*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, 14 Dec 2010 20:55:14 +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/t1292360107jabv53hkzybl3hl.htm/, Retrieved Tue, 14 Dec 2010 21:55:10 +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/t1292360107jabv53hkzybl3hl.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 1000.00 3 2.1 2547000.00 4 9.1 10550.00 4 15.8 0.02 1 5.2 160000.00 4 10.9 3300.00 1 8.3 52160.00 1 11 0.43 4 3.2 465000.00 5 7.6 0.55 2 6.3 0.08 1 8.6 3000.00 2 6.6 0.79 2 9.5 0.20 2 4.8 1410.00 1 12 60000.00 1 3.3 27660.00 5 11 0.12 2 4.7 85000.00 1 10.4 0.10 3 7.4 1040.00 4 2.1 521000.00 5 7.7 0.01 4 17.9 0.01 1 6.1 62000.00 1 8.2 0.12 1 8.4 1350.00 3 11.9 0.02 3 10.8 0.05 3 13.8 1700.00 1 14.3 3500.00 1 15.2 0.48 2 10 10000.00 4 11.9 1620.00 2 6.5 192000.00 4 7.5 2500.00 5 10.6 0.28 3 7.4 4235.00 1 8.4 6800.00 2 5.7 0.75 2 4.9 3600.00 3 3.2 55500.00 5 8.1 0.06 2 11 0.90 2 4.9 2000.00 3 13.2 0.10 2 9.7 4190.00 4 12.8 3500.00 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Cons[t] = + 11.7472992244928 -2.59759973978495e-06Inc[t] -1.10910450405552Price[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) 11.7472992244928 0.976676 12.0278 0 0 Inc -2.59759973978495e-06 1e-06 -2.0657 0.044644 0.022322 Price -1.10910450405552 0.346701 -3.199 0.002527 0.001264 Multiple Linear Regression - Regression Statistics Multiple R 0.548131167864913 R-squared 0.300447777184954 Adjusted R-squared 0.269356567282063 F-TEST (value) 9.66343150116579 F-TEST (DF numerator) 2 F-TEST (DF denominator) 45 p-value 0.000322443161042463 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.13399795049866 Sum Squared Residuals 441.987441917842 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.3 8.4173881125865 -2.11738811258649 2 2.1 0.69479467103849 1.40520532896151 3 9.1 7.28347653101603 1.81652346898397 4 15.8 10.6381946684853 5.16180533151467 5 5.2 6.89526524990517 -1.69526524990517 6 10.9 10.629622641296 0.270377358703963 7 8.3 10.5027039180101 -2.20270391801014 8 11 7.31088009130288 3.68911990869712 9 3.2 4.99389282521524 -1.79389282521524 10 7.6 9.52908878770195 -1.92908878770195 11 6.3 10.6381945126293 -4.33819451262935 12 8.6 9.52129741716245 -0.921297417162453 13 6.6 9.52908816427801 -2.92908816427801 14 9.5 9.52908969686186 -0.0290896968618593 15 4.8 10.6345321048042 -5.83453210480423 16 12 10.4823387360502 1.51766126394977 17 3.3 6.12992709541279 -2.82992709541279 18 11 9.52908990466984 1.47091009533016 19 4.7 10.4173987425556 -5.71739874255561 20 10.4 8.41998545256631 1.98001454743369 21 7.4 7.30817970454139 0.0918202954586114 22 2.1 4.84842723978728 -2.74842723978728 23 7.7 7.31088118229477 0.389118817705232 24 17.9 10.6381946944613 7.26180530553867 25 6.1 10.4771435365707 -4.37714353657066 26 8.2 10.6381944087254 -2.43819440872536 27 8.4 8.41647895267758 -0.0164789526775763 28 11.9 8.4199856603743 3.48001433962571 29 10.8 8.4199855824463 2.3800144175537 30 13.8 10.6337788008797 3.16622119912031 31 14.3 10.6291031213481 3.67089687865192 32 15.2 9.52908896953393 5.67091103046607 33 10 7.28490521087292 2.71509478912708 34 11.9 9.52488210480336 2.37511789519664 35 6.5 6.81214205823205 -0.312142058232054 36 7.5 6.19528270486578 1.30471729513422 37 10.6 8.41998498499836 2.18001501500164 38 7.4 10.6271938855393 -3.22719388553934 39 8.4 9.51142653815127 -1.11142653815127 40 5.7 9.529088268182 -3.829088268182 41 4.9 8.41063435326306 -3.51063435326306 42 3.2 6.05760991865718 -2.85760991865718 43 8.1 9.52909006052582 -1.42909006052582 44 11 9.52908787854204 1.47091212145796 45 4.9 8.41479051284672 -3.51479051284672 46 13.2 9.52908995662183 3.67091004337817 47 9.7 7.29999726536107 2.40000273463893 48 