*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, 18 Nov 2009 10:06:03 -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/18/t1258564008btnboe7p57vzyaz.htm/, Retrieved Wed, 18 Nov 2009 18:07:00 +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/18/t1258564008btnboe7p57vzyaz.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 1.6 8.8 1.8 8.3 1.6 7.5 1.5 7.2 1.5 7.4 1.3 8.8 1.4 9.3 1.4 9.3 1.3 8.7 1.3 8.2 1.2 8.3 1.1 8.5 1.4 8.6 1.2 8.5 1.5 8.2 1.1 8.1 1.3 7.9 1.5 8.6 1.1 8.7 1.4 8.7 1.3 8.5 1.5 8.4 1.6 8.5 1.7 8.7 1.1 8.7 1.6 8.6 1.3 8.5 1.7 8.3 1.6 8 1.7 8.2 1.9 8.1 1.8 8.1 1.9 8 1.6 7.9 1.5 7.9 1.6 8 1.6 8 1.7 7.9 2 8 2 7.7 1.9 7.2 1.7 7.5 1.8 7.3 1.9 7 1.7 7 2 7 2.1 7.2 2.4 7.3 2.5 7.1 2.5 6.8 2.6 6.4 2.2 6.1 2.5 6.5 2.8 7.7 2.8 7.9 2.9 7.5 3 6.9 3.1 6.6 2.9 6.9 2.7

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation inflatie[t] = + 5.7573134816754 -0.500081806282723graad[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) 5.7573134816754 0.542603 10.6105 0 0 graad -0.500081806282723 0.06833 -7.3187 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.692901934975365 R-squared 0.480113091492605 Adjusted R-squared 0.471149524104546 F-TEST (value) 53.5627246058552 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 8.54115778103903e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.390122286645665 Sum Squared Residuals 8.82733311518324 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.6 1.30658540575918 0.293414594240823 2 1.8 1.35659358638743 0.443406413612566 3 1.6 1.60663448952879 -0.00663448952879472 4 1.5 2.00669993455497 -0.506699934554974 5 1.5 2.15672447643979 -0.656724476439791 6 1.3 2.05670811518325 -0.756708115183246 7 1.4 1.35659358638743 0.0434064136125667 8 1.4 1.10655268324607 0.293447316753928 9 1.3 1.10655268324607 0.193447316753929 10 1.3 1.40660176701571 -0.106601767015706 11 1.2 1.65664267015707 -0.456642670157068 12 1.1 1.60663448952879 -0.506634489528795 13 1.4 1.50661812827225 -0.106618128272251 14 1.2 1.45660994764398 -0.256609947643978 15 1.5 1.50661812827225 -0.00661812827225051 16 1.1 1.65664267015707 -0.556642670157068 17 1.3 1.70665085078534 -0.40665085078534 18 1.5 1.80666721204188 -0.306667212041884 19 1.1 1.45660994764398 -0.356609947643978 20 1.4 1.40660176701571 -0.00660176701570632 21 1.3 1.40660176701571 -0.106601767015706 22 1.5 1.50661812827225 -0.00661812827225051 23 1.6 1.55662630890052 0.0433736910994774 24 1.7 1.50661812827225 0.193381871727749 25 1.1 1.40660176701571 -0.306601767015706 26 1.6 1.40660176701571 0.193398232984294 27 1.3 1.45660994764398 -0.156609947643978 28 1.7 1.50661812827225 0.193381871727749 29 1.6 1.60663448952879 -0.00663448952879472 30 1.7 1.75665903141361 -0.0566590314136122 31 1.9 1.65664267015707 0.243357329842932 32 1.8 1.70665085078534 0.09334914921466 33 1.9 1.70665085078534 0.19334914921466 34 1.6 1.75665903141361 -0.156659031413612 35 1.5 1.80666721204188 -0.306667212041884 36 1.6 1.80666721204188 -0.206667212041884 37 1.6 1.75665903141361 -0.156659031413612 38 1.7 1.75665903141361 -0.0566590314136122 39 2 1.80666721204188 0.193332787958116 40 2 1.75665903141361 0.243340968586388 41 1.9 1.90668357329843 -0.00668357329842911 42 1.7 2.15672447643979 -0.456724476439791 43 1.8 2.00669993455497 -0.206699934554974 44 1.9 2.10671629581152 -0.206716295811519 45 1.7 2.25674083769634 -0.556740837696335 46 2 2.25674083769634 -0.256740837696335 47 2.1 2.25674083769634 -0.156740837696335 48 2.4 2.15672447643979 0.243275523560209 49 2.5 2.10671629581152 0.393283704188482 50 2.5 2.20673265706806 0.293267342931937 51 2.6 2.35675719895288 0.24324280104712 52 2.2 2.55678992146597 -0.356789921465969 53 2.5 2.70681446335079 -0.206814463350786 54 2.8 2.50678174083770 0.293218259162303 55 2.8 1.90668357329843 0.89331642670157 56 2.9 1.80666721204188 1.09333278795812 57 3 2.00669993455497 0.993300065445026 58 3.1 2.30674901832461 0.793250981675393 59 2.9 2.45677356020942 0.443226439790575 60 2.7 2.30674901832461 0.393250981675393 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0168300578963813 0.0336601157927627 0.983169942103619 6 0.0174311597824925 0.0348623195649850 0.982568840217507 7 0.0239728775968421 0.0479457551936843 0.976027122403158 8 0.0203298527906185 0.040659705581237 0.979670147209382 9 0.0186706092278559 0.0373412184557118 0.981329390772144 10 0.0121445106088978 0.0242890212177956 0.987855489391102 11 