*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 08:37:17 -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/t1258731716deyxzjsv6jmj4g0.htm/, Retrieved Fri, 20 Nov 2009 16:42:07 +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/t1258731716deyxzjsv6jmj4g0.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 «

101.6 595454 594167 611324 612613 610763 94.6 590865 595454 594167 611324 612613 95.9 589379 590865 595454 594167 611324 104.7 584428 589379 590865 595454 594167 102.8 573100 584428 589379 590865 595454 98.1 567456 573100 584428 589379 590865 113.9 569028 567456 573100 584428 589379 80.9 620735 569028 567456 573100 584428 95.7 628884 620735 569028 567456 573100 113.2 628232 628884 620735 569028 567456 105.9 612117 628232 628884 620735 569028 108.8 595404 612117 628232 628884 620735 102.3 597141 595404 612117 628232 628884 99 593408 597141 595404 612117 628232 100.7 590072 593408 597141 595404 612117 115.5 579799 590072 593408 597141 595404 100.7 574205 579799 590072 593408 597141 109.9 572775 574205 579799 590072 593408 114.6 572942 572775 574205 579799 590072 85.4 619567 572942 572775 574205 579799 100.5 625809 619567 572942 572775 574205 114.8 619916 625809 619567 572942 572775 116.5 587625 619916 625809 619567 572942 112.9 565742 587625 619916 625809 619567 102 557274 565742 587625 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 Niet_werkende_werkzoekenden[t] = + 68970.6356205958 -418.292868757629X[t] + 0.930452455967116y1[t] + 0.0330128831486056y2[t] + 0.187881114683878y3[t] -0.219786393126637y4[t] + 17114.8877611796M1[t] + 18207.1937484543M2[t] + 12179.1884528439M3[t] + 7221.81459494864M4[t] + 8205.4763419188M5[t] + 3015.34139103115M6[t] + 20206.3648793463M7[t] + 58365.2100442902M8[t] + 23794.5572772682M9[t] + 7833.16211110014M10[t] -10011.4779756030M11[t] -53.8205884163031t + 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) 68970.6356205958 42530.599897 1.6217 0.112932 0.056466 X -418.292868757629 205.895392 -2.0316 0.04905 0.024525 y1 0.930452455967116 0.162167 5.7376 1e-06 1e-06 y2 0.0330128831486056 0.211066 0.1564 0.876517 0.438258 y3 0.187881114683878 0.220374 0.8526 0.399112 0.199556 y4 -0.219786393126637 0.143742 -1.529 0.134328 0.067164 M1 17114.8877611796 5565.002289 3.0755 0.00383 0.001915 M2 18207.1937484543 6728.246588 2.7061 0.010046 0.005023 M3 12179.1884528439 7006.031481 1.7384 0.090033 0.045017 M4 7221.81459494864 5854.495009 1.2336 0.224753 0.112377 M5 8205.4763419188 5106.233484 1.607 0.116132 0.058066 M6 3015.34139103115 5469.509261 0.5513 0.584573 0.292286 M7 20206.3648793463 5608.539099 3.6028 0.00088 0.00044 M8 58365.2100442902 7108.172592 8.211 0 0 M9 23794.5572772682 12273.106661 1.9388 0.059792 0.029896 M10 7833.16211110014 12856.167414 0.6093 0.545864 0.272932 M11 -10011.4779756030 8789.739214 -1.139 0.261653 0.130826 t -53.8205884163031 112.933153 -0.4766 0.636328 0.318164 Multiple Linear Regression - Regression Statistics Multiple R 0.990937737242827 R-squared 0.981957599091935 Adjusted R-squared 0.974092962798675 F-TEST (value) 124.857343998675 F-TEST (DF numerator) 17 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6734.34761480654 Sum Squared Residuals 1768706074.08497 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 595454 597419.875994766 -1965.87599476635 2 590865 601368.718165465 -10503.7181654651 3 589379 587575.581188341 1803.41881165846 4 584428 581361.939168099 3066.06083190139 5 573100 577285.758000202 -4185.75800020164 6 567456 564033.575160032 3422.42483996780 7 569028 568332.710313161 695.289686839065 8 620735 620477.591272211 257.408727789035 9 628884 629254.524102224 -370.524102223715 10 628232 616744.260872112 11487.7391278883 11 612117 610930.969709374 1186.0302906255 12 595404 594826.360223601 577.639776398962 13 597141 596610.138730408 530.861269592154 14 593408 597209.038761307 -3801.03876130654 15 590072 587401.88001582 2670.11998418011 16 579799 576972.364110527 2826.63588947326 17 574205 573341.141502384 863.858497616324 18 572775 558898.4903902 13876.5096097998 19 572942 571357.600442895 1584.39955710515 20 619567 622991.812585438 -3424.81258543762 21 625809 626399.790911864 -590.79091186413 22 619916 