W3_Mini_1

*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: Wed, 24 Nov 2010 18:22:43 +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/Nov/24/t1290622874v4x8es3flznovts.htm/, Retrieved Wed, 24 Nov 2010 19:21:16 +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/Nov/24/t1290622874v4x8es3flznovts.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:

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563668 276444 586111 289742 604378 303725 600991 298305 544686 266795 537034 259497 551531 266148 563250 271037 574761 276239 580112 279681 575093 277509 557560 271115 564478 275902 580523 287224 596594 300713 586570 293860 536214 264221 523597 256167 536535 262572 536322 263276 532638 264291 528222 263903 516141 260376 501866 255603 506174 261076 517945 270976 533590 285257 528379 280445 477580 250741 469357 243803 490243 253158 492622 255542 507561 262522 516922 268381 514258 267153 509846 266424 527070 276427 541657 286994 564591 303598 555362 296806 498662 263290 511038 264981 525919 272566 531673 276475 548854 284678 560576 291542 557274 291413 565742 295916 587625 309119 619916 327616

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 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Totaal[t] = + 58274.0716935709 + 1.85595409215134`vrouwen `[t] -1074.75162536165t + 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) 58274.0716935709 22760.081464 2.5604 0.01373 0.006865 `vrouwen ` 1.85595409215134 0.083518 22.2221 0 0 t -1074.75162536165 98.715181 -10.8874 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.959318170134875 R-squared 0.920291351550926 Adjusted R-squared 0.916899494170114 F-TEST (value) 271.323716839386 F-TEST (DF numerator) 2 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9907.2855273743 Sum Squared Residuals 4613252406.48324 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 563668 570266.693118893 -6598.69311889312 2 586111 593872.41901096 -7761.41901095998 3 604378 618749.47345615 -14371.4734561504 4 600991 607615.450651329 -6624.45065132856 5 544686 548059.585582278 -3373.5855822783 6 537034 533440.080992396 3593.9190076038 7 551531 544709.280033933 6821.71996606691 8 563250 552708.287965099 10541.7120349007 9 574761 561288.209527109 13472.7904728911 10 580112 566601.651886932 13510.3481130678 11 575093 561495.767973418 13597.2320265822 12 557560 548554.04588284 9005.95411715947 13 564478 556363.746496607 8114.25350339268 14 580523 576302.107102583 4220.8928974169 15 596594 600262.320226251 -3668.32022625083 16 586570 586468.715207376 101.284792623924 17 536214 530385.340244741 5828.65975525903 18 523597 514362.734361192 9234.26563880754 19 536535 525175.36869606 11359.6313039399 20 536322 525407.208751573 10914.791248427 21 532638 526216.250529745 6421.74947025503 22 528222 524421.388716629 3800.6112833714 23 516141 516800.687008249 -659.687008249191 24 501866 506867.466501049 -5001.46650104921 25 506174 515950.351622032 -9776.35162203183 26 517945 533249.545508968 -15304.5455089684 27 533590 558679.67427362 -25089.67427362 28 528379 548674.071556826 -20295.0715568261 29 477580 492470.059578201 -14890.0595782012 30 469357 478518.698461494 -9161.69846149355 31 490243 494806.397368208 -4563.39736820765 32 492622 498156.240298535 -5534.24029853479 33 507561 510036.04823639 -2475.04823638946 34 516922 519835.331636943 -2913.33163694249 35 514258 516481.468386419 -2223.468386419 36 509846 514053.726227879 -4207.72622787903 37 527070 531544.083386307 -4474.08338630719 38 541657 550081.198652709 -8424.19865270872 39 564591 579822.708773428 -15231.7087734279 40 555362 566142.316954174 -10780.3169541743 41 498662 502863.407976268 -4201.4079762685 42 511038 504927.074720735 6110.92527926525 43 525919 517929.734884341 7989.26511565901 44 531673 524109.907805199 7563.09219480108 45 548854 538259.547597755 10594.4524022453 46 560576 549924.06486092 10651.9351390802 47 557274 548609.895157671 8664.10484232937 48 565742 555892.504809266 9849.49519073355 49 587625 579321.915062579 8303.08493742112 50 619916 612576.74627974 7339.25372025949 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0355707952316873 0.0711415904633745 0.964429204768313 7 0.020387770626767 0.0407755412535341 0.979612229373233 8 0.0149712561213209 0.0299425122426417 0.98502874387868 9 0.00920545442692068 0.0184109088538414 0.99079454557308 10 0.00338652662324117 0.00677305324648234 0.99661347337676 11 0.00143224059660558 0.00286448119321117 0.998567759403394 12 0.00483029515130093 0.00966059030260185 0.9951697048487 13 0.00773481474243434 0.0154696294848687 0.992265185257566 14 0.0101142502436436 0.0202285004872871 0.989885749756356 15 0.01402052428391 0.0280410485678199 0.98597947571609 16 0.0108897631304216 0.0217795262608433 0.989110236869578 17 0.0222669623602285 0.0445339247204571 0.977733037639771 18 0.0305177119701311 0.0610354239402621 0.969482288029869 19 0.0394857906495533 0.0789715812991065 0.960514209350447 20 0.0755822748884553 0.151164549776911 0.924417725111545 21 0.18810681920187 0.37621363840374 0.81189318079813 22 0.504124286928236 0.991751426143528 0.495875713071764 23 0.878516971627668 0.242966056744663 0.121483028372332 24 0.986407583684718 0.027184832630564 0.013592416315282 25 0.998391418633231 0.00321716273353739 0.0016085813667687 26 0.999597122502321 0.00080575499535742 0.00040287749767871 27 0.99975636351669 0.000487272966621689 0.000243636483310844 28 0.999640398126031 0.000719203747937394 0.000359601873968697 29 0.999676888387396 0.000646223225208974 0.000323111612604487 30 0.999441734075011 0.00111653184997736 0.00055826592498868 31 0.99896515214126 0.00206969571747882 0.00103484785873941 32 0.997817212989563 0.00436557402087303 0.00218278701043652 33 0.997854654292603 0.00429069141479326 0.00214534570739663 34 0.998529914337692 0.00294017132461553 0.00147008566230776 35 0.99916782514193 0.00166434971613797 0.000832174858068985 36 0.998765220720859 0.00246955855828282 0.00123477927914141 37 0.999057070402245 0.00188585919551019 0.000942929597755093 38 0.9985750623998 0.00284987520040126 0.00142493760020063 39 0.995876330772892 0.00824733845421523 0.00412366922710761 40 0.988980963620341 0.0220380727593173 0.0110190363796587 41 0.99978548000632 0.000429039987361776 0.000214519993680888 42 0.999594824934225 0.000810350131549287 0.000405175065774644 43 0.998299947057144 0.00340010588571181 0.00170005294285591 44 0.999097394539874 0.00180521092025243 0.000902605460126216 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 22 0.564102564102564 NOK 5% type I error level 32 0.82051282051282 NOK 10% type I error level 35 0.897435897435897 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/10iuoo1290622953.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/10iuoo1290622953.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/1bb9c1290622953.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/1bb9c1290622953.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/2bb9c1290622953.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/2bb9c1290622953.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/3bb9c1290622953.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/3bb9c1290622953.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/4m28x1290622953.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/4m28x1290622953.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/5m28x1290622953.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/5m28x1290622953.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/6m28x1290622953.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/6m28x1290622953.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/7xbp01290622953.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/7xbp01290622953.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/8qlp31290622953.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/8qlp31290622953.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/9qlp31290622953.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290622874v4x8es3flznovts/9qlp31290622953.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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.

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