*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, 19 Nov 2010 12:14:29 +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/Nov/19/t1290168880hjzkhccokokcg9s.htm/, Retrieved Fri, 19 Nov 2010 13:14:51 +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/Nov/19/t1290168880hjzkhccokokcg9s.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 «

6 101.82 107.34 93.63 99.85 101.76 6 101.68 107.34 93.63 99.91 102.37 6 101.68 107.34 93.63 99.87 102.38 6 102.45 107.34 96.13 99.86 102.86 6 102.45 107.34 96.13 100.10 102.87 6 102.45 107.34 96.13 100.10 102.92 6 102.45 107.34 96.13 100.12 102.95 6 102.45 107.34 96.13 99.95 103.02 6 102.45 112.60 96.13 99.94 104.08 6 102.52 112.60 96.13 100.18 104.16 6 102.52 112.60 96.13 100.31 104.24 6 102.85 112.60 96.13 100.65 104.33 7 102.85 112.61 96.13 100.65 104.73 7 102.85 112.61 96.13 100.69 104.86 7 103.25 112.61 96.13 101.26 105.03 7 103.25 112.61 98.73 101.26 105.62 7 103.25 112.61 98.73 101.38 105.63 7 103.25 112.61 98.73 101.38 105.63 7 104.45 112.61 98.73 101.38 105.94 7 104.45 112.61 98.73 101.44 106.61 7 104.45 118.65 98.73 101.40 107.69 7 104.80 118.65 98.73 101.40 107.78 7 104.80 118.65 98.73 100.58 107.93 7 105.29 118.65 98.73 100.58 108.48 8 105.29 114.29 98.73 100.58 108.14 8 105.29 114.29 98.73 100.59 108.48 8 105.29 114.29 98.73 100.81 108.48 8 106.04 114.29 101.67 100.75 1 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Cultuuruitgaves[t] = + 67.2314876034128 + 0.0808949042160449Jaar[t] + 0.111902922150725Bioscoop[t] + 0.177271331756085Schouwburg[t] + 0.12737147086898Eendagattractie[t] -0.0868387869852808DVDhuren[t] + 0.169151767769644t + 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) 67.2314876034128 8.061054 8.3403 0 0 Jaar 0.0808949042160449 0.15474 0.5228 0.603391 0.301695 Bioscoop 0.111902922150725 0.020322 5.5064 1e-06 1e-06 Schouwburg 0.177271331756085 0.021622 8.1987 0 0 Eendagattractie 0.12737147086898 0.032484 3.9211 0.000264 0.000132 DVDhuren -0.0868387869852808 0.09024 -0.9623 0.340437 0.170218 t 0.169151767769644 0.020911 8.0889 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.998679902214923 R-squared 0.997361547088008 Adjusted R-squared 0.997051140863068 F-TEST (value) 3213.08487701933 F-TEST (DF numerator) 6 F-TEST (DF denominator) 51 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.302735427596799 Sum Squared Residuals 4.67408569523305 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.76 101.563207017546 0.196792982453661 2 102.37 101.711482048995 0.658517951004521 3 102.38 101.884107368245 0.495892631755464 4 102.86 102.458721451113 0.401278548887465 5 102.87 102.607031910006 0.262968089994293 6 102.92 102.776183677775 0.143816322224646 7 102.95 102.943598669805 0.00640133019470953 8 103.02 103.127513031362 -0.107513031362439 9 104.08 104.229980392039 -0.149980392038939 10 104.16 104.386124055483 -0.226124055482666 11 104.24 104.543986780944 -0.303986780944226 12 104.33 104.720541325449 -0.390541325448609 13 104.73 104.972360710752 -0.242360710751853 14 104.86 105.138038927042 -0.278038927042091 15 105.03 105.302453755090 -0.272453755090413 16 105.62 105.802771347119 -0.182771347119403 17 105.63 105.961502460451 -0.331502460450823 18 105.63 106.130654228220 -0.500654228220467 19 105.94 106.434089502571 -0.494089502570979 20 106.61 106.598030943122 0.0119690568784954 21 107.69 107.841375106177 -0.151375106177315 22 107.78 108.049692896700 -0.269692896699709 23 107.93 108.290052469797 -0.360052469797278 24 108.48 108.514036669421 -0.0340366694207805 25 108.14 107.99118033495 0.148819665050059 26 108.48 108.159463714850 0.320536285150271 27 108.48 108.309510949483 0.170489050517389 28 108.89 108.942272360439 -0.0522723604392201 29 108.93 109.100233835994 -0.170233835993784 30 109.21 109.251149458497 -0.0411494584965328 31 109.47 109.389907650821 0.0800923491786774 32 109.8 109.568449612417 0.231550387582927 33 111.73 111.378145785349 0.351854214650788 34 111.85 111.603302155360 0.246697844640147 35 112.12 111.878761699173 0.241238300827324 36 112.15 112.064681191543 0.0853188084569473 37 112.17 112.303438821221 -0.133438821220658 38 112.67 112.518837892239 0.151162107761357 39 112.8 112.674963841960 0.125036158039501 40 113.44 113.642734732079 -0.202734732078646 41 113.53 113.808412948369 -0.278412948368875 42 114.53 114.027921031106 0.502078968893654 43 114.51 114.163205671952 0.346794328048273 44 115.05 114.757606257616 0.292393742383989 45 116.67 116.161240739081 0.508759260918834 46 117.07 116.541131279776 0.528868720223899 47 