12.8 10.6291031213481 2.17089687865192 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.548065966241032 0.903868067517935 0.451934033758968 7 0.589946205394782 0.820107589210435 0.410053794605218 8 0.611078169250891 0.777843661498217 0.388921830749109 9 0.528331376951531 0.943337246096937 0.471668623048469 10 0.470308422936735 0.94061684587347 0.529691577063265 11 0.563137743247323 0.873724513505353 0.436862256752677 12 0.455885911883235 0.91177182376647 0.544114088116765 13 0.420006906664814 0.840013813329628 0.579993093335186 14 0.323855286905817 0.647710573811634 0.676144713094183 15 0.491848540880776 0.98369708176155 0.508151459119224 16 0.457398231509379 0.914796463018757 0.542601768490621 17 0.439748814108082 0.879497628216164 0.560251185891918 18 0.385390961321994 0.770781922643988 0.614609038678006 19 0.535389114144826 0.929221771710348 0.464610885855174 20 0.492693628071461 0.985387256142923 0.507306371928539 21 0.405355520892928 0.810711041785857 0.594644479107072 22 0.373264304434698 0.746528608869396 0.626735695565302 23 0.297262701393048 0.594525402786095 0.702737298606952 24 0.672481232413992 0.655037535172017 0.327518767586008 25 0.715408050377561 0.569183899244878 0.284591949622439 26 0.702034308320413 0.595931383359174 0.297965691679587 27 0.624675505068031 0.750648989863937 0.375324494931969 28 0.629976525721377 0.740046948557247 0.370023474278623 29 0.582595670279091 0.834808659441817 0.417404329720909 30 0.56052318683067 0.878953626338659 0.43947681316933 31 0.569578251962044 0.860843496075911 0.430421748037956 32 0.74875943617238 0.502481127655242 0.251240563827621 33 0.726420802507983 0.547158394984033 0.273579197492017 34 0.699265586873751 0.601468826252498 0.300734413126249 35 0.682675809009839 0.634648381980322 0.317324190990161 36 0.5843510113201 0.8312979773598 0.4156489886799 37 0.532819137508318 0.934361724983364 0.467180862491682 38 0.514020150373789 0.971959699252422 0.485979849626211 39 0.398911663561878 0.797823327123756 0.601088336438122 40 0.464090383536003 0.928180767072006 0.535909616463997 41 0.488989597149079 0.977979194298158 0.511010402850921 42 0.356848162792289 0.713696325584578 0.643151837207711 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/t1292360107jabv53hkzybl3hl/10imzq1292360105.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/10imzq1292360105.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/1t22f1292360105.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/1t22f1292360105.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/2t22f1292360105.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/2t22f1292360105.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/3t22f1292360105.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/3t22f1292360105.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/44c2h1292360105.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/44c2h1292360105.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/54c2h1292360105.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/54c2h1292360105.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/6e31k1292360105.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/6e31k1292360105.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/7e31k1292360105.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/7e31k1292360105.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/87uin1292360105.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/87uin1292360105.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/97uin1292360105.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360107jabv53hkzybl3hl/97uin1292360105.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