model 3

*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: Thu, 19 Nov 2009 01:41:46 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj.htm/, Retrieved Thu, 19 Nov 2009 09:43:05 +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/2009/Nov/19/t1258620172732i9u30cbvrrgj.htm/}, year = {2009}, } @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 = {2009}, 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 «

149 0 139 0 135 0 130 0 127 0 122 0 117 0 112 0 113 0 149 0 157 0 157 0 147 0 137 0 132 0 125 0 123 0 117 0 114 0 111 0 112 0 144 0 150 0 149 0 134 0 123 0 116 0 117 0 111 0 105 0 102 0 95 0 93 0 124 0 130 0 124 0 115 0 106 0 105 0 105 0 101 0 95 0 93 0 84 0 87 0 116 0 120 0 117 1 109 1 105 1 107 1 109 1 109 1 108 1 107 1 99 1 103 1 131 1 137 1 135 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WLH[t] = + 160.480347826087 + 16.6904347826087X[t] -11.6597391304348M1[t] -19.6053913043478M2[t] -21.7510434782609M3[t] -22.6966956521739M4[t] -24.8423478260870M5[t] -28.7880000000000M6[t] -30.7336521739131M7[t] -36.2793043478261M8[t] -34.0249565217391M9[t] -1.97060869565218M10[t] + 4.88373913043478M11[t] -0.854347826086956t + 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) 160.480347826087 3.396873 47.2436 0 0 X 16.6904347826087 2.903911 5.7476 1e-06 0 M1 -11.6597391304348 4.055404 -2.8751 0.006098 0.003049 M2 -19.6053913043478 4.049985 -4.8409 1.5e-05 7e-06 M3 -21.7510434782609 4.045766 -5.3762 2e-06 1e-06 M4 -22.6966956521739 4.042749 -5.6142 1e-06 1e-06 M5 -24.8423478260870 4.040938 -6.1477 0 0 M6 -28.7880000000000 4.040334 -7.1252 0 0 M7 -30.7336521739131 4.040938 -7.6056 0 0 M8 -36.2793043478261 4.042749 -8.9739 0 0 M9 -34.0249565217391 4.045766 -8.41 0 0 M10 -1.97060869565218 4.049985 -0.4866 0.628872 0.314436 M11 4.88373913043478 4.055404 1.2043 0.234651 0.117326 t -0.854347826086956 0.069857 -12.2299 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.948465481240342 R-squared 0.899586769104474 Adjusted R-squared 0.87120911689487 F-TEST (value) 31.7005354234340 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.35662522963395 Sum Squared Residuals 1858.70747826087 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 149 147.966260869565 1.03373913043479 2 139 139.166260869565 -0.166260869565168 3 135 136.166260869565 -1.16626086956523 4 130 134.366260869565 -4.36626086956519 5 127 131.366260869565 -4.36626086956524 6 122 126.566260869565 -4.56626086956522 7 117 123.766260869565 -6.76626086956524 8 112 117.366260869565 -5.36626086956517 9 113 118.766260869565 -5.76626086956521 10 149 149.966260869565 -0.966260869565233 11 157 155.966260869565 1.03373913043478 12 157 150.228173913043 6.7718260869565 13 147 137.714086956522 9.28591304347826 14 137 128.914086956522 8.08591304347821 15 132 125.914086956522 6.08591304347826 16 125 124.114086956522 0.885913043478253 17 123 121.114086956522 1.88591304347827 18 117 116.314086956522 0.68591304347826 19 114 113.514086956522 0.485913043478262 20 111 107.114086956522 3.88591304347825 21 112 108.514086956522 3.48591304347826 22 144 139.714086956522 4.28591304347826 23 150 145.714086956522 4.28591304347826 24 149 139.976 9.024 25 134 127.461913043478 6.53808695652174 26 123 118.661913043478 4.33808695652174 27 116 115.661913043478 0.338086956521741 28 117 113.861913043478 3.13808695652174 29 111 110.861913043478 0.138086956521751 30 105 106.061913043478 -1.06191304347826 31 102 103.261913043478 -1.26191304347826 32 95 96.8619130434783 -1.86191304347827 33 93 98.2619130434783 -5.26191304347826 34 124 129.461913043478 -5.46191304347825 35 130 135.461913043478 -5.46191304347826 36 124 129.723826086957 -5.72382608695652 37 115 117.209739130435 -2.20973913043478 38 106 108.409739130435 -2.40973913043478 39 105 105.409739130435 -0.409739130434784 40 105 103.609739130435 1.39026086956522 41 101 100.609739130435 0.390260869565224 42 95 95.8097391304348 -0.809739130434784 43 93 