paper

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Wed, 02 Dec 2009 11:07:48 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3.htm/, Retrieved Wed, 02 Dec 2009 19:18:16 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3.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 «

109 102,86 108,6 102,55 108,8 102,28 108,5 102,26 108,3 102,57 108,2 103,08 108 102,76 107,9 102,51 108 102,87 109,3 103,14 109,6 103,12 109 103,16 108,7 102,48 108,3 102,57 108,4 102,88 107,8 102,63 107,8 102,38 107,6 101,69 107,7 101,96 107,6 102,19 107,6 101,87 108,6 101,6 108,6 101,63 108,2 101,22 107,5 101,21 107,1 101,49 107 101,64 106,9 101,66 106,6 101,77 106,3 101,82 106,1 101,78 105,9 101,28 106 101,29 107,2 101,37 107,2 101,12 106,4 101,51 106,1 102,24 105,9 102,94 106,1 103,09 105,9 103,46 105,8 103,64 105,7 104,39 105,6 104,15 105,3 105,21 105,5 105,8 106,5 105,91 106,5 105,39 106,1 105,46 105,9 104,72 105,8 103,14 106,2 102,63 106,5 102,32 106,6 101,93 106,7 100,62 106,6 100,6 106,5 99,63 106,8 98,9 107,8 98,32 107,9 99,22 107,4 98,81

