Review

*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, 01 Dec 2010 18:10:05 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3.htm/, Retrieved Wed, 01 Dec 2010 19:08:28 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3.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 «

102.38 0 102.37 101.76 102.86 0 102.38 102.37 102.87 0 102.86 102.38 102.92 0 102.87 102.86 102.95 0 102.92 102.87 103.02 0 102.95 102.92 104.08 0 103.02 102.95 104.16 0 104.08 103.02 104.24 0 104.16 104.08 104.33 0 104.24 104.16 104.73 0 104.33 104.24 104.86 0 104.73 104.33 105.03 0 104.86 104.73 105.62 0 105.03 104.86 105.63 0 105.62 105.03 105.63 0 105.63 105.62 105.94 0 105.63 105.63 106.61 0 105.94 105.63 107.69 0 106.61 105.94 107.78 0 107.69 106.61 107.93 0 107.78 107.69 108.48 0 107.93 107.78 108.14 0 108.48 107.93 108.48 0 108.14 108.48 108.48 0 108.48 108.14 108.89 0 108.48 108.48 108.93 0 108.89 108.48 109.21 0 108.93 108.89 109.47 0 109.21 108.93 109.8 0 109.47 109.21 111.73 0 109.8 109.47 111.85 0 111.73 109.8 112.12 0 111.85 111.73 112.15 0 112.12 111.85 112.17 0 112.15 112.12 112.67 1 112.17 112.15 112.8 1 112.67 112.17 113.44 1 112.8 112.67 113.53 1 113.44 112.8 114.53 1 113.53 113.44 114.51 1 114.53 113.53 115.05 1 114.51 114.53 116.67 1 115.05 114.51 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Vrijetijdsbesteding[t] = + 23.2386693776659 + 0.328012898057582x[t] + 0.706706849753679`y-1`[t] + 0.0657834257010602`y-2`[t] -0.171640512276879M1[t] + 0.205094115152176M2[t] -0.190683624134502M3[t] + 0.0147835749451843M4[t] -0.127753109719908M5[t] + 0.0443784760656002M6[t] + 1.02445797837301M7[t] + 0.1945017340139M8[t] -0.00732604085153967M9[t] + 0.0380826032926670M10[t] -0.144087846353902M11[t] + 0.06890686556891t + 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) 23.2386693776659 7.472222 3.11 0.003442 0.001721 x 0.328012898057582 0.154567 2.1221 0.040068 0.020034 `y-1` 0.706706849753679 0.148857 4.7475 2.6e-05 1.3e-05 `y-2` 0.0657834257010602 0.139888 0.4703 0.640725 0.320363 M1 -0.171640512276879 0.154281 -1.1125 0.272557 0.136279 M2 0.205094115152176 0.145419 1.4104 0.166162 0.083081 M3 -0.190683624134502 0.160855 -1.1854 0.242839 0.121419 M4 0.0147835749451843 0.145301 0.1017 0.919468 0.459734 M5 -0.127753109719908 0.15189 -0.8411 0.405299 0.202649 M6 0.0443784760656002 0.147301 0.3013 0.764763 0.382382 M7 1.02445797837301 0.154241 6.6419 0 0 M8 0.1945017340139 0.237914 0.8175 0.418468 0.209234 M9 -0.00732604085153967 0.169158 -0.0433 0.965671 0.482835 M10 0.0380826032926670 0.163564 0.2328 0.81708 0.40854 M11 -0.144087846353902 0.162537 -0.8865 0.380652 0.190326 t 0.06890686556891 0.021924 3.1429 0.003147 0.001573 Multiple Linear Regression - Regression Statistics Multiple R 0.999427087849627 R-squared 0.998854503927587 Adjusted R-squared 0.998424942900431 F-TEST (value) 2325.29126430064 F-TEST (DF numerator) 15 F-TEST (DF denominator) 40 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.216308063638897 Sum Squared Residuals 1.87156713580837 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 102.38 102.175637339582 0.204362660418009 2 102.86 102.668473790755 0.191526209244890 3 102.87 102.681480039176 0.188519960823865 4 102.92 102.994497216659 -0.0744972166587715 5 102.95 102.956860574307 -0.0068605743072809 6 103.02 103.222389402439 -0.202389402439369 7 104.08 104.322818752569 -0.242818752569475 8 104.16 104.315483474317 -0.155483474317248 9 104.24 104.308829544244 -0.0688295442441368 10 104.33 104.484944275994 -0.154944275993627 11 104.73 104.440546982450 0.289453017550119 12 104.86 104.942144942587 -0.082144942587269 13 105.03 104.957596556628 0.0724034433723023 14 105.62 105.531930059425 0.0880699405750773 15 105.63 105.633199409431 -0.00319940943101672 16 105.63 105.953452763741 -0.323452763740769 17 105.94 105.880480778902 0.0595192210984051 18 106.61 106.340598353680 0.269401646320347 19 107.69 107.883471172858 -0.193471172858273 20 107.78 107.929740087022 -0.149740087021747 21 107.93 107.931468893960 -0.00146889396019063 22 108.48 108.157710939449 0.322289060550539 23 108.14 108.443003636591 -0.303003636591486 