W8-model neutraal

*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: Sun, 28 Nov 2010 14:31:42 +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/Nov/28/t1290954656rxzzptxatitv98k.htm/, Retrieved Sun, 28 Nov 2010 15:30:56 +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/Nov/28/t1290954656rxzzptxatitv98k.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 «

465 0 459 0 465 0 468 0 467 0 463 0 460 0 462 0 461 0 476 0 476 0 471 0 453 0 443 0 442 0 444 0 438 0 427 0 424 0 416 0 406 0 431 0 434 0 418 0 412 0 404 0 409 0 412 0 406 0 398 0 397 0 385 0 390 0 413 1 413 1 401 1 397 1 397 1 409 1 419 1 424 1 428 1 430 1 424 1 433 1 456 1 459 1 446 1 441 1 439 1 454 1 460 1 457 1 451 1 444 1 437 1 443 1 471 1 469 1 454 1 444 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 Y[t] = + 435.818181818182 + 0.360389610389608X[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) 435.818181818182 4.372077 99.6822 0 0 X 0.360389610389608 6.453179 0.0558 0.955653 0.477826 Multiple Linear Regression - Regression Statistics Multiple R 0.00727044638050349 R-squared 5.28593905717763e-05 Adjusted R-squared -0.0168953972299271 F-TEST (value) 0.00311886890524440 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.955652519278202 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 25.1156701148520 Sum Squared Residuals 37217.0162337662 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 465 435.818181818182 29.1818181818183 2 459 435.818181818182 23.1818181818182 3 465 435.818181818182 29.1818181818182 4 468 435.818181818182 32.1818181818182 5 467 435.818181818182 31.1818181818182 6 463 435.818181818182 27.1818181818182 7 460 435.818181818182 24.1818181818182 8 462 435.818181818182 26.1818181818182 9 461 435.818181818182 25.1818181818182 10 476 435.818181818182 40.1818181818182 11 476 435.818181818182 40.1818181818182 12 471 435.818181818182 35.1818181818182 13 453 435.818181818182 17.1818181818182 14 443 435.818181818182 7.18181818181818 15 442 435.818181818182 6.18181818181818 16 444 435.818181818182 8.18181818181818 17 438 435.818181818182 2.18181818181818 18 427 435.818181818182 -8.81818181818182 19 424 435.818181818182 -11.8181818181818 20 416 435.818181818182 -19.8181818181818 21 406 435.818181818182 -29.8181818181818 22 431 435.818181818182 -4.81818181818182 23 434 435.818181818182 -1.81818181818182 24 418 435.818181818182 -17.8181818181818 25 412 435.818181818182 -23.8181818181818 26 404 435.818181818182 -31.8181818181818 27 409 435.818181818182 -26.8181818181818 28 412 435.818181818182 -23.8181818181818 29 406 435.818181818182 -29.8181818181818 30 398 435.818181818182 -37.8181818181818 31 397 435.818181818182 -38.8181818181818 32 385 435.818181818182 -50.8181818181818 33 390 435.818181818182 -45.8181818181818 34 413 436.178571428571 -23.1785714285714 35 413 436.178571428571 -23.1785714285714 36 401 436.178571428571 -35.1785714285714 37 397 436.178571428571 -39.1785714285714 38 397 436.178571428571 -39.1785714285714 39 409 436.178571428571 -27.1785714285714 40 419 436.178571428571 -17.1785714285714 41 424 436.178571428571 -12.1785714285714 42 428 436.178571428571 -8.17857142857143 43 430 436.178571428571 -6.17857142857143 44 424 436.178571428571 -12.1785714285714 45 433 436.178571428571 -3.17857142857143 46 456 436.178571428571 19.8214285714286 47 459 436.178571428571 22.8214285714286 48 446 436.178571428571 9.82142857142857 49 441 436.178571428571 4.82142857142857 50 439 436.178571428571 2.82142857142857 51 454 436.178571428571 17.8214285714286 52 460 436.178571428571 23.8214285714286 53 457 436.178571428571 20.8214285714286 54 451 436.178571428571 14.8214285714286 55 444 436.178571428571 7.82142857142857 56 437 436.178571428571 0.821428571428572 57 443 436.178571428571 6.82142857142857 58 471 436.178571428571 34.8214285714286 59 469 436.178571428571 32.8214285714286 60 454 436.178571428571 17.8214285714286 61 444 436.178571428571 7.82142857142857 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00614644816813294 0.0122928963362659 0.993853551831867 6 0.000966301916401296 0.00193260383280259 0.999033698083599 7 0.000264518819849101 0.000529037639698203 0.99973548118015 8 4.4817575359626e-05 8.9635150719252e-05 0.99995518242464 9 8.77238983586924e-06 1.75447796717385e-05 0.999991227610164 10 9.55615654112471e-05 0.000191123130822494 0.999904438434589 11 0.000203983603882882 0.000407967207765763 