*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, 17 Dec 2010 11:54:43 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98.htm/, Retrieved Fri, 17 Dec 2010 12:52:46 +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/2010/Dec/17/t1292586755k81cd0luv4lko98.htm/}, year = {2010}, } @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 = {2010}, 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 «

101.82 107.34 93.63 99.85 101.76 101.68 107.34 93.63 99.91 102.37 101.68 107.34 93.63 99.87 102.38 102.45 107.34 96.13 99.86 102.86 102.45 107.34 96.13 100.10 102.87 102.45 107.34 96.13 100.10 102.92 102.45 107.34 96.13 100.12 102.95 102.45 107.34 96.13 99.95 103.02 102.45 112.60 96.13 99.94 104.08 102.52 112.60 96.13 100.18 104.16 102.52 112.60 96.13 100.31 104.24 102.85 112.60 96.13 100.65 104.33 102.85 112.61 96.13 100.65 104.73 102.85 112.61 96.13 100.69 104.86 103.25 112.61 96.13 101.26 105.03 103.25 112.61 98.73 101.26 105.62 103.25 112.61 98.73 101.38 105.63 103.25 112.61 98.73 101.38 105.63 104.45 112.61 98.73 101.38 105.94 104.45 112.61 98.73 101.44 106.61 104.45 118.65 98.73 101.40 107.69 104.80 118.65 98.73 101.40 107.78 104.80 118.65 98.73 100.58 107.93 105.29 118.65 98.73 100.58 108.48 105.29 114.29 98.73 100.58 108.14 105.29 114.29 98.73 100.59 108.48 105.29 114.29 98.73 100.81 108.48 106.04 114.29 101.67 100.75 108.89 105.94 114.29 101.67 100.75 108.93 105.94 114.29 etc...

