Seatbelt Law part 2

*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, 20 Nov 2009 15:38:53 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge.htm/, Retrieved Fri, 20 Nov 2009 23:39:49 +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/2009/Nov/20/t1258756776jdhh1urot8f9jge.htm/}, year = {2009}, } @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 = {2009}, 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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8.9 1,6 8.8 1,3 8.3 1,1 7.5 1,6 7.2 1,9 7.4 1,6 8.8 1,7 9.3 1,6 9.3 1,4 8.7 2,1 8.2 1,9 8.3 1,7 8.5 1,8 8.6 2 8.5 2,5 8.2 2,1 8.1 2,1 7.9 2,3 8.6 2,4 8.7 2,4 8.7 2,3 8.5 1,7 8.4 2 8.5 2,3 8.7 2 8.7 2 8.6 1,3 8.5 1,7 8.3 1,9 8 1,7 8.2 1,6 8.1 1,7 8.1 1,8 8 1,9 7.9 1,9 7.9 1,9 8 2 8 2,1 7.9 1,9 8 1,9 7.7 1,3 7.2 1,3 7.5 1,4 7.3 1,2 7 1,3 7 1,8 7 2,2 7.2 2,6 7.3 2,8 7.1 3,1 6.8 3,9 6.4 3,7 6.1 4,6 6.5 5,1 7.7 5,2 7.9 4,9 7.5 5,1 6.9 4,8 6.6 3,9 6.9 3,5

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 TWIB[t] = + 8.60763772508468 -0.353182385451952GI[t] + 0.392854341237291M1[t] + 0.374045284364414M2[t] + 0.168172579782492M3[t] -0.110636477090391M4[t] -0.294127295418079M5[t] -0.360000000000001M6[t] + 0.421190943127117M7[t] + 0.485872704581921M8[t] + 0.35293635229096M9[t] + 0.0811909431271165M10[t] -0.147063647709040M11[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) 8.60763772508468 0.337874 25.4759 0 0 GI -0.353182385451952 0.077065 -4.5829 3.4e-05 1.7e-05 M1 0.392854341237291 0.400835 0.9801 0.33206 0.16603 M2 0.374045284364414 0.400542 0.9338 0.355155 0.177578 M3 0.168172579782492 0.400376 0.42 0.676372 0.338186 M4 -0.110636477090391 0.400171 -0.2765 0.783397 0.391698 M5 -0.294127295418079 0.399886 -0.7355 0.465673 0.232836 M6 -0.360000000000001 0.399874 -0.9003 0.372559 0.18628 M7 0.421190943127117 0.399901 1.0532 0.297616 0.148808 M8 0.485872704581921 0.399886 1.215 0.230424 0.115212 M9 0.35293635229096 0.399877 0.8826 0.381938 0.190969 M10 0.0811909431271165 0.399901 0.203 0.83999 0.419995 M11 -0.147063647709040 0.399877 -0.3678 0.714694 0.357347 Multiple Linear Regression - Regression Statistics Multiple R 0.65086810078016 R-squared 0.423629284613173 Adjusted R-squared 0.276470804088877 F-TEST (value) 2.87872831456174 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.00472867820596834 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.632256148149712 Sum Squared Residuals 18.7881483330362 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 8.43540024959888 0.464599750401121 2 8.8 8.52254590836156 0.277454091638439 3 8.3 8.38730968087003 -0.0873096808700303 4 7.5 7.93190943127117 -0.431909431271172 5 7.2 7.6424638973079 -0.442463897307898 6 7.4 7.68254590836156 -0.282545908361562 7 8.8 8.42841861294348 0.371581387056518 8 9.3 8.52841861294348 0.771581387056518 9 9.3 8.46611873774291 0.833881262257088 10 8.7 7.9471456587627 0.752854341237297 11 8.2 7.78952754501694 0.410472454983062 12 8.3 8.00722766981637 0.292772330183634 13 8.5 8.36476377250846 0.135236227491537 14 8.6 8.2753182385452 0.324681761454805 15 8.5 7.8928543412373 0.607145658762702 16 8.2 7.7553182385452 0.444681761454804 17 8.1 7.57182742021751 0.528172579782492 18 7.9 7.4353182385452 0.464681761454805 19 8.6 8.18119094312712 0.418809056872882 20 8.7 8.24587270458192 0.454127295418077 21 8.7 8.14825459083616 0.551745409163843 22 8.5 8.08841861294348 0.411581387056516 23 8.4 7.75420930647174 0.645790693528259 24 8.5 7.7953182385452 0.704681761454804 25 8.7 8.29412729541807 0.405872704581927 26 8.7 8.2753182385452 0.424681761454804 27 8.6 8.31667320377964 0.28332679622036 28 8.5 7.89659119272598 0.603408807274024 29 8.3 7.6424638973079 0.657536102692103 30 8 7.64722766981637 0.352772330183633 31 8.2 8.46373685148868 -0.263736851488680 32 8.1 8.49310037439829 -0.393100374398289 33 8.1 8.32484578356213 -0.224845783562133 34 8 8.0177821358531 -0.0177821358530932 35 7.9 7.78952754501694 0.110472454983063 36 7.9 7.93659119272598 -0.0365911927259764 37 8 8.29412729541807 -0.294127295418073 38 8 8.24 -0.24 39 7.9 8.10476377250847 -0.204763772508468 40 8 7.82595471563559 0.174045284364415 41 7.7 7.85437332857907 -0.154373328579069 42 7.2 7.78850062399715 -0.588500623997148 