SHW WS7

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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: Wed, 18 Nov 2009 07:48:40 -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/18/t1258555896rkuqhcod7f92bg0.htm/, Retrieved Wed, 18 Nov 2009 15:51:51 +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/18/t1258555896rkuqhcod7f92bg0.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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Dataseries X:

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7.8 2.61 7.8 8.3 8.5 8.6 8 2.26 7.8 7.8 8.3 8.5 8.6 2.41 8 7.8 7.8 8.3 8.9 2.26 8.6 8 7.8 7.8 8.9 2.03 8.9 8.6 8 7.8 8.6 2.86 8.9 8.9 8.6 8 8.3 2.55 8.6 8.9 8.9 8.6 8.3 2.27 8.3 8.6 8.9 8.9 8.3 2.26 8.3 8.3 8.6 8.9 8.4 2.57 8.3 8.3 8.3 8.6 8.5 3.07 8.4 8.3 8.3 8.3 8.4 2.76 8.5 8.4 8.3 8.3 8.6 2.51 8.4 8.5 8.4 8.3 8.5 2.87 8.6 8.4 8.5 8.4 8.5 3.14 8.5 8.6 8.4 8.5 8.5 3.11 8.5 8.5 8.6 8.4 8.5 3.16 8.5 8.5 8.5 8.6 8.5 2.47 8.5 8.5 8.5 8.5 8.5 2.57 8.5 8.5 8.5 8.5 8.5 2.89 8.5 8.5 8.5 8.5 8.5 2.63 8.5 8.5 8.5 8.5 8.5 2.38 8.5 8.5 8.5 8.5 8.5 1.69 8.5 8.5 8.5 8.5 8.5 1.96 8.5 8.5 8.5 8.5 8.6 2.19 8.5 8.5 8.5 8.5 8.4 1.87 8.6 8.5 8.5 8.5 8.1 1.6 8.4 8.6 8.5 8.5 8 1.63 8.1 8.4 8.6 8.5 8 1.22 8 8.1 8.4 8.6 8 1.21 8 8 8.1 8.4 8 1.49 8 8 8 8.1 7.9 1.64 8 8 8 8 7.8 1.66 7.9 8 8 8 7.8 1.77 7.8 7.9 8 8 7.9 1.82 7.8 7.8 7.9 8 8.1 1.78 7.9 7.8 7.8 7.9 8 1.28 8.1 7.9 7.8 7.8 7.6 1.29 8 8.1 7.9 7.8 7.3 1.37 7.6 8 8.1 7.9 7 1.12 7.3 7.6 8 8.1 6.8 1.51 7 7.3 7.6 8 7 2.24 6.8 7 7.3 7.6 7 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] = + 0.64027424928453 + 0.0377069120685262X[t] + 1.36847147956655Y1[t] -0.520279112579848Y2[t] -0.356940166912918Y3[t] + 0.433839342938828Y4[t] + 0.00539445361760528M1[t] -0.097791454205246M2[t] + 0.0385041622066780M3[t] -0.0180838134340601M4[t] -0.100888043206709M5[t] + 0.00891596322495888M6[t] -0.0471410167153764M7[t] + 0.0400868743274814M8[t] -0.0691759095235761M9[t] -0.0311358304896136M10[t] -0.00207945143914559M11[t] -0.00491688463380581t + 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) 0.64027424928453 0.680519 0.9409 0.352718 0.176359 X 0.0377069120685262 0.021874 1.7238 0.092867 0.046434 Y1 1.36847147956655 0.13186 10.3782 0 0 Y2 -0.520279112579848 0.242107 -2.149 0.03807 0.019035 Y3 -0.356940166912918 0.241719 -1.4767 0.148003 0.074001 Y4 0.433839342938828 0.140082 3.097 0.003665 0.001832 M1 0.00539445361760528 0.090308 0.0597 0.952681 0.47634 M2 -0.097791454205246 0.090125 -1.0851 0.28473 0.142365 M3 0.0385041622066780 0.090617 0.4249 0.673298 0.336649 M4 -0.0180838134340601 0.091132 -0.1984 0.843762 0.421881 M5 -0.100888043206709 0.090185 -1.1187 0.270297 0.135149 M6 0.00891596322495888 0.090649 0.0984 0.922165 0.461083 M7 -0.0471410167153764 0.090196 -0.5226 0.604252 0.302126 M8 0.0400868743274814 0.089741 0.4467 0.657632 0.328816 M9 -0.0691759095235761 0.094395 -0.7328 0.468154 0.234077 M10 -0.0311358304896136 0.094576 -0.3292 0.743802 0.371901 M11 -0.00207945143914559 0.094437 -0.022 0.982548 0.491274 t -0.00491688463380581 0.002338 -2.1031 0.042131 0.021065 Multiple Linear Regression - Regression Statistics Multiple R 0.986148532657664 R-squared 0.972488928462864 Adjusted R-squared 0.96018134382783 F-TEST (value) 79.0154166963502 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.133055583251779 Sum Squared Residuals 0.672743952909903 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.8 7.79195469548762 0.0080453045123828 2 8 7.9587981391856 0.0412018608143949 3 8.6 8.461229418556 0.138770581443997 4 8.9 8.89417591522573 0.00582408477427472 5 8.9 8.82476815398298 0.0752318460170184 6 8.6 8.67747204746378 -0.0774720474637813 7 8.3 8.34748915196785 -0.0474891519678527 8 8.3 8.29493631578336 0.00506368421664129 9 8.3 8.44354536202564 -0.143545362025639 10 8.4 8.46528794635927 -0.065287946359266 11 8.5 8.5149762418852 -0.0149762418851976 12 8.4 8.58526890264796 -0.185268902647964 13 8.6 8.3517506677087 0.248249332291298 14 8.5 8.5906344883706 -0.0906344883705972 15 8.5 8.57036906691977 -0.0703690669197686 16 8.5 8.44498894286469 0.0550110571353125 17 8.5 8.48161505934072 0.0183849406592841 18 8.5 