M2

*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: Thu, 19 Nov 2009 00:34:35 -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/19/t1258617097gziup5lodpwmvvx.htm/, Retrieved Thu, 19 Nov 2009 08:51:50 +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/19/t1258617097gziup5lodpwmvvx.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:

» Textbox « » Textfile « » CSV «

21 2472,81 19 2407,6 25 2454,62 21 2448,05 23 2497,84 23 2645,64 19 2756,76 18 2849,27 19 2921,44 19 2981,85 22 3080,58 23 3106,22 20 3119,31 14 3061,26 14 3097,31 14 3161,69 15 3257,16 11 3277,01 17 3295,32 16 3363,99 20 3494,17 24 3667,03 23 3813,06 20 3917,96 21 3895,51 19 3801,06 23 3570,12 23 3701,61 23 3862,27 23 3970,1 27 4138,52 26 4199,75 17 4290,89 24 4443,91 26 4502,64 24 4356,98 27 4591,27 27 4696,96 26 4621,4 24 4562,84 23 4202,52 23 4296,49 24 4435,23 17 4105,18 21 4116,68 19 3844,49 22 3720,98 22 3674,4 18 3857,62 16 3801,06 14 3504,37 12 3032,6 14 3047,03 16 2962,34 8 2197,82 3 2014,45 0 1862,83 5 1905,41 1 1810,99 1 1670,07 3 1864,44

