SHW 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: Fri, 20 Nov 2009 04:57:18 -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/20/t1258718293ji7bo351hflmli1.htm/, Retrieved Fri, 20 Nov 2009 12:58:28 +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/20/t1258718293ji7bo351hflmli1.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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8.6 0 8.5 0 8.3 0 7.8 0 7.8 0 8 0 8.6 0 8.9 0 8.9 0 8.6 0 8.3 0 8.3 0 8.3 0 8.4 0 8.5 0 8.4 0 8.6 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.6 0 8.4 0 8.1 0 8 0 8 0 8 0 8 0 7.9 0 7.8 0 7.8 0 7.9 0 8.1 0 8 0 7.6 0 7.3 0 7 0 6.8 0 7 0 7.1 0 7.2 0 7.1 1 6.9 1 6.7 1 6.7 1 6.6 1 6.9 1 7.3 1 7.5 1 7.3 1 7.1 1 6.9 1 7.1 1

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 8.77083333333333 -0.391666666666667X[t] -0.0127083333333361M1[t] -0.0279166666666659M2[t] -0.0431249999999993M3[t] -0.098333333333333M4[t] -0.0535416666666664M5[t] -0.0687499999999997M6[t] + 0.036041666666667M7[t] + 0.0808333333333339M8[t] + 0.0256250000000003M9[t] -0.00958333333333323M10[t] -0.0647916666666663M11[t] -0.0247916666666667t + 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) 8.77083333333333 0.221833 39.538 0 0 X -0.391666666666667 0.182346 -2.1479 0.03702 0.01851 M1 -0.0127083333333361 0.257051 -0.0494 0.960783 0.480392 M2 -0.0279166666666659 0.256295 -0.1089 0.913737 0.456868 M3 -0.0431249999999993 0.255609 -0.1687 0.866761 0.43338 M4 -0.098333333333333 0.254994 -0.3856 0.701549 0.350774 M5 -0.0535416666666664 0.25445 -0.2104 0.834269 0.417134 M6 -0.0687499999999997 0.253978 -0.2707 0.787838 0.393919 M7 0.036041666666667 0.253578 0.1421 0.887596 0.443798 M8 0.0808333333333339 0.25325 0.3192 0.751032 0.375516 M9 0.0256250000000003 0.252994 0.1013 0.919763 0.459881 M10 -0.00958333333333323 0.252812 -0.0379 0.969926 0.484963 M11 -0.0647916666666663 0.252702 -0.2564 0.798789 0.399394 t -0.0247916666666667 0.004298 -5.7683 1e-06 0 Multiple Linear Regression - Regression Statistics Multiple R 0.843611399723022 R-squared 0.711680193742636 Adjusted R-squared 0.630198509365556 F-TEST (value) 8.73423517424017 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.40547959937010e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.399499233642769 Sum Squared Residuals 7.34158333333334 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.6 8.73333333333334 -0.133333333333346 2 8.5 8.69333333333333 -0.193333333333332 3 8.3 8.65333333333333 -0.353333333333333 4 7.8 8.57333333333333 -0.773333333333332 5 7.8 8.59333333333333 -0.793333333333333 6 8 8.55333333333333 -0.553333333333332 7 8.6 8.63333333333333 -0.0333333333333324 8 8.9 8.65333333333333 0.246666666666668 9 8.9 8.57333333333333 0.326666666666667 10 8.6 8.51333333333333 0.0866666666666665 11 8.3 8.43333333333333 -0.133333333333332 12 8.3 8.47333333333333 -0.173333333333332 13 8.3 8.43583333333333 -0.135833333333329 14 8.4 8.39583333333333 0.00416666666666725 15 8.5 8.35583333333333 0.144166666666666 16 8.4 8.27583333333333 0.124166666666667 17 8.6 8.29583333333333 0.304166666666666 18 8.5 8.25583333333333 0.244166666666667 19 8.5 8.33583333333333 0.164166666666667 20 8.5 8.35583333333333 0.144166666666667 21 8.5 8.27583333333333 0.224166666666667 22 8.5 8.21583333333333 0.284166666666667 23 8.5 8.13583333333333 0.364166666666667 24 8.5 8.17583333333333 0.324166666666667 25 8.5 8.13833333333333 0.36166666666667 26 8.5 8.09833333333333 0.401666666666666 27 8.5 8.05833333333333 0.441666666666666 28 8.5 7.97833333333333 0.521666666666667 29 8.6 7.99833333333333 0.601666666666666 30 8.4 7.95833333333333 0.441666666666667 31 8.1 8.03833333333333 0.0616666666666664 32 8 8.05833333333333 -0.0583333333333335 33 8 7.97833333333333 0.0216666666666666 34 8 7.91833333333333 0.0816666666666668 35 8 7.83833333333333 0.161666666666666 36 7.9 7.87833333333333 0.0216666666666671 37 7.8 7.84083333333333 -0.0408333333333306 38 7.8 7.80083333333333 -0.000833333333334452 39 7.9 7.76083333333333 0.139166666666666 40 8.1 7.68083333333333 0.419166666666666 41 8 7.70083333333333 