SHWWS7model1c

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 19 Nov 2009 09:11:14 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp.htm/, Retrieved Thu, 19 Nov 2009 17:12:26 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp.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 «

161 0 149 0 139 0 135 0 130 0 127 0 122 0 117 0 112 0 113 0 149 0 157 0 157 0 147 0 137 0 132 0 125 0 123 0 117 0 114 0 111 0 112 0 144 0 150 0 149 0 134 0 123 0 116 0 117 0 111 0 105 0 102 0 95 0 93 0 124 0 130 0 124 0 115 0 106 0 105 0 105 0 101 0 95 0 93 0 84 0 87 0 116 0 120 0 117 0 109 0 105 0 107 0 109 1 109 1 108 1 107 1 99 1 103 1 131 1 137 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 120.538461538462 -7.66346153846155X[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 120.538461538462 2.548976 47.289 0 0 X -7.66346153846155 6.980659 -1.0978 0.276823 0.138411 Multiple Linear Regression - Regression Statistics Multiple R 0.142675247853586 R-squared 0.0203562263500823 Adjusted R-squared 0.00346581645956656 F-TEST (value) 1.20519433702509 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.276822685864108 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 18.3809299964163 Sum Squared Residuals 19595.7980769231 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 161 120.538461538461 40.4615384615387 2 149 120.538461538462 28.4615384615385 3 139 120.538461538462 18.4615384615385 4 135 120.538461538462 14.4615384615385 5 130 120.538461538462 9.46153846153845 6 127 120.538461538462 6.46153846153845 7 122 120.538461538462 1.46153846153845 8 117 120.538461538462 -3.53846153846155 9 112 120.538461538462 -8.53846153846155 10 113 120.538461538462 -7.53846153846155 11 149 120.538461538462 28.4615384615385 12 157 120.538461538462 36.4615384615385 13 157 120.538461538462 36.4615384615385 14 147 120.538461538462 26.4615384615385 15 137 120.538461538462 16.4615384615385 16 132 120.538461538462 11.4615384615385 17 125 120.538461538462 4.46153846153845 18 123 120.538461538462 2.46153846153845 19 117 120.538461538462 -3.53846153846155 20 114 120.538461538462 -6.53846153846155 21 111 120.538461538462 -9.53846153846155 22 112 120.538461538462 -8.53846153846155 23 144 120.538461538462 23.4615384615385 24 150 120.538461538462 29.4615384615385 25 149 120.538461538462 28.4615384615385 26 134 120.538461538462 13.4615384615385 27 123 120.538461538462 2.46153846153845 28 116 120.538461538462 -4.53846153846155 29 117 120.538461538462 -3.53846153846155 30 111 120.538461538462 -9.53846153846155 31 105 120.538461538462 -15.5384615384615 32 102 120.538461538462 -18.5384615384615 33 95 120.538461538462 -25.5384615384615 34 93 120.538461538462 -27.5384615384615 35 124 120.538461538462 3.46153846153845 36 130 120.538461538462 9.46153846153845 37 124 120.538461538462 3.46153846153845 38 115 120.538461538462 -5.53846153846155 39 106 120.538461538462 -14.5384615384615 40 105 120.538461538462 -15.5384615384615 41 105 120.538461538462 -15.5384615384615 42 101 120.538461538462 -19.5384615384615 43 95 120.538461538462 -25.5384615384615 44 93 120.538461538462 -27.5384615384615 45 84 120.538461538462 -36.5384615384615 46 87 120.538461538462 -33.5384615384615 47 116 120.538461538462 -4.53846153846155 48 120 120.538461538462 -0.538461538461546 49 117 120.538461538462 -3.53846153846155 50 109 120.538461538462 -11.5384615384615 51 105 120.538461538462 -15.5384615384615 52 107 120.538461538462 -13.5384615384615 53 109 112.875 -3.875 54 109 112.875 -3.875 55 108 112.875 -4.875 56 107 112.875 -5.875 57 99 112.875 -13.875 58 103 112.875 -9.875 59 131 112.875 18.125 60 137 112.875 24.125 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.420936966267954 0.841873932535908 0.579063033732046 6 0.375278471813465 0.750556943626929 0.624721528186535 7 0.370090971768182 0.740181943536364 0.629909028231818 8 0.394240744311886 0.788481488623771 0.605759255688114 9 0.442474140300047 0.884948280600095 0.557525859699953 10 0.436208318024551 0.872416636049103 0.563791681975448 11 0.463373721417359 0.926747442834718 0.536626278582641 12 0.599613849023301 