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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 05:01:44 -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/t1258718601pt3rpvsrip8wqld.htm/, Retrieved Fri, 20 Nov 2009 13:03:37 +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/t1258718601pt3rpvsrip8wqld.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:

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User-defined keywords:

Dataseries X:

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1,6 0,55 1,6 1,6 1,6 0,56 1,6 1,6 1,61 0,56 1,6 1,6 1,61 0,56 1,61 1,6 1,62 0,56 1,61 1,61 1,63 0,56 1,62 1,61 1,63 0,55 1,63 1,62 1,63 0,56 1,63 1,63 1,63 0,55 1,63 1,63 1,63 0,55 1,63 1,63 1,64 0,56 1,63 1,63 1,64 0,55 1,64 1,63 1,64 0,55 1,64 1,64 1,65 0,55 1,64 1,64 1,65 0,55 1,65 1,64 1,65 0,53 1,65 1,65 1,65 0,53 1,65 1,65 1,65 0,53 1,65 1,65 1,66 0,53 1,65 1,65 1,67 0,54 1,66 1,65 1,68 0,54 1,67 1,66 1,68 0,54 1,68 1,67 1,68 0,55 1,68 1,68 1,68 0,55 1,68 1,68 1,69 0,54 1,68 1,68 1,7 0,55 1,69 1,68 1,7 0,56 1,7 1,69 1,71 0,58 1,7 1,7 1,73 0,59 1,71 1,7 1,73 0,6 1,73 1,71 1,73 0,6 1,73 1,73 1,74 0,6 1,73 1,73 1,74 0,59 1,74 1,73 1,74 0,6 1,74 1,74 1,75 0,6 1,74 1,74 1,78 0,62 1,75 1,74 1,82 0,65 1,78 1,75 1,83 0,68 1,82 1,78 1,84 0,73 1,83 1,82 1,85 0,78 1,84 1,83 1,86 0,78 1,85 1,84 1,86 0,82 1,86 1,85 1,87 0,82 1,86 1,86 1,87 0,81 1,87 1,86 1,87 0,83 1,87 1,87 1,87 0,85 1,87 1,87 1,87 0,86 1,87 1,87 1,87 0,85 1,87 1,87 1,87 0,85 1,87 1,87 1,88 0,82 1,87 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 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 Y[t] = + 0.190434608909453 + 0.0267422785774102X[t] + 1.41632685691026Y1[t] -0.543206943585124Y2[t] + 0.00256413973686938M1[t] -0.00102366937728268M2[t] -0.00371296611758367M3[t] -0.00393033843172351M4[t] + 0.00283302253654549M5[t] -0.00625541430959138M6[t] -0.00126574100982694M7[t] -0.00247493219970349M8[t] -0.00449074038810014M9[t] -0.00542218980057909M10[t] + 0.000158447376802211M11[t] + 0.000576813092250926t + 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) 0.190434608909453 0.184536 1.032 0.308138 0.154069 X 0.0267422785774102 0.055413 0.4826 0.631945 0.315973 Y1 1.41632685691026 0.136422 10.3819 0 0 Y2 -0.543206943585124 0.190211 -2.8558 0.006708 0.003354 M1 0.00256413973686938 0.00509 0.5037 0.617156 0.308578 M2 -0.00102366937728268 0.005139 -0.1992 0.843108 0.421554 M3 -0.00371296611758367 0.005151 -0.7209 0.475088 0.237544 M4 -0.00393033843172351 0.00513 -0.7661 0.448005 0.224002 M5 0.00283302253654549 0.005144 0.5507 0.584807 0.292403 M6 -0.00625541430959138 0.0051 -1.2266 0.226978 0.113489 M7 -0.00126574100982694 0.005141 -0.2462 0.806734 0.403367 M8 -0.00247493219970349 0.005092 -0.4861 0.629496 0.314748 M9 -0.00449074038810014 0.005112 -0.8785 0.384798 0.192399 M10 -0.00542218980057909 0.005442 -0.9963 0.324953 0.162477 M11 0.000158447376802211 0.005402 0.0293 0.976744 0.488372 t 0.000576813092250926 0.000482 1.1978 0.237866 0.118933 Multiple Linear Regression - Regression Statistics Multiple R 0.99797045554514 R-squared 0.995945030140976 Adjusted R-squared 0.994461504582796 F-TEST (value) 671.336617458153 F-TEST (DF numerator) 15 F-TEST (DF denominator) 41 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0075530743384303 Sum Squared Residuals 0.00233900621043602 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.6 1.60527567627637 -0.00527567627636534 2 1.6 1.60253210304024 -0.00253210304023638 3 1.61 1.60041961939219 0.00958038060781355 4 1.61 1.6149423287394 -0.00494232873939999 5 1.62 1.61685043336407 0.00314956663593134 6 1.63 1.62250207817929 0.00749792182071448 7 1.63 1.63653234091878 -0.00653234091877781 8 1.63 1.63073531617108 -0.00073531617107516 9 1.63 1.62902889828916 0.000971101710844663 10 1.63 1.62867426196893 0.00132573803107269 11 1.64 1.63509913502433 0.00490086497566637 12 1.64 1.64941334652311 -0.00941334652311083 13 1.64 1.64712222991638 -0.0071222299163799 14 1.65 1.64411123389448 0.00588876610552125 15 1.65 1.65616201881553 -0.00616201881553127 16 1.65 1.65055454458624 -0.000554544586242875 17 1.65 1.65789471864676 -0.0078947186467628 18 1.65 1.64938309489288 0.000616905107123137 19 1.66 1.65494958128489 0.00505041871510778 20 1.67 1.66874789454214 0.00125210545785672 21 1.68 1.67604009857925 0.0039599014207511 22 1.68 1.68441666139227 -0.00441666139227222 23 1.68 1.68540946501183 -0.00540946501182732 24 