WS 7.2

*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, 27 Nov 2009 12:17:27 -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/27/t12593496082sd6pryvvmhugqw.htm/, Retrieved Fri, 27 Nov 2009 20:20:20 +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/27/t12593496082sd6pryvvmhugqw.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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100.00 100.00 94.97 106.73 107.50 104.81 124.27 96.15 107.06 88.46 79.71 88.46 163.41 91.35 144.83 92.31 166.82 91.35 154.26 87.50 132.60 85.58 157.51 86.54 104.02 97.12 106.03 99.04 113.23 98.08 117.64 92.31 113.34 88.46 66.62 89.42 185.99 90.38 174.57 90.38 208.19 88.46 163.81 86.54 162.46 86.54 148.16 86.54 113.41 94.23 105.63 96.15 111.79 94.23 132.36 89.42 110.75 86.54 67.37 86.54 178.29 87.50 156.38 87.50 189.71 87.50 152.80 88.46 150.80 84.62 160.40 79.81 127.25 80.77 108.47 77.88 117.09 74.04 147.25 75.96 116.19 75.96 75.83 76.92 181.94 75.96 179.12 73.08 183.15 68.27 197.90 65.38 155.42 62.50 162.54 66.35 125.90 78.85 105.50 83.65 121.11 79.81 137.51 75.96 97.20 72.12 69.74 75.00 152.58 79.81 146.59 80.77 161.16 78.85 152.84 74.04 121.95 69.23 140.12 70.19

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 183.652974945091 -0.383983963036890X[t] -34.9039253829419M1[t] -43.9415014112019M2[t] -34.8759253829419M3[t] -18.8397134824402M4[t] -43.1400229154509M5[t] -79.8253983109354M6[t] + 21.4276619130444M7[t] + 9.20993699214136M8[t] + 29.9799198151845M9[t] + 11.5351919396662M10[t] -9.17372492090307M11[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) 183.652974945091 15.568197 11.7967 0 0 X -0.383983963036890 0.186769 -2.0559 0.045367 0.022683 M1 -34.9039253829419 8.173825 -4.2702 9.4e-05 4.7e-05 M2 -43.9415014112019 8.316968 -5.2834 3e-06 2e-06 M3 -34.8759253829419 8.173825 -4.2668 9.5e-05 4.8e-05 M4 -18.8397134824402 7.987562 -2.3586 0.022552 0.011276 M5 -43.1400229154509 7.887285 -5.4696 2e-06 1e-06 M6 -79.8253983109354 7.90807 -10.0942 0 0 M7 21.4276619130444 7.955661 2.6934 0.009774 0.004887 M8 9.20993699214136 7.949751 1.1585 0.252505 0.126253 M9 29.9799198151845 7.899319 3.7953 0.000422 0.000211 M10 11.5351919396662 7.857788 1.468 0.148767 0.074383 M11 -9.17372492090307 7.844008 -1.1695 0.248091 0.124046 Multiple Linear Regression - Regression Statistics Multiple R 0.945413171743223 R-squared 0.893806065305581 Adjusted R-squared 0.866692720277218 F-TEST (value) 32.9655401932369 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.4023356200969 Sum Squared Residuals 7229.44265517568 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 110.350653258460 -10.3506532584602 2 94.97 98.728865158962 -3.75886515896208 3 107.5 108.531690396253 -1.03169039625281 4 124.27 127.893203416654 -3.62320341665409 5 107.06 106.545730659397 0.514269340602919 6 79.71 69.8603552639124 9.84964473608757 7 163.41 170.003701834716 -6.59370183471576 8 144.83 157.417352309297 -12.5873523092973 9 166.82 178.555959736856 -11.7359597368558 10 154.26 161.589570119030 -7.3295701190295 11 132.6 141.617902467491 -9.01790246749109 12 157.51 150.423002783879 7.08699721612125 13 104.02 111.456527072006 -7.4365270720065 14 106.03 101.681701834716 4.34829816528426 15 113.23 111.115902467491 2.11409753250891 16 117.64 129.367701834716 -11.7277018347158 17 113.34 106.545730659397 6.79426934060295 18 66.62 69.4917306593971 -2.87173065939706 19 185.99 170.376166278862 15.6138337211385 20 174.57 158.158441357958 16.4115586420415 21 208.19 179.665673390032 28.5243266099676 22 163.81 161.958194723545 1.85180527645510 23 162.46 141.249277862976 21.2107221370243 24 148.16 150.423002783879 -2.26300278387875 25 113.41 112.566240725183 0.843759274816883 26 105.63 102.791415487892 2.83858451210764 27 111.79 112.594240725183 -0.804240725183107 28 132.36 130.477415487892 1.88258451210765 29 110.75 107.282979868428 3.46702013157213 30 67.37 70.5976044729433 -3.22760447294327 31 178.29 171.482040092408 6.80795990759222 32 156.38 159.264315171505 -2.88431517150469 33 189.71 180.034297994548 9.67570200545222 34 152.8 161.220945514514 -8.42094551451407 35 150.8 141.986527072006 8.81347292799352 36 160.4 153.007214855117 7.392785144883 37 127.25 117.734664867660 9.51533513234035 38 108.47 109.806802492576 -1.33680249257633 39 117.09 120.346876938898 -3.25687693889792 40 147.25 135.645839630369 11.6041603696311 41 116.19 111.345530197358 4.84446980264183 42 75.83 74.2915301973582 1.53846980264179 43 181.94 175.913215025854 6.0267849741465 44 179.12 164.801363918497 14.3186360815034 45 183.15 187.418309603747 -4.26830960374717 46 197.9 170.083295381405 27.8167046185945 47 155.42 150.480252334382 4.93974766561748 48 162.54 158.175638997594 4.36436100240643 49 125.9 118.471914076690 7.42808592330952 50 105.5 107.591215025853 -2.09121502585348 51 121.11 118.131289472175 2.97871052782493 52 137.51 135.645839630369 1.86416036963108 53 97.2 112.820028615420 -15.6200286154198 54 69.74 75.028779406389 -5.28877940638904 55 152.58 174.434876768161 -21.8548767681614 56 146.59 161.848527242743 -15.2585272427430 57 161.16 183.355759274817 -22.1957592748169 58 152.84 166.757994261506 -13.9179942615060 59 121.95 147.896040263144 -25.9460402631442 60 140.12 156.701140579532 -16.5811405795319 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0462049157128131 0.0924098314256263 0.953795084287187 17 0.0205300139976160 0.0410600279952321 0.979469986002384 18 0.0244655583061009 0.0489311166122018 0.975534441693899 19 0.0854295001072718 0.170859000214544 0.914570499892728 20 0.214075384712602 0.428150769425204 0.785924615287398 21 0.55326397911564 0.89347204176872 0.44673602088436 22 0.450547761336154 0.901095522672309 0.549452238663846 23 0.639588625068107 0.720822749863786 0.360411374931893 24 0.551956364459396 0.896087271081207 0.448043635540604 25 0.454537691440005 0.909075382880011 0.545462308559995 26 0.373369230083308 0.746738460166616 0.626630769916692 27 0.303468327446582 0.606936654893164 0.696531672553418 28 0.226478221073379 0.452956442146758 0.773521778926621 29 0.166209487801753 0.332418975603506 0.833790512198247 30 0.121679466141774 0.243358932283547 0.878320533858226 31 0.0923686218723035 0.184737243744607 0.907631378127697 32 0.0627474729403694 0.125494945880739 0.93725252705963 33 0.0683408925645353 0.136681785129071 0.931659107435465 34 0.0442539237258365 0.088507847451673 0.955746076274164 35 0.0843838428730088 0.168767685746018 0.915616157126991 36 0.23036577454869 0.46073154909738 0.76963422545131 37 0.166085436997913 0.332170873995827 0.833914563002087 38 0.178705551680612 0.357411103361223 0.821294448319388 39 0.236410368984428 0.472820737968856 0.763589631015572 40 0.193733845820396 0.387467691640793 0.806266154179604 41 0.519965809110813 0.960068381778374 0.480034190889187 42 0.502095592632518 0.995808814734964 0.497904407367482 43 0.497230156102068 0.994460312204135 0.502769843897932 44 0.34614327425465 0.6922865485093 0.65385672574535 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 2 0.0689655172413793 NOK 10% type I error level 4 0.137931034482759 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/101b2b1259349443.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/101b2b1259349443.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/1sv411259349442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/1sv411259349442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/269rk1259349443.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/269rk1259349443.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/3q6lc1259349443.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/3q6lc1259349443.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/4jxuc1259349443.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/4jxuc1259349443.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/5l9r41259349443.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/5l9r41259349443.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/6do7o1259349443.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/6do7o1259349443.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/739bu1259349443.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/739bu1259349443.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/8ks1z1259349443.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/8ks1z1259349443.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/9fhyz1259349443.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593496082sd6pryvvmhugqw/9fhyz1259349443.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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