Case Q3

*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: Sat, 22 Nov 2008 11:09:11 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/22/t122737744937sn5jlf7r7x708.htm/, Retrieved Sat, 22 Nov 2008 18:10:50 +0000

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/2008/Nov/22/t122737744937sn5jlf7r7x708.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

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User-defined keywords:

Dataseries X:

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0,9059 0 0,8883 1 0,8924 1 0,8833 0 0,8700 0 0,8758 1 0,8858 1 0,9170 1 0,9554 1 0,9922 1 0,9778 1 0,9808 1 0,9811 1 1,0014 1 1,0183 1 1,0622 1 1,0773 1 1,0807 1 1,0848 1 1,1582 1 1,1663 1 1,1372 1 1,1139 1 1,1222 1 1,1692 1 1,1702 1 1,2286 1 1,2613 1 1,2646 1 1,2262 1 1,1985 0 1,2007 1 1,2138 1 1,2266 1 1,2176 0 1,2218 1 1,2490 1 1,2991 1 1,3408 1 1,3119 0 1,3014 0 1,3201 1 1,2938 0 1,2694 0 1,2165 0 1,2037 0 1,2292 1 1,2256 0 1,2015 0 1,1786 0 1,1856 1 1,2103 1 1,1938 0 1,2020 1 1,2271 1 1,2770 1 1,2650 0 1,2684 1 1,2811 1 1,2727 0 1,2611 0 1,2881 1 1,3213 1 1,2999 0 1,3074 1 1,3242 1 1,3516 1 1,3511 0 1,3419 0 1,3716 1 1,3622 0 1,3896 1 1,4227 1 1,4684 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 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation y[t] = + 0.915412011076497 + 0.0300483731394946x[t] + 0.00256322682644082M1[t] + 0.00242863433823974M2[t] + 0.00951129613146747M3[t] + 0.0251694730147742M4[t] + 0.0164201299950001M5[t] -0.00253673292785442M6[t] + 0.00323004843220317M7[t] + 0.0188473720790959M8[t] + 0.015089424582571M9[t] + 0.00552395718296547M10[t] + 0.00119934301977398M11[t] + 0.00634934301977397t + 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) 0.915412011076497 0.038193 23.9678 0 0 x 0.0300483731394946 0.020434 1.4705 0.146653 0.073327 M1 0.00256322682644082 0.040326 0.0636 0.94953 0.474765 M2 0.00242863433823974 0.040388 0.0601 0.95225 0.476125 M3 0.00951129613146747 0.042295 0.2249 0.822835 0.411418 M4 0.0251694730147742 0.04205 0.5986 0.551717 0.275858 M5 0.0164201299950001 0.042012 0.3908 0.697299 0.34865 M6 -0.00253673292785442 0.042252 -0.06 0.952325 0.476163 M7 0.00323004843220317 0.041772 0.0773 0.938621 0.469311 M8 0.0188473720790959 0.041754 0.4514 0.653334 0.326667 M9 0.015089424582571 0.041902 0.3601 0.720028 0.360014 M10 0.00552395718296547 0.041853 0.132 0.895438 0.447719 M11 0.00119934301977398 0.041724 0.0287 0.977164 0.488582 t 0.00634934301977397 0.000408 15.5756 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.89809725814331 R-squared 0.80657868508453 Adjusted R-squared 0.764670733519511 F-TEST (value) 19.2464354606585 F-TEST (DF numerator) 13 F-TEST (DF denominator) 60 p-value 1.11022302462516e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0722646153436207 Sum Squared Residuals 0.313330477845687 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 0.9059 0.924324580922712 -0.0184245809227117 2 0.8883 0.96058770459378 -0.0722877045937794 3 0.8924 0.974019709406781 -0.0816197094067811 4 0.8833 0.965978856170367 -0.0826788561703672 5 0.87 0.963578856170367 -0.093578856170367 6 0.8758 0.981019709406781 -0.105219709406781 7 0.8858 0.993135833786613 -0.107335833786613 8 0.917 1.01510250045328 -0.0981025004532792 9 0.9554 1.01769389597653 -0.0622938959765283 10 0.9922 1.01447777159670 -0.0222777715966968 11 0.9778 1.01650250045328 -0.0387025004532793 12 0.9808 1.02165250045328 -0.0408525004532793 13 0.9811 1.03056507029949 -0.049465070299494 14 1.0014 1.03677982083107 -0.0353798208310667 15 1.0183 1.05021182564407 -0.0319118256440686 16 1.0622 1.07221934554715 -0.0100193455471493 17 1.0773 1.06981934554715 0.00748065445285071 18 1.0807 1.05721182564407 0.0234881743559314 19 1.0848 1.0693279500239 0.0154720499760998 20 1.1582 1.09129461669057 0.0669053833094331 21 1.1663 1.09388601221382 0.072413987786184 22 1.1372 1.09066988783398 0.0465301121660156 23 1.1139 1.09269461669057 0.0212053833094330 24 1.1222 1.09784461669057 0.0243553833094332 25 1.1692 1.10675718653678 0.0624428134632184 26 1.1702 1.11297193706835 0.0572280629316454 27 1.2286 1.12640394188136 0.102196058118644 28 1.2613 1.14841146178444 0.112888538215563 29 1.2646 1.14601146178444 0.118588538215563 30 1.2262 1.13340394188136 0.0927960581186437 31 1.1985 1.11547169312169 0.0830283068783068 32 1.2007 1.16748673292785 0.0332132670721456 33 1.2138 1.17007812845110 0.0437218715488965 34 1.2266 1.16686200407127 0.059737995928728 35 1.2176 1.13883835978836 0.0787616402116402 36 1.2218 1.17403673292785 0.0477632670721456 37 1.249 1.18294930277407 