workshop 7

*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: Thu, 19 Nov 2009 10:24:44 -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/19/t1258651684dt3c3m3i032q2fh.htm/, Retrieved Thu, 19 Nov 2009 18:28:16 +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/19/t1258651684dt3c3m3i032q2fh.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:

workshop 7

Dataseries X:

» Textbox « » Textfile « » CSV «

0.6348 1.5291 0.634 1.5358 0.62915 1.5355 0.62168 1.5287 0.61328 1.5334 0.6089 1.5225 0.60857 1.5135 0.62672 1.5144 0.62291 1.4913 0.62393 1.4793 0.61838 1.4663 0.62012 1.4749 0.61659 1.4745 0.6116 1.4775 0.61573 1.4678 0.61407 1.4658 0.62823 1.4572 0.64405 1.4721 0.6387 1.4624 0.63633 1.4636 0.63059 1.4649 0.62994 1.465 0.63709 1.4673 0.64217 1.4679 0.65711 1.4621 0.66977 1.4674 0.68255 1.4695 0.68902 1.4964 0.71322 1.5155 0.70224 1.5411 0.70045 1.5476 0.69919 1.54 0.69693 1.5474 0.69763 1.5485 0.69278 1.559 0.70196 1.5544 0.69215 1.5657 0.6769 1.5734 0.67124 1.567 0.66532 1.5547 0.67157 1.54 0.66428 1.5192 0.66576 1.527 0.66942 1.5387 0.6813 1.5431 0.69144 1.5426 0.69862 1.5216 0.695 1.5364 0.69867 1.5469 0.68968 1.5501 0.69233 1.5494 0.68293 1.5475 0.68399 1.5448 0.66895 1.5391 0.68756 1.5578 0.68527 1.5528 0.6776 1.5496 0.68137 1.549 0.67933 1.5449 0.67922 1.5479

