The Seatbelt Law - Q3

*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: Sun, 23 Nov 2008 13:54:10 -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/Nov/23/t1227473778eeeojmcib3wq9uq.htm/, Retrieved Sun, 23 Nov 2008 20:56:28 +0000

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/Nov/23/t1227473778eeeojmcib3wq9uq.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}, }

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Original text written by user:

IsPrivate?

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User-defined keywords:

Dataseries X:

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2173 2363 2126 1905 2121 1983 1734 2074 2049 2406 2558 2251 2059 2397 1747 1707 2319 1631 1627 1791 2034 1997 2169 2028 2253 2218 1855 2187 1852 1570 1851 1954 1828 2251 2277 2085 2282 2266 1878 2267 2069 1746 2299 2360 2214 2825 2355 2333 3016 2155 2172 2150 2533 2058 2160 2259 2498 2695 2799 2945 2930 2318 2540 2570 2669 2450 2842 3439 2677 2979 2257 2842 2546 2455 2293 2379 2478 2054 2272 2351 2271 2542 2304 2194 2722 2395 2146 1894 2548 2087 2063 2481 2476 2212 2834 2148 2598

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Bouwvergunningen[t] = + 2353.25 + 155.527777777778M1[t] -32.375M2[t] -258.625000000001M3[t] -220.875M4[t] -29.6250000000004M5[t] -405.874999999999M6[t] -247.25M7[t] -14.6250000000002M8[t] -97.3750000000001M9[t] + 135.125M10[t] + 90.8749999999999M11[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) 2353.25 112.585764 20.9018 0 0 M1 155.527777777778 154.734333 1.0051 0.317688 0.158844 M2 -32.375 159.220315 -0.2033 0.839359 0.41968 M3 -258.625000000001 159.220315 -1.6243 0.10801 0.054005 M4 -220.875 159.220315 -1.3872 0.168999 0.084499 M5 -29.6250000000004 159.220315 -0.1861 0.852839 0.426419 M6 -405.874999999999 159.220315 -2.5491 0.012594 0.006297 M7 -247.25 159.220315 -1.5529 0.124167 0.062084 M8 -14.6250000000002 159.220315 -0.0919 0.92703 0.463515 M9 -97.3750000000001 159.220315 -0.6116 0.542452 0.271226 M10 135.125 159.220315 0.8487 0.39845 0.199225 M11 90.8749999999999 159.220315 0.5708 0.569675 0.284837 Multiple Linear Regression - Regression Statistics Multiple R 0.48935329547902 R-squared 0.239466647796177 Adjusted R-squared 0.1410446845698 F-TEST (value) 2.43306107647322 F-TEST (DF numerator) 11 F-TEST (DF denominator) 85 p-value 0.0109407357077824 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 318.440629438400 Sum Squared Residuals 8619376.93055555 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2173 2508.77777777778 -335.777777777776 2 2363 2320.875 42.1250000000004 3 2126 2094.625 31.3749999999995 4 1905 2132.375 -227.374999999999 5 2121 2323.625 -202.625 6 1983 1947.375 35.6250000000005 7 1734 2106 -372.000000000001 8 2074 2338.625 -264.625 9 2049 2255.875 -206.875000000000 10 2406 2488.375 -82.375 11 2558 2444.125 113.875000000000 12 2251 2353.25 -102.250000000000 13 2059 2508.77777777778 -449.777777777778 14 2397 2320.875 76.125 15 1747 2094.625 -347.625 16 1707 2132.375 -425.375 17 2319 2323.625 -4.62499999999991 18 1631 1947.375 -316.375000000000 19 1627 2106 -479 20 1791 2338.625 -547.625 21 2034 2255.875 -221.875 22 1997 2488.375 -491.375 23 2169 2444.125 -275.125 24 2028 2353.25 -325.25 25 2253 2508.77777777778 -255.777777777778 26 2218 2320.875 -102.875 27 1855 2094.625 -239.625 28 2187 2132.375 54.6249999999998 29 1852 2323.625 -471.625 30 1570 1947.375 -377.375 31 1851 2106 -255 32 1954 2338.625 -384.625 33 1828 2255.875 -427.875 34 2251 2488.375 -237.375 35 2277 2444.125 -167.125 36 2085 2353.25 -268.25 37 2282 2508.77777777778 -226.777777777778 38 2266 2320.875 -54.875 39 1878 2094.625 -216.625 40 2267 2132.375 134.625000000000 41 2069 2323.625 -254.625 42 1746 1947.375 -201.375000000000 43 2299 2106 193 44 2360 2338.625 21.3750000000000 45 2214 2255.875 -41.875 46 2825 2488.375 336.625 47 2355 2444.125 -89.125 48 2333 2353.25 -20.2500000000002 49 3016 2508.77777777778 507.222222222222 50 2155 2320.875 -165.875 51 2172 2094.625 77.375 52 2150 2132.375 17.6249999999998 53 2533 2323.625 209.375 54 2058 1947.375 110.625000000000 55 2160 2106 54.0000000000002 56 2259 2338.625 -79.625 57 2498 2255.875 242.125 58 2695 2488.375 206.625 59 2799 2444.125 354.875 60 2945 2353.25 591.75 61 2930 2508.77777777778 421.222222222222 62 2318 2320.875 -2.87499999999999 63 2540 2094.625 445.375 64 2570 2132.375 437.625 65 2669 2323.625 345.375 66 2450 1947.375 502.625 67 2842 2106 736 68 3439 2338.625 1100.375 69 2677 2255.875 421.125 70 2979 2488.375 490.625 71 2257 2444.125 -187.125 72 2842 2353.25 488.75 73 2546 2508.77777777778 37.2222222222221 74 2455 2320.875 134.125 75 2293 2094.625 198.375 76 2379 2132.375 246.625 77 2478 2323.625 154.375 78 2054 1947.375 106.625000000000 79 2272 2106 166.000000000000 80 2351 2338.625 