Workshop 7

*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: Fri, 20 Nov 2009 09:29:41 -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/20/t12587349840xo0yj9l7jyutc2.htm/, Retrieved Fri, 20 Nov 2009 17:36:36 +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/20/t12587349840xo0yj9l7jyutc2.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

105.4 102.7 105.4 102.5 105.6 102.2 105.7 102.9 105.8 103.1 105.8 103 105.8 102.8 105.9 102.5 106.1 101.9 106.4 101.9 106.4 101.8 106.3 102 106.2 102.6 106.2 102.5 106.3 102.5 106.4 101.6 106.5 101.4 106.6 100.8 106.6 101.1 106.6 101.3 106.8 101.2 107 101.3 107.2 101.1 107.3 101.3 107.5 101.2 107.6 101.6 107.6 101.7 107.7 101.5 107.7 100.9 107.7 101.5 107.7 101.4 107.6 101.6 107.7 101.7 107.9 101.4 107.9 101.8 107.9 101.7 107.8 101.4 107.6 101.2 107.4 101 107 101.7 107 102.4 107.2 102 107.5 102.1 107.8 102 107.8 101.8 107.7 102.7 107.6 102.3 107.6 101.9 107.5 102 107.5 102.3 107.6 102.8 107.6 102.4 107.9 102.3 107.6 102.7 107.5 102.7 107.5 102.9 107.6 103 107.7 102.2 107.8 102.3 107.9 102.8 107.9 102.8

