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 13:08:51 -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/t1229717449q9ucxuc1kz8dbi8.htm/, Retrieved Fri, 19 Dec 2008 21:10:59 +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/t1229717449q9ucxuc1kz8dbi8.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 5 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 117506.517915441 + 5.70890541302999invoer[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) 117506.517915441 24808.875164 4.7365 9e-06 5e-06 invoer 5.70890541302999 1.669763 3.419 0.000981 0.000491 Multiple Linear Regression - Regression Statistics Multiple R 0.353225978679813 R-squared 0.124768592014311 Adjusted R-squared 0.114095038258388 F-TEST (value) 11.6895079996270 F-TEST (DF numerator) 1 F-TEST (DF denominator) 82 p-value 0.000981469509051314 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 27720.5469390125 Sum Squared Residuals 63011155253.0357 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 180144 183470.065510296 -3326.06551029616 2 173666 192761.879960543 -19095.8799605434 3 165688 201998.888918826 -36310.8889188259 4 161570 186873.714917544 -25303.7149175442 5 156145 200161.192266372 -44016.1922663715 6 153730 193171.779369199 -39441.7793691989 7 182698 186223.470591 -3525.47059100008 8 200765 189742.439887592 11022.5601124082 9 176512 197633.288949482 -21121.2889494818 10 166618 200693.262250866 -34075.2622508659 11 158644 205428.799290974 -46784.7992909743 12 159585 199047.955710831 -39462.9557108307 13 163095 200201.725494804 -37106.725494804 14 159044 198551.851830438 -39507.8518304383 15 155511 206331.948127316 -50820.9481273156 16 153745 196103.30229879 -42358.3022987898 17 150569 201050.639729722 -50481.6397297216 18 150605 199606.857550766 -49001.8575507663 19 179612 188924.353741905 -9312.35374190457 20 194690 186711.582003814 7978.41799618586 21 189917 192420.487416844 -2503.48741684413 22 184128 196909.970633651 -12781.9706336509 23 175335 194328.403605879 -18993.4036058788 24 179566 188534.435502195 -8968.43550219462 25 181140 191453.969730418 -10313.9697304182 26 177876 192048.837674456 -14172.8376744559 27 175041 199429.881482962 -24388.8814829624 28 169292 196461.821558728 -27169.8215587281 29 166070 196035.366324375 -29965.3663243747 30 166972 195001.483554075 -28029.483554075 31 206348 190595.350356298 15752.6496437015 32 215706 184513.653419798 31192.3465802024 33 202108 196575.999666989 5532.00033301132 34 195411 203607.087573676 -8196.0875736764 35 193111 195451.916191163 -2340.91619116306 36 195198 197540.233791249 -2342.23379124944 37 198770 197214.255292165 1555.74470783458 38 194163 196110.723875827 -1947.72387582673 39 190420 201696.316931935 -11276.3169319353 40 189733 196621.100019752 -6888.10001975161 41 186029 192795.562502480 -6766.56250248021 42 191531 195679.130626602 -4148.13062660166 43 232571 191551.021122440 41019.9788775603 44 243477 182546.935505009 60930.0644949912 45 227247 197346.701897748 29900.2981022523 46 217859 203109.841912201 14749.1580877985 47 208679 195060.285279829 13618.7147201708 48 213188 198902.949513340 14285.0504866603 49 216234 194881.596540401 21352.4034595986 50 213586 197472.868707376 16113.1312926243 51 209465 211907.836044222 -2442.83604422202 52 204045 201549.598062820 2495.4019371796 53 200237 195516.426822330 4720.57317766969 54 203666 211166.249231069 -7500.24923106943 55 241476 196457.254434398 45018.7455656024 56 260307 195921.759106655 64385.2408933446 57 243324 208109.701272933 35214.2987270668 58 244460 208634.349680391 35825.6503196094 59 233575 207388.095628726 26186.9043712738 60 237217 209326.839906991 27890.1600930088 61 235243 202363.117084177 32879.8829158228 62 230354 206183.516586577 24170.4834134232 63 227184 217258.793087855 9925.20691214497 64 221678 208423.691070650 13254.3089293502 65 217142 206415.298146346 10726.7018536541 66 219452 217383.818116400 2068.18188359961 67 256446 198320.641161211 58125.3588387894 68 265845 202648.562354829 63196.4376451713 69 248624 215987.419852373 32636.5801476267 70 241114 208162.794093274 32951.2059067257 71 229245 218256.138863511 10988.8611364886 72 231805 218839.018106182 12965.9818938183 73 219277 215196.165562127 4080.83443787271 74 219313 214185.689304021 5127.31069597902 75 212610 233283.119691689 -20673.1196916889 76 214771 210208.865793304 4562.1342066957 77 211142 219673.660077567 -8531.66007756671 78 211457 220776.620603364 -9319.62060336411 79 240048 206907.976683490 33140.0233165097 80 240636 209776.130762997 30859.8692370034 81 230580 219411.050428567 11168.9495714327 82 208795 223237.729726921 -14442.7297269214 83 197922 223262.278020197 -25340.2780201974 84 194596 213665.608020894 -19069.6080208940 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0423894702301166 