births met monthly dummies

*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, 26 Nov 2010 11:03:41 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws.htm/, Retrieved Fri, 26 Nov 2010 12:05:36 +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/2010/Nov/26/t129076953357c3lxe9juygrws.htm/}, year = {2010}, } @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 = {2010}, 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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9700 9081 9084 9743 8587 9731 9563 9998 9437 10038 9918 9252 9737 9035 9133 9487 8700 9627 8947 9283 8829 9947 9628 9318 9605 8640 9214 9567 8547 9185 9470 9123 9278 10170 9434 9655 9429 8739 9552 9687 9019 9672 9206 9069 9788 10312 10105 9863 9656 9295 9946 9701 9049 10190 9706 9765 9893 9994 10433 10073 10112 9266 9820 10097 9115 10411 9678 10408 10153 10368 10581 10597 10680 9738 9556

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 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Births[t] = + 9793 + 52.5714285714238M1[t] -679.571428571429M2[t] -320.857142857143M3[t] -79.3333333333333M4[t] -956.833333333334M5[t] + 9.66666666666673M6[t] -364.666666666667M7[t] -185.333333333333M8[t] -230M9[t] + 345.166666666667M10[t] + 223.5M11[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) 9793 158.732403 61.695 0 0 M1 52.5714285714238 216.315802 0.243 0.808771 0.404385 M2 -679.571428571429 216.315802 -3.1416 0.00256 0.00128 M3 -320.857142857143 216.315802 -1.4833 0.142985 0.071493 M4 -79.3333333333333 224.481516 -0.3534 0.724963 0.362482 M5 -956.833333333334 224.481516 -4.2624 6.9e-05 3.4e-05 M6 9.66666666666673 224.481516 0.0431 0.965788 0.482894 M7 -364.666666666667 224.481516 -1.6245 0.109266 0.054633 M8 -185.333333333333 224.481516 -0.8256 0.412143 0.206072 M9 -230 224.481516 -1.0246 0.309478 0.154739 M10 345.166666666667 224.481516 1.5376 0.12915 0.064575 M11 223.5 224.481516 0.9956 0.32324 0.16162 Multiple Linear Regression - Regression Statistics Multiple R 0.701030851771318 R-squared 0.491444255135219 Adjusted R-squared 0.402648807619146 F-TEST (value) 5.5345658914131 F-TEST (DF numerator) 11 F-TEST (DF denominator) 63 p-value 4.08818017527679e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 388.813391926637 Sum Squared Residuals 9524078.7857143 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9700 9845.57142857146 -145.571428571458 2 9081 9113.42857142857 -32.4285714285714 3 9084 9472.14285714286 -388.142857142857 4 9743 9713.66666666667 29.3333333333335 5 8587 8836.16666666667 -249.166666666666 6 9731 9802.66666666667 -71.6666666666666 7 9563 9428.33333333333 134.666666666667 8 9998 9607.66666666667 390.333333333333 9 9437 9563 -126 10 10038 10138.1666666667 -100.166666666667 11 9918 10016.5 -98.5 12 9252 9793 -541 13 9737 9845.57142857142 -108.571428571424 14 9035 9113.42857142857 -78.4285714285714 15 9133 9472.14285714286 -339.142857142857 16 9487 9713.66666666667 -226.666666666667 17 8700 8836.16666666667 -136.166666666667 18 9627 9802.66666666667 -175.666666666667 19 8947 9428.33333333333 -481.333333333333 20 9283 9607.66666666667 -324.666666666667 21 8829 9563 -734 22 9947 10138.1666666667 -191.166666666667 23 9628 10016.5 -388.5 24 9318 9793 -475 25 9605 9845.57142857142 -240.571428571424 26 8640 9113.42857142857 -473.428571428571 27 9214 9472.14285714286 -258.142857142857 28 9567 9713.66666666667 -146.666666666667 29 8547 8836.16666666667 -289.166666666667 30 9185 9802.66666666667 -617.666666666667 31 9470 9428.33333333333 41.6666666666667 32 9123 9607.66666666667 -484.666666666667 33 9278 9563 -285 34 10170 10138.1666666667 31.8333333333335 35 9434 10016.5 -582.5 36 9655 9793 -138 37 9429 9845.57142857142 -416.571428571424 38 8739 9113.42857142857 -374.428571428572 39 9552 9472.14285714286 79.8571428571428 40 9687 9713.66666666667 -26.6666666666667 41 9019 8836.16666666667 182.833333333333 42 9672 9802.66666666667 -130.666666666667 43 9206 9428.33333333333 -222.333333333333 44 9069 9607.66666666667 -538.666666666667 45 9788 9563 225 46 10312 10138.1666666667 173.833333333333 47 10105 10016.5 88.5000000000002 48 9863 9793 70.0000000000001 49 9656 9845.57142857142 -189.571428571424 50 9295 9113.42857142857 181.571428571429 51 9946 9472.14285714286 473.857142857143 52 9701 9713.66666666667 -12.6666666666667 53 9049 8836.16666666667 212.833333333333 54 10190 9802.66666666667 387.333333333333 55 9706 9428.33333333333 277.666666666667 56 9765 9607.66666666667 157.333333333333 57 9893 9563 330 58 9994 10138.1666666667 -144.166666666667 59 10433 10016.5 416.5 60 10073 9793 280 61 10112 9845.57142857142 266.428571428576 62 9266 9113.42857142857 152.571428571429 63 9820 9472.14285714286 347.857142857143 64 10097 9713.66666666667 383.333333333333 65 9115 8836.16666666667 278.833333333333 66 