M3

*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, 19 Nov 2009 00:58:21 -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/19/t1258618894rzai7tgryny2i3c.htm/, Retrieved Thu, 19 Nov 2009 09:21:46 +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/19/t1258618894rzai7tgryny2i3c.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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21 2472.81 19 2407.6 25 2454.62 21 2448.05 23 2497.84 23 2645.64 19 2756.76 18 2849.27 19 2921.44 19 2981.85 22 3080.58 23 3106.22 20 3119.31 14 3061.26 14 3097.31 14 3161.69 15 3257.16 11 3277.01 17 3295.32 16 3363.99 20 3494.17 24 3667.03 23 3813.06 20 3917.96 21 3895.51 19 3801.06 23 3570.12 23 3701.61 23 3862.27 23 3970.1 27 4138.52 26 4199.75 17 4290.89 24 4443.91 26 4502.64 24 4356.98 27 4591.27 27 4696.96 26 4621.4 24 4562.84 23 4202.52 23 4296.49 24 4435.23 17 4105.18 21 4116.68 19 3844.49 22 3720.98 22 3674.4 18 3857.62 16 3801.06 14 3504.37 12 3032.6 14 3047.03 16 2962.34 8 2197.82 3 2014.45 0 1862.83 5 1905.41 1 1810.99 1 1670.07 3 1864.44

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 Consvertr[t] = + 3.55671726525572 + 0.00638085941225368Aand[t] -0.338286051714473M1[t] -2.24724893355562M2[t] + 0.00822180782049912M3[t] -0.964857073231493M4[t] + 0.0778597387859022M5[t] -0.493834744584925M6[t] -0.0836314772970198M7[t] -2.52054447590919M8[t] -3.12456273564482M9[t] -0.332805124311363M10[t] + 0.34971383130199M11[t] -0.191708221875839t + 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) 3.55671726525572 2.470807 1.4395 0.156638 0.078319 Aand 0.00638085941225368 0.000536 11.9096 0 0 M1 -0.338286051714473 2.045623 -0.1654 0.869362 0.434681 M2 -2.24724893355562 2.150727 -1.0449 0.301423 0.150712 M3 0.00822180782049912 2.145392 0.0038 0.996958 0.498479 M4 -0.964857073231493 2.142051 -0.4504 0.654467 0.327234 M5 0.0778597387859022 2.139806 0.0364 0.971128 0.485564 M6 -0.493834744584925 2.138465 -0.2309 0.818372 0.409186 M7 -0.0836314772970198 2.136255 -0.0391 0.968938 0.484469 M8 -2.52054447590919 2.134929 -1.1806 0.243693 0.121847 M9 -3.12456273564482 2.133861 -1.4643 0.149776 0.074888 M10 -0.332805124311363 2.133192 -0.156 0.876691 0.438346 M11 0.34971383130199 2.132826 0.164 0.87046 0.43523 t -0.191708221875839 0.024927 -7.6909 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.900153925921666 R-squared 0.810277090352189 Adjusted R-squared 0.757800540875134 F-TEST (value) 15.4407463605527 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 8.77964367873574e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.3718656391664 Sum Squared Residuals 534.365460763779 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 21 18.8053759548805 2.19462404511954 2 19 16.2886090088904 2.71139099110961 3 25 18.6523995379548 6.34760046204518 4 21 17.4456901886885 3.55430981131151 5 23 18.6144017689662 4.38559823103384 6 23 18.7940900848506 4.20590991514943 7 19 19.7216262281523 -0.721626228152276 8 18 17.6832983118919 0.316701688108146 9 19 17.3480784540627 1.65192154593727 10 19 20.3335955606146 -1.33359556061459 11 22 21.4543885441239 0.545611455876085 12 23 21.0765717262763 1.92342827372373 13 20 20.6301029023924 -0.630102902392358 14 14 18.1590229097940 -4.15902290979404 15 14 20.4528154111061 -6.45281541110607 16 14 19.6988280371391 -5.69882803713913 17 15 21.1590172753685 -6.15901727536854 18 11 20.5222746294551 -9.52227462945511 19 17 20.8576032107055 -3.85760321070554 20 16 18.667155606057 -2.66715560605699 21 20 18.7020894027327 1.29791059726730 22 24 22.4051341501925 1.59486584980751 23 23 23.8277417839014 -0.827741783901411 24 20 23.955671883069 -3.95567188306899 25 21 23.2824273156736 -2.28242731567359 26 19 20.5790840404692 -1.57908404046924 27 23 21.1692508873037 1.83074911269635 28 23 20.8434829884931 2.15651701150694 29 23 22.7196404518073 0.280359548192708 30 23 22.6442858169839 0.355714183016064 31 27 23.9374452046078 3.06255479539223 32 26 21.6995240059320 4.30047599406795 33 17 21.4853490511534 -4.48534905115338 34 24 25.0617975478741 -1.06179754787406 35 26 25.9273561548932 0.0726438451067698 36 24 24.4564981197265 -0.456498119726525 37 27 25.4214753978331 1.57852460216687 38 27 23.9951973253972 3.00480267460277 39 26 25.5768221077076 0.423177892292376 40 24 