WS7

*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, 23 Nov 2009 01:19:48 -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/23/t12589645064gfh363ivqb499l.htm/, Retrieved Mon, 23 Nov 2009 09:21:58 +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/23/t12589645064gfh363ivqb499l.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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286602 326011 283042 328282 276687 317480 277915 317539 277128 313737 277103 312276 275037 309391 270150 302950 267140 300316 264993 304035 287259 333476 291186 337698 292300 335932 288186 323931 281477 313927 282656 314485 280190 313218 280408 309664 276836 302963 275216 298989 274352 298423 271311 301631 289802 329765 290726 335083 292300 327616 278506 309119 269826 295916 265861 291413 269034 291542 264176 284678 255198 276475 253353 272566 246057 264981 235372 263290 258556 296806 260993 303598 254663 286994 250643 276427 243422 266424 247105 267153 248541 268381 245039 262522 237080 255542 237085 253158 225554 243803 226839 250741 247934 280445 248333 285257 246969 270976 245098 261076 246263 255603 255765 260376 264319 263903 268347 264291 273046 263276 273963 262572 267430 256167 271993 264221 292710 293860 295881 300713 293299 287224

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] = + 101374.890620487 + 0.568142581778025X[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) 101374.890620487 15522.664701 6.5308 0 0 X 0.568142581778025 0.053107 10.698 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.812305162469938 R-squared 0.659839676975313 Adjusted R-squared 0.654074247771505 F-TEST (value) 114.447624565309 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 1.99840144432528e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10679.9604537033 Sum Squared Residuals 6729631762.26736 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 286602 286595.621848522 6.37815147811898 2 283042 287885.87365174 -4843.87365173995 3 276687 281748.797483374 -5061.79748337376 4 277915 281782.317895699 -3867.31789569866 5 277128 279622.239799779 -2494.23979977862 6 277103 278792.183487801 -1689.18348780092 7 275037 277153.092139371 -2116.09213937132 8 270150 273493.685770139 -3343.68577013907 9 267140 271997.198209736 -4857.19820973575 10 264993 274110.120471368 -9117.12047136822 11 287259 290836.806221495 -3577.80622149504 12 291186 293235.504201762 -2049.50420176186 13 292300 292232.164402342 67.835597658133 14 288186 285413.885278424 2772.11472157620 15 281477 279730.186890316 1746.81310968356 16 282656 280047.210450949 2608.78954905142 17 280190 279327.373799836 862.62620016418 18 280408 277308.195064197 3099.80493580328 19 276836 273501.071623702 3334.92837629782 20 275216 271243.273003716 3972.72699628369 21 274352 270921.70430243 3430.29569757005 22 271311 272744.305704774 -1433.30570477385 23 289802 288728.429100517 1073.57089948321 24 290726 291749.811350412 -1023.81135041232 25 292300 287507.490692276 4792.50930772419 26 278506 276998.557357128 1507.4426428723 27 269826 269497.370849912 328.629150087557 28 265861 266939.024804166 -1078.02480416600 29 269034 267012.315197215 2021.68480278464 30 264176 263112.584515891 1063.41548410899 31 255198 258452.110917566 -3254.11091756587 32 253353 256231.241565396 -2878.24156539557 33 246057 251921.880082609 -5864.88008260926 34 235372 250961.150976823 -15589.1509768226 35 258556 270003.017747695 -11447.0177476949 36 260993 273861.842163131 -12868.8421631312 37 254663 264428.402735289 -9765.4027352889 38 250643 258424.840073641 -7781.84007364053 39 243422 252741.709828115 -9319.70982811495 40 247105 253155.885770231 -6050.88577023113 41 248541 253853.564860655 -5312.56486065454 42 245039 250524.817474017 -5485.8174740171 43 237080 246559.182253206 -9479.18225320649 44 237085 245204.730338248 -8119.73033824768 45 225554 239889.756485714 -14335.7564857143 46 226839 243831.52971809 -16992.5297180902 47 247934 260707.636967225 -12773.6369672246 48 248333 263441.539070740 -15108.5390707405 49 246969 255327.894860369 -8358.89486036852 50 245098 249703.283300766 -4605.28330076607 51 246263 246593.838950695 -330.838950694946 52 255765 249305.583493521 6459.41650647854 53 264319 251309.422379453 13009.5776205475 54 268347 251529.861701182 16817.1382988176 55 273046 250953.196980678 22092.8030193223 56 273963 250553.224603106 23409.775396894 57 267430 246914.271366818 20515.7286331823 58 271993 251490.091720458 20502.9082795420 59 292710 268329.269701777 24380.7302982232 60 295881 272222.750814702 23658.2491852984 61 293299 264559.075529098 28739.9244709021 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0132403475098827 0.0264806950197655 0.986759652490117 6 0.00293267700110569 0.00586535400221138 0.997067322998894 7 0.0005034579679524 