*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, 09 Dec 2009 12:33:28 -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/09/t1260387460nhxe5q00zsg88xe.htm/, Retrieved Wed, 09 Dec 2009 20:37:52 +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/09/t1260387460nhxe5q00zsg88xe.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:

hypothesepaper

Dataseries X:

» Textbox « » Textfile « » CSV «

627 0 696 0 825 0 677 0 656 0 785 0 412 0 352 0 839 0 729 0 696 0 641 0 695 0 638 0 762 0 635 0 721 0 854 0 418 0 367 0 824 0 687 0 601 0 676 0 740 0 691 0 683 0 594 0 729 0 731 0 386 0 331 0 707 0 715 0 657 0 653 0 642 0 643 0 718 0 654 0 632 0 731 0 392 1 344 1 792 1 852 1 649 1 629 1 685 1 617 1 715 1 715 1 629 1 916 1 531 1 357 1 917 1 828 1 708 1 858 1 775 1 785 1 1006 1 789 1 734 1 906 1 532 1 387 1 991 1 841 1

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 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 653.571428571429 + 56.4285714285714X[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) 653.571428571429 23.887958 27.3599 0 0 X 56.4285714285714 37.770179 1.494 0.139802 0.069901 Multiple Linear Regression - Regression Statistics Multiple R 0.178271700568352 R-squared 0.0317807992235320 Adjusted R-squared 0.0175422815650547 F-TEST (value) 2.23203004595145 F-TEST (DF numerator) 1 F-TEST (DF denominator) 68 p-value 0.139802175645581 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 154.811663838692 Sum Squared Residuals 1629732.28571429 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 627 653.571428571426 -26.5714285714261 2 696 653.571428571429 42.4285714285714 3 825 653.571428571429 171.428571428571 4 677 653.571428571429 23.4285714285714 5 656 653.571428571429 2.42857142857136 6 785 653.571428571429 131.428571428571 7 412 653.571428571429 -241.571428571429 8 352 653.571428571429 -301.571428571429 9 839 653.571428571429 185.428571428571 10 729 653.571428571429 75.4285714285714 11 696 653.571428571429 42.4285714285714 12 641 653.571428571429 -12.5714285714286 13 695 653.571428571429 41.4285714285714 14 638 653.571428571429 -15.5714285714286 15 762 653.571428571429 108.428571428571 16 635 653.571428571429 -18.5714285714286 17 721 653.571428571429 67.4285714285714 18 854 653.571428571429 200.428571428571 19 418 653.571428571429 -235.571428571429 20 367 653.571428571429 -286.571428571429 21 824 653.571428571429 170.428571428571 22 687 653.571428571429 33.4285714285714 23 601 653.571428571429 -52.5714285714286 24 676 653.571428571429 22.4285714285714 25 740 653.571428571429 86.4285714285714 26 691 653.571428571429 37.4285714285714 27 683 653.571428571429 29.4285714285714 28 594 653.571428571429 -59.5714285714286 29 729 653.571428571429 75.4285714285714 30 731 653.571428571429 77.4285714285714 31 386 653.571428571429 -267.571428571429 32 331 653.571428571429 -322.571428571429 33 707 653.571428571429 53.4285714285714 34 715 653.571428571429 61.4285714285714 35 657 653.571428571429 3.42857142857136 36 653 653.571428571429 -0.571428571428637 37 642 653.571428571429 -11.5714285714286 38 643 653.571428571429 -10.5714285714286 39 718 653.571428571429 64.4285714285714 40 654 653.571428571429 0.428571428571363 41 632 653.571428571429 -21.5714285714286 42 731 653.571428571429 77.4285714285714 43 392 710 -318 44 344 710 -366 45 792 710 82 46 852 710 142 47 649 710 -61 48 629 710 -81 49 685 710 -25 50 617 710 -93 51 715 710 5 52 715 710 5 53 629 710 -81 54 916 710 206 55 531 710 -179 56 357 710 -353 57 917 710 207 58 828 710 118 59 708 710 -2 60 858 710 148 61 775 710 65 62 785 710 75 63 1006 710 296 64 789 710 79 65 734 710 24 66 906 710 196 67 532 710 -178 68 387 710 -323 69 991 710 281 70 841 710 131 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.180055513589125 0.360111027178251 0.819944486410875 6 0.119383772481348 0.238767544962697 0.880616227518652 7 0.483171774776129 0.966343549552258 0.516828225223871 8 0.753579751642003 0.492840496715995 0.246420248357997 9 0.781011148979996 0.437977702040009 0.218988851020004 10 0.709655016828741 0.580689966342518 0.290344983171259 11 0.618433340635988 0.763133318728024 0.381566659364012 12 0.520138026901099 0.959723946197803 0.479861973098901 13 0.426759888257732 0.853519776515463 0.573240111742268 14 0.337433616522776 0.674867233045551 0.662566383477224 15 0.289909058227384 0.579818116454768 0.710090941772616 16 0.219856116270021 0.439712232540042 0.78014388372998 17 0.167781575531254 