0.800772301953398 0.400386150976699 13 0.720694540961736 0.558610918076528 0.279305459038264 14 0.731757039232395 0.536485921535209 0.268242960767605 15 0.691963846891638 0.616072306216725 0.308036153108362 16 0.643543795831406 0.712912408337188 0.356456204168594 17 0.601687249925391 0.796625500149219 0.398312750074609 18 0.563625921114136 0.872748157771727 0.436374078885864 19 0.55229182522244 0.89541634955512 0.44770817477756 20 0.552377488283887 0.895245023432225 0.447622511716113 21 0.563948962844308 0.872102074311384 0.436051037155692 22 0.555185430655762 0.889629138688475 0.444814569344238 23 0.614350069140483 0.771299861719033 0.385649930859517 24 0.768111433306085 0.463777133387831 0.231888566693915 25 0.901344014622304 0.197311970755393 0.0986559853776963 26 0.922741167342644 0.154517665314711 0.0772588326573555 27 0.919440773450351 0.161118453099298 0.0805592265496492 28 0.913350433613652 0.173299132772695 0.0866495663863475 29 0.905759749663791 0.188480500672417 0.0942402503362085 30 0.900604252248676 0.198791495502648 0.0993957477513242 31 0.905321656892939 0.189356686214123 0.0946783431070615 32 0.913675109455069 0.172649781089863 0.0863248905449314 33 0.940190609115269 0.119618781769462 0.0598093908847312 34 0.961299380655878 0.077401238688244 0.038700619344122 35 0.958595382787078 0.0828092344258448 0.0414046172129224 36 0.971923050291214 0.0561538994175727 0.0280769497087863 37 0.976011192582759 0.0479776148344826 0.0239888074172413 38 0.970921303788835 0.0581573924223301 0.0290786962111650 39 0.961620875344841 0.076758249310317 0.0383791246551585 40 0.94936826666261 0.101263466674782 0.0506317333373911 41 0.932798642130414 0.134402715739172 0.0672013578695859 42 0.915301894352135 0.16939621129573 0.084698105647865 43 0.909670240583188 0.180659518833624 0.0903297594168118 44 0.911046483382958 0.177907033234083 0.0889535166170417 45 0.959053571354948 0.0818928572901033 0.0409464286450517 46 0.984823105398162 0.0303537892036766 0.0151768946018383 47 0.972345571812192 0.0553088563756148 0.0276544281878074 48 0.958882051070713 0.0822358978585749 0.0411179489292874 49 0.937815466843213 0.124369066313574 0.0621845331567871 50 0.89500835741233 0.209983285175340 0.104991642587670 51 0.831474301899539 0.337051396200923 0.168525698100462 52 0.739261007800039 0.521477984399921 0.260738992199961 53 0.61972858509427 0.760542829811459 0.380271414905730 54 0.478409926591222 0.956819853182444 0.521590073408778 55 0.333299148050906 0.666598296101813 0.666700851949094 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 2 0.0392156862745098 OK 10% type I error level 10 0.196078431372549 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/1065l41258647070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/1065l41258647070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/1cb9g1258647070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/1cb9g1258647070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/22nil1258647070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/22nil1258647070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/37eam1258647070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/37eam1258647070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/4n8k91258647070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/4n8k91258647070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/5hqee1258647070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/5hqee1258647070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/6s7l11258647070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/6s7l11258647070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/7payw1258647070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/7payw1258647070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/8d4so1258647070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/8d4so1258647070.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/9yxty1258647070.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586471341bo6ws0mn443swp/9yxty1258647070.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by