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: Tue, 29 Dec 2009 07:22:22 -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/Dec/29/t12620965963v8429bfieh3l7t.htm/, Retrieved Tue, 29 Dec 2009 15:23:28 +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/Dec/29/t12620965963v8429bfieh3l7t.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:

Paper

Dataseries X:

» Textbox « » Textfile « » CSV «

2529 314 2196 318 3202 320 2718 323 2728 325 2354 327 2697 330 2651 331 2067 332 2641 334 2539 334 2294 334 2712 339 2314 345 3092 346 2677 352 2813 355 2668 358 2939 361 2617 363 2231 364 2481 365 2421 366 2408 370 2560 371 2100 371 3315 372 2801 373 2403 373 3024 374 2507 375 2980 375 2211 376 2471 376 2594 377 2452 377 2232 378 2373 379 3127 380 2802 384 2641 389 2787 390 2619 391 2806 392 2193 393 2323 394 2529 394 2412 395 2262 396 2154 397 3230 398 2295 399 2715 400 2733 400 2317 401 2730 401 1913 406 2390 407 2484 423 1960 427

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 3255.2677926153 -1.88573019166673X[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) 3255.2677926153 535.849547 6.075 0 0 X -1.88573019166673 1.443548 -1.3063 0.196602 0.098301 Multiple Linear Regression - Regression Statistics Multiple R 0.169058749276525 R-squared 0.0285808607069431 Adjusted R-squared 0.0118322548570629 F-TEST (value) 1.70646207589555 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.196601812988957 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 309.957469882705 Sum Squared Residuals 5572270.7218931 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2529 2663.14851243195 -134.148512431948 2 2196 2655.60559166528 -459.605591665281 3 3202 2651.83413128195 550.165868718053 4 2718 2646.17694070695 71.8230592930527 5 2728 2642.40548032361 85.5945196763861 6 2354 2638.63401994028 -284.634019940280 7 2697 2632.97682936528 64.0231706347198 8 2651 2631.09109917361 19.9089008263865 9 2067 2629.20536898195 -562.205368981947 10 2641 2625.43390859861 15.5660914013867 11 2539 2625.43390859861 -86.4339085986133 12 2294 2625.43390859861 -331.433908598613 13 2712 2616.00525764028 95.9947423597203 14 2314 2604.69087649028 -290.690876490279 15 3092 2602.80514629861 489.194853701387 16 2677 2591.49076514861 85.5092348513878 17 2813 2585.83357457361 227.166425426388 18 2668 2580.17638399861 87.8236160013881 19 2939 2574.51919342361 364.480806576388 20 2617 2570.74773304028 46.2522669597218 21 2231 2568.86200284861 -337.862002848612 22 2481 2566.97627265694 -85.9762726569448 23 2421 2565.09054246528 -144.090542465278 24 2408 2557.54762169861 -149.547621698611 25 2560 2555.66189150694 4.33810849305561 26 2100 2555.66189150694 -455.661891506944 27 3315 2553.77616131528 761.223838684722 28 2801 2551.89043112361 249.109568876389 29 2403 2551.89043112361 -148.890431123611 30 3024 2550.00470093194 473.995299068056 31 2507 2548.11897074028 -41.1189707402775 32 2980 2548.11897074028 431.881029259722 33 2211 2546.23324054861 -335.233240548611 34 2471 2546.23324054861 -75.2332405486108 35 2594 2544.34751035694 49.652489643056 36 2452 2544.34751035694 -92.347510356944 37 2232 2542.46178016528 -310.461780165277 38 2373 2540.57604997361 -167.576049973611 39 3127 2538.69031978194 588.309680218056 40 2802 2531.14739901528 270.852600984723 41 2641 2521.71874805694 119.281251943057 42 2787 2519.83301786528 267.166982134723 43 2619 2517.94728767361 101.052712326390 44 2806 2516.06155748194 289.938442518057 45 2193 2514.17582729028 -321.175827290276 46 2323 2512.29009709861 -189.290097098610 47 2529 2512.29009709861 16.7099029013903 48 2412 2510.40436690694 -98.4043669069429 49 2262 2508.51863671528 -246.518636715276 50 2154 2506.63290652361 -352.632906523609 51 3230 2504.74717633194 725.252823668057 52 2295 2502.86144614028 -207.861446140276 53 2715 2500.97571594861 214.024284051391 54 2733 2500.97571594861 232.024284051391 55 2317 2499.08998575694 -182.089985756943 56 2730 2499.08998575694 230.910014243057 57 1913 2489.66133479861 -576.661334798609 58 2390 2487.77560460694 -97.7756046069422 59 2484 2457.60392154027 26.3960784597255 60 1960 2450.06100077361 -490.061000773608 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.808356201273515 0.38328759745297 0.191643798726485 6 0.817956700410502 0.364086599178995 