workshop 7

*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 03:34:01 -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/t1258713337hhxeuxcbu6njcng.htm/, Retrieved Fri, 20 Nov 2009 11:35:50 +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/20/t1258713337hhxeuxcbu6njcng.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:

workshop 7

Dataseries X:

» Textbox « » Textfile « » CSV «

0,61328 1,5334 0,62168 0,62915 0,634 0,6348 0,6089 1,5225 0,61328 0,62168 0,62915 0,634 0,60857 1,5135 0,6089 0,61328 0,62168 0,62915 0,62672 1,5144 0,60857 0,6089 0,61328 0,62168 0,62291 1,4913 0,62672 0,60857 0,6089 0,61328 0,62393 1,4793 0,62291 0,62672 0,60857 0,6089 0,61838 1,4663 0,62393 0,62291 0,62672 0,60857 0,62012 1,4749 0,61838 0,62393 0,62291 0,62672 0,61659 1,4745 0,62012 0,61838 0,62393 0,62291 0,6116 1,4775 0,61659 0,62012 0,61838 0,62393 0,61573 1,4678 0,6116 0,61659 0,62012 0,61838 0,61407 1,4658 0,61573 0,6116 0,61659 0,62012 0,62823 1,4572 0,61407 0,61573 0,6116 0,61659 0,64405 1,4721 0,62823 0,61407 0,61573 0,6116 0,6387 1,4624 0,64405 0,62823 0,61407 0,61573 0,63633 1,4636 0,6387 0,64405 0,62823 0,61407 0,63059 1,4649 0,63633 0,6387 0,64405 0,62823 0,62994 1,465 0,63059 0,63633 0,6387 0,64405 0,63709 1,4673 0,62994 0,63059 0,63633 0,6387 0,64217 1,4679 0,63709 0,62994 0,63059 0,63633 0,65711 1,4621 0,64217 0,63709 0,62994 0,63059 0,66977 1,4674 0,65711 0,64217 0,6370 etc...

