Home » date » 2010 » Dec » 13 »

Bonus - Multiple Regression PS

*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: Mon, 13 Dec 2010 20:43:53 +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/13/t1292273010555rtu39tw275dq.htm/, Retrieved Mon, 13 Dec 2010 21:43:32 +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/13/t1292273010555rtu39tw275dq.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 «
2,00 4,50 1000,00 6600,00 42,00 3,00 1,00 3,00 1,80 69,00 2547000,00 4603000,00 624,00 3,00 5,00 4,00 0,70 27,00 10550,00 179500,00 180,00 4,00 4,00 4,00 3,90 19,00 23,00 300,00 35,00 1,00 1,00 1,00 1,00 30,40 160000,00 169000,00 392,00 4,00 5,00 4,00 3,60 28,00 3300,00 25600,00 63,00 1,00 2,00 1,00 1,40 50,00 52160,00 440000,00 230,00 1,00 1,00 1,00 1,50 7,00 425,00 6400,00 112,00 5,00 4,00 4,00 0,70 30,00 465000,00 423000,00 281,00 5,00 5,00 5,00 2,10 3,50 75,00 1200,00 42,00 1,00 1,00 1,00 0,00 50,00 3000,00 25000,00 28,00 2,00 2,00 2,00 4,10 6,00 785,00 3500,00 42,00 2,00 2,00 2,00 1,20 10,40 200,00 5000,00 120,00 2,00 2,00 2,00 0,50 20,00 27660,00 115000,00 148,00 5,00 5,00 5,00 3,40 3,90 120,00 1000,00 16,00 3,00 1,00 2,00 1,50 41,00 85000,00 325000,00 310,00 1,00 3,00 1,00 3,40 9,00 101,00 4000,00 28,00 5,00 1,00 3,00 0,80 7,60 1040,00 5500,00 68,00 5,00 3,00 4,00 0,80 46,00 521000,00 655000,00 336,00 5,00 5,00 5,00 1,40 2,60 5,00 140,00 21,50 5,00 2,00 4,00 2,00 24,00 10,00 250,00 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'RServer@AstonUniversity' @ vre.aston.ac.uk


Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.77763738061614 -0.0133949634614039L[t] + 1.37031210793507e-06Wb[t] + 2.96123778267419e-07Wbr[t] -0.00500701775504269Tg[t] + 0.900361471653855P[t] + 0.360021324193483S[t] -1.73845778307009D[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)3.777637380616140.4132689.140900
L-0.01339496346140390.014311-0.9360.3558690.177935
Wb1.37031210793507e-062e-060.74830.4594340.229717
Wbr2.96123778267419e-071e-060.26940.7892270.394614
Tg-0.005007017755042690.002158-2.31970.0264970.013248
P0.9003614716538550.3379062.66450.0117040.005852
S0.3600213241934830.2117011.70060.0981470.049074
D-1.738457783070090.419569-4.14340.0002140.000107


Multiple Linear Regression - Regression Statistics
Multiple R0.787651358957768
R-squared0.620394663268018
Adjusted R-squared0.54224062335261
F-TEST (value)7.9381010110228
F-TEST (DF numerator)7
F-TEST (DF denominator)34
p-value1.06575639513551e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.939636820152983
Sum Squared Residuals30.0191900287651


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
121.35612241831730.643877581682701
21.82.12960841655679-0.329608416556788
30.70.6700212332972540.0299787667027454
43.92.869932820512181.03006717948782
510.1246957625120020.875304237487998
63.62.98118542077970.618814579220295
71.41.67997007865093-0.27997007865093
81.52.11362574541116-0.613625745411159
90.70.3408970398901850.359102960109815
102.13.0428433975087-0.942843397508697
1102.02305676673973-2.02305676673973
124.12.532935007919081.56706499208092
131.22.08309233687984-0.883092336879835
140.50.4502816149342150.0497183850657847
153.43.229935473282070.170064526717932
161.52.13095279291095-0.630952792910953
173.43.164664442210770.235335557789233
180.81.96645245490865-1.16645245490865
190.8-0.003370157417221580.803370157417222
201.41.90322677714845-0.503226777148446
2122.72782011663321-0.727820116633206
221.91.099035044661560.800964955338435
231.32.38588398848225-1.08588398848226
2422.34689427846869-0.346894278468691
255.64.077059945066291.52294005493371
263.13.52000870112606-0.420008701126056
271.81.96501302730277-0.165013027302766
280.90.7913235070276160.108676492972385
291.82.20780797183028-0.407807971830278
301.91.244268581248980.65573141875102
310.90.997944429777653-0.097944429777653
322.61.456212392005351.14378760799465
332.42.92865473841119-0.528654738411193
341.22.03422815680023-0.83422815680023
350.91.60581372360984-0.705813723609844
360.50.787594042033552-0.287594042033552
370.60.4911774774500440.108822522549956
382.32.102770883818910.19722911618109
390.50.527886940280408-0.0278869402804076
402.63.46160046703619-0.861600467036191
410.60.1552800427707440.444719957229256
426.64.095591701205452.50440829879455


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.8677923197015680.2644153605968630.132207680298432
120.8743541160205610.2512917679588780.125645883979439
130.9136694840510280.1726610318979450.0863305159489724
140.8511536516042070.2976926967915870.148846348395793
150.786353142054450.4272937158911010.213646857945551
160.711591202030520.576817595938960.28840879796948
170.6409433464995440.7181133070009120.359056653500456
180.6473278135972360.7053443728055280.352672186402764
190.5731301283344340.8537397433311320.426869871665566
200.491023383375740.982046766751480.50897661662426
210.4228143679877390.8456287359754780.577185632012261
220.4386158032124610.8772316064249220.561384196787539
230.5092969931540590.9814060136918820.490703006845941
240.5638430084620080.8723139830759850.436156991537993
250.6389035248003840.7221929503992310.361096475199616
260.5924902868090260.8150194263819480.407509713190974
270.4819250697068860.9638501394137720.518074930293114
280.3672351061571130.7344702123142270.632764893842887
290.3148463565720550.6296927131441110.685153643427945
300.3465896702553310.6931793405106610.65341032974467
310.2249404333045690.4498808666091390.775059566695431


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/103qx71292273020.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/103qx71292273020.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/1x70v1292273020.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/1x70v1292273020.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/2pyzy1292273020.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/2pyzy1292273020.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/3pyzy1292273020.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/3pyzy1292273020.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/4pyzy1292273020.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/4pyzy1292273020.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/5iqh11292273020.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/5iqh11292273020.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/6iqh11292273020.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/6iqh11292273020.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/7shg41292273020.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/7shg41292273020.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/8shg41292273020.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/8shg41292273020.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/93qx71292273020.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273010555rtu39tw275dq/93qx71292273020.ps (open in new window)


 
Parameters (Session):
par1 = pearson ;
 
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