Multi

*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: Sun, 22 Nov 2009 11:56:10 -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/22/t1258916349wnii96rg9pmx85b.htm/, Retrieved Sun, 22 Nov 2009 19:59:22 +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/22/t1258916349wnii96rg9pmx85b.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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17823,2 1,2218 17872 1,249 17420,4 1,2991 16704,4 1,3408 15991,2 1,3119 16583,6 1,3014 19123,5 1,3201 17838,7 1,2938 17209,4 1,2694 18586,5 1,2165 16258,1 1,2037 15141,6 1,2292 19202,1 1,2256 17746,5 1,2015 19090,1 1,1786 18040,3 1,1856 17515,5 1,2103 17751,8 1,1938 21072,4 1,202 17170 1,2271 19439,5 1,277 19795,4 1,265 17574,9 1,2684 16165,4 1,2811 19464,6 1,2727 19932,1 1,2611 19961,2 1,2881 17343,4 1,3213 18924,2 1,2999 18574,1 1,3074 21350,6 1,3242 18594,6 1,3516 19823,1 1,3511 20844,4 1,3419 19640,2 1,3716 17735,4 1,3622 19813,6 1,3896 22160 1,4227 20664,3 1,4684 17877,4 1,457 20906,5 1,4718 21164,1 1,4748 21374,4 1,5527 22952,3 1,5751 21343,5 1,5557 23899,3 1,5553 22392,9 1,577 18274,1 1,4975 22786,7 1,437 22321,5 1,3322 17842,2 1,2732 16373,5 1,3449 15993,8 1,3239 16446,1 1,2785 17729 1,305 16643 1,319 16196,7 1,365 18252,1 1,4016 17570,4 1,4088 15836,8 1,4268

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 EUDO[t] = + 0.82485539822803 + 2.71267840761339e-05UITV[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) 0.82485539822803 0.106285 7.7608 0 0 UITV 2.71267840761339e-05 6e-06 4.8189 1.1e-05 5e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.534702307917198 R-squared 0.285906558091978 Adjusted R-squared 0.273594602197012 F-TEST (value) 23.2218634091175 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.07793610141238e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.089609615924503 Sum Squared Residuals 0.465733229435943 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.2218 1.30834149617378 -0.0865414961737837 2 1.249 1.30966528323670 -0.0606652832366949 3 1.2991 1.29741482754791 0.00168517245208699 4 1.3408 1.2779920501494 0.062807949850599 5 1.3119 1.25864522774630 0.0532547722536977 6 1.3014 1.27471513463300 0.0266848653669959 7 1.3201 1.34361445350798 -0.0235144535079765 8 1.2938 1.30876196132696 -0.0149619613269597 9 1.2694 1.29169107610785 -0.0222910761078486 10 1.2165 1.32904737045909 -0.112547370459093 11 1.2037 1.26588536641622 -0.0621853664162225 12 1.2292 1.23559831199522 -0.00639831199521889 13 1.2256 1.34574661873636 -0.120146618736361 14 1.2015 1.30626087183514 -0.104760871835140 15 1.1786 1.34270841891983 -0.164108418919834 16 1.1856 1.31423072099671 -0.128630720996708 17 1.2103 1.29999458471355 -0.0896945847135533 18 1.1938 1.30640464379074 -0.112604643790744 19 1.202 1.39648184299395 -0.194481842993954 20 1.2271 1.29062228081525 -0.0635222808152489 21 1.277 1.35218651727603 -0.075186517276035 22 1.265 1.36184093972873 -0.096840939728731 23 1.2684 1.30160591568768 -0.0332059156876757 24 1.2811 1.26337071353236 0.0177292864676351 25 1.2727 1.35286739955635 -0.0801673995563459 26 1.2611 1.36554917111194 -0.104449171111938 27 1.2881 1.36633856052855 -0.078238560528554 28 1.3213 1.29532606517405 0.0259739348259493 29 1.2999 1.33820808544160 -0.0383080854416030 30 1.3074 1.32871099833655 -0.0213109983365487 31 1.3242 1.40402851432393 -0.0798285143239343 32 1.3516 1.32926709741011 0.0223329025898906 33 1.3511 1.36259235164764 -0.0114923516476398 34 1.3419 1.39029693622460 -0.0483969362245954 35 1.3716 1.35763086284012 0.0139691371598850 36 1.3622 1.30595976453190 0.056240235468105 37 1.3896 1.36233464719892 0.0272653528010834 38 1.4227 1.42598493335516 -0.0032849333551571 39 1.4684 1.38541140241248 0.0829885975875163 40 1.457 1.30981176787071 0.147188232129294 41 1.4718 1.39198150951572 0.0798184904842767 42 1.4748 1.39896936909374 0.0758306309062647 43 1.5527 1.40467413178495 0.148025868215054 44 1.5751 1.44747748437868 0.127622515621322 45 1.5557 1.40383591415699 0.151864085843006 46 1.5553 1.47316654889878 0.082133451101223 47 1.577 1.43230276136649 0.144697238633511 48 1.4975 1.32057296311371 0.176927036886292 49 1.437 1.44298528893567 -0.00598528893567027 50 1.3322 1.43036590898345 -0.0981659089834528 51 1.2732 1.30885690507123 -0.0356569050712261 52 1.3449 1.26901579729861 0.0758842027013917 53 1.3239 1.2587157573849 0.0651842426150998 54 1.2785 1.27098520182254 0.00751479817746438 55 1.305 1.30578615311381 -0.000786153113807886 56 1.319 1.27632646560713 0.0426735343928736 57 1.365 1.26421978187395 0.100780218126052 58 1.4016 1.31997617386403 0.0816238261359665 59 1.4088 1.30148384515933 0.107316154840667 60 1.4268 1.25445685228495 0.172343147715053 