*Unverified author*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sat, 19 Feb 2011 13:01:29 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl.htm/, Retrieved Sat, 19 Feb 2011 14:14:36 +0100

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

521 103 2708 553 234 349 42 1146 836 93 341 58 783 492 110 440 46 3314 419 437 367 64 3096 601 367 537 55 1847 459 192 423 46 2001 56 176 470 98 3428 701 273 380 126 3363 3567 171 453 49 1405 321 217 452 52 7120 730 394 429 89 1198 1260 249 351 70 4474 1078 621 462 47 1490 363 192 492 110 4757 1523 295 363 48 1238 692 157 372 63 2703 700 136 490 88 4057 647 346 347 28 1143 872 104 329 32 810 508 138 442 41 5211 464 694 337 51 4417 637 504 485 59 2779 542 306 402 31 2713 97 218 446 98 5791 751 340 383 112 5152 3749 241 426 34 2041 451 367 426 50 13207 1176 556 407 66 1506 1687 309 391 65 6619 1278 792 418 38 2168 378 301 465 91 7846 1588 468 336 37 1495 868 197 349 51 3659 850 194 503 118 8476 75 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 7 seconds R Server 'George Udny Yule' @ 216.218.223.82 Multiple Linear Regression - Estimated Regression Equation Cons[t] = + 372.191566591711 + 0.944504163917124Inc[t] + 0.0011644936945463Price[t] -0.0317530980421622Rds[t] + 0.0279306889646903Elec[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) 372.191566591711 17.755587 20.9619 0 0 Inc 0.944504163917124 0.253301 3.7288 0.000526 0.000263 Price 0.0011644936945463 0.002224 0.5235 0.603145 0.301572 Rds -0.0317530980421622 0.009677 -3.2814 0.001976 0.000988 Elec 0.0279306889646903 0.044503 0.6276 0.533363 0.266681 Multiple Linear Regression - Regression Statistics Multiple R 0.602502948652427 R-squared 0.36300980313487 Adjusted R-squared 0.307619351233554 F-TEST (value) 6.55365303358804 F-TEST (DF numerator) 4 F-TEST (DF denominator) 46 p-value 0.000292300025725667 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 46.4093977458369 Sum Squared Residuals 99076.2811600395 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 521 461.605262400429 59.3947375995712 2 349 389.247215360649 -40.2472153606493 3 341 415.334458211106 -74.3344582111065 4 440 418.399053233529 21.6009467664708 5 367 427.412056487425 -60.4120564874245 6 537 417.078135740848 119.921864259152 7 423 421.106537782111 1.89346221788934 8 470 454.111015400299 15.888984599701 9 380 386.628130636598 -6.6281306365976 10 453 415.976599298292 37.0234007017083 11 452 417.421908101881 34.5780918981189 12 429 424.593338645485 4.40666135451462 13 351 426.631921012932 -75.631921012932 14 462 412.154675592606 49.8453244073941 15 492 441.506106053922 50.4938939460776 16 363 401.381383975862 -38.3813839758618 17 372 416.414360444533 -44.4143604445332 18 490 449.152047883697 40.8479521163035 19 347 375.18478963382 -28.1847896338196 20 329 391.082801001351 -62.0828010013507 21 442 421.634874604526 20.365125395474 22 337 419.355191385642 -82.3551913856423 23 485 422.490051924309 62.5099480756908 24 402 407.639306750659 -5.6393067506591 25 446 457.146415259038 -11.1464152590381 26 383 371.664435945156 11.335564054844 27 426 402.611355428489 23.3886445715112 28 426 412.984062778226 13.0159372217744 29 407 391.34567540719 15.6543245928101 30 391 422.832767372678 -31.8327673726778 31 418 407.011813468773 10.9881865312271 32 465 429.925705780101 35.0742942198986 33 336 386.819795355439 -50.8197953554388 34 349 403.050581703142 -54.0505817031417 35 503 482.77004539276 20.2299546072401 36 384 327.447815350782 56.552184649218 37 334 391.668118986771 -57.6681189867714 38 463 423.361854174926 39.6381458250737 39 399 426.396826340799 -27.3968263407994 40 523 434.515724404903 88.4842755950968 41 395 420.234011102258 -25.2340111022579 42 489 488.715763223648 0.28423677635202 43 392 408.279825236058 -16.2798252360583 44 443 403.60097369101 39.3990263089904 45 463 467.504777356167 -4.50477735616743 46 434 406.555605497637 27.4443945023626 47 421 441.818814383636 -20.8188143836361 