0.182043299589498 7 0.712078930523946 0.575842138952109 0.287921069476054 8 0.59185140863206 0.816297182735879 0.408148591367940 9 0.748792836186914 0.502414327626172 0.251207163813086 10 0.668069734578913 0.663860530842175 0.331930265421087 11 0.570949124558232 0.858101750883537 0.429050875441768 12 0.543169611806343 0.913660776387314 0.456830388193657 13 0.491047260924885 0.98209452184977 0.508952739075115 14 0.450264295046452 0.900528590092904 0.549735704953548 15 0.623166739508828 0.753666520982343 0.376833260491172 16 0.537866052743506 0.924267894512988 0.462133947256494 17 0.467505247857187 0.935010495714375 0.532494752142813 18 0.383041973092349 0.766083946184699 0.61695802690765 19 0.347828669755178 0.695657339510356 0.652171330244822 20 0.283856454738477 0.567712909476954 0.716143545261523 21 0.367385499535321 0.734770999070641 0.632614500464679 22 0.313677462033896 0.627354924067792 0.686322537966104 23 0.276894665633876 0.553789331267752 0.723105334366124 24 0.241445600553641 0.482891201107282 0.758554399446359 25 0.186732459222345 0.373464918444689 0.813267540777655 26 0.292406285368288 0.584812570736576 0.707593714631712 27 0.607692734093474 0.784614531813053 0.392307265906526 28 0.55487388628699 0.89025222742602 0.44512611371301 29 0.52165971019415 0.95668057961170 0.47834028980585 30 0.563917046525257 0.872165906949486 0.436082953474743 31 0.500139539245088 0.999720921509824 0.499860460754912 32 0.519637634523007 0.960724730953986 0.480362365476993 33 0.579371181269068 0.841257637461864 0.420628818730932 34 0.524590805588127 0.950818388823747 0.475409194411873 35 0.449137131263151 0.898274262526303 0.550862868736849 36 0.403608374883501 0.807216749767001 0.5963916251165 37 0.484425819282047 0.968851638564095 0.515574180717953 38 0.509518399989663 0.980963200020674 0.490481600010337 39 0.585667386639226 0.828665226721549 0.414332613360774 40 0.524065424272398 0.951869151455204 0.475934575727602 41 0.442448797877912 0.884897595755823 0.557551202122088 42 0.393019165534458 0.786038331068916 0.606980834465542 43 0.31654790569296 0.63309581138592 0.68345209430704 44 0.291678742126569 0.583357484253139 0.708321257873431 45 0.310768371714150 0.621536743428301 0.68923162828585 46 0.27297610202806 0.54595220405612 0.72702389797194 47 0.202017221626908 0.404034443253816 0.797982778373092 48 0.152124573833710 0.304249147667421 0.84787542616629 49 0.142874622981690 0.285749245963381 0.85712537701831 50 0.194275991573068 0.388551983146137 0.805724008426931 51 0.504997063989181 0.990005872021638 0.495002936010819 52 0.451526402757658 0.903052805515316 0.548473597242342 53 0.371501608440473 0.743003216880946 0.628498391559527 54 0.332101959552765 0.66420391910553 0.667898040447235 55 0.213182187525170 0.426364375050340 0.78681781247483 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/109kg11262096536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/109kg11262096536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/1xt5j1262096536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/1xt5j1262096536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/2pzg71262096536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/2pzg71262096536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/3ms731262096536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/3ms731262096536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/4v8za1262096536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/4v8za1262096536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/5qsnr1262096536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/5qsnr1262096536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/6i5131262096536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/6i5131262096536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/7kff01262096536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/7kff01262096536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/89i8r1262096536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/89i8r1262096536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/99ljd1262096536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620965963v8429bfieh3l7t/99ljd1262096536.ps (open in new window)

Parameters (Session):

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