Home » date » 2011 » Apr » 20 »

dividend project

*Unverified author*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Wed, 20 Apr 2011 03:53:28 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz.htm/, Retrieved Wed, 20 Apr 2011 05:59:36 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
0.103638248 0.161963 1.8049 0.353861 0.132478037 0.165501 3.6967 0.357803 0.135816148 0.118078 2.5405 0.357531 0.148739627 0.126748 1.9518 0.325875 0.132908957 0.163447 1.3482 0.334919 0.136347327 0.096188 1.3459 0.344185 0.134656272 0.108369 1.8102 0.321689 0.141001855 0.108262 1.74 0.35336 0.132556598 0.070497 1.2274 0.281717 0.135787029 0.120334 1.3482 0.346899 0.134742217 0.109403 1.2096 0.338057 0.14266942 0.106887 1.4936 0.352255 0.137347743 0.099173 1.1971 0.284856 0.152776103 0.116039 1.3657 0.242718 0.143613001 0.128575 1.513 0.368384 0.153429967 0.110503 1.5941 0.434267 0.144245369 0.105957 1.5822 0.303746 0.153933404 0.106399 1.6498 0.368653 0.153028351 0.106466 1.709 0.366548 0.156417222 0.131275 1.4455 0.374977 0.146599282 0.139812 1.1474 0.349582 0.148219365 0.142927 1.2846 0.383001 0.136491073 0.144568 1.3725 0.435807 0.146048788 0.148735 1.2815 0.423048 0.146239279 0.154986 1.2046 0.389757 0.139762404 0.162922 1.1968 0.479903 0.060755392 0.1532 1.1857 0.205177 0.0603 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 time4 seconds
R Server'Herman Ole Andreas Wold' @ www.yougetit.org


Multiple Linear Regression - Estimated Regression Equation
div/rev/share[t] = + 0.100447462934908 -0.576381870047455roa[t] + 0.0115228239094356currentR[t] + 0.256366870998517ebitmargin[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)0.1004474629349080.0252843.97280.0002890.000144
roa-0.5763818700474550.104923-5.49342e-061e-06
currentR0.01152282390943560.0070091.6440.1080220.054011
ebitmargin0.2563668709985170.0431555.94061e-060


Multiple Linear Regression - Regression Statistics
Multiple R0.847488246747138
R-squared0.718236328374537
Adjusted R-squared0.697104053002628
F-TEST (value)33.9876476022672
F-TEST (DF numerator)3
F-TEST (DF denominator)40
p-value4.37336833414292e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0202559442723589
Sum Squared Residuals0.0164121311345964


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
10.1036382480.118610708328958-0.0149724603289583
20.1324780370.139380945750077-0.00690290875007674
30.1358161480.153322282380336-0.0175061343803362
40.1487396270.1334260154632110.015313611536789
50.1329089570.1076367826839150.0252721743160853
60.1363473270.148752643813117-0.0124053168131171
70.1346562720.141314554265237-0.00665828226523732
80.1410018550.148686720058284-0.00768486505828404
90.1325565980.146180290105703-0.0136236921057028
100.1357870290.1355576093618330.000229419638167115
110.1347422170.137994180316105-0.00325196331610496
120.142669420.146356735925861-0.00368731592586101
130.1373477430.130107557643830.00724018535616962
140.1527761030.1115262619246050.0412498410753947
150.1436130010.138214649974450.00539835102555007
160.1534299670.166455742710998-0.0130257757109981
170.1442453690.1354775927181140.00876777628188594
180.1539334040.1526417793237320.0012916246762683
190.1530283510.1527456606504250.000282690349574795
200.1564172220.1375708550919280.0188463669080719
210.1465992820.1227048925709230.0238943894290771
220.1482193650.1310579189479990.0171614460520009
230.1364910730.144662641510838-0.0081715685108383
240.1460487880.1379412963755220.00810749162447819
250.1462392790.1249175186448080.021321760355192
260.1397624040.14336392205065-0.00360151805065007
270.0607553920.078408958243918-0.0176535662439181
280.0603469950.0785809197584204-0.0182339247584204
290.1334816460.144891067599158-0.0114094215991579
300.1451258450.1424150132747610.00271083172523858
310.0618243480.0738375196972115-0.0120131716972115
320.0653241010.0684870247263032-0.00316292372630319
330.0633782410.0765580699265607-0.0131798289265607
340.0651178830.0780734184574382-0.0129555354574382
350.0654891460.0693272260344472-0.0038380800344472
360.0690397380.06588306753161360.00315667046838644
370.0683362140.0688858357884652-0.000549621788465198
380.0631010920.0790699629735454-0.0159688709735454
390.0703866410.0844504619227693-0.0140638209227693
400.0759281560.0804347798387418-0.00450662383874184
410.0755326350.107050428757713-0.0315177937577127
420.0771869640.0879088966774528-0.0107219326774528
430.0793884150.0861802189465748-0.00679180394657485
440.1692312510.08141716025344580.0878140907465542


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.2520607416456930.5041214832913850.747939258354307
80.1690911320359030.3381822640718050.830908867964097
90.1798117232791520.3596234465583050.820188276720848
100.1006447005120590.2012894010241170.899355299487941
110.05060965887884240.1012193177576850.949390341121158
120.02765261852432360.05530523704864720.972347381475676
130.01278499589926860.02556999179853710.987215004100731
140.01763523248158110.03527046496316210.98236476751842
150.01401302925521790.02802605851043580.985986970744782
160.01783347247695490.03566694495390970.982166527523045
170.009311085448564050.01862217089712810.990688914551436
180.007273875171629340.01454775034325870.99272612482837
190.007814304069201660.01562860813840330.992185695930798
200.008477998098062940.01695599619612590.991522001901937
210.007488531911645950.01497706382329190.992511468088354
220.004739686649661330.009479373299322650.995260313350339
230.003409788033417830.006819576066835650.996590211966582
240.001647725975135860.003295451950271720.998352274024864
250.001428292241208650.002856584482417290.998571707758791
260.000687202110799880.001374404221599760.9993127978892
270.0222537560551730.04450751211034610.977746243944827
280.0364025144515410.0728050289030820.96359748554846
290.02624526111360180.05249052222720360.973754738886398
300.07240510353009280.1448102070601860.927594896469907
310.05303847096639590.1060769419327920.946961529033604
320.03124885667669530.06249771335339050.968751143323305
330.02055444820214550.0411088964042910.979445551797854
340.01788155043350340.03576310086700680.982118449566497
350.01735477721104590.03470955442209180.982645222788954
360.01073247384500490.02146494769000970.989267526154995
370.004023918632330450.00804783726466090.99597608136767


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.193548387096774NOK
5% type I error level200.64516129032258NOK
10% type I error level240.774193548387097NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/10k4nr1303271604.png (open in new window)
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/10k4nr1303271604.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/1x3oa1303271604.png (open in new window)
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/1x3oa1303271604.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/24tkh1303271604.png (open in new window)
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/24tkh1303271604.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/3wfse1303271604.png (open in new window)
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/3wfse1303271604.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/41bho1303271604.png (open in new window)
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/41bho1303271604.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/58j7x1303271604.png (open in new window)
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/58j7x1303271604.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/6ngl31303271604.png (open in new window)
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/6ngl31303271604.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/7sglj1303271604.png (open in new window)
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/7sglj1303271604.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/8lmd81303271604.png (open in new window)
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/8lmd81303271604.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/991o91303271604.png (open in new window)
http://www.freestatistics.org/blog/date/2011/Apr/20/t130327196784a96dtuwri3dpz/991o91303271604.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