werkloosheid - Afrika

*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 16:36:52 -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/t122972995934fg0z5og2ywk0q.htm/, Retrieved Sat, 20 Dec 2008 00:39:29 +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/t122972995934fg0z5og2ywk0q.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 356,4 173666 394,3 165688 410,9 161570 385,9 156145 523,7 153730 439,1 182698 399,3 200765 372,9 176512 483,2 166618 468,7 158644 498,3 159585 434,4 163095 371,6 159044 408,7 155511 444,5 153745 383 150569 388,9 150605 385,1 179612 347,2 194690 315,6 189917 300,9 184128 371,2 175335 340,3 179566 301,9 181140 327,4 177876 398,6 175041 379,9 169292 379,7 166070 418,4 166972 367,9 206348 362,5 215706 296,7 202108 343 195411 488,3 193111 402,5 195198 500,7 198770 412,8 194163 385,9 190420 461,9 189733 357,4 186029 316,9 191531 339,2 232571 372,3 243477 264,8 227247 325,9 217859 324,1 208679 324,3 213188 318,2 216234 323,4 213586 295,9 209465 425 204045 337,8 200237 322,7 203666 430,2 241476 403,8 260307 333,7 243324 358,1 244460 426,7 233575 376 237217 312 235243 349,3 230354 340,3 227184 455,7 221678 352,3 217142 481,4 219452 731,9 256446 382,2 265845 392,8 248624 351,6 241114 276,5 229245 371,3 231805 439 219277 394,4 219313 445,5 212610 560 214771 331,8 211142 404,2 211457 489,8 240048 323,9 240636 269,4 230580 319,2 208795 337,6 197922 399,5 194596 316,7

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 154551.076702475 + 6.65258468743316Afrika[t] + 7828.00349363026M1[t] + 3072.48596086521M2[t] -2884.3580509503M3[t] -6244.89590528947M4[t] -11413.5937899456M5[t] -11212.6276231597M6[t] + 22962.727876162M7[t] + 34109.4844265709M8[t] + 18226.5056228648M9[t] + 8536.66740490845M10[t] -1248.74773898274M11[t] + 928.120234244064t + 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) 154551.076702475 11196.033243 13.8041 0 0 Afrika 6.65258468743316 24.485203 0.2717 0.786654 0.393327 M1 7828.00349363026 7411.556117 1.0562 0.294514 0.147257 M2 3072.48596086521 7397.627529 0.4153 0.679168 0.339584 M3 -2884.3580509503 7596.470358 -0.3797 0.70532 0.35266 M4 -6244.89590528947 7396.894397 -0.8443 0.401401 0.2007 M5 -11413.5937899456 7424.093429 -1.5374 0.12871 0.064355 M6 -11212.6276231597 7627.163287 -1.4701 0.146018 0.073009 M7 22962.727876162 7378.217301 3.1122 0.002688 0.001344 M8 34109.4844265709 7495.437091 4.5507 2.2e-05 1.1e-05 M9 18226.5056228648 7390.261041 2.4663 0.016104 0.008052 M10 8536.66740490845 7375.046326 1.1575 0.251 0.1255 M11 -1248.74773898274 7376.902282 -0.1693 0.866066 0.433033 t 928.120234244064 62.930956 14.7482 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.90285260272637 R-squared 0.81514282224978 Adjusted R-squared 0.780812203524738 F-TEST (value) 23.7439012905179 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13788.4806232388 Sum Squared Residuals 13308553852.8202 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 180144 165678.181612951 14465.8183870491 2 173666 162102.917274083 11563.0827259167 3 165688 157184.626402323 8503.37359767666 4 161570 154585.894165042 6984.10583495759 5 156145 151262.042684559 4882.95731544138 6 153730 151828.320421032 1901.67957896827 7 182698 186667.023284038 -3969.02328403767 8 200765 198566.271832942 2198.72816705766 9 176512 184345.193354504 -7833.19335450421 10 166618 175487.012892824 -8869.01289282416 11 158644 166826.634489925 -8182.63448992503 12 159585 168578.402301625 -8993.40230162488 13 163095 176916.743711128 -13821.7437111284 14 159044 173336.157304511 -14292.1573045111 15 155511 168545.59605875 -13034.5960587499 16 153745 165704.044480378 -11959.0444803776 17 150569 161502.717079621 -10933.7170796214 18 150605 162606.523658839 -12001.5236588391 19 179612 197457.866432751 -17845.8664327512 20 194690 209322.521541281 -14632.5215412812 21 189917 194269.869976914 -4352.86997691393 22 184128 185975.828696728 -1847.82869672820 23 175335 176912.968920239 -1577.96892023938 24 179566 178834.377641469 731.622358531221 25 181140 187760.142278873 -6620.14227887264 26 177876 184406.409010097 -6530.40901009688 27 175041 179253.281898870 -4212.28189887045 28 169292 176819.533761838 -7527.53376183785 29 166070 172836.411138829 -6766.41113882948 30 166972 173629.542013144 -6657.54201314401 31 206348 208697.093789398 -2349.09378939764 32 215706 220334.230501618 -4628.2305016175 33 202108 205687.386603184 -3579.38660318362 34 195411 197892.289174555 -2481.28917455537 35 193111 188464.202498726 4646.79750127352 36 195198 191294.354288259 3903.64571174077 37 198770 199465.715822108 -695.71582210819 38 194163 195459.363995495 -1296.36399549524 39 190420 190936.236654169 -516.236654168734 40 189733 187808.623934237 1924.37606576314 41 186029 183298.616603984 2730.38339601621 42 191531 184576.055643543 6954.94435645655 43 232571 219899.731930263 12671.2680697368 44 243477 231259.455861017 12217.5441389829 45 227247 216711.070215957 10535.9297840427 46 217859 207937.377579808 9921.62242019237 47 208679 199081.413187098 