WorkShop7 (SHW)

*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, 27 Nov 2009 09:41:59 -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/27/t1259340218ifax0o123nf9ps1.htm/, Retrieved Fri, 27 Nov 2009 17:43:50 +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/27/t1259340218ifax0o123nf9ps1.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 «

277051 1 277838 276610 277026 1 277051 277838 274960 1 277026 277051 270073 1 274960 277026 267063 1 270073 274960 264916 1 267063 270073 287182 1 264916 267063 291109 1 287182 264916 292223 1 291109 287182 288109 1 292223 291109 281400 1 288109 292223 282579 1 281400 288109 280113 1 282579 281400 280331 1 280113 282579 276759 1 280331 280113 275139 1 276759 280331 274275 1 275139 276759 271234 1 274275 275139 289725 1 271234 274275 290649 1 289725 271234 292223 1 290649 289725 278429 0 292223 290649 269749 0 278429 292223 265784 0 269749 278429 268957 0 265784 269749 264099 0 268957 265784 255121 0 264099 268957 253276 0 255121 264099 245980 0 253276 255121 235295 0 245980 253276 258479 0 235295 245980 260916 0 258479 235295 254586 0 260916 258479 250566 0 254586 260916 243345 0 250566 254586 247028 0 243345 250566 248464 0 247028 243345 244962 0 248464 247028 237003 0 244962 248464 237008 0 237003 244962 225477 0 237008 237003 226762 0 225477 237008 247857 0 226762 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] = + 9690.06414062205 + 8887.14053601723X[t] + 0.928450603723507Y1[t] + 0.00697821046662423Y2[t] -293.055756716993M1[t] -3225.60445747154M2[t] -6294.3963344095M3[t] -4703.55564690838M4[t] -9394.0620252704M5[t] -6208.55988822182M6[t] + 16596.0512996447M7[t] -1103.97720103963M8[t] -5033.72634393642M9[t] -7293.7916697285M10[t] -7370.59193338102M11[t] + 248.332473555430t + 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) 9690.06414062205 10137.93728 0.9558 0.344764 0.172382 X 8887.14053601723 2265.832236 3.9222 0.000327 0.000164 Y1 0.928450603723507 0.148114 6.2685 0 0 Y2 0.00697821046662423 0.148656 0.0469 0.962787 0.481394 M1 -293.055756716993 2607.516637 -0.1124 0.911064 0.455532 M2 -3225.60445747154 2570.643895 -1.2548 0.216663 0.108331 M3 -6294.3963344095 2420.166044 -2.6008 0.012877 0.006438 M4 -4703.55564690838 2348.260993 -2.003 0.051819 0.02591 M5 -9394.0620252704 2394.298419 -3.9235 0.000326 0.000163 M6 -6208.55988822182 2375.438718 -2.6136 0.01247 0.006235 M7 16596.0512996447 2418.689014 6.8616 0 0 M8 -1103.97720103963 4390.783027 -0.2514 0.802737 0.401369 M9 -5033.72634393642 2547.754411 -1.9758 0.054939 0.02747 M10 -7293.7916697285 2573.647354 -2.834 0.007101 0.003551 M11 -7370.59193338102 2469.135564 -2.9851 0.004763 0.002382 t 248.332473555430 62.860902 3.9505 3e-04 0.00015 Multiple Linear Regression - Regression Statistics Multiple R 0.98676705339317 R-squared 0.97370921766224 Adjusted R-squared 0.964090638758182 F-TEST (value) 101.232128714086 F-TEST (DF numerator) 15 F-TEST (DF denominator) 41 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3476.52994540491 Sum Squared Residuals 495536678.91318 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 277051 278421.583027982 -1370.58302798227 2 277026 275015.245418106 2010.75458189406 3 274960 272166.082897993 2793.91710200691 4 270073 272086.902656495 -2013.90265649525 5 267063 263092.973668468 3970.02633153218 6 264916 263698.069447314 1217.93055268632 7 287182 284736.625249037 2445.37475096334 8 291109 287942.828146544 3166.17185345640 9 292223 288062.813832274 4160.18616772568 10 288109 287112.778385088 996.221614911926 11 281400 283472.438537732 -2072.43853773232 12 282579 284833.679486428 -2254.67948642805 13 280113 285836.782651036 -5723.78265103592 14 280331 280871.234545195 -540.234545194788 15 276759 278235.969106413 -1476.96910641328 16 275139 276760.237960851 -1621.23796085119 17 274275 270789.047910226 3485.95208977426 18 271234 273409.396498257 -2175.3964982567 19 289725 293632.892699912 -3907.89269991223 20 290649 293327.956048206 -2678.95604820578 21 292223 290633.461826443 1589.53817355670 22 278429 281202.417554921 -2773.41755492139 23 269749 268577.885840337 1171.11415966330 24 265784 268041.601571776 -2257.6015717765 25 268957 264255.000778001 4701.99922199908 26 