*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 16:27:00 +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/t1292084764yfxyyom9ml5kf3n.htm/, Retrieved Sat, 11 Dec 2010 17:26:14 +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/t1292084764yfxyyom9ml5kf3n.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 4,5 1 7 42 3 1 3 1,8 69 2.547 4.603 624 3 5 4 0,7 27 11 180 180 4 4 4 3,9 19 0,023 0,3 35 1 1 1 1 30,4 160 169 392 4 5 4 3,6 28 3 26 63 1 2 1 1,4 50 52 440 230 1 1 1 1,5 7 0,425 6 112 5 4 4 0,7 30 465 423 281 5 5 5 2,1 3,5 0,075 1 42 1 1 1 0 50 3 25 28 2 2 2 4,1 6 0,785 4 42 2 2 2 1,2 10,4 0,2 5 120 2 2 2 0,5 20 28 115 148 5 5 5 3,4 3,9 0,12 1 16 3 1 2 1,5 41 85 325 310 1 3 1 3,4 9 0,101 4 28 5 1 3 0,8 7,6 1 6 68 5 3 4 0,8 46 521 655 336 5 5 5 1,4 2,6 0,005 0,14 21,5 5 2 4 2 24 0,01 0,25 50 1 1 1 1,9 100 62 1.320 267 1 1 1 1,3 3,2 0,023 0,4 19 4 1 3 2 2 0,048 0,33 30 4 1 3 5,6 5 2 6 12 2 1 1 3,1 6,5 4 11 120 2 1 1 1,8 12 0,48 16 140 2 2 2 0,9 20,2 10 115 170 4 4 4 1,8 13 2 11 17 2 1 2 1,9 27 192 180 115 4 4 4 0,9 18 3 12 31 5 5 5 2,6 4,7 0,28 2 21 3 1 3 2,4 9,8 4 50 52 1 1 1 1,2 29 7 179 164 2 3 2 0,9 7 0,75 12 225 2 2 2 0,5 6 4 21 225 3 2 3 0,6 20 56 175 151 5 5 5 2,3 4,5 0,9 3 60 2 1 2 0,5 7,5 2 12 200 3 1 3 2,6 2,3 0,104 3 46 3 2 2 0,6 24 4 58 210 4 3 4 6,6 3 4 4 etc...

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 13 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation PS[t] = + 3.58737153871768 -0.0094560197051181L[t] + 0.00508884300409277Wb[t] -0.00390837673295982Wbr[t] -0.000786575888854624Tg[t] + 0.810406161455018P[t] + 0.327261431599046S[t] -1.66026239739375D[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) 3.58737153871768 0.467805 7.6685 0 0 L -0.0094560197051181 0.011399 -0.8295 0.412587 0.206293 Wb 0.00508884300409277 0.002756 1.8462 0.073579 0.03679 Wbr -0.00390837673295982 0.002151 -1.8174 0.07798 0.03899 Tg -0.000786575888854624 0.002026 -0.3882 0.700288 0.350144 P 0.810406161455018 0.373808 2.168 0.037252 0.018626 S 0.327261431599046 0.225862 1.4489 0.156514 0.078257 D -1.66026239739375 0.463823 -3.5795 0.001061 0.00053 Multiple Linear Regression - Regression Statistics Multiple R 0.731211507658874 R-squared 0.534670268932763 Adjusted R-squared 0.438867089007155 F-TEST (value) 5.58092402932702 F-TEST (DF numerator) 7 F-TEST (DF denominator) 34 p-value 0.000242790446010499 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.04033754608211 Sum Squared Residuals 36.7982751327971 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2 1.26720619236899 0.732793807631013 2 1.8 -0.134470097765854 1.93447009776585 3 0.7 0.452565590439178 0.247434409560822 4 3.9 2.85652673424004 1.04347326575996 5 1 1.38215221827602 -0.382152218276021 6 3.6 2.99136406719121 0.60863593280879 7 1.4 0.95599736839603 0.444002631603971 8 1.5 2.13182234320539 -0.631822343205394 9 0.7 1.18275773995869 -0.482757739958694 10 2.1 2.99511776457053 -0.895117764570533 11 0 1.96491393058276 -1.96491393058276 12 4.1 2.44077085930208 1.65922914069792 13 1.2 2.33092610337855 -1.13092610337855 14 0.5 0.361888171190629 0.138111828809371 15 3.4 2.97256525325018 0.427434746749823 16 1.5 2.25009348125726 -0.750093481257256 17 3.4 2.86362874938815 0.53637125061185 18 0.8 1.83642272362023 -1.03642272362023 19 0.8 0.366431556972319 0.433568443027681 20 1.4 1.61085685824459 -0.210856858244591 21 2 2.79757726125923 -0.797577261259228 22 1.9 2.21950821050824 -0.319508210508243 23 1.3 2.12881991170684 -0.828819911706844 24 2 2.13191560802199 -0.131915608021995 