W8

*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, 26 Nov 2010 13:29:37 +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/Nov/26/t1290778626c3l9i648hpdgykd.htm/, Retrieved Fri, 26 Nov 2010 14:37:16 +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/Nov/26/t1290778626c3l9i648hpdgykd.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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16571771,60 17972385,83 17535166,38 16198106,13 17487530,67 13768040,14 16198892,67 16896235,55 16571771,60 17535166,38 16198106,13 17487530,67 16554237,93 16697955,94 16198892,67 16571771,60 17535166,38 16198106,13 19554176,37 19691579,52 16554237,93 16198892,67 16571771,60 17535166,38 15903762,33 15930700,75 19554176,37 16554237,93 16198892,67 16571771,60 18003781,65 17444615,98 15903762,33 19554176,37 16554237,93 16198892,67 18329610,38 17699369,88 18003781,65 15903762,33 19554176,37 16554237,93 16260733,42 15189796,81 18329610,38 18003781,65 15903762,33 19554176,37 14851949,20 15672722,75 16260733,42 18329610,38 18003781,65 15903762,33 18174068,44 17180794,3 14851949,20 16260733,42 18329610,38 18003781,65 18406552,23 17664893,45 18174068,44 14851949,20 16260733,42 18329610,38 18466459,42 17862884,98 18406552,23 18174068,44 14851949,20 16260733,42 16016524,60 16162288,88 18466459,42 18406552,23 18174068,44 14851949,20 17428458,32 17463628,82 16016524,60 18466459,42 18406552,23 18174068,44 171 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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 3151494.74893363 + 0.86246309155318X[t] + 0.057069425232684Y1[t] -0.00910064194217095Y2[t] + 0.0398947625434009Y3[t] -0.119766534283845Y4[t] -1281242.59489156M1[t] -490303.731102045M2[t] + 268741.195930662M3[t] + 399800.043282565M4[t] -201692.974633398M5[t] + 235204.245288726M6[t] + 645793.772386501M7[t] + 822791.718607045M8[t] -988516.699854688M9[t] + 617633.42020277M10[t] + 439029.687609985M11[t] -17613.1437160194t + 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) 3151494.74893363 935594.491107 3.3684 0.001744 0.000872 X 0.86246309155318 0.078585 10.9749 0 0 Y1 0.057069425232684 0.080247 0.7112 0.481321 0.240661 Y2 -0.00910064194217095 0.079353 -0.1147 0.909298 0.454649 Y3 0.0398947625434009 0.078227 0.51 0.613008 0.306504 Y4 -0.119766534283845 0.074535 -1.6069 0.116365 0.058182 M1 -1281242.59489156 509065.081879 -2.5169 0.01618 0.00809 M2 -490303.731102045 480760.953342 -1.0198 0.314249 0.157125 M3 268741.195930662 466154.149058 0.5765 0.567672 0.283836 M4 399800.043282565 440200.241513 0.9082 0.369485 0.184742 M5 -201692.974633398 386083.800141 -0.5224 0.604419 0.30221 M6 235204.245288726 387701.951221 0.6067 0.547683 0.273841 M7 645793.772386501 465962.142496 1.3859 0.173846 0.086923 M8 822791.718607045 379989.340758 2.1653 0.036709 0.018355 M9 -988516.699854688 443508.823958 -2.2289 0.031815 0.015907 M10 617633.42020277 548277.712182 1.1265 0.267019 0.133509 M11 439029.687609985 518014.262562 0.8475 0.40201 0.201005 t -17613.1437160194 4517.678382 -3.8987 0.000381 0.00019 Multiple Linear Regression - Regression Statistics Multiple R 0.97944652981867 R-squared 0.959315504773834 Adjusted R-squared 0.941114546383181 F-TEST (value) 52.7068676376117 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 485956.855387267 Sum Squared Residuals 8973854481319.48 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 16571771.6 17255177.5905915 -683405.990591523 2 16198892.67 16536303.1443226 -337410.474322625 3 16554237.93 17301985.2164883 -747747.28648832 4 19554176.37 19822424.0769997 -268247.706999724 5 15903762.33 16228176.2145419 -324413.884541911 6 18003781.65 17776362.7355703 227418.914429663 7 18329610.38 18619246.3232642 -289635.943264194 8 16260733.42 16108775.6804458 151957.739554234 9 14851949.2 15096302.2582609 -244353.058260939 10 18174068.44 17685411.7125495 488656.727450484 11 18406552.23 17987564.0292963 418988.200703702 12 18466459.42 17876294.9950413 590164.424958676 13 16016524.6 15413301.3655188 603223.234481239 14 17428458.32 16780019.3652301 648438.954769853 15 17167191.42 17002463.9914871 164727.428512923 16 19629987.6 18996870.5035644 633117.096435603 17 17183629.01 16812994.9249657 370634.08503428 18 18344657.85 18132634.9406470 212022.909352959 19 19301440.71 