*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: Wed, 01 Dec 2010 12:56:55 +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/01/t1291208166s09stulgq5bmt68.htm/, Retrieved Wed, 01 Dec 2010 13:56:18 +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/01/t1291208166s09stulgq5bmt68.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:

» Textbox « » Textfile « » CSV «

1 162556 1081 807 213118 6282154 1 29790 309 444 81767 4321023 1 87550 458 412 153198 4111912 0 84738 588 428 -26007 223193 1 54660 302 315 126942 1491348 1 42634 156 168 157214 1629616 0 40949 481 263 129352 1398893 1 45187 353 267 234817 1926517 1 37704 452 228 60448 983660 1 16275 109 129 47818 1443586 0 25830 115 104 245546 1073089 0 12679 110 122 48020 984885 1 18014 239 393 -1710 1405225 0 43556 247 190 32648 227132 1 24811 505 280 95350 929118 0 6575 159 63 151352 1071292 0 7123 109 102 288170 638830 1 21950 519 265 114337 856956 1 37597 248 234 37884 992426 0 17821 373 277 122844 444477 1 12988 119 73 82340 857217 1 22330 84 67 79801 711969 0 13326 102 103 165548 702380 0 16189 295 290 116384 358589 0 7146 105 83 134028 297978 0 15824 64 56 63838 585715 1 27664 282 236 74996 657954 0 11920 182 73 31080 209458 0 8568 37 34 32168 786690 0 14416 361 139 49857 439798 1 3369 28 26 87161 688779 1 11819 85 70 106113 574339 1 6984 45 40 80570 741409 1 4519 49 42 102129 597793 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 28 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation group[t] = + 0.141829395088363 -3.30450842805763e-06costs[t] -0.000396266602596711trades[t] + 0.00163242981862731orders[t] + 9.16354136508386e-07dividends[t] + 1.18426873582471e-07wealth[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) 0.141829395088363 0.054666 2.5945 0.009801 0.0049 costs -3.30450842805763e-06 4e-06 -0.7932 0.42811 0.214055 trades -0.000396266602596711 0.000384 -1.032 0.302644 0.151322 orders 0.00163242981862731 0.000809 2.0176 0.04426 0.02213 dividends 9.16354136508386e-07 1e-06 1.2616 0.207803 0.103901 wealth 1.18426873582471e-07 0 1.3678 0.172092 0.086046 Multiple Linear Regression - Regression Statistics Multiple R 0.247742376698528 R-squared 0.0613762852122355 Adjusted R-squared 0.050333653273556 F-TEST (value) 5.5581210668853 F-TEST (DF numerator) 5 F-TEST (DF denominator) 425 p-value 5.66819583613931e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.437304029136044 Sum Squared Residuals 81.2747959069127 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1 1.43293580773021 -0.432935807730206 2 1 1.23239332153249 -0.232393321532494 3 1 0.970935167108132 0.0290648328918682 4 0 0.330087587124547 -0.330087587124547 5 1 0.648687351154247 0.351312648845753 6 1 0.550429629527855 0.449570370472145 7 0 0.529436650649830 -0.529436650649830 8 1 0.731812138092546 0.268187861907454 9 1 0.382201256901982 0.617798743098018 10 1 0.47021650616859 0.52978349383141 11 0 0.532765652379081 -0.532765652379081 12 0 0.416138821339318 -0.416138821339318 13 1 0.793988618821752 0.206011381178248 14 0 0.266997703192944 -0.266997703192944 15 1 0.514213858229405 0.485786141770595 16 0 0.425500734457257 -0.425500734457257 17 0 0.581326574540565 -0.581326574540565 18 1 0.502486773064741 0.497513226935259 19 1 0.453549320382927 0.546450679617073 20 0 0.552521996417692 -0.552521996417692 21 1 0.34789221956738 0.65210778043262 22 1 0.301568364324885 0.698431635675115 23 0 0.460395855687362 -0.460395855687362 24 0 0.593954241776886 -0.593954241776886 25 0 0.370204774679179 -0.370204774679179 26 0 0.283456472626497 -0.283456472626497 27 1 0.470562059201013 0.529437940798987 28 0 0.202772252362625 -0.202772252362625 29 0 0.276999633455811 -0.276999633455811 30 0 0.275817673172989 -0.275817673172989 31 1 0.323484503057307 0.676515496942693 32 1 0.348615094694138 0.651384905305862 33 1 0.327849306549435 0.672150693450565 34 1 0.340422398004029 0.65957760199597 35 0 0.498090638998052 -0.498090638998052 36 0 0.50202496078373 -0.50202496078373 37 0 0.349436712272704 -0.349436712272704 38 1 0.408359863403235 0.591640136596765 39 1 0.479144193597736 0.520855806402264 40 0 0.382462629261056 -0.382462629261056 41 0 0.259148079914606 -0.259148079914606 42 0 0.293072478942926 -0.293072478942926 43 0 0.327991159415495 -0.327991159415495 44 0 0.373866694289537 -0.373866694289537 45 0 0.274893952216932 -0.274893952216932 46 1 0.298503715698519 0.701496284301481 47 1 0.356538177050985 0.643461822949015 48 1 0.409279093008055 0.590720906991945 49 0 0.354305305853467 -0.354305305853467 50 0 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1.26377495577614e-05 6.31887477888072e-06 323 0.99999110820418 1.77835916419528e-05 8.89179582097642e-06 324 0.999998649789033 