SHW paper

*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: Sun, 06 Dec 2009 05:38:48 -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/06/t1260103363h7k29j70wet1j1d.htm/, Retrieved Sun, 06 Dec 2009 13:42:55 +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/06/t1260103363h7k29j70wet1j1d.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 «

8,9 0 8,8 0 8,3 0 7,5 0 7,2 0 7,4 0 8,8 0 9,3 0 9,3 0 8,7 0 8,2 0 8,3 0 8,5 0 8,6 0 8,5 0 8,2 0 8,1 0 7,9 0 8,6 0 8,7 0 8,7 0 8,5 0 8,4 0 8,5 0 8,7 0 8,7 0 8,6 0 8,5 0 8,3 0 8 0 8,2 0 8,1 0 8,1 0 8 0 7,9 0 7,9 0 8 0 8 0 7,9 0 8 0 7,7 0 7,2 0 7,5 0 7,3 0 7 0 7 0 7 0 7,2 0 7,3 1 7,1 1 6,8 1 6,4 1 6,1 1 6,5 1 7,7 1 7,9 1 7,5 1 6,9 1 6,6 1 6,9 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 Y[t] = + 8.72 -0.437500000000001X[t] + 0.253402777777773M1[t] + 0.237638888888889M2[t] + 0.0418750000000011M3[t] -0.233888888888888M4[t] -0.449652777777777M5[t] -0.505416666666666M6[t] + 0.278819444444445M7[t] + 0.403055555555556M8[t] + 0.287291666666667M9[t] + 0.0115277777777778M10[t] -0.164236111111111M11[t] -0.0242361111111111t + 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) 8.72 0.246824 35.3288 0 0 X -0.437500000000001 0.202888 -2.1564 0.036318 0.018159 M1 0.253402777777773 0.286009 0.886 0.380228 0.190114 M2 0.237638888888889 0.285168 0.8333 0.408966 0.204483 M3 0.0418750000000011 0.284405 0.1472 0.883588 0.441794 M4 -0.233888888888888 0.283721 -0.8244 0.413988 0.206994 M5 -0.449652777777777 0.283116 -1.5882 0.119085 0.059543 M6 -0.505416666666666 0.28259 -1.7885 0.08028 0.04014 M7 0.278819444444445 0.282145 0.9882 0.328218 0.164109 M8 0.403055555555556 0.28178 1.4304 0.159363 0.079681 M9 0.287291666666667 0.281496 1.0206 0.312787 0.156393 M10 0.0115277777777778 0.281293 0.041 0.967488 0.483744 M11 -0.164236111111111 0.281171 -0.5841 0.561996 0.280998 t -0.0242361111111111 0.004782 -5.0681 7e-06 3e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.849220859124403 R-squared 0.72117606757199 Adjusted R-squared 0.6423779997119 F-TEST (value) 9.15220496081801 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 6.96668389643662e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.444505506071113 Sum Squared Residuals 9.08891666666667 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 8.94916666666669 -0.0491666666666857 2 8.8 8.90916666666667 -0.109166666666665 3 8.3 8.68916666666667 -0.389166666666665 4 7.5 8.38916666666666 -0.889166666666665 5 7.2 8.14916666666667 -0.949166666666666 6 7.4 8.06916666666667 -0.669166666666665 7 8.8 8.82916666666667 -0.0291666666666649 8 9.3 8.92916666666667 0.370833333333336 9 9.3 8.78916666666666 0.510833333333335 10 8.7 8.48916666666667 0.210833333333334 11 8.2 8.28916666666666 -0.089166666666666 12 8.3 8.42916666666667 -0.129166666666665 13 8.5 8.65833333333333 -0.158333333333328 14 8.6 8.61833333333333 -0.0183333333333327 15 8.5 8.39833333333333 0.101666666666667 16 8.2 8.09833333333333 0.101666666666666 17 8.1 7.85833333333333 0.241666666666666 18 7.9 7.77833333333333 0.121666666666667 19 8.6 8.53833333333333 0.0616666666666667 20 8.7 8.63833333333333 0.0616666666666663 21 8.7 8.49833333333333 0.201666666666666 22 8.5 8.19833333333333 0.301666666666667 23 8.4 7.99833333333333 0.401666666666668 24 8.5 8.13833333333333 0.361666666666667 25 8.7 8.3675 0.332500000000004 26 8.7 8.3275 0.372499999999999 27 8.6 8.1075 0.492499999999999 28 8.5 7.8075 0.6925 29 8.3 7.5675 0.7325 30 8 7.4875 0.5125 31 8.2 8.2475 -0.0475000000000008 32 8.1 8.3475 -0.247500000000001 33 8.1 8.2075 -0.107500000000000 34 8 7.9075 0.0924999999999999 35 7.9 7.7075 0.1925 36 7.9 7.8475 0.0525000000000001 37 8 8.07666666666666 -0.0766666666666623 38 8 8.03666666666667 -0.0366666666666675 39 7.9 7.81666666666667 0.0833333333333324 40 8 7.51666666666667 0.483333333333333 41 7.7 7.27666666666667 0.423333333333332 42 7.2 7.19666666666667 0.00333333333333241 43 7.5 