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 Consvertr[t] = -2.54623902332245 + 0.0061421420368986Aand[t] + 0.609520892164507M1[t] -0.280403213295958M2[t] + 1.75852696995038M3[t] + 0.577457909719086M4[t] + 1.42655819316205M5[t] + 0.676750919876607M6[t] + 0.879589447508637M7[t] -1.76292560165979M8[t] -2.55132966649962M9[t] + 0.0562001706321281M10[t] + 0.551095836096717M11[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) -2.54623902332245 3.479677 -0.7317 0.467881 0.233941 Aand 0.0061421420368986 0.000795 7.722 0 0 M1 0.609520892164507 3.036513 0.2007 0.841757 0.420879 M2 -0.280403213295958 3.175646 -0.0883 0.930007 0.465004 M3 1.75852696995038 3.172402 0.5543 0.581933 0.290967 M4 0.577457909719086 3.171445 0.1821 0.856286 0.428143 M5 1.42655819316205 3.171394 0.4498 0.654865 0.327432 M6 0.676750919876607 3.172038 0.2133 0.831958 0.415979 M7 0.879589447508637 3.171353 0.2774 0.782699 0.391349 M8 -1.76292560165979 3.171463 -0.5559 0.580881 0.29044 M9 -2.55132966649962 3.171321 -0.8045 0.425075 0.212537 M10 0.0562001706321281 3.171369 0.0177 0.985935 0.492967 M11 0.551095836096717 3.171478 0.1738 0.86278 0.43139 Multiple Linear Regression - Regression Statistics Multiple R 0.755982426459113 R-squared 0.571509429115009 Adjusted R-squared 0.464386786393761 F-TEST (value) 5.33509456634839 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 1.24190173838024e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.01428849726404 Sum Squared Residuals 1206.86827842214 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 21 13.2516321191053 7.74836788089471 2 19 11.9611789314187 7.03882106858135 3 25 14.2889126332400 10.7110873667600 4 21 13.0674896998262 7.93251030017375 5 23 14.2224072352864 8.7775927647136 6 23 14.3804085550546 8.61959144494545 7 19 15.2657619058268 3.73423809417324 8 18 13.1914564164918 4.80854358350818 9 19 12.8463307424550 6.15366925754504 10 19 15.8249073800358 3.17509261996425 11 22 16.9262167288033 5.07378327119665 12 23 16.5326054145327 6.4673945854673 13 20 17.2225269459602 2.77747305403978 14 14 15.9760514952578 -1.97605149525779 15 14 18.2364058989343 -4.23640589893432 16 14 17.4507679430386 -3.45076794303856 17 15 18.8862585267442 -3.88625852674423 18 11 18.2583727728912 -7.25837277289122 19 17 18.5736739212189 -1.57367392121887 20 16 16.3529397657243 -0.352939765724267 21 20 16.3641197512479 3.6358802487521 22 24 20.0333802608779 3.96661973912206 23 23 21.4252129279908 1.57478707200917 24 20 21.5184277915648 -1.51842779156477 25 21 21.9900575950009 -0.990057595000907 26 19 20.5200081741554 -1.52000817415537 27 23 21.1404720754003 1.85952792459965 28 23 20.7670332716009 2.23296672839915 29 23 22.6029300946919 0.397069905308057 30 23 22.5154299972453 0.484570002754728 31 27 23.7527280867318 3.24727191326823 32 26 21.4862963944826 4.51370360551736 33 17 21.2576871548858 -4.25768715488575 34 24 24.8050875665037 -0.805087566503718 35 26 25.6607112337954 0.339288766204634 36 24 24.214950988604 -0.214950988603991 37 27 26.2635143385935 0.736485661406522 38 27 26.0227532250128 0.97724677498718 39 26 27.5975831559511 -1.59758315595110 40 24 26.0568302580390 -2.05683025803903 41 23 24.6927939227467 -1.69279392274669 42 23 24.5201637366686 -1.52016373666860 43 24 25.5751630504999 -1.57516305049995 44 17 20.9054340220531 -3.90543402205314 45 21 20.1876645906376 0.812335409362354 46 19 21.1233647867460 -2.12336478674596 47 22 20.8596444892332 1.14035551076679 48 22 20.0224476770578 1.97755232294225 49 18 21.7573318332228 -3.75733183322282 50 16 20.5200081741554 -4.52000817415537 51 14 20.7366262364743 -6.73662623647426 52 12 16.6578788274953 -4.65787882749532 53 14 17.5956102205307 -3.59561022053073 54 16 16.3256249381403 -0.325624938140343 55 8 11.8326730357227 -3.83267303572266 56 3 8.06387340124813 -5.06387340124813 57 0 6.34419776077374 -6.34419776077374 58 5 9.21326000583663 -4.21326000583663 59 1 9.12821462017726 -8.12821462017726 60 1 7.71156812824078 -6.71156812824078 61 3 9.51493716811727 -6.51493716811727 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.603801658701031 0.792396682597938 0.396198341298969 17 0.440081188389555 0.88016237677911 0.559918811610445 18 0.583774238933011 0.832451522133978 0.416225761066989 19 0.532931215638896 0.934137568722208 0.467068784361104 20 0.465176475811577 0.930352951623153 0.534823524188423 21 0.65103566925367 0.697928661492658 0.348964330746329 22 0.914156102432267 0.171687795135466 0.085843897567733 23 0.918912635458432 0.162174729083135 0.0810873645415675 24 0.87973559826693 0.240528803466142 0.120264401733071 25 0.89012698181446 0.219746036371081 0.109873018185540 26 0.901891923506315 0.196216152987370 0.0981080764936852 27 0.955927426496677 0.0881451470066467 0.0440725735033233 28 0.98298781708092 0.0340243658381601 0.0170121829190800 29 0.981270809227924 0.0374583815441519 0.0187291907720759 30 0.979128338545642 0.0417433229087159 0.0208716614543579 31 0.988856975702336 0.0222860485953271 0.0111430242976635 32 0.997664826876392 0.00467034624721592 0.00233517312360796 33 0.998271835845545 0.0034563283089101 0.00172816415445505 34 0.996338295795995 0.00732340840800958 0.00366170420400479 35 0.992243572928488 0.0155128541430249 0.00775642707151247 36 0.986080628513796 0.0278387429724074 0.0139193714862037 37 0.978625033343156 0.0427499333136877 0.0213749666568438 38 0.977991124953334 0.0440177500933315 0.0220088750466658 39 0.966198403559523 0.0676031928809541 0.0338015964404771 40 0.93498640973738 0.130027180525239 0.0650135902626197 41 0.881838797131366 0.236322405737268 0.118161202868634 42 0.865082462583303 0.269835074833394 0.134917537416697 43 0.814025158619457 0.371949682761087 0.185974841380543 44 0.824818792678278 0.350362414643445 0.175181207321723 45 0.677084700825407 0.645830598349187 0.322915299174593 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.1 NOK 5% type I error level 11 0.366666666666667 NOK 10% type I error level 13 0.433333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/103lw1258616066.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/103lw1258616066.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/10h6001258616066.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/10h6001258616066.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/2ulic1258616066.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/2ulic1258616066.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/3g2y51258616066.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/3g2y51258616066.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/488tg1258616066.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/488tg1258616066.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/5oobi1258616066.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/5oobi1258616066.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/6xvap1258616066.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/6xvap1258616066.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/709fw1258616066.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/709fw1258616066.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/8xd1e1258616066.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/8xd1e1258616066.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/9qxiy1258616066.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258617097gziup5lodpwmvvx/9qxiy1258616066.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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by