*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, 14 Nov 2009 04:27:12 -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/14/t1258198096u19u9nuhqnpgtog.htm/, Retrieved Sat, 14 Nov 2009 12:28:28 +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/14/t1258198096u19u9nuhqnpgtog.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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5560 36.68 3922 36.7 3759 36.71 4138 36.72 4634 36.73 3996 36.73 4308 36.87 4429 37.31 5219 37.39 4929 37.42 5755 37.51 5592 37.67 4163 37.67 4962 37.71 5208 37.78 4755 37.79 4491 37.84 5732 37.88 5731 38.34 5040 38.58 6102 38.72 4904 38.83 5369 38.9 5578 38.92 4619 38.94 4731 39.1 5011 39.14 5299 39.16 4146 39.32 4625 39.34 4736 39.44 4219 39.92 5116 40.19 4205 40.2 4121 40.27 5103 40.28 4300 40.3 4578 40.34 3809 40.4 5526 40.43 4247 40.48 3830 40.48 4394 40.63 4826 40.74 4409 40.77 4569 40.91 4106 40.92 4794 41.03 3914 41 3793 41.04 4405 41.33 4022 41.44 4100 41.46 4788 41.55 3163 41.55 3585 41.81 3903 41.78 4178 41.84 3863 41.84 4187 41.86

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] = + 12017.7830247441 -174.383836222070X[t] -719.912886653628M1[t] -823.449856480296M2[t] -765.857775875422M3[t] -449.979957771428M4[t] -864.265695270547M5[t] -588.434180183885M6[t] -686.588928026133M7[t] -679.82747414218M8[t] -132.737858192417M9[t] -513.330989656872M10[t] -419.160565518212M11[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) 12017.7830247441 1895.375948 6.3406 0 0 X -174.383836222070 46.980391 -3.7118 0.000544 0.000272 M1 -719.912886653628 375.895987 -1.9152 0.061564 0.030782 M2 -823.449856480296 375.542108 -2.1927 0.033315 0.016657 M3 -765.857775875422 375.029626 -2.0421 0.046772 0.023386 M4 -449.979957771428 374.846949 -1.2004 0.235985 0.117993 M5 -864.265695270547 374.568512 -2.3074 0.025487 0.012743 M6 -588.434180183885 374.432193 -1.5715 0.122766 0.061383 M7 -686.588928026133 373.759183 -1.837 0.07254 0.03627 M8 -679.82747414218 372.975923 -1.8227 0.074712 0.037356 M9 -132.737858192417 372.841934 -0.356 0.723421 0.361711 M10 -513.330989656872 372.781014 -1.377 0.175026 0.087513 M11 -419.160565518212 372.756004 -1.1245 0.266515 0.133258 Multiple Linear Regression - Regression Statistics Multiple R 0.562255368238571 R-squared 0.316131099113091 Adjusted R-squared 0.141526273354732 F-TEST (value) 1.81055190049898 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.0738665033626638 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 589.359817260218 Sum Squared Residuals 16325214.7274469 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5560 4901.47102546502 658.528974534976 2 3922 4794.44637891388 -872.446378913876 3 3759 4850.29462115653 -1091.29462115653 4 4138 5164.4286008983 -1026.42860089830 5 4634 4748.39902503696 -114.399025036963 6 3996 5024.23054012363 -1028.23054012363 7 4308 4901.66205521029 -593.662055210287 8 4429 4831.69462115653 -402.69462115653 9 5219 5364.83353020853 -145.833530208527 10 4929 4979.00888365741 -50.0088836574102 11 5755 5057.48476253608 697.515237463916 12 5592 5448.74391425876 143.256085741236 13 4163 4728.83102760514 -565.831027605136 14 4962 4618.31870432959 343.681295670415 15 5208 4663.70391639891 544.296083601085 16 4755 4977.83789614069 -222.837896140689 17 4491 4554.83296683046 -63.8329668304644 18 5732 4823.68912846824 908.310871531756 19 5731 4645.31781596384 1085.68218403616 20 5040 4610.2271491545 429.772850845499 21 6102 5132.90302803317 969.096971966826 22 4904 4733.12767458429 170.872325415709 23 5369 4815.09123018741 553.908769812594 24 5578 5230.76411898118 347.235881018824 25 4619 4507.36355560311 111.636444396893 26 4731 4375.92517198091 355.074828019092 27 5011 4426.5418991369 584.458100863101 28 5299 4738.93204051645 560.067959483547 29 4146 4296.7448892218 -150.744889221801 30 4625 4569.08872758402 55.9112724159788 31 4736 4453.49559611957 282.504403880433 32 4219 4376.55280861693 -157.552808616926 33 5116 4876.55878878673 239.441211213269 34 4205 4494.22181896005 -289.221818960054 35 4121 4576.18537456317 -455.185374563169 36 5103 4993.60210171916 109.397898280839 37 4300 4270.20153834109 29.7984616589081 38 4578 4159.68921506554 418.31078493446 39 3809 4206.81826549709 -397.818265497091 40 5526 4517.46456851442 