werkloosheid - Amerika

*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 16:03:54 -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/20/t12297280688u7qnjeil0nq7vw.htm/, Retrieved Sat, 20 Dec 2008 00:07:58 +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/20/t12297280688u7qnjeil0nq7vw.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:

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180144 966.2 173666 1153.2 165688 1328.3 161570 1144.5 156145 1477.1 153730 1234.9 182698 1119.1 200765 1356.9 176512 1217 166618 1440.5 158644 1556.6 159585 1303.6 163095 1421.5 159044 1172.5 155511 1422.1 153745 1263 150569 1428.1 150605 1347 179612 1224.2 194690 1201.3 189917 997.8 184128 1248.8 175335 1268.6 179566 1016.7 181140 1194.3 177876 1181.8 175041 1150.7 169292 1247.2 166070 1260.6 166972 1249.3 206348 1223.2 215706 1153 202108 1191.5 195411 1303.1 193111 1267.1 195198 1125.2 198770 1322.4 194163 1089.2 190420 1147.3 189733 1196.4 186029 1190.2 191531 1146 232571 1139.8 243477 1045.6 227247 1050.9 217859 1117.3 208679 1120 213188 1052.1 216234 1065.8 213586 1092.5 209465 1422 204045 1367.5 200237 1136.3 203666 1293.7 241476 1154.8 260307 1206.7 243324 1199 244460 1265 233575 1247.1 237217 1116.5 235243 1153.9 230354 1077.4 227184 1132.5 221678 1058.8 217142 1195.1 219452 1263.4 256446 1023.1 265845 1141 248624 1116.3 241114 1135.6 229245 1210.5 231805 1230 219277 1136.5 219313 1068.7 212610 1372.5 214771 1049.9 211142 1302.2 211457 1305.9 240048 1173.5 240636 1277.4 230580 1238.6 208795 1508.6 197922 1423.4 194596 1375.1

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 werkloosheid[t] = + 312055.291460218 -94.0763140234483Amerika[t] -1908.17722848997M1[t] -11324.1281362288M2[t] -585.07019078121M3[t] -12307.3357791522M4[t] -7334.94395376222M5[t] -7902.9441416341M6[t] + 16121.5250407447M7[t] + 32225.3166130878M8[t] + 12511.3884359601M9[t] + 17494.6897606502M10[t] + 9655.4437268423M11[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) 312055.291460218 32113.486742 9.7173 0 0 Amerika -94.0763140234483 26.083423 -3.6067 0.000573 0.000286 M1 -1908.17722848997 13660.961113 -0.1397 0.889307 0.444654 M2 -11324.1281362288 13734.786527 -0.8245 0.412425 0.206213 M3 -585.07019078121 13947.680946 -0.0419 0.966658 0.483329 M4 -12307.3357791522 13666.027616 -0.9006 0.370857 0.185428 M5 -7334.94395376222 13958.466608 -0.5255 0.600884 0.300442 M6 -7902.9441416341 13854.693373 -0.5704 0.570196 0.285098 M7 16121.5250407447 13673.339104 1.179 0.242315 0.121158 M8 32225.3166130878 13673.536628 2.3568 0.021197 0.010599 M9 12511.3884359601 13682.08104 0.9144 0.363583 0.181791 M10 17494.6897606502 13981.328113 1.2513 0.214936 0.107468 M11 9655.4437268423 14043.026523 0.6876 0.493969 0.246985 Multiple Linear Regression - Regression Statistics Multiple R 0.596591358602322 R-squared 0.355921249158965 Adjusted R-squared 0.247062868735128 F-TEST (value) 3.26958060347027 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 0.00086247781716775 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 25555.6884687268 Sum Squared Residuals 46369618130.8536 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 180144 219250.579622273 -39106.5796222727 2 173666 192242.357992149 -18576.3579921486 3 165688 186508.653352090 -20820.6533520904 4 161570 192077.614281229 -30507.6142812293 5 156145 165760.224062420 -9615.22406242038 6 153730 187977.507131028 -34247.5071310276 7 182698 222896.013477322 -40198.0134773218 8 