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werk[t] = + 123.834438162153 -0.162585407011427Infl[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) 123.834438162153 9.158136 13.5218 0 0 Infl -0.162585407011427 0.089501 -1.8166 0.074451 0.037226 Multiple Linear Regression - Regression Statistics Multiple R 0.232018542744099 R-squared 0.0538326041770954 Adjusted R-squared 0.0375193732146316 F-TEST (value) 3.29993514472775 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.0744510475409212 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.09425369665578 Sum Squared Residuals 69.4486868534011 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 109 107.110903196958 1.88909680304221 2 108.6 107.161304673131 1.43869532686872 3 108.8 107.205202733024 1.59479726697563 4 108.5 107.208454441165 1.29154555883541 5 108.3 107.158052964991 1.14194703500895 6 108.2 107.075134407415 1.12486559258478 7 108 107.127161737659 0.872838262341122 8 107.9 107.167808089412 0.732191910588271 9 108 107.109277342888 0.890722657112379 10 109.3 107.065379282995 2.23462071700546 11 109.6 107.068630991135 2.53136900886523 12 109 107.062127574854 1.93787242514569 13 108.7 107.172685651622 1.52731434837793 14 108.3 107.158052964991 1.14194703500895 15 108.4 107.107651488818 1.29234851118250 16 107.8 107.148297840570 0.651702159429632 17 107.8 107.188944192323 0.611055807676775 18 107.6 107.301128123161 0.298871876838888 19 107.7 107.257230063268 0.442769936731981 20 107.6 107.219835419655 0.380164580344602 21 107.6 107.271862749899 0.328137250100946 22 108.6 107.315760809792 1.28423919020786 23 108.6 107.310883247582 1.28911675241820 24 108.2 107.377543264456 0.822456735543526 25 107.5 107.379169118527 0.120830881473408 26 107.1 107.333645204563 -0.233645204563398 27 107 107.309257393512 -0.309257393511677 28 106.9 107.306005685371 -0.406005685371443 29 106.6 107.288121290600 -0.688121290600198 30 106.3 107.279992020250 -0.979992020249624 31 106.1 107.28649543653 -1.18649543653008 32 105.9 107.367788140036 -1.46778814003579 33 106 107.366162285966 -1.36616228596568 34 107.2 107.353155453405 -0.153155453404759 35 107.2 107.393801805158 -0.193801805157616 36 106.4 107.330393496423 -0.930393496423156 37 106.1 107.211706149305 -1.11170614930483 38 105.9 107.097896364397 -1.19789636439682 39 106.1 107.073508553345 -0.973508553345113 40 105.9 107.013351952751 -1.11335195275087 41 105.8 106.984086579489 -1.18408657948883 42 105.7 106.862147524230 -1.16214752423025 43 105.6 106.901168021913 -1.301168021913 44 105.3 106.728827490481 -1.42882749048089 45 105.5 106.632902100344 -1.13290210034414 46 106.5 106.615017705573 -0.115017705572884 47 106.5 106.699562117219 -0.199562117218825 48 106.1 106.688181138728 -0.588181138728032 49 105.9 106.808494339916 -0.908494339916476 50 105.8 107.065379282995 -1.26537928299454 51 106.2 107.148297840570 -0.948297840570362 52 106.5 107.198699316744 -0.698699316743908 53 106.6 107.262107625478 -0.662107625478368 54 106.7 107.475094508663 -0.77509450866333 55 106.6 107.478346216804 -0.878346216803568 56 106.5 107.636054061605 -1.13605406160465 57 106.8 107.754741408723 -0.95474140872299 58 107.8 107.849040944790 -0.049040944789619 59 107.9 107.702714078479 0.197285921520675 60 107.4 107.769374095354 -0.36937409535401 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0277350413595933 0.0554700827191867 0.972264958640407 6 0.0182095873357224 0.0364191746714448 0.981790412664278 7 0.0136629609652923 0.0273259219305847 0.986337039034708 8 0.0128357792674516 0.0256715585349031 0.987164220732548 9 0.00629976962737472 0.0125995392547494 0.993700230372625 10 0.0212915605622442 0.0425831211244884 0.978708439437756 11 0.0592245493437149 0.118449098687430 0.940775450656285 12 0.0556412370974384 0.111282474194877 0.944358762902562 13 0.052859835207675 0.10571967041535 0.947140164792325 14 0.0448522977372353 0.0897045954744705 0.955147702262765 15 0.0486072052330965 0.097214410466193 0.951392794766904 16 0.0669050053397991 0.133810010679598 0.9330949946602 17 0.0682642844193315 0.136528568838663 0.931735715580668 18 0.0494471499174794 0.098894299834959 0.95055285008252 19 0.0396528248393827 0.0793056496787655 0.960347175160617 20 0.0385357351552127 0.0770714703104254 0.961464264844787 21 0.0305024218486113 0.0610048436972226 0.969497578151389 22 0.167218121918078 0.334436243836157 0.832781878081922 23 0.488566995900592 0.977133991801185 0.511433004099408 24 0.718358957420488 0.563282085159025 0.281641042579512 25 0.753087256499345 0.493825487001309 0.246912743500655 26 0.79690156730496 0.406196865390082 0.203098432695041 27 0.842080298819716 0.315839402360569 0.157919701180284 28 0.87407241763755 0.251855164724902 0.125927582362451 29 0.916889951323217 0.166220097353565 0.0831100486767825 30 0.957882637610248 0.0842347247795037 0.0421173623897518 31 0.980929047538742 0.0381419049225151 0.0190709524612575 32 0.990680924028377 0.0186381519432464 0.00931907597162318 33 0.9938794700316 0.0122410599367987 0.00612052996839937 34 0.99348155150707 0.0130368969858601 0.00651844849293004 35 0.993267107353073 0.0134657852938546 0.00673289264692732 36 0.990961982992451 0.0180760340150974 0.00903801700754871 37 0.995377306318904 0.00924538736219268 0.00462269368109634 38 0.998969606377 0.00206078724600067 0.00103039362300034 39 0.999412983592582 0.00117403281483560 0.000587016407417801 40 0.999675221259237 0.000649557481525337 0.000324778740762668 41 0.999765088956586 0.000469822086828205 0.000234911043414103 42 0.999774949569205 0.000450100861590332 0.000225050430795166 43 0.99977869817208 0.000442603655841777 0.000221301827920888 44 0.999827908101825 0.000344183796350067 0.000172091898175034 45 0.99971891264697 0.000562174706060139 0.000281087353030069 46 0.99965315114444 0.000693697711118801 0.000346848855559401 47 0.999692978867646 0.000614042264708039 0.000307021132354019 48 0.999562652425761 0.000874695148476913 0.000437347574238456 49 0.99899944236684 0.0020011152663185 0.00100055763315925 50 0.997668330721128 0.004663338557743 0.0023316692788715 51 0.993581325285567 0.0128373494288671 0.00641867471443355 52 0.98426867374113 0.0314626525177405 0.0157313262588703 53 0.969937248750462 0.0601255024990769 0.0300627512495384 54 0.928699203514263 0.142601592971475 0.0713007964857373 55 0.845950790351675 0.30809841929665 0.154049209648325 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 27 0.529411764705882 NOK 10% type I error level 36 0.705882352941177 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/100la01259777264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/100la01259777264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/1m44y1259777264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/1m44y1259777264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/28q5d1259777264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/28q5d1259777264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/35iad1259777264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/35iad1259777264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/4mril1259777264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/4mril1259777264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/5igpg1259777264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/5igpg1259777264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/6fxrc1259777264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/6fxrc1259777264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/7iq2i1259777264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/7iq2i1259777264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/8it6b1259777264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/8it6b1259777264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/91ze81259777264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t1259777884qp4g74kkt1g3rr3/91ze81259777264.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