werkloosheid/invoer

*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, 19 Dec 2008 13:30:17 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/19/t12297191519c0ug9jp5bfcp2t.htm/, Retrieved Fri, 19 Dec 2008 21:39:11 +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/2008/Dec/19/t12297191519c0ug9jp5bfcp2t.htm/}, year = {2008}, } @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 = {2008}, 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 «

180144 11554.5 173666 13182.1 165688 14800.1 161570 12150.7 156145 14478.2 153730 13253.9 182698 12036.8 200765 12653.2 176512 14035.4 166618 14571.4 158644 15400.9 159585 14283.2 163095 14485.3 159044 14196.3 155511 15559.1 153745 13767.4 150569 14634 150605 14381.1 179612 12509.9 194690 12122.3 189917 13122.3 184128 13908.7 175335 13456.5 179566 12441.6 181140 12953 177876 13057.2 175041 14350.1 169292 13830.2 166070 13755.5 166972 13574.4 206348 12802.6 215706 11737.3 202108 13850.2 195411 15081.8 193111 13653.3 195198 14019.1 198770 13962 194163 13768.7 190420 14747.1 189733 13858.1 186029 13188 191531 13693.1 232571 12970 243477 11392.8 227247 13985.2 217859 14994.7 208679 13584.7 213188 14257.8 216234 13553.4 213586 14007.3 209465 16535.8 204045 14721.4 200237 13664.6 203666 16405.9 241476 13829.4 260307 13735.6 243324 15870.5 244460 15962.4 233575 15744.1 237217 16083.7 235243 14863.9 230354 15533.1 227184 17473.1 221678 15925.5 217142 15573.7 219452 17495 256446 14155.8 265845 14913.9 248624 17250.4 241114 15879.8 229245 17647.8 231805 17749.9 219277 17111.8 219313 16934.8 212610 20280 214771 16238.2 211142 17896.1 211457 18089.3 240048 15660 240636 16162.4 230580 17850.1 208795 18520.4 197922 18524.7 194596 16843.7

