review

*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, 24 Nov 2009 02:26:23 -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/24/t1259054936kzcuudc7s16g0p1.htm/, Retrieved Tue, 24 Nov 2009 10:29:08 +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/24/t1259054936kzcuudc7s16g0p1.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 «

3,2 0 1,9 0 0 0 0,6 0 0,2 0 0,9 0 2,4 0 4,7 0 9,4 0 12,5 0 15,8 0 18,2 0 16,8 1 17,3 1 19,3 1 17,9 1 20,2 1 18,7 1 20,1 1 18,2 1 18,4 1 18,2 1 18,9 1 19,9 1 21,3 1 20 1 19,5 1 19,6 1 20,9 1 21 1 19,9 1 19,6 1 20,9 1 21,7 1 22,9 1 21,5 1 21,3 1 23,5 1 21,6 1 24,5 1 22,2 1 23,5 1 20,9 1 20,7 1 18,1 1 17,1 1 14,8 1 13,8 1 15,2 1 16 1 17,6 1 15 1 15 1 16,3 1 19,4 1 21,3 1 20,5 1 21,1 1 21,6 1 22,6 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 Y[t] = + 5.81666666666664 + 13.6895833333334X[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) 5.81666666666664 1.043263 5.5755 1e-06 0 X 13.6895833333334 1.166403 11.7366 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.838867982627093 R-squared 0.703699492276849 Adjusted R-squared 0.698590862833346 F-TEST (value) 137.747217734073 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.61396904664594 Sum Squared Residuals 757.524791666667 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.2 5.81666666666671 -2.61666666666671 2 1.9 5.8166666666667 -3.9166666666667 3 0 5.81666666666666 -5.81666666666666 4 0.6 5.81666666666666 -5.21666666666666 5 0.2 5.81666666666666 -5.61666666666666 6 0.9 5.81666666666666 -4.91666666666666 7 2.4 5.81666666666666 -3.41666666666666 8 4.7 5.81666666666666 -1.11666666666666 9 9.4 5.81666666666666 3.58333333333334 10 12.5 5.81666666666666 6.68333333333334 11 15.8 5.81666666666666 9.98333333333334 12 18.2 5.81666666666666 12.3833333333333 13 16.8 19.50625 -2.70625 14 17.3 19.50625 -2.20625 15 19.3 19.50625 -0.206249999999999 16 17.9 19.50625 -1.60625 17 20.2 19.50625 0.69375 18 18.7 19.50625 -0.80625 19 20.1 19.50625 0.593750000000002 20 18.2 19.50625 -1.30625 21 18.4 19.50625 -1.10625000000000 22 18.2 19.50625 -1.30625 23 18.9 19.50625 -0.606250000000001 24 19.9 19.50625 0.393749999999999 25 21.3 19.50625 1.79375 26 20 19.50625 0.49375 27 19.5 19.50625 -0.00624999999999976 28 19.6 19.50625 0.0937500000000017 29 20.9 19.50625 1.39375 30 21 19.50625 1.49375 31 19.9 19.50625 0.393749999999999 32 19.6 19.50625 0.0937500000000017 33 20.9 19.50625 1.39375 34 21.7 19.50625 2.19375 35 22.9 19.50625 3.39375 36 21.5 19.50625 1.99375 37 21.3 19.50625 1.79375 38 23.5 19.50625 3.99375 39 21.6 19.50625 2.09375 40 24.5 19.50625 4.99375 41 22.2 19.50625 2.69375 42 23.5 19.50625 3.99375 43 20.9 19.50625 1.39375 44 20.7 19.50625 1.19375 45 18.1 19.50625 -1.40625000000000 46 17.1 19.50625 -2.40625 47 14.8 19.50625 -4.70625 48 13.8 19.50625 -5.70625 49 15.2 19.50625 -4.30625 50 16 19.50625 -3.50625 51 17.6 19.50625 -1.90625 52 15 19.50625 -4.50625 53 15 19.50625 -4.50625 54 16.3 19.50625 -3.20625 55 19.4 19.50625 -0.106250000000001 56 21.3 19.50625 1.79375 57 20.5 19.50625 0.99375 58 21.1 19.50625 1.59375000000000 59 21.6 19.50625 2.09375 60 22.6 19.50625 3.09375 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.123150511609714 0.246301023219428 0.876849488390286 6 0.0607720626191958 0.121544125238392 0.939227937380804 7 0.0468095311278893 0.0936190622557786 0.95319046887211 8 0.16595332683969 0.33190665367938 0.83404667316031 9 0.840565676979509 0.318868646040983 0.159434323020491 10 0.994658913575692 0.0106821728486168 0.00534108642430838 11 0.999935870279595 0.000128259440810105 6.41297204050523e-05 12 0.999998957410045 2.0851799097921e-06 1.04258995489605e-06 13 0.999997853739072 4.29252185671588e-06 2.14626092835794e-06 14 0.999995380202273 9.23959545498073e-06 4.61979772749037e-06 15 0.999989151627274 2.16967454527043e-05 1.08483727263521e-05 16 0.99997648242797 4.70351440596933e-05 2.35175720298467e-05 