multiple regression paper

*The author of this computation has been verified*

R Software Module: Patrick.Wessa/rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Tue, 14 Dec 2010 09:38:58 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv.htm/, Retrieved Tue, 14 Dec 2010 10:48:21 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

6.3 1 3 2.1 2547 4 9.1 10.55 4 15.8 0.023 1 5.2 160 4 10.9 3.3 1 8.3 52.16 1 11 0.425 4 3.2 465 5 6.3 0.075 1 8.6 3 2 6.6 0.785 2 9.5 0.2 2 3.3 27.66 5 11 0.12 2 4.7 85 1 10.4 0.101 3 7.4 1.04 4 2.1 521 5 7.7 0.005 4 17.9 0.01 1 6.1 62 1 11.9 0.023 3 10.8 0.048 3 13.8 1.7 1 14.3 3.5 1 15.2 0.48 2 10 10 4 11.9 1.62 2 6.5 192 4 7.5 2.5 5 10.6 0.28 3 7.4 4.235 1 8.4 6.8 2 5.7 0.75 2 4.9 3.6 3 3.2 55.5 5 11 0.9 2 4.9 2 3 13.2 0.104 2 9.7 4.19 4 12.8 3.5 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation SWS[t] = + 12.4555798543405 -0.00260911694231715BodyW[t] -1.28218471390278ODI[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 12.4555798543405 1.081005 11.5222 0 0 BodyW -0.00260911694231715 0.00127 -2.0539 0.046731 0.023365 ODI -1.28218471390278 0.368019 -3.484 0.001236 0.000618 Multiple Linear Regression - Regression Statistics Multiple R 0.596267051905123 R-squared 0.355534397187627 Adjusted R-squared 0.322484879094684 F-TEST (value) 10.7576272727423 F-TEST (DF numerator) 2 F-TEST (DF denominator) 39 p-value 0.000190280805482668 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.15864460417671 Sum Squared Residuals 389.104393684292 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.3 8.60641659568987 -2.30641659568987 2 2.1 0.681420146647628 1.41857985335237 3 9.1 7.29931481498796 1.80068518501204 4 15.8 11.1733351307481 4.62666486925192 5 5.2 6.90938228795867 -1.70938228795867 6 10.9 11.1647850545281 -0.264785054528106 7 8.3 11.0373036007265 -2.73730360072649 8 11 7.32573212402893 3.67426787597107 9 3.2 4.83141690664916 -1.63141690664916 10 6.3 11.1731994566671 -4.87319945666708 11 8.6 9.88338307570802 -1.28338307570802 12 6.6 9.88916226973525 -3.28916226973525 13 9.5 9.8906886031465 -0.390688603146509 14 3.3 5.97248811020214 -2.67248811020214 15 11 9.8908973325019 1.10910266749811 16 4.7 10.9516202003408 -6.2516202003408 17 10.4 8.60876219182102 1.79123780817898 18 7.4 7.3241275171094 0.0758724828905983 19 2.1 4.68530635787940 -2.58530635787940 20 7.7 7.3268279531447 0.373172046855300 21 17.9 11.1733690492683 6.72663095073167 22 6.1 11.0116298900141 -4.91162989001409 23 11.9 8.60896570294252 3.29103429705748 24 10.8 8.60890047501896 2.19109952498104 25 13.8 11.1689596416358 2.63104035836419 26 14.3 11.1642632311396 3.13573676886036 27 15.2 9.88995805040266 5.31004194959734 28 10 7.30074982930624 2.69925017069376 29 11.9 9.88698365708842 2.01301634291158 30 6.5 6.82589054580452 -0.325890545804519 31 7.5 6.03813349247084 1.46186650752916 32 10.6 8.60829515988834 1.99170484011166 33 7.4 11.1623455301870 -3.76234553018704 34 8.4 9.87346843132721 -1.47346843132722 35 5.7 9.88925358882823 -4.18925358882823 36 4.9 8.59963289163985 -3.69963289163985 37 3.2 5.89985029452803 -2.69985029452803 38 11 9.88886222128689 1.11113777871311 39 4.9 8.60380747874756 -3.70380747874756 40 13.2 9.89093907837297 3.30906092162703 41 9.7 7.3159087987411 2.38409120125890 42 12.8 11.1642632311396 1.63573676886036 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.535716424562647 0.928567150874705 0.464283575437353 7 0.575878308666456 0.848243382667088 0.424121691333544 8 0.594100404240695 0.81179919151861 0.405899595759305 9 0.509370119143885 0.98125976171223 0.490629880856115 10 0.636830824485833 0.726338351028333 0.363169175514167 11 0.530057552620195 0.93988489475961 0.469942447379805 12 0.504459627955339 0.991080744089321 0.495540372044661 13 0.398605559704363 0.797211119408726 0.601394440295637 14 0.370791301020504 0.741582602041008 0.629208698979496 15 0.303195381515265 0.60639076303053 0.696804618484735 16 0.503717700513812 0.992564598972375 0.496282299486188 17 0.451582972949025 0.903165945898051 0.548417027050975 18 0.358137564548431 0.716275129096863 0.641862435451569 19 0.326337313348861 0.652674626697722 0.673662686651139 20 0.249144081238706 0.498288162477412 0.750855918761294 21 0.582059857957743 0.835880284084515 0.417940142042257 22 0.665114123862316 0.669771752275367 0.334885876137684 23 0.660226211909544 0.679547576180912 0.339773788090456 24 0.604196699926048 0.791606600147904 0.395803300073952 25 0.561674477371757 0.876651045256485 0.438325522628243 26 0.546465236393336 0.907069527213328 0.453534763606664 27 0.707992254636305 0.58401549072739 0.292007745363695 28 0.677564825264249 0.644870349471502 0.322435174735751 29 0.633782513717054 0.732434972565892 0.366217486282946 30 0.61060705770592 0.77878588458816 0.38939294229408 31 0.501906721577069 0.996186556845863 0.498093278422932 32 0.436401712786906 0.872803425573813 0.563598287213094 33 0.428915269787507 0.857830539575014 0.571084730212493 34 0.315601554792573 0.631203109585146 0.684398445207427 35 0.397470864479233 0.794941728958465 0.602529135520767 36 0.445495445461359 0.890990890922718 0.554504554538641 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/10k4vq1292319528.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/10k4vq1292319528.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/1w3yx1292319528.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/1w3yx1292319528.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/2w3yx1292319528.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/2w3yx1292319528.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/37cxi1292319528.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/37cxi1292319528.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/47cxi1292319528.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/47cxi1292319528.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/57cxi1292319528.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/57cxi1292319528.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/6z3w31292319528.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/6z3w31292319528.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/7sceo1292319528.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/7sceo1292319528.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/8sceo1292319528.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/8sceo1292319528.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/9sceo1292319528.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292320091y89j494dn7q8opv/9sceo1292319528.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 0 ;

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