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: Fri, 18 Dec 2009 08:50:26 -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/Dec/18/t1261151823rsh7wg3gxn9l3ka.htm/, Retrieved Fri, 18 Dec 2009 16:57:21 +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/Dec/18/t1261151823rsh7wg3gxn9l3ka.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:

» Textbox « » Textfile « » CSV «

3397 562 3971 561 4625 555 4486 544 4132 537 4685 543 3172 594 4280 611 4207 613 4158 611 3933 594 3151 595 3616 591 4221 589 4436 584 4807 573 4849 567 5024 569 3521 621 4650 629 5393 628 5147 612 4845 595 3995 597 4493 593 4680 590 5463 580 4761 574 5307 573 5069 573 3501 620 4952 626 5152 620 5317 588 5189 566 4030 557 4420 561 4571 549 4551 532 4819 526 5133 511 4532 499 3339 555 4380 565 4632 542 4719 527 4212 510 3615 514 3420 517 4571 508 4407 493 4386 490 4386 469 4744 478 3185 528 3890 534 4520 518 3990 506 3809 502 3236 516 3551 528 3264 533 3579 536 3537 537 3038 524 2888 536 2198 587

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation wng[t] = + 2850.76591223988 + 2.49252485896077totWL[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) 2850.76591223988 1194.301291 2.387 0.019907 0.009953 totWL 2.49252485896077 2.13589 1.167 0.247485 0.123742 Multiple Linear Regression - Regression Statistics Multiple R 0.143252252118493 R-squared 0.0205212077370203 Adjusted R-squared 0.00545230324066681 F-TEST (value) 1.36182479237201 F-TEST (DF numerator) 1 F-TEST (DF denominator) 65 p-value 0.247484906516652 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 705.283778420669 Sum Squared Residuals 32332638.5267168 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3397 4251.56488297585 -854.564882975851 2 3971 4249.07235811687 -278.072358116870 3 4625 4234.11720896311 390.882791036894 4 4486 4206.69943551454 279.300564485463 5 4132 4189.25176150181 -57.2517615018118 6 4685 4204.20691065558 480.793089344424 7 3172 4331.32567846258 -1159.32567846258 8 4280 4373.69860106491 -93.6986010649086 9 4207 4378.68365078283 -171.683650782830 10 4158 4373.69860106491 -215.698601064909 11 3933 4331.32567846258 -398.325678462576 12 3151 4333.81820332154 -1182.81820332154 13 3616 4323.8481038857 -707.848103885693 14 4221 4318.86305416777 -97.8630541677717 15 4436 4306.40042987297 129.599570127032 16 4807 4278.9826564244 528.0173435756 17 4849 4264.02750727064 584.972492729365 18 5024 4269.01255698856 754.987443011444 19 3521 4398.62384965452 -877.623849654516 20 4650 4418.5640485262 231.435951473798 21 5393 4416.07152366724 976.928476332758 22 5147 4376.19112592387 770.80887407613 23 4845 4333.81820332154 511.181796678464 24 3995 4338.80325303946 -343.803253039458 25 4493 4328.83315360361 164.166846396385 26 4680 4321.35557902673 358.644420973268 27 5463 4296.43033043712 1166.56966956288 28 4761 4281.47518128336 479.52481871664 29 5307 4278.9826564244 1028.0173435756 30 5069 4278.9826564244 790.0173435756 31 3501 4396.13132479556 -895.131324795556 32 4952 4411.08647394932 540.91352605068 33 5152 4396.13132479556 755.868675204444 34 5317 4316.37052930881 1000.62947069119 35 5189 4261.53498241167 927.465017588326 36 4030 4239.10225868103 -209.102258681027 37 4420 4249.07235811687 170.92764188313 38 4571 4219.16205980934 351.837940190659 39 4551 4176.78913720701 374.210862792992 40 4819 4161.83398805324 657.166011946757 41 5133 4124.44611516883 1008.55388483117 42 4532 4094.5358168613 437.464183138697 43 3339 4234.11720896311 -895.117208963106 44 4380 4259.04245755271 120.957542447287 45 4632 4201.71438579662 430.285614203384 46 4719 4164.3265129122 554.673487087796 47 4212 4121.95359030987 90.046409690129 48 3615 4131.92368974571 -516.923689745714 49 3420 4139.40126432260 -719.401264322596 50 4571 4116.96854059195 454.031459408050 51 4407 4079.58066770754 327.419332292462 52 4386 4072.10309313066 313.896906869344 53 4386 4019.76007109248 366.239928907520 54 4744 4042.19279482313 701.807205176874 55 3185 4166.81903777117 -981.819037771165 56 3890 4181.77418692493 -291.774186924929 57 4520 4141.89378918156 378.106210818443 58 3990 4111.98349087403 -121.983490874028 59 3809 4102.01339143818 -293.013391438185 60 3236 4136.90873946364 -900.908739463636 61 3551 4166.81903777117 -615.819037771165 62 3264 4179.28166206597 -915.281662065969 63 3579 4186.75923664285 -607.759236642851 64 3537 4189.25176150181 -652.251761501812 65 3038 4156.84893833532 -1118.84893833532 66 2888 4186.75923664285 -1298.75923664285 67 2198 4313.87800444985 -2115.87800444985 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.28820444697216 0.57640889394432 0.71179555302784 6 0.185373323967342 0.370746647934684 0.814626676032658 7 0.102516541217132 0.205033082434265 0.897483458782868 8 0.248046787762369 0.496093575524739 