43 7.5 8.53437332857907 -1.03437332857907 44 7.3 8.66969156712426 -1.36969156712426 45 7 8.50143697628811 -1.50143697628811 46 7 8.05310037439829 -1.05310037439829 47 7 7.68357282938135 -0.683572829381351 48 7.2 7.68936352290961 -0.48936352290961 49 7.3 8.01158138705651 -0.711581387056511 50 7.1 7.88681761454805 -0.786817614548048 51 6.8 7.39839900160456 -0.598399001604565 52 6.4 7.19022642182207 -0.790226421822071 53 6.1 6.68887145658763 -0.588871456587628 54 6.5 6.44640755927973 0.0535924407202706 55 7.7 7.19228026386165 0.507719736138349 56 7.9 7.36291674095204 0.537083259047959 57 7.5 7.15934391157069 0.34065608842931 58 6.9 6.99355321804243 -0.0935532180424317 59 6.6 7.08316277411303 -0.483162774113033 60 6.9 7.37149937600285 -0.471499376002853 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.137131864230522 0.274263728461043 0.862868135769478 17 0.183265914157754 0.366531828315508 0.816734085842246 18 0.106944309658697 0.213888619317394 0.893055690341303 19 0.0656363183912584 0.131272636782517 0.934363681608742 20 0.0678727928025737 0.135745585605147 0.932127207197426 21 0.0633767214806115 0.126753442961223 0.936623278519388 22 0.039984051266795 0.07996810253359 0.960015948733205 23 0.0272920432912573 0.0545840865825146 0.972707956708743 24 0.0195943489384082 0.0391886978768163 0.980405651061592 25 0.0122971280938679 0.0245942561877358 0.987702871906132 26 0.00814074004471278 0.0162814800894256 0.991859259955287 27 0.00551291287994843 0.0110258257598969 0.994487087120052 28 0.0103628550259667 0.0207257100519333 0.989637144974033 29 0.0213198915970755 0.0426397831941511 0.978680108402924 30 0.0201054472101092 0.0402108944202184 0.97989455278989 31 0.0171065499792461 0.0342130999584922 0.982893450020754 32 0.0275980588693543 0.0551961177387085 0.972401941130646 33 0.0433881974510744 0.0867763949021488 0.956611802548926 34 0.051120578365403 0.102241156730806 0.948879421634597 35 0.0592118104646421 0.118423620929284 0.940788189535358 36 0.0591438199298446 0.118287639859689 0.940856180070155 37 0.0624043755829381 0.124808751165876 0.937595624417062 38 0.0721441672557746 0.144288334511549 0.927855832744225 39 0.0827058208301824 0.165411641660365 0.917294179169818 40 0.20276950789919 0.40553901579838 0.79723049210081 41 0.667402999938511 0.665194000122977 0.332597000061489 42 0.852162565608627 0.295674868782747 0.147837434391373 43 0.781878129806175 0.436243740387649 0.218121870193825 44 0.777428403724546 0.445143192550907 0.222571596275454 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 8 0.275862068965517 NOK 10% type I error level 12 0.413793103448276 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/10u2fa1258756729.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/10u2fa1258756729.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/11nxh1258756729.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/11nxh1258756729.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/2ka9w1258756729.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/2ka9w1258756729.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/307gq1258756729.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/307gq1258756729.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/4wuql1258756729.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/4wuql1258756729.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/5u2je1258756729.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/5u2je1258756729.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/6upn21258756729.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/6upn21258756729.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/7ez4z1258756729.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/7ez4z1258756729.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/8dnei1258756729.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/8dnei1258756729.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/9xs4c1258756729.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756776jdhh1urot8f9jge/9xs4c1258756729.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal 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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