24 108.48 108.451898903734 0.0281010962663753 25 108.48 108.567079221204 -0.0870792212035485 26 108.89 109.035087078940 -0.145087078939877 27 108.93 108.997966013621 -0.0679660136211085 28 109.21 109.327579556797 -0.117579556797304 29 109.47 109.454458992660 0.0155410073398196 30 109.8 109.897660584147 -0.0976605841468559 31 111.73 111.196963903124 0.53303609687584 32 111.85 111.821567274840 0.0284327251600797 33 112.12 111.900413199117 0.219586800883138 34 112.15 112.213433569348 -0.0634335693476048 35 112.17 112.139132715702 0.0308672842981526 36 112.67 112.696247965448 -0.0262479654483509 37 112.8 112.948183412131 -0.148183412131247 38 113.44 113.518588508448 -0.0785885084477151 39 113.53 113.652561863913 -0.122561863913436 40 114.53 114.032640937489 0.497359062511456 41 114.51 114.671638476459 -0.161638476459133 42 115.05 114.964326216520 0.085673783480452 43 116.67 116.393618614749 0.276381385251174 44 117.07 116.812957382438 0.257042617561835 45 116.92 117.069288362679 -0.149288362678811 46 117 117.103911215209 -0.103911215209306 47 117.02 117.037316665257 -0.0173166652567858 48 117.35 117.269708188231 0.0802918117692447 49 117.36 117.401503470456 -0.0415034704555154 50 117.82 117.875920562432 -0.0559205624323758 51 117.88 117.874792673858 0.00520732614169591 52 118.24 118.221829525315 0.0181704746853892 53 118.5 118.406561177672 0.093438822328189 54 118.8 118.855025443215 -0.0550254432145738 55 119.76 120.133127556699 -0.373127556699266 56 120.09 120.070251781383 0.0197482186170809 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.59327597184291 0.81344805631418 0.40672402815709 20 0.461137030999229 0.922274061998459 0.538862969000771 21 0.316776325507231 0.633552651014463 0.683223674492769 22 0.386520979425882 0.773041958851765 0.613479020574118 23 0.758542288985018 0.482915422029965 0.241457711014982 24 0.66034310465233 0.67931379069534 0.33965689534767 25 0.583097518635039 0.833804962729921 0.416902481364961 26 0.528490627029093 0.943018745941813 0.471509372970907 27 0.431548809099579 0.863097618199158 0.568451190900421 28 0.587648621771383 0.824702756457234 0.412351378228617 29 0.604914950745774 0.790170098508452 0.395085049254226 30 0.593942568360867 0.812114863278265 0.406057431639133 31 0.848814559312246 0.302370881375507 0.151185440687754 32 0.794269493746678 0.411461012506645 0.205730506253322 33 0.721252462475384 0.557495075049233 0.278747537524616 34 0.645879462399648 0.708241075200704 0.354120537600352 35 0.501839969889391 0.996320060221217 0.498160030110609 36 0.351417850372815 0.70283570074563 0.648582149627185 37 0.215683697632834 0.431367395265669 0.784316302367166 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/10vazi1291226997.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/10vazi1291226997.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/16rk71291226997.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/16rk71291226997.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/2hijr1291226997.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/2hijr1291226997.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/3hijr1291226997.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/3hijr1291226997.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/4hijr1291226997.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/4hijr1291226997.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/5hijr1291226997.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/5hijr1291226997.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/6r9ic1291226997.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/6r9ic1291226997.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/7kjhx1291226997.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/7kjhx1291226997.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/8kjhx1291226997.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/8kjhx1291226997.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/9kjhx1291226997.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291226898e3rjil450m2rge3/9kjhx1291226997.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