0.0131426002946508 0.0262852005893016 0.98685739970535 12 0.0210848729183832 0.0421697458367663 0.978915127081617 13 0.0103121618079682 0.0206243236159364 0.989687838192032 14 0.00781324333056755 0.0156264866611351 0.992186756669432 15 0.0040022274985321 0.0080044549970642 0.995997772501468 16 0.00572719759652053 0.0114543951930411 0.99427280240348 17 0.00350795923190956 0.00701591846381912 0.99649204076809 18 0.00221217504118104 0.00442435008236207 0.997787824958819 19 0.00271063062135205 0.0054212612427041 0.997289369378648 20 0.00131618637994298 0.00263237275988596 0.998683813620057 21 0.000672121507244338 0.00134424301448868 0.999327878492756 22 0.000362450972046442 0.000724901944092883 0.999637549027954 23 0.000267368143706141 0.000534736287412282 0.999732631856294 24 0.000308188387292328 0.000616376774584656 0.999691811612708 25 0.000399771582034562 0.000799543164069124 0.999600228417965 26 0.000271841052124205 0.00054368210424841 0.999728158947876 27 0.000155861555115762 0.000311723110231524 0.999844138444884 28 0.000155432960374423 0.000310865920748846 0.999844567039626 29 0.000100775002932878 0.000201550005865756 0.999899224997067 30 9.33516591240936e-05 0.000186703318248187 0.999906648340876 31 0.000207780119549703 0.000415560239099406 0.99979221988045 32 0.0002116978427644 0.0004233956855288 0.999788302157236 33 0.000291364944340292 0.000582729888680584 0.99970863505566 34 0.000184564527405341 0.000369129054810683 0.999815435472595 35 0.000148649309678246 0.000297298619356492 0.999851350690322 36 0.000116772114238006 0.000233544228476011 0.999883227885762 37 0.000100089653803875 0.000200179307607751 0.999899910346196 38 9.73333001080194e-05 0.000194666600216039 0.999902666699892 39 0.000170023326422777 0.000340046652845554 0.999829976673577 40 0.00026401540070706 0.00052803080141412 0.999735984599293 41 0.00033034789961764 0.00066069579923528 0.999669652100382 42 0.000733762966888625 0.00146752593377725 0.999266237033111 43 0.00189175364912669 0.00378350729825338 0.998108246350873 44 0.00491431350839403 0.00982862701678805 0.995085686491606 45 0.0434025645287109 0.0868051290574217 0.95659743547129 46 0.131030393977846 0.262060787955692 0.868969606022154 47 0.32696122650702 0.65392245301404 0.67303877349298 48 0.477268580686862 0.954537161373723 0.522731419313138 49 0.60228741309326 0.795425173813481 0.397712586906740 50 0.666910001255248 0.666179997489505 0.333089998744752 51 0.623093537781672 0.753812924436657 0.376906462218328 52 0.881970868396418 0.236058263207164 0.118029131603582 53 0.921998361546548 0.156003276906905 0.0780016384534524 54 0.871681857407541 0.256636285184917 0.128318142592459 55 0.828718032256892 0.342563935486215 0.171281967743108 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 29 0.568627450980392 NOK 5% type I error level 40 0.784313725490196 NOK 10% type I error level 41 0.80392156862745 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/10108t1258563958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/10108t1258563958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/1ajh71258563958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/1ajh71258563958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/2w2j31258563958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/2w2j31258563958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/33vvu1258563958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/33vvu1258563958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/4wlz91258563958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/4wlz91258563958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/59hf41258563958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/59hf41258563958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/6ctx61258563958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/6ctx61258563958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/7lz1h1258563958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/7lz1h1258563958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/80ybe1258563958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/80ybe1258563958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/9kpyz1258563958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564008btnboe7p57vzyaz/9kpyz1258563958.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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