48 434 412.522500699632 21.4774993003678 49 484 549.818087712125 -65.8180877121253 50 367 389.691212537649 -22.6912125376488 51 380 395.164213848495 -15.1642138484949 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.989661378173793 0.0206772436524137 0.0103386218262069 9 0.977783832234837 0.044432335530325 0.0222161677651625 10 0.97314631169155 0.0537073766169013 0.0268536883084506 11 0.952693603723755 0.0946127925524905 0.0473063962762453 12 0.91742720141029 0.165145597179421 0.0825727985897104 13 0.929766344309397 0.140467311381205 0.0702336556906027 14 0.929138605216757 0.141722789566486 0.0708613947832432 15 0.91403815245254 0.171923695094919 0.0859618475474594 16 0.889970865335968 0.220058269328065 0.110029134664032 17 0.906037643368028 0.187924713263944 0.0939623566319721 18 0.887717596007815 0.224564807984371 0.112282403992185 19 0.843366285016639 0.313267429966722 0.156633714983361 20 0.845665896169432 0.308668207661135 0.154334103830568 21 0.817868530241211 0.364262939517578 0.182131469758789 22 0.910649169735885 0.178701660528231 0.0893508302641155 23 0.94357266697643 0.112854666047141 0.0564273330235703 24 0.913191002831618 0.173617994336763 0.0868089971683816 25 0.900982285617989 0.198035428764023 0.0990177143820115 26 0.865693234740234 0.268613530519532 0.134306765259766 27 0.841319581329715 0.31736083734057 0.158680418670285 28 0.780324928712181 0.439350142575639 0.219675071287819 29 0.728429807047728 0.543140385904544 0.271570192952272 30 0.708843068812592 0.582313862374816 0.291156931187408 31 0.636422919306415 0.72715416138717 0.363577080693585 32 0.584727724710284 0.830544550579432 0.415272275289716 33 0.578275550473482 0.843448899053035 0.421724449526517 34 0.608216675811232 0.783566648377536 0.391783324188768 35 0.584159426750827 0.831681146498345 0.415840573249173 36 0.600382382203558 0.799235235592885 0.399617617796442 37 0.676226684607744 0.647546630784513 0.323773315392256 38 0.631083092932616 0.737833814134768 0.368916907067384 39 0.56950370567032 0.86099258865936 0.43049629432968 40 0.875909132395772 0.248181735208456 0.124090867604228 41 0.85391851949231 0.29216296101538 0.14608148050769 42 0.79524864249286 0.40950271501428 0.20475135750714 43 0.676588238716643 0.646823522566715 0.323411761283357 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 2 0.0555555555555556 NOK 10% type I error level 4 0.111111111111111 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/10bfdp1298120480.png (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/10bfdp1298120480.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/1gmej1298120480.png (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/1gmej1298120480.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/252mz1298120480.png (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/252mz1298120480.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/35mn61298120480.png (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/35mn61298120480.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/4wqqk1298120480.png (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/4wqqk1298120480.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/55rkb1298120480.png (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/55rkb1298120480.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/65wrx1298120480.png (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/65wrx1298120480.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/7dc181298120480.png (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/7dc181298120480.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/8d0yo1298120480.png (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/8d0yo1298120480.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/9kdsn1298120480.png (open in new window) http://www.freestatistics.org/blog/date/2011/Feb/19/t1298121263c51dj0j7gibufyl/9kdsn1298120480.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