*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, 20 Nov 2009 10:50:38 -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/20/t1258740790v3gk7fqrb2la17m.htm/, Retrieved Fri, 20 Nov 2009 19:13:22 +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/20/t1258740790v3gk7fqrb2la17m.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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594 139 595 135 591 130 589 127 584 122 573 117 567 112 569 113 621 149 629 157 628 157 612 147 595 137 597 132 593 125 590 123 580 117 574 114 573 111 573 112 620 144 626 150 620 149 588 134 566 123 557 116 561 117 549 111 532 105 526 102 511 95 499 93 555 124 565 130 542 124 527 115 510 106 514 105 517 105 508 101 493 95 490 93 469 84 478 87 528 116 534 120 518 117 506 109 502 105 516 107 528 109 533 109 536 108 537 107 524 99 536 103 587 131 597 137 581 135 564 124

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 295.989857812736 + 2.19348434427469X[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) 295.989857812736 14.664053 20.1847 0 0 X 2.19348434427469 0.122304 17.9347 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.920450295083232 R-squared 0.84722874571881 Adjusted R-squared 0.84459475857603 F-TEST (value) 321.652574516703 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16.2389882592519 Sum Squared Residuals 15294.874901679 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 594 600.884181666918 -6.8841816669177 2 595 592.11024428982 2.88975571017992 3 591 581.142822568447 9.85717743155344 4 589 574.562369535622 14.4376304643775 5 584 563.594947814249 20.405052185751 6 573 552.627526092876 20.3724739071245 7 567 541.660104371502 25.3398956284979 8 569 543.853588715777 25.1464112842233 9 621 622.819025109666 -1.81902510966577 10 629 640.366899863863 -11.3668998638633 11 628 640.366899863863 -12.3668998638633 12 612 618.432056421116 -6.43205642111638 13 595 596.49721297837 -1.49721297836942 14 597 585.529791256996 11.4702087430040 15 593 570.175400847073 22.8245991529269 16 590 565.788432158524 24.2115678414763 17 580 552.627526092876 27.3724739071245 18 574 546.047073060051 27.9529269399486 19 573 539.466620027227 33.5333799727726 20 573 541.660104371502 31.3398956284979 21 620 611.851603388292 8.1483966117077 22 626 625.01250945394 0.987490546059543 23 620 622.819025109666 -2.81902510966577 24 588 589.916759945545 -1.91675994554534 25 566 565.788432158524 0.211567841476303 26 557 550.434041748601 6.56595825139917 27 561 552.627526092876 8.37247390712447 28 549 539.466620027227 9.53337997277264 29 532 526.305713961579 5.69428603842081 30 526 519.725260928755 6.2747390712449 31 511 504.370870518832 6.62912948116776 32 499 499.983901830283 -0.98390183028285 33 555 567.981916502798 -12.9819165027984 34 565 581.142822568447 -16.1428225684466 35 542 567.981916502798 -25.9819165027984 36 527 548.240557404326 -21.2405574043261 37 510 528.499198305854 -18.4991983058539 38 514 526.305713961579 -12.3057139615792 39 517 526.305713961579 -9.30571396157919 40 508 517.53177658448 -9.5317765844804 41 493 504.370870518832 -11.3708705188322 42 490 499.983901830283 -9.98390183028285 43 469 480.24254273181 -11.2425427318106 44 478 486.822995764635 -8.82299576463468 45 528 550.434041748601 -22.4340417486008 46 534 559.2079791257 -25.2079791256996 47 518 552.627526092876 -34.6275260928755 48 506 535.079651338678 -29.079651338678 49 502 526.305713961579 -24.3057139615792 50 516 530.692682650129 -14.6926826501286 51 528 535.079651338678 -7.07965133867797 52 533 535.079651338678 -2.07965133867797 53 536 532.886166994403 3.11383300559673 54 537 530.692682650129 6.30731734987142 55 524 513.144807895931 10.8551921040690 56 536 521.91874527303 14.0812547269702 57 587 583.336306912721 3.66369308727874 58 597 596.49721297837 0.502787021630576 59 581 592.11024428982 -11.1102442898200 60 564 567.981916502798 -3.98191650279839 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00150816497513730 0.00301632995027460 0.998491835024863 6 0.00353797580830073 0.00707595161660145 0.9964620241917 7 0.00136014244386616 0.00272028488773233 0.998639857556134 8 0.000329924873886748 0.000659849747773496 0.999670075126113 9 0.000604093640244319 0.00120818728048864 0.999395906359756 10 0.000173797636251672 0.000347595272503343 0.999826202363748 11 4.20460973741466e-05 