Model 1

*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: Mon, 30 Nov 2009 14:53:26 -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/30/t1259618062rl8q4abeum0hzjn.htm/, Retrieved Mon, 30 Nov 2009 22:54:34 +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/30/t1259618062rl8q4abeum0hzjn.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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2756.76 10001.60 2849.27 10411.75 2921.44 10673.38 2981.85 10539.51 3080.58 10723.78 3106.22 10682.06 3119.31 10283.19 3061.26 10377.18 3097.31 10486.64 3161.69 10545.38 3257.16 10554.27 3277.01 10532.54 3295.32 10324.31 3363.99 10695.25 3494.17 10827.81 3667.03 10872.48 3813.06 10971.19 3917.96 11145.65 3895.51 11234.68 3801.06 11333.88 3570.12 10997.97 3701.61 11036.89 3862.27 11257.35 3970.10 11533.59 4138.52 11963.12 4199.75 12185.15 4290.89 12377.62 4443.91 12512.89 4502.64 12631.48 4356.98 12268.53 4591.27 12754.80 4696.96 13407.75 4621.40 13480.21 4562.84 13673.28 4202.52 13239.71 4296.49 13557.69 4435.23 13901.28 4105.18 13200.58 4116.68 13406.97 3844.49 12538.12 3720.98 12419.57 3674.40 12193.88 3857.62 12656.63 3801.06 12812.48 3504.37 12056.67 3032.60 11322.38 3047.03 11530.75 2962.34 11114.08 2197.82 9181.73 2014.45 8614.55 1862.83 8595.56 1905.41 8396.20 1810.99 7690.50 1670.07 7235.47 1864.44 7992.12 2052.02 8398.37 2029.60 8593.01 2070.83 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 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Dow [t] = + 4917.93272132236 + 1.82235335164621Bel20[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) 4917.93272132236 299.406265 16.4256 0 0 Bel20 1.82235335164621 0.086177 21.1467 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.940845637839431 R-squared 0.885190514241486 Adjusted R-squared 0.883211040349098 F-TEST (value) 447.184738149556 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 562.16119851205 Sum Squared Residuals 18329462.3605253 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10001.6 9941.72354700654 59.8764529934612 2 10411.75 10110.3094555674 301.440544432635 3 10673.38 10241.8286969557 431.551303044322 4 10539.51 10351.9170629286 187.592937071376 5 10723.78 10531.8380093367 191.941990663345 6 10682.06 10578.5631492729 103.496850727135 7 10283.19 10602.4177546459 -319.227754645913 8 10377.18 10496.6301425829 -119.450142582851 9 10486.64 10562.3259809097 -75.6859809096972 10 10545.38 10679.6490896887 -134.269089688681 11 10554.27 10853.6291641703 -299.359164170343 12 10532.54 10889.8028782005 -357.262878200521 13 10324.31 10923.1701680692 -598.860168069165 14 10695.25 11048.3111727267 -353.061172726709 15 10827.81 11285.545132044 -457.735132044014 16 10872.48 11600.5571324096 -728.077132409579 17 10971.19 11866.6753923505 -895.485392350474 18 11145.65 12057.8402589382 -912.190258938163 19 11234.68 12016.9284261937 -782.248426193705 20 11333.88 11844.8071521307 -510.927152130721 21 10997.97 11423.9528691015 -425.982869101544 22 11036.89 11663.5741113095 -626.684111309505 23 11257.35 11956.3534007850 -699.003400784985 24 11533.59 12152.857762693 -619.267762692996 25 11963.12 12459.7785141773 -496.658514177252 26 12185.15 12571.3612098985 -386.211209898549 27 12377.62 12737.4504943676 -359.830494367585 28 12512.89 13016.3070042365 -503.417004236489 29 12631.48 13123.3338165787 -491.853816578672 30 12268.53 12857.8898273779 -589.359827377882 31 12754.8 13284.8489941351 -530.048994135077 32 13407.75 13477.4535198706 -69.7035198705634 33 13480.21 13339.7565006202 140.453499379824 34 13673.28 13233.0394883478 440.240511652227 35 13239.71 12576.4091286826 663.300871317389 36 13557.69 12747.6556731368 810.034326863197 37 13901.28 13000.4889771442 900.791022855802 38 13200.58 12399.0212534334 801.558746566633 39 13406.97 12419.9783169773 986.991683022701 40 12538.12 11923.9519581927 614.168041807286 41 12419.57 11698.8730957309 720.696904269108 42 12193.88 11613.9878766112 579.892123388788 43 12656.63 11947.8794576998 708.75054230017 44 12812.48 11844.8071521307 967.67284786928 45 12056.67 11304.1331362308 752.536863769195 46 11322.38 10444.4014955247 877.978504475329 47 11530.75 10470.6980543889 1060.05194561107 48 11114.08 10316.362949038 797.717050961992 49 9181.73 8923.13736463744 258.592635362556 50 8614.55 8588.97243054608 25.5775694539222 51 8595.56 8312.66721536948 282.892784630521 52 8396.2 8390.26302108257 5.93697891742667 53 7690.5 8218.19641762014 -527.696417620138 54 7235.47 7961.39038330615 -725.920383306153 55 7992.12 8315.60120426563 -323.481204265629 56 8398.37 8657.43824596743 -259.068245967425 57 8593.01 8616.58108382352 -23.5710838235165 58 8679.75 8691.71671251189 -11.9667125118907 59 9374.63 9097.3361215213 277.293878478694 60 9634.97 9370.433994799 264.536005200993 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0259618272747727 