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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 220056.614158371 -5.04575072429907invoer[t] + 5574.86255590247M1[t] + 2255.00845545131M2[t] + 5887.48571546812M3[t] -7880.62856212393M4[t] -11066.3380033862M5[t] -8159.91405504897M6[t] + 15859.6711581921M7[t] + 25505.1739943878M8[t] + 19120.6900731122M9[t] + 11487.4427833903M10[t] + 792.023633316998M11[t] + 1202.34699906132t + 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) 220056.614158371 19897.545828 11.0595 0 0 invoer -5.04575072429907 1.53061 -3.2966 0.001539 0.00077 M1 5574.86255590247 6921.633969 0.8054 0.423302 0.211651 M2 2255.00845545131 6890.339127 0.3273 0.74444 0.37222 M3 5887.48571546812 7326.610241 0.8036 0.424362 0.212181 M4 -7880.62856212393 6892.360449 -1.1434 0.256774 0.128387 M5 -11066.3380033862 6873.056423 -1.6101 0.111876 0.055938 M6 -8159.91405504897 6912.423841 -1.1805 0.241809 0.120904 M7 15859.6711581921 7193.680104 2.2047 0.030766 0.015383 M8 25505.1739943878 7305.660618 3.4912 0.000837 0.000419 M9 19120.6900731122 6870.162976 2.7831 0.006916 0.003458 M10 11487.4427833903 6917.439881 1.6606 0.101257 0.050629 M11 792.023633316998 6886.677626 0.115 0.908768 0.454384 t 1202.34699906132 101.986336 11.7893 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.916414775691669 R-squared 0.839816041106012 Adjusted R-squared 0.810067591597128 F-TEST (value) 28.2305819284876 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12835.3514702565 Sum Squared Residuals 11532237315.5512 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 180144 168532.696969421 11611.3030305786 2 173666 158202.725989162 15463.2740108377 3 165688 154873.525576324 10814.4744236755 4 161570 155675.970266752 5894.02973324829 5 156145 141948.623013745 14196.3769862553 6 153730 152234.906572903 1495.09342709741 7 182698 183598.021991749 -900.021991749388 8 200765 191335.671080548 9429.32891945156 9 176512 179179.297507208 -2667.29750720802 10 166618 170043.874828323 -3425.87482832316 11 158644 156365.352451505 2278.64754849494 12 159585 162415.311401798 -2830.31140179845 13 163095 168172.774735381 -5077.7747353814 14 159044 167513.489593314 -8469.48959331398 15 155511 165471.964765317 -9960.96476531734 16 153745 161946.669059513 -8201.66905951328 17 150569 155590.659039635 -5021.65903963476 18 150605 160975.500345209 -10370.5003452085 19 179612 195639.041312819 -16027.0413128194 20 194690 208442.624128815 -13752.6241288147 21 189917 198214.736482301 -8297.73648230134 22 184128 187815.857822052 -3687.857822052 23 175335 180604.474148568 -5269.47414856804 24 179566 186135.729924403 -6569.72992440348 25 181140 190332.542558961 -9192.54255896073 26 177876 187689.268232099 -9813.26823209894 27 175041 186000.441379731 -10959.4413797308 28 169292 176057.959902763 -6765.95990276315 29 166070 174451.515039667 -8381.51503966735 30 166972 179474.071443236 -12502.0714432365 31 206348 208590.314064553 -2242.31406455286 32 215706 224813.402146406 -9107.40214640568 33 202108 208970.09851882 -6862.09851881989 34 195411 196324.851636113 -913.851636112615 35 193111 194039.634394762 -928.634394761824 36 195198 192604.222145558 2593.77785444245 37 198770 199669.544066879 -899.544066878827 38 194163 198527.380580496 -4364.380580496 39 190420 198425.44233092 -8005.4423309199 40 189733 190345.347446291 -612.347446291063 41 186029 191743.142564443 -5714.14256444294 42 191531 193303.304820998 -1772.30482099800 43 232571 222173.819382041 10397.1806179589 44 243477 240979.827259663 2497.17274033744 45 227247 222717.086159775 4529.91384022464 46 217859 211192.500512935 6666.49948706509 47 208679 208813.936883185 -134.936883184587 48 213188 205827.965436403 7360.03456359679 49 216234 216159.401801563 74.5981984367158 50 213586 211751.628446414 1834.37155358590 51 209465 203828.271999102 5636.72800089799 52 204045 200417.514834740 3627.48516526047 53 200237 203766.501757978 -3529.50175797785 54 203666 194043.356244855 9622.64375514467 55 241476 232265.665198314 9210.33480168571 56 260307 243586.806451511 16720.1935484895 57 243324 227632.49630799 15691.5036920098 58 244460 220737.891525767 23722.1084742334 59 233575 212346.306757869 21228.6932421310 60 237217 211043.093177641 26173.9068223586 61 235243 223975.109466105 11267.8905338948 62 230354 218480.985980014 11873.0140199856 63 227184 213527.053833952 13656.9461660477 64 