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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkl[t] = + 138.628299151492 -0.309228385849689Inflatie[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 138.628299151492 15.837795 8.753 0 0 Inflatie -0.309228385849689 0.155277 -1.9915 0.051066 0.025533 Multiple Linear Regression - Regression Statistics Multiple R 0.250968372520995 R-squared 0.0629851240058372 Adjusted R-squared 0.0471035159381394 F-TEST (value) 3.965916029243 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.0510662732824825 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.745791631113164 Sum Squared Residuals 32.8161042652676 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 105.4 106.870543924729 -1.47054392472873 2 105.4 106.932389601899 -1.53238960189884 3 105.6 107.025158117654 -1.42515811765376 4 105.7 106.808698247559 -1.10869824755897 5 105.8 106.746852570389 -0.94685257038904 6 105.8 106.777775408974 -0.977775408974007 7 105.8 106.839621086144 -1.03962108614395 8 105.9 106.932389601899 -1.03238960189884 9 106.1 107.117926633409 -1.01792663340867 10 106.4 107.117926633409 -0.717926633408654 11 106.4 107.148849471994 -0.748849471993626 12 106.3 107.087003794824 -0.787003794823696 13 106.2 106.901466763314 -0.701466763313879 14 106.2 106.932389601899 -0.732389601898846 15 106.3 106.932389601899 -0.632389601898852 16 106.4 107.210695149164 -0.810695149163565 17 106.5 107.272540826334 -0.772540826333504 18 106.6 107.458077857843 -0.858077857843326 19 106.6 107.365309342088 -0.76530934208842 20 106.6 107.303463664918 -0.703463664918482 21 106.8 107.334386503503 -0.534386503503446 22 107 107.303463664918 -0.303463664918476 23 107.2 107.365309342088 -0.165309342088412 24 107.3 107.303463664918 -0.00346366491847871 25 107.5 107.334386503503 0.165613496496557 26 107.6 107.210695149164 0.389304850836424 27 107.6 107.179772310579 0.420227689421396 28 107.7 107.241617987749 0.458382012251466 29 107.7 107.427155019258 0.272844980741654 30 107.7 107.241617987749 0.458382012251466 31 107.7 107.272540826334 0.427459173666498 32 107.6 107.210695149164 0.389304850836424 33 107.7 107.179772310579 0.520227689421404 34 107.9 107.272540826334 0.627459173666501 35 107.9 107.148849471994 0.751150528006374 36 107.9 107.179772310579 0.720227689421407 37 107.8 107.272540826334 0.527459173666493 38 107.6 107.334386503503 0.265613496496551 39 107.4 107.396232180673 0.00376781932662412 40 107 107.179772310579 -0.179772310578599 41 107 106.963312440484 0.0366875595161843 42 107.2 107.087003794824 0.11299620517631 43 107.5 107.056080956239 0.443919043761274 44 107.8 107.087003794824 0.712996205176304 45 107.8 107.148849471994 0.651150528006366 46 107.7 106.870543924729 0.829456075271093 47 107.6 106.994235279069 0.605764720931207 48 107.6 107.117926633409 0.482073366591334 49 107.5 107.087003794824 0.412996205176307 50 107.5 106.994235279069 0.505764720931213 51 107.6 106.839621086144 0.760378913856051 52 107.6 106.963312440484 0.636687559516179 53 107.9 106.994235279069 0.905764720931219 54 107.6 106.870543924729 0.729456075271084 55 107.5 106.870543924729 0.62945607527109 56 107.5 106.808698247559 0.691301752441029 57 107.6 106.777775408974 0.82222459102599 58 107.7 107.025158117654 0.674841882346249 59 107.8 106.994235279069 0.80576472093121 60 107.9 106.839621086144 1.06037891385606 61 107.9 106.839621086144 1.06037891385606 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0250964473554707 0.0501928947109415 0.97490355264453 6 0.00808807516243836 0.0161761503248767 0.991911924837562 7 0.00362281824282651 0.00724563648565303 0.996377181757173 8 0.00575133818551825 0.0115026763710365 0.994248661814482 9 0.0143788168960112 0.0287576337920223 0.985621183103989 10 0.0288702907906725 0.057740581581345 0.971129709209328 11 0.0258361420904461 0.0516722841808922 0.974163857909554 12 0.0222763819609327 0.0445527639218653 0.977723618039067 13 0.0393816861219216 0.0787633722438432 0.960618313878078 14 0.0668008641662129 0.133601728332426 0.933199135833787 15 0.148875668308552 0.297751336617104 0.851124331691448 16 0.176396926584072 0.352793853168145 0.823603073415928 17 0.20326415380815 0.4065283076163 0.79673584619185 18 0.216057773958339 0.432115547916678 0.783942226041661 19 0.267248008247637 0.534496016495274 0.732751991752363 20 0.4109292809249 0.8218585618498 0.5890707190751 21 0.573923001319284 0.852153997361433 0.426076998680717 22 0.772852443290473 0.454295113419055 0.227147556709527 23 0.875509536159217 0.248980927681566 0.124490463840783 24 0.954212949923405 0.0915741001531894 0.0457870500765947 25 0.982421326803674 0.0351573463926512 0.0175786731963256 26 0.99749170260408 0.00501659479184145 0.00250829739592073 27 0.999493670515697 0.00101265896860644 0.000506329484303221 28 0.999799622669283 0.000400754661433188 0.000200377330716594 29 0.999707960862644 0.000584078274711001 0.000292039137355501 30 0.999809315256334 0.000381369487332844 0.000190684743666422 31 0.999817234786196 0.000365530427608411 0.000182765213804206 32 0.999815782399926 0.000368435200148179 0.000184217600074089 33 0.999853065793984 0.000293868412032747 0.000146934206016374 34 0.999903303322528 0.000193393354943552 9.6696677471776e-05 35 0.999962288705724 7.54225885522308e-05 3.77112942761154e-05 36 0.999981516437207 3.69671255865227e-05 1.84835627932614e-05 37 0.999981627305686 3.67453886278648e-05 1.83726943139324e-05 38 0.99996396756905 7.20648618988063e-05 3.60324309494032e-05 39 0.999912832345486 0.000174335309026987 8.71676545134933e-05 40 0.999964467091453 7.10658170943442e-05 3.55329085471721e-05 41 0.999998647473245 2.7050535106731e-06 1.35252675533655e-06 42 0.99999983477046 3.30459081437608e-07 1.65229540718804e-07 43 0.999999776918393 4.4616321401979e-07 2.23081607009895e-07 44 0.99999962322536 7.53549279051864e-07 3.76774639525932e-07 45 0.99999929529489 1.40941022172136e-06 7.04705110860679e-07 46 0.999998721258512 2.55748297697552e-06 1.27874148848776e-06 47 0.999996178287939 7.64342412227882e-06 3.82171206113941e-06 48 0.99998634159904 2.73168019188187e-05 1.36584009594094e-05 49 0.999971626301324 5.67473973513382e-05 2.83736986756691e-05 50 0.999957617940749 8.47641185018692e-05 4.23820592509346e-05 51 0.999868815800677 0.000262368398646081 0.000131184199323040 52 0.999647590370066 0.000704819259868431 0.000352409629934215 53 0.999033139145377 0.00193372170924666 0.000966860854623332 54 0.996646245815013 0.00670750836997473 0.00335375418498737 55 0.992747936783642 0.0145041264327169 0.00725206321635846 56 0.987681598895539 0.0246368022089223 0.0123184011044612 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 30 0.576923076923077 NOK 5% type I error level 37 0.711538461538462 NOK 10% type I error level 42 0.807692307692308 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/10gm221258734577.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/10gm221258734577.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/182ey1258734577.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/182ey1258734577.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/24w5a1258734577.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/24w5a1258734577.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/3ydar1258734577.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/3ydar1258734577.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/4n5171258734577.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/4n5171258734577.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/5juzm1258734577.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/5juzm1258734577.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/6h3kl1258734577.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/6h3kl1258734577.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/7wzd91258734577.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/7wzd91258734577.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/864ho1258734577.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/864ho1258734577.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/9atd01258734577.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587349840xo0yj9l7jyutc2/9atd01258734577.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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