17 0.999950939524722 9.81209505559624e-05 4.90604752779812e-05 18 0.999893894988669 0.000212210022662064 0.000106105011331032 19 0.999785142135737 0.00042971572852514 0.00021485786426257 20 0.99958853462418 0.000822930751640584 0.000411465375820292 21 0.9992248840589 0.00155023188219967 0.000775115941099837 22 0.998619900890076 0.0027601982198477 0.00138009910992385 23 0.997499867086882 0.00500026582623705 0.00250013291311852 24 0.995636392467387 0.00872721506522592 0.00436360753261296 25 0.993551895057171 0.0128962098856571 0.00644810494282857 26 0.989296854609021 0.0214062907819580 0.0107031453909790 27 0.982573705044473 0.0348525899110531 0.0174262949555265 28 0.972537194843779 0.0549256103124421 0.0274628051562210 29 0.96057623512777 0.0788475297444597 0.0394237648722299 30 0.94499826472776 0.110003470544480 0.0550017352722401 31 0.92034635515082 0.15930728969836 0.07965364484918 32 0.887624602549118 0.224750794901764 0.112375397450882 33 0.852556212465746 0.294887575068508 0.147443787534254 34 0.821808416355114 0.356383167289772 0.178191583644886 35 0.81557080968427 0.368858380631461 0.184429190315731 36 0.777407066153003 0.445185867693993 0.222592933846996 37 0.73164099938193 0.536718001236139 0.268359000618070 38 0.752725467546986 0.494549064906028 0.247274532453014 39 0.715006401732449 0.569987196535102 0.284993598267551 40 0.800244456904326 0.399511086191349 0.199755543095674 41 0.795241526256726 0.409516947486549 0.204758473743275 42 0.851539750509942 0.296920498980117 0.148460249490058 43 0.828197532163548 0.343604935672903 0.171802467836452 44 0.80151495184863 0.396970096302739 0.198485048151370 45 0.736437190854881 0.527125618290238 0.263562809145119 46 0.669160922956759 0.661678154086482 0.330839077043241 47 0.676518038013102 0.646963923973797 0.323481961986898 48 0.753027737804964 0.493944524390072 0.246972262195036 49 0.759913271547688 0.480173456904624 0.240086728452312 50 0.737644145915463 0.524711708169073 0.262355854084537 51 0.65361523832442 0.692769523351161 0.346384761675581 52 0.728247526775111 0.543504946449778 0.271752473224889 53 0.87532539099499 0.249349218010021 0.124674609005010 54 0.981158307257745 0.0376833854845093 0.0188416927422547 55 0.981734628503734 0.0365307429925325 0.0182653714962663 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.274509803921569 NOK 5% type I error level 20 0.392156862745098 NOK 10% type I error level 23 0.450980392156863 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/1082mr1259054777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/1082mr1259054777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/1ys341259054777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/1ys341259054777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/26fb91259054777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/26fb91259054777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/3u69u1259054777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/3u69u1259054777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/47pev1259054777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/47pev1259054777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/51fz11259054777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/51fz11259054777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/63epa1259054777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/63epa1259054777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/7dzge1259054777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/7dzge1259054777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/8x6sl1259054777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/8x6sl1259054777.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/97gxw1259054777.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259054936kzcuudc7s16g0p1/97gxw1259054777.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by