*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: Thu, 19 Nov 2009 07:44:25 -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/19/t1258642864wjsswx725la5ftd.htm/, Retrieved Thu, 19 Nov 2009 16:01:16 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd.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 «

286602 0 283042 0 276687 0 277915 0 277128 0 277103 0 275037 0 270150 0 267140 0 264993 0 287259 0 291186 0 292300 0 288186 0 281477 0 282656 0 280190 0 280408 0 276836 0 275216 0 274352 0 271311 0 289802 0 290726 0 292300 0 278506 0 269826 0 265861 0 269034 0 264176 0 255198 0 253353 0 246057 0 235372 0 258556 0 260993 0 254663 0 250643 0 243422 0 247105 0 248541 0 245039 0 237080 0 237085 0 225554 0 226839 0 247934 0 248333 0 246969 1 245098 1 246263 1 255765 1 264319 1 268347 1 273046 1 273963 1 267430 1 271993 1 292710 1 295881 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 266232.75 + 582.583333333325X[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) 266232.75 2616.953032 101.7339 0 0 X 582.583333333325 5851.684873 0.0996 0.921038 0.460519 Multiple Linear Regression - Regression Statistics Multiple R 0.0130715179340899 R-squared 0.000170864581101233 Adjusted R-squared -0.0170675687881900 F-TEST (value) 0.00991183928613908 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.921038382478863 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 18130.7824467722 Sum Squared Residuals 19066065783.6667 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 286602 266232.750000000 20369.2500000004 2 283042 266232.75 16809.25 3 276687 266232.75 10454.25 4 277915 266232.75 11682.25 5 277128 266232.75 10895.25 6 277103 266232.75 10870.25 7 275037 266232.75 8804.25 8 270150 266232.75 3917.24999999999 9 267140 266232.75 907.249999999992 10 264993 266232.75 -1239.75000000001 11 287259 266232.75 21026.25 12 291186 266232.75 24953.25 13 292300 266232.75 26067.25 14 288186 266232.75 21953.25 15 281477 266232.75 15244.25 16 282656 266232.75 16423.25 17 280190 266232.75 13957.25 18 280408 266232.75 14175.25 19 276836 266232.75 10603.25 20 275216 266232.75 8983.25 21 274352 266232.75 8119.25 22 271311 266232.75 5078.24999999999 23 289802 266232.75 23569.25 24 290726 266232.75 24493.25 25 292300 266232.75 26067.25 26 278506 266232.75 12273.25 27 269826 266232.75 3593.24999999999 28 265861 266232.75 -371.750000000008 29 269034 266232.75 2801.24999999999 30 264176 266232.75 -2056.75000000001 31 255198 266232.75 -11034.75 32 253353 266232.75 -12879.75 33 246057 266232.75 -20175.75 34 235372 266232.75 -30860.75 35 258556 266232.75 -7676.75 36 260993 266232.75 -5239.75000000001 37 254663 266232.75 -11569.75 38 250643 266232.75 -15589.75 39 243422 266232.75 -22810.75 40 247105 266232.75 -19127.75 41 248541 266232.75 -17691.75 42 245039 266232.75 -21193.75 43 237080 266232.75 -29152.75 44 237085 266232.75 -29147.75 45 225554 266232.75 -40678.75 46 226839 266232.75 -39393.75 47 247934 266232.75 -18298.75 48 248333 266232.75 -17899.75 49 246969 266815.333333333 -19846.3333333333 50 245098 266815.333333333 -21717.3333333333 51 246263 266815.333333333 -20552.3333333333 52 255765 266815.333333333 -11050.3333333333 53 264319 266815.333333333 -2496.33333333333 54 268347 266815.333333333 1531.66666666667 55 273046 266815.333333333 6230.66666666667 56 273963 266815.333333333 7147.66666666667 57 267430 266815.333333333 614.666666666668 58 271993 266815.333333333 5177.66666666667 59 292710 266815.333333333 25894.6666666667 60 295881 266815.333333333 29065.6666666667 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0268636797989692 0.0537273595979385 0.97313632020103 6 0.0072856343989968 0.0145712687979936 0.992714365601003 7 0.00249669077861382 0.00499338155722765 0.997503309221386 8 0.00227827485692729 0.00455654971385457 0.997721725143073 9 0.00261447622289175 0.0052289524457835 0.997385523777108 10 0.0029072966288041 0.0058145932576082 0.997092703371196 11 0.00313053848275274 0.00626107696550548 0.996869461517247 12 0.00514403982102575 0.0102880796420515 0.994855960178974 13 0.00762269107469671 0.0152453821493934 0.992377308925303 