25 5.6 3.80519131225159 1.79480868774841 26 3.1 3.696692889041 -0.596692889041002 27 1.8 2.25849768605185 -0.458497686051852 28 0.9 0.773687927960818 0.126312072039182 29 1.8 2.04580599410783 -0.245805994107826 30 1.9 1.42477360695551 0.475226393044487 31 0.9 0.748171317989398 0.151828682010602 32 2.6 1.29771099879576 1.30228900120424 33 2.4 2.75614233041578 -0.356142330415776 34 1.2 1.80224281024561 -0.602242810245615 35 0.9 2.25592632856774 -1.35592632856774 36 0.5 1.39688946150080 -0.896889461500795 37 0.6 0.267513443661074 0.332486556338926 38 2.3 2.11802868493976 0.181971315060242 39 0.5 1.10010610215389 -0.60010610215389 40 2.6 3.28346086475779 -0.683460864757789 41 0.6 0.571275001682291 0.0287249983177086 42 6.6 3.84052463935822 2.75947536064177 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.889415192356413 0.221169615287175 0.110584807643587 12 0.908310145515471 0.183379708969058 0.091689854484529 13 0.944663471821004 0.110673056357992 0.0553365281789958 14 0.900621473307166 0.198757053385669 0.0993785266928344 15 0.872015911749053 0.255968176501894 0.127984088250947 16 0.816609069227583 0.366781861544833 0.183390930772417 17 0.749466975169671 0.501066049660658 0.250533024830329 18 0.744648562825293 0.510702874349413 0.255351437174706 19 0.67935420003276 0.64129159993448 0.32064579996724 20 0.597003372397963 0.805993255204073 0.402996627602037 21 0.569372988344453 0.861254023311095 0.430627011655547 22 0.45685833620463 0.91371667240926 0.54314166379537 23 0.528185311856685 0.94362937628663 0.471814688143315 24 0.582818813948354 0.834362372103291 0.417181186051646 25 0.680487898828463 0.639024202343075 0.319512101171537 26 0.649243457016803 0.701513085966394 0.350756542983197 27 0.552399784585884 0.895200430828231 0.447600215414116 28 0.434884335469386 0.869768670938773 0.565115664530614 29 0.376963635713993 0.753927271427986 0.623036364286007 30 0.424585017592288 0.849170035184576 0.575414982407712 31 0.290907426716765 0.58181485343353 0.709092573283235 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/11/t1292084764yfxyyom9ml5kf3n/10mtcr1292084805.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/10mtcr1292084805.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/1faff1292084805.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/1faff1292084805.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/2pjxi1292084805.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/2pjxi1292084805.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/3pjxi1292084805.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/3pjxi1292084805.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/4pjxi1292084805.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/4pjxi1292084805.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/50tel1292084805.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/50tel1292084805.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/60tel1292084805.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/60tel1292084805.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/7t2d61292084805.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/7t2d61292084805.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/8t2d61292084805.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/8t2d61292084805.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/9mtcr1292084805.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292084764yfxyyom9ml5kf3n/9mtcr1292084805.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