*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 20 Nov 2009 06:18:27 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5.htm/, Retrieved Fri, 20 Nov 2009 14:22:25 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5.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 «

280 1258 557 1199 831 1158 1081 1427 1318 934 1578 709 1859 1186 2141 986 2428 1033 2715 1257 3004 1105 3309 1179 269 1092 537 1092 813 1087 1068 2028 1411 2039 1675 2010 1958 754 2242 760 2524 715 2836 855 3143 971 3522 815 285 915 574 843 865 761 1147 1858 1516 2968 1789 4061 2087 3661 2372 3269 2669 2857 2966 2568 3270 2274 3652 1987 329 683 658 381 988 71 1303 1772 1603 3485 1929 5181 2235 4479 2544 3782 2872 3067 3198 2489 3544 1903 3903 1330 332 736 665 483 1001 242 1329 1334 1639 2423 1975 3523 2304 2986 2640 2462 2992 1908 3330 1575 3690 1237 4063 904

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 3354.30967526023 + 0.0263231337674931X[t] -3290.22659632555M1[t] -2995.82535874672M2[t] -2699.26097355517M3[t] -2448.52086637209M4[t] -2163.18883251067M5[t] -1898.93604713372M6[t] -1595.21647601783M7[t] -1294.91359184834M8[t] -985.284579903292M9[t] -677.293648311444M10[t] -357.902102736634M11[t] + 8.41029637407708t + 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) 3354.30967526023 47.110127 71.2015 0 0 X 0.0263231337674931 0.014295 1.8414 0.072021 0.03601 M1 -3290.22659632555 56.484817 -58.2498 0 0 M2 -2995.82535874672 56.500306 -53.0232 0 0 M3 -2699.26097355517 56.609733 -47.6819 0 0 M4 -2448.52086637209 56.870559 -43.0543 0 0 M5 -2163.18883251067 58.989503 -36.6707 0 0 M6 -1898.93604713372 62.774943 -30.2499 0 0 M7 -1595.21647601783 59.861038 -26.6487 0 0 M8 -1294.91359184834 58.148033 -22.2693 0 0 M9 -985.284579903292 56.978523 -17.2922 0 0 M10 -677.293648311444 56.532069 -11.9807 0 0 M11 -357.902102736634 56.120128 -6.3774 0 0 t 8.41029637407708 0.725339 11.595 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.997355409116792 R-squared 0.994717812094524 Adjusted R-squared 0.993225019860367 F-TEST (value) 666.347124090398 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 88.5111261077739 Sum Squared Residuals 360374.094463848 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 280 105.607877588275 174.392122411725 2 557 406.866346648887 150.133653351113 3 831 710.761779730045 120.238220269955 4 1081 976.993106270657 104.006893729343 5 1318 1257.75813155878 60.2418684412153 6 1578 1524.49850821213 53.5014917878694 7 1859 1849.18451050918 9.81548949081726 8 2141 2152.63306429926 -11.6330642992569 9 2428 2471.90955990545 -43.9095599054542 10 2715 2794.20716983530 -79.2071698352957 11 3004 3118.00789545153 -114.007895451526 12 3309 3486.26820646103 -177.268206461031 13 269 202.161793871788 66.8382061282116 14 537 504.97332782469 32.0266721753097 15 813 809.816393721478 3.18360627852161 16 1068 1093.73686615385 -25.7368661538465 17 1411 1387.76875086079 23.2312491392118 18 1675 1659.66846173256 15.3315382674365 19 1958 1938.73647321055 19.2635267894478 20 2242 2247.60759255673 -5.60759255672876 21 2524 2564.46235985632 -40.4623598563159 22 2836 2884.54882654969 -48.54882654969 23 3143 3215.40415201561 -72.4041520156063 24 3522 3577.61014225859 -55.6101422585885 25 285 298.426155683867 -13.4261556838671 26 574 599.34242400551 -25.3424240055096 27 865 902.1586086022 -37.1586086022 28 1147 1190.18548990230 -43.1854899022977 29 1516 1513.14649861971 2.85350138028586 30 1789 1814.58076557862 -25.5807655786167 31 2087 2116.18137956158 -29.1813795615795 32 2372 2414.57589166829 -42.575891668294 33 2669 2721.77006887521 -52.7700688752107 34 2966 3030.56391118233 -64.5639111823307 35 3270 3350.62675180357 -80.6267518035745 36 3652 3709.38441152301 -57.3844115230150 37 329 393.242745138733 -64.2427451387334 38 658 688.104692693852 -30.1046926938525 39 988 984.919202791555 3.08079720844485 40 1303 1288.84525688722 14.1547431127822 41 1603 1627.67911526643 -24.679115266433 42 1929 1944.98623188713 -15.9862318871341 43 