200765 216628.457574889 -15863.4575748889 9 176512 210075.805729642 -33563.8057296415 10 166618 194033.050870091 -27415.0508700910 11 158644 175271.544778161 -16627.5447781608 12 159585 189417.408499251 -29832.4084992509 13 163095 176417.633847396 -13322.6338473963 14 159044 190426.685131496 -31382.6851314961 15 155511 177684.295096691 -22173.2950966910 16 153745 180929.571069451 -27184.5710694507 17 150569 170369.963449569 -19800.9634495693 18 150605 177431.552328999 -26826.5523289991 19 179612 213008.592873457 -33396.5928734573 20 194690 231266.732036937 -36576.7320369375 21 189917 230697.333763581 -40780.3337635814 22 184128 212067.480268386 -27939.480268386 23 175335 202365.523216914 -27030.5232169138 24 179566 216407.902992578 -36841.9029925782 25 181140 197791.772393524 -16651.7723935238 26 177876 189551.775411078 -11675.775411078 27 175041 203216.606722655 -28175.6067226549 28 169292 182415.976831021 -13123.9768310211 29 166070 186127.746048497 -20057.7460484969 30 166972 186622.80820909 -19650.80820909 31 206348 213102.669187481 -6754.6691874808 32 215706 235810.61800427 -20104.61800427 33 202108 212474.751737239 -10366.7517372395 34 195411 206959.136416913 -11548.1364169128 35 193111 202506.637687949 -9395.63768794901 36 195198 206200.622921034 -11002.622921034 37 198770 185740.59656712 13029.4034328799 38 194163 198263.242089649 -4100.24208964932 39 190420 203536.466190335 -13116.4661903346 40 189733 187195.053583412 2537.94641658770 41 186029 192750.718555748 -6721.71855574766 42 191531 196340.891447712 -4809.8914477122 43 232571 220948.633777036 11622.3662229636 44 243477 245914.414130388 -2437.41413038837 45 227247 225701.881488936 1545.11851106374 46 217859 224438.515562469 -6579.51556246947 47 208679 216345.263480798 -7666.26348079824 48 213188 213077.601476148 110.398523851909 49 216234 209880.578745537 6353.42125446311 50 213586 197952.790253372 15633.2097466281 51 209465 177693.702728093 31771.2972719066 52 204045 171098.596254000 32946.4037459997 53 200237 197821.431881612 2415.56811838847 54 203666 182445.819866449 21220.1801335511 55 241476 219537.489066685 21938.5109333153 56 260307 230758.719941211 29548.2800587892 57 243324 211769.179382064 31554.8206179364 58 244460 210543.443981206 33916.5560187938 59 233575 204388.163968418 29186.836031582 60 237217 207019.086853038 30197.913146962 61 235243 201592.455480071 33650.5445199289 62 230354 199373.342595126 30980.657404874 63 227184 204928.795637882 22255.2043621184 64 221678 200139.954393039 21538.0456069612 65 217142 192289.744617033 24852.2553829672 66 219452 185296.332181359 34155.6678186406 67 256446 231927.339623573 24518.6603764272 68 265845 236939.533772551 28905.4662274486 69 248624 219549.290551803 29074.7094481973 70 241114 222716.919015840 18397.0809841596 71 229245 207831.357061676 21413.6429383238 72 231805 196341.425211377 35463.5747886234 73 219277 203229.383344079 16047.6166559209 74 219313 200191.80652713 19121.19347287 75 212610 182350.480272254 30259.5197277460 76 214771 200977.233587847 13793.7664121525 77 211142 182214.171385121 28927.8286148785 78 211457 181298.088835363 30158.9111646372 79 240048 217778.261994446 22269.7380055538 80 240636 224107.524539753 16528.4754602470 81 230580 208043.757346735 22536.2426532650 82 208795 187626.453885094 21168.5461149058 83 197922 187802.509806084 10119.4901939160 84 194596 182690.952046574 11905.0479534257 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0256867514463054 