*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, 17 Dec 2010 12:34:10 +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/Dec/17/t1292589221whfhpo8xpm6l38l.htm/, Retrieved Fri, 17 Dec 2010 13:33:54 +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/2010/Dec/17/t1292589221whfhpo8xpm6l38l.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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2 2,3 2,8 2,4 2,3 2,7 2,7 2,9 3 2,2 2,3 2,8 2,8 2,8 2,2 2,6 2,8 2,5 2,4 2,3 1,9 1,7 2 2,1 1,7 1,8 1,8 1,8 1,3 1,3 1,3 1,2 1,4 2,2 2,9 3,1 3,5 3,6 4,4 4,1 5,1 5,8 5,9 5,4 5,5 4,8 3,2 2,7 2,1 1,9 0,6 0,7 -0,2 -1 -1,7 -0,7 -1 -0,9 0 0,3 0,8

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 19 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation HPC[t] = + 3.11176470588236 -0.176633986928106M1[t] + 0.0267320261437896M2[t] -0.0679411764705897M3[t] -0.082614379084969M4[t] -0.117287581699348M5[t] -0.0919607843137269M6[t] -0.206633986928106M7[t] -0.0813071895424855M8[t] -0.115980392156864M9[t] -0.250653594771243M10[t] -0.145326797385622M11[t] -0.0253267973856209t + 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) 3.11176470588236 0.915522 3.3989 0.00137 0.000685 M1 -0.176633986928106 1.067714 -0.1654 0.869299 0.434649 M2 0.0267320261437896 1.120679 0.0239 0.981068 0.490534 M3 -0.0679411764705897 1.119248 -0.0607 0.951848 0.475924 M4 -0.082614379084969 1.117966 -0.0739 0.941399 0.4707 M5 -0.117287581699348 1.116834 -0.105 0.916799 0.4584 M6 -0.0919607843137269 1.115851 -0.0824 0.934661 0.46733 M7 -0.206633986928106 1.115019 -0.1853 0.85376 0.42688 M8 -0.0813071895424855 1.114338 -0.073 0.942138 0.471069 M9 -0.115980392156864 1.113808 -0.1041 0.9175 0.45875 M10 -0.250653594771243 1.113429 -0.2251 0.822843 0.411421 M11 -0.145326797385622 1.113202 -0.1305 0.896678 0.448339 t -0.0253267973856209 0.012989 -1.9499 0.057048 0.028524 Multiple Linear Regression - Regression Statistics Multiple R 0.280701934462105 R-squared 0.0787935760107679 Adjusted R-squared -0.15150802998654 F-TEST (value) 0.34213211701046 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.976538597787572 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.76000733435786 Sum Squared Residuals 148.686039215686 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2 2.90980392156862 -0.909803921568623 2 2.3 3.0878431372549 -0.787843137254901 3 2.8 2.9678431372549 -0.167843137254902 4 2.4 2.9278431372549 -0.527843137254901 5 2.3 2.8678431372549 -0.567843137254902 6 2.7 2.8678431372549 -0.167843137254902 7 2.7 2.72784313725490 -0.0278431372549023 8 2.9 2.8278431372549 0.0721568627450983 9 3 2.7678431372549 0.232156862745098 10 2.2 2.60784313725490 -0.407843137254902 11 2.3 2.6878431372549 -0.387843137254902 12 2.8 2.80784313725490 -0.00784313725490308 13 2.8 2.60588235294118 0.194117647058822 14 2.8 2.78392156862745 0.0160784313725485 15 2.2 2.66392156862745 -0.463921568627451 16 2.6 2.62392156862745 -0.0239215686274506 17 2.8 2.56392156862745 0.236078431372549 18 2.5 2.56392156862745 -0.0639215686274504 19 2.4 2.42392156862745 -0.0239215686274508 20 2.3 2.52392156862745 -0.223921568627451 21 1.9 2.46392156862745 -0.563921568627451 22 1.7 2.30392156862745 -0.603921568627451 23 2 2.38392156862745 -0.383921568627451 24 2.1 2.50392156862745 -0.403921568627452 25 1.7 2.30196078431373 -0.601960784313726 26 1.8 2.48 -0.68 27 1.8 2.36 -0.56 28 1.8 2.32 -0.52 29 1.3 2.26 -0.96 30 1.3 2.26 -0.96 31 1.3 2.12 -0.82 32 1.2 2.22 -1.02 33 1.4 2.16 -0.76 34 2.2 2 0.200000000000001 35 2.9 2.08 0.82 36 3.1 2.2 0.899999999999999 37 3.5 1.99803921568627 1.50196078431373 38 3.6 2.17607843137255 1.42392156862745 39 4.4 2.05607843137255 2.34392156862745 40 4.1 2.01607843137255 2.08392156862745 41 5.1 1.95607843137255 3.14392156862745 42 5.8 1.95607843137255 3.84392156862745 43 5.9 1.81607843137255 4.08392156862745 44 5.4 1.91607843137255 3.48392156862745 45 5.5 1.85607843137255 3.64392156862745 46 4.8 1.69607843137255 3.10392156862745 47 3.2 1.77607843137255 1.42392156862745 48 2.7 1.89607843137255 0.80392156862745 49 2.1 1.69411764705882 