*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: Mon, 06 Dec 2010 14:42:23 +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/06/t12916464819ms5jmtsuxnfabk.htm/, Retrieved Mon, 06 Dec 2010 15:41:24 +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/06/t12916464819ms5jmtsuxnfabk.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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User-defined keywords:

Dataseries X:

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3484,74 13830,14 9349,44 7977 -5,6 6 1 2,77 3411,13 14153,22 9327,78 8241 -6,2 3 1 2,76 3288,18 15418,03 9753,63 8444 -7,1 2 1,2 2,76 3280,37 16666,97 10443,5 8490 -1,4 2 1,2 2,46 3173,95 16505,21 10853,87 8388 -0,1 2 0,8 2,46 3165,26 17135,96 10704,02 8099 -0,9 -8 0,7 2,47 3092,71 18033,25 11052,23 7984 0 0 0,7 2,71 3053,05 17671 10935,47 7786 0,1 -2 0,9 2,8 3181,96 17544,22 10714,03 8086 2,6 3 1,2 2,89 2999,93 17677,9 10394,48 9315 6 5 1,3 3,36 3249,57 18470,97 10817,9 9113 6,4 8 1,5 3,31 3210,52 18409,96 11251,2 9023 8,6 8 1,9 3,5 3030,29 18941,6 11281,26 9026 6,4 9 1,8 3,51 2803,47 19685,53 10539,68 9787 7,7 11 1,9 3,71 2767,63 19834,71 10483,39 9536 9,2 13 2,2 3,71 2882,6 19598,93 10947,43 9490 8,6 12 2,1 3,71 2863,36 17039,97 10580,27 9736 7,4 13 2,2 4,21 2897,06 16969,28 10582,92 9694 8,6 15 2,7 4,21 3012,61 16973,38 10654,41 9647 6,2 13 2,8 4,21 3142,95 16329,89 11014,51 9753 6 16 2,9 4,5 3032,93 16153,34 10967,87 10070 6,6 10 3,4 4,51 3045,78 15311,7 10433,56 10137 5,1 14 3 4,51 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation BEL_20[t] = + 961.213187415473 + 0.0452365407202592Nikkei[t] + 0.249168737014703DJ_Indust[t] -0.177843191456367Goudprijs[t] -51.3749707456488Conjunct_Seizoenzuiver[t] + 29.9047276015519Cons_vertrouw[t] -105.787821541009Alg_consumptie_index_BE[t] + 127.079892680716Gem_rente_kasbon_1j[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) 961.213187415473 756.282401 1.271 0.210572 0.105286 Nikkei 0.0452365407202592 0.023743 1.9053 0.063444 0.031722 DJ_Indust 0.249168737014703 0.053385 4.6674 3e-05 1.5e-05 Goudprijs -0.177843191456367 0.045979 -3.868 0.000367 0.000183 Conjunct_Seizoenzuiver -51.3749707456488 11.300252 -4.5464 4.4e-05 2.2e-05 Cons_vertrouw 29.9047276015519 7.37313 4.0559 0.000206 0.000103 Alg_consumptie_index_BE -105.787821541009 63.506334 -1.6658 0.103026 0.051513 Gem_rente_kasbon_1j 127.079892680716 106.533689 1.1929 0.239465 0.119732 Multiple Linear Regression - Regression Statistics Multiple R 0.935503325245988 R-squared 0.875166471546301 Adjusted R-squared 0.854844734356164 F-TEST (value) 43.0655343762174 F-TEST (DF numerator) 7 F-TEST (DF denominator) 43 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 164.31351893068 Sum Squared Residuals 1160954.09764546 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3484.74 3211.12558000918 273.614419990819 2 3411.13 3113.23300491279 297.89699508721 3 3288.18 3235.63015453458 52.5498454654203 4 3280.37 3124.87982844467 155.490171555333 5 3173.95 3213.48141240209 -39.5314124020916 6 3165.26 3009.97538921254 155.284610787461 7 3092.71 3381.2802191535 -288.570219153496 8 3053.05 3296.3459662075 -243.2959662075 9 3181.96 3182.86905003614 -0.90905003613946 10 2999.93 2825.00844066043 174.921559339567 11 3249.57 3043.96417087456 205.60582912544 12 3210.52 3033.98010585727 176.539894142734 13 3030.29 3219.76538734896 -189.475387348965 14 2803.47 2941.16017600913 -137.690176009131 15 2767.63 2929.5321486251 -161.902148625098 16 2882.6 3054.17036158531 -171.570361585309 17 2863.36 2947.6935014458 -84.3335014457977 18 2897.06 2897.89102111436 -0.831021114359997 19 3012.61 2977.1598863713 35.4501136287043 20 3142.95 3145.18847236493 -2.23847236492843 21 3032.93 2807.32797961433 225.602020385666 22 3045.78 2863.20275092172 182.577249078283 23 3110.52 2933.32828574331 177.191714256686 24 3013.24 3018.26610625947 -5.02610625947204 25 2987.1 3106.73928796235 -119.63928796235 26 2995.55 3027.69978405082 -32.1497840508221 27 2833.18 2867.10337957087 -33.9233795708695 28 2848.96 3016.59390160801 -167.633901608005 29 2794.83 3012.5054151716 -217.6754151716 30 2845.26 3048.17102372439 -202.911023724388 31 2915.02 3051.25008922407 -136.230089224068 32 2892.63 2925.56955072037 -32.9395507203656 33 2604.42 2535.23296335721 69.1870366427897 