0.0519236545495454 0.974038172725227 6 0.00835126558638588 0.0167025311727718 0.991648734413614 7 0.0229490776880301 0.0458981553760602 0.97705092231197 8 0.0102566436469389 0.0205132872938778 0.989743356353061 9 0.00356841218212355 0.00713682436424711 0.996431587817876 10 0.00115061112900905 0.00230122225801810 0.99884938887099 11 0.000390649043447764 0.000781298086895529 0.999609350956552 12 0.000130592949265558 0.000261185898531117 0.999869407050734 13 0.000104752772896403 0.000209505545792807 0.999895247227104 14 3.48255859290535e-05 6.9651171858107e-05 0.99996517441407 15 1.28562904762731e-05 2.57125809525463e-05 0.999987143709524 16 4.43191999286247e-06 8.86383998572493e-06 0.999995568080007 17 1.75876532809935e-06 3.51753065619869e-06 0.999998241234672 18 9.12240921493083e-07 1.82448184298617e-06 0.999999087759079 19 6.03598741576539e-07 1.20719748315308e-06 0.999999396401258 20 7.79364428420168e-07 1.55872885684034e-06 0.999999220635572 21 3.27823519663107e-07 6.55647039326214e-07 0.99999967217648 22 1.46369988589294e-07 2.92739977178587e-07 0.999999853630011 23 9.53428539438808e-08 1.90685707887762e-07 0.999999904657146 24 1.57105203837021e-07 3.14210407674043e-07 0.999999842894796 25 1.48033885076120e-06 2.96067770152239e-06 0.99999851966115 26 1.24888557454121e-05 2.49777114908242e-05 0.999987511144255 27 6.14864548042761e-05 0.000122972909608552 0.999938513545196 28 0.000173604195655737 0.000347208391311474 0.999826395804344 29 0.000544329545920756 0.00108865909184151 0.99945567045408 30 0.00188118299873136 0.00376236599746272 0.998118817001269 31 0.0178923026905337 0.0357846053810674 0.982107697309466 32 0.174826021828398 0.349652043656796 0.825173978171602 33 0.590479498573433 0.819041002853134 0.409520501426567 34 0.892562508625755 0.214874982748489 0.107437491374245 35 0.962008742605908 0.0759825147881841 0.0379912573940921 36 0.986029250187099 0.0279414996258028 0.0139707498129014 37 0.994235665824382 0.0115286683512361 0.00576433417561803 38 0.996029006162244 0.00794198767551256 0.00397099383775628 39 0.996935201574452 0.0061295968510952 0.0030647984255476 40 0.99737186957427 0.00525626085146005 0.00262813042573003 41 0.997072065542604 0.00585586891479137 0.00292793445739568 42 0.997583650667385 0.00483269866522957 0.00241634933261479 43 0.998903153587368 0.00219369282526378 0.00109684641263189 44 0.999245877623049 0.00150824475390245 0.000754122376951223 45 0.99983246389211 0.000335072215779261 0.000167536107889631 46 0.99966579087919 0.000668418241619215 0.000334209120809608 47 0.999344838283781 0.00131032343243818 0.000655161716219088 48 0.99860199304713 0.00279601390573988 0.00139800695286994 49 0.996615602194608 0.00676879561078458 0.00338439780539229 50 0.991934927863503 0.016130144272993 0.0080650721364965 51 0.999213345565845 0.00157330886831008 0.000786654434155039 52 0.999682442731405 0.000635114537190204 0.000317557268595102 53 0.998697245522438 0.00260550895512381 0.00130275447756190 54 0.996691189635801 0.00661762072839725 0.00330881036419862 55 0.983040637131942 0.0339187257361151 0.0169593628680576 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 38 0.745098039215686 NOK 5% type I error level 46 0.901960784313726 NOK 10% type I error level 48 0.941176470588235 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/10ng1s1259618000.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/10ng1s1259618000.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/1sbg51259618000.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/1sbg51259618000.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/2ai5r1259618000.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/2ai5r1259618000.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/34ifc1259618000.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/34ifc1259618000.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/4wzst1259618000.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/4wzst1259618000.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/54ydq1259618000.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/54ydq1259618000.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/654dj1259618000.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/654dj1259618000.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/7vfdn1259618000.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/7vfdn1259618000.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/8del71259618000.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/8del71259618000.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/9jfoi1259618000.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618062rl8q4abeum0hzjn/9jfoi1259618000.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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