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: Fri, 17 Dec 2010 12:34:51 +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/17/t1292589196wdsx1bsr1i6wr3b.htm/, Retrieved Fri, 17 Dec 2010 13:33:16 +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/17/t1292589196wdsx1bsr1i6wr3b.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 «

108,35 98,68 100,70 104,38 97,72 15.38 31.27 109,87 99,21 99,62 103,97 98,01 15.03 35.83 111,30 99,36 99,83 103,32 97,78 15.21 37.12 115,50 100,72 100,74 105,01 98,04 15.20 36.77 116,22 102,27 100,84 104,88 98,54 14.60 35.17 116,63 102,62 100,85 104,46 98,39 13.79 37.25 116,84 102,97 99,71 104,71 98,58 14.54 33.77 116,63 102,88 100,80 106,09 98,91 14.31 30.59 117,03 102,90 100,06 106,54 98,68 13.93 33.59 117,00 103,01 100,57 104,36 98,59 14.82 37.24 117,14 103,02 99,79 105,31 99,13 14.46 34.81 116,64 103,73 99,90 105,07 98,70 14.85 34.94 117,24 104,18 100,12 105,39 99,00 14.95 34.47 117,52 103,73 100,40 105,65 98,80 14.43 30.48 117,83 103,78 100,51 108,25 98,80 14.84 30.94 119,79 103,61 100,70 107,71 99,29 14.39 30.60 120,86 103,84 100,62 108,58 99,69 15.70 28.42 120,75 103,86 99,70 108,27 100,01 15.34 25.89 120,63 104,14 99,48 107,62 99,85 13.98 26.32 120,89 104,05 99,36 108,80 99,66 14.75 27.18 120,23 104,01 99,39 109,26 101,18 14.81 25.85 121,19 104,49 99,45 108,58 101,47 14.67 26.32 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 4 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Coffee[t] = -126.644403449146 + 1.11859932108523Tea[t] + 0.375159164294309Sugar[t] + 0.536573708762059Water[t] + 0.322868064613424Soda[t] + 0.115372489905679SaraLee[t] + 0.0139559697604042Starbucks[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) -126.644403449146 46.441652 -2.727 0.009364 0.004682 Tea 1.11859932108523 0.162903 6.8667 0 0 Sugar 0.375159164294309 0.313024 1.1985 0.237605 0.118802 Water 0.536573708762059 0.195769 2.7409 0.009036 0.004518 Soda 0.322868064613424 0.240095 1.3448 0.186098 0.093049 SaraLee 0.115372489905679 0.17171 0.6719 0.505415 0.252707 Starbucks 0.0139559697604042 0.06569 0.2125 0.832807 0.416403 Multiple Linear Regression - Regression Statistics Multiple R 0.974260332908292 R-squared 0.949183196278576 Adjusted R-squared 0.941746590855928 F-TEST (value) 127.636622132450 F-TEST (DF numerator) 6 F-TEST (DF denominator) 41 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.16191788971606 Sum Squared Residuals 55.3521804801312 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 108.35 111.286568463747 -2.93656846374714 2 109.87 111.371149575270 -1.50114957527021 3 111.3 111.233460581552 0.0665394184476699 4 115.5 114.080867448028 1.41913255197175 5 116.22 115.852338716747 0.367661283252621 6 116.63 115.909385603676 0.720614396323876 7 116.84 116.106664870891 0.733335129109454 8 116.63 117.191016943971 -0.561016943971362 9 117.03 117.100996026014 -0.0709960260141837 10 117 116.370205119849 0.629794880151317 11 117.14 116.697413640241 0.442586359758654 12 116.64 117.312085255530 -0.672085255529643 13 117.24 118.171531915554 -0.931531915553823 14 117.52 117.732464324328 -0.212464324328356 15 117.83 119.278475908187 -1.44847590818745 16 119.79 118.971387163572 0.818612836428159 17 120.86 119.915332574445 0.944667425554969 18 120.75 119.452695360816 1.29730463918384 19 120.63 119.132030834267 1.49796916573295 20 120.89 119.658988790938 1.23101120906196 21 120.23 120.351443866879 -0.121443866879432 22 121.19 120.630049864838 0.559950135161718 23 120.79 120.060471064039 0.729528935960546 24 120.09 121.251284854193 -1.16128485419304 25 120.86 121.490990184969 -0.630990184968984 26 121.1 122.661861741368 -1.56186174136849 27 121.47 121.60880218324 -0.138802183239909 28 122.01 123.741377513773 -1.73137751377329 29 123.94 124.649735507669 -0.709735507669219 30 125.78 124.18003899988 1.59996100011995 31 125.31 125.348127055936 -0.0381270559364169 32 125.79 125.164539476358 0.625460523641864 33 126.12 125.915584177891 0.204415822109054 34 125.57 124.239269388141 1.33073061185877 