10411 9802.66666666667 608.333333333333 67 9678 9428.33333333333 249.666666666667 68 10408 9607.66666666667 800.333333333333 69 10153 9563 590 70 10368 10138.1666666667 229.833333333333 71 10581 10016.5 564.5 72 10597 9793 804 73 10680 9845.57142857142 834.428571428576 74 9738 9113.42857142857 624.571428571429 75 9556 9472.14285714286 83.8571428571428 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.000584137802515681 0.00116827560503136 0.999415862197484 16 0.0048917514157289 0.0097835028314578 0.995108248584271 17 0.00137590625534639 0.00275181251069277 0.998624093744654 18 0.000367179587400708 0.000734359174801416 0.9996328204126 19 0.0131169820726594 0.0262339641453188 0.98688301792734 20 0.0519488335149573 0.103897667029915 0.948051166485043 21 0.0937355692366544 0.187471138473309 0.906264430763346 22 0.0565177568364923 0.113035513672985 0.943482243163508 23 0.0430969787306103 0.0861939574612205 0.95690302126939 24 0.0306359946796065 0.061271989359213 0.969364005320393 25 0.0186369493789682 0.0372738987579364 0.981363050621032 26 0.0238930934001812 0.0477861868003623 0.976106906599819 27 0.0160348375985519 0.0320696751971038 0.983965162401448 28 0.00906644548223689 0.0181328909644738 0.990933554517763 29 0.0059140507557244 0.0118281015114488 0.994085949244276 30 0.0151609099901537 0.0303218199803074 0.984839090009846 31 0.0100072375399697 0.0200144750799394 0.98999276246003 32 0.0187076573868038 0.0374153147736076 0.981292342613196 33 0.0174508826431245 0.0349017652862489 0.982549117356876 34 0.0112068401682585 0.0224136803365171 0.988793159831741 35 0.0235050174053388 0.0470100348106776 0.976494982594661 36 0.0282712760385842 0.0565425520771684 0.971728723961416 37 0.0395815469910386 0.0791630939820773 0.960418453008961 38 0.0521789413550356 0.104357882710071 0.947821058644964 39 0.0549462827500068 0.109892565500014 0.945053717249993 40 0.0387798598230812 0.0775597196461623 0.961220140176919 41 0.0372331066704967 0.0744662133409934 0.962766893329503 42 0.0438800226041321 0.0877600452082642 0.956119977395868 43 0.0417868514754037 0.0835737029508073 0.958213148524596 44 0.18688231968697 0.373764639373941 0.81311768031303 45 0.239106629659687 0.478213259319375 0.760893370340313 46 0.199023820375587 0.398047640751174 0.800976179624413 47 0.232406797918075 0.46481359583615 0.767593202081925 48 0.292995396445318 0.585990792890637 0.707004603554682 49 0.49172249801946 0.98344499603892 0.50827750198054 50 0.474514840188051 0.949029680376101 0.525485159811949 51 0.537865566686801 0.924268866626398 0.462134433313199 52 0.518878932916911 0.962242134166178 0.481121067083089 53 0.443590386596853 0.887180773193707 0.556409613403147 54 0.439032896735927 0.878065793471854 0.560967103264073 55 0.361547578893112 0.723095157786223 0.638452421106888 56 0.489716281376109 0.979432562752219 0.510283718623891 57 0.448329287925774 0.896658575851548 0.551670712074226 58 0.404797858045738 0.809595716091476 0.595202141954262 59 0.325641595163702 0.651283190327405 0.674358404836298 60 0.372579555970128 0.745159111940256 0.627420444029872 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.0869565217391304 NOK 5% type I error level 16 0.347826086956522 NOK 10% type I error level 24 0.521739130434783 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/10gjqd1290769410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/10gjqd1290769410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/1r1b11290769410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/1r1b11290769410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/2kaa41290769410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/2kaa41290769410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/3kaa41290769410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/3kaa41290769410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/4kaa41290769410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/4kaa41290769410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/5kaa41290769410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/5kaa41290769410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/6cj971290769410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/6cj971290769410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/75s9s1290769410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/75s9s1290769410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/85s9s1290769410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/85s9s1290769410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/9gjqd1290769410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t129076953357c3lxe9juygrws/9gjqd1290769410.ps (open in new window)

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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