12.3750000000000 81 2271 2255.875 15.125 82 2542 2488.375 53.625 83 2304 2444.125 -140.125 84 2194 2353.25 -159.250000000000 85 2722 2508.77777777778 213.222222222222 86 2395 2320.875 74.125 87 2146 2094.625 51.375 88 1894 2132.375 -238.375 89 2548 2323.625 224.375 90 2087 1947.375 139.625000000000 91 2063 2106 -42.9999999999998 92 2481 2338.625 142.375 93 2476 2255.875 220.125 94 2212 2488.375 -276.375 95 2834 2444.125 389.875 96 2148 2353.25 -205.25 97 2598 2508.77777777778 89.222222222222 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.13254760087232 0.26509520174464 0.86745239912768 16 0.0792301969863026 0.158460393972605 0.920769803013697 17 0.0460078574799325 0.092015714959865 0.953992142520067 18 0.0537465802442903 0.107493160488581 0.94625341975571 19 0.0302197005336868 0.0604394010673737 0.969780299466313 20 0.029735567466791 0.059471134933582 0.97026443253321 21 0.0145002509855862 0.0290005019711724 0.985499749014414 22 0.0256895917365725 0.0513791834731451 0.974310408263428 23 0.0320243947312974 0.0640487894625947 0.967975605268703 24 0.0244102433571635 0.048820486714327 0.975589756642836 25 0.0168504435659259 0.0337008871318518 0.983149556434074 26 0.0109040745998459 0.0218081491996917 0.989095925400154 27 0.00653714207802479 0.0130742841560496 0.993462857921975 28 0.00980285201663146 0.0196057040332629 0.990197147983369 29 0.0165881555936744 0.0331763111873488 0.983411844406326 30 0.0163302468177757 0.0326604936355513 0.983669753182224 31 0.0137916429746112 0.0275832859492225 0.986208357025389 32 0.0119731323448385 0.0239462646896769 0.988026867655162 33 0.0135975646733782 0.0271951293467564 0.986402435326622 34 0.00993315409712729 0.0198663081942546 0.990066845902873 35 0.00652404567750848 0.0130480913550170 0.993475954322492 36 0.00482466589793478 0.00964933179586957 0.995175334102065 37 0.00435032374594041 0.00870064749188082 0.99564967625406 38 0.00255122040997409 0.00510244081994818 0.997448779590026 39 0.00189121508986976 0.00378243017973951 0.99810878491013 40 0.00248661612878332 0.00497323225756665 0.997513383871217 41 0.00216890555917873 0.00433781111835747 0.997831094440821 42 0.00171306010424784 0.00342612020849568 0.998286939895752 43 0.00757846166422607 0.0151569233284521 0.992421538335774 44 0.0128829891120284 0.0257659782240568 0.987117010887972 45 0.0121687622365274 0.0243375244730548 0.987831237763473 46 0.0346833931385202 0.0693667862770403 0.96531660686148 47 0.0250826331815657 0.0501652663631314 0.974917366818434 48 0.0208950618317090 0.0417901236634179 0.97910493816829 49 0.106786424245808 0.213572848491617 0.893213575754191 50 0.0894632521177868 0.178926504235574 0.910536747882213 51 0.078862078029154 0.157724156058308 0.921137921970846 52 0.0607840513972631 0.121568102794526 0.939215948602737 53 0.0657578866631249 0.131515773326250 0.934242113336875 54 0.0599314909173532 0.119862981834706 0.940068509082647 55 0.0542052289713013 0.108410457942603 0.945794771028699 56 0.0622736226558237 0.124547245311647 0.937726377344176 57 0.0660800595198729 0.132160119039746 0.933919940480127 58 0.0578990450107015 0.115798090021403 0.942100954989298 59 0.0697923088131521 0.139584617626304 0.930207691186848 60 0.171306199309422 0.342612398618844 0.828693800690578 61 0.207621993631726 0.415243987263453 0.792378006368274 62 0.163264248861832 0.326528497723664 0.836735751138168 63 0.193915024737183 0.387830049474366 0.806084975262817 64 0.233454255330385 0.46690851066077 0.766545744669615 65 0.223247581128234 0.446495162256467 0.776752418871766 66 0.270330799187453 0.540661598374906 0.729669200812547 67 0.48883790599459 0.97767581198918 0.51116209400541 68 0.941195124918855 0.117609750162291 0.0588048750811454 69 0.940219840537974 0.119560318924052 0.0597801594620261 70 0.975585301995515 0.0488293960089691 0.0244146980044845 71 0.972355504141403 0.0552889917171947 0.0276444958585973 72 0.996763125028116 0.00647374994376788 0.00323687497188394 73 0.99374171703692 0.0125165659261581 0.00625828296307905 74 0.98740455289317 0.0251908942136581 0.0125954471068291 75 0.977886072869449 0.0442278542611022 0.0221139271305511 76 0.98836719347888 0.0232656130422394 0.0116328065211197 77 0.975668783943935 0.0486624321121308 0.0243312160560654 78 0.949973524674712 0.100052950650576 0.0500264753252879 79 0.920421148793778 0.159157702412444 0.0795788512062218 80 0.858096481204713 0.283807037590575 0.141903518795287 81 0.778272766764211 0.443454466471577 0.221727233235789 82 0.726943867399876 0.546112265200248 0.273056132600124 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.117647058823529 NOK 5% type I error level 31 0.455882352941176 NOK 10% type I error level 39 0.573529411764706 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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