*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: Sun, 19 Dec 2010 13:09:36 +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/19/t12927640439tjt8exdomg5i81.htm/, Retrieved Sun, 19 Dec 2010 14:07:23 +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/19/t12927640439tjt8exdomg5i81.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 0 3 14,3 0,544068044 1 9,1 1,02325246 4 15,8 -1,638272164 1 10,9 0,51851394 1 8,3 1,717337583 1 11 -0,37161107 4 3,2 2,667452953 5 2,1 3,406028945 4 7,4 0,626853415 1 9,5 -0,698970004 2 3,3 1,441852176 5 5,7 -0,124938737 2 7,4 0,017033339 4 11 -0,920818754 2 6,6 -0,105130343 2 2,1 2,716837723 5 17,9 -2 1 12,8 0,544068044 1 6,1 1,792391689 1 6,3 -1,124938737 1 11,9 -1,638272164 3 13,8 0,230448921 1 15,2 -0,318758763 2 10 1 4 11,9 0,209515015 2 6,5 2,283301229 4 7,5 0,397940009 5 10,6 -0,552841969 3 8,4 0,832508913 2 4,9 0,556302501 3 4,7 1,929418926 1 3,2 1,744292983 5 10,4 -0,995678626 3 5,2 2,204119983 4 11 -0,045757491 2 4,9 0,301029996 3 13,2 -0,982966661 2 9,7 0,622214023 4

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation SWS[t] = + 11.6991087210001 -1.81485814734191`log(wb)`[t] -0.80621691930904D[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) 11.6991087210001 0.941095 12.4314 0 0 `log(wb)` -1.81485814734191 0.37295 -4.8662 2.3e-05 1.1e-05 D -0.80621691930904 0.336956 -2.3927 0.022068 0.011034 Multiple Linear Regression - Regression Statistics Multiple R 0.757704457897525 R-squared 0.574116045517782 Adjusted R-squared 0.550455825824325 F-TEST (value) 24.2650344314664 F-TEST (DF numerator) 2 F-TEST (DF denominator) 36 p-value 2.12443282854302e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.66067288469349 Sum Squared Residuals 254.850487176355 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.3 9.28045796307299 -2.98045796307299 2 14.3 9.90548547932928 4.39451452067072 3 9.1 6.61718297994529 2.48281702005471 4 15.8 13.8661233860899 1.93387661391007 5 10.9 9.9518625531717 0.94813744682829 6 8.3 7.77616769744705 0.523832302552955 7 11 9.14866242179589 1.85133757820411 8 3.2 2.82697540005161 0.373024599948394 9 2.1 2.29278166284831 -0.192781662848311 10 7.4 9.75524177428921 -2.35524177428921 11 9.5 11.3552062888890 -1.85520628888904 12 3.3 5.05126695557864 -1.75126695557864 13 5.7 10.3134209671451 -4.61342096714508 14 7.4 8.44332794970336 -1.04332794970336 15 11 11.7578303003042 -0.757830300304155 16 6.6 10.2774715419084 -3.67747154190843 17 2.1 2.7373490478625 -0.6373490478625 18 17.9 14.5226080963749 3.37739190362511 19 12.8 9.90548547932929 2.89451452067071 20 6.1 7.63995514168148 -1.53995514168148 21 6.3 12.9344960337960 -6.63449603379604 22 11.9 12.2536895474719 -0.353689547471852 23 13.8 10.4746596998681 3.32534030013194 24 15.2 10.6651768204492 4.53482317955079 25 10 6.65938289642203 3.34061710357797 26 11.9 9.70643485041881 2.19356514958119 27 6.5 4.33037320547749 2.16962679452251 28 7.5 6.94581945696794 0.554180543032061 29 10.6 10.2837877147052 0.316212285294819 30 8.4 8.57578929888921 -0.175789298889214 31 4.9 8.27084783674645 -3.37084783674645 32 4.7 7.39127014420428 -2.69127014420428 33 3.2 4.50237979290602 -1.30237979290602 34 10.4 11.0874734296033 -0.687473429603286 35 5.2 4.47407593489727 0.725924065102726 36 11 10.1697182377253 0.8302817622747 37 4.9 8.73413122223808 -3.83413122223808 38 13.2 11.8706199356634 1.32938006433665 39 9.7 7.345010854732 2.35498914526799 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.712187317271278 0.575625365457443 0.287812682728722 7 0.596031172984503 0.807937654030995 0.403968827015497 8 0.443469083852791 0.886938167705582 0.556530916147209 9 0.314913513837011 0.629827027674022 0.685086486162989 10 0.383692779738502 0.767385559477005 0.616307220261498 11 0.363931831610967 0.727863663221935 0.636068168389033 12 0.302725560090153 0.605451120180306 0.697274439909847 13 0.506416030895124 0.987167938209751 0.493583969104876 14 0.40759565058882 0.81519130117764 0.59240434941118 15 0.31203306600729 0.62406613201458 0.68796693399271 16 0.372820354312307 0.745640708624614 0.627179645687693 17 0.283998062732427 0.567996125464854 0.716001937267573 18 0.346617301558219 0.693234603116437 0.653382698441781 19 0.355475504163258 0.710951008326516 0.644524495836742 20 0.295695785358704 0.591391570717408 0.704304214641296 21 0.73928158571412 0.521436828571761 0.260718414285881 22 0.665948284357399 0.668103431285202 0.334051715642601 23 0.697427813259401 0.605144373481199 0.302572186740599 24 0.841280365175721 0.317439269648557 0.158719634824279 25 0.87113482885487 0.257730342290258 0.128865171145129 26 0.874577049769459 0.250845900461083 0.125422950230541 27 0.881125657518662 0.237748684962677 0.118874342481338 28 0.809434321670801 0.381131356658398 0.190565678329199 29 0.712547243316935 0.574905513366129 0.287452756683065 30 0.606569034072032 0.786861931855936 0.393430965927968 31 0.632457155873452 0.735085688253096 0.367542844126548 32 0.542013859135083 0.915972281729833 0.457986140864917 33 0.401089266390941 0.802178532781883 0.598910733609059 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/19/t12927640439tjt8exdomg5i81/109df01292764168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/109df01292764168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/1lc071292764168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/1lc071292764168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/2lc071292764168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/2lc071292764168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/3lc071292764168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/3lc071292764168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/4vlh91292764168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/4vlh91292764168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/5vlh91292764168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/5vlh91292764168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/6vlh91292764168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/6vlh91292764168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/76czu1292764168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/76czu1292764168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/8z4yf1292764168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/8z4yf1292764168.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/9z4yf1292764168.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927640439tjt8exdomg5i81/9z4yf1292764168.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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