Home » date » 2009 » Nov » 18 »

*Unverified author*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Wed, 18 Nov 2009 12:45:07 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx.htm/, Retrieved Wed, 18 Nov 2009 20:46:07 +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/2009/Nov/18/t125857355555i9wxztl8eggxx.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 «
902.2 0 891.9 0 874 0 930.9 0 944.2 0 935.9 0 937.1 0 885.1 0 892.4 0 987.3 0 946.3 0 799.6 0 875.4 0 846.2 0 880.6 0 885.7 0 868.9 0 882.5 0 789.6 0 773.3 0 804.3 0 817.8 0 836.7 0 721.8 0 760.8 0 841.4 0 1045.6 0 949.2 1 850.1 1 957.4 0 851.8 0 913.9 0 888 0 973.8 0 927.6 1 833 1 879.5 1 797.3 1 834.5 1 735.1 1 835 1 892.8 1 697.2 1 821.1 1 732.7 1 797.6 1 866.3 1 826.3 1 778.6 1 779.2 1 951 1 692.3 1 841.4 1 857.3 1 760.7 1 841.2 0 810.3 0 1007.4 1 931.3 0 931.2 0
 
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'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 852.701226415094 -45.3792452830189X[t] + 13.2015330188675M1[t] + 5.43957547169818M2[t] + 91.7176179245284M3[t] + 22.6315094339623M4[t] + 52.2495518867925M5[t] + 80.771745283019M6[t] -16.7902122641509M7[t] + 14.1119811320755M8[t] -6.92997641509435M9[t] + 93.7239150943396M10[t] + 78.9219575471698M11[t] -0.338042452830185t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)852.70122641509436.82917523.152900
X-45.379245283018925.827817-1.7570.0855740.042787
M113.201533018867544.1489360.2990.766270.383135
M25.4395754716981844.0238940.12360.9022020.451101
M391.717617924528443.9104542.08870.0422930.021146
M422.631509433962344.5801840.50770.6141180.307059
M552.249551886792544.4334241.17590.2456820.122841
M680.77174528301943.6406051.85080.070620.03531
M7-16.790212264150943.574387-0.38530.7017740.350887
M814.111981132075543.5882060.32380.747590.373795
M9-6.9299764150943543.605561-0.15890.8744240.437212
M1093.723915094339643.4476922.15720.0362520.018126
M1178.921957547169843.4295621.81720.0756980.037849
t-0.3380424528301850.724578-0.46650.6430330.321517


Multiple Linear Regression - Regression Statistics
Multiple R0.609636459867138
R-squared0.371656613199336
Adjusted R-squared0.194081308233931
F-TEST (value)2.09295213245863
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value0.0333177956843731
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation68.6586098693227
Sum Squared Residuals216844.216622641


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1902.2865.56471698113436.635283018866
2891.9857.46471698113234.435283018868
3874943.404716981132-69.404716981132
4930.9873.98056603773656.9194339622642
5944.2903.26056603773640.9394339622642
6935.9931.4447169811324.45528301886807
7937.1833.544716981132103.555283018868
8885.1864.10886792452820.9911320754718
9892.4842.72886792452849.6711320754718
10987.3943.04471698113244.2552830188679
11946.3927.90471698113218.395283018868
12799.6848.644716981132-49.0447169811319
13875.4861.5082075471713.8917924528306
14846.2853.40820754717-7.20820754716972
15880.6939.34820754717-58.7482075471697
16885.7869.92405660377415.7759433962265
17868.9899.204056603773-30.3040566037735
18882.5927.38820754717-44.8882075471698
19789.6829.48820754717-39.8882075471698
20773.3860.052358490566-86.752358490566
21804.3838.672358490566-34.3723584905660
22817.8938.98820754717-121.18820754717
23836.7923.84820754717-87.1482075471697
24721.8844.58820754717-122.788207547170
25760.8857.451698113207-96.651698113207
26841.4849.351698113208-7.95169811320757
271045.6935.291698113208110.308301886792
28949.2820.488301886792128.711698113208
29850.1849.7683018867920.331698113207585
30957.4923.33169811320834.0683018867924
31851.8825.43169811320826.3683018867924
32913.9855.99584905660457.9041509433962
33888834.61584905660453.3841509433962
34973.8934.93169811320838.8683018867924
35927.6874.41245283018953.1875471698113
36833795.15245283018937.8475471698113
37879.5808.01594339622671.484056603774
38797.3799.915943396226-2.61594339622653
39834.5885.855943396226-51.3559433962265
40735.1816.43179245283-81.3317924528302
41835845.71179245283-10.7117924528302
42892.8873.89594339622618.9040566037735
43697.2775.995943396226-78.7959433962264
44821.1806.56009433962314.5399056603773
45732.7785.180094339623-52.4800943396226
46797.6885.495943396226-87.8959433962264
47866.3870.355943396227-4.05594339622652
48826.3791.09594339622635.2040566037735
49778.6803.959433962264-25.3594339622637
50779.2795.859433962264-16.6594339622642
51951881.79943396226469.2005660377358
52692.3812.375283018868-120.075283018868
53841.4841.655283018868-0.255283018868008
54857.3869.839433962264-12.5394339622643
55760.7771.939433962264-11.2394339622642
56841.2847.88283018868-6.68283018867928
57810.3826.50283018868-16.2028301886794
581007.4881.439433962264125.960566037736
59931.3911.67867924528319.6213207547168
60931.2832.41867924528398.781320754717


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.04631859923810520.09263719847621040.953681400761895
180.01352541914841790.02705083829683580.986474580851582
190.05602325332846280.1120465066569260.943976746671537
200.03859396360026360.07718792720052730.961406036399736
210.01747798698179620.03495597396359230.982522013018204
220.03724777698651110.07449555397302220.962752223013489
230.02463904210488210.04927808420976430.975360957895118
240.0288440064494550.057688012898910.971155993550545
250.02980523740409940.05961047480819880.9701947625959
260.04988399597546430.09976799195092860.950116004024536
270.6132282960203090.7735434079593820.386771703979691
280.8390646026514450.3218707946971090.160935397348555
290.7973327220853040.4053345558293920.202667277914696
300.7873005499037130.4253989001925740.212699450096287
310.7214085662040.5571828675919990.278591433795999
320.7324719877864390.5350560244271230.267528012213561
330.7370197154922480.5259605690155050.262980284507752
340.6994243895346820.6011512209306360.300575610465318
350.674881645459390.650236709081220.32511835454061
360.5870362193948030.8259275612103950.412963780605197
370.6633334847036060.6733330305927870.336666515296394
380.6280299503018220.7439400993963570.371970049698178
390.6080091913356220.7839816173287560.391990808664378
400.6867869295100050.6264261409799890.313213070489995
410.6083907007468370.7832185985063270.391609299253163
420.8077712456595280.3844575086809440.192228754340472
430.9409113051688090.1181773896623830.0590886948311914


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.111111111111111NOK
10% type I error level90.333333333333333NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/10gmjb1258573502.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/10gmjb1258573502.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/1keta1258573502.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/1keta1258573502.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/2abe51258573502.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/2abe51258573502.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/3lox71258573502.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/3lox71258573502.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/47d8s1258573502.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/47d8s1258573502.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/5z1uh1258573502.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/5z1uh1258573502.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/63w3l1258573502.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/63w3l1258573502.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/756pg1258573502.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/756pg1258573502.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/8v1v21258573502.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/8v1v21258573502.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/9543t1258573502.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/18/t125857355555i9wxztl8eggxx/9543t1258573502.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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