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: Fri, 20 Nov 2009 09:05:32 -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/t1258733305q8zikmg1ccx8kv9.htm/, Retrieved Fri, 20 Nov 2009 17:08:37 +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/t1258733305q8zikmg1ccx8kv9.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 «

0.62915 1.5355 0.634 0.6348 0.62168 1.5287 0.62915 0.634 0.61328 1.5334 0.62168 0.62915 0.6089 1.5225 0.61328 0.62168 0.60857 1.5135 0.6089 0.61328 0.62672 1.5144 0.60857 0.6089 0.62291 1.4913 0.62672 0.60857 0.62393 1.4793 0.62291 0.62672 0.61838 1.4663 0.62393 0.62291 0.62012 1.4749 0.61838 0.62393 0.61659 1.4745 0.62012 0.61838 0.6116 1.4775 0.61659 0.62012 0.61573 1.4678 0.6116 0.61659 0.61407 1.4658 0.61573 0.6116 0.62823 1.4572 0.61407 0.61573 0.64405 1.4721 0.62823 0.61407 0.6387 1.4624 0.64405 0.62823 0.63633 1.4636 0.6387 0.64405 0.63059 1.4649 0.63633 0.6387 0.62994 1.465 0.63059 0.63633 0.63709 1.4673 0.62994 0.63059 0.64217 1.4679 0.63709 0.62994 0.65711 1.4621 0.64217 0.63709 0.66977 1.4674 0.65711 0.64217 0.68255 1.4695 0.66977 0.65711 0.68902 1.4964 0.68255 0.66977 0.71322 1.5155 0.68902 0.68255 0.70224 1.5411 0.71322 0.68902 0.70045 1.5476 0.70224 0.71322 0.69919 1.54 0.70045 0.70224 0.69693 1.5474 0.69919 0.70045 0.69763 1.5485 0.69693 0.69919 0.69278 1.559 0.6 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation britse_pond[t] = + 0.136039597495012 -0.0837778395373193Zwitserse_frank[t] + 1.12933820928760`Britse_pond_-1`[t] -0.155747217813694`Britse_pond_-2`[t] + 0.00722085111187932M1[t] + 0.000961566541320923M2[t] + 0.0125782436648632M3[t] -0.000865431373813849M4[t] + 0.00785537504436781M5[t] + 0.0073783304629954M6[t] + 0.00229776282336032M7[t] + 0.00716246700968827M8[t] + 0.0033528548036246M9[t] + 0.00607844505430859M10[t] + 0.00488969511372625M11[t] + 0.000143463291902331t + 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) 0.136039597495012 0.063436 2.1445 0.037823 0.018911 Zwitserse_frank -0.0837778395373193 0.054173 -1.5465 0.129487 0.064744 `Britse_pond_-1` 1.12933820928760 0.149549 7.5516 0 0 `Britse_pond_-2` -0.155747217813694 0.169166 -0.9207 0.362477 0.181238 M1 0.00722085111187932 0.005727 1.2608 0.214325 0.107162 M2 0.000961566541320923 0.005705 0.1685 0.866967 0.433483 M3 0.0125782436648632 0.005729 2.1955 0.033708 0.016854 M4 -0.000865431373813849 0.005805 -0.1491 0.882207 0.441104 M5 0.00785537504436781 0.005738 1.3691 0.178252 0.089126 M6 0.0073783304629954 0.005725 1.2888 0.204527 0.102264 M7 0.00229776282336032 0.005703 0.4029 0.689055 0.344527 M8 0.00716246700968827 0.0057 1.2566 0.215825 0.107913 M9 0.0033528548036246 0.005698 0.5884 0.559383 0.279691 M10 0.00607844505430859 0.005695 1.0673 0.291946 0.145973 M11 0.00488969511372625 0.005987 0.8168 0.418665 0.209332 t 0.000143463291902331 0.000112 1.2752 0.209232 0.104616 Multiple Linear Regression - Regression Statistics Multiple R 0.973851080073204 R-squared 0.948385926159746 Adjusted R-squared 0.929952328359655 F-TEST (value) 51.4487696023764 F-TEST (DF numerator) 15 F-TEST (DF denominator) 42 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.00845799712280262 Sum Squared Residuals 0.00300458404383217 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 0.62915 0.631895130109449 -0.00274513010944870 2 0.62168 0.620996305598852 0.000683694401148332 3 0.61328 0.624681907751489 -0.0114019077514888 4 0.6089 0.603971865214723 0.00492813478527658 5 0.60857 0.609951910753599 -0.00138191075359847 6 0.62672 0.609852420613504 0.0168675793864960 7 0.62291 0.627399469439532 -0.00448946943953186 8 0.62393 0.626283380411506 -0.00235338041150552 9 0.61838 0.625451665284673 -0.00707166528467286 10 0.62012 0.621173540183522 -0.00105354018352215 11 0.61659 0.622991210213683 -0.0064012102136835 12 0.6116 0.613736080835466 -0.00213608083546645 13 0.61573 0.616827430297297 -0.00109743029729740 14 0.61407 0.616320510118964 -0.00225051011896407 15 0.62823 0.626283202517442 0.00194679748255837 16 0.64405 0.627984670386644 0.016065329613356 17 0.6387 0.653322335006928 -0.014622335006928 18 0.63633 0.644382339904512 -0.00805233990451194 19 0.63059 0.637493040424672 -0.00690304042467217 20 0.62994 0.636379549703856 -0.00643954970385632 21 0.63709 0.632680630952973 0.00440936904702711 22 0.64217 0.643675421679822 -0.00150542167982209 23 0.65711 0.647739491996272 0.00937050800372833 24 0.66977 