612095.76772932 7820.23227068054 23 587625 596932.371915395 -9307.37191539508 24 565742 569081.311792408 -3339.31179240771 25 557274 566795.571056221 -9521.57105622053 26 560576 552787.724801416 7788.27519858387 27 548854 552777.224828446 -3923.22482844574 28 531673 534539.929866863 -2866.92986686344 29 525919 526890.544414083 -971.544414082744 30 511038 511458.956821315 -420.956821314821 31 498662 510603.977599991 -11941.9775999914 32 555362 549729.373812067 5632.62618793326 33 564591 561027.752742092 3563.24725790764 34 541657 552945.107008256 -11288.1070082559 35 527070 523244.161809776 3825.83819022427 36 509846 511950.723182472 -2104.72318247165 37 514258 511730.139375319 2527.86062468074 38 516922 514129.392734919 2792.60726508106 39 507561 509763.489806872 -2202.48980687243 40 492622 501999.687160432 -9377.6871604319 41 490243 485114.0866875 5128.91331250016 42 469357 478249.14075206 -8892.14075206053 43 477580 472280.648953864 5299.35104613632 44 528379 528507.72484173 -128.724841729892 45 533590 536430.044701778 -2840.04470177786 46 517945 525964.864390313 -8019.86439031294 47 506174 501878.496565455 4295.5034345453 48 501866 496999.60480152 4866.3951984804 49 516141 507712.274843286 8428.725156714 50 528222 524498.125536893 3723.8744631067 51 532638 530985.82416052 1652.1758394796 52 536322 529970.079694079 6351.9203059207 53 536535 537370.469395832 -835.469395832101 54 523597 531582.836876392 -7985.83687639227 55 536214 531851.062690089 4362.93730991082 56 586570 588906.497488555 -2336.49748855479 57 596594 596355.887542042 238.112457958066 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.00432868412210498 0.00865736824420997 0.995671315877895 22 0.136928882489933 0.273857764979865 0.863071117510067 23 0.270614969384768 0.541229938769535 0.729385030615233 24 0.187114406169665 0.374228812339329 0.812885593830335 25 0.243700794424293 0.487401588848585 0.756299205575707 26 0.388966722135473 0.777933444270947 0.611033277864527 27 0.302034587279304 0.604069174558609 0.697965412720696 28 0.239272177252540 0.478544354505081 0.76072782274746 29 0.343585823314465 0.68717164662893 0.656414176685535 30 0.42032738528274 0.84065477056548 0.57967261471726 31 0.546924484112696 0.906151031774608 0.453075515887304 32 0.7645807738151 0.4708384523698 0.2354192261849 33 0.885226967837038 0.229546064325923 0.114773032162962 34 0.914183531378126 0.171632937243749 0.0858164686218744 35 0.879853313770679 0.240293372458642 0.120146686229321 36 0.767751501330214 0.464496997339573 0.232248498669786 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0625 NOK 5% type I error level 1 0.0625 NOK 10% type I error level 1 0.0625 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/10hu6a1258731432.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/10hu6a1258731432.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/1pwh31258731432.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/1pwh31258731432.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/23r791258731432.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/23r791258731432.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/3tj1g1258731432.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/3tj1g1258731432.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/4vopj1258731432.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/4vopj1258731432.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/5fimt1258731432.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/5fimt1258731432.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/69yzh1258731432.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/69yzh1258731432.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/7kbxq1258731432.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/7kbxq1258731432.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/89zjc1258731432.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/89zjc1258731432.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/99zx01258731432.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731716deyxzjsv6jmj4g0/99zx01258731432.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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