221678 208770.090376347 12907.9096236531 65 217142 208561.823038954 8580.17696104565 66 219452 202976.193119757 16475.8068802429 67 256446 245046.896150639 11399.1038493611 68 265845 252069.562361805 13775.4376381952 69 248624 235098.028872266 13525.9711277343 70 241114 235582.834524330 5531.16547567049 71 229245 217168.875092757 12076.1249072433 72 231805 217064.02730955 14740.9726904499 73 219277 227060.930401689 -7783.93040168917 74 219313 225836.521178500 -6523.52117850027 75 212610 213792.300114653 -1182.30011465314 76 214771 221620.448113594 -6849.4481135944 77 211142 211271.735545578 -129.735545578046 78 211457 214405.667453042 -2948.66745304199 79 240048 251885.241899884 -11837.2418998841 80 240636 260198.106571253 -19562.1065712533 81 230580 246500.256151639 -15920.2561516395 82 208795 236687.189150481 -27892.1891504812 83 197922 227172.420271355 -29250.4202713547 84 194596 236064.650604646 -41468.6506046458 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00970776767021311 0.0194155353404262 0.990292232329787 18 0.00613405530316608 0.0122681106063322 0.993865944696834 19 0.00172489554264300 0.00344979108528601 0.998275104457357 20 0.000458112896335411 0.000916225792670821 0.999541887103665 21 0.00175136791206394 0.00350273582412788 0.998248632087936 22 0.00327306567721958 0.00654613135443917 0.99672693432278 23 0.00113673736802877 0.00227347473605753 0.998863262631971 24 0.000452320963838074 0.000904641927676148 0.999547679036162 25 0.000238448042676724 0.000476896085353448 0.999761551957323 26 8.68455249418926e-05 0.000173691049883785 0.999913154475058 27 3.43507680292593e-05 6.87015360585186e-05 0.99996564923197 28 9.64412439461914e-05 0.000192882487892383 0.999903558756054 29 3.65147629511189e-05 7.30295259022379e-05 0.999963485237049 30 2.55985766800136e-05 5.11971533600272e-05 0.99997440142332 31 0.000430125819605945 0.00086025163921189 0.999569874180394 32 0.000270604319137421 0.000541208638274843 0.999729395680863 33 0.000337716686712586 0.000675433373425173 0.999662283313287 34 0.000688151595531976 0.00137630319106395 0.999311848404468 35 0.000563270796744732 0.00112654159348946 0.999436729203255 36 0.00122911105409434 0.00245822210818869 0.998770888945906 37 0.00156337763040418 0.00312675526080835 0.998436622369596 38 0.00157399078831488 0.00314798157662977 0.998426009211685 39 0.00123237264590115 0.00246474529180231 0.998767627354099 40 0.00187518598167968 0.00375037196335937 0.99812481401832 41 0.00170013073869135 0.00340026147738271 0.998299869261309 42 0.00222028266382663 0.00444056532765325 0.997779717336173 43 0.00941552638091201 0.0188310527618240 0.990584473619088 44 0.0087221334127139 0.0174442668254278 0.991277866587286 45 0.0112376737510120 0.0224753475020239 0.988762326248988 46 0.0166440236320479 0.0332880472640957 0.983355976367952 47 0.0129812731219869 0.0259625462439738 0.987018726878013 48 0.0147605747068106 0.0295211494136213 0.98523942529319 49 0.0129342407360057 0.0258684814720113 0.987065759263994 50 0.0134262761790421 0.0268525523580842 0.986573723820958 51 0.0177921616292219 0.0355843232584438 0.982207838370778 52 0.0339143455490405 0.067828691098081 0.96608565445096 53 0.0680833708558731 0.136166741711746 0.931916629144127 54 0.20104344871447 0.40208689742894 0.79895655128553 55 0.458014537997073 0.916029075994145 0.541985462002927 56 0.552872576143259 0.894254847713482 0.447127423856741 57 0.767187545721523 0.465624908556955 0.232812454278477 58 0.779177695880997 0.441644608238006 0.220822304119003 59 0.749144144947926 0.501711710104147 0.250855855052074 60 0.708231980379217 0.583536039241565 0.291768019620783 61 0.611484885569427 0.777030228861145 0.388515114430573 62 0.540382643230552 0.919234713538895 0.459617356769448 63 0.42815029894445 0.8563005978889 0.57184970105555 64 0.482103370889723 0.964206741779446 0.517896629110277 65 0.506694699730649 0.986610600538702 0.493305300269351 66 0.696646655032265 0.606706689935471 0.303353344967735 67 0.687757007866022 0.624485984267957 0.312242992133978 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 24 0.470588235294118 NOK 5% type I error level 35 0.686274509803922 NOK 10% type I error level 36 0.705882352941177 NOK

Charts produced by software:

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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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