primary composite outcome

R Software Module: rwasp_logisticregression.wasp (opens new window with default values)

Title produced by software: Bias-Reduced Logistic Regression

Date of computation: Fri, 10 Oct 2008 03:14:33 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Oct/10/t1223630382q08n7j4h2tlnswy.htm/, Retrieved Fri, 10 Oct 2008 09:19:42 +0000

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/2008/Oct/10/t1223630382q08n7j4h2tlnswy.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

Post a new message

Original text written by user:

These are the results for your model with missing data treated properly. Every missing value obtains the label NA.

IsPrivate?

This computation is private

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

0 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 1 1 0 1 0 1 1 0 1 1 1 0 0 0 1 0 0 0 1 1 1 0 1 1 1 1 0 0 0 1 1 0 0 1 1 1 0 0 1 1 NA 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 1 NA 1 0 1 0 1 1 0 1 0 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 1 0 1 1 1 NA 0 0 1 0 1 1 0 0 1 1 0 0 1 0 1 0 0 1 0 NA 0 0 0 1 NA 1 0 0 1 0 0 0 1 0 NA 1 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 NA 0 0 0 1 NA 0 0 1 1 NA 1 0 1 0 0 0 0 1 0 1 0 0 0 1 NA 0 0 1 1 NA 0 0 0 0 NA 1 0 0 0 NA 1 0 1 0 1 1 0 0 0 NA 0 0 1 0 1 1 1 1 0 NA 1 0 1 1 0 1 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 1 0 1 1 0 1 1 1 1 0 1 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1 1 0 1 1 1 1 0 0 1 0 0 0 0 1 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 1 1 1 1 0 0 1 1 0 0 0 1 1 0 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 NA NA 0 0 0 0 1 0 1 0 1 1 0 1 1 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 0 1 1 1 NA 1 1 1 1 0 0 0 1 0 0 0 1 1 1 1 0 1 0 0 NA 0 1 0 0 1 0 1 1 1 0 0 0 0 0 1 0 1 0 1 0 0 1 1 1 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 NA 0 0 0 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 0 0 1 1 1 0 0 0 0 0 1 0 1 1 1 0 0 0 1 NA 0 1 1 1 1 NA 0 0 1 1 1 0 1 0 0 0 0 0 0 1 0 0 1 1 NA 0 0 1 1 NA 0 0 1 0 NA 0 0 0 1 NA 0 0 1 1 1 1 0 1 1 1 0 0 0 0 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 1 1 0 1 1 1 1 0 1 1 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 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 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Coefficients of Bias-Reduced Logistic Regression Variable Parameter S.E. t-stat 2-sided p-value (Intercept) -4.0395331117045 1.41585758558950 -2.85306456865337 0.00506916242873401 IM 2.32936982723884 1.39225749319048 1.67308837526949 0.0968108013675668 UC 0.346307056786085 0.74478023294557 0.464978850763073 0.642755580144504 DIA -0.75902651943117 0.706295301011985 -1.07465888325129 0.284597508132181 TEN -0.180483905955498 0.692816011645475 -0.260507700344336 0.794900832245201 Summary of Bias-Reduced Logistic Regression Deviance 53.8962223457521 Penalized deviance 50.3047894402064 Residual Degrees of Freedom 125 ROC Area 0.758196721311475 Hosmer–Lemeshow test Chi-square NA Degrees of Freedom NA P(>Chi) NA Fit of Logistic Regression Index Actual Fitted Error 1 0 0.00683362080067247 -0.00683362080067247 2 0 0.0908445198161321 -0.0908445198161321 3 0 0.0908445198161321 -0.0908445198161321 4 1 0.131170694811021 0.86882930518898 5 0 0.066009123056486 -0.066009123056486 6 0 0.153142538219492 -0.153142538219492 7 0 0.0173010927062556 -0.0173010927062556 8 0 0.0173010927062556 -0.0173010927062556 9 0 0.0908445198161321 -0.0908445198161321 10 0 0.00683362080067247 -0.00683362080067247 11 0 0.066009123056486 -0.066009123056486 12 0 0.106892552575036 -0.106892552575036 13 0 0.0242870285101468 -0.0242870285101468 14 0 0.106892552575036 -0.106892552575036 15 1 0.106892552575036 0.893107447424964 16 0 0.0115180777575777 -0.0115180777575777 17 0 0.106892552575036 -0.106892552575036 18 0 0.106892552575036 -0.106892552575036 19 0 0.106892552575036 -0.106892552575036 20 0 0.0908445198161321 -0.0908445198161321 21 0 0.0908445198161321 -0.0908445198161321 22 0 0.175905232564779 -0.175905232564779 23 0 0.066009123056486 -0.066009123056486 24 0 0.066009123056486 -0.066009123056486 25 0 0.066009123056486 -0.066009123056486 26 0 0.00817424056313886 -0.00817424056313886 27 0 0.0144854810572694 -0.0144854810572694 28 0 0.0173010927062556 -0.0173010927062556 29 0 0.00817424056313886 -0.00817424056313886 30 0 0.0780465130700821 -0.0780465130700821 31 0 0.0115180777575777 -0.0115180777575777 32 0 0.066009123056486 -0.066009123056486 33 0 0.0115180777575777 -0.0115180777575777 34 1 0.0780465130700821 0.921953486929918 35 0 0.0242870285101468 -0.0242870285101468 36 0 0.106892552575036 -0.106892552575036 37 0 0.0144854810572694 -0.0144854810572694 38 0 0.0173010927062556 -0.0173010927062556 39 0 0.153142538219492 -0.153142538219492 40 0 0.0780465130700821 -0.0780465130700821 41 0 0.066009123056486 -0.066009123056486 42 0 0.066009123056486 -0.066009123056486 43 0 0.175905232564779 -0.175905232564779 44 0 0.0908445198161321 -0.0908445198161321 45 0 0.175905232564779 -0.175905232564779 46 0 0.0908445198161321 -0.0908445198161321 47 0 0.106892552575036 -0.106892552575036 48 0 0.066009123056486 -0.066009123056486 49 0 0.0908445198161321 -0.0908445198161321 50 0 0.106892552575036 -0.106892552575036 51 0 0.0908445198161321 -0.0908445198161321 52 0 0.0173010927062556 -0.0173010927062556 53 0 0.0115180777575777 -0.0115180777575777 54 0 0.0908445198161321 -0.0908445198161321 55 0 0.175905232564779 -0.175905232564779 56 0 0.0908445198161321 -0.0908445198161321 57 1 0.175905232564779 0.824094767435221 58 0 0.0908445198161321 -0.0908445198161321 59 0 0.0908445198161321 -0.0908445198161321 60 0 0.066009123056486 -0.066009123056486 61 0 0.131170694811021 -0.131170694811021 62 0 0.0096343776786181 -0.0096343776786181 63 0 0.0203580654727409 -0.0203580654727409 64 0 0.0908445198161321 -0.0908445198161321 65 0 0.0908445198161321 -0.0908445198161321 66 0 0.0242870285101468 -0.0242870285101468 67 0 0.0115180777575777 -0.0115180777575777 68 0 0.175905232564779 -0.175905232564779 69 1 0.00683362080067247 0.993166379199327 70 1 0.0096343776786181 0.990365622321382 71 0 0.0908445198161321 -0.0908445198161321 72 0 0.0908445198161321 -0.0908445198161321 73 0 0.175905232564779 -0.175905232564779 74 0 0.066009123056486 -0.066009123056486 75 0 0.0908445198161321 -0.0908445198161321 76 0 0.0115180777575777 -0.0115180777575777 77 0 0.0115180777575777 -0.0115180777575777 78 0 0.0908445198161321 -0.0908445198161321 79 0 0.0908445198161321 -0.0908445198161321 80 0 0.0908445198161321 -0.0908445198161321 81 0 0.0096343776786181 -0.0096343776786181 82 0 0.066009123056486 -0.066009123056486 83 0 0.0144854810572694 -0.0144854810572694 84 0 0.066009123056486 -0.066009123056486 85 0 0.0908445198161321 -0.0908445198161321 86 0 0.0242870285101468 -0.0242870285101468 87 0 0.0144854810572694 -0.0144854810572694 88 1 0.0144854810572694 0.98551451894273 89 