0.0010069159359048 0.999496542032048 8 8.29568067824298e-05 0.000165913613564860 0.999917043193218 9 1.81534415275648e-05 3.63068830551295e-05 0.999981846558472 10 4.91911599076504e-05 9.83823198153008e-05 0.999950808840092 11 1.15262898782414e-05 2.30525797564829e-05 0.999988473710122 12 2.17465907612864e-06 4.34931815225728e-06 0.999997825340924 13 5.85283806650808e-07 1.17056761330162e-06 0.999999414716193 14 1.13602347473571e-06 2.27204694947142e-06 0.999998863976525 15 1.11894119699443e-06 2.23788239398887e-06 0.999998881058803 16 1.15859513856500e-06 2.31719027712999e-06 0.999998841404862 17 4.85092075425531e-07 9.70184150851062e-07 0.999999514907925 18 4.61748039037482e-07 9.23496078074964e-07 0.99999953825196 19 3.87307181790463e-07 7.74614363580927e-07 0.999999612692818 20 2.87235768435983e-07 5.74471536871966e-07 0.999999712764231 21 1.35012701319770e-07 2.70025402639540e-07 0.999999864987299 22 3.72634735256993e-08 7.45269470513987e-08 0.999999962736526 23 1.21443754269571e-08 2.42887508539143e-08 0.999999987855624 24 3.19467191727093e-09 6.38934383454185e-09 0.999999996805328 25 2.71597421103899e-09 5.43194842207798e-09 0.999999997284026 26 7.9396733428708e-10 1.58793466857416e-09 0.999999999206033 27 1.95802156417436e-10 3.91604312834873e-10 0.999999999804198 28 4.75395047798621e-11 9.50790095597242e-11 0.99999999995246 29 1.31069875992248e-11 2.62139751984495e-11 0.999999999986893 30 2.97190265064178e-12 5.94380530128356e-12 0.999999999997028 31 1.03998642371178e-12 2.07997284742356e-12 0.99999999999896 32 2.82293129104374e-13 5.64586258208748e-13 0.999999999999718 33 1.47922835230242e-13 2.95845670460484e-13 0.999999999999852 34 1.10551234765795e-11 2.21102469531590e-11 0.999999999988945 35 5.10012645788801e-11 1.02002529157760e-10 0.999999999948999 36 7.5790343657931e-10 1.51580687315862e-09 0.999999999242096 37 1.31553416829773e-09 2.63106833659546e-09 0.999999998684466 38 8.95998009601702e-10 1.79199601920340e-09 0.999999999104002 39 5.43765547389829e-10 1.08753109477966e-09 0.999999999456235 40 2.29437849878274e-10 4.58875699756549e-10 0.999999999770562 41 9.9812183764888e-11 1.99624367529776e-10 0.999999999900188 42 3.80800286954430e-11 7.61600573908859e-11 0.99999999996192 43 1.7514352521686e-11 3.5028705043372e-11 0.999999999982486 44 6.57514171431219e-12 1.31502834286244e-11 0.999999999993425 45 7.49742139224187e-12 1.49948427844837e-11 0.999999999992503 46 8.86165975820452e-11 1.77233195164090e-10 0.999999999911383 47 2.37035481321005e-09 4.74070962642011e-09 0.999999997629645 48 6.26464822946639e-06 1.25292964589328e-05 0.99999373535177 49 0.000627901776450772 0.00125580355290154 0.99937209822355 50 0.0176944288489665 0.0353888576979331 0.982305571151033 51 0.208970277020018 0.417940554040037 0.791029722979982 52 0.741790752738851 0.516418494522298 0.258209247261149 53 0.94851096343334 0.102978073133319 0.0514890365666593 54 0.98264905411334 0.0347018917733185 0.0173509458866592 55 0.969800335794673 0.0603993284106542 0.0301996642053271 56 0.941279634731138 0.117440730537724 0.058720365268862 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 44 0.846153846153846 NOK 5% type I error level 47 0.903846153846154 NOK 10% type I error level 48 0.923076923076923 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/10aryk1258964378.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/10aryk1258964378.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/1dimy1258964378.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/1dimy1258964378.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/2ekzu1258964378.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/2ekzu1258964378.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/3p3o81258964378.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/3p3o81258964378.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/4awra1258964378.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/4awra1258964378.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/5v3wi1258964378.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/5v3wi1258964378.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/6lqsh1258964378.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/6lqsh1258964378.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/700en1258964378.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/700en1258964378.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/8wat31258964378.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/8wat31258964378.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/9b1p11258964378.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589645064gfh363ivqb499l/9b1p11258964378.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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