*The author of this computation has been verified*

R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)

Title produced by software: Recursive Partitioning (Regression Trees)

Date of computation: Tue, 14 Dec 2010 14:48:39 +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/14/t1292337994dlfkzx8e56aw4kt.htm/, Retrieved Tue, 14 Dec 2010 15:46:35 +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/14/t1292337994dlfkzx8e56aw4kt.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 «

8 350 165 3693 11.5 8 318 150 3436 11 8 302 140 3449 10.5 8 429 198 4341 10 8 440 215 4312 8.5 8 455 225 4425 10 8 383 170 3563 10 8 340 160 3609 8 8 455 225 3086 10 4 113 95 2372 15 6 199 97 2774 15.5 4 97 46 1835 20.5 4 110 87 2672 17.5 4 104 95 2375 17.5 4 121 113 2234 12.5 8 360 215 4615 14 8 307 200 4376 15 8 304 193 4732 18.5 4 97 88 2130 14.5 4 113 95 2228 14 6 250 100 3329 15.5 6 232 100 3288 15.5 8 350 165 4209 12 8 318 150 4096 13 8 400 170 4746 12 8 400 175 5140 12 4 140 72 2408 19 6 250 100 3282 15 4 122 86 2220 14 4 116 90 2123 14 4 88 76 2065 14.5 4 71 65 1773 19 4 97 60 1834 19 4 91 70 1955 20.5 4 97.5 80 2126 17 4 122 86 2226 16.5 8 350 165 4274 12 8 318 150 4135 13.5 8 351 153 4129 13 8 429 208 4633 11 8 350 155 4502 13.5 8 400 190 4422 12.5 3 70 97 2330 13.5 8 307 130 4098 14 8 302 140 4294 16 4 121 112 2933 14.5 4 121 76 2511 18 4 122 86 2395 16 4 120 97 2506 14.5 4 98 80 2164 15 8 350 175 4100 13 8 304 150 3672 11.5 8 302 137 4042 14.5 8 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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Goodness of Fit Correlation 0.7911 R-squared 0.6259 RMSE 1.6141 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 11.5 12.6461538461538 -1.14615384615385 2 11 13.5877551020408 -2.58775510204082 3 10.5 13.5877551020408 -3.08775510204082 4 10 10.7214285714286 -0.721428571428572 5 8.5 10.7214285714286 -2.22142857142857 6 10 10.7214285714286 -0.721428571428572 7 10 10.7214285714286 -0.721428571428572 8 8 12.6461538461538 -4.64615384615385 9 10 10.7214285714286 -0.721428571428572 10 15 15.2875 -0.2875 11 15.5 15.2875 0.2125 12 20.5 19.6111111111111 0.88888888888889 13 17.5 15.2875 2.2125 14 17.5 15.2875 2.2125 15 12.5 13.5877551020408 -1.08775510204082 16 14 12.6461538461538 1.35384615384615 17 15 12.6461538461538 2.35384615384615 18 18.5 12.6461538461538 5.85384615384615 19 14.5 15.2875 -0.7875 20 14 15.2875 -1.2875 21 15.5 16.045 -0.545000000000002 22 15.5 16.045 -0.545000000000002 23 12 12.6461538461538 -0.646153846153846 24 13 13.5877551020408 -0.587755102040816 25 12 10.7214285714286 1.27857142857143 26 12 10.7214285714286 1.27857142857143 27 19 16.7848484848485 2.21515151515151 28 15 16.045 -1.045 29 14 15.2875 -1.2875 30 14 15.2875 -1.2875 31 14.5 15.2875 -0.7875 32 19 16.7848484848485 2.21515151515151 33 19 19.6111111111111 -0.611111111111111 34 20.5 16.7848484848485 3.71515151515151 35 17 15.2875 1.7125 36 16.5 15.2875 1.2125 37 12 12.6461538461538 -0.646153846153846 38 13.5 13.5877551020408 -0.0877551020408163 39 13 13.5877551020408 -0.587755102040816 40 11 10.7214285714286 0.278571428571428 41 13.5 13.5877551020408 -0.0877551020408163 42 12.5 10.7214285714286 1.77857142857143 43 13.5 15.2875 -1.7875 44 14 13.5877551020408 0.412244897959184 45 16 13.5877551020408 2.41224489795918 46 