Home » date » 2010 » Dec » 28 »

*The author of this computation has been verified*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Tue, 28 Dec 2010 12:53:33 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl.htm/, Retrieved Tue, 28 Dec 2010 13:57:07 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
4.607 5.485 4.969 5.231 3.642 3.532 3.607 4.397 3.830 4.071 2.894 2.614 757 882 748 874 624 533 244 205 390 286 123 384 1.485 2.655 1.295 2.005 757 617 1.210 2.198 1.075 1.631 601 496 275 457 219 373 156 121 7.666 8.849 7.045 8.112 7.356 5.774 6.101 7.099 5.586 6.573 5.717 4.572 1.566 1.750 1.459 1.539 1.640 1.201 1.866 2.111 1.599 2.441 1.375 1.123 645 534 538 967 379 552 157 125 168 222 106 51 285 389 230 357 216 149 100 124 135 134 48 44 128 109 107 188 85 56 551 831 421 572 541 271 1.303 1.123 1.200 1.414 1.362 729 391 365 321 398 442 249 147 150 146 152 157 130 633 538 641 716 618 284 131 168 147 129 127 54 4.642 7.891 5.078 5.565 2.721 2.643 1.804 3.880 2.221 1.682 1.215 1.162 1.990 2.946 1.989 2.776 990 828 190 249 167 279 90 147 658 815 700 829 424 506 2.681 3.427 2.769 3.587 1.565 1.568 1.038 1.489 1.096 1.290 641 719 1.020 1.142 1.103 1.370 618 595 383 397 355 499 287 212 239 399 214 429 19 42 5.359 7.882 5.079 6.543 3.742 2.950 566 736 542 676 434 346 150 321 85 18 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Multiple Linear Regression - Estimated Regression Equation
VlaamsGewest[t] = + 4.24548645333661 + 0.0793346820558553Zelfstandigen[t] + 1.1421704037988Arbeiders[t] -0.161796871191036Bedienden[t] + 0.0837696508067514Gepensioneerden[t] -0.0720956413728646A.N.A.[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)4.2454864533366113.4979780.31450.7550420.377521
Zelfstandigen0.07933468205585530.0933340.850.4012670.200634
Arbeiders1.14217040379880.07703414.826900
Bedienden-0.1617968711910360.099888-1.61980.114520.05726
Gepensioneerden0.08376965080675140.0538861.55460.129310.064655
A.N.A.-0.07209564137286460.054433-1.32450.1941770.097088


Multiple Linear Regression - Regression Statistics
Multiple R0.980271404865744
R-squared0.96093202719746
Adjusted R-squared0.95518673707944
F-TEST (value)167.255614156615
F-TEST (DF numerator)5
F-TEST (DF denominator)34
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation54.9858190098112
Sum Squared Residuals102796.969934110


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
14.6079.5601697505981-4.95316975059809
23.6078.36412999715296-4.75712999715296
3757800.996957898815-43.9969578988150
4244402.300589357734-158.300589357734
51.48524.5414429140296-23.0564429140296
61.2119.9699285855832-18.7599285855832
7275234.6310145502840.3689854497198
87.66611.8815426485572-4.21554264855724
96.10110.2746462238380-4.17364622383796
101.5665.852538743348-4.286538743348
111.8665.87856614485098-4.01256614485098
12645496.592213091124148.407786908876
13157175.330749419607-18.3307494196067
14285247.39638164129037.6036183587098
15100147.44394581982-47.44394581982
16128107.77045262167520.2295473783248
17551484.25999919418166.7400008058187
181.303-46.967225286439848.2702252864398
19391354.51856124384736.4814387561525
20147162.08884509349-15.0888450934899
21633694.506696510315-61.5066965103154
22131171.346547031823-40.3465470318231
234.6429.8084465914482-5.16644659144819
241.8046.83592813966201-5.03192813966201
251.9929.5385984873545-27.5485984873545
26190166.54216194814223.4578380518581
27658733.330866178044-75.3308661780438
282.6817.1177244177385-4.43672441773849
291.0387.26629661368272-6.22829661368272
301.0214.2469764838206-13.2269764838206
31383369.23282366424913.7671763357510
32239209.4772396932829.5227603067201
335.3599.71403286126088-4.35503286126088
34566583.728422915375-17.7284229153750
3515098.362771246785451.6372287532146
361.6255.7728264921531-4.1478264921531
37973848.68384571799124.316154282010
381.339-35.514996664951036.8539966649510
39437506.011735303891-69.0117353038909
40269261.2146067145557.78539328544528


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
91.06046549054568e-062.12093098109136e-060.99999893953451
101.28043196102449e-082.56086392204898e-080.99999998719568
117.1758188639975e-101.4351637727995e-090.999999999282418
121.01469595734947e-102.02939191469893e-100.99999999989853
130.0007738206905711460.001547641381142290.999226179309429
140.0002308456737258130.0004616913474516260.999769154326274
150.0003140737287965440.0006281474575930880.999685926271203
160.0001061230042250210.0002122460084500420.999893876995775
170.0003193669886770480.0006387339773540950.999680633011323
180.0006012198780588650.001202439756117730.999398780121941
190.04253291184024810.08506582368049610.957467088159752
200.02746601410208970.05493202820417930.97253398589791
210.02362966122713390.04725932245426780.976370338772866
220.03139922686970440.06279845373940890.968600773130296
230.02024214849091680.04048429698183360.979757851509083
240.01417912350779720.02835824701559430.985820876492203
250.008109163524492010.01621832704898400.991890836475508
260.006556702347741370.01311340469548270.993443297652259
270.06140891528258630.1228178305651730.938591084717414
280.039118533568640.078237067137280.96088146643136
290.01877664274266460.03755328548532920.981223357257335
300.007968752689763770.01593750537952750.992031247310236
310.3797204663088840.7594409326177670.620279533691116


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.434782608695652NOK
5% type I error level170.739130434782609NOK
10% type I error level210.91304347826087NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/10hvz1293540805.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/10hvz1293540805.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/10wrbq1293540805.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/10wrbq1293540805.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/20hvz1293540805.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/20hvz1293540805.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/3a8uk1293540805.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/3a8uk1293540805.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/4a8uk1293540805.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/4a8uk1293540805.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/5a8uk1293540805.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/5a8uk1293540805.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/6q3jc1293540805.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/6q3jc1293540805.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/7q3jc1293540805.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/7q3jc1293540805.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/8wrbq1293540805.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/8wrbq1293540805.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/9wrbq1293540805.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354102749ik5lbmtg853vl/9wrbq1293540805.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

Creative Commons License

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