0.299166666666666 42 7.6 7.66083333333333 -0.0608333333333342 43 7.3 7.74083333333333 -0.440833333333334 44 7 7.76083333333333 -0.760833333333334 45 6.8 7.68083333333333 -0.880833333333334 46 7 7.62083333333333 -0.620833333333334 47 7.1 7.54083333333333 -0.440833333333334 48 7.2 7.58083333333333 -0.380833333333333 49 7.1 7.15166666666666 -0.0516666666666639 50 6.9 7.11166666666667 -0.211666666666667 51 6.7 7.07166666666667 -0.371666666666667 52 6.7 6.99166666666667 -0.291666666666667 53 6.6 7.01166666666667 -0.411666666666667 54 6.9 6.97166666666667 -0.0716666666666666 55 7.3 7.05166666666667 0.248333333333333 56 7.5 7.07166666666667 0.428333333333333 57 7.3 6.99166666666667 0.308333333333333 58 7.1 6.93166666666667 0.168333333333333 59 6.9 6.85166666666667 0.0483333333333334 60 7.1 6.89166666666667 0.208333333333333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.704784811578244 0.590430376843513 0.295215188421756 18 0.588718905301102 0.822562189397797 0.411281094698898 19 0.517363933622046 0.96527213275591 0.482636066377954 20 0.555780630775282 0.888438738449436 0.444219369224718 21 0.542206003808919 0.915587992382162 0.457793996191081 22 0.437626347525395 0.875252695050791 0.562373652474605 23 0.335387222481108 0.670774444962215 0.664612777518892 24 0.255197111423309 0.510394222846618 0.744802888576691 25 0.175504230592647 0.351008461185293 0.824495769407353 26 0.115880593290941 0.231761186581883 0.884119406709059 27 0.0736332816240045 0.147266563248009 0.926366718375995 28 0.0538058778594568 0.107611755718914 0.946194122140543 29 0.0411193671631924 0.0822387343263849 0.958880632836808 30 0.0245229300661803 0.0490458601323605 0.97547706993382 31 0.0348393528139029 0.0696787056278059 0.965160647186097 32 0.0674241668549775 0.134848333709955 0.932575833145022 33 0.0852494967145368 0.170498993429074 0.914750503285463 34 0.0730256113007672 0.146051222601534 0.926974388699233 35 0.0515474709441717 0.103094941888343 0.948452529055828 36 0.0362661271398535 0.072532254279707 0.963733872860146 37 0.0295650368542659 0.0591300737085318 0.970434963145734 38 0.0251087093129786 0.0502174186259573 0.974891290687021 39 0.0290225176271013 0.0580450352542025 0.970977482372899 40 0.0714955244046203 0.142991048809241 0.92850447559538 41 0.413410404768391 0.826820809536781 0.586589595231609 42 0.695867905412348 0.608264189175305 0.304132094587652 43 0.600623385442089 0.798753229115822 0.399376614557911 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 1 0.0370370370370370 OK 10% type I error level 7 0.259259259259259 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/10wuy1258718233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/10wuy1258718233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/10y0w51258718234.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/10y0w51258718234.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/2n7te1258718233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/2n7te1258718233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/3h84a1258718233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/3h84a1258718233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/4p8le1258718233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/4p8le1258718233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/5owdl1258718234.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/5owdl1258718234.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/6j3e31258718234.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/6j3e31258718234.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/708e81258718234.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/708e81258718234.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/89rkn1258718234.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/89rkn1258718234.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/9wxrm1258718234.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718293ji7bo351hflmli1/9wxrm1258718234.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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