264099 264489.089711916 -390.089711916333 27 255121 257180.359137456 -2059.35913745561 28 253276 250650.002631836 2625.99736816436 29 245980 244432.18698959 1547.81301041018 30 235295 241079.171197116 -5784.1711971162 31 258479 254160.707134188 4318.29286581208 32 260916 258159.647724949 2756.352275051 33 254586 256902.64800834 -2316.64800834005 34 250566 249030.828733441 1535.17126655924 35 243345 245425.817444121 -2080.81744412143 36 247028 246312.347635495 715.652364505387 37 248464 249636.718268067 -1172.71826806723 38 244962 248311.457856964 -3349.45785696365 39 237003 242249.585149571 -5246.58514957147 40 237008 236674.782262539 333.217737461498 41 225477 232181.711033647 -6704.71103364666 42 226762 224909.616623767 1852.38337623276 43 247857 249075.153566083 -1218.15356608320 44 248256 251218.090024951 -2962.09002495136 45 246892 248054.330496289 -1162.33049628913 46 245021 244778.97532655 242.024673450216 47 246186 243203.858177810 2982.14182219046 48 255688 251891.371306301 3796.62869369917 49 264242 260676.915274914 3565.08472508635 50 268270 266000.972467819 2269.02753218071 51 272969 266980.003708567 5988.99629143345 52 273886 273210.074488279 675.925511720576 53 267353 269652.08039807 -2299.08039806996 54 271916 267026.746233546 4889.25376645381 55 292633 294270.62135078 -1637.62135077999 56 295804 296085.47805535 -281.478055350261 57 293222 295492.745836653 -2270.74583665321 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0833254008830628 0.166650801766126 0.916674599116937 20 0.034848733241147 0.069697466482294 0.965151266758853 21 0.0137149034846943 0.0274298069693886 0.986285096515306 22 0.00411816034350669 0.00823632068701338 0.995881839656493 23 0.00724274021581504 0.0144854804316301 0.992757259784185 24 0.0139680217011936 0.0279360434023872 0.986031978298806 25 0.0192445653618458 0.0384891307236916 0.980755434638154 26 0.0175481131990219 0.0350962263980439 0.982451886800978 27 0.0464164804442520 0.0928329608885039 0.953583519555748 28 0.0252352531868923 0.0504705063737846 0.974764746813108 29 0.0477426805230214 0.0954853610460427 0.952257319476979 30 0.191333385215373 0.382666770430746 0.808666614784627 31 0.406493444786295 0.81298688957259 0.593506555213705 32 0.441611110255308 0.883222220510615 0.558388889744692 33 0.495378492365108 0.990756984730216 0.504621507634892 34 0.616527167709834 0.766945664580333 0.383472832290166 35 0.515326343686595 0.96934731262681 0.484673656313405 36 0.538852301972627 0.922295396054745 0.461147698027373 37 0.60556961575371 0.78886076849258 0.39443038424629 38 0.916108967322827 0.167782065354347 0.0838910326771735 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.05 NOK 5% type I error level 6 0.3 NOK 10% type I error level 10 0.5 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/10uh6f1259340115.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/10uh6f1259340115.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/1p2ma1259340114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/1p2ma1259340114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/2xuk01259340114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/2xuk01259340114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/3o4z01259340114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/3o4z01259340114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/4nq3e1259340114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/4nq3e1259340114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/5eskh1259340114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/5eskh1259340114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/6coio1259340114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/6coio1259340114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/7rof11259340114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/7rof11259340114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/81foo1259340115.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/81foo1259340115.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/9ns6x1259340115.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259340218ifax0o123nf9ps1/9ns6x1259340115.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