multi regression "t"

*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: Thu, 19 Nov 2009 13:09:01 -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/19/t1258661563dr5fzr6368uocdr.htm/, Retrieved Thu, 19 Nov 2009 21:12:55 +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/19/t1258661563dr5fzr6368uocdr.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 «

8,9 6,3 8,2 6,2 7,6 6,1 7,7 6,3 8,1 6,5 8,3 6,6 8,3 6,5 7,9 6,2 7,8 6,2 8 5,9 8,5 6,1 8,6 6,1 8,5 6,1 8 6,1 7,8 6,1 8 6,4 8,2 6,7 8,3 6,9 8,2 7 8,1 7 8 6,8 7,8 6,4 7,8 5,9 7,7 5,5 7,6 5,5 7,6 5,6 7,6 5,8 7,8 5,9 8 6,1 8 6,1 7,9 6 7,7 6 7,4 5,9 6,9 5,5 6,7 5,6 6,5 5,4 6,4 5,2 6,7 5,2 6,8 5,2 6,9 5,5 6,9 5,8 6,7 5,8 6,4 5,5 6,2 5,3 5,9 5,1 6,1 5,2 6,7 5,8 6,8 5,8 6,6 5,5 6,4 5 6,4 4,9 6,7 5,3 7,1 6,1 7,1 6,5 6,9 6,8 6,4 6,6 6 6,4 6 6,4

