seabelt law

*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: Mon, 23 Nov 2009 08:17:01 -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/23/t1258989522vurcchrdhn8cvsf.htm/, Retrieved Mon, 23 Nov 2009 16:18:54 +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/23/t1258989522vurcchrdhn8cvsf.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:

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Dataseries X:

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105,29 0 101,23 0 102,33 0 100,26 0 104,13 0 103,54 0 100,02 0 98,66 0 108,64 0 105,67 0 102,66 0 100,3 0 95,13 0 93,2 0 102,84 0 101,36 0 102,55 0 103,12 0 96,3 0 99,13 0 102,23 0 104,3 0 99,58 0 98,45 0 96,23 0 97,62 0 102,32 0 105,23 0 100,05 0 102,66 0 100,98 0 99,2 0 98,36 0 102,56 0 97,33 0 96,22 0 99,22 0 102,32 0 104,22 0 100,06 0 107,23 0 99,62 0 98,32 1 101,23 1 102,33 1 100,6 1 95,63 1 94,63 1 95,66 1 100,78 1 90,36 1 95,45 1 103,65 1 99,89 1 97,68 1 99,62 1 98,33 1 96,23 1 102,65 1 99,35 1 92,65 1 100,6 1 97,67 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 101.008333333333 -2.75547619047619X[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) 101.008333333333 0.512361 197.143 0 0 X -2.75547619047619 0.887435 -3.105 0.002885 0.001443 Multiple Linear Regression - Regression Statistics Multiple R 0.369429779656944 R-squared 0.136478362097378 Adjusted R-squared 0.122322269672745 F-TEST (value) 9.64096291571896 F-TEST (DF numerator) 1 F-TEST (DF denominator) 61 p-value 0.00288509416574256 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.32047698505423 Sum Squared Residuals 672.559611904763 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 105.29 101.008333333333 4.28166666666683 2 101.23 101.008333333333 0.221666666666666 3 102.33 101.008333333333 1.32166666666666 4 100.26 101.008333333333 -0.748333333333332 5 104.13 101.008333333333 3.12166666666666 6 103.54 101.008333333333 2.53166666666667 7 100.02 101.008333333333 -0.988333333333342 8 98.66 101.008333333333 -2.34833333333334 9 108.64 101.008333333333 7.63166666666666 10 105.67 101.008333333333 4.66166666666667 11 102.66 101.008333333333 1.65166666666666 12 100.3 101.008333333333 -0.70833333333334 13 95.13 101.008333333333 -5.87833333333334 14 93.2 101.008333333333 -7.80833333333334 15 102.84 101.008333333333 1.83166666666667 16 101.36 101.008333333333 0.351666666666662 17 102.55 101.008333333333 1.54166666666666 18 103.12 101.008333333333 2.11166666666667 19 96.3 101.008333333333 -4.70833333333334 20 99.13 101.008333333333 -1.87833333333334 21 102.23 101.008333333333 1.22166666666667 22 104.3 101.008333333333 3.29166666666666 23 99.58 101.008333333333 -1.42833333333334 24 98.45 101.008333333333 -2.55833333333333 25 96.23 101.008333333333 -4.77833333333333 26 97.62 101.008333333333 -3.38833333333333 27 102.32 101.008333333333 1.31166666666666 28 105.23 101.008333333333 4.22166666666667 29 100.05 101.008333333333 -0.95833333333334 30 102.66 101.008333333333 1.65166666666666 31 100.98 101.008333333333 -0.0283333333333335 32 99.2 101.008333333333 -1.80833333333333 33 98.36 101.008333333333 -2.64833333333334 34 102.56 101.008333333333 1.55166666666666 35 97.33 101.008333333333 -3.67833333333334 36 96.22 101.008333333333 -4.78833333333334 37 99.22 101.008333333333 -1.78833333333334 38 102.32 101.008333333333 1.31166666666666 39 104.22 101.008333333333 3.21166666666666 40 100.06 101.008333333333 -0.948333333333335 41 107.23 101.008333333333 6.22166666666667 42 99.62 101.008333333333 -1.38833333333333 43 98.32 98.2528571428571 0.0671428571428499 44 101.23 98.2528571428571 2.97714285714286 45 102.33 98.2528571428571 4.07714285714285 46 100.6 98.2528571428571 2.34714285714285 47 95.63 98.2528571428571 -2.62285714285715 48 94.63 98.2528571428571 -3.62285714285715 49 95.66 98.2528571428571 -2.59285714285715 50 100.78 98.2528571428571 2.52714285714286 51 90.36 98.2528571428571 -7.89285714285714 52 95.45 98.2528571428571 -2.80285714285714 53 103.65 98.2528571428571 5.39714285714286 54 99.89 98.2528571428571 1.63714285714286 55 97.68 98.2528571428571 -0.572857142857137 56 99.62 98.2528571428571 1.36714285714286 57 98.33 98.2528571428571 0.077142857142855 58 96.23 98.2528571428571 -2.02285714285714 59 102.65 98.2528571428571 4.39714285714286 60 99.35 98.2528571428571 1.09714285714285 61 92.65 98.2528571428571 -5.60285714285714 62 100.6 98.2528571428571 2.34714285714285 63 97.67 98.2528571428571 -0.582857142857142 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.313008567118233 0.626017134236465 0.686991432881767 6 0.180692635540418 