1008.53543148558 41 4247 4094.4596392042 152.540360795800 42 3830 4370.29115429086 -540.291154290862 43 4394 4245.9788310153 148.021168984697 44 4826 4233.55806291483 592.441937085171 45 4409 4775.41616377793 -366.416163777929 46 4569 4370.40929524239 198.590704757615 47 4106 4462.83588101882 -356.835881018823 48 4794 4862.81422455261 -68.8142245526083 49 3914 4148.13285298564 -234.132852985642 50 3793 4037.62052971009 -244.620529710091 51 4405 4044.64129781057 360.358702189434 52 4022 4341.33689393013 -319.336893930132 53 4100 3923.56347970657 176.436520293429 54 4788 4183.70044953325 604.299550466753 55 3163 4085.545701691 -922.545701690999 56 3585 4046.96735815721 -461.967358157214 57 3903 4599.28848919364 -696.288489193638 58 4178 4208.23232755586 -30.2323275558589 59 3863 4302.40275169452 -439.402751694519 60 4187 4718.07564048829 -531.07564048829 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.98718275466095 0.0256344906780978 0.0128172453390489 17 0.98253826973966 0.0349234605206814 0.0174617302603407 18 0.988249051879452 0.0235018962410954 0.0117509481205477 19 0.985484931311676 0.0290301373766484 0.0145150686883242 20 0.971487191980522 0.0570256160389559 0.0285128080194780 21 0.964789418678295 0.0704211626434107 0.0352105813217054 22 0.959601405351012 0.0807971892979765 0.0403985946489882 23 0.969319959199203 0.0613600816015939 0.0306800408007969 24 0.954059458887378 0.0918810822252439 0.0459405411126220 25 0.949467904209826 0.101064191580348 0.0505320957901738 26 0.919864583201895 0.160270833596210 0.0801354167981048 27 0.882796764585574 0.234406470828852 0.117203235414426 28 0.832866973608645 0.33426605278271 0.167133026391355 29 0.857193899594449 0.285612200811102 0.142806100405551 30 0.831336055060833 0.337327889878334 0.168663944939167 31 0.795570989715494 0.408858020569012 0.204429010284506 32 0.78896452473448 0.422070950531039 0.211035475265520 33 0.778365488638518 0.443269022722965 0.221634511361482 34 0.78923113276568 0.421537734468639 0.210768867234320 35 0.823529320892866 0.352941358214267 0.176470679107134 36 0.753615042836195 0.492769914327611 0.246384957163806 37 0.667522008075206 0.664955983849588 0.332477991924794 38 0.586104811595947 0.827790376808106 0.413895188404053 39 0.642565435667711 0.714869128664577 0.357434564332289 40 0.709038090798675 0.581923818402651 0.290961909201325 41 0.599509828505141 0.800980342989717 0.400490171494859 42 0.94848212015703 0.103035759685940 0.0515178798429699 43 0.952004144197051 0.0959917116058979 0.0479958558029490 44 0.989043095114513 0.0219138097709738 0.0109569048854869 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 5 0.172413793103448 NOK 10% type I error level 11 0.379310344827586 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/10wpm01258198027.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/10wpm01258198027.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/1a7nm1258198027.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/1a7nm1258198027.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/2juqt1258198027.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/2juqt1258198027.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/3g8961258198027.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/3g8961258198027.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/4ur981258198027.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/4ur981258198027.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/5flru1258198027.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/5flru1258198027.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/6fwcp1258198027.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/6fwcp1258198027.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/72b331258198027.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/72b331258198027.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/81lim1258198027.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/81lim1258198027.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/9a5mj1258198027.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258198096u19u9nuhqnpgtog/9a5mj1258198027.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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