0.066050697225931 38 1.2991 1.18916405330564 0.109935946694358 39 1.3408 1.20259605811864 0.138203941881356 40 1.3119 1.19455520488223 0.117344795117770 41 1.3014 1.19215520488223 0.109244795117770 42 1.3201 1.20959605811864 0.110503941881356 43 1.2938 1.19166380935898 0.102136190641019 44 1.2694 1.21363047602565 0.0557695239743527 45 1.2165 1.21622187154890 0.000278128451103460 46 1.2037 1.21300574716906 -0.00930574716906494 47 1.2292 1.24507884916514 -0.0158788491651419 48 1.2256 1.22018047602565 0.00541952397435265 49 1.2015 1.22909304587186 -0.0275930458718621 50 1.1786 1.23530779640343 -0.0567077964034349 51 1.1856 1.27878817435593 -0.0931881743559313 52 1.2103 1.30079569425901 -0.0904956942590121 53 1.1938 1.26834732111952 -0.0745473211195174 54 1.202 1.28578817435593 -0.0837881743559314 55 1.2271 1.29790429873576 -0.0708042987357628 56 1.277 1.31987096540243 -0.0428709654024296 57 1.265 1.29241398778618 -0.0274139877861841 58 1.2684 1.31924623654585 -0.0508462365458471 59 1.2811 1.32127096540243 -0.0401709654024297 60 1.2727 1.29637259226293 -0.023672592262935 61 1.2611 1.30528516210915 -0.0441851621091496 62 1.2881 1.34154828578022 -0.0534482857802173 63 1.3213 1.35498029059322 -0.0336802905932190 64 1.2999 1.34693943735680 -0.0470394373568049 65 1.3074 1.3745878104963 -0.0671878104962997 66 1.3242 1.36198029059322 -0.0377802905932189 67 1.3516 1.37409641497305 -0.0224964149730506 68 1.3511 1.36601470850022 -0.0149147085002226 69 1.3419 1.36860610402347 -0.0267061040234715 70 1.3716 1.39543835278313 -0.0238383527831347 71 1.3622 1.36741470850022 -0.00521470850022241 72 1.3896 1.40261308163972 -0.0130130816397173 73 1.4227 1.41152565148593 0.0111743485140681 74 1.4684 1.41774040201750 0.0506595979824951 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.332099296865484 0.664198593730967 0.667900703134516 18 0.331692696043308 0.663385392086616 0.668307303956692 19 0.261981552526556 0.523963105053111 0.738018447473444 20 0.26157589472784 0.52315178945568 0.73842410527216 21 0.180573844143670 0.361147688287340 0.81942615585633 22 0.125520163027010 0.251040326054021 0.87447983697299 23 0.0939288212562365 0.187857642512473 0.906071178743764 24 0.0657753440149032 0.131550688029806 0.934224655985097 25 0.039834319354102 0.079668638708204 0.960165680645898 26 0.0251928516003289 0.0503857032006579 0.974807148399671 27 0.0154162766541761 0.0308325533083521 0.984583723345824 28 0.0125819275878277 0.0251638551756555 0.987418072412172 29 0.0101716790796142 0.0203433581592284 0.989828320920386 30 0.00536511688209149 0.0107302337641830 0.994634883117908 31 0.00349905459732725 0.00699810919465449 0.996500945402673 32 0.00668486024956251 0.0133697204991250 0.993315139750438 33 0.0106706225695262 0.0213412451390524 0.989329377430474 34 0.0111712995741207 0.0223425991482414 0.98882870042588 35 0.0070501482821289 0.0141002965642578 0.992949851717871 36 0.00547204012290127 0.0109440802458025 0.994527959877099 37 0.00592851637687016 0.0118570327537403 0.99407148362313 38 0.00408966765431634 0.00817933530863268 0.995910332345684 39 0.00804989540243211 0.0160997908048642 0.991950104597568 40 0.0164423551205603 0.0328847102411206 0.98355764487944 41 0.0476934720980804 0.0953869441961608 0.95230652790192 42 0.218374085991575 0.436748171983151 0.781625914008425 43 0.576026252370589 0.847947495258822 0.423973747629411 44 0.83742202052205 0.3251559589559 0.16257797947795 45 0.937655540711902 0.124688918576197 0.0623444592880983 46 0.976509151545501 0.046981696908998 0.023490848454499 47 0.993615423403953 0.0127691531920945 0.00638457659604725 48 0.99856249020003 0.00287501959993828 0.00143750979996914 49 0.9994029772085 0.00119404558300074 0.000597022791500372 50 0.999081841963989 0.00183631607202282 0.00091815803601141 51 0.999376641188434 0.00124671762313217 0.000623358811566083 52 0.999123334236003 0.00175333152799383 0.000876665763996913 53 0.998479333619903 0.00304133276019417 0.00152066638009709 54 0.996089400359724 0.0078211992805516 0.0039105996402758 55 0.989146064367052 0.0217078712658969 0.0108539356329485 56 0.967898957607142 0.0642020847857151 0.0321010423928576 57 0.938083719583527 0.123832560832946 0.061916280416473 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.219512195121951 NOK 5% type I error level 24 0.585365853658537 NOK 10% type I error level 28 0.682926829268293 NOK

Charts produced by software:

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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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