Model4

*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 16:04:12 -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/21/t1258758384vlb06yj9dniay4u.htm/, Retrieved Sat, 21 Nov 2009 00:06:35 +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/21/t1258758384vlb06yj9dniay4u.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 «

537 20,1 544 555 561 562 543 19,9 537 544 555 561 594 20 543 537 544 555 611 22,6 594 543 537 544 613 20,6 611 594 543 537 611 20,1 613 611 594 543 594 20,2 611 613 611 594 595 21,8 594 611 613 611 591 22 595 594 611 613 589 19,5 591 595 594 611 584 17,5 589 591 595 594 573 18,2 584 589 591 595 567 18,8 573 584 589 591 569 19,7 567 573 584 589 621 18,8 569 567 573 584 629 18,5 621 569 567 573 628 18,7 629 621 569 567 612 18,5 628 629 621 569 595 19,3 612 628 629 621 597 18,9 595 612 628 629 593 21,4 597 595 612 628 590 22,5 593 597 595 612 580 25 590 593 597 595 574 22,9 580 590 593 597 573 22,9 574 580 590 593 573 21,3 573 574 580 590 620 22,3 573 573 574 580 626 20,9 620 573 573 574 620 19,9 626 620 573 573 588 20,2 620 626 620 573 566 19,8 588 620 626 620 557 17,7 566 588 620 626 561 18,1 557 566 588 620 549 17,6 561 557 566 588 532 18,2 549 561 557 566 526 16 532 549 561 557 511 16,3 526 532 549 561 499 17,3 511 526 532 549 555 19 499 511 526 532 565 18,6 555 499 511 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 58.2998057129362 -0.668637279101902X[t] + 0.961166527491067Y1[t] + 0.0391683255838521Y2[t] + 0.0478769811627857Y3[t] -0.127097092762909Y4[t] -4.4972676862563M1[t] + 6.69352067183267M2[t] + 56.6788489417115M3[t] + 16.6872113068170M4[t] -4.55219702469572M5[t] -14.5874408744148M6[t] -9.10435377427196M7[t] + 9.94245587206924M8[t] + 9.88391350018948M9[t] + 2.43364712654867M10[t] -5.18399963045349M11[t] -0.254141845139373t + 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) 58.2998057129362 27.458505 2.1232 0.040137 0.020069 X -0.668637279101902 0.344845 -1.939 0.059767 0.029884 Y1 0.961166527491067 0.161395 5.9554 1e-06 0 Y2 0.0391683255838521 0.224454 0.1745 0.862371 0.431186 Y3 0.0478769811627857 0.222987 0.2147 0.831114 0.415557 Y4 -0.127097092762909 0.165009 -0.7702 0.445797 0.222898 M1 -4.4972676862563 4.845255 -0.9282 0.359024 0.179512 M2 6.69352067183267 5.063393 1.3219 0.193889 0.096944 M3 56.6788489417115 5.335302 10.6234 0 0 M4 16.6872113068170 11.212649 1.4882 0.144727 0.072364 M5 -4.55219702469572 10.654938 -0.4272 0.671555 0.335778 M6 -14.5874408744148 10.20374 -1.4296 0.160789 0.080395 M7 -9.10435377427196 5.147178 -1.7688 0.084747 0.042374 M8 9.94245587206924 5.180107 1.9194 0.062278 0.031139 M9 9.88391350018948 5.87144 1.6834 0.10029 0.050145 M10 2.43364712654867 6.063902 0.4013 0.690365 0.345183 M11 -5.18399963045349 4.995633 -1.0377 0.305798 0.152899 t -0.254141845139373 0.110433 -2.3013 0.026803 0.013401 Multiple Linear Regression - Regression Statistics Multiple R 0.991290024248878 R-squared 0.98265591217534 Adjusted R-squared 0.975095668764592 F-TEST (value) 129.976755877757 F-TEST (DF numerator) 17 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.82215927009146 Sum Squared Residuals 1815.1324271533 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 537 540.152218825339 -3.15221882533875 2 543 543.903410726035 -0.903410726035087 3 594 599.296490072511 -5.29649007251066 4 611 607.629685674612 3.37031432538824 5 613 606.987767164605 6.01223283539486 6 611 601.30003818193 9.69996181807006 7 594 598.950080254068 -4.95008025406777 8 595 598.189864175547 -3.18986417554682 9 591 597.688809347421 -6.68880934742142 10 589 587.290782047774 1.70921795222589 11 584 580.885789204651 3.11421079534892 12 573 580.144826588557 -7.14482658855679 13 567 564.636195668105 2.36380433189497 14 569 568.788027163206 0.211972836793970 15 621 620.91714891164 0.0828510883598304 16 629 632.041762829455 -3.04176282945526 17 628 620.998906866175 7.00109313382533 18 612 612.430837539255 -0.430837539255436 19 595 595.481007231167 -0.481007231167442 20 597 596.509952044054 0.490047955945787 21 593 595.143211543495 -2.14321154349531 22 590 584.156617663346 5.84338233665436 23 580 573.829467517936 6.17053248206424 24 574 569.988591227525 4.0114087724753 25 573 559.443256702907 13.5567432970926 26 573 570.156067848087 2.84393215191329 27 620 620.163157708793 -0.163157708792835 28 626 626.743002786996 -0.743002786996366 29 620 613.653097449597 6.34690255040347 30 588 599.881349474215 -11.8813494742152 31 566 568.699109334762 -2.69910933476215 32 557 565.447020955037 -8.44702095503706 33 561 554.585199075481 6.41480092451887 34 549 553.721074058793 -4.72107405879328 35 532 536.436021271952 -4.43602127195213 36 526 527.362411956453 -1.36241195645339 37 511 514.89463839645 -3.8946383964502 38 499 511.221396197816 -12.2213961978163 39 555 549.567764724425 5.43223527557525 40 565 562.989173627661 2.01082637233926 41 542 555.033829943566 -13.0338299435664 42 527 527.302437158433 -0.302437158433059 43 510 510.641209357457 -0.641209357457052 44 514 509.666333717808 4.33366628219154 45 517 515.473033498945 1.52696650105468 46 508 510.831526230087 -2.83152623008697 47 493 497.848722005461 -4.84872200546102 48 490 485.504170227465 4.49582977253489 49 469 477.873690407199 -8.87369040719865 50 478 467.931098064856 10.0689019351442 51 528 528.055438582632 -0.0554385826315939 52 534 535.596375081276 -1.59637508127588 53 518 524.326398576057 -6.32639857605727 54 506 503.085337646166 2.91466235383363 55 502 493.228593822546 8.77140617745442 56 516 509.186829107553 6.81317089244655 57 528 527.109746534657 0.890253465343169 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.210926310164594 0.421852620329188 0.789073689835406 22 0.120344048047937 0.240688096095874 0.879655951952063 23 0.0604029867858278 0.120805973571656 0.939597013214172 24 0.0390324428211412 0.0780648856422823 0.96096755717886 25 0.0862966100043207 0.172593220008641 0.91370338999568 26 0.0809994378614731 0.161998875722946 0.919000562138527 27 0.0445846410200745 0.0891692820401489 0.955415358979925 28 0.0245077663027179 0.0490155326054359 0.975492233697282 29 0.182278051434818 0.364556102869636 0.817721948565182 30 0.626556396540206 0.746887206919588 0.373443603459794 31 0.713829045464549 0.572341909070901 0.286170954535451 32 0.620223175556669 0.759553648886661 0.379776824443331 33 0.543080048758 0.913839902484001 0.456919951242000 34 0.418451084413337 0.836902168826673 0.581548915586664 35 0.479300710712457 0.958601421424913 0.520699289287543 36 0.435161707796765 0.87032341559353 0.564838292203235 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 1 0.0625 NOK 10% type I error level 3 0.1875 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/109k831258758247.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/109k831258758247.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/16h3u1258758247.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/16h3u1258758247.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/21zqm1258758247.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/21zqm1258758247.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/3di8m1258758247.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/3di8m1258758247.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/4x10f1258758247.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/4x10f1258758247.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/5zddx1258758247.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/5zddx1258758247.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/6ue7k1258758247.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/6ue7k1258758247.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/7202a1258758247.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/7202a1258758247.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/8l3wg1258758247.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/8l3wg1258758247.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/992oc1258758247.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758384vlb06yj9dniay4u/992oc1258758247.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