14.5 13.5877551020408 0.912244897959184 47 18 15.2875 2.7125 48 16 15.2875 0.7125 49 14.5 15.2875 -0.7875 50 15 15.2875 -0.2875 51 13 12.6461538461538 0.353846153846154 52 11.5 13.5877551020408 -2.08775510204082 53 14.5 13.5877551020408 0.912244897959184 54 12.5 13.5877551020408 -1.08775510204082 55 12 13.5877551020408 -1.58775510204082 56 13 13.5877551020408 -0.587755102040816 57 11 10.7214285714286 0.278571428571428 58 11 10.7214285714286 0.278571428571428 59 16.5 16.045 0.454999999999998 60 18 16.045 1.955 61 16.5 18.345 -1.845 62 16 16.045 -0.0450000000000017 63 14 13.5877551020408 0.412244897959184 64 12.5 12.6461538461538 -0.146153846153846 65 15 15.2875 -0.2875 66 19.5 16.7848484848485 2.71515151515151 67 16.5 15.2875 1.2125 68 18.5 15.2875 3.2125 69 14 15.2875 -1.2875 70 13 13.5877551020408 -0.587755102040816 71 9.5 10.7214285714286 -1.22142857142857 72 15.5 15.2875 0.2125 73 14 15.2875 -1.2875 74 11 13.5877551020408 -2.58775510204082 75 14 15.2875 -1.2875 76 11 12.6461538461538 -1.64615384615385 77 16.5 16.045 0.454999999999998 78 16 16.045 -0.0450000000000017 79 16.5 15.2875 1.2125 80 21 16.7848484848485 4.21515151515151 81 17 18.15 -1.15 82 18 18.15 -0.149999999999999 83 14 13.5877551020408 0.412244897959184 84 14.5 13.5877551020408 0.912244897959184 85 16 13.5877551020408 2.41224489795918 86 15.5 13.5877551020408 1.91224489795918 87 15.5 16.7848484848485 -1.28484848484849 88 14.5 15.2875 -0.7875 89 19 19.6111111111111 -0.611111111111111 90 14.5 15.2875 -0.7875 91 14 15.2875 -1.2875 92 15 15.2875 -0.2875 93 16 16.7848484848485 -0.784848484848485 94 16 16.045 -0.0450000000000017 95 19.5 18.345 1.155 96 11.5 10.7214285714286 0.778571428571428 97 14 13.5877551020408 0.412244897959184 98 13.5 13.5877551020408 -0.0877551020408163 99 21 18.15 2.85 100 19 18.15 0.850000000000001 101 19 18.15 0.850000000000001 102 13.5 16.045 -2.545 103 12 13.5877551020408 -1.58775510204082 104 17 15.2875 1.7125 105 16 16.045 -0.0450000000000017 106 13.5 15.2875 -1.7875 107 16.5 16.7848484848485 -0.284848484848485 108 14.5 16.045 -1.545 109 15 15.2875 -0.2875 110 17 18.345 -1.345 111 13.5 13.5877551020408 -0.0877551020408163 112 17.5 19.6111111111111 -2.11111111111111 113 16.9 15.2875 1.6125 114 14.9 15.2875 -0.387499999999999 115 15.3 15.2875 0.0125000000000011 116 13 13.5877551020408 -0.587755102040816 117 13.9 13.5877551020408 0.312244897959184 118 12.8 13.5877551020408 -0.787755102040816 119 14.5 16.045 -1.545 120 17.6 18.345 -0.744999999999997 121 22.2 19.6111111111111 2.58888888888889 122 22.1 19.6111111111111 2.48888888888889 123 17.7 18.15 -0.449999999999999 124 16.2 18.15 -1.95 125 17.8 16.045 1.755 126 17 16.7848484848485 0.215151515151515 127 16.4 15.2875 1.1125 128 15.7 16.045 -0.345000000000002 129 13.2 13.5877551020408 -0.387755102040817 130 16.7 13.5877551020408 3.11224489795918 131 12.1 12.6461538461538 -0.546153846153846 132 15 13.5877551020408 1.41224489795918 133 14 13.5877551020408 0.412244897959184 134 14.8 15.2875 -0.487499999999999 135 18.6 19.6111111111111 -1.01111111111111 136 16.8 16.7848484848485 0.0151515151515156 137 12.5 13.5877551020408 -1.08775510204082 138 13.7 13.5877551020408 0.112244897959183 139 16.9 16.045 0.854999999999997 140 17.7 18.15 -0.449999999999999 141 11.1 10.7214285714286 0.378571428571428 142 11.4 