14 0.00625146213837943 0.0125029242767589 0.99374853786162 15 0.00336327271799783 0.00672654543599567 0.996636727282002 16 0.00190009021256220 0.00380018042512440 0.998099909787438 17 0.000997310756496163 0.00199462151299233 0.999002689243504 18 0.00053210483538169 0.00106420967076338 0.999467895164618 19 0.000283631118851084 0.000567262237702168 0.999716368881149 20 0.000159441400477832 0.000318882800955664 0.999840558599522 21 9.35829471347002e-05 0.000187165894269400 0.999906417052865 22 6.73070376131386e-05 0.000134614075226277 0.999932692962387 23 0.000142015683768027 0.000284031367536055 0.999857984316232 24 0.000410898240111605 0.00082179648022321 0.999589101759888 25 0.00201916592697714 0.00403833185395427 0.997980834073023 26 0.00252866503921871 0.00505733007843741 0.997471334960781 27 0.00365627685221632 0.00731255370443263 0.996343723147784 28 0.00651363244613518 0.0130272648922704 0.993486367553865 29 0.0101052178142287 0.0202104356284574 0.989894782185771 30 0.0185029351104796 0.0370058702209593 0.98149706488952 31 0.0485864753559896 0.0971729507119791 0.95141352464401 32 0.0973085037841553 0.194617007568311 0.902691496215845 33 0.204766937458545 0.409533874917091 0.795233062541455 34 0.455806426254125 0.91161285250825 0.544193573745875 35 0.469523241001475 0.93904648200295 0.530476758998525 36 0.497624859807746 0.995249719615492 0.502375140192254 37 0.517770293466604 0.964459413066791 0.482229706533396 38 0.537018352466459 0.925963295067082 0.462981647533541 39 0.570074499701558 0.859851000596884 0.429925500298442 40 0.573777583499874 0.852444833000251 0.426222416500126 41 0.57101959644601 0.857960807107979 0.428980403553989 42 0.566229923330903 0.867540153338195 0.433770076669097 43 0.576603404609444 0.846793190781112 0.423396595390556 44 0.570088862327838 0.859822275344324 0.429911137672162 45 0.646003140286486 0.707993719427029 0.353996859713514 46 0.715004605065213 0.569990789869574 0.284995394934787 47 0.643340273026858 0.713319453946283 0.356659726973142 48 0.561268046329986 0.877463907340028 0.438731953670014 49 0.570906208087578 0.858187583824845 0.429093791912423 50 0.644113165972442 0.711773668055116 0.355886834027558 51 0.766204842025012 0.467590315949977 0.233795157974988 52 0.80277441153467 0.394451176930662 0.197225588465331 53 0.766149709248902 0.467700581502195 0.233850290751098 54 0.689863052618623 0.620273894762753 0.310136947381377 55 0.552931724160543 0.894136551678914 0.447068275839457 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 18 0.352941176470588 NOK 5% type I error level 25 0.490196078431373 NOK 10% type I error level 27 0.529411764705882 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/102sfv1258641860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/102sfv1258641860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/1tslq1258641859.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/1tslq1258641859.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/2zzjx1258641859.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/2zzjx1258641859.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/30vhb1258641859.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/30vhb1258641859.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/47b8i1258641859.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/47b8i1258641859.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/5qapu1258641859.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/5qapu1258641859.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/6453d1258641859.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/6453d1258641859.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/7ne431258641860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/7ne431258641860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/8tg2c1258641860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/8tg2c1258641860.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/9eckc1258641860.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258642864wjsswx725la5ftd/9eckc1258641860.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