8.40921947482933e-05 0.999957953902626 12 1.03873661505277e-05 2.07747323010555e-05 0.99998961263385 13 5.38679202537246e-06 1.07735840507449e-05 0.999994613207975 14 1.89827393233831e-06 3.79654786467663e-06 0.999998101726068 15 4.22451908174427e-06 8.44903816348855e-06 0.999995775480918 16 5.4025931050441e-06 1.08051862100882e-05 0.999994597406895 17 4.0188041111402e-06 8.0376082222804e-06 0.99999598119589 18 2.59973881449693e-06 5.19947762899386e-06 0.999997400261186 19 4.20728308965899e-06 8.41456617931797e-06 0.99999579271691 20 7.3606063258039e-06 1.47212126516078e-05 0.999992639393674 21 2.14644127143787e-05 4.29288254287574e-05 0.999978535587286 22 2.14307786120394e-05 4.28615572240789e-05 0.999978569221388 23 9.01847660517522e-06 1.80369532103504e-05 0.999990981523395 24 2.22718507655450e-05 4.45437015310900e-05 0.999977728149234 25 0.000354156788832783 0.000708313577665566 0.999645843211167 26 0.00173943589879551 0.00347887179759102 0.998260564101205 27 0.00366197234927203 0.00732394469854406 0.996338027650728 28 0.00998389881270964 0.0199677976254193 0.99001610118729 29 0.0378777010563375 0.075755402112675 0.962122298943662 30 0.0847317655014376 0.169463531002875 0.915268234498562 31 0.158788945262687 0.317577890525375 0.841211054737313 32 0.272186388663840 0.544372777327679 0.72781361133616 33 0.32865293619586 0.65730587239172 0.67134706380414 34 0.375591516952235 0.75118303390447 0.624408483047765 35 0.590107057977864 0.819785884044272 0.409892942022136 36 0.705418254156091 0.589163491687819 0.294581745843909 37 0.768565162019142 0.462869675961717 0.231434837980858 38 0.760610842408552 0.478778315182896 0.239389157591448 39 0.728466787781028 0.543066424437943 0.271533212218972 40 0.691354027496877 0.617291945006246 0.308645972503123 41 0.656646093765286 0.686707812469428 0.343353906234714 42 0.605805074535818 0.788389850928364 0.394194925464182 43 0.555846650988652 0.888306698022695 0.444153349011348 44 0.487019128659022 0.974038257318045 0.512980871340978 45 0.50545915898387 0.98908168203226 0.49454084101613 46 0.558258150034554 0.883483699930892 0.441741849965446 47 0.794063256927184 0.411873486145632 0.205936743072816 48 0.922702930283894 0.154594139432212 0.0772970697161058 49 0.984794193844174 0.0304116123116513 0.0152058061558256 50 0.99577508070873 0.00844983858253842 0.00422491929126921 51 0.99707950454876 0.00584099090247895 0.00292049545123947 52 0.996051080455724 0.00789783908855175 0.00394891954427587 53 0.989710750497369 0.0205784990052627 0.0102892495026313 54 0.968844735728933 0.062310528542133 0.0311552642710665 55 0.913582679578395 0.17283464084321 0.086417320421605 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 26 0.509803921568627 NOK 5% type I error level 29 0.568627450980392 NOK 10% type I error level 31 0.607843137254902 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/10c63h1258739434.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/10c63h1258739434.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/1o4km1258739434.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/1o4km1258739434.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/20i4t1258739434.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/20i4t1258739434.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/33ypx1258739434.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/33ypx1258739434.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/4qxbr1258739434.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/4qxbr1258739434.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/5lxmw1258739434.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/5lxmw1258739434.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/6908h1258739434.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/6908h1258739434.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/78kw81258739434.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/78kw81258739434.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/8n0qz1258739434.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/8n0qz1258739434.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/939d01258739434.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740790v3gk7fqrb2la17m/939d01258739434.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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