paperMR2(werk)

*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 13:32:08 +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/t129293819118ohsn902ajw0xi.htm/, Retrieved Tue, 21 Dec 2010 14:30:02 +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/t129293819118ohsn902ajw0xi.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 «

595 0 597 0 593 0 590 0 580 0 574 0 573 0 573 0 620 0 626 0 620 0 588 0 566 0 557 0 561 0 549 0 532 0 526 0 511 0 499 0 555 0 565 0 542 0 527 0 510 0 514 0 517 0 508 0 493 0 490 0 469 0 478 0 528 0 534 0 518 0 506 0 502 1 516 1 528 1 533 1 536 1 537 1 524 1 536 1 587 1 597 1 581 1 564 1 558 1 575 1 580 1 575 1 563 1 552 1 537 1 545 1 601 1 604 1 586 1 564 1 549 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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 545.544217687075 + 10.6394557823129X[t] -4.19727891156465M1[t] + 2.00000000000002M2[t] + 6.00000000000003M3[t] + 1.20000000000003M4[t] -8.99999999999995M5[t] -14M6[t] -27.0000000000000M7[t] -23.6000000000000M8[t] + 28.4000000000001M9[t] + 35.4M10[t] + 19.6000000000000M11[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) 545.544217687075 16.15886 33.7613 0 0 X 10.6394557823129 9.177616 1.1593 0.252075 0.126038 M1 -4.19727891156465 21.326852 -0.1968 0.844809 0.422405 M2 2.00000000000002 22.254536 0.0899 0.928765 0.464383 M3 6.00000000000003 22.254536 0.2696 0.788618 0.394309 M4 1.20000000000003 22.254536 0.0539 0.957221 0.478611 M5 -8.99999999999995 22.254536 -0.4044 0.687706 0.343853 M6 -14 22.254536 -0.6291 0.532276 0.266138 M7 -27.0000000000000 22.254536 -1.2132 0.230976 0.115488 M8 -23.6000000000000 22.254536 -1.0605 0.294243 0.147122 M9 28.4000000000001 22.254536 1.2761 0.208045 0.104022 M10 35.4 22.254536 1.5907 0.118245 0.059123 M11 19.6000000000000 22.254536 0.8807 0.382858 0.191429 Multiple Linear Regression - Regression Statistics Multiple R 0.518642621269286 R-squared 0.268990168597076 Adjusted R-squared 0.0862377107463449 F-TEST (value) 1.47188263162393 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.168108290720798 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 35.1875103460552 Sum Squared Residuals 59431.7224489796 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 595 541.34693877551 53.6530612244896 2 597 547.544217687075 49.4557823129252 3 593 551.544217687075 41.4557823129252 4 590 546.744217687075 43.2557823129252 5 580 536.544217687075 43.4557823129252 6 574 531.544217687075 42.4557823129252 7 573 518.544217687075 54.4557823129251 8 573 521.944217687075 51.0557823129252 9 620 573.944217687075 46.0557823129253 10 626 580.944217687075 45.0557823129252 11 620 565.144217687075 54.8557823129252 12 588 545.544217687075 42.4557823129252 13 566 541.34693877551 24.6530612244898 14 557 547.544217687075 9.45578231292517 15 561 551.544217687075 9.45578231292516 16 549 546.744217687075 2.25578231292518 17 532 536.544217687075 -4.54421768707483 18 526 531.544217687075 -5.54421768707483 19 511 518.544217687075 -7.5442176870748 20 499 521.944217687075 -22.9442176870748 21 555 573.944217687075 -18.9442176870748 22 565 580.944217687075 -15.9442176870748 23 542 565.144217687075 -23.1442176870748 24 527 545.544217687075 -18.5442176870748 25 510 541.34693877551 -31.3469387755102 26 514 547.544217687075 -33.5442176870748 27 517 551.544217687075 -34.5442176870749 28 508 546.744217687075 -38.7442176870748 29 493 536.544217687075 -43.5442176870748 30 490 531.544217687075 -41.5442176870748 31 469 518.544217687075 -49.5442176870748 32 478 521.944217687075 -43.9442176870748 33 528 573.944217687075 -45.9442176870749 34 534 580.944217687075 -46.9442176870748 35 518 565.144217687075 -47.1442176870748 36 506 545.544217687075 -39.5442176870748 37 502 551.986394557823 -49.9863945578231 38 516 558.183673469388 -42.1836734693878 39 528 562.183673469388 -34.1836734693878 40 533 557.383673469388 -24.3836734693878 41 536 547.183673469388 -11.1836734693878 42 537 542.183673469388 -5.18367346938776 43 524 529.183673469388 -5.18367346938774 44 536 532.583673469388 3.41632653061222 45 587 584.583673469388 2.41632653061222 46 597 591.583673469388 5.41632653061223 47 581 575.783673469388 5.21632653061224 48 564 