*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 07:20:51 -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/t1258726980fg6dws72zz72gna.htm/, Retrieved Fri, 20 Nov 2009 15:23:12 +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/t1258726980fg6dws72zz72gna.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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20.3 3016 20 2155 19.2 2172 21.8 2150 21.3 2533 21.5 2058 19.5 2160 19.5 2260 19.7 2498 18.7 2695 19.7 2799 20 2946 19.7 2930 19.2 2318 19.7 2540 22 2570 21.8 2669 22.8 2450 21 2842 25 3440 23.3 2678 25 2981 26.8 2260 25.3 2844 26.5 2546 27.8 2456 22 2295 22.3 2379 28 2479 25 2057 27.3 2280 25.8 2351 27.3 2276 23.5 2548 24.5 2311 18 2201 21.3 2725 21.8 2408 20.5 2139 22.3 1898 18.7 2537 22.3 2068 17.7 2063 19.7 2520 20.5 2434 18.5 2190 10 2794 14.2 2070 15.5 2615 16.5 2265 20.5 2139 15.7 2428 11.7 2137 7.5 1823 3.5 2063 4.5 1806 2.2 1758 5 2243 2.3 1993 6.1 1932 3.3 2465

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 Y[t] = -11.7933478162042 + 0.0118874959627300X[t] -2.72840580123097M1[t] + 5.26960218428549M2[t] + 5.34326942832257M3[t] + 5.45041954136613M4[t] + 2.71934529229835M5[t] + 6.7542162589432M6[t] + 2.47083702763941M7[t] + 1.26704031006233M8[t] + 2.70974721819855M9[t] -0.158659463850549M10[t] -0.449909867577547M11[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) -11.7933478162042 7.137969 -1.6522 0.105022 0.052511 X 0.0118874959627300 0.002738 4.3409 7.3e-05 3.6e-05 M1 -2.72840580123097 3.88133 -0.703 0.485478 0.242739 M2 5.26960218428549 3.956646 1.3318 0.189204 0.094602 M3 5.34326942832257 3.96983 1.346 0.184635 0.092318 M4 5.45041954136613 3.963075 1.3753 0.175423 0.087712 M5 2.71934529229835 3.955818 0.6874 0.495121 0.24756 M6 6.7542162589432 4.039534 1.672 0.101026 0.050513 M7 2.47083702763941 3.963817 0.6233 0.536007 0.268003 M8 1.26704031006233 3.95644 0.3202 0.750171 0.375085 M9 2.70974721819855 3.955467 0.6851 0.496599 0.248299 M10 -0.158659463850549 3.967549 -0.04 0.968268 0.484134 M11 -0.449909867577547 3.951867 -0.1138 0.909834 0.454917 Multiple Linear Regression - Regression Statistics Multiple R 0.562727797596643 R-squared 0.316662574187969 Adjusted R-squared 0.145828217734961 F-TEST (value) 1.85362347927406 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.0656190615320313 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.24683593753813 Sum Squared Residuals 1873.10204306486 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 20.3 21.3309342061586 -1.03093420615859 2 20 19.0938081677645 0.90619183223555 3 19.2 19.3695628431679 -0.169562843167947 4 21.8 19.2151880450314 2.58481195496855 5 21.3 21.0370247496893 0.262975250310743 6 21.5 19.4253351340374 2.07466486596263 7 19.5 16.3544804909320 3.14551950906797 8 19.5 16.3394333696280 3.16056663037205 9 19.7 20.6113643168939 -0.911364316893922 10 18.7 20.0847943395026 -1.38479433950263 11 19.7 21.0298435158996 -1.32984351589956 12 20 23.2272152899984 -3.22721528999842 13 19.7 20.3086095533638 -0.608609553363768 14 19.2 21.0314700096894 -1.83147000968945 15 19.7 23.7441613574526 -4.0441613574526 16 22 24.2079363493781 -2.20793634937806 17 21.8 22.6537242006205 -0.853724200620545 18 22.8 24.0852335514275 -1.28523355142753 19 21 24.4617527375139 -3.46175273751391 20 25 30.3666786056494 -5.36667860564939 21 23.3 22.7511135901853 0.548886409814676 22 25 23.4846181848434 1.51538181515658 23 26.8 14.6224831919881 12.1775168080119 24 25.3 22.0146907018000 3.28530929820005 25 26.5 15.7438111036754 10.7561888963246 26 27.8 22.6719444525462 5.12805554745381 27 22 20.8317248465837 1.16827515341626 28 22.3 21.9374246204966 0.362575379503377 29 28 20.3950999677018 7.60490003229815 30 25 19.4134476380746 5.58655236192537 31 27.3 17.7809800064596 9.51901999354037 32 25.8 17.4211955022364 8.37880449776362 33 27.3 17.9723402131679 9.32765978683215 34 23.5 18.3373324329813 5.16266756701868 35 24.5 15.2287454860873 9.2712545139127 36 18 14.3710307977645 3.62896920223545 37 21.3 17.8716728810041 3.42832711899589 38 21.8 22.1013446463351 -0.301344646335149 39 20.5 18.9772754763979 1.52272452360214 40 22.3 16.2195390624235 6.08046093757652 41 18.7 21.0845747335402 -2.38457473354018 42 22.3 19.5442100936647 2.75578990633534 43 17.7 15.2013933825472 2.49860661745278 44 19.7 