*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 26 Nov 2010 22:32:59 +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/27/t129081525409cp12syyysgjvy.htm/, Retrieved Sat, 27 Nov 2010 00:47:44 +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/27/t129081525409cp12syyysgjvy.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 «

4143 0 4429 0 5219 0 4929 0 5761 0 5592 0 4163 0 4962 0 5208 0 4755 0 4491 0 5732 0 5731 0 5040 0 6102 0 4904 0 5369 0 5578 0 4619 0 4731 0 5011 0 5299 0 4146 0 4625 0 4736 0 4219 0 5116 0 4205 0 4121 0 5103 1 4300 1 4578 1 3809 1 5657 1 4248 1 3830 1 4736 1 4839 1 4411 1 4570 1 4104 1 4801 1 3953 1 3828 1 4440 1 4026 1 4109 1 4785 1 3224 1 3552 1 3940 1 3913 1 3681 1 4309 1 3830 1 4143 1 4087 1 3818 1 3380 1 3430 1 3458 1 3970 1 5260 1 5024 1 5634 1 6549 1 4676 1

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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 4928.82758620690 -612.906533575318X[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) 4928.82758620690 120.417939 40.931 0 0 X -612.906533575318 159.895703 -3.8332 0.000288 0.000144 Multiple Linear Regression - Regression Statistics Multiple R 0.42938509090449 R-squared 0.184371556291057 Adjusted R-squared 0.171823426387843 F-TEST (value) 14.6931501118606 F-TEST (DF numerator) 1 F-TEST (DF denominator) 65 p-value 0.000287981500547541 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 648.470447711547 Sum Squared Residuals 27333404.9010889 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4143 4928.82758620689 -785.827586206892 2 4429 4928.8275862069 -499.827586206897 3 5219 4928.8275862069 290.172413793103 4 4929 4928.8275862069 0.172413793103402 5 5761 4928.8275862069 832.172413793103 6 5592 4928.8275862069 663.172413793103 7 4163 4928.8275862069 -765.827586206897 8 4962 4928.8275862069 33.1724137931034 9 5208 4928.8275862069 279.172413793103 10 4755 4928.8275862069 -173.827586206897 11 4491 4928.8275862069 -437.827586206897 12 5732 4928.8275862069 803.172413793103 13 5731 4928.8275862069 802.172413793103 14 5040 4928.8275862069 111.172413793103 15 6102 4928.8275862069 1173.17241379310 16 4904 4928.8275862069 -24.8275862068966 17 5369 4928.8275862069 440.172413793103 18 5578 4928.8275862069 649.172413793103 19 4619 4928.8275862069 -309.827586206897 20 4731 4928.8275862069 -197.827586206897 21 5011 4928.8275862069 82.1724137931034 22 5299 4928.8275862069 370.172413793103 23 4146 4928.8275862069 -782.827586206897 24 4625 4928.8275862069 -303.827586206897 25 4736 4928.8275862069 -192.827586206897 26 4219 4928.8275862069 -709.827586206897 27 5116 4928.8275862069 187.172413793103 28 4205 4928.8275862069 -723.827586206897 29 4121 4928.8275862069 -807.827586206897 30 5103 4315.92105263158 787.078947368421 31 4300 4315.92105263158 -15.9210526315790 32 4578 4315.92105263158 262.078947368421 33 3809 4315.92105263158 -506.921052631579 34 5657 4315.92105263158 1341.07894736842 35 4248 4315.92105263158 -67.921052631579 36 3830 4315.92105263158 -485.921052631579 37 4736 4315.92105263158 420.078947368421 38 4839 4315.92105263158 523.078947368421 39 4411 4315.92105263158 95.078947368421 40 4570 4315.92105263158 254.078947368421 41 4104 4315.92105263158 -211.921052631579 42 4801 4315.92105263158 485.078947368421 43 3953 4315.92105263158 -362.921052631579 44 3828 4315.92105263158 -487.921052631579 45 4440 4315.92105263158 124.078947368421 46 4026 4315.92105263158 -289.921052631579 47 4109 4315.92105263158 -206.921052631579 48 4785 4315.92105263158 469.078947368421 49 3224 4315.92105263158 -1091.92105263158 50 3552 4315.92105263158 -763.921052631579 51 3940 4315.92105263158 -375.921052631579 52 3913 4315.92105263158 -402.921052631579 53 3681 4315.92105263158 -634.921052631579 54 4309 4315.92105263158 -6.92105263157897 55 3830 4315.92105263158 -485.921052631579 56 4143 4315.92105263158 -172.921052631579 57 4087 4315.92105263158 -228.921052631579 58 3818 4315.92105263158 -497.921052631579 59 3380 4315.92105263158 -935.921052631579 60 3430 4315.92105263158 -885.921052631579 61 3458 4315.92105263158 -857.921052631579 62 3970 4315.92105263158 -345.921052631579 63 5260 4315.92105263158 944.078947368421 64 5024 4315.92105263158 708.078947368421 65 5634 4315.92105263158 1318.07894736842 66 6549 4315.92105263158 2233.07894736842 67 4676 4315.92105263158 360.078947368421 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.7091788959996 0.581642208000799 0.290821104000399 6 0.676834585895131 0.646330828209738 0.323165414104869 7 0.702702759176838 0.594594481646324 0.297297240823162 8 0.582382452766156 0.835235094467687 0.417617547233844 9 0.482888243451887 0.965776486903773 0.517111756548113 10 0.376207566691325 