556.183673469388 7.81632653061226 49 558 551.986394557823 6.01360544217689 50 575 558.183673469388 16.8163265306122 51 580 562.183673469388 17.8163265306122 52 575 557.383673469388 17.6163265306122 53 563 547.183673469388 15.8163265306122 54 552 542.183673469388 9.81632653061225 55 537 529.183673469388 7.81632653061225 56 545 532.583673469388 12.4163265306122 57 601 584.583673469388 16.4163265306122 58 604 591.583673469388 12.4163265306122 59 586 575.783673469388 10.2163265306122 60 564 556.183673469388 7.81632653061226 61 549 551.986394557823 -2.98639455782311 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.919326002351541 0.161347995296918 0.0806739976484588 17 0.955016470342563 0.0899670593148745 0.0449835296574373 18 0.97385126406117 0.0522974718776581 0.0261487359388291 19 0.992048797750323 0.0159024044993551 0.00795120224967753 20 0.997543289550355 0.0049134208992893 0.00245671044964465 21 0.99858388804382 0.00283222391236138 0.00141611195618069 22 0.999011321177394 0.00197735764521141 0.000988678822605706 23 0.999490634881303 0.00101873023739495 0.000509365118697475 24 0.999516219732954 0.000967560534091572 0.000483780267045786 25 0.99978576588727 0.000428468225460701 0.000214234112730351 26 0.999843905634964 0.000312188730072564 0.000156094365036282 27 0.999852406867019 0.000295186265962432 0.000147593132981216 28 0.999825696632566 0.00034860673486803 0.000174303367434015 29 0.99975621248701 0.000487575025978344 0.000243787512989172 30 0.999631703010466 0.000736593979068524 0.000368296989534262 31 0.999503625753755 0.000992748492490514 0.000496374246245257 32 0.999129089265477 0.00174182146904656 0.000870910734523282 33 0.99848047875307 0.00303904249386027 0.00151952124693013 34 0.99738339436255 0.00523321127489999 0.00261660563744999 35 0.995532457085976 0.00893508582804763 0.00446754291402381 36 0.991595603413326 0.0168087931733478 0.0084043965866739 37 0.995052444201836 0.009895111596327 0.0049475557981635 38 0.998151989571896 0.00369602085620875 0.00184801042810438 39 0.999513416914768 0.000973166170464658 0.000486583085232329 40 0.999902013960632 0.000195972078735137 9.79860393675685e-05 41 0.999948766975025 0.000102466049949760 5.12330249748799e-05 42 0.999886751080518 0.00022649783896362 0.00011324891948181 43 0.999689305051771 0.000621389896457535 0.000310694948228768 44 0.998651317250684 0.00269736549863194 0.00134868274931597 45 0.997341099457962 0.00531780108407562 0.00265890054203781 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 25 0.833333333333333 NOK 5% type I error level 27 0.9 NOK 10% type I error level 29 0.966666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/10twzn1292938318.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/10twzn1292938318.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/1mv2c1292938318.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/1mv2c1292938318.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/2xnjf1292938318.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/2xnjf1292938318.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/3xnjf1292938318.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/3xnjf1292938318.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/4xnjf1292938318.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/4xnjf1292938318.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/5pei01292938318.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/5pei01292938318.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/6pei01292938318.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/6pei01292938318.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/70ni31292938318.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/70ni31292938318.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/80ni31292938318.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/80ni31292938318.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/9twzn1292938318.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293819118ohsn902ajw0xi/9twzn1292938318.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 = 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.

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