*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: Wed, 22 Dec 2010 07:24:23 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok.htm/, Retrieved Wed, 22 Dec 2010 08:22:09 +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/2010/Dec/22/t129300252652zos0glzbwomok.htm/}, year = {2010}, } @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 = {2010}, 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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-999.00 -999.00 38.60 6654.00 5712.00 645.00 3.00 5.00 3.00 6.30 2.00 4.50 1.00 6600.00 42.00 3.00 1.00 3.00 -999.00 -999.00 14.00 3.39 44.50 60.00 1.00 1.00 1.00 -999.00 -999.00 -999.00 0.92 5.70 25.00 5.00 2.00 3.00 2.10 1.80 69.00 2547.00 4603.00 624.00 3.00 5.00 4.00 9.10 0.70 27.00 10.55 179.50 180.00 4.00 4.00 4.00 15.80 3.90 19.00 0.02 0.30 35.00 1.00 1.00 1.00 5.20 1.00 30.40 160.00 169.00 392.00 4.00 5.00 4.00 10.90 3.60 28.00 3.30 25.60 63.00 1.00 2.00 1.00 8.30 1.40 50.00 52.16 440.00 230.00 1.00 1.00 1.00 11.00 1.50 7.00 0.43 6.40 112.00 5.00 4.00 4.00 3.20 0.70 30.00 465.00 423.00 281.00 5.00 5.00 5.00 7.60 2.70 -999.00 0.55 2.40 -999.00 2.00 1.00 2.00 -999.00 -999.00 40.00 187.10 419.00 365.00 5.00 5.00 5.00 6.30 2.10 3.50 0.08 1.20 42.00 1.00 1.00 1.00 8.60 0.00 50.00 3.00 25.00 28.00 2.00 2.00 2.00 6.60 4.10 6.00 0.79 3500.00 42.00 2.00 2.00 2.00 9.50 1.20 10.40 0.20 5.00 120.00 2.00 2.00 2.00 4.80 1.30 34.00 1.41 17.50 -999.00 1.00 2.00 1.00 12.00 6.10 7.00 60.00 81.00 -999 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 5 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation PS[t] = -88.2745862297677 + 0.85459151446654SWS[t] + 0.0141510400094479L[t] -0.0210073835664919Wb[t] + 0.00242742421534027Wbr[t] + 0.0344938766541348Tg[t] -9.18488699988641P[t] -1.64139874995659S[t] + 44.1134440268902D[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) -88.2745862297677 51.817459 -1.7036 0.094319 0.04716 SWS 0.85459151446654 0.055676 15.3493 0 0 L 0.0141510400094479 0.093199 0.1518 0.879892 0.439946 Wb -0.0210073835664919 0.035368 -0.594 0.555067 0.277533 Wbr 0.00242742421534027 0.023306 0.1042 0.917441 0.458721 Tg 0.0344938766541348 0.082084 0.4202 0.676018 0.338009 P -9.18488699988641 42.276019 -0.2173 0.82884 0.41442 S -1.64139874995659 28.36796 -0.0579 0.954077 0.477038 D 44.1134440268902 56.064845 0.7868 0.434886 0.217443 Multiple Linear Regression - Regression Statistics Multiple R 0.917484263475886 R-squared 0.84177737372589 Adjusted R-squared 0.817894713156213 F-TEST (value) 35.2463818371503 F-TEST (DF numerator) 8 F-TEST (DF denominator) 53 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 170.137619942577 Sum Squared Residuals 1534180.91514542 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -999 -948.555734397744 -50.4442656022556 2 2 37.7660275796213 -35.7660275796213 3 -999 -906.419798398121 -92.5802016018793 4 -999 -872.158442128454 -126.841557871546 5 1.8 34.3804058209188 -32.5804058209188 6 0.7 59.4559002880929 -58.7559002880929 7 3.9 -40.0084185014817 43.9084185014817 8 1 58.6773685901522 -57.6773685901522 9 3.6 -44.7516181513735 48.3516181513736 10 1.4 -39.2808532238034 40.6808532238034 11 1.5 49.0585403430403 -47.5585403430403 12 0.7 82.2715756265265 -81.5715756265265 13 2.7 -62.165975405535 64.865975405535 14 -999 -765.33280193653 -233.667198063469 15 2.1 -48.1039976337015 50.2039976337015 16 0 -12.679738649789 12.679738649789 17 4.1 -6.0469276999905 10.1469276999905 18 1.2 -9.28727862928345 10.4872786292835 19 1.3 -86.4921753374575 87.7921753374575 20 6.1 -80.1564769290836 86.2564769290836 21 0.3 -770.844195454929 771.144195454929 22 0.5 86.0675612535236 -85.5675612535236 23 3.4 -19.2362536457809 22.6362536457809 24 -999 -907.762948532285 -91.2370514677145 25 1.5 -43.977065664638 45.477065664638 26 -999 -906.795110878667 -92.2048891213327 27 3.4 6.48846071687163 -3.08846071687163 28 0.8 46.0991705063717 -45.2991705063717 29 0.8 82.8418537549773 -82.0418537549773 30 -999 -906.677612698009 -92.322387301991 31 -999 -800.54975061075 -198.45024938925 32 1.4 46.330852857483 -44.930852857483 33 2 -37.6255242686178 39.6255242686178 34 1.9 -37.2477084637492 39.1477084637492 35 2.4 -70.2619758171777 72.6619758171777 36 2.8 9.45489247113303 -6.65489247113303 37 1.3 16.555655930026 -15.255655930026 38 2 15.9772565180949 -13.9772565180949 39 5.6 -51.9146901125782 57.5146901125782 40 3.1 -47.7677189981349 50.8677189981349 41 1 -764.126855550669 765.126855550669 42 1.8 -3.68398191286387 5.48398191286387 43 0.9 59.6388520115793 -58.7388520115793 44 1.8 -9.12523180564468 10.9252318056447 45 1.9 51.1812843319288 -49.2812843319288 46 0.9 85.8715237845048 -84.9715237845048 47 -999 -873.065175796665 -125.934824203335 48 2.6 24.717967491024 -22.117967491024 49 2.4 -46.6978180934293 49.0978180934293 50 1.2 -9.80406504626521 11.0040650462652 51 0.9 -8.95481673579384 9.85481673579384 52 0.5 25.2371635871362 -24.7371635871362 53 -999 -770.234235058669 -228.765764941331 54 0.6 85.6463836264685 -85.0463836264685 55 -999 -872.152093055055 -126.847906944945 56 2.2 -56.7302538146706 58.9302538146706 57 2.3 -8.5376473295442 10.8376473295442 58 0.5 26.0499352037865 -25.5499352037865 59 2.6 -17.9813151443078 20.5813151443078 60 0.6 62.4410920436442 -61.8410920436442 61 6.6 -52.7722350622919 59.3722350622919 62 -999 -925.64320776337 -73.3567922366302 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 5.36097740204375e-06 1.07219548040875e-05 0.999994639022598 13 9.94207442238263e-08 1.98841488447653e-07 0.999999900579256 14 3.20571633008404e-09 6.41143266016807e-09 0.999999996794284 15 1.43235542769201e-10 2.86471085538403e-10 0.999999999856764 16 2.33405127437989e-12 4.66810254875978e-12 0.999999999997666 17 4.98649667663022e-14 9.97299335326044e-14 0.99999999999995 18 7.72314644137518e-16 1.54462928827504e-15 1 19 2.31599083003535e-17 4.6319816600707e-17 1 20 3.89231698142251e-19 7.78463396284501e-19 1 21 0.975254145233265 0.0494917095334696 0.0247458547667348 22 0.9632157901293 0.0735684197413997 0.0367842098706999 23 0.942926572736458 0.114146854527084 0.0570734272635419 24 0.921344673653232 0.157310652693536 0.0786553263467682 25 0.895768791875893 0.208462416248214 0.104231208124107 26 0.875297008283259 0.249405983433483 0.124702991716741 27 0.827237559106787 0.345524881786425 0.172762440893213 28 0.769990128516174 0.460019742967651 0.230009871483826 29 0.960558703152777 0.0788825936944469 0.0394412968472235 30 0.97448802326309 0.0510239534738175 0.0255119767369088 31 0.97350113396139 0.0529977320772214 0.0264988660386107 32 0.957749537030227 0.084500925939545 0.0422504629697725 33 0.935412755235149 0.129174489529702 0.0645872447648511 34 0.995091412374848 0.00981717525030456 0.00490858762515228 35 0.991643360694292 0.016713278611415 0.00835663930570748 36 0.998437299864542 0.00312540027091669 0.00156270013545835 37 0.996823707418168 0.00635258516366447 0.00317629258183223 38 0.994500249805242 0.010999500389517 0.00549975019475848 39 0.989195408167737 0.0216091836645257 0.0108045918322628 40 0.9795822485883 0.040835502823398 0.020417751411699 41 1 7.67736743576465e-21 3.83868371788232e-21 42 1 8.10011098887522e-20 4.05005549443761e-20 43 1 3.6863971545603e-18 1.84319857728015e-18 44 1 1.8761645851048e-16 9.38082292552399e-17 45 0.999999999999997 6.3986562081802e-15 3.1993281040901e-15 46 0.999999999999805 3.88939445767372e-13 1.94469722883686e-13 47 0.999999999984824 3.03514858741087e-11 1.51757429370543e-11 48 0.999999999155808 1.68838441101757e-09 8.44192205508783e-10 49 0.999999926592543 1.46814914756527e-07 7.34074573782636e-08 50 0.999994784992048 1.04300159049688e-05 5.21500795248441e-06 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 22 0.564102564102564 NOK 5% type I error level 27 0.692307692307692 NOK 10% type I error level 32 0.82051282051282 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/10pdhv1293002655.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/10pdhv1293002655.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/1juk21293002655.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/1juk21293002655.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/2juk21293002655.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/2juk21293002655.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/3bljn1293002655.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/3bljn1293002655.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/4bljn1293002655.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/4bljn1293002655.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/5bljn1293002655.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/5bljn1293002655.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/6mviq1293002655.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/6mviq1293002655.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/7xmza1293002655.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/7xmza1293002655.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/8xmza1293002655.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/8xmza1293002655.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/9xmza1293002655.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t129300252652zos0glzbwomok/9xmza1293002655.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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