WS 7 (1)

*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: Tue, 23 Nov 2010 08:40:45 +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/23/t1290501548rbpchrjiuxu31bn.htm/, Retrieved Tue, 23 Nov 2010 09:39:18 +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/23/t1290501548rbpchrjiuxu31bn.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 «

1.39 1.08 1.34 1.12 1.33 1.12 1.3 1.16 1.28 1.16 1.29 1.16 1.29 1.16 1.28 1.15 1.27 1.17 1.26 1.16 1.29 1.19 1.36 1.13 1.33 1.14 1.35 1.13 1.31 1.16 1.3 1.17 1.32 1.14 1.33 1.14 1.36 1.11 1.35 1.12 1.4 1.08 1.41 1.07 1.4 1.09 1.4 1.08 1.4 1.08 1.41 1.08 1.4 1.09 1.39 1.08 1.41 1.07 1.42 1.07 1.43 1.07 1.42 1.08 1.42 1.07 1.43 1.06 1.43 1.06 1.43 1.06 1.46 1.04 1.47 1.03 1.47 1.03 1.46 1.04 1.47 1.03 1.49 1.02 1.5 1.01 1.47 1.03 1.48 1.02 1.49 1.01 1.49 1.02 1.5 1.01 1.48 1.02 1.46 1.03 1.43 1.04 1.44 1.04 1.43 1.03

