paper dummys

*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 13:36:43 +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/t129259303084xf4d8wt5lu1gk.htm/, Retrieved Fri, 17 Dec 2010 14:37:10 +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/t129259303084xf4d8wt5lu1gk.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:

» Textbox « » Textfile « » CSV «

100.00 0 100.42 0 100.50 0 101.14 0 101.98 0 102.31 0 103.27 0 103.80 0 103.46 0 105.06 0 106.08 0 106.74 0 107.35 0 108.96 0 109.85 0 109.81 0 109.99 0 111.60 0 112.74 0 112.78 0 113.66 0 115.37 0 116.26 0 116.24 0 116.73 0 118.76 0 119.78 0 120.23 0 121.48 0 124.07 0 125.82 0 126.92 0 128.48 0 131.44 0 133.51 0 134.58 0 136.68 0 140.10 0 142.45 0 143.91 0 146.19 0 149.84 0 152.31 0 153.62 0 155.79 0 159.89 0 163.21 0 165.32 0 167.68 0 171.79 0 175.38 0 177.81 0 181.09 0 186.48 0 191.07 0 194.23 0 197.82 0 204.41 0 209.26 0 212.24 0 214.88 0 218.87 0 219.86 0 219.75 0 220.89 0 224.02 0 222.27 0 217.27 1 213.23 1 212.44 1 207.87 1 199.46 1 198.19 1 199.77 1 200.10 1 195,76 1 191,27 1 195,79 1 192,7 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation woningprijs_us[t] = + 144.780298507463 + 57.2072014925373Dummy_[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) 144.780298507463 4.554014 31.7918 0 0 Dummy_ 57.2072014925373 11.684693 4.8959 5e-06 3e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.487233748468161 R-squared 0.237396725646336 Adjusted R-squared 0.227492787018366 F-TEST (value) 23.9699310106693 F-TEST (DF numerator) 1 F-TEST (DF denominator) 77 p-value 5.27065705901997e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 37.2762134418027 Sum Squared Residuals 106992.738819030 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 144.780298507463 -44.7802985074627 2 100.42 144.780298507463 -44.3602985074627 3 100.5 144.780298507463 -44.2802985074627 4 101.14 144.780298507463 -43.6402985074627 5 101.98 144.780298507463 -42.8002985074627 6 102.31 144.780298507463 -42.4702985074627 7 103.27 144.780298507463 -41.5102985074627 8 103.8 144.780298507463 -40.9802985074627 9 103.46 144.780298507463 -41.3202985074627 10 105.06 144.780298507463 -39.7202985074627 11 106.08 144.780298507463 -38.7002985074627 12 106.74 144.780298507463 -38.0402985074627 13 107.35 144.780298507463 -37.4302985074627 14 108.96 144.780298507463 -35.8202985074627 15 109.85 144.780298507463 -34.9302985074627 16 109.81 144.780298507463 -34.9702985074627 17 109.99 144.780298507463 -34.7902985074627 18 111.6 144.780298507463 -33.1802985074627 19 112.74 144.780298507463 -32.0402985074627 20 112.78 144.780298507463 -32.0002985074627 21 113.66 144.780298507463 -31.1202985074627 22 115.37 144.780298507463 -29.4102985074627 23 116.26 144.780298507463 -28.5202985074627 24 116.24 144.780298507463 -28.5402985074627 25 116.73 144.780298507463 -28.0502985074627 26 118.76 144.780298507463 -26.0202985074627 27 119.78 144.780298507463 -25.0002985074627 28 120.23 144.780298507463 -24.5502985074627 29 121.48 144.780298507463 -23.3002985074627 30 124.07 144.780298507463 -20.7102985074627 31 125.82 144.780298507463 -18.9602985074627 32 126.92 144.780298507463 -17.8602985074627 33 128.48 144.780298507463 -16.3002985074627 34 131.44 144.780298507463 -13.3402985074627 35 133.51 