19044395.6015091 257045.108490907 20 18147463.68 18245323.2018766 -97859.5218765806 21 16192909.22 15479850.9243100 713058.295689977 22 18374420.6 18210070.1089061 164350.491093864 23 20515191.95 19995441.6895382 519750.260461832 24 18957217.2 19055087.5352568 -97870.3352567868 25 16471529.53 16661988.8011975 -190459.271197479 26 18746813.27 19005408.4648715 -258595.194871476 27 19009453.59 18593483.6626872 415969.927312781 28 19211178.55 19819389.4885690 -608210.93856897 29 20547653.75 20657485.5872439 -109831.837243946 30 19325754.03 19337006.8269402 -11252.7969402468 31 20605542.58 20814511.3051512 -208968.725151206 32 20056915.06 20316430.3722358 -259515.312235764 33 16141449.72 16548889.8962452 -407440.176245162 34 20359793.22 20777786.1336861 -417992.91368611 35 19711553.27 19943535.4150014 -231982.145001435 36 15638580.7 16516330.1473086 -877749.447308581 37 14384486 14452100.9127655 -67614.9127655282 38 13855616.12 14017338.9027664 -161722.782766369 39 14308336.46 14180190.9487047 128145.511295329 40 15290621.44 15751882.1601039 -461260.720103876 41 14423755.53 14173719.3150554 250036.214944576 42 13779681.49 14214193.6732223 -434512.183222282 43 15686348.94 15930217.8604883 -243868.920488343 44 14733828.17 14993193.0620991 -259364.892099093 45 12522497.94 12583763.0011839 -61265.0611838757 46 16189383.57 16424397.8748582 -235014.304858239 47 16059123.25 16765879.5661641 -706756.316164099 48 16007123.26 15621667.9023933 385455.357606691 49 15806842.33 15468585.3899267 338256.940073290 50 15159951.13 15050661.6328094 109289.497190618 51 15692144.17 15653239.7506327 38904.4193672878 52 18908869.11 18204266.8407630 704602.269236968 53 16969881.42 17156305.998193 -186424.578192998 54 16997477.78 16991154.6236201 6323.15637990719 55 19858875.65 19373447.1695872 485428.480412837 56 17681170.13 17216388.1433428 464781.986657203 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.330538660591570 0.661077321183139 0.66946133940843 22 0.473882073160261 0.947764146320523 0.526117926839738 23 0.555712885935467 0.888574228129066 0.444287114064533 24 0.542042824984014 0.915914350031973 0.457957175015986 25 0.58631710035399 0.827365799292019 0.413682899646010 26 0.556679098195949 0.886641803608103 0.443320901804051 27 0.543375962608688 0.913248074782625 0.456624037391312 28 0.604170063521513 0.791659872956973 0.395829936478487 29 0.541498865635815 0.917002268728371 0.458501134364185 30 0.520683006577582 0.958633986844835 0.479316993422418 31 0.438547202913205 0.87709440582641 0.561452797086795 32 0.35853197466508 0.71706394933016 0.64146802533492 33 0.259988109689895 0.519976219379789 0.740011890310105 34 0.205948007528703 0.411896015057405 0.794051992471297 35 0.433992225050289 0.867984450100577 0.566007774949711 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/10dhce1290778169.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/10dhce1290778169.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/1eouq1290778168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/1eouq1290778168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/2eouq1290778168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/2eouq1290778168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/3pxta1290778168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/3pxta1290778168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/4pxta1290778168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/4pxta1290778168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/5pxta1290778168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/5pxta1290778168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/6iotd1290778168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/6iotd1290778168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/7agay1290778168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/7agay1290778168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/8agay1290778168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/8agay1290778168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/9agay1290778168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290778626c3l9i648hpdgykd/9agay1290778168.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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