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 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation vrijetijdsbesteding[t] = + 35.2689359098687 + 0.0686290527084394bios[t] + 0.349632828334539schouwburg[t] + 0.533715451658278eedagsacttractie[t] -0.279710824870231huurDVD[t] + 0.316008799723908M1[t] + 0.695631656879148M2[t] + 0.734592742605774M3[t] -0.547693856402015M4[t] -0.484995255361616M5[t] -0.147579851748444M6[t] + 0.045631283121235M7[t] + 0.381593475385275M8[t] -0.370011739420981M9[t] -0.13577970566168M10[t] -0.188061168684507M11[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) 35.2689359098687 14.406688 2.4481 0.018621 0.00931 bios 0.0686290527084394 0.040939 1.6764 0.101098 0.050549 schouwburg 0.349632828334539 0.056553 6.1824 0 0 eedagsacttractie 0.533715451658278 0.091555 5.8295 1e-06 0 huurDVD -0.279710824870231 0.174489 -1.603 0.116423 0.058211 M1 0.316008799723908 0.397969 0.7941 0.431628 0.215814 M2 0.695631656879148 0.39816 1.7471 0.087926 0.043963 M3 0.734592742605774 0.398371 1.844 0.072247 0.036123 M4 -0.547693856402015 0.531577 -1.0303 0.308757 0.154379 M5 -0.484995255361616 0.531154 -0.9131 0.366404 0.183202 M6 -0.147579851748444 0.52955 -0.2787 0.781851 0.390925 M7 0.045631283121235 0.527995 0.0864 0.93154 0.46577 M8 0.381593475385275 0.522941 0.7297 0.46962 0.23481 M9 -0.370011739420981 0.393307 -0.9408 0.352202 0.176101 M10 -0.13577970566168 0.390147 -0.348 0.729562 0.364781 M11 -0.188061168684507 0.410028 -0.4587 0.648847 0.324423 Multiple Linear Regression - Regression Statistics Multiple R 0.99601068857855 R-squared 0.99203729176272 Adjusted R-squared 0.989193467392262 F-TEST (value) 348.839155493686 F-TEST (DF numerator) 15 F-TEST (DF denominator) 42 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.579534663866987 Sum Squared Residuals 14.1061379181837 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.76 102.144994525268 -0.384994525267722 2 102.37 102.498226665551 -0.128226665551124 3 102.38 102.548376184273 -0.168376184272574 4 102.86 102.656019693245 0.203980306755325 5 102.87 102.651587696316 0.218412303683786 6 102.92 102.989003099929 -0.0690030999293898 7 102.95 103.176620018302 -0.22662001830166 8 103.02 103.560133050794 -0.540133050793647 9 104.08 104.650393621276 -0.570393621275765 10 104.16 104.822299090756 -0.662299090755801 11 104.24 104.733655220500 -0.493655220499847 12 104.33 104.849262296122 -0.519262296122256 13 104.73 105.168767424130 -0.438767424129506 14 104.86 105.53720184829 -0.677201848289943 15 105.03 105.444179384924 -0.414179384923909 16 105.62 105.549552960228 0.0704470397723576 17 105.63 105.578686262284 0.0513137377163745 18 105.63 105.916101665897 -0.286101665896798 19 105.94 106.191667664017 -0.251667664016602 20 106.61 106.510847206788 0.0991527932115737 21 107.69 107.882212708118 -0.192212708117599 22 107.78 108.140464910325 -0.36046491032485 23 107.93 108.317546323696 -0.387546323695609 24 108.48 108.539235728207 -0.0592357282072548 25 108.14 107.330845396393 0.809154603607425 26 108.48 107.707671145299 0.772328854700892 27 108.48 107.685095849554 0.794904150445718 28 108.89 108.040187117445 0.849812882554624 29 108.93 108.096022813215 0.833977186785076 30 109.21 108.374698943605 0.835301056394638 31 109.47 108.470011289770 0.999988710229548 32 109.8 108.737002787748 1.06299721225181 33 111.73 111.210152100104 0.51984789989551 34 111.85 111.417588838572 0.432411161428172 35 112.12 111.430504975622 0.689495024377992 36 112.15 111.649230442762 0.500769557238488 37 112.17 111.928876835252 0.241123164747705 38 112.67 112.34365634771 0.326343652290066 39 112.8 112.340660809706 0.459339190293973 40 113.44 114.404770092596 -0.964770092595634 41 113.53 114.456280260641 -0.92628026064122 42 114.53 114.824578737973 -0.29457873797319 43 114.51 114.908702651143 -0.398702651143479 44 115.05 115.485085303151 -0.435085303150866 45 116.67 116.598836088135 0.0711639118651307 46 117.07 116.774980726708 0.295019273292036 47 116.92 116.728293480183 0.191706519817464 48 117 116.922271532909 0.0777284670910233 49 117.02 117.246515818958 -0.226515818957902 50 117.35 117.64324399315 -0.293243993149891 51 117.36 118.031687771543 -0.671687771543208 52 117.82 117.979470136487 -0.159470136486672 53 117.88 118.057422967544 -0.177422967544017 54 118.24 118.425617552595 -0.18561755259526 55 118.5 118.622998376768 -0.122998376767807 56 118.8 118.986931651519 -0.186931651518871 57 119.76 119.588405482367 0.171594517632724 58 120.09 119.794666433640 0.295333566360443 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0333304328523004 0.0666608657046009 0.9666695671477 20 0.0565366688518876 0.113073337703775 0.943463331148112 21 0.0458266069010975 0.091653213802195 0.954173393098902 22 0.0520464973896608 0.104092994779322 0.94795350261034 23 0.103622472471212 0.207244944942424 0.896377527528788 24 0.302459205299881 0.604918410599762 0.697540794700119 25 0.239374227585357 0.478748455170714 0.760625772414643 26 0.170378287574510 0.340756575149019 0.82962171242549 27 0.105809059415743 0.211618118831487 0.894190940584257 28 0.207439272472346 0.414878544944693 0.792560727527654 29 0.189699787574926 0.379399575149851 0.810300212425074 30 0.127967688722201 0.255935377444402 0.8720323112778 31 0.145294584121482 0.290589168242964 0.854705415878518 32 0.609501014550346 0.780997970899309 0.390498985449654 33 0.594599409179392 0.810801181641216 0.405400590820608 34 0.689710410412257 0.620579179175485 0.310289589587743 35 0.578061433307055 0.84387713338589 0.421938566692945 36 0.487109584288505 0.97421916857701 0.512890415711495 37 0.463116476722374 0.926232953444748 0.536883523277626 38 0.426808221779824 0.853616443559648 0.573191778220176 39 0.408366854632799 0.816733709265597 0.591633145367201 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 2 0.0952380952380952 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/10hzbx1292586873.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/10hzbx1292586873.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/1aye31292586873.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/1aye31292586873.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/2lpdo1292586873.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/2lpdo1292586873.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/3lpdo1292586873.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/3lpdo1292586873.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/4dhcr1292586873.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/4dhcr1292586873.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/5dhcr1292586873.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/5dhcr1292586873.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/6dhcr1292586873.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/6dhcr1292586873.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/768uu1292586873.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/768uu1292586873.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/868uu1292586873.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/868uu1292586873.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/9hzbx1292586873.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292586755k81cd0luv4lko98/9hzbx1292586873.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ; par4 = no ;

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