Model 3 - WZM & WZM<25j

*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: Thu, 19 Nov 2009 14:42:35 -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/19/t1258667031eefblv66m4rzch6.htm/, Retrieved Thu, 19 Nov 2009 22:44:03 +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/19/t1258667031eefblv66m4rzch6.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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8.2 20.3 8 20.3 7.5 20.3 6.8 15.8 6.5 15.8 6.6 15.8 7.6 23.2 8 23.2 8.1 23.2 7.7 20.9 7.5 20.9 7.6 20.9 7.8 19.8 7.8 19.8 7.8 19.8 7.5 20.6 7.5 20.6 7.1 20.6 7.5 21.1 7.5 21.1 7.6 21.1 7.7 22.4 7.7 22.4 7.9 22.4 8.1 20.5 8.2 20.5 8.2 20.5 8.2 18.4 7.9 18.4 7.3 18.4 6.9 17.6 6.6 17.6 6.7 17.6 6.9 18.5 7 18.5 7.1 18.5 7.2 17.3 7.1 17.3 6.9 17.3 7 16.2 6.8 16.2 6.4 16.2 6.7 18.5 6.6 18.5 6.4 18.5 6.3 16.3 6.2 16.3 6.5 16.3 6.8 16.8 6.8 16.8 6.4 16.8 6.1 14.8 5.8 14.8 6.1 14.8 7.2 21.4 7.3 21.4 6.9 21.4 6.1 16.1 5.8 16.1 6.2 16.1 7.1 19.6 7.7 19.6 7.9 19.6 7.7 18.9 7.4 18.9 7.5 18.9 8 21.9 8.1 21.9 8 21.9

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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 2.04749099291583 + 0.268190282588306X[t] + 0.411430607756518M1[t] + 0.479213827669841M2[t] + 0.330330380916495M3[t] + 0.527218052971105M4[t] + 0.295001272884426M5[t] + 0.146117826131079M6[t] -0.218701515485238M7[t] -0.184251628905251M8[t] -0.249801742325264M9[t] -0.122233106493307M10[t] -0.221116553246654M11[t] -0.00111655324665354t + 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) 2.04749099291583 0.437171 4.6835 1.9e-05 9e-06 X 0.268190282588306 0.020264 13.2348 0 0 M1 0.411430607756518 0.188097 2.1873 0.032988 0.016494 M2 0.479213827669841 0.188015 2.5488 0.01363 0.006815 M3 0.330330380916495 0.187956 1.7575 0.084399 0.042199 M4 0.527218052971105 0.190227 2.7715 0.007598 0.003799 M5 0.295001272884426 0.190082 1.552 0.126406 0.063203 M6 0.146117826131079 0.18996 0.7692 0.445063 0.222531 M7 -0.218701515485238 0.191431 -1.1425 0.258214 0.129107 M8 -0.184251628905251 0.191606 -0.9616 0.34045 0.170225 M9 -0.249801742325264 0.191801 -1.3024 0.198208 0.099104 M10 -0.122233106493307 0.196255 -0.6228 0.535972 0.267986 M11 -0.221116553246654 0.196224 -1.1269 0.264694 0.132347 t -0.00111655324665354 0.002033 -0.5491 0.58517 0.292585 Multiple Linear Regression - Regression Statistics Multiple R 0.907202855997636 R-squared 0.823017021930267 Adjusted R-squared 0.78118468165924 F-TEST (value) 19.6741807079885 F-TEST (DF numerator) 13 F-TEST (DF denominator) 55 p-value 4.44089209850063e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.310240057275317 Sum Squared Residuals 5.29368912260055 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 7.90206778396833 0.297932216031667 2 8 7.96873445063499 0.0312655493650105 3 7.5 7.81873445063499 -0.318734450634988 4 6.8 6.80764929779557 -0.0076492977955676 5 6.5 6.57431596446223 -0.074315964462234 6 6.6 6.42431596446223 0.175684035537766 7 7.6 8.04298816075273 -0.442988160752732 8 8 8.07632149408606 -0.0763214940860637 9 8.1 8.0096548274194 0.090345172580602 10 7.7 7.5192692600516 0.180730739948404 11 7.5 7.4192692600516 0.080730739948404 12 7.6 7.6392692600516 -0.0392692600515964 13 7.8 7.75457400371432 0.0454259962856753 14 7.8 7.821240670381 -0.0212406703809934 15 7.8 7.671240670381 0.128759329619006 16 7.5 8.0815640152596 -0.581564015259596 17 7.5 7.84823068192626 -0.348230681926263 18 7.1 7.69823068192626 -0.598230681926263 19 7.5 7.46638992835745 0.0336100716425545 20 7.5 7.49972326169078 0.000276738309221314 21 7.6 7.43305659502411 0.166943404975887 22 7.7 7.90815604497421 -0.208156044974213 23 7.7 7.80815604497421 -0.108156044974213 24 7.9 8.02815604497421 -0.128156044974213 25 8.1 7.9289085625663 0.171091437433703 26 8.2 7.99557522923296 0.204424770767034 27 8.2 7.84557522923297 0.354424770767033 28 8.2 7.47814675460548 0.721853245394521 29 7.9 7.24481342127214 0.655186578727855 30 7.3 7.09481342127215 0.205186578727855 31 6.9 6.51432530033853 0.38567469966147 32 6.6 6.54765863367186 0.0523413663281359 33 6.7 6.4809919670052 0.219008032994803 34 6.9 6.84881530391998 0.0511846960800246 35 7 6.74881530391998 0.251184696080024 36 7.1 6.96881530391998 0.131184696080024 37 7.2 7.05730101932387 0.142698980676127 38 7.1 7.12396768599054 -0.0239676859905426 39 6.9 6.97396768599054 -0.0739676859905425 40 7 6.87472949395136 0.125270506048638 41 6.8 6.64139616061803 0.158603839381971 42 6.4 6.49139616061803 -0.0913961606180284 43 6.7 6.74229791570816 -0.0422979157081632 44 6.6 6.7756312490415 -0.175631249041497 45 6.4 6.70896458237483 -0.30896458237483 46 6.3 6.24539804326586 0.0546019567341405 47 6.2 6.14539804326586 0.0546019567341408 48 