Werkloosheid- Azië

*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: Wed, 17 Dec 2008 07:33:28 -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/17/t1229524771jgdv65gkxm63mp5.htm/, Retrieved Wed, 17 Dec 2008 15:39:37 +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/17/t1229524771jgdv65gkxm63mp5.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 1235.8 173666 1147.1 165688 1376.9 161570 1157.7 156145 1506 153730 1271.3 182698 1240.2 200765 1408.3 176512 1334.6 166618 1601.2 158644 1566.4 159585 1297.5 163095 1487.6 159044 1320.9 155511 1514 153745 1290.9 150569 1392.5 150605 1288.2 179612 1304.4 194690 1297.8 189917 1211 184128 1454 175335 1405.7 179566 1160.8 181140 1492.1 177876 1263 175041 1376.3 169292 1368.6 166070 1427.6 166972 1339.8 206348 1248.3 215706 1309.8 202108 1424 195411 1590.5 193111 1423.1 195198 1355.3 198770 1515 194163 1385.6 190420 1430 189733 1494.2 186029 1580.9 191531 1369.8 232571 1407.5 243477 1388.3 227247 1478.5 217859 1630.4 208679 1413.5 213188 1493.8 216234 1641.3 213586 1465 209465 1725.1 204045 1628.4 200237 1679.8 203666 1876 241476 1669.4 260307 1712.4 243324 1768.8 244460 1820.5 233575 1776.2 237217 1693.7 235243 1799.1 230354 1917.5 227184 1887.2 221678 1787.8 217142 1803.8 219452 2196.4 256446 1759.5 265845 2002.6 248624 2056.8 241114 1851.1 229245 1984.3 231805 1725.3 219277 2096.6 219313 1792.2 212610 2029.9 214771 1785.3 211142 2026.5 211457 1930.8 240048 1845.5 240636 1943.1 230580 2066.8 208795 2354.4 197922 2190.7 194596 1929.6

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 9 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 176169.70192712 -18.3539480086108`Azië`[t] + 11367.9863194578M1[t] + 3996.50801380844M2[t] + 1049.58641584915M3[t] -5241.37960167324M4[t] -7910.89164512382M5[t] -7962.44532586479M6[t] + 23375.6264412199M7[t] + 35551.0150642940M8[t] + 20438.1356703591M9[t] + 13286.6974291506M10[t] + 1914.16390003506M11[t] + 1111.74629907713t + 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) 176169.70192712 14280.03113 12.3368 0 0 `Azië` -18.3539480086108 12.513405 -1.4667 0.146924 0.073462 M1 11367.9863194578 7705.617025 1.4753 0.14462 0.07231 M2 3996.50801380844 7315.093366 0.5463 0.586571 0.293286 M3 1049.58641584915 7656.646322 0.1371 0.89136 0.44568 M4 -5241.37960167324 7319.100889 -0.7161 0.476297 0.238149 M5 -7910.89164512382 7614.433623 -1.0389 0.302411 0.151205 M6 -7962.44532586479 7506.662169 -1.0607 0.292465 0.146232 M7 23375.6264412199 7276.420988 3.2125 0.00199 0.000995 M8 35551.0150642940 7371.12256 4.823 8e-06 4e-06 M9 20438.1356703591 7440.311517 2.7469 0.007642 0.003821 M10 13286.6974291506 7936.006965 1.6742 0.098548 0.049274 M11 1914.16390003506 7560.992382 0.2532 0.800884 0.400442 t 1111.74629907713 140.528749 7.9112 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.905795546824204 R-squared 0.820465572646559 Adjusted R-squared 0.787123464709491 F-TEST (value) 24.6074895503057 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13588.5189511088 Sum Squared Residuals 12925349309.9250 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 180144 165967.625596614 14176.3744033862 2 173666 161335.888778405 12330.1112215948 3 165688 155282.976227144 10405.0237728557 4 161570 154126.941912187 7443.05808781351 5 156145 146176.496076414 9968.50392358609 6 153730 151544.360292371 2185.63970762898 7 182698 184564.986141601 -1866.98614160067 8 200765 194766.822403504 5998.17759649554 9 176512 182118.375276881 -5606.3752768813 10 166618 171185.520795654 -4567.52079565424 11 158644 161563.450956316 -2919.45095631549 12 159585 165696.409974873 -6111.40997487304 13 163095 174687.057076971 -11592.0570769711 14 159044 171486.928203434 -12442.9282034342 15 155511 166107.605544089 -10596.6055440893 16 153745 165023.151626365 -11278.1516263652 17 150569 161600.624764317 -11031.6247643168 18 150605 164575.134159951 -13970.1341599511 19 179612 196727.618268373 -17115.6182683735 20 194690 210135.889247382 -15445.8892473816 21 189917 197727.878839671 -7810.8788396712 22 184128 187228.177531447 -3100.17753144737 23 175335 177853.885990225 -2518.88599022488 24 179566 181546.350256576 -1980.35025657575 25 181140 187945.419899858 -6805.41989985793 26 177876 185890.577382058 -8014.57738205842 27 175041 181975.899773801 -6934.89977380067 28 169292 176938.005455022 -7646.00545502171 29 166070 174297.356778140 -8227.35677814023 30 166972 176969.026031632 -9997.02603163243 31 206348 211098.230340582 -4750.23034058216 32 215706 223256.597460204 -7550.59746020386 33 202108 207159.443502763 -5051.44350276272 34 195411 198063.819217198 -2652.81921719761 35 193111 190875.482883801 2235.51711619933 36 195198 191317.462957827 3880.53704217344 37 198770 200866.070079386 -2096.07007938636 38 194163 196981.338945128 -2818.33894512836 39 190420 194331.248354664 -3911.24835466388 40 189733 187973.705174066 1759.29482593419 41 186029 184824.652137346 1204.34786265419 42 191531 189759.363180300 1771.63681970028 43 232571 221517.237406537 11053.7625934631 44 243477 235156.768130454 8320.2318695465 45 227247 219500.108925219 7746.89107478095 46 217859 210672.452280580 7186.54771942035 47 208679 204392.636373609 4286.36362639107 48 213188 202116.396747560 11071.6032524404 49 216234 211888.922034824 4345.07796517556 50 213586 208864.991062170 4721.00893782973 51 209465 202255.953886248 7209.04611375154 52 204045 198851.560940236 5193.43905976414 53 200237 196350.40226822 3886.59773178018 54 203666 193809.550287267 9856.44971273345 55 241476 230051.294012007 11424.7059879926 56 260307 242549.209169788 17757.7908302116 57 243324 227512.913407245 15811.0865927551 58 244460 220524.322353068 23935.6776469316 59 233575 211076.615019811 22498.3849801886 60 237217 211788.398129564 25428.6018704361 61 235243 222333.624627991 12909.3753720087 62 230354 213900.785177200 16453.2148228005 63 227184 212621.734502978 14562.2654970217 64 221678 209266.897216589 12411.1027834111 65 217142 207415.468304078 9726.5316959223 66 219452 201269.900934233 18182.0990657667 67 256446 241738.558885357 14707.4411146429 68 265845 250563.849046615 15281.1509533849 69 248624 235567.931969691 13056.0680303094 70 241114 233303.647132930 7810.35286706953 71 229245 220598.114028145 8646.88597185487 72 231805 224549.368961417 7255.6310385826 73 219277 230214.280684355 -10937.2806843552 74 219313 229541.490451604 -10228.4904516040 75 212610 223343.581711075 -10733.5817110751 76 214771 222653.737675536 -7882.73767553605 77 211142 216668.999671486 -5526.99967148569 78 211457 219485.665114246 -8028.6651142459 79 240048 253501.074945542 -13453.0749455422 80 240636 264996.864542053 -24360.8645420531 81 230580 248725.34807853 -18145.3480785302 82 208795 237407.060689122 -28612.0606891223 83 197922 230150.814748093 -32228.8147480935 