7.95666666666667 -0.456666666666667 44 7.3 8.05666666666667 -0.756666666666667 45 7 7.91666666666667 -0.916666666666667 46 7 7.61666666666667 -0.616666666666667 47 7 7.41666666666667 -0.416666666666668 48 7.2 7.55666666666667 -0.356666666666667 49 7.3 7.34833333333333 -0.0483333333333286 50 7.1 7.30833333333333 -0.208333333333334 51 6.8 7.08833333333333 -0.288333333333334 52 6.4 6.78833333333333 -0.388333333333334 53 6.1 6.54833333333333 -0.448333333333335 54 6.5 6.46833333333333 0.0316666666666661 55 7.7 7.22833333333333 0.471666666666666 56 7.9 7.32833333333333 0.571666666666667 57 7.5 7.18833333333333 0.311666666666666 58 6.9 6.88833333333333 0.0116666666666668 59 6.6 6.68833333333333 -0.0883333333333339 60 6.9 6.82833333333333 0.0716666666666668 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.763272986883318 0.473454026233364 0.236727013116682 18 0.663658660915657 0.672682678168687 0.336341339084343 19 0.61720079524951 0.76559840950098 0.38279920475049 20 0.688278132113271 0.623443735773457 0.311721867886729 21 0.692614250039703 0.614771499920594 0.307385749960297 22 0.5949368730876 0.810126253824799 0.405063126912400 23 0.490293205543560 0.980586411087119 0.509706794456440 24 0.39414057761592 0.78828115523184 0.60585942238408 25 0.293065531120951 0.586131062241902 0.706934468879049 26 0.208967753431983 0.417935506863965 0.791032246568017 27 0.149903391464101 0.299806782928203 0.850096608535899 28 0.158257162711284 0.316514325422568 0.841742837288716 29 0.161887014314864 0.323774028629728 0.838112985685136 30 0.11640255593637 0.23280511187274 0.88359744406363 31 0.134005007089429 0.268010014178859 0.86599499291057 32 0.232436153184985 0.464872306369969 0.767563846815015 33 0.271990506827929 0.543981013655858 0.728009493172071 34 0.228788617950317 0.457577235900633 0.771211382049683 35 0.170423928884568 0.340847857769136 0.829576071115432 36 0.122844805327084 0.245689610654168 0.877155194672916 37 0.0943285301574636 0.188657060314927 0.905671469842536 38 0.072938591028606 0.145877182057212 0.927061408971394 39 0.0602556338848406 0.120511267769681 0.93974436611516 40 0.12438910617667 0.24877821235334 0.87561089382333 41 0.487699775977345 0.97539955195469 0.512300224022655 42 0.582373067199639 0.835253865600722 0.417626932800361 43 0.454610415466257 0.909220830932514 0.545389584533743 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/06/t1260103363h7k29j70wet1j1d/10l23r1260103124.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/10l23r1260103124.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/1fu661260103124.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/1fu661260103124.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/2njsz1260103124.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/2njsz1260103124.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/3wpez1260103124.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/3wpez1260103124.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/4354i1260103124.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/4354i1260103124.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/5nz1s1260103124.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/5nz1s1260103124.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/60pp71260103124.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/60pp71260103124.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/7wmub1260103124.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/7wmub1260103124.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/8y0ig1260103124.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/8y0ig1260103124.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/948ep1260103124.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260103363h7k29j70wet1j1d/948ep1260103124.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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