0 0.0173010927062556 -0.0173010927062556 90 0 0.0908445198161321 -0.0908445198161321 91 0 0.0780465130700821 -0.0780465130700821 92 0 0.106892552575036 -0.106892552575036 93 0 0.0242870285101468 -0.0242870285101468 94 0 0.0908445198161321 -0.0908445198161321 95 0 0.131170694811021 -0.131170694811021 96 0 0.106892552575036 -0.106892552575036 97 0 0.0144854810572694 -0.0144854810572694 98 1 0.0780465130700821 0.921953486929918 99 0 0.106892552575036 -0.106892552575036 100 0 0.153142538219492 -0.153142538219492 101 0 0.0780465130700821 -0.0780465130700821 102 0 0.131170694811021 -0.131170694811021 103 0 0.203614279014127 -0.203614279014127 104 0 0.0203580654727409 -0.0203580654727409 105 0 0.0908445198161321 -0.0908445198161321 106 0 0.0203580654727409 -0.0203580654727409 107 1 0.175905232564779 0.824094767435221 108 0 0.00817424056313886 -0.00817424056313886 109 1 0.0115180777575777 0.988481922242422 110 0 0.0908445198161321 -0.0908445198161321 111 0 0.00683362080067247 -0.00683362080067247 112 0 0.203614279014127 -0.203614279014127 113 0 0.106892552575036 -0.106892552575036 114 0 0.106892552575036 -0.106892552575036 115 0 0.0144854810572694 -0.0144854810572694 116 0 0.106892552575036 -0.106892552575036 117 0 0.0096343776786181 -0.0096343776786181 118 0 0.153142538219492 -0.153142538219492 119 0 0.00817424056313886 -0.00817424056313886 120 0 0.0908445198161321 -0.0908445198161321 121 0 0.106892552575036 -0.106892552575036 122 0 0.00683362080067247 -0.00683362080067247 123 0 0.0908445198161321 -0.0908445198161321 124 0 0.066009123056486 -0.066009123056486 125 0 0.066009123056486 -0.066009123056486 126 0 0.0908445198161321 -0.0908445198161321 127 0 0.203614279014127 -0.203614279014127 128 0 0.0908445198161321 -0.0908445198161321 129 1 0.203614279014127 0.796385720985873 130 0 0.175905232564779 -0.175905232564779 Type I & II errors for various threshold values Threshold Type I Type II 0.01 0.181818181818182 0.907563025210084 0.02 0.363636363636364 0.747899159663866 0.03 0.363636363636364 0.680672268907563 0.04 0.363636363636364 0.680672268907563 0.05 0.363636363636364 0.680672268907563 0.06 0.363636363636364 0.680672268907563 0.07 0.363636363636364 0.554621848739496 0.08 0.545454545454545 0.521008403361345 0.09 0.545454545454545 0.521008403361345 0.1 0.545454545454545 0.26890756302521 0.11 0.636363636363636 0.142857142857143 0.12 0.636363636363636 0.142857142857143 0.13 0.636363636363636 0.142857142857143 0.14 0.727272727272727 0.117647058823529 0.15 0.727272727272727 0.117647058823529 0.16 0.727272727272727 0.0840336134453782 0.17 0.727272727272727 0.0840336134453782 0.18 0.909090909090909 0.0252100840336134 0.19 0.909090909090909 0.0252100840336134 0.2 0.909090909090909 0.0252100840336134 0.21 1 0 0.22 1 0 0.23 1 0 0.24 1 0 0.25 1 0 0.26 1 0 0.27 1 0 0.28 1 0 0.29 1 0 0.3 1 0 0.31 1 0 0.32 1 0 0.33 1 0 0.34 1 0 0.35 1 0 0.36 1 0 0.37 1 0 0.38 1 0 0.39 1 0 0.4 1 0 0.41 1 0 0.42 1 0 0.43 1 0 0.44 1 0 0.45 1 0 0.46 1 0 0.47 1 0 0.48 1 0 0.49 1 0 0.5 1 0 0.51 1 0 0.52 1 0 0.53 1 0 0.54 1 0 0.55 1 0 0.56 1 0 0.57 1 0 0.58 1 0 0.59 1 0 0.6 1 0 0.61 1 0 0.62 1 0 0.63 1 0 0.64 1 0 0.65 1 0 0.66 1 0 0.67 1 0 0.68 1 0 0.69 1 0 0.7 1 0 0.71 1 0 0.72 1 0 0.73 1 0 0.74 1 0 0.75 1 0 0.76 1 0 0.77 1 0 0.78 1 0 0.79 1 0 0.8 1 0 0.81 1 0 0.82 1 0 0.83 1 0 0.84 1 0 0.85 1 0 0.86 1 0 0.87 1 0 0.88 1 0 0.89 1 0 0.9 1 0 0.91 1 0 0.92 1 0 0.93 1 0 0.94 1 0 0.95 1 0 0.96 1 0 0.97 1 0 0.98 1 0 0.99 1 0