1.68 1.68582783072728 -0.00582783072727603 25 1.69 1.68870136077062 0.00129863922937779 26 1.7 1.70012105610360 -0.000121056103597770 27 1.7 1.70700719437457 -0.00700719437457315 28 1.71 1.70246941128838 0.00753058871161881 29 1.73 1.72424027670378 0.0057597232962222 30 1.73 1.73889054343802 -0.00889054343801988 31 1.73 1.73359289095833 -0.00359289095833275 32 1.74 1.73296051286071 0.00703948713929288 33 1.74 1.74541736354789 -0.00541736354788989 34 1.74 1.73989808057758 0.000101919422415291 35 1.75 1.74605553084722 0.00394446915278307 36 1.78 1.76117201070332 0.0188279892966836 37 1.82 1.80217296816122 0.0178270318387845 38 1.83 1.84032110646549 -0.0103211064654933 39 1.84 1.83198072757201 0.00801927242798865 40 1.85 1.84240848141224 0.00759151858775569 41 1.86 1.85847985460602 0.00152014539398443 42 1.86 1.85976912112848 0.000230878871522624 43 1.87 1.85990353808464 0.0100964619153585 44 1.87 1.87316700577034 -0.00316700577034434 45 1.87 1.86683078680990 0.00316921319010442 46 1.87 1.86701099606122 0.00298900393878422 47 1.87 1.87343586911662 -0.00343586911662212 48 1.87 1.87358681204630 -0.00358681204629672 49 1.87 1.87672776487542 -0.00672776487541703 50 1.88 1.87291450049619 0.00708549950380622 51 1.88 1.8844304398457 -0.00443043984569778 52 1.87 1.87962523397373 -0.00962523397373163 53 1.87 1.87253471667938 -0.00253471667937518 54 1.87 1.86945516236134 0.000544837638659635 55 1.87 1.87502164875336 -0.00502164875335573 56 1.87 1.87438927065573 -0.00438927065573010 57 1.87 1.87268285277381 -0.00268285277381029 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.335470259803381 0.670940519606761 0.66452974019662 20 0.273979008304637 0.547958016609273 0.726020991695363 21 0.192449702264332 0.384899404528663 0.807550297735668 22 0.112062041326575 0.224124082653150 0.887937958673425 23 0.0656932717900766 0.131386543580153 0.934306728209923 24 0.0439483747862394 0.0878967495724789 0.95605162521376 25 0.0329823803792105 0.0659647607584209 0.96701761962079 26 0.0157731448872266 0.0315462897744533 0.984226855112773 27 0.0130887001165756 0.0261774002331512 0.986911299883424 28 0.00575522857443537 0.0115104571488707 0.994244771425565 29 0.00271325379204709 0.00542650758409418 0.997286746207953 30 0.0041171699637249 0.0082343399274498 0.995882830036275 31 0.00429878838672432 0.00859757677344865 0.995701211613276 32 0.00266506178877294 0.00533012357754587 0.997334938211227 33 0.0122168000508741 0.0244336001017483 0.987783199949126 34 0.0318472424158655 0.063694484831731 0.968152757584134 35 0.0711666125185204 0.142333225037041 0.92883338748148 36 0.318642799724139 0.637285599448277 0.681357200275861 37 0.824253655055578 0.351492689888844 0.175746344944422 38 0.713193273940103 0.573613452119793 0.286806726059897 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.2 NOK 5% type I error level 8 0.4 NOK 10% type I error level 11 0.55 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/101qfd1258718500.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/101qfd1258718500.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/1x9fw1258718500.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/1x9fw1258718500.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/2f0zj1258718500.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/2f0zj1258718500.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/3ewnd1258718500.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/3ewnd1258718500.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/47xzt1258718500.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/47xzt1258718500.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/5t0q91258718500.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/5t0q91258718500.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/68dum1258718500.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/68dum1258718500.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/78nwt1258718500.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/78nwt1258718500.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/8b2d51258718500.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/8b2d51258718500.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/9wdzp1258718500.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718601pt3rpvsrip8wqld/9wdzp1258718500.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') }

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