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0680389520275418 0.136077904055084 0.931961047972458 6 0.0213866909295609 0.0427733818591219 0.978613309070439 7 0.0484911579490474 0.0969823158980948 0.951508842050953 8 0.0194961620368426 0.0389923240736852 0.980503837963157 9 0.00853158642016578 0.0170631728403316 0.991468413579834 10 0.00776369785433023 0.0155273957086605 0.99223630214567 11 0.01715041046357 0.03430082092714 0.98284958953643 12 0.0112836045482085 0.0225672090964169 0.988716395451791 13 0.0083707861979769 0.0167415723959538 0.991629213802023 14 0.00848908976108815 0.0169781795221763 0.991510910238912 15 0.0122390356246309 0.0244780712492618 0.98776096437537 16 0.0142367372244303 0.0284734744488606 0.98576326277557 17 0.0112550590055374 0.0225101180110748 0.988744940994463 18 0.0115106353851468 0.0230212707702937 0.988489364614853 19 0.0143433130702333 0.0286866261404666 0.985656686929767 20 0.0105159956284337 0.0210319912568674 0.989484004371566 21 0.0103201138626563 0.0206402277253125 0.989679886137344 22 0.0102217632700013 0.0204435265400025 0.989778236729999 23 0.007029235826497 0.014058471652994 0.992970764173503 24 0.00419742869240824 0.00839485738481649 0.995802571307592 25 0.00427934633721960 0.00855869267443919 0.99572065366278 26 0.00535039530183814 0.0107007906036763 0.994649604698162 27 0.00728165331231167 0.0145633066246233 0.992718346687688 28 0.00741848715748327 0.0148369743149665 0.992581512842517 29 0.00816520493227677 0.0163304098645535 0.991834795067723 30 0.00869624636296177 0.0173924927259235 0.991303753637038 31 0.0190598851610973 0.0381197703221946 0.980940114838903 32 0.0264570995783551 0.0529141991567103 0.973542900421645 33 0.0371859206238326 0.0743718412476651 0.962814079376167 34 0.0572615637722851 0.114523127544570 0.942738436227715 35 0.0751060083579315 0.150212016715863 0.924893991642069 36 0.0825238371143128 0.165047674228626 0.917476162885687 37 0.101554404463711 0.203108808927422 0.89844559553629 38 0.137231035226420 0.274462070452839 0.86276896477358 39 0.199222981516519 0.398445963033038 0.800777018483481 40 0.33212415546889 0.66424831093778 0.66787584453111 41 0.350304488609884 0.700608977219768 0.649695511390116 42 0.342816431028333 0.685632862056667 0.657183568971667 43 0.452978566396098 0.905957132792197 0.547021433603902 44 0.490352365139488 0.980704730278977 0.509647634860512 45 0.586429463015877 0.827141073968246 0.413570536984123 46 0.553928420820472 0.892143158359056 0.446071579179528 47 0.771633337769014 0.456733324461972 0.228366662230986 48 0.932278395239436 0.135443209521129 0.0677216047605644 49 0.936841292161853 0.126317415676295 0.0631587078381474 50 0.899844478684905 0.200311042630191 0.100155521315095 51 0.906988160046388 0.186023679907224 0.0930118399536118 52 0.845999562792767 0.308000874414466 0.154000437207233 53 0.765185938951235 0.46962812209753 0.234814061048765 54 0.80294331568022 0.39411336863956 0.19705668431978 55 0.810393271430595 0.379213457138809 0.189606728569404 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0392156862745098 NOK 5% type I error level 25 0.490196078431373 NOK 10% type I error level 28 0.549019607843137 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/10syf61258916165.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/10syf61258916165.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/1wdth1258916165.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/1wdth1258916165.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/2xx431258916165.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/2xx431258916165.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/3jmix1258916165.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/3jmix1258916165.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/4ti1i1258916165.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/4ti1i1258916165.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/5iaks1258916165.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/5iaks1258916165.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/6v2js1258916165.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/6v2js1258916165.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/7a8c01258916165.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/7a8c01258916165.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/8gzoc1258916165.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/8gzoc1258916165.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/97ard1258916165.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258916349wnii96rg9pmx85b/97ard1258916165.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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