0.658630354605163 0.0111396453948369 25 0.68255 0.677789293841361 0.00476070615863913 26 0.68902 0.681881031216325 0.00713896878367483 27 0.71322 0.697357383667039 0.0158626163329613 28 0.70224 0.708234759393614 -0.00599475939361406 29 0.70045 0.700385256937636 6.47430623638514e-05 30 0.69919 0.700376976285619 -0.00118697628561933 31 0.69693 0.693675737301494 0.00325426269850557 32 0.69763 0.696235686297689 0.00139431370231089 33 0.69278 0.692832395527146 -5.23955271461109e-05 34 0.70196 0.69050051376409 0.0114594862359105 35 0.69215 0.699631236296295 -0.00748123629629452 36 0.6769 0.681731347817392 -0.00483134781739225 37 0.67124 0.673937312909329 -0.00269731290932901 38 0.66532 0.664835049864073 0.000484950135927078 39 0.67157 0.672022571574559 -0.000452571574559055 40 0.66428 0.668445326227665 -0.00416532622766519 41 0.66576 0.667449833132316 -0.00168983313231583 42 0.66942 0.668942868887867 0.000477131112133344 43 0.6813 0.667540014009798 0.0137599859902020 44 0.69144 0.685436573516936 0.00600342648306443 45 0.69862 0.693130971727608 0.0054890282723924 46 0.695 0.701289484799096 -0.00628948479909576 47 0.69867 0.69415806149375 0.00451193850624968 48 0.68968 0.693852216741978 -0.00417221674197820 49 0.69233 0.690550832842564 0.00177916715743597 50 0.68293 0.688987103201786 -0.00605710320178615 51 0.68399 0.689944934489472 -0.0059549344894718 52 0.66895 0.679783378777353 -0.0108333787773533 53 0.68756 0.669930664169522 0.0176293358304785 54 0.68527 0.693375394308498 -0.0081053943084981 55 0.6776 0.683221738824503 -0.0056217388245035 56 0.68137 0.679974810070013 0.00139518992998652 57 0.67933 0.6821043365076 -0.00277433650760055 58 0.67922 0.68183103957347 -0.00261103957347046 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.736396268403515 0.527207463192971 0.263603731596485 20 0.689496366767047 0.621007266465907 0.310503633232953 21 0.590286424555473 0.819427150889053 0.409713575444527 22 0.569252141189396 0.861495717621208 0.430747858810604 23 0.698187011106344 0.603625977787312 0.301812988893656 24 0.678057863611538 0.643884272776925 0.321942136388462 25 0.617943306370704 0.764113387258593 0.382056693629296 26 0.50634926434576 0.98730147130848 0.49365073565424 27 0.629536847226388 0.740926305547223 0.370463152773612 28 0.873124188290454 0.253751623419091 0.126875811709546 29 0.820852948479733 0.358294103040535 0.179147051520267 30 0.774182363111295 0.451635273777411 0.225817636888705 31 0.67786661140391 0.64426677719218 0.32213338859609 32 0.584939512186765 0.83012097562647 0.415060487813235 33 0.511527165741098 0.976945668517804 0.488472834258902 34 0.557429485448918 0.885141029102164 0.442570514551082 35 0.604349466257598 0.791301067484804 0.395650533742402 36 0.531236288708211 0.937527422583577 0.468763711291789 37 0.573611745989005 0.85277650802199 0.426388254010995 38 0.544185124255841 0.911629751488318 0.455814875744159 39 0.451443429602118 0.902886859204237 0.548556570397882 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/10lojn1258733128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/10lojn1258733128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/1gliw1258733128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/1gliw1258733128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/272v61258733128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/272v61258733128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/319ju1258733128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/319ju1258733128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/4gjxa1258733128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/4gjxa1258733128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/5b77x1258733128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/5b77x1258733128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/6cskm1258733128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/6cskm1258733128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/7hqcb1258733128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/7hqcb1258733128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/8tci61258733128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/8tci61258733128.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/9wp0w1258733128.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733305q8zikmg1ccx8kv9/9wp0w1258733128.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