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 Britse_pond[t] = + 0.114636991739404 -0.0565949872591202Zwitserse_frank[t] + 1.12444792345794`Britse_pond_-1`[t] -0.000546810967661961`Britse_pond_-2`[t] -0.259096619888014`Britse_pond_-3`[t] + 0.0820675914217389`Britse_pond_-4`[t] + 0.0101173405338365M1[t] -0.00198837719947519M2[t] + 0.00440236865833959M3[t] + 0.00684260124238548M4[t] -0.000281218482007058M5[t] + 0.00516663440438992M6[t] + 0.00236758392844985M7[t] + 0.00385981556788097M8[t] + 0.00338930082332849M9[t] -0.00207401615927181M10[t] + 0.00643574267259323M11[t] + 0.000137273166313686t + 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) 0.114636991739404 0.074497 1.5388 0.132139 0.066069 Zwitserse_frank -0.0565949872591202 0.075054 -0.7541 0.45546 0.22773 `Britse_pond_-1` 1.12444792345794 0.161788 6.9501 0 0 `Britse_pond_-2` -0.000546810967661961 0.239349 -0.0023 0.998189 0.499095 `Britse_pond_-3` -0.259096619888014 0.238048 -1.0884 0.283264 0.141632 `Britse_pond_-4` 0.0820675914217389 0.176238 0.4657 0.644115 0.322058 M1 0.0101173405338365 0.006141 1.6475 0.107704 0.053852 M2 -0.00198837719947519 0.005958 -0.3338 0.7404 0.3702 M3 0.00440236865833959 0.006451 0.6824 0.499138 0.249569 M4 0.00684260124238548 0.006066 1.1281 0.266353 0.133176 M5 -0.000281218482007058 0.006061 -0.0464 0.963238 0.481619 M6 0.00516663440438992 0.006088 0.8487 0.401372 0.200686 M7 0.00236758392844985 0.005884 0.4024 0.689638 0.344819 M8 0.00385981556788097 0.006028 0.6403 0.525823 0.262911 M9 0.00338930082332849 0.006231 0.544 0.589646 0.294823 M10 -0.00207401615927181 0.006309 -0.3288 0.744147 0.372074 M11 0.00643574267259323 0.006382 1.0084 0.319657 0.159829 t 0.000137273166313686 0.00013 1.0526 0.299195 0.149597 Multiple Linear Regression - Regression Statistics Multiple R 0.973540091625546 R-squared 0.947780310002276 Adjusted R-squared 0.924418869740136 F-TEST (value) 40.5702858799454 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.00872717634948795 Sum Squared Residuals 0.00289421706733235 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 0.61328 0.624640860936968 -0.0113608609369677 2 0.6089 0.605038988385295 0.00386101161470527 3 0.60857 0.608693297534306 -0.000123297534305677 4 0.62672 0.612414561712568 0.0143054382874317 5 0.62291 0.627589745045723 -0.00467974504572314 6 0.62393 0.629285985572241 -0.0053559855722411 7 0.61838 0.62377927737256 -0.00539927737256051 8 0.62012 0.621157506471576 -0.00103750647157630 9 0.61659 0.622229521000326 -0.00563952100032589 10 0.6116 0.614285134785 -0.00268513478500017 11 0.61573 0.616965770013257 -0.00123577001325742 12 0.61407 0.616484597669385 -0.00241459766938451 13 0.62823 0.626360279913249 0.00186972008675103 14 0.64405 0.627992074017129 0.0160579259828712 15 0.6387 0.65361912726506 -0.0149191272650592 16 0.63633 0.646299231751325 -0.00996923175132522 17 0.63059 0.633640264137794 -0.00305026413779446 18 0.62994 0.635451171765817 -0.00551117176581673 19 0.63709 0.63210647090523 0.00498352909477051 20 0.64217 0.64303489120496 -0.000864891204959784 21 0.65711 0.648435531133738 0.00867446886626213 22 0.66977 0.6577001232951 0.0120698767048996 23 0.68255 0.67972621962479 0.00282378037521004 24 0.68902 0.682814866659478 0.00620513334052245 25 0.71322 0.697202632531643 0.0160173674683568 26 0.70224 0.708721179076764 -0.00648117907676355 27 0.70045 0.701894128346416 -0.00144412834641643 28 0.69919 0.697155837316589 0.00203416268341068 29 0.69693 0.693165578859644 0.00376442114035555 30 0.69763 0.695710567896963 0.00191943210303661 31 0.69278 0.693422453312738 -0.000642453312737655 32 0.70196 0.690340493059181 0.0116195069408186 33 0.69215 0.699325971704917 -0.007175971704917 34 0.6769 0.683842358553382 -0.00694235855338182 35 0.67124 0.67293259706391 -0.0016925970639105 36 0.66532 0.664269327851756 0.00105067214824406 37 0.67157 0.671848391489266 -0.000278391489266206 38 0.66428 0.668303115399183 -0.00402311539918325 39 0.66576 0.667258400014424 -0.00149840001442421 40 0.66942 0.668736719577007 0.000683280422992628 41 0.6813 0.668017561999982 0.0132824380000182 42 0.69144 0.686005689809962 0.00543431019003803 43 0.69862 0.695100979468859 0.00351902053114107 44 0.695 0.701183169430719 -0.00618316943071869 45 0.69867 0.69452897616102 0.00414102383898078 46 0.68968 0.692122383366518 -0.00244238336651763 47 0.69233 0.692225413298042 0.000104586701957885 48 0.68293 0.687771207819382 -0.00484120781938201 49 0.68399 0.690237835128874 -0.00624783512887399 50 0.66895 0.67836464312163 -0.00941464312162963 51 0.68756 0.669575046839794 0.0179849531602055 52 0.68527 0.69232364964251 -0.00705364964250976 53 0.6776 0.686916849956856 -0.00931684995685614 54 0.68137 0.677856584955017 0.00351341504498319 55 0.67933 0.681790818940613 -0.00246081894061341 56 0.67922 0.682753939833564 -0.00353393983356378 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.517317116627362 0.965365766745276 0.482682883372638 22 0.747890290074132 0.504219419851735 0.252109709925868 23 0.737875272389856 0.524249455220289 0.262124727610144 24 0.620158929113562 0.759682141772876 0.379841070886438 25 0.656276509906873 0.687446980186254 0.343723490093127 26 0.822699765940368 0.354600468119265 0.177300234059632 27 0.726234970338066 0.547530059323868 0.273765029661934 28 0.62088807888979 0.758223842220421 0.379111921110211 29 0.518592909430766 0.962814181138469 0.481407090569234 30 0.447971804408402 0.895943608816804 0.552028195591598 31 0.458954632294838 0.917909264589676 0.541045367705162 32 0.478612284558085 0.95722456911617 0.521387715441915 33 0.47219329940616 0.94438659881232 0.52780670059384 34 0.407685981514617 0.815371963029234 0.592314018485383 35 0.662471918044358 0.675056163911283 0.337528081955642 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/10l2u81258713237.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/10l2u81258713237.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/17fge1258713237.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/17fge1258713237.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/2eynp1258713237.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/2eynp1258713237.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/3yyjs1258713237.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/3yyjs1258713237.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/4b9qr1258713237.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/4b9qr1258713237.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/5z0ih1258713237.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/5z0ih1258713237.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/6u58e1258713237.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/6u58e1258713237.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/71vgv1258713237.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/71vgv1258713237.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/8snvv1258713237.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/8snvv1258713237.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/9agj71258713237.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713337hhxeuxcbu6njcng/9agj71258713237.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') }

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