35 125.44 124.856323932151 0.583676067848552 36 126.12 124.918260082092 1.20173991790849 37 126.01 126.055500963984 -0.0455009639843438 38 126.5 126.693104733134 -0.193104733134405 39 126.13 126.458326555763 -0.328326555763218 40 126.66 125.518683911135 1.14131608886536 41 126.33 126.424091947799 -0.0940919477993204 42 126.61 127.559362280375 -0.949362280374524 43 126.36 127.392999077399 -1.03299907739879 44 126.83 127.38252257839 -0.552522578390062 45 125.9 128.455387995298 -2.55538799529801 46 126.29 126.937758749482 -0.647758749481735 47 126.37 124.533573120258 1.83642687974182 48 125.11 124.265469075938 0.844530924062021 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.451120733095265 0.902241466190531 0.548879266904735 11 0.290688745267803 0.581377490535606 0.709311254732197 12 0.49145875544314 0.98291751088628 0.50854124455686 13 0.525244824117871 0.949510351764257 0.474755175882129 14 0.514807435554273 0.970385128891454 0.485192564445727 15 0.549949654899034 0.900100690201931 0.450050345100966 16 0.616089990717905 0.76782001856419 0.383910009282095 17 0.609444427769704 0.781111144460591 0.390555572230296 18 0.611066084945323 0.777867830109354 0.388933915054677 19 0.562397397854417 0.875205204291166 0.437602602145583 20 0.570320056756084 0.859359886487832 0.429679943243916 21 0.754254727571139 0.491490544857723 0.245745272428862 22 0.68668729451542 0.62662541096916 0.31331270548458 23 0.621057154033502 0.757885691932997 0.378942845966498 24 0.627720939368242 0.744558121263517 0.372279060631758 25 0.565239563338128 0.869520873323745 0.434760436661872 26 0.701502044449329 0.596995911101343 0.298497955550671 27 0.78556890881833 0.42886218236334 0.21443109118167 28 0.988219925665133 0.0235601486697343 0.0117800743348672 29 0.999208643256787 0.00158271348642592 0.000791356743212961 30 0.999264406155596 0.00147118768880873 0.000735593844404367 31 0.998610340980862 0.00277931803827606 0.00138965901913803 32 0.996683464348246 0.00663307130350878 0.00331653565175439 33 0.992148166731964 0.0157036665360726 0.00785183326803628 34 0.98375867991465 0.0324826401707016 0.0162413200853508 35 0.979392827319527 0.0412143453609454 0.0206071726804727 36 0.95062059568004 0.0987588086399194 0.0493794043199597 37 0.910283580291431 0.179432839417138 0.0897164197085688 38 0.820380125767573 0.359239748464854 0.179619874232427 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.137931034482759 NOK 5% type I error level 8 0.275862068965517 NOK 10% type I error level 9 0.310344827586207 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/105r1i1292589282.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/105r1i1292589282.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/1zrnp1292589282.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/1zrnp1292589282.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/2rims1292589282.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/2rims1292589282.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/3rims1292589282.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/3rims1292589282.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/4rims1292589282.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/4rims1292589282.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/52rlv1292589282.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/52rlv1292589282.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/62rlv1292589282.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/62rlv1292589282.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/7vi2y1292589282.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/7vi2y1292589282.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/8vi2y1292589282.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/8vi2y1292589282.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/95r1i1292589282.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292589196wdsx1bsr1i6wr3b/95r1i1292589282.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