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 X[t] = + 5.84831039520222 + 0.448276619582171Y[t] + 0.0237866131382666M1[t] -0.117936154009505M2[t] -0.224486583115485M3[t] -0.12758893331283M4[t] -0.015518945468392M5[t] -0.0248281013158757M6[t] -0.122412998030213M7[t] -0.306204700394689M8[t] -0.449996402759166M9[t] -0.386891507948712M10[t] -0.0756910638313447M11[t] -0.0334495708940197t + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 5.84831039520222 0.774291 7.5531 0 0 Y 0.448276619582171 0.122045 3.673 0.000646 0.000323 M1 0.0237866131382666 0.236036 0.1008 0.920187 0.460093 M2 -0.117936154009505 0.236405 -0.4989 0.620353 0.310177 M3 -0.224486583115485 0.236168 -0.9505 0.34703 0.173515 M4 -0.12758893331283 0.236315 -0.5399 0.591981 0.29599 M5 -0.015518945468392 0.244118 -0.0636 0.949599 0.4748 M6 -0.0248281013158757 0.249726 -0.0994 0.921255 0.460628 M7 -0.122412998030213 0.249527 -0.4906 0.626162 0.313081 M8 -0.306204700394689 0.244954 -1.25 0.21789 0.108945 M9 -0.449996402759166 0.241298 -1.8649 0.068873 0.034437 M10 -0.386891507948712 0.237591 -1.6284 0.110582 0.055291 M11 -0.0756910638313447 0.248838 -0.3042 0.762426 0.381213 t -0.0334495708940197 0.003317 -10.0854 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.920223356592374 R-squared 0.846811026018135 Adjusted R-squared 0.801550647341675 F-TEST (value) 18.7097644955976 F-TEST (DF numerator) 13 F-TEST (DF denominator) 44 p-value 8.81517081552374e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.351111530753584 Sum Squared Residuals 5.42428950923751 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 8.6627901408141 0.237209859185902 2 8.2 8.44279014081413 -0.242790140814131 3 7.6 8.25796247885591 -0.657962478855914 4 7.7 8.41106588168098 -0.711065881680984 5 8.1 8.57934162254784 -0.479341622547837 6 8.3 8.58141055776455 -0.281410557764549 7 8.3 8.40554842819798 -0.105548428197976 8 7.9 8.05382416906483 -0.153824169064829 9 7.8 7.87658289580633 -0.076582895806333 10 8 7.77175523384812 0.228244766151884 11 8.5 8.1391614309879 0.360838569012104 12 8.6 8.18140292392522 0.418597076074778 13 8.5 8.17173996616947 0.328260033830531 14 8 7.99656762812768 0.00343237187232173 15 7.8 7.85656762812768 -0.056567628127678 16 8 8.05449869291096 -0.054498692910965 17 8.2 8.26760209573603 -0.0676020957360353 18 8.3 8.31449869291097 -0.0144986929109648 19 8.2 8.22829188726083 -0.0282918872608263 20 8.1 8.01105061400233 0.0889493859976702 21 8 7.7441540168274 0.255845983172601 22 7.8 7.59449869291096 0.205501307089035 23 7.8 7.64811125634323 0.151888743656773 24 7.7 7.51104210144768 0.188957898552316 25 7.6 7.50137914369193 0.0986208563080689 26 7.6 7.37103446760836 0.228965532391643 27 7.6 7.3206897915248 0.279310208475209 28 7.8 7.42896553239164 0.371034467608356 29 8 7.5972412732585 0.402758726741504 30 8 7.55448254651699 0.445517453483008 31 7.9 7.37862041695042 0.521379583049582 32 7.7 7.16137914369192 0.538620856308078 33 7.4 6.93931020847521 0.460689791524791 34 6.9 6.78965488455878 0.110345115441225 35 6.7 7.11223341974034 -0.412233419740339 36 6.5 7.06481958876123 -0.564819588761231 37 6.4 6.96550130708904 -0.565501307089043 38 6.7 6.79032896904725 -0.090328969047252 39 6.8 6.65032896904725 0.149671030952747 40 6.9 6.84826003383054 0.0517399661694614 41 6.9 7.06136343665561 -0.161363436655608 42 6.7 7.0186047099141 -0.318604709914105 43 6.4 6.7530872564311 -0.353087256431097 44 6.2 6.44619065925617 -0.246190659256167 45 5.9 6.17929406208124 -0.279294062081236 46 6.1 6.25377704795589 -0.153777047955888 47 6.7 6.80049389292854 -0.100493892928537 48 6.8 6.84273538586586 -0.042735385865863 49 6.6 6.69858944223546 -0.098589442235459 50 6.4 6.29927879440258 0.100721205597419 51 6.4 6.11445113244436 0.285548867555635 52 6.7 6.35720985918587 0.342790140814131 53 7.1 6.79445157180202 0.305548428197976 54 7.1 6.93100349289339 0.168996507106611 55 6.9 6.93445201115968 -0.034452011159683 56 6.4 6.62755541398475 -0.227555413984753 57 6 6.36065881680982 -0.360658816809822 58 6 6.39031414072626 -0.390314140726257 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.126431160513478 0.252862321026957 0.873568839486522 18 0.141174429739971 0.282348859479943 0.858825570260029 19 0.151807444768731 0.303614889537463 0.848192555231269 20 0.0861039136991613 0.172207827398323 0.913896086300839 21 0.0433814834496858 0.0867629668993715 0.956618516550314 22 0.0405923924815952 0.0811847849631903 0.959407607518405 23 0.0877823662841692 0.175564732568338 0.912217633715831 24 0.0869880230815024 0.173976046163005 0.913011976918498 25 0.0641046757107843 0.128209351421569 0.935895324289216 26 0.0544185819617961 0.108837163923592 0.945581418038204 27 0.0781687422722486 0.156337484544497 0.921831257727751 28 0.111009741086210 0.222019482172421 0.88899025891379 29 0.0965843349273213 0.193168669854643 0.903415665072679 30 0.0749500339699477 0.149900067939895 0.925049966030052 31 0.0782850978189508 0.156570195637902 0.921714902181049 32 0.113823580532326 0.227647161064651 0.886176419467674 33 0.383007225410385 0.76601445082077 0.616992774589615 34 0.878692520762069 0.242614958475862 0.121307479237931 35 0.94766769029534 0.104664619409319 0.0523323097046596 36 0.9798066356593 0.0403867286813983 0.0201933643406991 37 0.984268200137132 0.0314635997257352 0.0157317998628676 38 0.965304621074648 0.0693907578507036 0.0346953789253518 39 0.948507027799715 0.102985944400569 0.0514929722002845 40 0.931103765123004 0.137792469753992 0.0688962348769959 41 0.851584758937722 0.296830482124555 0.148415241062278 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 2 0.08 NOK 10% type I error level 5 0.2 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/10rnh21258661337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/10rnh21258661337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/1ivby1258661337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/1ivby1258661337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/2kos71258661337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/2kos71258661337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/3h2e81258661337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/3h2e81258661337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/4rfvy1258661337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/4rfvy1258661337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/5v4lp1258661337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/5v4lp1258661337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/6y2ue1258661337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/6y2ue1258661337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/7q0ji1258661337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/7q0ji1258661337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/8wtkm1258661337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/8wtkm1258661337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/9zw701258661337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258661563dr5fzr6368uocdr/9zw701258661337.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