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 39 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation eu/us[t] = + 2.81747388264865 -1.31118267745398`us/ch`[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) 2.81747388264865 0.039141 71.9828 0 0 `us/ch` -1.31118267745398 0.03603 -36.3916 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.981284059913598 R-squared 0.962918406240514 Adjusted R-squared 0.962191316166798 F-TEST (value) 1324.34541613258 F-TEST (DF numerator) 1 F-TEST (DF denominator) 51 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0136782808318764 Sum Squared Residuals 0.00954186369229955 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.39 1.40139659099835 -0.0113965909983464 2 1.34 1.34894928390019 -0.008949283900191 3 1.33 1.34894928390019 -0.0189492839001910 4 1.3 1.29650197680203 0.00349802319796813 5 1.28 1.29650197680203 -0.0165019768020319 6 1.29 1.29650197680203 -0.00650197680203188 7 1.29 1.29650197680203 -0.00650197680203188 8 1.28 1.30961380357657 -0.0296138035765717 9 1.27 1.28339015002749 -0.0133901500274921 10 1.26 1.29650197680203 -0.0365019768020319 11 1.29 1.25716649647841 0.0328335035215877 12 1.36 1.33583745712565 0.0241625428743486 13 1.33 1.32272563035111 0.00727436964888843 14 1.35 1.33583745712565 0.0141625428743486 15 1.31 1.29650197680203 0.0134980231979681 16 1.3 1.28339015002749 0.0166098499725080 17 1.32 1.32272563035111 -0.00272563035111158 18 1.33 1.32272563035111 0.00727436964888843 19 1.36 1.36206111067473 -0.00206111067473081 20 1.35 1.34894928390019 0.00105071609980903 21 1.4 1.40139659099835 -0.00139659099835056 22 1.41 1.41450841777289 -0.00450841777289041 23 1.4 1.38828476422381 0.0117152357761893 24 1.4 1.40139659099835 -0.00139659099835056 25 1.4 1.40139659099835 -0.00139659099835056 26 1.41 1.40139659099835 0.00860340900164945 27 1.4 1.38828476422381 0.0117152357761893 28 1.39 1.40139659099835 -0.0113965909983506 29 1.41 1.41450841777289 -0.00450841777289041 30 1.42 1.41450841777289 0.0054915822271096 31 1.43 1.41450841777289 0.0154915822271096 32 1.42 1.40139659099835 0.0186034090016495 33 1.42 1.41450841777289 0.0054915822271096 34 1.43 1.42762024454743 0.00237975545256975 35 1.43 1.42762024454743 0.00237975545256975 36 1.43 1.42762024454743 0.00237975545256975 37 1.46 1.45384389809651 0.00615610190349007 38 1.47 1.46695572487105 0.00304427512895021 39 1.47 1.46695572487105 0.00304427512895021 40 1.46 1.45384389809651 0.00615610190349007 41 1.47 1.46695572487105 0.00304427512895021 42 1.49 1.48006755164559 0.00993244835441039 43 1.5 1.49317937842013 0.00682062157987055 44 1.47 1.46695572487105 0.00304427512895021 45 1.48 1.48006755164559 -6.75516455896238e-05 46 1.49 1.49317937842013 -0.00317937842012946 47 1.49 1.48006755164559 0.00993244835441039 48 1.5 1.49317937842013 0.00682062157987055 49 1.48 1.48006755164559 -6.75516455896238e-05 50 1.46 1.46695572487105 -0.0069557248710498 51 1.43 1.45384389809651 -0.0238438980965100 52 1.44 1.45384389809651 -0.0138438980965099 53 1.43 1.46695572487105 -0.0369557248710498 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.313746225999553 0.627492451999106 0.686253774000447 6 0.171315934096949 0.342631868193897 0.828684065903051 7 0.0861208821893242 0.172241764378648 0.913879117810676 8 0.323557837045587 0.647115674091173 0.676442162954413 9 0.250287600178877 0.500575200357754 0.749712399821123 10 0.770540113343542 0.458919773312915 0.229459886656458 11 0.99286726981463 0.0142654603707388 0.00713273018536942 12 0.999377884814268 0.00124423037146334 0.000622115185731669 13 0.999106550553382 0.00178689889323586 0.000893449446617928 14 0.999241171114927 0.00151765777014608 0.000758828885073039 15 0.999027748391961 0.00194450321607752 0.000972251608038762 16 0.998932287373444 0.00213542525311212 0.00106771262655606 17 0.998095215270129 0.00380956945974258 0.00190478472987129 18 0.996853023402968 0.00629395319406446 0.00314697659703223 19 0.9947004238804 0.0105991522392001 0.00529957611960006 20 0.991219366892004 0.0175612662159911 0.00878063310799553 21 0.986140138887108 0.0277197222257835 0.0138598611128917 22 0.979106094989216 0.0417878100215682 0.0208939050107841 23 0.97582740015438 0.048345199691239 0.0241725998456195 24 0.962979384696863 0.0740412306062742 0.0370206153031371 25 0.945407859700664 0.109184280598672 0.0545921402993361 26 0.927741832422426 0.144516335155148 0.0722581675775742 27 0.91317636560864 0.173647268782719 0.0868236343913595 28 0.9131423906896 0.173715218620799 0.0868576093103997 29 0.888092116677861 0.223815766644278 0.111907883322139 30 0.847476785926414 0.305046428147173 0.152523214073586 31 0.843273341621624 0.313453316756752 0.156726658378376 32 0.87567110067609 0.248657798647822 0.124328899323911 33 0.84223770201654 0.31552459596692 0.15776229798346 34 0.794987995870136 0.410024008259729 0.205012004129864 35 0.750126923054113 0.499746153891774 0.249873076945887 36 0.736044946847992 0.527910106304015 0.263955053152008 37 0.732474096266359 0.535051807467281 0.267525903733641 38 0.672850581656471 0.654298836687058 0.327149418343529 39 0.611288518268456 0.777422963463088 0.388711481731544 40 0.697699373021766 0.604601253956468 0.302300626978234 41 0.676607795578726 0.646784408842547 0.323392204421274 42 0.651887027883729 0.696225944232542 0.348112972116271 43 0.543684817525358 0.912630364949285 0.456315182474642 44 0.559887307457068 0.880225385085864 0.440112692542932 45 0.445737403924325 0.891474807848651 0.554262596075675 46 0.38487677793764 0.76975355587528 0.61512322206236 47 0.36648118615386 0.73296237230772 0.63351881384614 48 0.234382347510309 0.468764695020617 0.765617652489691 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.159090909090909 NOK 5% type I error level 13 0.295454545454545 NOK 10% type I error level 14 0.318181818181818 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/10vwv41290501605.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/10vwv41290501605.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/16vgs1290501605.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/16vgs1290501605.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/2hmxv1290501605.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/2hmxv1290501605.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/3hmxv1290501605.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/3hmxv1290501605.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/4hmxv1290501605.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/4hmxv1290501605.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/5avwg1290501605.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/5avwg1290501605.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/6avwg1290501605.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/6avwg1290501605.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/7k4d11290501605.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/7k4d11290501605.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/8k4d11290501605.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/8k4d11290501605.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/9vwv41290501605.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290501548rbpchrjiuxu31bn/9vwv41290501605.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