12.6461538461538 -1.24615384615385 143 14.5 13.5877551020408 0.912244897959184 144 14.5 15.2875 -0.7875 145 18.2 15.2875 2.9125 146 15.8 15.2875 0.512500000000001 147 15.9 15.2875 0.612500000000001 148 16.4 16.7848484848485 -0.384848484848487 149 14.5 15.2875 -0.7875 150 12.8 15.2875 -2.4875 151 21.5 19.6111111111111 1.88888888888889 152 14.4 16.7848484848485 -2.38484848484848 153 18.6 16.7848484848485 1.81515151515152 154 13.2 13.5877551020408 -0.387755102040817 155 12.8 13.5877551020408 -0.787755102040816 156 18.2 16.045 2.155 157 15.8 18.345 -2.545 158 17.2 18.15 -0.95 159 17.2 18.345 -1.145 160 16.7 18.345 -1.645 161 18.7 18.15 0.550000000000001 162 13.2 13.5877551020408 -0.387755102040817 163 13.4 12.6461538461538 0.753846153846155 164 13.7 13.5877551020408 0.112244897959183 165 16.5 16.7848484848485 -0.284848484848485 166 14.7 15.2875 -0.5875 167 14.5 15.2875 -0.7875 168 17.6 18.345 -0.744999999999997 169 15.9 15.2875 0.612500000000001 170 13.6 13.5877551020408 0.0122448979591834 171 15.8 13.5877551020408 2.21224489795918 172 14.9 16.7848484848485 -1.88484848484848 173 16.6 16.7848484848485 -0.184848484848484 174 18.2 18.345 -0.145 175 17.3 18.345 -1.045 176 16.6 16.045 0.555 177 15.4 13.5877551020408 1.81224489795918 178 13.2 13.5877551020408 -0.387755102040817 179 15.2 13.5877551020408 1.61224489795918 180 14.3 13.5877551020408 0.712244897959184 181 15 13.5877551020408 1.41224489795918 182 14 16.7848484848485 -2.78484848484849 183 15.2 16.7848484848485 -1.58484848484849 184 15 15.2875 -0.2875 185 24.8 18.345 6.455 186 22.2 18.345 3.855 187 14.9 16.7848484848485 -1.88484848484848 188 19.2 16.7848484848485 2.41515151515151 189 16 15.2875 0.7125 190 11.3 13.5877551020408 -2.28775510204082 191 13.2 15.2875 -2.0875 192 14.7 15.2875 -0.5875 193 15.5 16.7848484848485 -1.28484848484849 194 16.4 16.7848484848485 -0.384848484848487 195 18.1 18.345 -0.244999999999997 196 20.1 18.345 1.755 197 15.8 15.2875 0.512500000000001 198 15.5 15.2875 0.2125 199 15 15.2875 -0.2875 200 15.2 15.2875 -0.0875000000000004 201 14.4 15.2875 -0.8875 202 19.2 16.7848484848485 2.41515151515151 203 19.9 18.345 1.555 204 13.8 16.7848484848485 -2.98484848484848 205 15.3 16.7848484848485 -1.48484848484848 206 15.1 15.2875 -0.1875 207 15.7 15.2875 0.4125 208 16.4 15.2875 1.1125 209 12.6 15.2875 -2.6875 210 12.9 15.2875 -2.3875 211 16.4 16.7848484848485 -0.384848484848487 212 16.1 19.6111111111111 -3.51111111111111 213 19.4 16.7848484848485 2.61515151515151 214 17.3 16.7848484848485 0.515151515151516 215 14.9 16.7848484848485 -1.88484848484848 216 16.2 16.7848484848485 -0.584848484848486 217 14.2 15.2875 -1.0875 218 14.8 15.2875 -0.487499999999999 219 20.4 18.345 2.055 220 13.8 13.5877551020408 0.212244897959184 221 15.8 16.045 -0.245000000000001 222 17.1 18.345 -1.245 223 16.6 18.345 -1.745 224 18.6 15.2875 3.3125 225 18 15.2875 2.7125 226 16 15.2875 0.7125 227 18 15.2875 2.7125 228 15.3 15.2875 0.0125000000000011 229 17.6 16.7848484848485 0.815151515151516 230 14.7 16.7848484848485 -2.08484848484849 231 14.5 15.2875 -0.7875 232 14.5 15.2875 -0.7875 233 15.7 16.7848484848485 -1.08484848484849 234 16.4 16.045 0.354999999999997 235 17 18.345 -1.345 236 13.9 15.2875 -1.3875 237 17.3 18.345 -1.045 238 15.6 15.2875 0.3125 239 11.6 15.2875 -3.6875 240 18.6 15.2875 3.3125

