999999999892.19663493610247e-131.09831746805123e-13
470.9999999999900141.99712396035696e-119.98561980178479e-12
480.9999999994034421.19311619498435e-095.96558097492173e-10
490.9999999441394561.11721088787161e-075.58605443935805e-08
500.9999957652978228.46940435602068e-064.23470217801034e-06


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.538461538461538NOK
5% type I error level300.769230769230769NOK
10% type I error level340.871794871794872NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/10yz181292933515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/10yz181292933515.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/128lh1292933515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/128lh1292933515.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/228lh1292933515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/228lh1292933515.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/328lh1292933515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/328lh1292933515.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/4uzkk1292933515.png (open in new window)
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http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/5uzkk1292933515.png (open in new window)
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http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/6uzkk1292933515.png (open in new window)
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http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/7nq251292933515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/7nq251292933515.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/8yz181292933515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/8yz181292933515.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/9yz181292933515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933504oel4ubckd1jyfp5/9yz181292933515.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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