*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 13:19:50 +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/t1292937481bs3vtd2d0tjp7o7.htm/, Retrieved Tue, 21 Dec 2010 14:18:12 +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/t1292937481bs3vtd2d0tjp7o7.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 2 4.5 1 6.6 42 3 1 3 2.1 1.8 69 2547 4603 624 3 5 4 9.1 0.7 27 10.55 179.5 180 4 4 4 15.8 3.9 19 0.023 0.3 35 1 1 1 5.2 1 30.4 160 169 392 4 5 4 10.9 3.6 28 3.3 25.6 63 1 2 1 8.3 1.4 50 52.16 440 230 1 1 1 11 1.5 7 0.425 6.4 112 5 4 4 3.2 0.7 30 465 423 281 5 5 5 6.3 2.1 3.5 0.075 1.2 42 1 1 1 8.6 0 50 3 25 28 2 2 2 6.6 4.1 6 0.785 3.5 42 2 2 2 9.5 1.2 10.4 0.2 5 120 2 2 2 3.3 0.5 20 27.66 115 148 5 5 5 11 3.4 3.9 0.12 1 16 3 1 2 4.7 1.5 41 85 325 310 1 3 1 10.4 3.4 9 0.101 4 28 5 1 3 7.4 0.8 7.6 1.04 5.5 68 5 3 4 2.1 0.8 46 521 655 336 5 5 5 7.7 1.4 2.6 0.005 0.14 21.5 5 2 4 17.9 2 24 0.01 0.25 50 1 1 1 6.1 1.9 100 62 1320 267 1 1 1 11.9 1.3 3.2 0.023 0.4 19 4 1 3 10.8 2 2 0.048 0.33 30 4 1 3 13.8 5.6 5 1.7 6.3 12 2 1 1 14.3 3.1 6.5 3.5 10.8 120 2 1 1 15.2 1.8 12 0.48 15.5 140 2 2 2 10 0.9 20.2 10 115 170 4 4 4 11.9 1.8 13 1.62 11.4 17 2 1 2 6.5 1.9 27 192 180 115 4 4 4 7.5 0.9 18 2.5 12.1 31 5 5 5 10.6 2.6 4.7 0.28 1.9 21 3 1 3 7.4 2.4 9.8 4.235 50.4 52 1 1 1 8.4 1.2 29 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 14 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation SWS[t] = + 12.9287686106582 + 0.106606074538538PS[t] + 0.000246988712017691L[t] + 0.00318595520151939WB[t] -0.00132557005859304WBR[t] -0.0132700427644034TG[t] + 1.31878955543532P[t] + 0.186037336578466S[t] -2.61378468965366D[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) 12.9287686106582 2.370019 5.4551 5e-06 2e-06 PS 0.106606074538538 0.52893 0.2016 0.841505 0.420753 L 0.000246988712017691 0.044702 0.0055 0.995625 0.497812 WB 0.00318595520151939 0.005694 0.5595 0.579597 0.289798 WBR -0.00132557005859304 0.003393 -0.3906 0.698575 0.349287 TG -0.0132700427644034 0.007165 -1.8522 0.072965 0.036482 P 1.31878955543532 1.145813 1.151 0.258019 0.129009 S 0.186037336578466 0.680124 0.2735 0.786147 0.393074 D -2.61378468965366 1.587451 -1.6465 0.109147 0.054573 Multiple Linear Regression - Regression Statistics Multiple R 0.735505789285703 R-squared 0.540968766072785 Adjusted R-squared 0.429688466938915 F-TEST (value) 4.86131660575427 F-TEST (DF numerator) 8 F-TEST (DF denominator) 33 p-value 0.000508434878703889 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.89799308463584 Sum Squared Residuals 277.146009313706 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.3 8.88123953957267 -2.58123953957267 2 2.1 1.30164059011887 0.798359409881132 3 9.1 5.98129467176615 3.11870532823385 4 15.8 11.7754913984449 4.02450860155511 5 5.2 3.87696401675581 1.32303598324419 6 10.9 11.5371120663796 -0.637112066379641 7 8.3 8.5122275146907 -0.212227514690696 8 11 8.47999060129845 2.52000939870155 9 3.2 4.65788455312067 -1.45788455312067 10 6.3 11.4858544965062 -5.18585449650618 11 8.6 10.3280598677158 -1.72805986771578 12 6.6 10.5899395367817 -3.98993953678175 13 9.5 9.24295319644862 0.257046803551381 14 3.3 5.4139390689726 -2.1139390689726 15 11 11.9937652039783 -0.99376520397835 16 4.7 8.08820380129715 -3.38820380129715 17 10.4 11.8555415111292 -1.45554151112918 18 7.4 8.80651146290563 -1.40651146290563 19 2.1 3.81352586561612 -1.71352586561612 20 7.7 9.30406740791544 -1.60406740791544 21 17.9 11.3751490200010 6.52485097999898 22 6.1 6.951736552899 -0.851736552899002 23 11.9 10.4354005972181 1.46459940278193 24 10.8 10.3639304313163 0.436069568683673 25 13.8 13.5746538487302 0.225346151269844 26 14.3 11.8751141809953 2.42488581900466 27 15.2 9.0288847496641 6.1711152503359 28 10 6.21738378449458 3.78261621550542 29 11.9 10.4843764879892 1.41562351201077 30 6.5 7.54920352718501 -1.04920352718501 31 7.5 7.06492505095818 0.435074949041824 32 10.6 9.22785977162097 1.37214022837903 33 7.4 11.3347274468650 -3.93472744686496 34 8.4 8.6400807555717 -0.240080755571703 35 5.7 7.80885273613695 -2.10885273613695 36 4.9 6.46851569620575 -1.56851569620575 37 3.2 5.39395233742796 -2.19395233742796 38 11 9.97433941110753 1.02566058889247 39 4.9 6.62103484299272 -1.72103484299272 40 13.2 11.6939818856835 1.50601811431646 41 9.7 5.52654856570306 4.17345143429694 42 12.8 13.6631419478192 -0.86314194781921 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.889813992174752 0.220372015650496 0.110186007825248 13 0.867632310015104 0.264735379969792 0.132367689984896 14 0.821205954667394 0.357588090665212 0.178794045332606 15 0.747850157105808 0.504299685788384 0.252149842894192 16 0.822035094687942 0.355929810624115 0.177964905312057 17 0.836483859386122 0.327032281227757 0.163516140613878 18 0.775693672591228 0.448612654817544 0.224306327408772 19 0.758590731832849 0.482818536334303 0.241409268167151 20 0.693128384591082 0.613743230817835 0.306871615408918 21 0.870733315599069 0.258533368801862 0.129266684400931 22 0.817493745384581 0.365012509230838 0.182506254615419 23 0.74544298434179 0.509114031316421 0.254557015658210 24 0.642345879747288 0.715308240505424 0.357654120252712 25 0.527871862127049 0.944256275745901 0.472128137872951 26 0.426066079841085 0.85213215968217 0.573933920158915 27 0.790121160182274 0.419757679635451 0.209878839817726 28 0.877981584437563 0.244036831124873 0.122018415562437 29 0.769277738816762 0.461444522366476 0.230722261183238 30 0.615182510508502 0.769634978982996 0.384817489491498 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/21/t1292937481bs3vtd2d0tjp7o7/107fr01292937574.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/107fr01292937574.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/1t5tr1292937574.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/1t5tr1292937574.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/2t5tr1292937574.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/2t5tr1292937574.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/3t5tr1292937574.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/3t5tr1292937574.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/44wsu1292937574.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/44wsu1292937574.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/54wsu1292937574.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/54wsu1292937574.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/64wsu1292937574.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/64wsu1292937574.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/7e5sx1292937574.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/7e5sx1292937574.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/87fr01292937574.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/87fr01292937574.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/97fr01292937574.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937481bs3vtd2d0tjp7o7/97fr01292937574.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