3e link

*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: Sat, 28 Nov 2009 01:33:23 -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/28/t1259397252m3xor6v1qr8oczr.htm/, Retrieved Sat, 28 Nov 2009 09:34:23 +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/28/t1259397252m3xor6v1qr8oczr.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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130 0 136.7 0 138.1 0 139.5 0 140.4 0 144.6 0 151.4 0 147.9 0 141.5 0 143.8 0 143.6 0 150.5 0 150.1 0 154.9 0 162.1 0 176.7 0 186.6 0 194.8 0 196.3 0 228.8 0 267.2 0 237.2 0 254.7 0 258.2 0 257.9 0 269.6 0 266.9 0 269.6 0 253.9 0 258.6 0 274.2 0 301.5 0 304.5 0 285.1 0 287.7 0 265.5 0 264.1 0 276.1 0 258.9 0 239.1 0 250.1 1 276.8 1 297.6 1 295.4 1 283 1 275.8 1 279.7 1 254.6 1 234.6 1 176.9 1 148.1 1 122.7 1 124.9 1 121.6 1 128.4 1 144.5 1 151.8 1 167.1 1 173.8 1 203.7 1 199.8 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y(t)[t] = + 157.120625 -106.098437500000`X(t)`[t] -11.9595138888888M1[t] -13.8189236111112M2[t] -24.945M3[t] -33.3510763888889M4[t] -13.5774652777778M5[t] -8.58354166666668M6[t] -1.38961805555559M7[t] + 9.54430555555554M8[t] + 12.4182291666666M9[t] + 1.51215277777778M10[t] + 4.50607638888888M11[t] + 3.10607638888889t + 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) 157.120625 31.491594 4.9893 9e-06 4e-06 `X(t)` -106.098437500000 27.467095 -3.8627 0.000342 0.000171 M1 -11.9595138888888 34.440599 -0.3473 0.729953 0.364977 M2 -13.8189236111112 36.131493 -0.3825 0.703843 0.351921 M3 -24.945 36.080203 -0.6914 0.49273 0.246365 M4 -33.3510763888889 36.044044 -0.9253 0.359544 0.179772 M5 -13.5774652777778 36.255018 -0.3745 0.709718 0.354859 M6 -8.58354166666668 36.156656 -0.2374 0.813379 0.40669 M7 -1.38961805555559 36.073217 -0.0385 0.969435 0.484717 M8 9.54430555555554 36.004806 0.2651 0.792104 0.396052 M9 12.4182291666666 35.951506 0.3454 0.731323 0.365661 M10 1.51215277777778 35.913387 0.0421 0.966593 0.483296 M11 4.50607638888888 35.890496 0.1256 0.900623 0.450312 t 3.10607638888889 0.740194 4.1963 0.000119 6e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.564508949422612 R-squared 0.318670353978221 Adjusted R-squared 0.130217473163687 F-TEST (value) 1.69098160028575 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 0.0945400973921531 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 56.7357872956924 Sum Squared Residuals 151290.629322917 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 130 148.267187500000 -18.2671874999996 2 136.7 149.513854166667 -12.8138541666669 3 138.1 141.493854166667 -3.39385416666668 4 139.5 136.193854166667 3.30614583333337 5 140.4 159.073541666667 -18.6735416666667 6 144.6 167.173541666667 -22.5735416666666 7 151.4 177.473541666667 -26.0735416666667 8 147.9 191.513541666667 -43.6135416666667 9 141.5 197.493541666667 -55.9935416666667 10 143.8 189.693541666667 -45.8935416666667 11 143.6 195.793541666667 -52.1935416666667 12 150.5 194.393541666667 -43.8935416666667 13 150.1 185.540104166667 -35.4401041666668 14 154.9 186.786770833333 -31.8867708333332 15 162.1 178.766770833333 -16.6667708333333 16 176.7 173.466770833333 3.23322916666663 17 186.6 196.346458333333 -9.74645833333334 18 194.8 204.446458333333 -9.64645833333334 19 196.3 214.746458333333 -18.4464583333333 20 228.8 228.786458333333 0.0135416666666783 21 267.2 234.766458333333 32.4335416666666 22 237.2 226.966458333333 10.2335416666666 23 254.7 233.066458333333 21.6335416666666 24 258.2 231.666458333333 26.5335416666666 25 257.9 222.813020833333 35.0869791666666 26 269.6 224.0596875 45.5403125 27 266.9 216.0396875 50.8603125 28 269.6 210.7396875 58.8603125 29 253.9 233.619375 20.2806250000000 30 258.6 241.719375 16.8806250000000 31 274.2 252.019375 22.180625 32 301.5 266.059375 35.440625 33 304.5 272.039375 32.460625 34 285.1 264.239375 20.8606250000000 35 287.7 270.339375 17.360625 36 265.5 268.939375 -3.439375 37 264.1 260.0859375 4.01406249999994 38 276.1 261.332604166667 14.7673958333334 39 258.9 253.312604166667 5.58739583333336 40 239.1 248.012604166667 -8.91260416666664 41 250.1 164.793854166667 85.3061458333333 42 276.8 172.893854166667 