0.75195321223763 9 0.194694698089121 0.389389396178243 0.805305301910879 10 0.130457815395840 0.260915630791681 0.86954218460416 11 0.0800119982694652 0.160023996538930 0.919988001730535 12 0.127993831752225 0.255987663504451 0.872006168247775 13 0.0970354303043047 0.194070860608609 0.902964569695695 14 0.0674344991695543 0.134868998339109 0.932565500830446 15 0.0529978761434145 0.105995752286829 0.947002123856586 16 0.0611170615804078 0.122234123160816 0.938882938419592 17 0.0642681401087376 0.128536280217475 0.935731859891262 18 0.0821371778262549 0.164274355652510 0.917862822173745 19 0.0704742057274205 0.140948411454841 0.92952579427258 20 0.0853278349024822 0.170655669804964 0.914672165097518 21 0.201169760950346 0.402339521900692 0.798830239049654 22 0.234299668904537 0.468599337809073 0.765700331095463 23 0.210246816067232 0.420493632134464 0.789753183932768 24 0.169371402317126 0.338742804634252 0.830628597682874 25 0.127974968802757 0.255949937605514 0.872025031197243 26 0.101211729902151 0.202423459804301 0.89878827009785 27 0.170054683405588 0.340109366811176 0.829945316594412 28 0.142908908235546 0.285817816471092 0.857091091764454 29 0.18703772149688 0.37407544299376 0.81296227850312 30 0.194691064623594 0.389382129247188 0.805308935376406 31 0.207209228784252 0.414418457568503 0.792790771215748 32 0.204496631067689 0.408993262135377 0.795503368932311 33 0.255810235054643 0.511620470109286 0.744189764945357 34 0.407007719723548 0.814015439447096 0.592992280276452 35 0.568574367583299 0.862851264833403 0.431425632416701 36 0.528051110441611 0.943897779116778 0.471948889558389 37 0.531520880823507 0.936958238352986 0.468479119176493 38 0.556441382537915 0.88711723492417 0.443558617462085 39 0.547712632276952 0.904574735446095 0.452287367723048 40 0.591618971633443 0.816762056733114 0.408381028366557 41 0.689635099574991 0.620729800850019 0.310364900425009 42 0.640610936798074 0.718778126403852 0.359389063201926 43 0.656983675938655 0.686032648122689 0.343016324061345 44 0.799180702148824 0.401638595702352 0.200819297851176 45 0.927764534708888 0.144470930582225 0.0722354652911124 46 0.983825689181224 0.0323486216375529 0.0161743108187765 47 0.978744201607347 0.0425115967853068 0.0212557983926534 48 0.973545212198063 0.0529095756038743 0.0264547878019372 49 0.972124109078888 0.0557517818422234 0.0278758909211117 50 0.979365086866953 0.0412698262660937 0.0206349131330469 51 0.967351078364922 0.0652978432701564 0.0326489216350782 52 0.94753433554424 0.104931328911518 0.0524656644557591 53 0.927745067439578 0.144509865120845 0.0722549325604225 54 0.895942377724367 0.208115244551267 0.104057622275633 55 0.886800965335589 0.226398069328822 0.113199034664411 56 0.891209897364687 0.217580205270626 0.108790102635313 57 0.980100655696227 0.0397986886075457 0.0198993443037728 58 0.968059109107007 0.063881781785987 0.0319408908929935 59 0.935180738547248 0.129638522905503 0.0648192614527516 60 0.914918264642208 0.170163470715584 0.0850817353577921 61 0.850862542493234 0.298274915013531 0.149137457506766 62 0.72654625037415 0.546907499251701 0.273453749625851 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 4 0.0689655172413793 NOK 10% type I error level 8 0.137931034482759 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/10bfbc1261151419.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/10bfbc1261151419.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/1lha71261151419.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/1lha71261151419.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/218dy1261151419.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/218dy1261151419.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/3ck0p1261151419.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/3ck0p1261151419.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/4qyrf1261151419.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/4qyrf1261151419.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/5t6by1261151419.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/5t6by1261151419.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/6lkp51261151419.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/6lkp51261151419.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/7lzci1261151419.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/7lzci1261151419.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/8fzbz1261151419.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/8fzbz1261151419.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/9zdlf1261151419.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261151823rsh7wg3gxn9l3ka/9zdlf1261151419.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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