Multiplelineairregression2

*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: Wed, 18 Nov 2009 08:58:15 -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/18/t1258560311llg1ejfg5nn24df.htm/, Retrieved Wed, 18 Nov 2009 17:05:24 +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/18/t1258560311llg1ejfg5nn24df.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 «

17823.2 0 17872 0 17420.4 0 16704.4 0 15991.2 0 16583.6 0 19123.5 0 17838.7 0 17209.4 0 18586.5 0 16258.1 0 15141.6 0 19202.1 0 17746.5 0 19090.1 0 18040.3 0 17515.5 0 17751.8 0 21072.4 0 17170 0 19439.5 0 19795.4 0 17574.9 0 16165.4 0 19464.6 0 19932.1 0 19961.2 0 17343.4 0 18924.2 0 18574.1 0 21350.6 0 18594.6 0 19823.1 0 20844.4 0 19640.2 0 17735.4 0 19813.6 0 22160 0 20664.3 0 17877.4 0 20906.5 0 21164.1 0 21374.4 0 22952.3 0 21343.5 0 23899.3 0 22392.9 0 18274.1 0 22786.7 0 22321.5 0 17842.2 1 16373.5 1 15993.8 1 16446.1 1 17729 1 16643 1 16196.7 1 18252.1 1 17570.4 1 15836.8 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation uitvoer[t] = + 17043.5515 -2064.45750000000dummy[t] + 2774.48850000001M1[t] + 2962.86850000000M2[t] + 2364.98000000000M3[t] + 637.139999999999M4[t] + 1235.58000000000M5[t] + 1473.28000000000M6[t] + 3499.32M7[t] + 2009.06M8[t] + 2171.78M9[t] + 3644.88M10[t] + 2056.64M11[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) 17043.5515 794.556172 21.4504 0 0 dummy -2064.45750000000 620.444132 -3.3274 0.001709 0.000855 M1 2774.48850000001 1116.799438 2.4843 0.0166 0.0083 M2 2962.86850000000 1116.799438 2.653 0.010847 0.005424 M3 2364.98000000000 1109.884205 2.1308 0.038364 0.019182 M4 637.139999999999 1109.884205 0.5741 0.568665 0.284332 M5 1235.58000000000 1109.884205 1.1133 0.271262 0.135631 M6 1473.28000000000 1109.884205 1.3274 0.190783 0.095391 M7 3499.32 1109.884205 3.1529 0.002815 0.001408 M8 2009.06 1109.884205 1.8102 0.076668 0.038334 M9 2171.78 1109.884205 1.9568 0.056331 0.028165 M10 3644.88 1109.884205 3.284 0.001937 0.000969 M11 2056.64 1109.884205 1.853 0.070163 0.035082 Multiple Linear Regression - Regression Statistics Multiple R 0.65483290912575 R-squared 0.428806138874093 Adjusted R-squared 0.282969408373862 F-TEST (value) 2.94031645802298 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.00403785006313562 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1754.88101308195 Sum Squared Residuals 144741546.393550 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 17823.2 19818.0399999999 -1994.83999999995 2 17872 20006.42 -2134.42000000001 3 17420.4 19408.5315 -1988.13150000000 4 16704.4 17680.6915 -976.291499999998 5 15991.2 18279.1315 -2287.93150000000 6 16583.6 18516.8315 -1933.2315 7 19123.5 20542.8715 -1419.3715 8 17838.7 19052.6115 -1213.9115 9 17209.4 19215.3315 -2005.93149999999 10 18586.5 20688.4315 -2101.9315 11 16258.1 19100.1915 -2842.09150000000 12 15141.6 17043.5515 -1901.95150000000 13 19202.1 19818.04 -615.940000000015 14 17746.5 20006.42 -2259.92000000000 15 19090.1 19408.5315 -318.431500000003 16 18040.3 17680.6915 359.608499999998 17 17515.5 18279.1315 -763.631499999999 18 17751.8 18516.8315 -765.0315 19 21072.4 20542.8715 529.5285 20 17170 19052.6115 -1882.6115 21 19439.5 19215.3315 224.168500000000 22 19795.4 20688.4315 -893.031499999999 23 17574.9 19100.1915 -1525.2915 24 16165.4 17043.5515 -878.1515 25 19464.6 19818.04 -353.440000000014 26 19932.1 20006.42 -74.3199999999988 27 19961.2 19408.5315 552.668499999999 28 17343.4 17680.6915 -337.291499999999 29 18924.2 18279.1315 645.068500000002 30 18574.1 18516.8315 57.2684999999996 31 21350.6 20542.8715 807.728499999997 32 18594.6 19052.6115 -458.011500000002 33 19823.1 19215.3315 607.768499999998 34 20844.4 20688.4315 155.968500000001 35 19640.2 19100.1915 540.0085 36 17735.4 17043.5515 691.848500000001 37 19813.6 19818.04 -4.4400000000139 38 22160 20006.42 2153.58000000000 39 20664.3 19408.5315 1255.76850000000 40 17877.4 17680.6915 