2235 2238.63725947231 -3.63725947231396 44 2544 2529.00321577994 14.9967842200572 45 2872 2828.22148345531 43.7785165446904 46 3198 3129.40794010362 68.5920598963763 47 3544 3441.78442566476 102.215574335241 48 3903 3793.0136691267 109.986330873303 49 332 495.561427717336 -163.561427717336 50 665 791.713208827061 -126.713208827061 51 1001 1090.34401515472 -89.3440151547216 52 1329 1378.23928078598 -49.2392807859809 53 1639 1700.64750369428 -61.6475036942798 54 1975 2002.26603258956 -27.2660325895552 55 2304 2300.26037724637 3.73962275362838 56 2640 2595.18023569578 44.8197643042226 57 2992 2898.63652790771 93.3634720922902 58 3330 3206.27215232906 123.727847670940 59 3690 3525.17677506453 164.823224935466 60 4063 3882.72357063067 180.276429369330 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0432420155915732 0.0864840311831465 0.956757984408427 18 0.0124204202621225 0.0248408405242451 0.987579579737877 19 0.141049668155528 0.282099336311056 0.858950331844472 20 0.144721986030596 0.289443972061191 0.855278013969404 21 0.112458304299074 0.224916608598149 0.887541695700926 22 0.100276518663554 0.200553037327109 0.899723481336446 23 0.1053307121427 0.2106614242854 0.8946692878573 24 0.228556982015282 0.457113964030564 0.771443017984718 25 0.30752487422407 0.61504974844814 0.69247512577593 26 0.299193524769613 0.598387049539226 0.700806475230387 27 0.242444803386222 0.484889606772445 0.757555196613778 28 0.169931059501039 0.339862119002077 0.830068940498961 29 0.162399000243328 0.324798000486656 0.837600999756672 30 0.115620030691176 0.231240061382353 0.884379969308824 31 0.0780872828559032 0.156174565711806 0.921912717144097 32 0.0487989865266399 0.0975979730532797 0.95120101347336 33 0.0352801939426494 0.0705603878852988 0.96471980605735 34 0.0371975284142834 0.0743950568285668 0.962802471585717 35 0.12827296952074 0.25654593904148 0.87172703047926 36 0.781256994601329 0.437486010797342 0.218743005398671 37 0.750415481664047 0.499169036671907 0.249584518335953 38 0.716515536829269 0.566968926341463 0.283484463170731 39 0.813087043343334 0.373825913313333 0.186912956656666 40 0.97601091162152 0.0479781767569609 0.0239890883784804 41 0.99963230612048 0.000735387759042033 0.000367693879521017 42 0.999210391142256 0.00157921771548736 0.00078960885774368 43 0.9991309805363 0.00173803892739876 0.000869019463699378 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.111111111111111 NOK 5% type I error level 5 0.185185185185185 NOK 10% type I error level 9 0.333333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/100nwy1258723100.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/100nwy1258723100.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/17m0l1258723100.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/17m0l1258723100.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/2sbpd1258723100.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/2sbpd1258723100.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/3lot41258723100.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/3lot41258723100.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/4ce4n1258723100.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/4ce4n1258723100.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/59f0g1258723100.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/59f0g1258723100.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/6pfuf1258723100.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/6pfuf1258723100.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/7366w1258723100.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/7366w1258723100.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/8wzka1258723100.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/8wzka1258723100.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/9drhs1258723100.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723327zr3jwpfzwgyxxm5/9drhs1258723100.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