Home » date » 2009 » Nov » 21 »

WS Multiple Regression analysis

*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, 21 Nov 2009 02:31:56 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0.htm/, Retrieved Sat, 21 Nov 2009 10:33:18 +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/2009/Nov/21/t1258795986ihv8ew8br9s8ru0.htm/},
    year = {2009},
}
@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 = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
WS Multiple Regression analysis
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
14.5 14.8 14.3 14.7 15.3 16 14.4 15.4 13.7 15 14.2 15.5 13.5 15.1 11.9 11.7 14.6 16.3 15.6 16.7 14.1 15 14.9 14.9 14.2 14.6 14.6 15.3 17.2 17.9 15.4 16.4 14.3 15.4 17.5 17.9 14.5 15.9 14.4 13.9 16.6 17.8 16.7 17.9 16.6 17.4 16.9 16.7 15.7 16 16.4 16.6 18.4 19.1 16.9 17.8 16.5 17.2 18.3 18.6 15.1 16.3 15.7 15.1 18.1 19.2 16.8 17.7 18.9 19.1 19 18 18.1 17.5 17.8 17.8 21.5 21.1 17.1 17.2 18.7 19.4 19 19.8 16.4 17.6 16.9 16.2 18.6 19.5 19.3 19.9 19.4 20 17.6 17.3 18.6 18.9 18.1 18.6 20.4 21.4 18.1 18.6 19.6 19.8 19.9 20.8 19.2 19.6 17.8 17.7 19.2 19.8 22 22.2 21.1 20.7 19.5 17.9 22.2 20.9 20.9 21.2 22.2 21.4 23.5 23 21.5 21.3 24.3 23.9 22.8 22.4 20.3 18.3 23.7 22.8 23.3 22.3 19.6 17.8 18 16.4 17.3 16 16.8 16.4 18.2 17.7 16.5 16.6 16 16.2 18.4 18.3
 
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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
Y[t] = -0.579523901807764 + 1.02934079084922X[t] -0.411228941058860M1[t] -0.95414467705487M2[t] -0.990635208661665M3[t] -1.21391964540537M4[t] -1.36022308765738M5[t] -1.31061465241099M6[t] -1.60787662081260M7[t] + 0.0232524650700799M8[t] -1.55744159337999M9[t] -1.31779819082949M10[t] -0.855700400480026M11[t] + 0.0206660927654979t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)-0.5795239018077640.528695-1.09610.2771250.138562
X1.029340790849220.03437529.944700
M1-0.4112289410588600.256803-1.60130.1142260.057113
M2-0.954144677054870.257203-3.70970.0004360.000218
M3-0.9906352086616650.270918-3.65660.0005180.000259
M4-1.213919645405370.259064-4.68581.5e-058e-06
M5-1.360223087657380.258245-5.26722e-061e-06
M6-1.310614652410990.268991-4.87238e-064e-06
M7-1.607876620812600.26967-5.962400
M80.02325246507007990.2687310.08650.9313180.465659
M9-1.557441593379990.280128-5.55981e-060
M10-1.317798190829490.281753-4.67711.6e-058e-06
M11-0.8557004004800260.270983-3.15780.0024260.001213
t0.02066609276549790.0032476.364100


Multiple Linear Regression - Regression Statistics
Multiple R0.988100561390578
R-squared0.976342719420376
Adjusted R-squared0.97153733430264
F-TEST (value)203.176789268527
F-TEST (DF numerator)13
F-TEST (DF denominator)64
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.46038124843887
Sum Squared Residuals13.5648572105045


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
114.514.26415695446730.235843045532726
214.313.63897323215190.661026767848087
315.314.96129182141460.338708178585409
414.414.14106900292680.258930997073150
513.713.60369533710070.0963046628993436
614.214.18864026053720.0113597394628484
713.513.5003080685613-0.000308068561344622
811.911.65234455832220.247655441677822
914.614.8272842305440-0.227284230544017
1015.615.49933004219970.100669957800292
1114.114.232214580871-0.132214580870997
1214.915.0056469950316-0.105646995031598
1314.214.3062819094835-0.106281909483471
1414.614.50457081984740.0954291801525889
1517.217.16503243721410.0349675627859135
1615.415.4184029069620-0.0184029069620428
1714.314.26342476662630.0365752333736828
1817.516.90705127176130.59294872823875
1914.514.5717738144267-0.0717738144266994
2014.414.16488741137640.23511258862356
2116.616.6192885300038-0.0192885300038222
2216.716.9825321044047-0.282532104404747
2316.616.9506255920951-0.350625592095097
2416.917.1064535317462-0.206453531746169
2515.715.9953521298584-0.295352129858354
2616.416.09070696113740.309293038862626
2718.418.6482344994191-0.248234499419129
2816.917.1074731273369-0.207473127336930
2916.516.36423130334090.135768696659112
3018.317.87558293854170.424417061458319
3115.115.2315032439524-0.131503243952363
3215.715.64808947358150.0519105264185206
3318.118.3083587503787-0.208358750378704
3416.817.0246570594209-0.224657059420876
3518.918.9484980497248-0.0484980497247513
361918.69258967303610.307410326963870
3718.117.78735642931820.312643570681843
3817.817.57390902334240.22609097665759
3921.520.95490919430350.545090805696458
4017.116.73786176601340.362138233986633
4118.718.8767741563951-0.176774156395147
421919.3587850007467-0.358785000746719
4316.416.8176393852423-0.417639385242326
4416.917.0283574567016-0.128357456701596
4518.618.8651541008194-0.265154100819445
4619.319.5371999124751-0.237199912475135
4719.420.1228978746750-0.722897874675023
4817.618.2200442326277-0.620044232627649
4918.619.4764266496930-0.876426649693039
5018.118.6453747692078-0.545374769207761
5120.421.5117045447443-1.11170454474428
5218.118.4269319863883-0.326931986388252
5319.619.53650358592080.0634964140791905
5419.920.6361189047819-0.736118904781916
5519.219.12431408012670.07568591987326
5617.818.8203617561614-1.0203617561614
5719.219.4219494512602-0.221949451260188
582222.1526768446143-0.152676844614318
5921.121.09142954145550.0085704585445521
6019.519.08564182032320.414358179676845
6122.221.78310134457750.416898655422544
6220.921.5696539386017-0.669653938601709
6322.221.75969765793030.440302342069745
6423.523.20402457931080.295975420689204
6521.521.32850788538060.171492114619383
6624.324.07506846960050.224931530399530
6722.822.25446140769050.545538592309473
6820.319.68595934385690.614040656143092
6923.722.75796493699380.942035063006176
7023.322.50360403688520.796395963114784
7119.618.35433436117871.24566563882132
721817.78962374723530.210376252764702
7317.316.98732458260230.31267541739775
7416.816.8768112557114-0.0768112557114228
7518.218.19912984497410.000870155025885298
7616.516.8642366310618-0.364236631061762
771616.3268629652356-0.326862965235565
7818.418.5587531540308-0.158753154030814


