DSHW-WS7-MiltReg.T

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 20 Nov 2009 09:13:36 -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/20/t1258733685vowvbwn3vzililt.htm/, Retrieved Fri, 20 Nov 2009 17:14:58 +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/20/t1258733685vowvbwn3vzililt.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:

SDHW, DSHW

Dataseries X:

» Textbox « » Textfile « » CSV «

2,0 0,0 2,0 1,7 1,6 1,4 2,1 0,0 2,0 2,0 1,7 1,6 2,5 0,0 2,1 2,0 2,0 1,7 2,5 0,0 2,5 2,1 2,0 2,0 2,6 0,0 2,5 2,5 2,1 2,0 2,7 0,0 2,6 2,5 2,5 2,1 3,7 0,0 2,7 2,6 2,5 2,5 4,0 0,0 3,7 2,7 2,6 2,5 5,0 0,0 4,0 3,7 2,7 2,6 5,1 0,0 5,0 4,0 3,7 2,7 5,1 0,0 5,1 5,0 4,0 3,7 5,0 0,0 5,1 5,1 5,0 4,0 5,1 0,0 5,0 5,1 5,1 5,0 4,7 0,0 5,1 5,0 5,1 5,1 4,5 0,0 4,7 5,1 5,0 5,1 4,5 0,0 4,5 4,7 5,1 5,0 4,6 0,0 4,5 4,5 4,7 5,1 4,6 0,0 4,6 4,5 4,5 4,7 4,6 0,0 4,6 4,6 4,5 4,5 4,6 0,0 4,6 4,6 4,6 4,5 5,3 0,0 4,6 4,6 4,6 4,6 5,4 0,0 5,3 4,6 4,6 4,6 5,3 0,0 5,4 5,3 4,6 4,6 5,2 0,0 5,3 5,4 5,3 4,6 5,0 0,0 5,2 5,3 5,4 5,3 4,2 0,0 5,0 5,2 5,3 5,4 4,3 0,0 4,2 5,0 5,2 5,3 4,3 0,0 4,3 4,2 5,0 5,2 4,3 0,0 4,3 4,3 4,2 5,0 4,0 0,0 4,3 4,3 4,3 4,2 4,0 0,0 4,0 4,3 4,3 4,3 4,1 0,0 4,0 4,0 4,3 4,3 4,4 0,0 4,1 4,0 4,0 4,3 3,6 0,0 4,4 4,1 4,0 4,0 3,7 0,0 3,6 4,4 4,1 4,0 3,8 0,0 3,7 3,6 4,4 4,1 3,3 0,0 3,8 3,7 3,6 4,4 3,3 0,0 3,3 3,8 3,7 3,6 3,3 0,0 3,3 3,3 3,8 3,7 3,5 0,0 3,3 3,3 3,3 3,8 3,3 0,0 3,5 3,3 3,3 3,3 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 IndGez[t] = + 0.680001490026424 -0.109916882560442InvlMex[t] + 0.829083916174935`Yt-1`[t] + 0.0406279848806237`Yt-2`[t] + 0.102037061011512`Yt-3`[t] -0.109824058538842`Yt-4`[t] -0.199444656948999M1[t] -0.252551856372627M2[t] -0.2362239526388M3[t] -0.0226862977284185M4[t] -0.0745510928749468M5[t] -0.161684153029778M6[t] + 0.160657728882051M7[t] + 0.0429349888681616M8[t] + 0.906708489222317M9[t] -0.040782689573798M10[t] -0.0505139202881374M11[t] -0.00275273476324471t + 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) 0.680001490026424 0.419206 1.6221 0.114016 0.057008 InvlMex -0.109916882560442 0.225445 -0.4876 0.628994 0.314497 `Yt-1` 0.829083916174935 0.169489 4.8917 2.4e-05 1.2e-05 `Yt-2` 0.0406279848806237 0.247835 0.1639 0.870756 0.435378 `Yt-3` 0.102037061011512 0.258943 0.3941 0.696003 0.348001 `Yt-4` -0.109824058538842 0.189244 -0.5803 0.565519 0.28276 M1 -0.199444656948999 0.307899 -0.6478 0.521491 0.260745 M2 -0.252551856372627 0.310744 -0.8127 0.422026 0.211013 M3 -0.2362239526388 0.295468 -0.7995 0.429557 0.214778 M4 -0.0226862977284185 0.330934 -0.0686 0.945747 0.472874 M5 -0.0745510928749468 0.344289 -0.2165 0.829864 0.414932 M6 -0.161684153029778 0.302225 -0.535 0.596146 0.298073 M7 0.160657728882051 0.303948 0.5286 0.600537 0.300269 M8 0.0429349888681616 0.309732 0.1386 0.890568 0.445284 M9 0.906708489222317 0.318013 2.8512 0.007354 0.003677 M10 -0.040782689573798 0.32261 -0.1264 0.900148 0.450074 M11 -0.0505139202881374 0.349013 -0.1447 0.885776 0.442888 t -0.00275273476324471 0.004832 -0.5697 0.572634 0.286317 Multiple Linear Regression - Regression Statistics Multiple R 0.950462850624025 R-squared 0.903379630416347 Adjusted R-squared 0.85506944562452 F-TEST (value) 18.6995689275274 F-TEST (DF numerator) 17 F-TEST (DF denominator) 34 p-value 1.68287606072681e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.342358847011439 Sum Squared Residuals 3.98512572431807 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2 2.21454512062515 -0.214545120625151 2 2.1 2.15911247629585 -0.0591124762958488 3 2.5 2.27522474933349 0.224775250666508 4 2.5 2.78875881687701 -0.288758816877015 5 2.6 2.76059618702064 -0.160596187020643 6 2.7 2.78345120227078 -0.0834512022707805 7 3.7 3.14608191610938 0.553918083890616 8 4 3.8689568620964 0.131043137903601 9 5 5.01855208766768 -0.0185520876676811 10 5.1 5.00063514090507 0.0993648590949268 11 5.1 5.03247461169022 0.0675253883097832 12 5 5.15338843915303 -0.153388439153031 13 5.1 4.7686623033856 