8.51710047751741 -0.0171004775174126 19 8.5 8.45989730415012 0.0401026958498757 20 8.5 8.5542745224211 -0.0542745224211044 21 8.5 8.43029105679842 0.0697089432015756 22 8.5 8.45398752318145 0.0460124768185504 23 8.5 8.45210924827083 0.0478907517291711 24 8.5 8.45945268133467 0.0405473186653293 25 8.6 8.46860284009423 0.131397159905769 26 8.4 8.4852809837323 -0.0852809837322994 27 8.1 8.28075664208062 -0.180756642080624 28 8 7.87820335112285 0.121796648877152 29 8 7.90903095626206 0.0909690437379361 30 8 8.08588310168334 -0.085883101683336 31 8 7.94100938629802 0.0589906137019749 32 7.9 7.98559249522347 -0.0855924952234731 33 7.8 7.73531981702333 0.0646801829766733 34 7.8 7.68777153505235 0.112228464947649 35 7.9 7.80151830302172 0.0984816969782847 36 8.1 7.92632982369838 0.173670176301621 37 8 8.08623638700936 -0.0862363870093555 38 7.6 7.70191367650947 -0.101913676509469 39 7.3 7.31294418159573 -0.0129441815957346 40 7 7.16204467974509 -0.162044679745092 41 6.8 6.93396368342064 -0.133963683420637 42 7 6.88231260178751 0.117687398212487 43 7.1 7.20241394128285 -0.102413941282846 44 7.2 7.2644085404438 -0.0644085404437963 45 7.1 7.09084376415261 0.00915623584739075 46 6.9 6.99295299540693 -0.0929529954069337 47 6.7 6.83139620682226 -0.131396206822258 48 6.7 6.72894859231899 -0.0289485923189865 49 6.6 6.90145540970009 -0.301455409700094 50 6.9 6.66337271220203 0.236627287797970 51 7.3 7.17470069084787 0.125299309152130 52 7.5 7.52058711104165 -0.0205871110416475 53 7.3 7.3506221469936 -0.0506221469936013 54 7.1 7.03723177154796 0.0627682284520427 55 6.9 6.84919021630115 0.0508097836988479 56 7.1 6.90078812612827 0.199211873871733 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0280315830093683 0.0560631660187367 0.971968416990632 22 0.158728140522985 0.317456281045969 0.841271859477015 23 0.0930542984084869 0.186108596816974 0.906945701591513 24 0.0500895361539847 0.100179072307969 0.949910463846015 25 0.0523332596285594 0.104666519257119 0.94766674037144 26 0.0418311358326226 0.0836622716652452 0.958168864167377 27 0.259069229524562 0.518138459049123 0.740930770475438 28 0.252553946277588 0.505107892555176 0.747446053722412 29 0.224727154855935 0.449454309711869 0.775272845144065 30 0.399813892821027 0.799627785642054 0.600186107178973 31 0.334593136106853 0.669186272213706 0.665406863893147 32 0.271332156121707 0.542664312243414 0.728667843878293 33 0.215817387098228 0.431634774196457 0.784182612901771 34 0.121649153121378 0.243298306242755 0.878350846878622 35 0.0580298936550745 0.116059787310149 0.941970106344926 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 2 0.133333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/10erer1258555715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/10erer1258555715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/1d2iz1258555715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/1d2iz1258555715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/2a4n71258555715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/2a4n71258555715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/30qs91258555715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/30qs91258555715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/441kj1258555715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/441kj1258555715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/5j6u31258555715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/5j6u31258555715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/6pd6j1258555715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/6pd6j1258555715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/7zj7y1258555715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/7zj7y1258555715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/8g1db1258555715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/8g1db1258555715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/9ryd91258555715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555896rkuqhcod7f92bg0/9ryd91258555715.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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