*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 12:01:33 +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/t1291291184d2tixj7dvbv4h07.htm/, Retrieved Thu, 02 Dec 2010 12:59:44 +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/t1291291184d2tixj7dvbv4h07.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 2466,92 9633,83 10238,83 24320 -8,8 -10 -0,1 3,37 2513,17 10066,24 9857,34 22680 -14,2 -12 -1 3,55 2443,27 10302,87 9634,97 22052 -17,8 -11 -1,2 3,53 2293,41 10430,35 9374,63 21467 -18,2 -11 -0,8 3,52 2070,83 9691,12 8679,75 21383 -22,8 -17 -1,7 3,54 2029,6 9810,31 8593 21777 -23,6 -18 -1,1 3,5 2052,02 9304,43 8398,37 21928 -27,6 -19 -0,4 3,44 1864,44 8767,96 7992,12 21814 -29,4 -22 0,6 3,38 1670,07 7764,58 7235,47 22937 -31,8 -24 0,6 3,35 1810,99 7694,78 7690,5 23595 -31,4 -24 1,9 3,68 1905,41 8331,49 8396,2 20830 -27,6 -20 2,3 3,92 1862,83 8460,94 8595,56 19650 -28,8 -25 2,6 4,05 2014,45 8531,45 8614,55 19195 -21,9 -22 3,1 4,14 2197,82 9117,03 9181,73 19644 -13,9 -17 4,7 4,53 2962,34 12123,53 11114,08 18483 -8 -9 5,5 4,54 3047,03 12989,35 11530,75 18079 -2,8 -11 5,4 4,9 3032,6 13168,91 11322,38 19178 -3,3 -13 5,9 4,92 3504,37 14084,6 12056,67 18391 -1,3 -11 5,8 4,45 3801,06 13995,33 12812,48 18441 0,5 -9 5,2 3,92 3857,62 13357,7 12656,63 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation BEL_20[t] = -1886.04000678096 + 0.191792840371528Nikkei[t] + 0.288303751656477DJ_Indust[t] + 0.0147718309612606Goudprijs[t] -9.98349305638144Conjunct_Seizoenzuiver[t] -2.50766896022587Cons_vertrouw[t] + 33.9568643858046Alg_consumptie_index_BE[t] -255.691840281079Gem_rente_kasbon_5j[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) -1886.04000678096 270.224455 -6.9795 0 0 Nikkei 0.191792840371528 0.014953 12.8265 0 0 DJ_Indust 0.288303751656477 0.033193 8.6858 0 0 Goudprijs 0.0147718309612606 0.008199 1.8016 0.07632 0.03816 Conjunct_Seizoenzuiver -9.98349305638144 6.033247 -1.6547 0.102872 0.051436 Cons_vertrouw -2.50766896022587 7.649392 -0.3278 0.744113 0.372057 Alg_consumptie_index_BE 33.9568643858046 17.241466 1.9695 0.053229 0.026614 Gem_rente_kasbon_5j -255.691840281079 56.189516 -4.5505 2.4e-05 1.2e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.9837054952436 R-squared 0.967676501372457 Adjusted R-squared 0.96414111871007 F-TEST (value) 273.711955332998 F-TEST (DF numerator) 7 F-TEST (DF denominator) 64 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 160.133985897204 Sum Squared Residuals 1641145.18011685 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2502.66 2708.8082449362 -206.148244936199 2 2466.92 2520.65788343882 -53.7378834388196 3 2513.17 2451.72071577507 61.4492842249317 4 2443.27 2455.47321046387 -12.2032104638688 5 2293.41 2416.35750331553 -122.947503315532 6 2070.83 2098.29620424375 -27.4662042437528 7 2029.6 2143.06199947823 -113.461999478229 8 2052.02 2073.70878135401 -21.6887813540108 9 1864.44 1926.80195762470 -62.3619576247026 10 1670.07 1569.45106639554 100.618933604459 11 1810.99 1652.74286621260 158.247133787398 12 1905.41 1841.71988517336 63.6901148266421 13 1862.83 1908.05860030339 -45.2286003033882 14 2014.45 1837.89267623246 176.557323767537 15 2197.82 1982.56027771883 215.259722281165 16 2962.34 3044.78372345458 -82.4437234545788 17 3047.03 3182.65793008691 -135.627930086909 18 3032.6 3195.12832183371 -162.528321833710 19 3504.37 3662.62139313115 -158.251393131149 20 3801.06 3956.29842765428 -155.238427654278 21 3857.62 3842.65385048212 14.9661495178816 22 3674.4 3524.36380455471 150.03619544529 23 3720.98 3662.70017341549 58.279826584514 24 3844.49 3682.14878243838 162.341217561616 25 4116.68 4276.06118970188 -159.381189701878 26 4105.18 4188.10491509472 -82.9249150947207 27 4435.23 4572.62116297434 -137.391162974343 28 4296.49 4296.5678631292 -0.0778631292035645 29 4202.52 4205.08179278783 -2.5617927878252 30 4562.84 4589.48099026381 -26.6409902638108 31 4621.4 4565.31268828803 56.0873117119668 32 4696.96 4571.73034500852 125.229654991476 33 4591.27 4377.34426916186 213.925730838142 34 4356.98 4190.67292615312 166.307073846883 35 4502.64 4403.06192168819 99.5780783118129 36 4443.91 4335.61262334952 108.297376650476 37 4290.89 4236.48218094527 54.4078190547265 38 4199.75 4045.36120653470 154.388793465295 39 4138.52 4018.99216934148 119.527830658519 40 3970.1 3812.15884936956 157.941150630440 41 3862.27 3695.22163010429 167.048369895706 42 3701.61 3507.86986753702 193.740132462985 43 3570.12 3505.75287642029 64.3671235797118 44 3801.06 3923.64733261763 -122.587332617627 45 3895.51 4075.79262323747 -180.282623237466 46 3917.96 3935.63905870863 -17.6790587086295 47 3813.06 3909.92717343976 -96.8671734397565 48 3667.03 3851.58081047638 -184.550810476384 49 3494.17 3785.90185801967 -291.731858019670 50 3363.99 3532.18819246 -168.198192460000 51 3295.32 3271.32318186744 23.9968181325598 52 3277.01 3303.22816215898 -26.2181621589777 53 3257.16 3169.84060308332 87.3193969166836 54 3161.69 3101.06364918295 60.6263508170478 55 3097.31 2987.19403896795 110.115961032050 56 3061.26 2847.85807442898 213.401925571025 57 3119.31 2856.50265543567 262.807344564328 58 3106.22 3020.11606222490 86.1039377750947 59 3080.58 2957.18251877454 123.397481225460 60 2981.85 2810.19182531178 171.658174688216 61 2921.44 2777.73903164141 143.700968358595 62 2849.27 2668.67035332043 180.599646679572 63 2756.76 2533.52907359763 223.230926402375 64 2645.64 2573.36355195115 72.2764480488501 65 2497.84 2517.61342566693 -19.7734256669316 66 2448.05 2583.28819996326 -135.238199963261 67 2454.62 2732.94972929247 -278.329729292475 68 2407.6 2585.60087290272 -178.000872902716 69 2472.81 2875.44001921294 -402.630019212943 70 2408.64 2694.24239617322 -285.602396173219 71 2440.25 2591.6935085745 -151.443508574502 72 2350.44 2644.52226973597 -294.082269735966 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.0484965329093405 0.096993065818681 0.95150346709066 12 0.0140072720680519 0.0280145441361038 0.985992727931948 13 0.0039420836672217 0.0078841673344434 0.996057916332778 14 0.0189754376443016 0.0379508752886033 0.981024562355698 15 0.0079552744297736 0.0159105488595472 0.992044725570226 16 0.00546217435753775 0.0109243487150755 0.994537825642462 17 0.00200021995311613 0.00400043990623225 0.997999780046884 18 0.00071342892673936 0.00142685785347872 0.99928657107326 19 0.00384171370064493 0.00768342740128986 0.996158286299355 20 0.00392192483839343 0.00784384967678685 0.996078075161607 21 0.00452231972003611 0.00904463944007221 0.995477680279964 22 0.00279969196889436 0.00559938393778871 0.997200308031106 23 0.00329199408916654 0.00658398817833308 0.996708005910833 24 0.0269257603029794 0.0538515206059587 0.97307423969702 25 0.0204240928189501 0.0408481856379002 0.97957590718105 26 0.0289309734970478 0.0578619469940957 0.971069026502952 27 0.0439551112189474 0.0879102224378948 0.956044888781053 28 0.0750148595795029 0.150029719159006 0.924985140420497 29 0.0650033081358408 0.130006616271682 0.93499669186416 30 0.0574988867244809 0.114997773448962 0.94250111327552 31 0.0452164989227985 0.090432997845597 0.954783501077201 32 0.038599399577901 0.077198799155802 0.961400600422099 33 0.0277735087970311 0.0555470175940622 0.972226491202969 34 0.0209779585088739 0.0419559170177477 0.979022041491126 35 0.0134200884532605 0.0268401769065211 0.98657991154674 36 0.0099988520056501 0.0199977040113002 0.99000114799435 37 0.00689975285117712 0.0137995057023542 0.993100247148823 38 0.0068458275219426 0.0136916550438852 0.993154172478057 39 0.00806254022299236 0.0161250804459847 0.991937459777008 40 0.0115249234641063 0.0230498469282127 0.988475076535894 41 0.0133420539612411 0.0266841079224822 0.986657946038759 42 0.00985189577536837 0.0197037915507367 0.990148104224632 43 0.0125486566980148 0.0250973133960297 0.987451343301985 44 0.0347410590338443 0.0694821180676887 0.965258940966156 45 0.0450023780168302 0.0900047560336603 0.95499762198317 46 0.0729364097158698 0.145872819431740 0.92706359028413 47 0.0795232964098499 0.159046592819700 0.92047670359015 48 0.114928651073188 0.229857302146376 0.885071348926812 49 0.146261945658693 0.292523891317385 0.853738054341307 50 0.119936900385912 0.239873800771824 0.880063099614088 51 0.155500878550178 0.311001757100357 0.844499121449822 52 0.119768219707957 0.239536439415915 0.880231780292043 53 0.0949506968418686 0.189901393683737 0.905049303158131 54 0.083336702194095 0.16667340438819 0.916663297805905 55 0.162458659567390 0.324917319134780 0.83754134043261 56 0.133519651346551 0.267039302693103 0.866480348653449 57 0.133921761959299 0.267843523918599 0.8660782380407 58 0.107240220978583 0.214480441957166 0.892759779021417 59 0.0814687134961375 0.162937426992275 0.918531286503863 60 0.299025090650402 0.598050181300804 0.700974909349598 61 0.4941459093829 0.9882918187658 0.505854090617101 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.156862745098039 NOK 5% type I error level 23 0.450980392156863 NOK 10% type I error level 32 0.627450980392157 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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