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/10/t1223630382q08n7j4h2tlnswy/17y811223630062.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/10/t1223630382q08n7j4h2tlnswy/17y811223630062.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/10/t1223630382q08n7j4h2tlnswy/225sr1223630062.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/10/t1223630382q08n7j4h2tlnswy/225sr1223630062.ps (open in new window)

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

library(brglm) roc.plot <- function (sd, sdc, newplot = TRUE, ...) { sall <- sort(c(sd, sdc)) sens <- 0 specc <- 0 for (i in length(sall):1) { sens <- c(sens, mean(sd >= sall[i], na.rm = T)) specc <- c(specc, mean(sdc >= sall[i], na.rm = T)) } if (newplot) { plot(specc, sens, xlim = c(0, 1), ylim = c(0, 1), type = 'l', xlab = '1-specificity', ylab = 'sensitivity', main = 'ROC plot', ...) abline(0, 1) } else lines(specc, sens, ...) npoints <- length(sens) area <- sum(0.5 * (sens[-1] + sens[-npoints]) * (specc[-1] - specc[-npoints])) lift <- (sens - specc)[-1] cutoff <- sall[lift == max(lift)][1] sensopt <- sens[-1][lift == max(lift)][1] specopt <- 1 - specc[-1][lift == max(lift)][1] list(area = area, cutoff = cutoff, sensopt = sensopt, specopt = specopt) } roc.analysis <- function (object, newdata = NULL, newplot = TRUE, ...) { if (is.null(newdata)) { sd <- object$fitted[object$y == 1] sdc <- object$fitted[object$y == 0] } else { sd <- predict(object, newdata, type = 'response')[newdata$y == 1] sdc <- predict(object, newdata, type = 'response')[newdata$y == 0] } roc.plot(sd, sdc, newplot, ...) } hosmerlem <- function (y, yhat, g = 10) { cutyhat <- cut(yhat, breaks = quantile(yhat, probs = seq(0, 1, 1/g)), include.lowest = T) obs <- xtabs(cbind(1 - y, y) ~ cutyhat) expect <- xtabs(cbind(1 - yhat, yhat) ~ cutyhat) chisq <- sum((obs - expect)^2/expect) P <- 1 - pchisq(chisq, g - 2) c('X^2' = chisq, Df = g - 2, 'P(>Chi)' = P) } x <- as.data.frame(t(y)) r <- brglm(x) summary(r) rc <- summary(r)$coeff try(hm <- hosmerlem(y[1,],r$fitted.values),silent=T) try(hm,silent=T) bitmap(file='test0.png') ra <- roc.analysis(r) dev.off() te <- array(0,dim=c(2,99)) for (i in 1:99) { threshold <- i / 100 numcorr1 <- 0 numfaul1 <- 0 numcorr0 <- 0 numfaul0 <- 0 for (j in 1:length(r$fitted.values)) { if (y[1,j] > 0.99) { if (r$fitted.values[j] >= threshold) numcorr1 = numcorr1 + 1 else numfaul1 = numfaul1 + 1 } else { if (r$fitted.values[j] < threshold) numcorr0 = numcorr0 + 1 else numfaul0 = numfaul0 + 1 } } te[1,i] <- numfaul1 / (numfaul1 + numcorr1) te[2,i] <- numfaul0 / (numfaul0 + numcorr0) } bitmap(file='test1.png') op <- par(mfrow=c(2,2)) plot((1:99)/100,te[1,],xlab='Threshold',ylab='Type I error', main='1 - Specificity') plot((1:99)/100,te[2,],xlab='Threshold',ylab='Type II error', main='1 - Sensitivity') plot(te[1,],te[2,],xlab='Type I error',ylab='Type II error', main='(1-Sens.) vs (1-Spec.)') plot((1:99)/100,te[1,]+te[2,],xlab='Threshold',ylab='Sum of Type I & II error', main='(1-Sens.) + (1-Spec.)') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Coefficients of Bias-Reduced Logistic Regression',5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'t-stat',header=TRUE) a<-table.element(a,'2-sided p-value',header=TRUE) a<-table.row.end(a) for (i in 1:length(rc[,1])) { a<-table.row.start(a) a<-table.element(a,labels(rc)[[1]][i],header=TRUE) a<-table.element(a,rc[i,1]) a<-table.element(a,rc[i,2]) a<-table.element(a,rc[i,3]) a<-table.element(a,2*(1-pt(abs(rc[i,3]),r$df.residual))) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Summary of Bias-Reduced Logistic Regression',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Deviance',1,TRUE) a<-table.element(a,r$deviance) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Penalized deviance',1,TRUE) a<-table.element(a,r$penalized.deviance) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual Degrees of Freedom',1,TRUE) a<-table.element(a,r$df.residual) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'ROC Area',1,TRUE) a<-table.element(a,ra$area) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Hosmer–Lemeshow test',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Chi-square',1,TRUE) phm <- array('NA',dim=3) for (i in 1:3) { try(phm[i] <- hm[i],silent=T) } a<-table.element(a,phm[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Degrees of Freedom',1,TRUE) a<-table.element(a,phm[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'P(>Chi)',1,TRUE) a<-table.element(a,phm[3]) 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,'Fit of Logistic Regression',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Index',1,TRUE) a<-table.element(a,'Actual',1,TRUE) a<-table.element(a,'Fitted',1,TRUE) a<-table.element(a,'Error',1,TRUE) a<-table.row.end(a) for (i in 1:length(r$fitted.values)) { a<-table.row.start(a) a<-table.element(a,i,1,TRUE) a<-table.element(a,y[1,i]) a<-table.element(a,r$fitted.values[i]) a<-table.element(a,y[1,i]-r$fitted.values[i]) 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,'Type I & II errors for various threshold values',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Threshold',1,TRUE) a<-table.element(a,'Type I',1,TRUE) a<-table.element(a,'Type II',1,TRUE) a<-table.row.end(a) for (i in 1:99) { a<-table.row.start(a) a<-table.element(a,i/100,1,TRUE) a<-table.element(a,te[1,i]) a<-table.element(a,te[2,i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable3.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