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292337994dlfkzx8e56aw4kt/2gf1g1292338110.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292337994dlfkzx8e56aw4kt/2gf1g1292338110.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292337994dlfkzx8e56aw4kt/3gf1g1292338110.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292337994dlfkzx8e56aw4kt/3gf1g1292338110.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292337994dlfkzx8e56aw4kt/49oji1292338110.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292337994dlfkzx8e56aw4kt/49oji1292338110.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

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

library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] if (par2 == 'kmeans') { cl <- kmeans(x[,par1], par3) print(cl) clm <- matrix(cbind(cl$centers,1:par3),ncol=2) clm <- clm[sort.list(clm[,1]),] for (i in 1:par3) { cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') } cl$cluster <- as.factor(cl$cluster) print(cl$cluster) x[,par1] <- cl$cluster } if (par2 == 'quantiles') { x[,par1] <- cut2(x[,par1],g=par3) } if (par2 == 'hclust') { hc <- hclust(dist(x[,par1])^2, 'cen') print(hc) memb <- cutree(hc, k = par3) dum <- c(mean(x[memb==1,par1])) for (i in 2:par3) { dum <- c(dum, mean(x[memb==i,par1])) } hcm <- matrix(cbind(dum,1:par3),ncol=2) hcm <- hcm[sort.list(hcm[,1]),] for (i in 1:par3) { memb[memb==hcm[i,2]] <- paste('C',i,sep='') } memb <- as.factor(memb) print(memb) x[,par1] <- memb } if (par2=='equal') { ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) x[,par1] <- as.factor(ed) } table(x[,par1]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='createtable') if (par2 != 'none') { m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) if (par4=='yes') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Prediction (training)',par3+1,TRUE) a<-table.element(a,'Prediction (testing)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Actual',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) a<-table.row.end(a) for (i in 1:10) { ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) if (i==1) { m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) m.ct.i.actu <- x[ind==1,par1] m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) m.ct.x.actu <- x[ind==2,par1] } else { m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) } } print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) numer <- numer + m.ct.i.tab[i,i] } print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) numer <- numer + m.ct.x.tab[i,i] } print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'Overall',1,TRUE) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.i.cp,4)) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.x.cp,4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() if (par2 == 'none') { forec <- predict(m) result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) colnames(result) <- c('Actuals','Forecasts','Residuals') print(result) } if (par2 != 'none') { print(cbind(as.factor(x[,par1]),predict(m))) myt <- table(as.factor(x[,par1]),predict(m)) print(myt) } bitmap(file='test2.png') if(par2=='none') { op <- par(mfrow=c(2,2)) plot(density(result$Actuals),main='Kernel Density Plot of Actuals') plot(density(result$Residuals),main='Kernel Density Plot of Residuals') plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') par(op) } if(par2!='none') { plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') } dev.off() if (par2 == 'none') { detcoef <- cor(result$Forecasts,result$Actuals) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goodness of Fit',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',1,TRUE) a<-table.element(a,round(detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'R-squared',1,TRUE) a<-table.element(a,round(detcoef*detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'RMSE',1,TRUE) a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$Actuals)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,result$Actuals[i]) a<-table.element(a,result$Forecasts[i]) a<-table.element(a,result$Residuals[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') } if (par2 != 'none') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) for (i in 1:par3) { a<-table.element(a,paste('C',i,sep=''),1,TRUE) } a<-table.row.end(a) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (j in 1:par3) { a<-table.element(a,myt[i,j]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.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.

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