Home » date » 2007 » Nov » 16 » attachments

Paper 20

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

Title produced by software: Multiple Regression

Date of computation: Fri, 16 Nov 2007 09:33:57 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6.htm/, Retrieved Fri, 16 Nov 2007 17:29:40 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

100,70 99,20 100,80 99,20 101,30 99,80 97,90 95,80 100,80 98,10 97,60 103,50 96,50 93,80 100,80 96,10 96,40 103,10 96,60 94,40 90,10 95,50 97,00 105,60 96,60 94,80 95,70 95,70 96,40 105,70 95,50 96,90 88,60 95,90 94,70 106,60 91,80 93,10 93,30 96,20 89,30 107,00 89,30 92,20 93,30 95,70 85,90 105,20 87,00 90,80 93,30 93,40 83,30 105,70 85,90 92,40 93,30 93,40 81,50 105,00 88,00 91,20 93,30 91,90 85,00 105,10 87,90 89,80 93,30 92,80 84,80 105,90 89,20 85,80 88,60 93,20 87,50 105,30 90,90 88,20 97,40 93,80 89,00 104,90 91,60 89,90 97,40 93,80 90,00 103,20 90,20 91,80 102,90 85,10 89,60 103,40 89,10 93,40 102,90 86,10 87,40 104,40 87,50 93,80 102,90 86,50 84,80 104,50 86,30 95,00 105,10 90,00 81,90 105,90 86,00 97,30 105,10 89,10 81,10 110,60 84,40 95,30 105,10 88,40 79,10 112,40 86,10 97,90 105,10 91,40 80,50 111,80 91,00 97,00 105,10 88,00 88,50 111,00 92,70 97,00 105,10 87,80 90,90 111,00 88,00 65,20 96,90 87,40 84,90 109,10 84,30 64,10 96,90 86,20 80,00 107,80 82,20 65,60 96,90 87,80 76,50 107,20 80,80 66,50 96,90 84,60 75,40 108,40 79,40 65,60 96,90 85,00 73,50 107,50 80,20 66,10 96,90 85,70 74,30 106,40 82,20 65,90 96,50 83,90 77,70 106,20 82,20 65,80 96,50 83,60 77,90 104,90 81,20 64,10 96,50 82,60 76,70 106,20 82,10 63,50 96,00 84,90 77,20 107,60 88,10 64,40 96,00 84,20 86,00 107,00 88,50 66,00 96,00 83,80 86,90 104,50 92,10 67,70 96,00 84,20 92,00 105,10 98,60 70,40 96,00 84,40 101,70 104,70 100,90 74,10 96,00 86,00 104,50 103,70 100,60 75,50 105,80 89,70 101,70 104,90 101,10 80,80 105,80 93,90 100,60 105,90 102,10 83,90 105,80 98,40 100,30 106,10 103,60 83,60 105,80 98,30 102,50 106,10 102,80 88,70 105,80 99,30 101,00 106,80 108,30 87,00 105,80 100,50 108,60 106,40 104,00 86,90 105,80 96,90 103,40 107,80 106,10 88,60 105,80 97,50 106,40 107,60 106,30 88,60 105,80 97,50 106,60 107,60 109,00 86,90 123,60 98,90 108,90 108,40 111,00 86,10 142,20 99,30 110,50 109,50 113,70 86,60 142,20 100,60 114,00 109,20 112,70 83,90 141,20 99,90 112,80 109,10 110,30 84,20 141,20 98,80 109,60 110,00 114,50 83,20 141,20 98,60 116,00 109,00 119,30 78,40 124,70 98,20 124,60 109,00 121,80 77,60 124,70 96,30 129,00 111,90 125,40 77,10 124,70 103,40 131,50 109,30 129,70 76,80 122,70 102,70 138,60 112,10 129,40 76,80 122,70 102,70 138,10 112,10 134,50 76,80 122,70 102,60 146,30 112,50 141,20 76,80 123,30 101,60 157,60 113,60 141,40 78,70 123,30 100,90 158,40 112,90 152,20 78,70 123,30 101,10 176,30 114,00 167,70 78,70 127,20 105,60 199,90 116,10 173,30 78,70 127,20 104,70 210,40 116,50 168,70 78,70 127,20 103,80 202,60 117,10 172,60 83,70 140,00 105,40 207,10 117,10 169,80 83,70 140,00 105,60 202,00 117,10 172,00 87,90 140,00 109,20 203,40 116,50 179,40 87,90 140,00 109,50 216,30 116,50 174,60 84,50 140,00 110,10 207,30 116,30 172,50 84,50 140,00 110,00 203,50 116,50 172,60 84,40 139,00 110,30 204,40 119,20 176,30 87,50 139,00 109,30 203,70 118,60 178,90 89,20 139,00 110,20 205,70 117,50 179,60 88,90 147,00 113,00 208,00 117,10 179,90 88,80 147,00 113,60 209,30 117,60 180,30 89,50 147,00 111,20 208,70 118,30 180,90 90,40 147,10 111,30 206,50 118,60 177,70 87,90 147,10 115,00 204,50 116,70

Text written by user:

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 compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation TM[t] = -7.79895678054507 -0.0335405225086837EGKS[t] + 0.0994588527017785BUIZEN[t] + 0.338478881529632NEGKS[t] + 0.624837353466008NF[t] + 0.0535379228849357`GM `[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) -7.79895678054507 7.161466 -1.089 0.27968 0.13984 EGKS -0.0335405225086837 0.016356 -2.0506 0.043843 0.021922 BUIZEN 0.0994588527017785 0.015323 6.4907 0 0 NEGKS 0.338478881529632 0.043035 7.8652 0 0 NF 0.624837353466008 0.009053 69.0174 0 0 `GM ` 0.0535379228849357 0.064902 0.8249 0.412076 0.206038 Multiple Linear Regression - Regression Statistics Multiple R 0.99943890991353 R-squared 0.998878134649145 Adjusted R-squared 0.99880233293625 F-TEST (value) 13177.5140230020 F-TEST (DF numerator) 5 F-TEST (DF denominator) 74 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.19634092346888 Sum Squared Residuals 105.911138782311 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100.7 101.115489396696 -0.415489396695537 2 97.9 98.7433925103924 -0.843392510392405 3 96.5 97.3622957990373 -0.862295799037326 4 96.6 96.5836216519973 0.0163783480026958 5 96.6 96.8253221746386 -0.225322174638585 6 95.5 95.1023856291979 0.397614370802138 7 91.8 92.4461333473256 -0.646133347325636 8 89.3 90.0862651138413 -0.786265113841327 9 87 87.7569122602662 -0.756912260266172 10 85.9 86.541063641994 -0.641063641994005 11 88 88.2658784761295 -0.265878476129506 12 87.9 88.5353290686331 -0.63532906863307 13 89.2 89.9923642042086 -0.792364204208559 14 90.9 91.9060330439262 -1.00603304392619 15 91.6 92.382837040223 -0.782837040223049 16 90.2 89.6821401111991 0.517859888800899 17 89.1 88.6458499019746 0.454150098025423 18 87.5 87.1486019188598 0.351398081140185 19 86.3 86.7747636201345 -0.474763620134514 20 86 86.1447477797742 -0.144747779774249 21 84.4 84.8215871619818 -0.421587161981748 22 86.1 86.5924679891695 -0.492467989169524 23 91 90.4276947516467 0.572305248353299 24 92.7 91.8596086236592 0.840391376340807 25 88 88.1244969203915 -0.124496920391489 26 84.3 84.6239145055816 -0.323914505581620 27 82.2 82.896116441404 -0.696116441404008 28 80.8 81.1597219689007 -0.359721968900693 29 79.4 80.0899248895885 -0.689924889588492 30 80.2 80.7510680130043 -0.551068013004276 31 82.2 82.2224700068794 -0.022470006879401 32 82.2 82.1796485656142 0.0203514343858335 33 81.2 81.2179830479405 -0.0179830479405009 34 82.1 82.3542511313849 -0.254251131384905 35 88.1 87.5535754008262 0.546424599173750 36 88.5 87.7930278231076 0.706972176892433 37 92.1 91.0901937438623 1.00980625613773 38 98.6 97.106837268861 1.49316273113894 39 100.9 99.2203102128462 1.67968978715379 40 100.6 99.7151230172282 0.884876982771768 41 101.1 100.325186384429 0.774813615571013 42 102.1 101.567622110073 0.532377889927401 43 103.6 102.918478556297 0.681521443702544 44 102.8 102.186121288853 0.613878711146758 45 108.3 107.376663552141 0.923336447858752 46 104 102.987292484901 1.01270751509888 47 106.1 104.997165401375 1.10283459862483 48 106.3 105.122132872068 1.17786712793164 49 109 108.903346023846 0.0966539761539607 50 111 111.974136135437 -0.97413613543695 51 113.7 114.568257780437 -0.868257780436668 52 112.7 113.567264504990 -0.867264504989893 53 110.3 111.23358017806 -0.933580178059907 54 114.5 115.144846063560 -0.644846063560175 55 119.3 118.902979189218 0.397020810781655 56 121.8 121.191246063936 0.608753936064255 57 125.4 125.034111168215 0.365888831785339 58 129.7 129.194571796179 0.50542820382055 59 129.4 128.882153119446 0.51784688055357 60 134.5 133.993386698869 0.506613301131277 61 141.2 140.834136938299 0.365863061700521 62 141.4 140.995868065216 0.404131934784417 63 152.2 152.307044183736 -0.107044183736498 64 167.7 169.076679856013 -1.37667985601293 65 173.3 175.354256243183 -2.05425624318331 66 168.7 170.208016646503 -1.50801664650274 67 172.6 174.666721649587 -2.06672164958653 68 169.8 171.547746923216 -1.74774692321580 69 172 173.468050243307 -1.46805024330748 70 179.4 181.629995767478 -2.22999576747787 71 174.6 176.312877107154 -1.71287710715413 72 172.5 173.915354860407 -1.41535486040731 73 172.6 174.627699734324 -2.02769973432402 74 176.3 173.715736331860 2.58426366813971 75 178.9 175.154131428731 3.74586857126923 76 179.6 178.323316019198 1.27668398080155 77 179.9 179.368814921315 0.531185078684633 78 180.3 178.195561373828 2.104438626172 79 180.9 176.850587876234 4.0494121237664 80 177.7 176.835414283752 0.864585716248428

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/1ha8s1195230832.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/1ha8s1195230832.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/2ytw91195230832.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/2ytw91195230832.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/3nqmq1195230832.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/3nqmq1195230832.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/4qutz1195230832.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/4qutz1195230832.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/5awe11195230832.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/5awe11195230832.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/6hqhk1195230832.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/6hqhk1195230832.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/7wsyg1195230832.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/7wsyg1195230832.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/87qph1195230832.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/87qph1195230832.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/9ln311195230832.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195230571jh907c36pq2mpa6/9ln311195230832.ps (open in new window)

Parameters:

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

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

library(lattice) 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)) 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$resid, main='Residual Normal Q-Q Plot') grid() dev.off() (myerror <- as.ts(mysum$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() 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')

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