0.0513735028926108 0.974313248553695 17 0.00893143479201208 0.0178628695840242 0.991068565207988 18 0.00227798632225588 0.00455597264451177 0.997722013677744 19 0.000613145632030563 0.00122629126406113 0.99938685436797 20 0.000566691311228524 0.00113338262245705 0.999433308688771 21 0.000229521582565855 0.00045904316513171 0.999770478417434 22 0.000149142825225981 0.000298285650451962 0.999850857174774 23 5.46711031281316e-05 0.000109342206256263 0.999945328896872 24 2.82899794774605e-05 5.65799589549209e-05 0.999971710020523 25 2.58402567137622e-05 5.16805134275245e-05 0.999974159743286 26 3.89092504840786e-05 7.78185009681573e-05 0.999961090749516 27 2.15337974140419e-05 4.30675948280838e-05 0.999978466202586 28 4.57240648714927e-05 9.14481297429854e-05 0.999954275935129 29 2.70664035969183e-05 5.41328071938366e-05 0.999972933596403 30 5.90944756669153e-05 0.000118188951333831 0.999940905524333 31 0.00150045897340005 0.00300091794680009 0.9984995410266 32 0.00232830712064900 0.00465661424129799 0.99767169287935 33 0.00981010870744317 0.0196202174148863 0.990189891292557 34 0.0194682769876510 0.0389365539753021 0.980531723012349 35 0.0308880595152696 0.0617761190305391 0.96911194048473 36 0.0681366173246525 0.136273234649305 0.931863382675348 37 0.164119132628398 0.328238265256796 0.835880867371602 38 0.233637637537222 0.467275275074444 0.766362362462778 39 0.336519378086819 0.673038756173638 0.663480621913181 40 0.476229554569734 0.952459109139467 0.523770445430267 41 0.548504886212253 0.902990227575495 0.451495113787747 42 0.701469784619103 0.597060430761794 0.298530215380897 43 0.835833002941348 0.328333994117304 0.164166997058652 44 0.882771147122981 0.234457705754038 0.117228852877019 45 0.93341752908716 0.133164941825679 0.0665824709128394 46 0.961632056605818 0.0767358867883644 0.0383679433941822 47 0.977499223430554 0.0450015531388911 0.0225007765694455 48 0.991770867339603 0.0164582653207939 0.00822913266039693 49 0.994481743375069 0.0110365132498626 0.00551825662493129 50 0.995477307872511 0.00904538425497725 0.00452269212748863 51 0.998091535052255 0.00381692989548908 0.00190846494774454 52 0.999363329105929 0.00127334178814237 0.000636670894071187 53 0.999899271596312 0.000201456807376842 0.000100728403688421 54 0.999923112116893 0.000153775766214976 7.68878831074881e-05 55 0.999879711900275 0.000240576199450519 0.000120288099725260 56 0.999866849036914 0.000266301926171081 0.000133150963085540 57 0.999840008567896 0.000319982864208806 0.000159991432104403 58 0.99987738727855 0.000245225442900212 0.000122612721450106 59 0.99987165008402 0.000256699831960287 0.000128349915980143 60 0.999745151452032 0.000509697095935783 0.000254848547967892 61 0.99980798359731 0.000384032805378618 0.000192016402689309 62 0.99970231110041 0.000595377799178101 0.000297688899589051 63 0.999547192228591 0.000905615542817225 0.000452807771408613 64 0.998822768603172 0.00235446279365675 0.00117723139682838 65 0.996832763943484 0.00633447211303297 0.00316723605651649 66 0.990559152035078 0.0188816959298429 0.00944084796492144 67 0.970671443213224 0.0586571135735523 0.0293285567867761 68 0.927698880307769 0.144602239384462 0.0723011196922308 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 31 0.584905660377358 NOK 5% type I error level 38 0.716981132075472 NOK 10% type I error level 42 0.79245283018868 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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