*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: Fri, 20 Nov 2009 11:17:33 -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/20/t1258741728jv37hpc280dbg3y.htm/, Retrieved Fri, 20 Nov 2009 19:28:59 +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/20/t1258741728jv37hpc280dbg3y.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 «

594 139 595 135 591 130 589 127 584 122 573 117 567 112 569 113 621 149 629 157 628 157 612 147 595 137 597 132 593 125 590 123 580 117 574 114 573 111 573 112 620 144 626 150 620 149 588 134 566 123 557 116 561 117 549 111 532 105 526 102 511 95 499 93 555 124 565 130 542 124 527 115 510 106 514 105 517 105 508 101 493 95 490 93 469 84 478 87 528 116 534 120 518 117 506 109 502 105 516 107 528 109 533 109 536 108 537 107 524 99 536 103 587 131 597 137 581 135 564 124

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 176.905248685416 + 3.04049881808095X[t] + 5.55389550870783M1[t] + 17.0753919629505M2[t] + 24.7482898354962M3[t] + 29.669786289739M4[t] + 35.4641806165276M5[t] + 38.9775773071543M6[t] + 47.2367697428724M7[t] + 45.180071397559M8[t] + 1.51650827343334M9[t] -8.72648463505236M10[t] -13.8292874716581M11[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) 176.905248685416 11.536304 15.3347 0 0 X 3.04049881808095 0.087246 34.8496 0 0 M1 5.55389550870783 5.035462 1.103 0.275662 0.137831 M2 17.0753919629505 5.05944 3.375 0.001488 0.000744 M3 24.7482898354962 5.08025 4.8715 1.3e-05 6e-06 M4 29.669786289739 5.125448 5.7887 1e-06 0 M5 35.4641806165276 5.224295 6.7883 0 0 M6 38.9775773071543 5.296415 7.3592 0 0 M7 47.2367697428724 5.498591 8.5907 0 0 M8 45.180071397559 5.450119 8.2897 0 0 M9 1.51650827343334 5.061516 0.2996 0.765792 0.382896 M10 -8.72648463505236 5.150959 -1.6941 0.096855 0.048427 M11 -13.8292874716581 5.108937 -2.7069 0.009438 0.004719 Multiple Linear Regression - Regression Statistics Multiple R 0.985073814323171 R-squared 0.970370419665202 Adjusted R-squared 0.962805420430785 F-TEST (value) 128.271053254117 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.94448891931475 Sum Squared Residuals 2966.40049688840 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 594 605.088479907375 -11.0884799073753 2 595 604.447981089295 -9.4479810892953 3 591 596.918384871436 -5.91838487143616 4 589 592.718384871436 -3.71838487143619 5 584 583.31028510782 0.689714892180035 6 573 571.621187708042 1.37881229195811 7 567 564.677886053355 2.32211394664476 8 569 565.661686526123 3.33831347387716 9 621 631.456080852911 -10.4560808529114 10 629 645.537078489073 -16.5370784890733 11 628 640.434275652468 -12.4342756524676 12 612 623.858574943316 -11.8585749433162 13 595 599.007482271214 -4.00748227121449 14 597 595.326484635052 1.67351536494766 15 593 581.715890781031 11.2841092189686 16 590 580.556389599112 9.44361040088764 17 580 568.107791017415 11.8922089825848 18 574 562.499691253799 11.5003087462010 19 573 561.637387235274 11.3626127647257 20 573 562.621187708042 10.3788122919581 21 620 616.253586762507 3.74641323749335 22 626 624.253586762507 1.74641323749335 23 620 616.11028510782 3.88971489218002 24 588 584.332090308264 3.6679096917362 25 566 556.440498818081 9.55950118191883 26 557 546.678503545757 10.3214964542429 27 561 557.391900236384 3.60809976361619 28 549 544.070403782141 4.92959621785904 29 532 531.621805200444 0.378194799556179 30 526 526.013705436828 -0.0137054368276273 31 511 512.989406145979 -1.98940614597905 32 499 504.851710164504 -5.85171016450381 33 555 555.443610400888 -0.443610400887622 34 565 563.443610400888 1.55638959911237 35 542 540.097814655796 1.90218534420379 36 527 526.562612764726 0.43738723527428 37 510 504.752018910705 5.247981089295 38 514 513.233016546867 0.766983453133338 39 517 520.905914419412 -3.9059144194124 40 508 513.665415601331 -5.66541560133144 41 493 501.216817019634 -8.2168170196343 42 490 498.649216074099 -8.64921607409907 43 469 479.543919147089 -10.5439191470886 44 478 486.608717256018 -8.6087172560181 45 528 531.11961985624 -3.11961985624001 46 534 533.038622220078 0.961377779921877 47 518 