Maandeffecten-Paper

*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: Tue, 21 Dec 2010 18:01:47 +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/21/t1292954391gerok26waaqds1w.htm/, Retrieved Tue, 21 Dec 2010 19:00:02 +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/21/t1292954391gerok26waaqds1w.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 «

8,1 9,9 11,5 23,4 25,4 27,9 26,1 18,8 14,1 11,5 15,8 12,4 4,5 -2,2 -4,2 -9,4 -14,5 -17,9 -15,1 -15,2 -15,7 -18 -18,1 -13,5 -9,9 -4,8 -1,7 -0,1 2,2 10,2 7,6 10,8 3,8 11 10,8 20,1 14,9 13 10,9 9,6 4 -1,1 -7,7 -8,9 -8 -7,1 -5,3 -2,5 -2,4 -2,9 -4,8 -7,2 1,7 2,2 13,4 12,3 13,7 4,4 -2,5

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation registraties_personenwagens[t] = + 4.12500000000001 -1.08500000000000M1[t] -1.525M2[t] -1.78499999999999M3[t] -0.86500000000001M4[t] -0.365000000000011M5[t] + 0.134999999999982M6[t] + 0.735000000000008M7[t] -0.565000000000003M8[t] -2.545M9[t] -3.76500000000001M10[t] -3.985M11[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) 4.12500000000001 6.629738 0.6222 0.53682 0.26841 M1 -1.08500000000000 8.894727 -0.122 0.903433 0.451717 M2 -1.525 8.894727 -0.1714 0.864606 0.432303 M3 -1.78499999999999 8.894727 -0.2007 0.841814 0.420907 M4 -0.86500000000001 8.894727 -0.0972 0.922942 0.461471 M5 -0.365000000000011 8.894727 -0.041 0.967441 0.483721 M6 0.134999999999982 8.894727 0.0152 0.987955 0.493977 M7 0.735000000000008 8.894727 0.0826 0.934494 0.467247 M8 -0.565000000000003 8.894727 -0.0635 0.949621 0.474811 M9 -2.545 8.894727 -0.2861 0.77604 0.38802 M10 -3.76500000000001 8.894727 -0.4233 0.674018 0.337009 M11 -3.985 8.894727 -0.448 0.656198 0.328099 Multiple Linear Regression - Regression Statistics Multiple R 0.120419064580162 R-squared 0.0145007511143612 Adjusted R-squared -0.216148009263129 F-TEST (value) 0.0628694084053545 F-TEST (DF numerator) 11 F-TEST (DF denominator) 47 p-value 0.999988437281317 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.2594755359841 Sum Squared Residuals 8263.2435 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.1 3.03999999999998 5.06000000000002 2 9.9 2.6 7.3 3 11.5 2.33999999999998 9.16000000000002 4 23.4 3.26000000000001 20.14 5 25.4 3.75999999999999 21.64 6 27.9 4.26000000000001 23.64 7 26.1 4.86 21.24 8 18.8 3.56000000000001 15.24 9 14.1 1.58000000000001 12.52 10 11.5 0.360000000000001 11.14 11 15.8 0.139999999999999 15.66 12 12.4 4.125 8.275 13 4.5 3.04000000000000 1.46000000000000 14 -2.2 2.60000000000001 -4.80000000000001 15 -4.2 2.34000000000000 -6.54 16 -9.4 3.25999999999999 -12.66 17 -14.5 3.75999999999998 -18.26 18 -17.9 4.25999999999999 -22.16 19 -15.1 4.86000000000001 -19.96 20 -15.2 3.56 -18.76 21 -15.7 1.58000000000001 -17.28 22 -18 0.360000000000003 -18.36 23 -18.1 0.140000000000004 -18.24 24 -13.5 4.125 -17.625 25 -9.9 3.04000000000001 -12.94 26 -4.8 2.60000000000001 -7.40000000000001 27 -1.7 2.34000000000000 -4.04 28 -0.1 3.25999999999999 -3.35999999999999 29 2.2 3.75999999999998 -1.55999999999998 30 10.2 4.25999999999999 5.94000000000001 31 7.6 4.86000000000001 2.73999999999999 32 10.8 3.56 7.24 33 3.8 1.58 2.22 34 11 0.359999999999999 10.64 35 10.8 0.140000000000001 10.66 36 20.1 4.12500000000001 15.975 37 14.9 3.04000000000001 11.86 38 13 2.60000000000001 10.4000000000000 39 10.9 2.34 8.56 40 9.6 3.25999999999999 6.34000000000001 41 4 3.75999999999999 0.240000000000014 42 -1.1 4.25999999999999 -5.35999999999999 43 -7.7 4.86000000000002 -12.5600000000000 44 -8.9 3.56 -12.46 45 -8 1.58000000000000 -9.58 46 -7.1 0.360000000000005 -7.46 47 -5.3 0.140000000000001 -5.44 48 -2.5 4.125 -6.625 49 -2.4 3.04000000000001 -5.44000000000001 50 -2.9 2.60000000000001 -5.50000000000001 51 -4.8 2.34000000000000 -7.14 52 -7.2 3.25999999999999 -10.46 53 1.7 3.75999999999998 -2.05999999999998 54 2.2 4.25999999999999 -2.05999999999999 55 13.4 4.86000000000001 8.54 56 12.3 3.56 8.74 57 13.7 1.58000000000000 12.12 58 4.4 0.359999999999999 4.04 59 -2.5 0.139999999999997 -2.64000000000000 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.293706594117367 0.587413188234734 0.706293405882633 16 0.69486974315083 0.610260513698339 0.305130256849170 17 0.91139525535008 0.177209489299840 0.0886047446499199 18 0.983197059807021 0.0336058803859573 0.0168029401929786 19 0.994342540113594 0.0113149197728128 0.00565745988640641 20 0.99692103397202 0.00615793205596074 0.00307896602798037 21 0.997841564635293 0.00431687072941367 0.00215843536470683 22 0.998685692946353 0.00262861410729316 0.00131430705364658 23 0.999283459828233 0.00143308034353424 0.000716540171767122 24 0.999578184031258 0.00084363193748474 0.00042181596874237 25 0.999533995020124 0.000932009959751519 0.000466004979875759 26 0.999158601731896 0.00168279653620835 0.000841398268104174 27 0.9982122516433 0.00357549671340105 0.00178774835670053 28 0.99627423379623 0.00745153240753921 0.00372576620376961 29 0.99241913147859 0.0151617370428213 0.00758086852141066 30 0.987618042400456 0.0247639151990886 0.0123819575995443 31 0.977679397990902 0.0446412040181964 0.0223206020090982 32 0.966026116735545 0.06794776652891 0.033973883264455 33 0.94196836402685 0.116063271946302 0.0580316359731511 34 0.927107733143547 0.145784533712907 0.0728922668564534 35 0.914783228034395 0.170433543931211 0.0852167719656054 36 0.9300410305072 0.139917938985602 0.0699589694928009 37 0.922833564786354 0.154332870427293 0.0771664352136464 38 0.90868543405685 0.182629131886300 0.0913145659431501 39 0.889750086378117 0.220499827243766 0.110249913621883 40 0.872921174614449 0.254157650771102 0.127078825385551 41 0.787080658430527 0.425838683138945 0.212919341569473 42 0.671862204781881 0.656275590436238 0.328137795218119 43 0.686737988371356 0.626524023257288 0.313262011628644 44 0.72393986687472 0.55212026625056 0.27606013312528 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.3 NOK 5% type I error level 14 0.466666666666667 NOK 10% type I error level 15 0.5 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/10bmo61292954500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/10bmo61292954500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/1ml9u1292954500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/1ml9u1292954500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/2xcqx1292954500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/2xcqx1292954500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/3xcqx1292954500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/3xcqx1292954500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/4xcqx1292954500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/4xcqx1292954500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/5xcqx1292954500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/5xcqx1292954500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/6q38i1292954500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/6q38i1292954500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/70up21292954500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/70up21292954500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/80up21292954500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/80up21292954500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/90up21292954500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292954391gerok26waaqds1w/90up21292954500.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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