84 194596 234140.612972184 -39544.6129721838 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00373821258652392 0.00747642517304784 0.996261787413476 18 0.0013027696243515 0.002605539248703 0.998697230375649 19 0.000577083868205368 0.00115416773641074 0.999422916131795 20 0.000168425569111556 0.000336851138223112 0.999831574430888 21 0.00139348643984568 0.00278697287969135 0.998606513560154 22 0.00196273896637791 0.00392547793275582 0.998037261033622 23 0.00110269171173634 0.00220538342347268 0.998897308288264 24 0.000942495881329359 0.00188499176265872 0.99905750411867 25 0.00320670347764272 0.00641340695528544 0.996793296522357 26 0.00240606265157853 0.00481212530315707 0.997593937348422 27 0.00124003588706244 0.00248007177412488 0.998759964112938 28 0.00186285493966441 0.00372570987932881 0.998137145060336 29 0.00106873283994147 0.00213746567988294 0.998931267160059 30 0.00101570718511769 0.00203141437023538 0.998984292814882 31 0.00158269854550196 0.00316539709100391 0.998417301454498 32 0.00111173853130238 0.00222347706260476 0.998888261468698 33 0.00228242232966987 0.00456484465933974 0.99771757767033 34 0.00258281673278384 0.00516563346556768 0.997417183267216 35 0.00217621410507282 0.00435242821014564 0.997823785894927 36 0.00415916077407257 0.00831832154814515 0.995840839225927 37 0.00391871325398935 0.0078374265079787 0.99608128674601 38 0.00419187193133991 0.00838374386267982 0.99580812806866 39 0.00344480139695353 0.00688960279390707 0.996555198603046 40 0.00601315784682536 0.0120263156936507 0.993986842153175 41 0.00816905173958506 0.0163381034791701 0.991830948260415 42 0.00936711330442266 0.0187342266088453 0.990632886695577 43 0.0219144195689498 0.0438288391378995 0.97808558043105 44 0.0247620852384952 0.0495241704769903 0.975237914761505 45 0.0291592616430198 0.0583185232860396 0.97084073835698 46 0.0309648294898998 0.0619296589797996 0.9690351705101 47 0.0285134836545912 0.0570269673091825 0.971486516345409 48 0.0308305462059149 0.0616610924118297 0.969169453794085 49 0.0299177393520237 0.0598354787040474 0.970082260647976 50 0.0353197690007818 0.0706395380015635 0.964680230999218 51 0.0422650959863051 0.0845301919726101 0.957734904013695 52 0.065017543383165 0.13003508676633 0.934982456616835 53 0.136536406187257 0.273072812374515 0.863463593812743 54 0.306173152024021 0.612346304048041 0.693826847975979 55 0.542517276811529 0.914965446376943 0.457482723188472 56 0.627195081684889 0.745609836630222 0.372804918315111 57 0.850770727033065 0.298458545933871 0.149229272966935 58 0.861245608505088 0.277508782989823 0.138754391494912 59 0.87494749892246 0.250105002155081 0.125052501077541 60 0.831696683473934 0.336606633052132 0.168303316526066 61 0.79685451401725 0.406290971965500 0.203145485982750 62 0.708716444136658 0.582567111726684 0.291283555863342 63 0.631056119508319 0.737887760983362 0.368943880491681 64 0.606490926604226 0.787018146791549 0.393509073395774 65 0.880520434578664 0.238959130842671 0.119479565421336 66 0.815965472322976 0.368069055354047 0.184034527677024 67 0.816571142011547 0.366857715976907 0.183428857988453 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.450980392156863 NOK 5% type I error level 28 0.549019607843137 NOK 10% type I error level 35 0.686274509803922 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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