*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: Thu, 02 Dec 2010 12:56:44 +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/02/t1291295168eqr6dp00b8bepjj.htm/, Retrieved Thu, 02 Dec 2010 14:06:18 +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/02/t1291295168eqr6dp00b8bepjj.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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2502,66 10169,02 10433,44 24977 -7,9 -15 0,3 3,36 12 2466,92 9633,83 10238,83 24320 -8,8 -10 -0,1 3,37 11 2513,17 10066,24 9857,34 22680 -14,2 -12 -1 3,55 10 2443,27 10302,87 9634,97 22052 -17,8 -11 -1,2 3,53 09 2293,41 10430,35 9374,63 21467 -18,2 -11 -0,8 3,52 08 2070,83 9691,12 8679,75 21383 -22,8 -17 -1,7 3,54 07 2029,6 9810,31 8593 21777 -23,6 -18 -1,1 3,5 06 2052,02 9304,43 8398,37 21928 -27,6 -19 -0,4 3,44 05 1864,44 8767,96 7992,12 21814 -29,4 -22 0,6 3,38 04 1670,07 7764,58 7235,47 22937 -31,8 -24 0,6 3,35 03 1810,99 7694,78 7690,5 23595 -31,4 -24 1,9 3,68 02 1905,41 8331,49 8396,2 20830 -27,6 -20 2,3 3,92 01 1862,83 8460,94 8595,56 19650 -28,8 -25 2,6 4,05 12 2014,45 8531,45 8614,55 19195 -21,9 -22 3,1 4,14 11 2197,82 9117,03 9181,73 19644 -13,9 -17 4,7 4,53 10 2962,34 12123,53 11114,08 18483 -8 -9 5,5 4,54 09 3047,03 12989,35 11530,75 18079 -2,8 -11 5,4 4,9 08 3032,6 13168,91 11322,38 19178 -3,3 -13 5,9 4,92 07 3504,37 14084,6 12056,67 18391 -1,3 -11 5,8 4,45 06 3801,06 13995,33 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation BEL_20[t] = -1881.92093448974 + 0.191100321953382Nikkei[t] + 0.289259310027567DJ_Indust[t] + 0.0145639845361721Goudprijs[t] -9.42821394015824Conjunct_Seizoenzuiver[t] -3.35520018942041Cons_vertrouw[t] + 32.6392820980445Alg_consumptie_index_BE[t] -254.219100476035Gem_rente_kasbon_5j[t] -1.32190944304161Maand[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) -1881.92093448974 272.991974 -6.8937 0 0 Nikkei 0.191100321953382 0.015432 12.3835 0 0 DJ_Indust 0.289259310027567 0.03376 8.5682 0 0 Goudprijs 0.0145639845361721 0.008322 1.7501 0.084975 0.042487 Conjunct_Seizoenzuiver -9.42821394015824 6.642913 -1.4193 0.160744 0.080372 Cons_vertrouw -3.35520018942041 8.724572 -0.3846 0.701852 0.350926 Alg_consumptie_index_BE 32.6392820980445 18.498163 1.7645 0.082502 0.041251 Gem_rente_kasbon_5j -254.219100476035 57.058457 -4.4554 3.5e-05 1.8e-05 Maand -1.32190944304161 6.377068 -0.2073 0.836451 0.418226 Multiple Linear Regression - Regression Statistics Multiple R 0.983716693372244 R-squared 0.96769853281922 Adjusted R-squared 0.963596759208963 F-TEST (value) 235.921975410639 F-TEST (DF numerator) 8 F-TEST (DF denominator) 63 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 161.344873074614 Sum Squared Residuals 1640026.58825019 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2502.66 2707.67994551663 -205.019945516626 2 2466.92 2516.97706924377 -50.0570692437716 3 2513.17 2449.18616376271 63.9838362372944 4 2443.27 2451.40226291509 -8.13226291509133 5 2293.41 2412.62913109455 -119.219131094554 6 2070.83 2099.50131485049 -28.6713148504925 7 2029.6 2144.89554104893 -115.295541048933 8 2052.02 2074.61294122374 -22.5929412237412 9 1864.44 1929.17218579978 -64.7321857997813 10 1670.07 1573.19983875250 96.8701612474971 11 1810.99 1657.15518938415 153.834810615853 12 1905.41 1846.80857765675 58.6014223432489 13 1862.83 1902.31990399739 -39.4899039973921 14 2014.45 1834.30236372566 180.147636274342 15 2197.82 1979.00581391919 218.814186080813 16 2962.34 3038.01345391172 -75.6734539117243 17 3047.03 3182.33655457386 -135.306554573857 18 3032.6 3196.36506079008 -163.765060790078 19 3504.37 3664.26620772756 -159.896207727562 20 3801.06 3959.35323829092 -158.293238290915 21 3857.62 3845.20043287096 12.4195671290381 22 3674.4 3526.62581228835 147.774187711654 23 3720.98 3667.21055535645 53.7694446435532 24 3844.49 3691.14506122603 153.344938773970 25 4116.68 4268.14222608313 -151.462226083132 26 4105.18 4186.3025012722 -81.1225012721991 27 4435.23 4567.10274279444 -131.872742794436 28 4296.49 4295.70212830935 0.787871690651293 29 4202.52 4206.01681959499 -3.49681959499111 30 4562.84 4591.18955058552 -28.3495505855178 31 4621.4 4567.00897914014 54.3910208598633 32 4696.96 4572.62547954475 124.334520455245 33 4591.27 4377.82315737226 213.446842627739 34 4356.98 4193.71028777419 163.269712225814 35 4502.64 4406.17204489626 96.4679551037408 36 4443.91 4342.51306538464 101.396934615356 37 4290.89 4235.42587031606 55.4641296839441 38 4199.75 4037.66127687256 162.088723127439 39 4138.52 4012.51476357641 126.00523642359 40 3970.1 3809.55824857668 160.541751423318 41 3862.27 3693.79587479137 168.474125208635 42 3701.61 3508.57633496669 193.033665033308 43 3570.12 