196.708500000001 41 20906.5 18279.1315 2627.3685 42 21164.1 18516.8315 2647.2685 43 21374.4 20542.8715 831.5285 44 22952.3 19052.6115 3899.6885 45 21343.5 19215.3315 2128.1685 46 23899.3 20688.4315 3210.8685 47 22392.9 19100.1915 3292.7085 48 18274.1 17043.5515 1230.54850000000 49 22786.7 19818.04 2968.65999999999 50 22321.5 20006.42 2315.08000000000 51 17842.2 17344.074 498.126 52 16373.5 15616.234 757.266 53 15993.8 16214.674 -220.873999999999 54 16446.1 16452.374 -6.27400000000014 55 17729 18478.414 -749.414000000001 56 16643 16988.154 -345.154 57 16196.7 17150.874 -954.174 58 18252.1 18623.974 -371.874000000001 59 17570.4 17035.734 534.666000000001 60 15836.8 14979.094 857.706 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.296390275917256 0.592780551834512 0.703609724082744 17 0.254622508027380 0.509245016054761 0.74537749197262 18 0.192475255691796 0.384950511383592 0.807524744308204 19 0.197574730692607 0.395149461385213 0.802425269307393 20 0.162464786761620 0.324929573523241 0.83753521323838 21 0.193531019909591 0.387062039819182 0.806468980090409 22 0.170446844727422 0.340893689454843 0.829553155272578 23 0.203444351208187 0.406888702416373 0.796555648791814 24 0.188054173474018 0.376108346948036 0.811945826525982 25 0.16639342038779 0.33278684077558 0.83360657961221 26 0.256995007418324 0.513990014836649 0.743004992581676 27 0.257978681690463 0.515957363380926 0.742021318309537 28 0.205614558455357 0.411229116910714 0.794385441544643 29 0.247899088498401 0.495798176996803 0.752100911501599 30 0.263276931103087 0.526553862206175 0.736723068896913 31 0.215486812178676 0.430973624357352 0.784513187821324 32 0.30969481250733 0.61938962501466 0.69030518749267 33 0.280444143108371 0.560888286216743 0.719555856891629 34 0.344830432422456 0.689660864844911 0.655169567577544 35 0.530049609191791 0.939900781616418 0.469950390808209 36 0.552798950920325 0.89440209815935 0.447201049079675 37 0.701615401788522 0.596769196422956 0.298384598211478 38 0.747086338666031 0.505827322667938 0.252913661333969 39 0.732667092945912 0.534665814108176 0.267332907054088 40 0.894532995366423 0.210934009267154 0.105467004633577 41 0.86869933990983 0.262601320180340 0.131300660090170 42 0.82137890208576 0.357242195828479 0.178621097914239 43 0.746031672085987 0.507936655828026 0.253968327914013 44 0.779018520711578 0.441962958576844 0.220981479288422 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/Nov/18/t1258560311llg1ejfg5nn24df/101niy1258559889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/101niy1258559889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/1fq711258559889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/1fq711258559889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/27o611258559889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/27o611258559889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/3qk6y1258559889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/3qk6y1258559889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/4w4c11258559889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/4w4c11258559889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/5e7x41258559889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/5e7x41258559889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/6f1d01258559889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/6f1d01258559889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/7a9m71258559889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/7a9m71258559889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/81n2i1258559889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/81n2i1258559889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/9zxmi1258559889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560311llg1ejfg5nn24df/9zxmi1258559889.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.

FreeStatistics.org is powered by