9597.58681290202 48 213188 201217.700393731 11970.2996062685 49 216234 210008.417561980 6225.58243801956 50 213586 205998.074184555 7587.92581544497 51 209465 201828.199090131 7636.80090986879 52 204045 198815.676085292 5229.32391470807 53 200237 194474.644406100 5762.35559390034 54 203666 196318.883661029 7347.11633897138 55 241476 231246.731158846 10229.2688411538 56 260307 242855.26175691 17451.7382430900 57 243324 228062.726253821 15261.2737461786 58 244460 219757.375579667 24702.6244203330 59 233575 210562.794626367 23012.2053736330 60 237217 212313.897179598 24903.1028204018 61 235243 221318.162316314 13924.8376836863 62 230354 217430.891755606 12923.1082443942 63 227184 213169.876250964 14014.1237490358 64 221678 210049.581374188 11628.4186258115 65 217142 206667.852406924 10474.1475930760 66 219452 209463.411272156 9988.58872784406 67 256446 242240.478140526 14205.5218594736 68 265845 254385.872322866 11459.1276771339 69 248624 239156.927264282 9467.0727357182 70 241114 229895.600170543 11218.3998294567 71 229245 221668.970289265 7576.02971073515 72 231805 224296.218245831 7508.7817541691 73 219277 232755.636696646 -13478.6366966457 74 219313 229268.186475653 -9955.18647565255 75 212610 225001.183644792 -12391.1836447922 76 214771 221050.646199025 -6279.64619902485 77 211142 217291.715679983 -6149.71567998298 78 211457 218990.263330257 -7533.26333025715 79 240048 252990.075264178 -12942.0752641778 80 240636 264702.386183366 -24066.3861833656 81 230580 250078.826331338 -19498.8263313378 82 208795 241439.515905874 -32644.5159058743 83 197922 232994.015988379 -35072.0159883792 84 194596 234620.049949487 -40024.0499494866 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0103127041680709 0.0206254083361419 0.98968729583193 18 0.00410413581241847 0.00820827162483693 0.995895864187581 19 0.00142525501116088 0.00285051002232176 0.99857474498884 20 0.000302382265132678 0.000604764530265356 0.999697617734867 21 0.000313984576618663 0.000627969153237326 0.999686015423381 22 0.00275379096311824 0.00550758192623648 0.997246209036882 23 0.00131901078163512 0.00263802156327023 0.998680989218365 24 0.00118040471675036 0.00236080943350073 0.99881959528325 25 0.00358379437136542 0.00716758874273084 0.996416205628635 26 0.00842055470738744 0.0168411094147749 0.991579445292613 27 0.00819714670422539 0.0163942934084508 0.991802853295775 28 0.00795181952219257 0.0159036390443851 0.992048180477807 29 0.00621862301398852 0.0124372460279770 0.993781376986012 30 0.0050807982940351 0.0101615965880702 0.994919201705965 31 0.0120445181184406 0.0240890362368812 0.98795548188156 32 0.0107952566627065 0.0215905133254130 0.989204743337293 33 0.0100520184237925 0.020104036847585 0.989947981576208 34 0.0148615388555599 0.0297230777111199 0.98513846114444 35 0.0178001178268254 0.0356002356536509 0.982199882173175 36 0.0254050553803895 0.0508101107607789 0.97459494461961 37 0.0247822338884495 0.0495644677768991 0.97521776611155 38 0.0267270725573906 0.0534541451147812 0.97327292744261 39 0.0274710106152768 0.0549420212305536 0.972528989384723 40 0.0354841240610045 0.070968248122009 0.964515875938995 41 0.0362506022209216 0.0725012044418433 0.963749397779078 42 0.0349055638850357 0.0698111277700715 0.965094436114964 43 0.0627244465700045 0.125448893140009 0.937275553429995 44 0.0708274955405098 0.141654991081020 0.92917250445949 45 0.0817969383495532 0.163593876699106 0.918203061650447 46 0.0903134731058692 0.180626946211738 0.90968652689413 47 0.0853825969031017 0.170765193806203 0.914617403096898 48 0.082717199932402 0.165434399864804 0.917282800067598 49 0.0755927127831872 0.151185425566374 0.924407287216813 50 0.0664156987162004 0.132831397432401 0.9335843012838 51 0.063610364164897 0.127220728329794 0.936389635835103 52 0.102307507811606 0.204615015623212 0.897692492188394 53 0.144213873803082 0.288427747606164 0.855786126196918 54 0.261578484176966 0.523156968353933 0.738421515823034 55 0.503347495041684 0.993305009916632 0.496652504958316 56 0.573231759452212 0.853536481095575 0.426768240547788 57 0.760482283386947 0.479035433226106 0.239517716613053 58 0.750646629194667 0.498706741610667 0.249353370805333 59 0.721603472982503 0.556793054034994 0.278396527017497 60 0.668133085373901 0.663733829252198 0.331866914626099 61 0.574010617075255 0.85197876584949 0.425989382924745 62 0.530881485849598 0.938237028300803 0.469118514150401 63 0.488851590975841 0.977703181951682 0.511148409024159 64 0.566358921646307 0.867282156707386 0.433641078353693 65 0.684474397110313 0.631051205779375 0.315525602889687 66 0.666313910247633 0.667372179504733 0.333686089752367 67 0.674936098122169 0.650127803755662 0.325063901877831 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.156862745098039 NOK 5% type I error level 20 0.392156862745098 NOK 10% type I error level 26 0.509803921568627 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 = 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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