0.331337696614397 14 4.7 4.78066555647428 -0.0806655564742768 15 4.5 4.4564662513618 0.043533748638203 16 4.5 4.50636930627673 -0.00636930627673205 17 4.6 4.39182894913235 0.208171050867654 18 4.6 4.408373757045 0.191626242955003 19 4.6 4.75399051438941 -0.153990514389412 20 4.6 4.64371874571343 -0.0437187457134295 21 5.3 5.49375710545046 -0.193757105450456 22 5.4 5.12387193321355 0.276128066786449 23 5.3 5.2227359487699 0.0772640512301022 24 5.2 5.26307748387342 -0.0630774838734171 25 5 4.90723576717958 0.0927642328204208 26 4.2 4.66031013931462 -0.460310139314622 27 4.3 4.00327127812186 0.296728721878136 28 4.3 4.25503719563358 0.0449628043664231 29 4.3 4.14481762711042 0.155182372889575 30 4 4.15299478512457 -0.152994785124574 31 4 4.21287635156679 -0.212876351566793 32 4.1 4.08021248132547 0.0197875186745276 33 4.4 4.99353052023042 -0.593530520230422 34 3.6 4.32902179757326 -0.729021797573259 35 3.7 3.67566280072106 0.0243371992789359 36 3.8 3.79345870240852 0.00654129759147848 37 3.3 3.56365563443097 -0.26365563443097 38 3.3 3.19537949357692 0.104620506423082 39 3.3 3.18786197035446 0.112138029645545 40 3.5 3.33664595414195 0.163354045858049 41 3.3 3.50275723673659 -0.202757236736587 42 3.3 3.25518025555965 0.0448197444403515 43 3.4 3.58705121793441 -0.187051217934410 44 3.4 3.5071119108647 -0.107111910864699 45 5.2 4.39416028665144 0.80583971334856 46 5.3 4.94647112830812 0.353528871691883 47 4.8 4.96912663881882 -0.169126638818821 48 5 4.79007537456503 0.209924625434969 49 4.6 4.5459011743787 0.0540988256213028 50 4.6 4.10453233433833 0.495467665661665 51 3.5 4.17717575082839 -0.677175750828391 52 3.5 3.41318872707073 0.0868112729292748 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.236818770499909 0.473637540999817 0.763181229500091 22 0.163536221849435 0.327072443698869 0.836463778150565 23 0.157996964564609 0.315993929129218 0.842003035435391 24 0.121213100806550 0.242426201613101 0.87878689919345 25 0.0708861248699219 0.141772249739844 0.929113875130078 26 0.121426373278215 0.242852746556430 0.878573626721785 27 0.0790698322225995 0.158139664445199 0.9209301677774 28 0.0447772214172969 0.0895544428345939 0.955222778582703 29 0.0288425855002310 0.0576851710004621 0.97115741449977 30 0.0111513803558030 0.0223027607116060 0.988848619644197 31 0.00691101143046316 0.0138220228609263 0.993088988569537 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 2 0.181818181818182 NOK 10% type I error level 4 0.363636363636364 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/10j90z1258733609.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/10j90z1258733609.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/10s101258733609.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/10s101258733609.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/2orun1258733609.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/2orun1258733609.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/32inb1258733609.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/32inb1258733609.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/4rqik1258733609.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/4rqik1258733609.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/5c5xs1258733609.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/5c5xs1258733609.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/6blim1258733609.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/6blim1258733609.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/74be11258733609.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/74be11258733609.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/8t5bm1258733609.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/8t5bm1258733609.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/9qo611258733609.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733685vowvbwn3vzililt/9qo611258733609.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