0.999796016396117 12 0.000132951635671652 0.000265903271343304 0.999867048364328 13 0.000293831134961862 0.000587662269923724 0.999706168865038 14 0.00265451625294922 0.00530903250589843 0.99734548374705 15 0.008374066538398 0.016748133076796 0.991625933461602 16 0.0141257956261295 0.028251591252259 0.98587420437387 17 0.0310403758459899 0.0620807516919797 0.96895962415401 18 0.0996116121454692 0.199223224290938 0.90038838785453 19 0.204785611294548 0.409571222589095 0.795214388705452 20 0.377181213368598 0.754362426737196 0.622818786631402 21 0.607828934705672 0.784342130588657 0.392171065294328 22 0.618799728368203 0.762400543263595 0.381200271631797 23 0.636540095680324 0.726919808639352 0.363459904319676 24 0.684781565643524 0.630436868712953 0.315218434356476 25 0.739473079542782 0.521053840914436 0.260526920457218 26 0.803245453921073 0.393509092157854 0.196754546078927 27 0.82585404428138 0.348291911437241 0.174145955718620 28 0.836170131714599 0.327659736570802 0.163829868285401 29 0.851434139374896 0.297131721250208 0.148565860625104 30 0.873426534745352 0.253146930509297 0.126573465254648 31 0.887442177989867 0.225115644020266 0.112557822010133 32 0.913822787696496 0.172354424607007 0.0861772123035036 33 0.919783690962022 0.160432618075956 0.0802163090379779 34 0.907985986573482 0.184028026853035 0.0920140134265176 35 0.897353598969299 0.205292802061402 0.102646401030701 36 0.922411564855987 0.155176870288025 0.0775884351440125 37 0.960518366796605 0.078963266406791 0.0394816332033955 38 0.98827389841395 0.0234522031720986 0.0117261015860493 39 0.99513472729009 0.00973054541982032 0.00486527270991016 40 0.996874074685343 0.0062518506293146 0.0031259253146573 41 0.997596499137128 0.00480700172574397 0.00240350086287199 42 0.997839366919654 0.00432126616069257 0.00216063308034628 43 0.997982686007253 0.00403462798549454 0.00201731399274727 44 0.999230413653173 0.00153917269365448 0.000769586346827238 45 0.999406340086427 0.00118731982714594 0.000593659913572971 46 0.998978019820471 0.00204396035905727 0.00102198017952863 47 0.998425546964678 0.00314890607064495 0.00157445303532247 48 0.996732937919948 0.00653412416010346 0.00326706208005173 49 0.99461843461799 0.0107631307640193 0.00538156538200963 50 0.992789783158598 0.0144204336828045 0.00721021684140226 51 0.984404381457952 0.0311912370840957 0.0155956185420478 52 0.97205115249661 0.0558976950067813 0.0279488475033906 53 0.945511886525362 0.108976226949277 0.0544881134746383 54 0.89019607777656 0.219607844446881 0.109803922223441 55 0.81041185510166 0.379176289796678 0.189588144898339 56 0.770956285882507 0.458087428234986 0.229043714117493 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 19 0.365384615384615 NOK 5% type I error level 26 0.5 NOK 10% type I error level 29 0.557692307692308 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/10ysvr1290954694.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/10ysvr1290954694.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/1xdn31290954694.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/1xdn31290954694.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/2k1xi1290954694.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/2k1xi1290954694.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/3k1xi1290954694.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/3k1xi1290954694.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/4k1xi1290954694.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/4k1xi1290954694.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/5dse31290954694.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/5dse31290954694.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/6dse31290954694.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/6dse31290954694.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/76jdo1290954694.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/76jdo1290954694.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/86jdo1290954694.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/86jdo1290954694.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/9ysvr1290954694.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290954656rxzzptxatitv98k/9ysvr1290954694.ps (open in new window)

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') }

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