34 2641.65 2440.31836976091 201.331630239087 35 2659.81 2474.22934092095 185.580659079054 36 2638.53 2729.67577051046 -91.145770510464 37 2720.25 2664.96105522495 55.2889447750484 38 2745.88 2641.53144570835 104.348554291649 39 2735.7 2751.33466849212 -15.6346684921185 40 2811.7 2628.59147947027 183.108520529727 41 2799.43 2615.94279803997 183.487201960032 42 2555.28 2488.34147236287 66.9385276371274 43 2304.98 2345.65948897455 -40.6794889745546 44 2214.95 2320.16144224852 -105.211442248525 45 2065.81 2106.73173833295 -40.9217383329518 46 1940.49 2133.35595178272 -192.86595178272 47 2042 2239.89706491664 -197.897064916639 48 1995.37 2042.16583677588 -46.7958367758845 49 1946.81 2027.14056126499 -80.3305612649878 50 1765.9 1676.16925279028 89.7307472097219 51 1635.25 1807.01746732396 -171.767467323959 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.604761025160254 0.790477949679492 0.395238974839746 12 0.544759977868897 0.910480044262207 0.455240022131103 13 0.46558543702144 0.93117087404288 0.53441456297856 14 0.525522315562066 0.948955368875868 0.474477684437934 15 0.548025164340219 0.903949671319563 0.451974835659782 16 0.448378039982623 0.896756079965246 0.551621960017377 17 0.661661392662711 0.676677214674577 0.338338607337289 18 0.649281931488045 0.701436137023909 0.350718068511955 19 0.578020799816308 0.843958400367384 0.421979200183692 20 0.537592459032903 0.924815081934194 0.462407540967097 21 0.438099511654557 0.876199023309114 0.561900488345443 22 0.362625028527326 0.725250057054652 0.637374971472674 23 0.315486114313821 0.630972228627641 0.68451388568618 24 0.340258038167381 0.680516076334762 0.659741961832619 25 0.441599884356019 0.883199768712039 0.55840011564398 26 0.444342394866198 0.888684789732397 0.555657605133802 27 0.750917403669371 0.498165192661258 0.249082596330629 28 0.837475958702918 0.325048082594164 0.162524041297082 29 0.847597250499707 0.304805499000585 0.152402749500293 30 0.950561710992003 0.0988765780159936 0.0494382890079968 31 0.976221833896062 0.0475563322078766 0.0237781661039383 32 0.962414377055056 0.075171245889889 0.0375856229449445 33 0.959775505168185 0.0804489896636296 0.0402244948318148 34 0.934270905894135 0.13145818821173 0.0657290941058652 35 0.947358633471692 0.105282733056615 0.0526413665283075 36 0.910881581400713 0.178236837198575 0.0891184185992875 37 0.944511131340806 0.110977737318388 0.0554888686591938 38 0.98982971694671 0.0203405661065814 0.0101702830532907 39 0.998671205719855 0.00265758856028921 0.0013287942801446 40 0.993078460984684 0.0138430780306329 0.00692153901531646 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0333333333333333 NOK 5% type I error level 4 0.133333333333333 NOK 10% type I error level 7 0.233333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/10zfbi1291646534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/10zfbi1291646534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/1lnvs1291646534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/1lnvs1291646534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/2lnvs1291646534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/2lnvs1291646534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/3lnvs1291646534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/3lnvs1291646534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/4eevd1291646534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/4eevd1291646534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/5eevd1291646534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/5eevd1291646534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/6eevd1291646534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/6eevd1291646534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/7p5cf1291646534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/7p5cf1291646534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/8zfbi1291646534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/8zfbi1291646534.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/9zfbi1291646534.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t12916464819ms5jmtsuxnfabk/9zfbi1291646534.ps (open in new window)

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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