6.5 6.36539804326586 0.134601956734141 49 6.8 6.90980723906988 -0.109807239069878 50 6.8 6.97647390573655 -0.176473905736547 51 6.4 6.82647390573655 -0.426473905736547 52 6.1 6.48586445936789 -0.385864459367891 53 5.8 6.25253112603456 -0.452531126034558 54 6.1 6.10253112603456 -0.00253112603455805 55 7.2 7.50665109625441 -0.306651096254409 56 7.3 7.53998442958774 -0.239984429587743 57 6.9 7.47331776292108 -0.573317762921076 58 6.1 6.17836134778836 -0.078361347788356 59 5.8 6.07836134778836 -0.278361347788356 60 6.2 6.29836134778836 -0.0983613477883555 61 7.1 7.6473413913573 -0.547341391357294 62 7.7 7.71400805802396 -0.0140080580239620 63 7.9 7.56400805802396 0.335991941976038 64 7.7 7.5720459790201 0.127954020979896 65 7.4 7.33871264568677 0.0612873543132294 66 7.5 7.18871264568677 0.311287354313229 67 8 7.62734759858872 0.37265240141128 68 8.1 7.66068093192205 0.439319068077946 69 8 7.59401426525539 0.405985734744613 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.305651909359663 0.611303818719325 0.694348090640337 18 0.285408614474545 0.57081722894909 0.714591385525455 19 0.196663468667016 0.393326937334032 0.803336531332984 20 0.134872220342560 0.269744440685121 0.86512777965744 21 0.0823713153555791 0.164742630711158 0.917628684644421 22 0.0632743176738866 0.126548635347773 0.936725682326113 23 0.0404894933426185 0.080978986685237 0.959510506657381 24 0.0368807862346054 0.0737615724692108 0.963119213765395 25 0.0200908020362554 0.0401816040725108 0.979909197963745 26 0.0178054538491912 0.0356109076983824 0.982194546150809 27 0.0368834626309870 0.0737669252619739 0.963116537369013 28 0.323475186732641 0.646950373465282 0.676524813267359 29 0.480515489330913 0.961030978661826 0.519484510669087 30 0.400650605108223 0.801301210216447 0.599349394891777 31 0.410322799071948 0.820645598143895 0.589677200928052 32 0.490017595792198 0.980035191584397 0.509982404207802 33 0.58706547759951 0.82586904480098 0.41293452240049 34 0.549204253333558 0.901591493332884 0.450795746666442 35 0.465520067703294 0.931040135406588 0.534479932296706 36 0.39544417462478 0.79088834924956 0.60455582537522 37 0.448993115349759 0.897986230699518 0.551006884650241 38 0.421193563349232 0.842387126698465 0.578806436650768 39 0.381112174079618 0.762224348159235 0.618887825920382 40 0.339885155492924 0.679770310985847 0.660114844507076 41 0.363190560865736 0.726381121731472 0.636809439134264 42 0.288060719222251 0.576121438444503 0.711939280777749 43 0.248779095865478 0.497558191730957 0.751220904134522 44 0.201107818780894 0.402215637561787 0.798892181219106 45 0.205892211832077 0.411784423664154 0.794107788167923 46 0.167827612696237 0.335655225392473 0.832172387303763 47 0.220355045126863 0.440710090253725 0.779644954873137 48 0.413941572716728 0.827883145433457 0.586058427283272 49 0.92996030966023 0.140079380679541 0.0700396903397707 50 0.977214743273346 0.045570513453308 0.022785256726654 51 0.968171731443027 0.0636565371139457 0.0318282685569728 52 0.92134207467432 0.157315850651359 0.0786579253256795 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 3 0.0833333333333333 NOK 10% type I error level 7 0.194444444444444 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/10xm701258666947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/10xm701258666947.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/1fjeg1258666947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/1fjeg1258666947.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/2bpjw1258666947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/2bpjw1258666947.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/3bzhj1258666947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/3bzhj1258666947.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/4rbdg1258666947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/4rbdg1258666947.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/5hfcm1258666947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/5hfcm1258666947.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/613ue1258666947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/613ue1258666947.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/7iahf1258666947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/7iahf1258666947.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/8pmbp1258666947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/8pmbp1258666947.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/94nf41258666947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258667031eefblv66m4rzch6/94nf41258666947.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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