144.780298507463 -11.2702985074627 36 134.58 144.780298507463 -10.2002985074627 37 136.68 144.780298507463 -8.10029850746268 38 140.1 144.780298507463 -4.68029850746269 39 142.45 144.780298507463 -2.33029850746270 40 143.91 144.780298507463 -0.870298507462688 41 146.19 144.780298507463 1.40970149253731 42 149.84 144.780298507463 5.05970149253732 43 152.31 144.780298507463 7.52970149253732 44 153.62 144.780298507463 8.83970149253732 45 155.79 144.780298507463 11.0097014925373 46 159.89 144.780298507463 15.1097014925373 47 163.21 144.780298507463 18.4297014925373 48 165.32 144.780298507463 20.5397014925373 49 167.68 144.780298507463 22.8997014925373 50 171.79 144.780298507463 27.0097014925373 51 175.38 144.780298507463 30.5997014925373 52 177.81 144.780298507463 33.0297014925373 53 181.09 144.780298507463 36.3097014925373 54 186.48 144.780298507463 41.6997014925373 55 191.07 144.780298507463 46.2897014925373 56 194.23 144.780298507463 49.4497014925373 57 197.82 144.780298507463 53.0397014925373 58 204.41 144.780298507463 59.6297014925373 59 209.26 144.780298507463 64.4797014925373 60 212.24 144.780298507463 67.4597014925373 61 214.88 144.780298507463 70.0997014925373 62 218.87 144.780298507463 74.0897014925373 63 219.86 144.780298507463 75.0797014925373 64 219.75 144.780298507463 74.9697014925373 65 220.89 144.780298507463 76.1097014925373 66 224.02 144.780298507463 79.2397014925373 67 222.27 144.780298507463 77.4897014925373 68 217.27 201.9875 15.2825000000000 69 213.23 201.9875 11.2425 70 212.44 201.9875 10.4525 71 207.87 201.9875 5.8825 72 199.46 201.9875 -2.52749999999999 73 198.19 201.9875 -3.7975 74 199.77 201.9875 -2.21749999999999 75 200.1 201.9875 -1.88750000000000 76 195.76 201.9875 -6.22750000000001 77 191.27 201.9875 -10.7175000000000 78 195.79 201.9875 -6.1975 79 192.7 201.9875 -9.28750000000001 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 2.01100455164322e-05 4.02200910328643e-05 0.999979889954484 6 1.31943171220010e-06 2.63886342440020e-06 0.999998680568288 7 1.84339307568611e-07 3.68678615137223e-07 0.999999815660692 8 2.55325186662573e-08 5.10650373325145e-08 0.999999974467481 9 1.98650912723852e-09 3.97301825447704e-09 0.99999999801349 10 4.90049645145354e-10 9.80099290290709e-10 0.99999999950995 11 1.71653438414597e-10 3.43306876829194e-10 0.999999999828347 12 5.8341018843367e-11 1.16682037686734e-10 0.99999999994166 13 1.98497590680164e-11 3.96995181360329e-11 0.99999999998015 14 1.24968048981193e-11 2.49936097962386e-11 0.999999999987503 15 7.94440337992365e-12 1.58888067598473e-11 0.999999999992056 16 3.45662700929429e-12 6.91325401858859e-12 0.999999999996543 17 1.34140119951974e-12 2.68280239903949e-12 0.999999999998659 18 8.43163499198224e-13 1.68632699839645e-12 0.999999999999157 19 6.56866958949766e-13 1.31373391789953e-12 0.999999999999343 20 4.13972527080627e-13 8.27945054161253e-13 0.999999999999586 21 3.01253961452752e-13 6.02507922905505e-13 0.999999999999699 22 3.26761341194683e-13 6.53522682389365e-13 0.999999999999673 23 3.79475065404576e-13 7.58950130809152e-13 0.99999999999962 24 3.68838375782137e-13 7.37676751564274e-13 0.999999999999631 25 3.68917647828166e-13 7.37835295656332e-13 0.999999999999631 26 5.64697323118202e-13 1.12939464623640e-12 0.999999999999435 27 9.68865502612558e-13 