3506.70160827661 63.4183917233942 44 3801.06 3927.45022691712 -126.390226917115 45 3895.51 4078.65670013303 -183.146700133032 46 3917.96 3940.35999441493 -22.3999944149300 47 3813.06 3911.68623431729 -98.6262343172928 48 3667.03 3852.86926536805 -185.839265368054 49 3494.17 3775.72657397467 -281.556573974671 50 3363.99 3527.35301809864 -163.363018098644 51 3295.32 3266.74859776635 28.5714022336481 52 3277.01 3303.63485148703 -26.6248514870341 53 3257.16 3166.85811840313 90.301881596873 54 3161.69 3100.15097264666 61.5390273533421 55 3097.31 2988.54797975787 108.762020242126 56 3061.26 2851.19789412654 210.062105873457 57 3119.31 2856.04379988562 263.26620011438 58 3106.22 3019.68328332930 86.5367166707018 59 3080.58 2960.16765850692 120.412341493079 60 2981.85 2818.62664994969 163.223350050313 61 2921.44 2771.31165379598 150.128346204023 62 2849.27 2664.91029184400 184.359708155996 63 2756.76 2530.5747812473 226.185218752702 64 2645.64 2569.93281266683 75.70718733317 65 2497.84 2514.72623002729 -16.8862300272921 66 2448.05 2584.63066430323 -136.580664303231 67 2454.62 2730.34588266038 -275.725882660376 68 2407.6 2589.29918247106 -181.699182471058 69 2472.81 2878.36232326484 -405.552323264843 70 2408.64 2698.24117148639 -289.601171486394 71 2440.25 2595.6434602399 -155.393460239898 72 2350.44 2650.34214685445 -299.902146854452 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.0181248920453580 0.0362497840907159 0.981875107954642 13 0.0145253859466247 0.0290507718932494 0.985474614053375 14 0.0402136461046304 0.0804272922092608 0.95978635389537 15 0.0172033280403929 0.0344066560807857 0.982796671959607 16 0.0120453350520615 0.0240906701041230 0.987954664947938 17 0.00454280300315557 0.00908560600631114 0.995457196996844 18 0.00167005323363354 0.00334010646726709 0.998329946766366 19 0.00651930625397917 0.0130386125079583 0.99348069374602 20 0.00436271303512712 0.00872542607025424 0.995637286964873 21 0.00339545451314511 0.00679090902629022 0.996604545486855 22 0.00164665163143871 0.00329330326287743 0.99835334836856 23 0.000966723676538598 0.00193344735307720 0.999033276323461 24 0.000979019705114744 0.00195803941022949 0.999020980294885 25 0.00316134017593127 0.00632268035186254 0.996838659824069 26 0.00659004143790365 0.0131800828758073 0.993409958562096 27 0.0119068436090170 0.0238136872180340 0.988093156390983 28 0.0180566011911639 0.0361132023823277 0.981943398808836 29 0.0136772448206461 0.0273544896412922 0.986322755179354 30 0.0121186342061726 0.0242372684123452 0.987881365793827 31 0.0111202288716439 0.0222404577432877 0.988879771128356 32 0.0112564763552908 0.0225129527105817 0.98874352364471 33 0.0104560214818918 0.0209120429637836 0.989543978518108 34 0.00738119019312186 0.0147623803862437 0.992618809806878 35 0.00433883937729519 0.00867767875459038 0.995661160622705 36 0.00373870856010668 0.00747741712021337 0.996261291439893 37 0.00216292026159969 0.00432584052319937 0.9978370797384 38 0.00163805972979537 0.00327611945959073 0.998361940270205 39 0.00124108600509434 0.00248217201018868 0.998758913994906 40 0.00154118115275632 0.00308236230551263 0.998458818847244 41 0.00174383055769052 0.00348766111538103 0.99825616944231 42 0.00132744290718193 0.00265488581436386 0.998672557092818 43 0.00318442601334118 0.00636885202668236 0.99681557398666 44 0.0112412326041063 0.0224824652082126 0.988758767395894 45 0.0149128352144064 0.0298256704288128 0.985087164785594 46 0.0283909735368529 0.0567819470737058 0.971609026463147 47 0.0323875488094735 0.064775097618947 0.967612451190527 48 0.0601679196955994 0.120335839391199 0.9398320803044 49 0.0521190784677275 0.104238156935455 0.947880921532273 50 0.0419667548951798 0.0839335097903596 0.95803324510482 51 0.0659438229610741 0.131887645922148 0.934056177038926 52 0.048473024841417 0.096946049682834 0.951526975158583 53 0.0328086403856169 0.0656172807712337 0.967191359614383 54 0.0295480117278258 0.0590960234556516 0.970451988272174 55 0.0552847884166502 0.110569576833300 0.94471521158335 56 0.0424297702554532 0.0848595405109064 0.957570229744547 57 0.0420662166893334 0.0841324333786668 0.957933783310667 58 0.0348674605825541 0.0697349211651082 0.965132539417446 59 0.0270167406323069 0.0540334812646137 0.972983259367693 60 0.832276910663795 0.335446178672410 0.167723089336205 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.346938775510204 NOK 5% type I error level 33 0.673469387755102 NOK 10% type I error level 44 0.897959183673469 NOK

Charts produced by software:

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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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