103.906145833333 43 297.6 183.193854166667 114.406145833333 44 295.4 197.233854166667 98.1661458333333 45 283 203.213854166667 79.7861458333333 46 275.8 195.413854166667 80.3861458333333 47 279.7 201.513854166667 78.1861458333333 48 254.6 200.113854166667 54.4861458333333 49 234.6 191.260416666667 43.3395833333333 50 176.9 192.507083333333 -15.6070833333333 51 148.1 184.487083333333 -36.3870833333333 52 122.7 179.187083333333 -56.4870833333333 53 124.9 202.066770833333 -77.1667708333333 54 121.6 210.166770833333 -88.5667708333334 55 128.4 220.466770833333 -92.0667708333333 56 144.5 234.506770833333 -90.0067708333333 57 151.8 240.486770833333 -88.6867708333333 58 167.1 232.686770833333 -65.5867708333333 59 173.8 238.786770833333 -64.9867708333333 60 203.7 237.386770833333 -33.6867708333333 61 199.8 228.533333333333 -28.7333333333334 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00944963479793712 0.0188992695958742 0.990550365202063 18 0.00424697605162508 0.00849395210325015 0.995753023948375 19 0.00136407682595592 0.00272815365191183 0.998635923174044 20 0.00581318126633801 0.0116263625326760 0.994186818733662 21 0.0577404730021315 0.115480946004263 0.942259526997869 22 0.0656403125001241 0.131280625000248 0.934359687499876 23 0.103901893639830 0.207803787279659 0.89609810636017 24 0.152827506068469 0.305655012136938 0.847172493931531 25 0.217631278336273 0.435262556672545 0.782368721663727 26 0.214596557757049 0.429193115514098 0.785403442242951 27 0.18128863986855 0.3625772797371 0.81871136013145 28 0.12982077954813 0.25964155909626 0.87017922045187 29 0.0900677807887017 0.180135561577403 0.909932219211298 30 0.0631192642971689 0.126238528594338 0.936880735702831 31 0.0408131453190893 0.0816262906381785 0.95918685468091 32 0.0239637598901851 0.0479275197803702 0.976036240109815 33 0.0128589910224323 0.0257179820448645 0.987141008977568 34 0.00778681815549105 0.0155736363109821 0.992213181844509 35 0.00547038921843133 0.0109407784368627 0.994529610781569 36 0.0174304618965683 0.0348609237931366 0.982569538103432 37 0.157762622548640 0.315525245097279 0.84223737745136 38 0.133540244660559 0.267080489321118 0.86645975533944 39 0.130308784518586 0.260617569037173 0.869691215481414 40 0.158522297893121 0.317044595786241 0.84147770210688 41 0.0980079039381092 0.196015807876218 0.90199209606189 42 0.083604671675589 0.167209343351178 0.916395328324411 43 0.128689131769583 0.257378263539165 0.871310868230417 44 0.172350486573763 0.344700973147526 0.827649513426237 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 9 0.321428571428571 NOK 10% type I error level 10 0.357142857142857 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/10gq7n1259397191.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/10gq7n1259397191.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/1vr4d1259397191.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/1vr4d1259397191.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/227z91259397191.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/227z91259397191.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/323h71259397191.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/323h71259397191.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/4c9r81259397191.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/4c9r81259397191.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/5djpk1259397191.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/5djpk1259397191.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/6rtn21259397191.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/6rtn21259397191.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/70lxs1259397191.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/70lxs1259397191.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/83y6h1259397191.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/83y6h1259397191.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/9nime1259397191.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259397252m3xor6v1qr8oczr/9nime1259397191.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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