0.335563151062508 0.832218424468746 18 0.200540818627621 0.401081637255242 0.799459181372379 19 0.313554813966737 0.627109627933474 0.686445186033263 20 0.501524023467243 0.996951953065515 0.498475976532757 21 0.512600895964377 0.974798208071246 0.487399104035623 22 0.438311360119607 0.876622720239214 0.561688639880393 23 0.372965153940521 0.745930307881043 0.627034846059479 24 0.304673300730126 0.609346601460253 0.695326699269874 25 0.259911927827804 0.519823855655608 0.740088072172196 26 0.206039795662453 0.412079591324905 0.793960204337547 27 0.158912697866623 0.317825395733245 0.841087302133377 28 0.124437242662337 0.248874485324675 0.875562757337663 29 0.0983605966302692 0.196721193260538 0.901639403369731 30 0.0773527500586076 0.154705500117215 0.922647249941392 31 0.144966687061836 0.289933374123673 0.855033312938164 32 0.318479017875380 0.636958035750761 0.68152098212462 33 0.263926414426431 0.527852828852861 0.73607358557357 34 0.216464545375375 0.43292909075075 0.783535454624625 35 0.168393778295235 0.336787556590469 0.831606221704765 36 0.127890412427595 0.255780824855189 0.872109587572405 37 0.0951194874512858 0.190238974902572 0.904880512548714 38 0.0691355171520694 0.138271034304139 0.93086448284793 39 0.0509514244222457 0.101902848844491 0.949048575577754 40 0.0350656282713019 0.0701312565426038 0.964934371728698 41 0.0243137070044244 0.0486274140088488 0.975686292995576 42 0.0168351478220358 0.0336702956440716 0.983164852177964 43 0.0246215744696011 0.0492431489392023 0.9753784255304 44 0.0562399682563096 0.112479936512619 0.94376003174369 45 0.0963120586585598 0.192624117317120 0.90368794134144 46 0.127758536717786 0.255517073435572 0.872241463282214 47 0.0991583122912132 0.198316624582426 0.900841687708787 48 0.0772624374663463 0.154524874932693 0.922737562533654 49 0.0565473809468212 0.113094761893642 0.943452619053179 50 0.0439781548286035 0.087956309657207 0.956021845171396 51 0.03076486924219 0.06152973848438 0.96923513075781 52 0.0206771819904315 0.041354363980863 0.979322818009569 53 0.0150022084000931 0.0300044168001862 0.984997791599907 54 0.0199999892633291 0.0399999785266583 0.98000001073667 55 0.0233795565769221 0.0467591131538442 0.976620443423078 56 0.149885587183437 0.299771174366874 0.850114412816563 57 0.161663547735441 0.323327095470882 0.838336452264559 58 0.125886338530561 0.251772677061123 0.874113661469439 59 0.088655227866818 0.177310455733636 0.911344772133182 60 0.067912759416155 0.13582551883231 0.932087240583845 61 0.0414833636590727 0.0829667273181453 0.958516636340927 62 0.023538114757116 0.047076229514232 0.976461885242884 63 0.0435327027455094 0.0870654054910188 0.95646729725449 64 0.0228562829276245 0.0457125658552491 0.977143717072375 65 0.00956985162007734 0.0191397032401547 0.990430148379923 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 10 0.163934426229508 NOK 10% type I error level 15 0.245901639344262 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/10rgs71260387203.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/10rgs71260387203.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/1uyxz1260387203.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/1uyxz1260387203.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/2d0bw1260387203.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/2d0bw1260387203.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/3e5te1260387203.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/3e5te1260387203.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/45tfw1260387203.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/45tfw1260387203.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/5nhue1260387203.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/5nhue1260387203.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/69e9n1260387203.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/69e9n1260387203.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/7l0pz1260387203.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/7l0pz1260387203.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/8p7hj1260387203.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/8p7hj1260387203.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/9u6md1260387203.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260387460nhxe5q00zsg88xe/9u6md1260387203.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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