Paper Multiple Regression

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Mon, 13 Dec 2010 14:36:13 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54.htm/, Retrieved Mon, 13 Dec 2010 15:35:08 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54.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 «

3 11 6 12 2 2 12 6 7 1 -7 39 4 11 -8 -1 19 6 9 -1 0 14 5 13 1 -3 15 4 12 -1 4 7 5 5 2 2 12 5 13 2 3 12 4 11 1 0 14 3 8 -1 -10 9 2 8 -2 -10 8 3 8 -2 -9 4 2 8 -1 -22 7 -1 0 -8 -16 3 0 3 -4 -18 5 -2 0 -6 -14 0 1 -1 -3 -12 -2 -4 -1 -3 -17 6 -2 -4 -7 -23 11 -2 1 -9 -28 9 -6 -1 -11 -31 17 -4 0 -13 -21 21 -2 -1 -11 -19 21 0 6 -9 -22 41 -5 0 -17 -22 57 -4 -3 -22 -25 65 -5 -3 -25 -16 68 -1 4 -20 -22 73 -2 1 -24 -21 71 -4 0 -24 -10 71 -1 -4 -22 -7 70 1 -2 -19 -5 69 1 3 -18 -4 65 -2 2 -17 7 57 1 5 -11 6 57 1 6 -11 3 57 3 6 -12 10 55 3 3 -10 0 65 1 4 -15 -2 65 1 7 -15 -1 64 0 5 -15 2 60 2 6 -13 8 43 2 1 -8 -6 47 -1 3 -13 -4 40 1 6 -9 4 31 0 0 -7 7 27 1 3 -4 3 24 1 4 -4 3 23 3 7 -2 8 17 2 6 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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation indicator[t] = + 0.269947045983855 + 0.258970092398895economical[t] -0.254850177345353unemployement[t] + 0.237994998300893financial[t] + 0.225051492669513capacity[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) 0.269947045983855 0.130103 2.0749 0.043746 0.021873 economical 0.258970092398895 0.006928 37.3812 0 0 unemployement -0.254850177345353 0.002067 -123.3205 0 0 financial 0.237994998300893 0.034249 6.9489 0 0 capacity 0.225051492669513 0.017251 13.0461 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.999221162082563 R-squared 0.998442930753627 Adjusted R-squared 0.998304524598394 F-TEST (value) 7213.86219472357 F-TEST (DF numerator) 4 F-TEST (DF denominator) 45 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.320567936851966 Sum Squared Residuals 4.62437109618868 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2 2.37209327422117 -0.372093274221165 2 1 0.73301554112935 0.26698445887065 3 -8 -8.05445410470899 0.054454104708986 4 -1 -1.37774299214578 0.377742992145784 5 1 0.817688959357037 0.182311040642963 6 -1 -0.677117986155409 -0.322882013844591 7 2 1.83710862901399 0.162891370986010 8 2 1.84532949884553 0.154670501154466 9 1 1.41620160760451 -0.416201607604511 10 -1 -0.783558500592313 -0.216441499407687 11 -2 -2.33700353615539 0.337003536155393 12 -2 -1.84415836050915 -0.155841639490853 13 -1 -0.803782557029731 -0.196217442970269 14 -8 -7.44934122651021 -0.550658773489789 15 -4 -3.96297048642599 -0.0370295135740053 16 -6 -6.14175550052481 0.141755500524815 17 -3 -3.3426907419693 0.342690741969302 18 -3 -3.50502519398527 0.505025193985268 19 -7 -7.03784155614932 0.0378415561493239 20 -9 -8.7406555339219 -0.259344466078099 21 -11 -10.9278886197683 -0.072111380231732 22 -13 -13.0425588264565 0.0425588264564821 23 -11 -11.2213201079167 0.221320107916668 24 -9 -8.6520294778305 -0.347970522169498 25 -17 -17.0662272494558 0.0662272494557975 26 -22 -21.5809895666891 -0.419010433310902 27 -25 -24.6346962609495 -0.365303739050497 28 -20 -20.5411755195053 0.541175519505344 29 -24 -24.2823964369349 0.282396436934915 30 -24 -24.2147674791166 0.214767479116610 31 -22 -21.5523174385041 -0.447682561495867 32 -19 -19.5944640020213 0.594464002021282 33 -18 -17.6964161765306 -0.303583823469426 34 -17 -17.3570818623225 0.357081862322457 35 -11 -11.0804699542606 0.0804699542605642 36 -11 -11.1143885539899 0.114388553989947 37 -12 -11.4153088345848 -0.58469116541515 38 -10 -9.76797231111041 -0.232027688889588 39 -15 -15.1571135124852 0.157113512485171 40 -15 -14.9998992192744 -0.000100780725575256 41 -15 -15.1741769331701 0.174176933170095 42 -13 -12.6768244573207 -0.323175542679303 43 -8 -7.91580835140388 -0.0841916485961194 44 -13 -12.8246723639335 -0.175327636066517 45 -9 -9.3716364631079 0.371636463107894 46 -7 -6.59452808212652 -0.40547191787348 47 -4 -3.88506761923899 -0.114932380761012 48 -4 -3.931345964129 -0.0686540358710028 49 -2 -2.52535131217332 0.525351312173321 50 0 -0.164446277077129 0.164446277077129 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.644922585115148 0.710154829769704 0.355077414884852 9 0.545098440173579 0.909803119652843 0.454901559826421 10 0.400563941026702 0.801127882053403 0.599436058973298 11 0.30276645209692 0.60553290419384 0.69723354790308 12 0.397501793811307 0.795003587622613 0.602498206188693 13 0.310790494435567 0.621580988871134 0.689209505564433 14 0.422543712515651 0.845087425031302 0.577456287484349 15 0.371004237089028 0.742008474178056 0.628995762910972 16 0.410694567715862 0.821389135431723 0.589305432284138 17 0.382973046128502 0.765946092257004 0.617026953871498 18 0.523771795549514 0.952456408900972 0.476228204450486 19 0.468988750195421 0.937977500390842 0.531011249804579 20 0.409142608127609 0.818285216255218 0.590857391872391 21 0.322064526931060 0.644129053862119 0.67793547306894 22 0.259511869974632 0.519023739949264 0.740488130025368 23 0.220623934032783 0.441247868065567 0.779376065967217 24 0.251822696887497 0.503645393774994 0.748177303112503 25 0.186515506192296 0.373031012384592 0.813484493807704 26 0.249876998617871 0.499753997235742 0.750123001382129 27 0.283794215004898 0.567588430009797 0.716205784995102 28 0.437177825175313 0.874355650350626 0.562822174824687 29 0.379974961154555 0.75994992230911 0.620025038845445 30 0.302112854250084 0.604225708500168 0.697887145749916 31 0.434300939786158 0.868601879572316 0.565699060213842 32 0.780793307805906 0.438413384388188 0.219206692194094 33 0.733573648549592 0.532852702900816 0.266426351450408 34 0.758671053373143 0.482657893253715 0.241328946626857 35 0.678022511974404 0.643954976051193 0.321977488025596 36 0.585853128864066 0.828293742271868 0.414146871135934 37 0.887675767353193 0.224648465293613 0.112324232646807 38 0.83069307115627 0.338613857687461 0.169306928843731 39 0.805188227301233 0.389623545397534 0.194811772698767 40 0.697897676456976 0.604204647086048 0.302102323543024 41 0.909672147988138 0.180655704023724 0.0903278520118622 42 0.993760731626018 0.0124785367479632 0.00623926837398161 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 1 0.0285714285714286 OK 10% type I error level 1 0.0285714285714286 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/106bre1292250961.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/106bre1292250961.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/1zau21292250961.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/1zau21292250961.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/2zau21292250961.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/2zau21292250961.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/3skbn1292250961.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/3skbn1292250961.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/4skbn1292250961.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/4skbn1292250961.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/5skbn1292250961.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/5skbn1292250961.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/63bbq1292250961.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/63bbq1292250961.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/7ekab1292250961.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/7ekab1292250961.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/8ekab1292250961.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/8ekab1292250961.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/9ekab1292250961.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292250908sctcqrhef2mjz54/9ekab1292250961.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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