2.70042193490472e-06 1.35021096745236e-06 325 0.999998034379681 3.93124063739906e-06 1.96562031869953e-06 326 0.999997156024934 5.68795013227043e-06 2.84397506613522e-06 327 0.999999676466382 6.47067236758102e-07 3.23533618379051e-07 328 0.999999977018416 4.59631673402198e-08 2.29815836701099e-08 329 0.999999963587106 7.28257873943055e-08 3.64128936971527e-08 330 0.999999942590553 1.14818893647510e-07 5.74094468237551e-08 331 0.999999909938807 1.80122385016513e-07 9.00611925082567e-08 332 0.999999859430437 2.81139125414555e-07 1.40569562707278e-07 333 0.999999781718395 4.3656320975369e-07 2.18281604876845e-07 334 0.999999666517262 6.66965475860828e-07 3.33482737930414e-07 335 0.999999488500486 1.02299902808434e-06 5.11499514042169e-07 336 0.999999224406866 1.55118626753208e-06 7.75593133766038e-07 337 0.999998845991197 2.30801760680604e-06 1.15400880340302e-06 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0.259190072286887 387 0.708593949892303 0.582812100215395 0.291406050107697 388 0.675204572847428 0.649590854305145 0.324795427152572 389 0.63967067105856 0.72065865788288 0.36032932894144 390 0.605732464864276 0.788535070271448 0.394267535135724 391 0.570772590051803 0.858454819896395 0.429227409948197 392 0.534503882462607 0.930992235074786 0.465496117537393 393 0.496320367696342 0.992640735392684 0.503679632303658 394 0.457490833437609 0.914981666875218 0.542509166562391 395 0.679936690665983 0.640126618668033 0.320063309334017 396 0.645056262868502 0.709887474262996 0.354943737131498 397 0.615747968098884 0.768504063802232 0.384252031901116 398 0.566579069894832 0.866841860210336 0.433420930105168 399 0.523060365083694 0.953879269832613 0.476939634916306 400 0.560880349046758 0.878239301906485 0.439119650953242 401 0.508301861129681 0.983396277740638 0.491698138870319 402 0.46906389533817 0.93812779067634 0.53093610466183 403 0.434042045069344 0.868084090138689 0.565957954930656 404 0.586088433962907 0.827823132074187 0.413911566037093 405 0.537323522781562 0.925352954436876 0.462676477218438 406 0.484772843448997 0.969545686897993 0.515227156551003 407 0.433190306622236 0.866380613244472 0.566809693377764 408 0.409676390585942 0.819352781171883 0.590323609414058 409 0.386003200318078 0.772006400636156 0.613996799681922 410 0.329375591148684 0.658751182297369 0.670624408851316 411 0.311945058566318 0.623890117132636 0.688054941433682 412 0.457787043422658 0.915574086845316 0.542212956577342 413 0.569282541035714 0.861434917928571 0.430717458964286 414 0.7821114532433 0.4357770935134 0.2178885467567 415 0.706023987070737 0.587952025858527 0.293976012929263 416 0.615686873257249 0.768626253485502 0.384313126742751 417 0.578660977066246 0.842678045867509 0.421339022933754 418 0.852895786083767 0.294208427832467 0.147104213916233 419 0.772716639362794 0.454566721274412 0.227283360637206 420 0.657774867799801 0.684450264400397 0.342225132200199 421 0.523320240096599 0.953359519806802 0.476679759903401 422 0.73305363476895 0.533892730462099 0.266946365231049 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 81 0.195652173913043 NOK 5% type I error level 156 0.376811594202899 NOK 10% type I error level 195 0.471014492753623 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/10iwpa1291208184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/10iwpa1291208184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/1bvag1291208184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/1bvag1291208184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/2bvag1291208184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/2bvag1291208184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/3m4r11291208184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/3m4r11291208184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/4m4r11291208184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/4m4r11291208184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/5m4r11291208184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/5m4r11291208184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/6kzgs1291208184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/6kzgs1291208184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/775q71291208184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/775q71291208184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/875q71291208184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/875q71291208184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/9iwpa1291208184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291208166s09stulgq5bmt68/9iwpa1291208184.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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