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Britse_pond[t] = -0.193118251040811 + 0.567705764717280Zwitserse_frank[t] -0.00746666831058105M1[t] -0.0138813841718164M2[t] -0.0103682668776646M3[t] -0.0144070773741441M4[t] -0.0067032868376685M5[t] -0.0114292644117932M6[t] -0.0105289028988846M7[t] -0.00748715228241673M8[t] -0.00750840906356317M9[t] -0.00316126934353599M10[t] + 8.93218259334423e-05M11[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) -0.193118251040811 0.147615 -1.3083 0.197149 0.098575 Zwitserse_frank 0.567705764717280 0.097028 5.8509 0 0 M1 -0.00746666831058105 0.017021 -0.4387 0.66291 0.331455 M2 -0.0138813841718164 0.017027 -0.8153 0.419029 0.209515 M3 -0.0103682668776646 0.017022 -0.6091 0.545377 0.272688 M4 -0.0144070773741441 0.017022 -0.8464 0.401643 0.200822 M5 -0.0067032868376685 0.017022 -0.3938 0.695508 0.347754 M6 -0.0114292644117932 0.017023 -0.6714 0.505245 0.252623 M7 -0.0105289028988846 0.017029 -0.6183 0.539366 0.269683 M8 -0.00748715228241673 0.01703 -0.4397 0.662203 0.331102 M9 -0.00750840906356317 0.017023 -0.4411 0.66119 0.330595 M10 -0.00316126934353599 0.017021 -0.1857 0.853459 0.426729 M11 8.93218259334423e-05 0.017027 0.0052 0.995836 0.497918 Multiple Linear Regression - Regression Statistics Multiple R 0.655418571879294 R-squared 0.429573504364294 Adjusted R-squared 0.283932696967943 F-TEST (value) 2.94954080551917 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.00394358814395668 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0269125781659805 Sum Squared Residuals 0.0340414825863804 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 0.6348 0.667493965477801 -0.0326939654778007 2 0.634 0.66488287824017 -0.0308828782401705 3 0.62915 0.668225683804907 -0.0390756838049072 4 0.62168 0.66032647410835 -0.0386464741083502 5 0.61328 0.670698481738997 -0.057418481738997 6 0.6089 0.659784511329454 -0.0508845113294539 7 0.60857 0.655575520959907 -0.047005520959907 8 0.62672 0.65912820676462 -0.0324082067646203 9 0.62291 0.645992946818505 -0.0230829468185049 10 0.62393 0.643527617361925 -0.0195976173619247 11 0.61838 0.63939803359007 -0.0210180335900694 12 0.62012 0.644190981340705 -0.0240709813407047 13 0.61659 0.636497230724237 -0.0199072307242367 14 0.6116 0.631785632157153 -0.0201856321571531 15 0.61573 0.629792003533547 -0.0140620035335473 16 0.61407 0.624617781507633 -0.0105477815076333 17 0.62823 0.62743930246754 0.000790697532459633 18 0.64405 0.631172140787703 0.0128778592122969 19 0.6387 0.626565756382854 0.0121342436171460 20 0.63633 0.630288753916983 0.0060412460830173 21 0.63059 0.631005514629969 -0.000415514629968741 22 0.62994 0.635409424926468 -0.00546942492646756 23 0.63709 0.639965739354787 -0.00287573935478673 24 0.64217 0.640217040987684 0.00195295901231635 25 0.65711 0.629457679241742 0.0276523207582576 26 0.66977 0.626051803933509 0.0437181960664914 27 0.68255 0.630757103333567 0.0517928966664333 28 0.68902 0.641989577907982 0.047030422092018 29 0.71322 0.660536548550558 0.0526834514494423 30 0.70224 0.670343838553195 0.0318961614468047 31 0.70045 0.674934287536766 0.0255157124632337 32 0.69919 0.673661474341383 0.0255285256586172 33 0.69693 0.677841240219144 0.0190887597808558 34 0.69763 0.68281285628036 0.0148171437196396 35 0.69278 0.692024357979361 0.000755642020638745 36 0.70196 0.689323589635728 0.0126364103642717 37 0.69215 0.688271996466453 0.00387800353354748 38 0.6769 0.68622861499354 -0.00932861499354018 39 0.67124 0.686108415393501 -0.0148684153935014 40 0.66532 0.675086823990999 -0.0097668239909994 41 0.67157 0.674445339786131 -0.00287533978613102 42 0.66428 0.657911082305887 0.00636891769411299 43 0.66576 0.66323954878359 0.00252045121640979 44 0.66942 0.67292345684725 -0.00350345684725027 45 0.6813 0.67540010543086 0.00589989456914017 46 0.69144 0.679463392268528 0.0119766077314716 47 0.69862 0.670792162378935 0.027827837621065 48 0.695 0.679104885870817 0.0158951141291827 49 0.69867 0.677599128089768 0.0210708719102324 50 0.68968 0.673001070675628 0.0166789293243724 51 0.69233 0.676116793934477 0.0162132060655226 52 0.68293 0.670999342485035 0.0119306575149649 53 0.68399 0.677170327456774 0.00681967254322608 54 0.66895 0.66920842702376 -0.000258427023760691 55 0.68756 0.680724886336883 0.00683511366311744 56 0.68527 0.680928108129764 0.00434189187023613 57 0.6776 0.679090192901522 -0.00149019290152228 58 0.68137 0.683096709162719 -0.00172670916271895 59 0.67933 0.684019706696848 -0.00468970669684757 60 0.67922 0.685633502165066 -0.00641350216506599 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.032494819893531 0.064989639787062 0.96750518010647 17 0.196575921116765 0.393151842233531 0.803424078883235 18 0.444409751691455 0.88881950338291 0.555590248308545 19 0.488159561609735 0.97631912321947 0.511840438390265 20 0.384986803645012 0.769973607290025 0.615013196354988 21 0.311145420274273 0.622290840548546 0.688854579725727 22 0.28313073480014 0.56626146960028 0.71686926519986 23 0.327334941887311 0.654669883774622 0.672665058112689 24 0.435030442032027 0.870060884064055 0.564969557967973 25 0.615077188504003 0.769845622991994 0.384922811495997 26 0.800461039466728 0.399077921066543 0.199538960533272 27 0.92625787312036 0.147484253759279 0.0737421268796395 28 0.983195400347946 0.0336091993041082 0.0168045996520541 29 0.99945036473539 0.00109927052921880 0.000549635264609399 30 0.99995786864813 8.4262703740447e-05 4.21313518702235e-05 31 0.99998717382457 2.56523508595981e-05 1.28261754297990e-05 32 0.999992080033146 1.58399337078415e-05 7.91996685392075e-06 33 0.9999915107246 1.69785508000576e-05 8.48927540002881e-06 34 0.999982894577992 3.42108440168021e-05 1.71054220084011e-05 35 0.999947199525987 0.000105600948025416 5.28004740127081e-05 36 0.99991505272951 0.000169894540981569 8.49472704907845e-05 37 0.999716123050386 0.000567753899227538 0.000283876949613769 38 0.999202157628246 0.00159568474350842 0.00079784237175421 39 0.998798719585686 0.00240256082862784 0.00120128041431392 40 0.997848514487594 0.00430297102481191 0.00215148551240596 41 0.994387402697305 0.0112251946053903 0.00561259730269516 42 0.982287388123075 0.0354252237538501 0.0177126118769250 43 0.985055615542179 0.0298887689156426 0.0149443844578213 44 0.998789733322462 0.00242053335507619 0.00121026667753809 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.448275862068966 NOK 5% type I error level 17 0.586206896551724 NOK 10% type I error level 18 0.620689655172414 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/10l1ni1258651479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/10l1ni1258651479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/18qt11258651479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/18qt11258651479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/2fzda1258651479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/2fzda1258651479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/33zth1258651479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/33zth1258651479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/4sp771258651479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/4sp771258651479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/5eurl1258651479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/5eurl1258651479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/6k0cg1258651479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/6k0cg1258651479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/7au6y1258651479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/7au6y1258651479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/8lyv51258651479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/8lyv51258651479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/96vx81258651479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258651684dt3c3m3i032q2fh/96vx81258651479.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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