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.03136885298751170.06273770597502340.968631147012488
180.08332861952085060.1666572390417010.91667138047915
190.0358712765012430.0717425530024860.964128723498757
200.01568129113475990.03136258226951970.98431870886524
210.007719978309313020.01543995661862600.992280021690687
220.004196930756593030.008393861513186060.995803069243407
230.002725941338018420.005451882676036840.997274058661982
240.0009828045274224030.001965609054844810.999017195472578
250.0003344379001535020.0006688758003070040.999665562099846
260.0001831744186485420.0003663488372970830.999816825581351
270.0001100411703308030.0002200823406616060.99988995882967
283.80091115760199e-057.60182231520398e-050.999961990888424
292.58571994736884e-055.17143989473768e-050.999974142800526
303.20870617793018e-056.41741235586037e-050.99996791293822
311.65660096607704e-053.31320193215408e-050.99998343399034
327.00936240805643e-061.40187248161129e-050.999992990637592
332.26355105250229e-064.52710210500459e-060.999997736448948
349.5325310797767e-071.90650621595534e-060.999999046746892
354.47920801806462e-078.95841603612924e-070.999999552079198
361.71605148427224e-063.43210296854447e-060.999998283948516
375.00507964693327e-061.00101592938665e-050.999994994920353
385.27640354207147e-061.05528070841429e-050.999994723596458
392.54640171539242e-055.09280343078485e-050.999974535982846
400.0003671333604073810.0007342667208147630.999632866639593
410.0004047455272623180.0008094910545246360.999595254472738
420.001891891669595150.00378378333919030.998108108330405
430.001206122506613430.002412245013226850.998793877493387
440.002103629374919860.004207258749839730.99789637062508
450.001243715969146790.002487431938293580.998756284030853
460.000903546826604390.001807093653208780.999096453173396
470.001577123120413280.003154246240826550.998422876879587
480.001234041465730520.002468082931461050.99876595853427
490.004022079701995720.008044159403991440.995977920298004
500.006799867187591720.01359973437518340.993200132812408
510.04291746570557210.08583493141114430.957082534294428
520.041156501845240.082313003690480.95884349815476
530.08316063423843930.1663212684768790.91683936576156
540.07817611131619920.1563522226323980.921823888683801
550.1357594209139100.2715188418278200.86424057908609
560.2266724154054760.4533448308109510.773327584594524
570.1699725571037810.3399451142075610.83002744289622
580.1151252920249870.2302505840499750.884874707975013
590.5281356502365720.9437286995268560.471864349763428
600.4762438814828890.9524877629657780.523756118517111
610.3640770651845780.7281541303691550.635922934815422


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level280.622222222222222NOK
5% type I error level310.688888888888889NOK
10% type I error level350.777777777777778NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/10dc511258795911.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/10dc511258795911.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/1k64k1258795911.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/1k64k1258795911.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/25dh31258795911.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/25dh31258795911.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/38b4p1258795911.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/38b4p1258795911.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/4qm4i1258795911.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/4qm4i1258795911.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/5r6wt1258795911.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/5r6wt1258795911.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/6q1qw1258795911.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/6q1qw1258795911.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/75wf71258795911.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/75wf71258795911.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/8jnyp1258795911.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/8jnyp1258795911.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/98nl31258795911.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258795986ihv8ew8br9s8ru0/98nl31258795911.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 1 ; 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')
}
 





Copyright

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.


Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

  • personalize online software applications according to your needs
  • enforce strict security rules with respect to the data that you upload (e.g. statistical data)
  • manage user sessions of online applications
  • alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.


FreeStatistics.org is powered by