0.361385271080835 0.819307364459582 7 0.163428046900544 0.326856093801089 0.836571953099456 8 0.204732588835041 0.409465177670082 0.79526741116496 9 0.562969956009813 0.874060087980374 0.437030043990187 10 0.554904692322255 0.89019061535549 0.445095307677745 11 0.454335783302329 0.908671566604657 0.545664216697671 12 0.405992939859817 0.811985879719633 0.594007060140183 13 0.718036332323582 0.563927335352836 0.281963667676418 14 0.942240720853137 0.115518558293725 0.0577592791468627 15 0.918976059339071 0.162047881321857 0.0810239406609286 16 0.882366888565906 0.235266222868188 0.117633111434094 17 0.842943893729583 0.314112212540833 0.157056106270417 18 0.805232139518342 0.389535720963316 0.194767860481658 19 0.855912393946712 0.288175212106575 0.144087606053288 20 0.823238690735461 0.353522618529078 0.176761309264539 21 0.774060231576175 0.45187953684765 0.225939768423825 22 0.766720516225692 0.466558967548616 0.233279483774308 23 0.716918440131498 0.566163119737005 0.283081559868502 24 0.68872138816948 0.622557223661041 0.311278611830521 25 0.748410196864583 0.503179606270835 0.251589803135417 26 0.746539801058973 0.506920397882053 0.253460198941027 27 0.692253058648106 0.615493882703788 0.307746941351894 28 0.72710327152145 0.545793456957099 0.272896728478549 29 0.666439222598592 0.667121554802816 0.333560777401408 30 0.615131683333943 0.769736633332115 0.384868316666057 31 0.542761967521487 0.914476064957026 0.457238032478513 32 0.486927886529268 0.973855773058536 0.513072113470732 33 0.455378568507788 0.910757137015577 0.544621431492212 34 0.398250912007543 0.796501824015087 0.601749087992457 35 0.409871100588833 0.819742201177667 0.590128899411167 36 0.503906531933044 0.992186936133912 0.496093468066956 37 0.473858171174224 0.947716342348449 0.526141828825776 38 0.405641046732867 0.811282093465734 0.594358953267133 39 0.371308874563455 0.74261774912691 0.628691125436545 40 0.329333493365063 0.658666986730126 0.670666506634937 41 0.464169656657476 0.928339313314951 0.535830343342524 42 0.390918268520355 0.78183653704071 0.609081731479645 43 0.316617430763006 0.633234861526013 0.683382569236994 44 0.290609691730729 0.581219383461457 0.709390308269271 45 0.304601222305529 0.609202444611057 0.695398777694471 46 0.266686775061799 0.533373550123599 0.7333132249382 47 0.249561212727644 0.499122425455288 0.750438787272356 48 0.256687856379585 0.513375712759169 0.743312143620415 49 0.223757710437115 0.44751542087423 0.776242289562885 50 0.191812235583634 0.383624471167269 0.808187764416366 51 0.556438862583793 0.887122274832413 0.443561137416207 52 0.546947612431393 0.906104775137214 0.453052387568607 53 0.69148165739408 0.61703668521184 0.30851834260592 54 0.60536520160254 0.78926959679492 0.39463479839746 55 0.486097565634969 0.972195131269938 0.513902434365031 56 0.374346335452743 0.748692670905485 0.625653664547257 57 0.24858785886979 0.49717571773958 0.75141214113021 58 0.169630226395225 0.339260452790450 0.830369773604775 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/10nyda1258989416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/10nyda1258989416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/1qilg1258989416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/1qilg1258989416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/23lk41258989416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/23lk41258989416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/3xxv41258989416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/3xxv41258989416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/4vzi21258989416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/4vzi21258989416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/5tgh21258989416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/5tgh21258989416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/6be551258989416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/6be551258989416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/7mw5y1258989416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/7mw5y1258989416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/8x8py1258989416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/8x8py1258989416.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/9rnqw1258989416.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258989522vurcchrdhn8cvsf/9rnqw1258989416.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 = Do not include Seasonal 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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