Model 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, 18 Nov 2009 11:45:45 -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/18/t1258570119u1nvv0c1054c9ir.htm/, Retrieved Wed, 18 Nov 2009 19:48:51 +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/18/t1258570119u1nvv0c1054c9ir.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 «

562 0 561 0 555 0 544 0 537 0 543 0 594 0 611 0 613 0 611 0 594 0 595 0 591 0 589 0 584 0 573 0 567 0 569 0 621 0 629 0 628 0 612 0 595 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 1 510 1 514 1 517 1 508 1 493 1 490 1 469 1 478 1 528 1 534 1 518 1 506 1 502 1 516 1 528 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 577.222222222222 -68.5972222222222X[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) 577.222222222222 4.312533 133.8476 0 0 X -68.5972222222222 8.420489 -8.1465 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.727580800319605 R-squared 0.529373820993717 Adjusted R-squared 0.521397106095305 F-TEST (value) 66.3648917800901 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 3.09468006776115e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 28.9293492922120 Sum Squared Residuals 49377.5277777778 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 562 577.222222222222 -15.222222222222 2 561 577.222222222222 -16.2222222222222 3 555 577.222222222222 -22.2222222222222 4 544 577.222222222222 -33.2222222222222 5 537 577.222222222222 -40.2222222222222 6 543 577.222222222222 -34.2222222222222 7 594 577.222222222222 16.7777777777778 8 611 577.222222222222 33.7777777777778 9 613 577.222222222222 35.7777777777778 10 611 577.222222222222 33.7777777777778 11 594 577.222222222222 16.7777777777778 12 595 577.222222222222 17.7777777777778 13 591 577.222222222222 13.7777777777778 14 589 577.222222222222 11.7777777777778 15 584 577.222222222222 6.77777777777777 16 573 577.222222222222 -4.22222222222223 17 567 577.222222222222 -10.2222222222222 18 569 577.222222222222 -8.22222222222223 19 621 577.222222222222 43.7777777777778 20 629 577.222222222222 51.7777777777778 21 628 577.222222222222 50.7777777777778 22 612 577.222222222222 34.7777777777778 23 595 577.222222222222 17.7777777777778 24 597 577.222222222222 19.7777777777778 25 593 577.222222222222 15.7777777777778 26 590 577.222222222222 12.7777777777778 27 580 577.222222222222 2.77777777777777 28 574 577.222222222222 -3.22222222222223 29 573 577.222222222222 -4.22222222222223 30 573 577.222222222222 -4.22222222222223 31 620 577.222222222222 42.7777777777778 32 626 577.222222222222 48.7777777777778 33 620 577.222222222222 42.7777777777778 34 588 577.222222222222 10.7777777777778 35 566 577.222222222222 -11.2222222222222 36 557 577.222222222222 -20.2222222222222 37 561 577.222222222222 -16.2222222222222 38 549 577.222222222222 -28.2222222222222 39 532 577.222222222222 -45.2222222222222 40 526 577.222222222222 -51.2222222222222 41 511 577.222222222222 -66.2222222222222 42 499 577.222222222222 -78.2222222222222 43 555 577.222222222222 -22.2222222222222 44 565 577.222222222222 -12.2222222222222 45 542 577.222222222222 -35.2222222222222 46 527 508.625 18.375 47 510 508.625 1.37500000000000 48 514 508.625 5.375 49 517 508.625 8.375 50 508 508.625 -0.625000000000003 51 493 508.625 -15.625 52 490 508.625 -18.625 53 469 508.625 -39.625 54 478 508.625 -30.625 55 528 508.625 19.375 56 534 508.625 25.375 57 518 508.625 9.375 58 506 508.625 -2.62500000000000 59 502 508.625 -6.625 60 516 508.625 7.375 61 528 508.625 19.375 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0975136889293678 0.195027377858736 0.902486311070632 6 0.0444477375667488 0.0888954751334977 0.955552262433251 7 0.239050726494406 0.478101452988812 0.760949273505594 8 0.530873350393748 0.938253299212505 0.469126649606252 9 0.67642619416788 0.64714761166424 0.32357380583212 10 0.729569424920655 0.540861150158689 0.270430575079345 11 0.672801118915848 0.654397762168304 0.327198881084152 12 0.61348017883493 0.77303964233014 0.38651982116507 13 0.536841755788242 0.926316488423517 0.463158244211758 14 0.454361416908714 