19.4301823199378 0.269817680062238 45 20.5 19.8505645752792 0.649435424720801 46 18.5 14.0816088783240 4.41839112167603 47 10 20.9704060360859 -10.9704060360859 48 14.2 12.8137688266469 1.38623117335308 49 15.5 16.5640483251038 -1.06404832510381 50 16.5 20.4014327236648 -3.90143272366476 51 20.5 18.9772754763979 1.52272452360214 52 15.7 22.5199119226704 -6.8199119226704 53 11.7 16.3295763484482 -4.62957634844817 54 7.5 16.6317735827958 -9.1317735827958 55 3.5 15.2013933825472 -11.7013933825472 56 4.5 10.9425102025485 -6.44251020254852 57 2.2 11.8146173044737 -9.6146173044737 58 5 14.7116461643487 -9.71164616434866 59 2.3 11.4485217699392 -9.14852176993915 60 6.1 11.1732943837902 -5.07329438379017 61 3.3 14.7809239306943 -11.4809239306943 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.000301552809064524 0.000603105618129048 0.999698447190935 17 1.88149035008250e-05 3.76298070016499e-05 0.999981185096499 18 2.52142447205668e-06 5.04284894411336e-06 0.999997478575528 19 1.58881285602661e-07 3.17762571205321e-07 0.999999841118714 20 4.85145410800488e-07 9.70290821600976e-07 0.99999951485459 21 1.01889402747616e-06 2.03778805495233e-06 0.999998981105973 22 9.23095697472539e-06 1.84619139494508e-05 0.999990769043025 23 0.000419190414576797 0.000838380829153594 0.999580809585423 24 0.000380816501967928 0.000761633003935856 0.999619183498032 25 0.00137837552418469 0.00275675104836938 0.998621624475815 26 0.00287348673473148 0.00574697346946296 0.997126513265268 27 0.00140479018916747 0.00280958037833494 0.998595209810833 28 0.000561716794522487 0.00112343358904497 0.999438283205478 29 0.00081196457930109 0.00162392915860218 0.999188035420699 30 0.000490320671869349 0.000980641343738698 0.99950967932813 31 0.000994583129193421 0.00198916625838684 0.999005416870807 32 0.000808250408777578 0.00161650081755516 0.999191749591222 33 0.00127117475292252 0.00254234950584504 0.998728825247077 34 0.000680222706057348 0.00136044541211470 0.999319777293943 35 0.00358039474925363 0.00716078949850727 0.996419605250746 36 0.00288411937768409 0.00576823875536818 0.997115880622316 37 0.00250988896747196 0.00501977793494391 0.997490111032528 38 0.00118603450319490 0.00237206900638980 0.998813965496805 39 0.000490602589862517 0.000981205179725034 0.999509397410137 40 0.0017051355275955 0.003410271055191 0.998294864472405 41 0.00109821467629287 0.00219642935258574 0.998901785323707 42 0.00116496769842403 0.00232993539684805 0.998835032301576 43 0.00674271223324974 0.0134854244664995 0.99325728776675 44 0.00328662818044431 0.00657325636088863 0.996713371819556 45 0.00297657320254801 0.00595314640509602 0.997023426797452 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 29 0.966666666666667 NOK 5% type I error level 30 1 NOK 10% type I error level 30 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/10k3yu1258726847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/10k3yu1258726847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/1gpg31258726847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/1gpg31258726847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/2yi221258726847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/2yi221258726847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/3eccp1258726847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/3eccp1258726847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/4klas1258726847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/4klas1258726847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/54cwl1258726847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/54cwl1258726847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/6cha21258726847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/6cha21258726847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/7gseb1258726847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/7gseb1258726847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/8sf6a1258726847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/8sf6a1258726847.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/9ectn1258726847.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726980fg6dws72zz72gna/9ectn1258726847.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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