4e multiple

*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, 24 Nov 2009 13:45:36 -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/24/t1259095580oo7ab03s8tdojwv.htm/, Retrieved Tue, 24 Nov 2009 21:46:32 +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/24/t1259095580oo7ab03s8tdojwv.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:

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2,40 2,00 1,70 1,00 1,20 1,40 2,00 2,00 2,40 1,70 1,00 1,20 2,10 2,00 2,00 2,40 1,70 1,00 2,00 2,00 2,10 2,00 2,40 1,70 1,80 2,00 2,00 2,10 2,00 2,40 2,70 2,00 1,80 2,00 2,10 2,00 2,30 2,00 2,70 1,80 2,00 2,10 1,90 2,00 2,30 2,70 1,80 2,00 2,00 2,00 1,90 2,30 2,70 1,80 2,30 2,00 2,00 1,90 2,30 2,70 2,80 2,00 2,30 2,00 1,90 2,30 2,40 2,00 2,80 2,30 2,00 1,90 2,30 2,00 2,40 2,80 2,30 2,00 2,70 2,00 2,30 2,40 2,80 2,30 2,70 2,00 2,70 2,30 2,40 2,80 2,90 2,00 2,70 2,70 2,30 2,40 3,00 2,00 2,90 2,70 2,70 2,30 2,20 2,00 3,00 2,90 2,70 2,70 2,30 2,00 2,20 3,00 2,90 2,70 2,80 2,21 2,30 2,20 3,00 2,90 2,80 2,25 2,80 2,30 2,20 3,00 2,80 2,25 2,80 2,80 2,30 2,20 2,20 2,45 2,80 2,80 2,80 2,30 2,60 2,50 2,20 2,80 2,80 2,80 2,80 2,50 2,60 2,20 2,80 2,80 2,50 2,64 2,80 2,60 2,20 2,80 2,40 2,75 2,50 2,80 2,60 2,20 2,30 2,93 2,40 2,50 2,80 2,60 1,90 3,00 2,30 2,40 2,50 2,80 1,70 3,17 1,90 2,30 2,40 2,50 2,00 3,25 1,70 1,90 2,30 2,40 2,10 3,39 2,00 1,70 1,90 2,30 1,70 3,50 2,10 2,00 1,70 1,90 1 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation X1[t] = + 0.185036980311549 + 0.073579736810377X2[t] + 1.11584285808511X3[t] -0.256942291868568X4[t] + 0.278431112422916X5[t] -0.293078735356719X6[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) 0.185036980311549 0.231657 0.7988 0.428211 0.214106 X2 0.073579736810377 0.07253 1.0145 0.315243 0.157621 X3 1.11584285808511 0.13542 8.2399 0 0 X4 -0.256942291868568 0.207051 -1.241 0.220411 0.110206 X5 0.278431112422916 0.211157 1.3186 0.193312 0.096656 X6 -0.293078735356719 0.156309 -1.875 0.06664 0.03332 Multiple Linear Regression - Regression Statistics Multiple R 0.930382067608297 R-squared 0.86561079172709 Adjusted R-squared 0.8521718708998 F-TEST (value) 64.4107367586582 F-TEST (DF numerator) 5 F-TEST (DF denominator) 50 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.442858093411831 Sum Squared Residuals 9.8061645450181 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2.4 1.89599412621650 0.504005873783495 2 2 2.50015404715485 -0.500154047154846 3 2.1 2.12747482538019 -0.0274748253801892 4 2 2.33158269188247 -0.331582691882465 5 1.8 1.87777661716823 -0.077776617168228 6 2.7 1.82537688012304 0.874623119876957 7 2.3 2.82387292599539 -0.523872925995388 8 1.9 2.11990937113072 -0.219909371130724 9 2 2.08555289289608 -0.0855528928960763 10 2.3 1.9247707886618 0.375229211338199 11 2.8 2.23968846607400 0.560311533926003 12 2.4 2.86560181294096 -0.465601812940959 13 2.3 2.34501498396384 -0.0450149839638354 14 2.7 2.38749955050719 0.312500449492806 15 2.7 2.60161911028057 0.0983808897194326 16 2.9 2.58823057643354 0.311769423566464 17 3 2.95207946655540 0.0479205334446046 18 2.2 2.89504379984750 -0.695043799847505 19 2.3 2.03236150667715 0.267638493322853 20 2.8 2.33417873488164 0.465821265118362 21 2.8 2.61729636073574 0.182703639264255 22 2.8 2.75113131432913 0.0488686856708722 23 2.2 2.87575494436699 -0.675754944366989 24 2.6 2.06338884867809 0.536611151321915 25 2.8 2.66389136703327 0.136108632966732 26 2.5 2.62752551760257 -0.127525517602565 27 2.4 2.53669765903566 -0.136697659035659 28 2.3 2.45389514175548 -0.153895141755482 29 1.9 2.23101058591234 -0.331010585912336 30 1.7 1.88295673648764 -0.182956736487639 31 2 1.76991622285626 0.230083777143744 32 2.1 2.08429413037546 0.0157058696245409 33 1.7 2.18843477133064 -0.488434771330645 34 1.8 1.85854847970796 -0.0585484797079644 35 1.8 2.02386613342073 -0.223866133420735 36 1.8 1.86484955941008 -0.0648495594100774 37 1.3 2.00992416479506 -0.709924164795057 38 1.3 1.43373182273839 -0.133731822738388 39 1.3 1.56956094235371 -0.26956094235371 40 1.2 1.43034538614225 -0.230345386142252 41 1.4 1.4653004680121 -0.0653004680121008 42 2.2 1.71416326881598 0.485836731184022 43 2.9 2.52760598566806 0.372394014331942 44 3.1 3.18813624885303 -0.0881362488530329 45 3.5 3.39557435902905 0.104425640970954 46 3.6 3.75096183430004 -0.15096183430004 47 4.4 3.610300311096 0.789699688903997 48 4.1 4.53003706627505 -0.430037066275055 49 5.1 3.90034199245427 1.19965800754573 50 5.8 5.28670455450261 0.513295445497391 51 5.9 5.50610429390723 0.393895706092768 52 5.4 5.8093342900144 -0.409334290014404 53 5.5 5.12754167512432 0.372458324875683 54 4.8 5.16968277705279 -0.369682777052794 55 3.2 4.15390626221353 -0.953906262213525 56 2.7 2.67350134884305 0.0264986511569475 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.496974768679411 0.993949537358823 0.503025231320589 10 0.33525812504257 0.67051625008514 0.66474187495743 11 0.483334929740901 0.966669859481802 0.516665070259099 12 0.405026140458623 0.810052280917246 0.594973859541377 13 0.313067085638713 0.626134171277425 0.686932914361287 14 0.291702957379306 0.583405914758612 0.708297042620694 15 0.216466296204152 0.432932592408305 0.783533703795848 16 0.238570409634258 0.477140819268516 0.761429590365742 17 0.210069613222579 0.420139226445159 0.78993038677742 18 0.258276415672042 0.516552831344084 0.741723584327958 19 0.19107972013187 0.38215944026374 0.80892027986813 20 0.159432753429131 0.318865506858263 0.840567246570869 21 0.113735253324280 0.227470506648560 0.88626474667572 22 0.0782806326040034 0.156561265208007 0.921719367395997 23 0.115178083700688 0.230356167401376 0.884821916299312 24 0.104946813795048 0.209893627590096 0.895053186204952 25 0.079964605751412 0.159929211502824 0.920035394248588 26 0.0547237146614571 0.109447429322914 0.945276285338543 27 0.0341851063691204 0.0683702127382408 0.96581489363088 28 0.0233631030411595 0.046726206082319 0.97663689695884 29 0.0233175188109267 0.0466350376218534 0.976682481189073 30 0.0161936102537599 0.0323872205075198 0.98380638974624 31 0.0125892433320088 0.0251784866640176 0.987410756667991 32 0.00848421394679435 0.0169684278935887 0.991515786053206 33 0.00565132118505816 0.0113026423701163 0.994348678814942 34 0.00318867771880137 0.00637735543760275 0.996811322281199 35 0.00160284528968789 0.00320569057937579 0.998397154710312 36 0.00078229157771132 0.00156458315542264 0.999217708422289 37 0.00129873383441236 0.00259746766882473 0.998701266165588 38 0.000693543337269381 0.00138708667453876 0.99930645666273 39 0.00040240760556238 0.00080481521112476 0.999597592394438 40 0.000294766327560781 0.000589532655121561 0.99970523367244 41 0.000322367248992252 0.000644734497984504 0.999677632751008 42 0.000831085179947803 0.00166217035989561 0.999168914820052 43 0.00235246909875911 0.00470493819751822 0.997647530901241 44 0.00317737231068529 0.00635474462137059 0.996822627689315 45 0.00395303077700117 0.00790606155400233 0.996046969222999 46 0.00898323892181925 0.0179664778436385 0.991016761078181 47 0.00822416335619052 0.0164483267123810 0.99177583664381 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.307692307692308 NOK 5% type I error level 20 0.512820512820513 NOK 10% type I error level 21 0.538461538461538 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/10tqvy1259095530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/10tqvy1259095530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/1uhru1259095530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/1uhru1259095530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/2r86j1259095530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/2r86j1259095530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/3eqy51259095530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/3eqy51259095530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/4glh01259095530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/4glh01259095530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/5f7ib1259095530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/5f7ib1259095530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/6nmpk1259095530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/6nmpk1259095530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/7cltl1259095530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/7cltl1259095530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/8x46o1259095530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/8x46o1259095530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/9fm2u1259095530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259095580oo7ab03s8tdojwv/9fm2u1259095530.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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