Experiment Multiple Regression PS D Tg

*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: Sat, 11 Dec 2010 15:31:10 +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/11/t1292081368lxo1h29p8rbkxw3.htm/, Retrieved Sat, 11 Dec 2010 16:29:38 +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/11/t1292081368lxo1h29p8rbkxw3.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 «

2.0 3 42.0 1.8 4 624.0 0.7 4 180.0 3.9 1 35.0 1.0 4 392.0 3.6 1 63.0 1.4 1 230.0 1.5 4 112.0 0.7 5 281.0 2.1 1 42.0 4.1 2 42.0 1.2 2 120.0 0.5 5 148.0 3.4 2 16.0 1.5 1 310.0 3.4 3 28.0 0.8 4 68.0 0.8 5 336.0 2.0 1 50.0 1.9 1 267.0 1.3 3 19.0 5.6 1 12.0 3.1 1 120.0 1.8 2 140.0 0.9 4 170.0 1.8 2 17.0 1.9 4 115.0 0.9 5 31.0 2.6 3 21.0 2.4 1 52.0 1.2 2 164.0 0.9 2 225.0 0.5 3 225.0 0.6 5 151.0 2.3 2 60.0 0.5 3 200.0 2.6 2 46.0 0.6 4 210.0 6.6 1 14.0

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 PS[t] = + 3.73727356178825 -0.498628257437098D[t] -0.0032533153842104Tg[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) 3.73727356178825 0.37911 9.858 0 0 D -0.498628257437098 0.130456 -3.8222 0.000505 0.000252 Tg -0.0032533153842104 0.001433 -2.2704 0.029267 0.014634 Multiple Linear Regression - Regression Statistics Multiple R 0.666719264617542 R-squared 0.444514577812156 Adjusted R-squared 0.413654276579498 F-TEST (value) 14.4040906944144 F-TEST (DF numerator) 2 F-TEST (DF denominator) 36 p-value 2.53575908029102e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.07629394981051 Sum Squared Residuals 41.7027119903536 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2 2.10474954334012 -0.104749543340123 2 1.8 -0.287308267707432 2.08730826770743 3 0.7 1.15716376288198 -0.457163762881985 4 3.9 3.12477926590379 0.775220734096213 5 1 0.467460901429381 0.532539098570619 6 3.6 3.03368643514590 0.566313564854105 7 1.4 2.49038276598276 -1.09038276598276 8 1.5 1.37838920900829 0.121610790991707 9 0.7 0.329950651639638 0.370049348360362 10 2.1 3.10200605821431 -1.00200605821431 11 4.1 2.60337780077722 1.49662219922278 12 1.2 2.34961920080880 -1.14961920080881 13 0.5 0.762641597739621 -0.262641597739621 14 3.4 2.68796400076669 0.712035999233313 15 1.5 2.23011753524593 -0.730117535245926 16 3.4 2.15029595871906 1.24970404128094 17 0.8 1.52153508591355 -0.721535085913551 18 0.8 0.151018305508066 0.648981694491934 19 2 3.07597953514063 -1.07597953514063 20 1.9 2.37001009676697 -0.470010096766973 21 1.3 2.17957579717696 -0.879575797176958 22 5.6 3.19960551974063 2.40039448025937 23 3.1 2.8482474582459 0.251752541754098 24 1.8 2.28455289312460 -0.484552893124597 25 0.9 1.18969691672409 -0.28969691672409 26 1.8 2.68471068538248 -0.884710685382476 27 1.9 1.36862926285566 0.531370737144338 28 0.9 1.14327949769224 -0.243279497692238 29 2.6 2.17306916640854 0.426930833591463 30 2.4 3.06947290437221 -0.66947290437221 31 1.2 2.20647332390355 -1.00647332390355 32 0.9 2.00802108546671 -1.10802108546671 33 0.5 1.50939282802962 -1.00939282802962 34 0.6 0.75288165158699 -0.152881651586990 35 2.3 2.54481812386143 -0.244818123861429 36 0.5 1.59072571263488 -1.09072571263488 37 2.6 2.59036453924037 0.00963546075962544 38 0.6 1.05956430135567 -0.459564301355674 39 6.6 3.19309888897220 3.40690111102780 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0336149617526779 0.0672299235053558 0.966385038247322 7 0.582629204610294 0.834741590779412 0.417370795389706 8 0.431028123246939 0.862056246493878 0.568971876753061 9 0.313351626229227 0.626703252458454 0.686648373770773 10 0.291235982062559 0.582471964125119 0.70876401793744 11 0.462474967978972 0.924949935957944 0.537525032021028 12 0.497976715625335 0.99595343125067 0.502023284374665 13 0.398669626351157 0.797339252702314 0.601330373648843 14 0.354039718030021 0.708079436060041 0.64596028196998 15 0.322763516831035 0.64552703366207 0.677236483168965 16 0.341509997678282 0.683019995356565 0.658490002321718 17 0.307742785728936 0.615485571457871 0.692257214271064 18 0.336281622092707 0.672563244185414 0.663718377907293 19 0.376649301577644 0.753298603155287 0.623350698422356 20 0.299423059933946 0.598846119867891 0.700576940066054 21 0.325745869581994 0.651491739163989 0.674254130418006 22 0.653136488628587 0.693727022742826 0.346863511371413 23 0.565721902776301 0.868556194447399 0.434278097223699 24 0.474104014529102 0.948208029058203 0.525895985470898 25 0.384579224840519 0.769158449681038 0.615420775159481 26 0.446758927360744 0.893517854721487 0.553241072639256 27 0.386084082909654 0.772168165819308 0.613915917090346 28 0.320725607957796 0.641451215915592 0.679274392042204 29 0.246446437659138 0.492892875318277 0.753553562340862 30 0.299980816288758 0.599961632577517 0.700019183711241 31 0.245146307446801 0.490292614893602 0.754853692553199 32 0.165882217579333 0.331764435158665 0.834117782420667 33 0.095801144216936 0.191602288433872 0.904198855783064 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 1 0.0357142857142857 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/105x671292081460.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/105x671292081460.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/1gesd1292081460.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/1gesd1292081460.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/2gesd1292081460.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/2gesd1292081460.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/39n9g1292081460.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/39n9g1292081460.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/49n9g1292081460.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/49n9g1292081460.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/59n9g1292081460.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/59n9g1292081460.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/6jeqj1292081460.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/6jeqj1292081460.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/7u5741292081460.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/7u5741292081460.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/8u5741292081460.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/8u5741292081460.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/9u5741292081460.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081368lxo1h29p8rbkxw3/9u5741292081460.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