518.81432292923 -0.814322929229542 48 506 508.31961985624 -2.31961985624002 49 502 501.711520092624 0.288479907375944 50 516 519.314014183029 -3.31401418302857 51 528 533.067909691736 -5.0679096917362 52 533 537.989406145979 -4.98940614597904 53 536 540.743301654687 -4.74330165468667 54 537 541.216199527232 -4.21619952723238 55 524 525.151401418303 -1.15140141830285 56 536 535.256698345313 0.743301654686659 57 587 576.727102127454 10.2728978725457 58 597 584.727102127454 12.2728978725457 59 581 573.543301654687 7.45669834531333 60 564 553.927102127454 10.0728978725457 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.000860697409510995 0.00172139481902199 0.99913930259049 17 0.0360528531048604 0.0721057062097209 0.96394714689514 18 0.0158405863107176 0.0316811726214353 0.984159413689282 19 0.0534852782521301 0.106970556504260 0.94651472174787 20 0.0577461666612851 0.115492333322570 0.942253833338715 21 0.0347221019332266 0.0694442038664532 0.965277898066773 22 0.0492381726962248 0.0984763453924497 0.950761827303775 23 0.165710813303324 0.331421626606648 0.834289186696676 24 0.869487035671714 0.261025928656572 0.130512964328286 25 0.953801574800654 0.0923968503986923 0.0461984251993462 26 0.9883610555523 0.0232778888953979 0.0116389444476989 27 0.994872268628226 0.0102554627435484 0.00512773137177421 28 0.999083447555212 0.00183310488957613 0.000916552444788067 29 0.9998897938836 0.000220412232801428 0.000110206116400714 30 0.99996935822558 6.12835488412234e-05 3.06417744206117e-05 31 0.999977840033939 4.43199321225238e-05 2.21599660612619e-05 32 0.999980275944666 3.94481106685859e-05 1.97240553342929e-05 33 0.999967889846978 6.42203060430797e-05 3.21101530215398e-05 34 0.999976234162866 4.75316742689485e-05 2.37658371344742e-05 35 0.999916232155737 0.000167535688525462 8.37678442627312e-05 36 0.99979054190154 0.000418916196919673 0.000209458098459837 37 0.999634198141887 0.000731603716226991 0.000365801858113495 38 0.9995683873176 0.000863225364799476 0.000431612682399738 39 0.999230412667315 0.00153917466537052 0.00076958733268526 40 0.99885311059875 0.00229377880250047 0.00114688940125023 41 0.998620551714781 0.00275889657043808 0.00137944828521904 42 0.9985015228922 0.00299695421559741 0.00149847710779870 43 0.993847317749444 0.0123053645011117 0.00615268225055586 44 0.97587868470241 0.0482426305951816 0.0241213152975908 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.551724137931034 NOK 5% type I error level 21 0.724137931034483 NOK 10% type I error level 25 0.862068965517241 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/10eqyc1258741048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/10eqyc1258741048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/1vi1n1258741048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/1vi1n1258741048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/22ib81258741048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/22ib81258741048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/39q6g1258741048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/39q6g1258741048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/4etmq1258741048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/4etmq1258741048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/5zmmr1258741048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/5zmmr1258741048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/6pwjk1258741048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/6pwjk1258741048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/7l2p61258741048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/7l2p61258741048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/8ieu91258741048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/8ieu91258741048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/9j1ib1258741048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258741728jv37hpc280dbg3y/9j1ib1258741048.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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.

FreeStatistics.org is powered by