0.752415133382651 0.623792433308675 11 0.316182765131223 0.632365530262447 0.683817234868777 12 0.377394945168196 0.754789890336392 0.622605054831804 13 0.415040557527508 0.830081115055016 0.584959442472492 14 0.325251433182128 0.650502866364256 0.674748566817872 15 0.493426238166391 0.986852476332783 0.506573761833609 16 0.410874373688836 0.821748747377672 0.589125626311164 17 0.357531019762217 0.715062039524434 0.642468980237783 18 0.348325380482981 0.696650760965963 0.651674619517019 19 0.307375205829824 0.614750411659649 0.692624794170176 20 0.25505652488145 0.5101130497629 0.74494347511855 21 0.200763460155192 0.401526920310384 0.799236539844808 22 0.173275276161143 0.346550552322286 0.826724723838857 23 0.211042772230083 0.422085544460166 0.788957227769917 24 0.174803755989369 0.349607511978738 0.825196244010631 25 0.138101250056964 0.276202500113927 0.861898749943036 26 0.143804716952915 0.287609433905829 0.856195283047085 27 0.122656738746581 0.245313477493163 0.877343261253419 28 0.123794432922149 0.247588865844297 0.876205567077851 29 0.128308846748679 0.256617693497359 0.87169115325132 30 0.110435428589801 0.220870857179601 0.8895645714102 31 0.0946072785427539 0.189214557085508 0.905392721457246 32 0.0694158233640045 0.138831646728009 0.930584176635995 33 0.070511226604095 0.14102245320819 0.929488773395905 34 0.151976862238057 0.303953724476114 0.848023137761943 35 0.121314751975003 0.242629503950006 0.878685248024997 36 0.117620547774751 0.235241095549502 0.882379452225249 37 0.094043889674829 0.188087779349658 0.90595611032517 38 0.078878421163086 0.157756842326172 0.921121578836914 39 0.0566954311066932 0.113390862213386 0.943304568893307 40 0.0407157135549407 0.0814314271098813 0.95928428644506 41 0.0300884472901759 0.0601768945803518 0.969911552709824 42 0.0239268686494418 0.0478537372988837 0.976073131350558 43 0.0188121076652083 0.0376242153304165 0.981187892334792 44 0.0160440804213925 0.0320881608427851 0.983955919578607 45 0.0101587471261795 0.0203174942523589 0.98984125287382 46 0.00685851648242198 0.0137170329648440 0.993141483517578 47 0.00425440510028575 0.0085088102005715 0.995745594899714 48 0.00316175258275038 0.00632350516550077 0.99683824741725 49 0.0072269348808452 0.0144538697616904 0.992773065119155 50 0.00788452841472898 0.0157690568294580 0.99211547158527 51 0.00524573971692708 0.0104914794338542 0.994754260283073 52 0.00349680094847543 0.00699360189695087 0.996503199051525 53 0.00311118761478018 0.00622237522956037 0.99688881238522 54 0.00161016569755772 0.00322033139511543 0.998389834302442 55 0.00114432762027467 0.00228865524054935 0.998855672379725 56 0.00058432339386578 0.00116864678773156 0.999415676606134 57 0.000300041342704096 0.000600082685408192 0.999699958657296 58 0.000223382146174107 0.000446764292348214 0.999776617853826 59 0.000630532708501073 0.00126106541700215 0.9993694672915 60 0.00249670778039643 0.00499341556079285 0.997503292219604 61 0.0214894113582672 0.0429788227165345 0.978510588641733 62 0.0899912773923654 0.179982554784731 0.910008722607635 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.189655172413793 NOK 5% type I error level 20 0.344827586206897 NOK 10% type I error level 22 0.379310344827586 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/1014wa1290810771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/1014wa1290810771.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/1clhg1290810771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/1clhg1290810771.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/25cy11290810771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/25cy11290810771.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/35cy11290810771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/35cy11290810771.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/45cy11290810771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/45cy11290810771.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/55cy11290810771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/55cy11290810771.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/6ylf41290810771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/6ylf41290810771.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/7qdfp1290810771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/7qdfp1290810771.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/8qdfp1290810771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/8qdfp1290810771.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/9qdfp1290810771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t129081525409cp12syyysgjvy/9qdfp1290810771.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