116.92 116.712019823285 0.207980176714557 48 117 116.909281499130 0.0907185008704657 49 117.02 117.172756521773 -0.152756521773314 50 117.35 117.373491749097 -0.0234917490968169 51 117.36 117.662015187647 -0.302015187646585 52 117.82 118.131664876574 -0.311664876574014 53 117.88 118.314612100168 -0.434612100168287 54 118.24 118.541335640938 -0.301335640938110 55 118.5 118.713593855021 -0.213593855020616 56 118.8 118.891429501489 -0.0914295014887906 57 119.76 119.746621323154 0.0133786768455240 58 120.09 119.907089212226 0.182910787774406 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.112420926588230 0.224841853176459 0.88757907341177 11 0.0476101742046196 0.0952203484092391 0.95238982579538 12 0.226824863996256 0.453649727992511 0.773175136003744 13 0.137016396197778 0.274032792395556 0.862983603802222 14 0.0768929576815509 0.153785915363102 0.923107042318449 15 0.0569124814266924 0.113824962853385 0.943087518573308 16 0.0412454120766625 0.082490824153325 0.958754587923337 17 0.0255529792966954 0.0511059585933908 0.974447020703305 18 0.0230581027798625 0.0461162055597251 0.976941897220137 19 0.0286498153864716 0.0572996307729431 0.971350184613528 20 0.228152379996633 0.456304759993266 0.771847620003367 21 0.274059147913091 0.548118295826183 0.725940852086909 22 0.271369270516584 0.542738541033167 0.728630729483416 23 0.312026723424966 0.624053446849932 0.687973276575034 24 0.355381698770068 0.710763397540136 0.644618301229932 25 0.320637473621236 0.641274947242472 0.679362526378764 26 0.479181607158958 0.958363214317916 0.520818392841042 27 0.497548358899356 0.995096717798711 0.502451641100644 28 0.49089171653865 0.9817834330773 0.50910828346135 29 0.425269774691213 0.850539549382427 0.574730225308787 30 0.366770523694434 0.733541047388867 0.633229476305566 31 0.387496712763241 0.774993425526483 0.612503287236759 32 0.425619467204316 0.851238934408632 0.574380532795684 33 0.661252217570711 0.677495564858577 0.338747782429289 34 0.592120657925002 0.815758684149997 0.407879342074998 35 0.524422772577486 0.951154454845027 0.475577227422514 36 0.580118632948774 0.839762734102451 0.419881367051226 37 0.655190680535218 0.689618638929563 0.344809319464782 38 0.582566476572728 0.834867046854545 0.417433523427272 39 0.521416848573965 0.95716630285207 0.478583151426035 40 0.502458639916762 0.995082720166476 0.497541360083238 41 0.901180212205295 0.197639575589409 0.0988197877947046 42 0.905651051585636 0.188697896828728 0.094348948414364 43 0.91740401254256 0.165191974914879 0.0825959874574393 44 0.885062565211636 0.229874869576727 0.114937434788364 45 0.824122136902139 0.351755726195722 0.175877863097861 46 0.983833829500153 0.0323323409996938 0.0161661704998469 47 0.983414399178296 0.0331712016434077 0.0165856008217039 48 0.944905285659149 0.110189428681702 0.0550947143408512 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 3 0.0769230769230769 NOK 10% type I error level 7 0.179487179487179 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/10v4751290168858.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/10v4751290168858.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/1plat1290168858.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/1plat1290168858.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/2hu9e1290168858.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/2hu9e1290168858.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/3hu9e1290168858.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/3hu9e1290168858.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/4hu9e1290168858.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/4hu9e1290168858.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/5al8h1290168858.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/5al8h1290168858.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/6al8h1290168858.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/6al8h1290168858.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/7ldp21290168858.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/7ldp21290168858.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/8ldp21290168858.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/8ldp21290168858.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/9v4751290168858.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168880hjzkhccokokcg9s/9v4751290168858.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 6 ; par2 = Do not include Seasonal 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