1.93773100522512e-12 0.999999999999031 28 1.63744441669999e-12 3.27488883339999e-12 0.999999999998363 29 3.33469778404103e-12 6.66939556808206e-12 0.999999999996665 30 1.06335714649433e-11 2.12671429298865e-11 0.999999999989366 31 4.09173835125839e-11 8.18347670251679e-11 0.999999999959083 32 1.62611152327241e-10 3.25222304654483e-10 0.999999999837389 33 7.29854215110618e-10 1.45970843022124e-09 0.999999999270146 34 4.4701904880355e-09 8.940380976071e-09 0.99999999552981 35 2.96445645874561e-08 5.92891291749122e-08 0.999999970355435 36 1.82711825717291e-07 3.65423651434581e-07 0.999999817288174 37 1.19097808117311e-06 2.38195616234622e-06 0.999998809021919 38 8.74436103872632e-06 1.74887220774526e-05 0.999991255638961 39 5.99528176632496e-05 0.000119905635326499 0.999940047182337 40 0.000353760417707741 0.000707520835415483 0.999646239582292 41 0.00186388486163813 0.00372776972327627 0.998136115138362 42 0.0086958118760719 0.0173916237521438 0.991304188123928 43 0.0327198350365011 0.0654396700730022 0.967280164963499 44 0.0974784535924416 0.194956907184883 0.902521546407558 45 0.233080807886962 0.466161615773925 0.766919192113038 46 0.441032799836917 0.882065599673834 0.558967200163083 47 0.667145399167196 0.665709201665608 0.332854600832804 48 0.845427840631014 0.309144318737972 0.154572159368986 49 0.946508506925576 0.106982986148849 0.0534914930744244 50 0.98572442861989 0.0285511427602199 0.0142755713801099 51 0.99700005850112 0.0059998829977604 0.0029999414988802 52 0.999538805454589 0.000922389090822222 0.000461194545411111 53 0.999947117201602 0.000105765596795475 5.28827983977377e-05 54 0.999993716013216 1.25679735673863e-05 6.28398678369316e-06 55 0.99999915515639 1.68968721823964e-06 8.44843609119822e-07 56 0.999999887345753 2.25308493315858e-07 1.12654246657929e-07 57 0.999999983228114 3.35437725972241e-08 1.67718862986120e-08 58 0.99999999347412 1.30517609016024e-08 6.5258804508012e-09 59 0.999999994723127 1.05537469194489e-08 5.27687345972446e-09 60 0.999999993472992 1.30540154689627e-08 6.52700773448134e-09 61 0.999999988708326 2.25833482064704e-08 1.12916741032352e-08 62 0.999999972954784 5.40904309465988e-08 2.70452154732994e-08 63 0.999999926251593 1.47496815057970e-07 7.37484075289852e-08 64 0.999999782162359 4.35675282900798e-07 2.17837641450399e-07 65 0.999999303941202 1.39211759580781e-06 6.96058797903904e-07 66 0.999997673396994 4.65320601276042e-06 2.32660300638021e-06 67 0.99999166262357 1.66747528597695e-05 8.33737642988475e-06 68 0.999992371440353 1.52571192941276e-05 7.6285596470638e-06 69 0.999990701026532 1.85979469360072e-05 9.29897346800361e-06 70 0.999994839334237 1.03213315268349e-05 5.16066576341745e-06 71 0.999997112714243 5.774571514733e-06 2.8872857573665e-06 72 0.999975939974361 4.81200512777255e-05 2.40600256388628e-05 73 0.999767114875214 0.000465770249571814 0.000232885124785907 74 0.998525408766616 0.00294918246676740 0.00147459123338370 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 61 0.871428571428571 NOK 5% type I error level 63 0.9 NOK 10% type I error level 64 0.914285714285714 NOK

Charts produced by software:

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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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