0.0847789404602332 0.957610529769883 6 0.0294500809059258 0.0589001618118517 0.970549919094074 7 0.0163299004061592 0.0326598008123184 0.98367009959384 8 0.0601427736588958 0.120285547317792 0.939857226341104 9 0.0355329011883112 0.0710658023766224 0.96446709881169 10 0.0176070689491032 0.0352141378982064 0.982392931050897 11 0.00907515585188258 0.0181503117037652 0.990924844148117 12 0.00492835865023185 0.0098567173004637 0.995071641349768 13 0.00239518057069317 0.00479036114138634 0.997604819429307 14 0.00135875369163615 0.00271750738327230 0.998641246308364 15 0.000787926379629677 0.00157585275925935 0.99921207362037 16 0.000832525292518918 0.00166505058503784 0.999167474707481 17 0.000832484217714047 0.00166496843542809 0.999167515782286 18 0.000968532038424282 0.00193706407684856 0.999031467961576 19 0.000517853602270637 0.00103570720454127 0.99948214639773 20 0.000523726195854871 0.00104745239170974 0.999476273804145 21 0.000622956302970664 0.00124591260594133 0.99937704369703 22 0.00076244055891285 0.0015248811178257 0.999237559441087 23 0.000492655587011493 0.000985311174022985 0.999507344412988 24 0.000277287388680232 0.000554574777360464 0.99972271261132 25 0.000173718578266528 0.000347437156533057 0.999826281421733 26 0.000111344256994801 0.000222688513989602 0.999888655743005 27 0.000119307616215591 0.000238615232431182 0.999880692383784 28 0.000111273935060825 0.000222547870121650 0.99988872606494 29 0.000137935478935557 0.000275870957871114 0.999862064521065 30 0.000202583251881837 0.000405166503763675 0.999797416748118 31 0.000959205065191206 0.00191841013038241 0.99904079493481 32 0.00209676152861577 0.00419352305723154 0.997903238471384 33 0.00736467372642737 0.0147293474528547 0.992635326273573 34 0.0263898003926336 0.0527796007852671 0.973610199607366 35 0.0325488544076349 0.0650977088152698 0.967451145592365 36 0.0461907355428568 0.0923814710857136 0.953809264457143 37 0.065811846852297 0.131623693704594 0.934188153147703 38 0.0815269304645615 0.163053860929123 0.918473069535439 39 0.118681050711594 0.237362101423188 0.881318949288406 40 0.155747953678989 0.311495907357978 0.84425204632101 41 0.229461165629955 0.45892233125991 0.770538834370045 42 0.33202278064563 0.66404556129126 0.66797721935437 43 0.54031101818262 0.919377963634759 0.459688981817379 44 0.654387469003555 0.69122506199289 0.345612530996445 45 0.77653475426044 0.446930491479119 0.223465245739560 46 0.851991629189334 0.296016741621333 0.148008370810666 47 0.876937666828726 0.246124666342549 0.123062333171274 48 0.902579606208027 0.194840787583946 0.0974203937919728 49 0.920598394943223 0.158803210113554 0.0794016050567772 50 0.941073275151177 0.117853449697645 0.0589267248488226 51 0.956773517281359 0.0864529654372827 0.0432264827186413 52 0.976205713191718 0.0475885736165645 0.0237942868082822 53 0.997179164170433 0.00564167165913348 0.00282083582956674 54 0.998777918733583 0.00244416253283419 0.00122208126641709 55 0.99925270988889 0.00149458022221909 0.000747290111109546 56 0.999689526855357 0.000620946289285997 0.000310473144642999 57 0.999770246054671 0.000459507890657765 0.000229753945328882 58 0.999811030073594 0.000377939852812303 0.000188969926406152 59 0.999719085654009 0.000561828691982815 0.000280914345991407 60 0.999595522828016 0.000808954343968739 0.000404477171984369 61 0.999405123266323 0.00118975346735435 0.000594876733677175 62 0.999038353528526 0.00192329294294896 0.000961646471474478 63 0.998269932842962 0.00346013431407621 0.00173006715703811 64 0.997474470169832 0.00505105966033597 0.00252552983016798 65 0.997762027899979 0.00447594420004135 0.00223797210002067 66 0.995645786363849 0.00870842727230284 0.00435421363615142 67 0.99439656473893 0.0112068705221422 0.00560343526107112 68 0.996971527478003 0.00605694504399485 0.00302847252199742 69 0.998482019749928 0.00303596050014416 0.00151798025007208 70 0.997699081318616 0.00460183736276714 0.00230091868138357 71 0.996028473599415 0.0079430528011701 0.00397152640058505 72 0.994709618700662 0.0105807625986754 0.00529038129933772 73 0.987883901717523 0.0242321965649548 0.0121160982824774 74 0.973693644595752 0.0526127108084951 0.0263063554042475 75 0.965499795398289 0.0690004092034226 0.0345002046017113 76 0.945757147545198 0.108485704909605 0.0542428524548023 77 0.889082159317 0.221835681365999 0.110917840683000 78 0.789975373612985 0.42004925277403 0.210024626387015 79 0.650099606455296 0.699800787089409 0.349900393544705 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 39 0.52 NOK 5% type I error level 47 0.626666666666667 NOK 10% type I error level 56 0.746666666666667 NOK

Charts produced by software:

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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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