24.0383718775982 -0.0383718775982237 41 23 22.5902292043165 0.409770795683464 42 23 22.4264358580393 0.573564141960659 43 24 23.5302113383075 0.469788661692517 44 17 18.7955874688052 -1.79558746880515 45 21 18.0732408704346 2.9267591295654 46 19 18.9364841364709 0.0635158635291115 47 22 18.6391949242010 3.36080507579905 48 22 17.8005524396003 4.19944756039965 49 18 18.4396592275232 -0.439659227523154 50 16 15.9780867154491 0.0219132845509014 51 14 16.1487120559278 -2.14871205592783 52 12 11.9736269080811 0.0263730919189082 53 14 12.9167112995415 1.08328870045853 54 16 11.6129136106710 4.38708638932896 55 8 6.95311401822693 1.04688598177307 56 3 3.15443460731396 -0.154434607313957 57 0 1.39124222161659 -1.39124222161659 58 5 4.26298860484797 0.737011395152033 59 1 4.15131859288049 -3.15131859288049 60 1 2.71070583132787 -1.71070583132787 61 3 3.42095920169731 -0.420959201697308 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.647755524161479 0.704488951677043 0.352244475838521 18 0.830795578812094 0.338408842375812 0.169204421187906 19 0.826986180301022 0.346027639397956 0.173013819698978 20 0.756837232235447 0.486325535529106 0.243162767764553 21 0.799472477190551 0.401055045618897 0.200527522809449 22 0.97219971065377 0.0556005786924605 0.0278002893462303 23 0.973584764978743 0.0528304700425139 0.0264152350212570 24 0.96991011601501 0.06017976796998 0.03008988398499 25 0.974194328669559 0.0516113426608827 0.0258056713304413 26 0.9805711393198 0.0388577213604003 0.0194288606802002 27 0.98372079373444 0.0325584125311222 0.0162792062655611 28 0.986951654214638 0.0260966915707243 0.0130483457853622 29 0.98235589807625 0.0352882038475021 0.0176441019237511 30 0.981367489741675 0.037265020516651 0.0186325102583255 31 0.987590511981154 0.0248189760376923 0.0124094880188461 32 0.99701846712486 0.00596306575027921 0.00298153287513961 33 0.997454965692443 0.00509006861511411 0.00254503430755705 34 0.99483988223823 0.0103202355235398 0.00516011776176991 35 0.989203787346101 0.0215924253077974 0.0107962126538987 36 0.983756121432018 0.0324877571359634 0.0162438785679817 37 0.970678904327817 0.0586421913443661 0.0293210956721831 38 0.96276336176941 0.0744732764611793 0.0372366382305896 39 0.950798534567316 0.0984029308653678 0.0492014654326839 40 0.905782373694776 0.188435252610447 0.0942176263052237 41 0.844776359345753 0.310447281308495 0.155223640654247 42 0.81308761571761 0.373824768564781 0.186912384282390 43 0.711392567503433 0.577214864993133 0.288607432496567 44 0.686073945463068 0.627852109073864 0.313926054536932 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0714285714285714 NOK 5% type I error level 11 0.392857142857143 NOK 10% type I error level 18 0.642857142857143 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/1050i21258617496.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/1050i21258617496.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/1kft21258617496.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/1kft21258617496.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/2vkns1258617496.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/2vkns1258617496.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/3uap71258617496.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/3uap71258617496.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/4n1gy1258617496.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/4n1gy1258617496.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/5a51r1258617496.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/5a51r1258617496.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/6yxoy1258617496.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/6yxoy1258617496.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/7at7q1258617496.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/7at7q1258617496.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/81ory1258617496.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/81ory1258617496.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/9wwg41258617496.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258618894rzai7tgryny2i3c/9wwg41258617496.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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