93.0097391304348 -0.00973913043477244 44 84 86.6097391304348 -2.60973913043479 45 87 88.0097391304348 -1.00973913043478 46 116 119.209739130435 -3.20973913043478 47 120 125.209739130435 -5.20973913043478 48 117 136.162086956522 -19.1620869565217 49 109 123.648 -14.648 50 105 114.848 -9.848 51 107 111.848 -4.848 52 109 110.048 -1.04800000000000 53 109 107.048 1.95200000000000 54 108 102.248 5.752 55 107 99.448 7.552 56 99 93.048 5.95199999999999 57 103 94.448 8.552 58 131 125.648 5.35200000000000 59 137 131.648 5.352 60 135 125.909913043478 9.09008695652174 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00551092296990859 0.0110218459398172 0.994489077030091 18 0.00134191423656699 0.00268382847313399 0.998658085763433 19 0.000190609220848364 0.000381218441696727 0.999809390779152 20 8.1028033268132e-05 0.000162056066536264 0.999918971966732 21 2.50820466470589e-05 5.01640932941178e-05 0.999974917953353 22 7.76144018733946e-06 1.55228803746789e-05 0.999992238559813 23 9.43547055425059e-06 1.88709411085012e-05 0.999990564529446 24 2.48579265945461e-05 4.97158531890921e-05 0.999975142073405 25 0.000935191182605203 0.00187038236521041 0.999064808817395 26 0.00519170557545799 0.0103834111509160 0.994808294424542 27 0.0166989238994287 0.0333978477988573 0.983301076100571 28 0.0123683478522021 0.0247366957044042 0.987631652147798 29 0.0100526233397734 0.0201052466795469 0.989947376660227 30 0.00747330827110629 0.0149466165422126 0.992526691728894 31 0.00441210531432477 0.00882421062864955 0.995587894685675 32 0.00465933489710667 0.00931866979421335 0.995340665102893 33 0.00643011678415346 0.0128602335683069 0.993569883215847 34 0.0152545862444550 0.0305091724889101 0.984745413755545 35 0.0501333282501822 0.100266656500364 0.949866671749818 36 0.261452271178027 0.522904542356055 0.738547728821973 37 0.557976425468011 0.884047149063979 0.442023574531989 38 0.75455849178661 0.490883016426781 0.245441508213391 39 0.879175544393209 0.241648911213582 0.120824455606791 40 0.976966599943315 0.0460668001133696 0.0230334000566848 41 0.99864846040118 0.00270307919764009 0.00135153959882005 42 0.998095054833004 0.00380989033399124 0.00190494516699562 43 0.995450289409835 0.0090994211803292 0.0045497105901646 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.481481481481481 NOK 5% type I error level 22 0.814814814814815 NOK 10% type I error level 22 0.814814814814815 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/10b88g1258620102.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/10b88g1258620102.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/1hrlg1258620102.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/1hrlg1258620102.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/2ht591258620102.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/2ht591258620102.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/3md3i1258620102.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/3md3i1258620102.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/4v3ea1258620102.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/4v3ea1258620102.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/5n0ke1258620102.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/5n0ke1258620102.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/6nqzh1258620102.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/6nqzh1258620102.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/72iow1258620102.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/72iow1258620102.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/8sgms1258620102.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/8sgms1258620102.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/97bgr1258620102.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620172732i9u30cbvrrgj/97bgr1258620102.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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