0.405882352941176 50 1.9 1.8721568627451 0.0278431372549018 51 0.6 1.75215686274510 -1.15215686274510 52 0.7 1.71215686274510 -1.01215686274510 53 -0.2 1.6521568627451 -1.8521568627451 54 -1 1.65215686274510 -2.6521568627451 55 -1.7 1.51215686274510 -3.2121568627451 56 -0.7 1.6121568627451 -2.3121568627451 57 -1 1.5521568627451 -2.5521568627451 58 -0.9 1.3921568627451 -2.2921568627451 59 0 1.4721568627451 -1.4721568627451 60 0.3 1.5921568627451 -1.2921568627451 61 0.8 1.39019607843137 -0.590196078431373 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0111321750190376 0.0222643500380752 0.988867824980962 17 0.00197603005051437 0.00395206010102874 0.998023969949486 18 0.000423622507114023 0.000847245014228047 0.999576377492886 19 9.40906718038906e-05 0.000188181343607781 0.999905909328196 20 3.28433449934913e-05 6.56866899869825e-05 0.999967156655007 21 2.90369671519759e-05 5.80739343039517e-05 0.999970963032848 22 6.61332579055521e-06 1.32266515811104e-05 0.99999338667421 23 1.22411756558044e-06 2.44823513116087e-06 0.999998775882434 24 3.21460393808537e-07 6.42920787617074e-07 0.999999678539606 25 7.9648074379939e-08 1.59296148759878e-07 0.999999920351926 26 1.95519261053518e-08 3.91038522107036e-08 0.999999980448074 27 4.20133902652341e-09 8.40267805304683e-09 0.99999999579866 28 8.86851316440512e-10 1.77370263288102e-09 0.999999999113149 29 6.60105038255032e-10 1.32021007651006e-09 0.999999999339895 30 5.10036308418355e-10 1.02007261683671e-09 0.999999999489964 31 3.30649947531936e-10 6.61299895063872e-10 0.99999999966935 32 4.71654399629039e-10 9.43308799258079e-10 0.999999999528346 33 5.63848792077113e-10 1.12769758415423e-09 0.999999999436151 34 3.38770359436159e-09 6.77540718872317e-09 0.999999996612296 35 5.57372018510203e-08 1.11474403702041e-07 0.999999944262798 36 6.62492956223794e-07 1.32498591244759e-06 0.999999337507044 37 0.000132745521241138 0.000265491042482275 0.999867254478759 38 0.00098702668318747 0.00197405336637494 0.999012973316812 39 0.00373914924367656 0.00747829848735312 0.996260850756323 40 0.0060112605081933 0.0120225210163866 0.993988739491807 41 0.0131309617567897 0.0262619235135795 0.98686903824321 42 0.0388821827480898 0.0777643654961795 0.96111781725191 43 0.134193415413995 0.26838683082799 0.865806584586005 44 0.164748019909935 0.329496039819871 0.835251980090065 45 0.334078787897395 0.66815757579479 0.665921212102605 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.766666666666667 NOK 5% type I error level 26 0.866666666666667 NOK 10% type I error level 27 0.9 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/107prp1292589230.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/107prp1292589230.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/1i6uv1292589230.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/1i6uv1292589230.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/2i6uv1292589230.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/2i6uv1292589230.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/3bxby1292589230.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/3bxby1292589230.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/4bxby1292589230.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/4bxby1292589230.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/5bxby1292589230.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/5bxby1292589230.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/636a11292589230.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/636a11292589230.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/7wgam1292589230.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/7wgam1292589230.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/8wgam1292589230.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/8wgam1292589230.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/9wgam1292589230.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589221whfhpo8xpm6l38l/9wgam1292589230.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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