0.908722833817428 0.545638583091286 15 0.366696390167425 0.73339278033485 0.633303609832575 16 0.286755820778164 0.573511641556328 0.713244179221836 17 0.224951576360000 0.449903152720001 0.77504842364 18 0.169203829522663 0.338407659045326 0.830796170477337 19 0.248948135680414 0.497896271360827 0.751051864319586 20 0.395625474009785 0.79125094801957 0.604374525990215 21 0.536997685399316 0.926004629201368 0.463002314600684 22 0.559458524298144 0.881082951403712 0.440541475701856 23 0.509808273622813 0.980383452754374 0.490191726377187 24 0.469921203119376 0.939842406238752 0.530078796880624 25 0.422059403547197 0.844118807094394 0.577940596452803 26 0.371574011280445 0.74314802256089 0.628425988719555 27 0.312963127122803 0.625926254245607 0.687036872877197 28 0.259769414669898 0.519538829339796 0.740230585330102 29 0.212215084796154 0.424430169592307 0.787784915203846 30 0.170247645664291 0.340495291328581 0.82975235433571 31 0.284663683954990 0.569327367909979 0.71533631604501 32 0.555191726417564 0.889616547164872 0.444808273582436 33 0.83861592818966 0.322768143620681 0.161384071810340 34 0.892494843916886 0.215010312166229 0.107505156083114 35 0.89682159295421 0.206356814091581 0.103178407045790 36 0.896085186265452 0.207829627469097 0.103914813734548 37 0.902898145688693 0.194203708622613 0.0971018543113066 38 0.902960141124809 0.194079717750382 0.0970398588751908 39 0.911127112485701 0.177745775028598 0.0888728875142988 40 0.921915602281722 0.156168795436555 0.0780843977182776 41 0.95851885615575 0.0829622876884983 0.0414811438442491 42 0.995166300338523 0.00966739932295435 0.00483369966147718 43 0.991385028187028 0.0172299436259435 0.00861497181297177 44 0.98775154368534 0.0244969126293182 0.0122484563146591 45 0.980383611757736 0.0392327764845271 0.0196163882422635 46 0.97421678359177 0.0515664328164587 0.0257832164082294 47 0.95544268758051 0.0891146248389817 0.0445573124194909 48 0.927446067043972 0.145107865912055 0.0725539329560275 49 0.890575104951999 0.218849790096003 0.109424895048001 50 0.83011766214455 0.339764675710901 0.169882337855450 51 0.771407038938938 0.457185922122124 0.228592961061062 52 0.715753326466391 0.568493347067218 0.284246673533609 53 0.860973693263731 0.278052613472537 0.139026306736269 54 0.96576142461516 0.068477150769679 0.0342385753848395 55 0.932028445272494 0.135943109455013 0.0679715547275064 56 0.92500097871829 0.149998042563421 0.0749990212817106 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0192307692307692 NOK 5% type I error level 4 0.0769230769230769 NOK 10% type I error level 9 0.173076923076923 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/1034gl1258569941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/1034gl1258569941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/15k8a1258569941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/15k8a1258569941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/2zcx81258569941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/2zcx81258569941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/33tv01258569941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/33tv01258569941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/4rp6d1258569941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/4rp6d1258569941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/5pnxh1258569941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/5pnxh1258569941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/6uzro1258569941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/6uzro1258569941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/7ids81258569941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/7ids81258569941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/8bs551258569941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/8bs551258569941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/9hc9y1258569941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570119u1nvv0c1054c9ir/9hc9y1258569941.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