*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, 20 Nov 2009 07:06:51 -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/20/t1258726193a5pn1ys93gmm5rz.htm/, Retrieved Fri, 20 Nov 2009 15:10:05 +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/20/t1258726193a5pn1ys93gmm5rz.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 «

613 0 611 0 594 0 595 0 591 0 589 0 584 0 573 0 567 0 569 0 621 0 629 0 628 0 612 0 595 0 597 0 593 0 590 0 580 0 574 0 573 0 573 0 620 0 626 0 620 0 588 0 566 0 557 0 561 0 549 0 532 0 526 0 511 0 499 0 555 0 565 0 542 0 527 0 510 0 514 0 517 0 508 0 493 0 490 0 469 0 478 0 528 0 534 0 518 1 506 1 502 1 516 1 528 1 533 1 536 1 537 1 524 1 536 1 587 1 597 1 581 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WklBe[t] = + 595.594736842105 -26.9736842105263X[t] -2.93684210526333M1[t] -21.4M2[t] -36.8M3[t] -34.4M4[t] -32.1999999999999M5[t] -36.3999999999999M6[t] -45.2M7[t] -50.2M8[t] -61.4M9[t] -59.2M10[t] -7.99999999999997M11[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) 595.594736842105 17.908126 33.2584 0 0 X -26.9736842105263 12.457042 -2.1653 0.035362 0.017681 M1 -2.93684210526333 24.069292 -0.122 0.903396 0.451698 M2 -21.4 25.079628 -0.8533 0.397741 0.19887 M3 -36.8 25.079628 -1.4673 0.148809 0.074405 M4 -34.4 25.079628 -1.3716 0.176556 0.088278 M5 -32.1999999999999 25.079628 -1.2839 0.205336 0.102668 M6 -36.3999999999999 25.079628 -1.4514 0.15318 0.07659 M7 -45.2 25.079628 -1.8023 0.077786 0.038893 M8 -50.2 25.079628 -2.0016 0.050995 0.025497 M9 -61.4 25.079628 -2.4482 0.018063 0.009031 M10 -59.2 25.079628 -2.3605 0.022362 0.011181 M11 -7.99999999999997 25.079628 -0.319 0.751123 0.375561 Multiple Linear Regression - Regression Statistics Multiple R 0.537167659901329 R-squared 0.288549094843869 Adjusted R-squared 0.110686368554837 F-TEST (value) 1.62231345973505 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.116971788217152 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 39.6543730028053 Sum Squared Residuals 75478.5263157894 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 613 592.657894736843 20.3421052631569 2 611 574.194736842105 36.8052631578947 3 594 558.794736842105 35.2052631578947 4 595 561.194736842105 33.8052631578948 5 591 563.394736842105 27.6052631578948 6 589 559.194736842105 29.8052631578948 7 584 550.394736842105 33.6052631578947 8 573 545.394736842105 27.6052631578947 9 567 534.194736842105 32.8052631578947 10 569 536.394736842105 32.6052631578948 11 621 587.594736842105 33.4052631578948 12 629 595.594736842105 33.4052631578948 13 628 592.657894736842 35.3421052631581 14 612 574.194736842105 37.8052631578947 15 595 558.794736842105 36.2052631578947 16 597 561.194736842105 35.8052631578947 17 593 563.394736842105 29.6052631578947 18 590 559.194736842105 30.8052631578947 19 580 550.394736842105 29.6052631578947 20 574 545.394736842105 28.6052631578947 21 573 534.194736842105 38.8052631578948 22 573 536.394736842105 36.6052631578947 23 620 587.594736842105 32.4052631578947 24 626 595.594736842105 30.4052631578948 25 620 592.657894736842 27.3421052631581 26 588 574.194736842105 13.8052631578947 27 566 558.794736842105 7.20526315789473 28 557 561.194736842105 -4.19473684210529 29 561 563.394736842105 -2.39473684210529 30 549 559.194736842105 -10.1947368421053 31 532 550.394736842105 -18.3947368421052 32 526 545.394736842105 -19.3947368421052 33 511 534.194736842105 -23.1947368421053 34 499 536.394736842105 -37.3947368421053 35 555 587.594736842105 -32.5947368421053 36 565 595.594736842105 -30.5947368421052 37 542 592.657894736842 -50.6578947368419 38 527 574.194736842105 -47.1947368421053 39 510 558.794736842105 -48.7947368421053 40 514 561.194736842105 -47.1947368421053 41 517 563.394736842105 -46.3947368421053 42 508 559.194736842105 -51.1947368421053 43 493 550.394736842105 -57.3947368421052 44 490 545.394736842105 -55.3947368421052 45 469 534.194736842105 -65.1947368421052 46 478 536.394736842105 -58.3947368421052 47 528 587.594736842105 -59.5947368421052 48 534 595.594736842105 -61.5947368421052 49 518 565.684210526316 -47.6842105263156 50 506 547.221052631579 -41.221052631579 51 502 531.821052631579 -29.821052631579 52 516 534.221052631579 -18.221052631579 53 528 536.421052631579 -8.421052631579 54 533 532.221052631579 0.778947368421 55 536 523.421052631579 12.5789473684210 56 537 518.421052631579 18.5789473684210 57 524 507.221052631579 16.7789473684210 58 536 509.421052631579 26.578947368421 59 587 560.621052631579 26.378947368421 60 597 568.621052631579 28.3789473684211 61 581 565.684210526316 15.3157894736844 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00579636704677684 0.0115927340935537 0.994203632953223 17 0.000844865598487998 0.00168973119697600 0.999155134401512 18 0.000115855535994366 0.000231711071988732 0.999884144464006 19 1.85471221270918e-05 3.70942442541836e-05 0.999981452877873 20 2.47079153541971e-06 4.94158307083941e-06 0.999997529208465 21 6.48299488131684e-07 1.29659897626337e-06 0.999999351700512 22 1.43604406882979e-07 2.87208813765959e-07 0.999999856395593 23 2.67486062750629e-08 5.34972125501259e-08 0.999999973251394 24 6.14066803656605e-09 1.22813360731321e-08 0.999999993859332 25 2.26669095644399e-09 4.53338191288799e-09 0.99999999773331 26 1.90142111547462e-06 3.80284223094923e-06 0.999998098578885 27 0.000125415563188628 0.000250831126377256 0.999874584436811 28 0.00436650205314035 0.0087330041062807 0.99563349794686 29 0.0168651874542079 0.0337303749084158 0.983134812545792 30 0.0664427232861858 0.132885446572372 0.933557276713814 31 0.189569048688651 0.379138097377301 0.81043095131135 32 0.302348641952331 0.604697283904662 0.697651358047669 33 0.48657172443494 0.97314344886988 0.51342827556506 34 0.640281147895894 0.719437704208213 0.359718852104106 35 0.699901950010715 0.60019609997857 0.300098049989285 36 0.722487602163875 0.555024795672251 0.277512397836125 37 0.793884998985751 0.412230002028498 0.206115001014249 38 0.89845675350425 0.203086492991501 0.101543246495751 39 0.940954382952636 0.118091234094727 0.0590456170473637 40 0.957752596538754 0.0844948069224927 0.0422474034612464 41 0.9637622215932 0.0724755568135993 0.0362377784067996 42 0.956456497260827 0.0870870054783461 0.0435435027391731 43 0.925502553071287 0.148994893857427 0.0744974469287134 44 0.86472190118234 0.27055619763532 0.13527809881766 45 0.758339319768784 0.483321360462433 0.241660680231216 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.4 NOK 5% type I error level 14 0.466666666666667 NOK 10% type I error level 17 0.566666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/10pr4v1258726007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/10pr4v1258726007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/1tm6p1258726007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/1tm6p1258726007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/2hd4j1258726007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/2hd4j1258726007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/3d8bz1258726007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/3d8bz1258726007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/45gcw1258726007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/45gcw1258726007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/59vuw1258726007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/59vuw1258726007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/61ky61258726007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/61ky61258726007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/7lyfa1258726007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/7lyfa1258726007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/8mjp61258726007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/8mjp61258726007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/994791258726007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz/994791258726007.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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.

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