paper - Multiple Regression (alle)

*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: Sat, 13 Dec 2008 09:21:20 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c.htm/, Retrieved Sat, 13 Dec 2008 17:51:43 +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/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c.htm/}, year = {2008}, } @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 = {2008}, 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 «

1.8 0.8 2.9 1.7 -0.1 2.9 1.4 -1.5 2.9 1.2 -4.4 1.4 1 -4.2 1.1 1.7 3.5 1.9 2.4 10 2.8 2 8.6 1.4 2.1 9.5 0.7 2 9.9 -0.8 1.8 10.4 -3.1 2.7 16 0.1 2.3 12.7 1 1.9 10.2 1.9 2 8.9 -0.5 2.3 12.6 1.5 2.8 13.6 3.9 2.4 14.8 1.9 2.3 9.5 2.6 2.7 13.7 1.7 2.7 17 1.4 2.9 14.7 2.8 3 17.4 0.5 2.2 9 1 2.3 9.1 1.5 2.8 12.2 1.8 2.8 15.9 2.7 2.8 12.9 3 2.2 10.9 -0.3 2.6 10.6 1.1 2.8 13.2 1.7 2.5 9.6 1.6 2.4 6.4 3 2.3 5.8 3.3 1.9 -1 6.7 1.7 -0.2 5.6 2 2.7 6 2.1 3.6 4.8 1.7 -0.9 5.9 1.8 0.3 4.3 1.8 -1.1 3.7 1.8 -2.5 5.6 1.3 -3.4 1.7 1.3 -3.5 3.2 1.3 -3.9 3.6 1.2 -4.6 1.7 1.4 -0.1 0.5 2.2 4.3 2.1 2.9 10.2 1.5 3.1 8.7 2.7 3.5 13.3 1.4 3.6 15 1.2 4.4 20.7 2.3 4.1 20.7 1.6 5.1 26.4 4.7 5.8 31.2 3.5 5.9 31.4 4.4 5.4 26.6 3.9 5.5 26.6 3.5 4.8 19.2 3

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation totale_inflatie[t] = + 0.793045803171116 + 0.110299880437482inflatie_energiedragers[t] + 0.099961772311484inflatie_onbewerkte_levensmiddelen[t] + 0.0169049684459392t + 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) 0.793045803171116 0.080992 9.7917 0 0 inflatie_energiedragers 0.110299880437482 0.004348 25.3658 0 0 inflatie_onbewerkte_levensmiddelen 0.099961772311484 0.022784 4.3874 5.1e-05 2.6e-05 t 0.0169049684459392 0.002437 6.9371 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.973276206517053 R-squared 0.947266574172225 Adjusted R-squared 0.944441569217166 F-TEST (value) 335.315013333248 F-TEST (DF numerator) 3 F-TEST (DF denominator) 56 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.276856081743618 Sum Squared Residuals 4.292360239912 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.8 1.18807981567035 0.611920184329654 2 1.7 1.10571489172255 0.59428510827745 3 1.4 0.968200027556014 0.431799972443986 4 1.2 0.515292684266028 0.684707315733972 5 1 0.524269097106018 0.475730902893982 6 1.7 1.47045256276976 0.229547437230241 7 2.4 2.29427234913967 0.105727650860330 8 2 2.01681100373706 -0.0168110037370560 9 2.1 2.06301262395869 0.0369873760413094 10 2 1.97409488611240 0.0259051138876031 11 1.8 1.81623771846066 -0.0162377184606642 12 2.7 2.77069968875325 -0.0706996887532535 13 2.3 2.51358064683584 -0.213580646835837 14 1.9 2.34470150926840 -0.444701509268405 15 2 1.97830837959806 0.0216916204019441 16 2.3 2.60324645028565 -0.303246450285648 17 2.8 2.97035955271663 -0.170359552716631 18 2.4 2.91970083306458 -0.519700833064582 19 2.3 2.42198967580990 -0.121989675809903 20 2.7 2.81218854701293 -0.112188547012932 21 2.7 3.16309458920912 -0.463094589209118 22 2.9 3.06625631388493 -0.166256313884926 23 3 3.15105888319565 -0.151058883195654 24 2.2 2.29142574212248 -0.0914257421224831 25 2.3 2.36934158476791 -0.0693415847679128 26 2.8 2.75816471426349 0.0418352857365073 27 2.8 3.27314483540845 -0.473144835408453 28 2.8 2.98913869423539 -0.18913869423539 29 2.2 2.45557005317847 -0.255570053178467 30 2.6 2.57933153872924 0.0206684612707611 31 2.8 2.94299325969952 -0.142993259699523 32 2.5 2.55282248133938 -0.052822481339377 33 2.4 2.35671431362145 0.0432856863785497 34 2.3 2.33742788549835 -0.0374278854983453 35 1.9 1.94416369282845 -0.0441636928284495 36 1.7 1.93935061608174 -0.239350616081742 37 2 2.31610994672097 -0.316109946720974 38 2.1 2.31233068078687 -0.212330680786867 39 1.7 1.94284413680677 -0.242844136806768 40 1.8 1.93217012607931 -0.132170126079311 41 1.8 1.73467819852588 0.0653218014741153 42 1.8 1.78709070175117 0.0129092982488319 43 1.3 1.31487486578859 -0.0148748657885858 44 1.3 1.47069250465800 -0.170692504658003 45 1.3 1.48346222985354 -0.183462229853543 46 1.2 1.23322991460142 -0.0332299146014250 47 1.4 1.62653021824225 -0.226530218242254 48 2.2 2.28869349631149 -0.08869349631149 49 2.9 2.89639069595168 0.00360930404831486 50 3.1 2.86779997051518 0.232200029484819 51 3.5 3.26213408496861 0.237865915031389 52 3.6 3.44655649569597 0.153443504304027 53 4.4 4.20212873217819 0.197871267821806 54 4.1 4.14906046000609 -0.0490604600060954 55 5.1 5.10455624111128 -0.00455624111128468 56 5.8 5.53094650888336 0.269053491116641 57 5.9 5.65987704849713 0.240122951502871 58 5.4 5.09736170468741 0.302638295312589 59 5.5 5.07428196420876 0.425718035791243 60 4.8 4.22498693126158 0.575013068738415 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.158651167381772 0.317302334763543 0.841348832618228 8 0.126299731087989 0.252599462175978 0.873700268912011 9 0.0698810277928177 0.139762055585635 0.930118972207182 10 0.0350058496512537 0.0700116993025075 0.964994150348746 11 0.0199297289571894 0.0398594579143788 0.98007027104281 12 0.0572819767158121 0.114563953431624 0.942718023284188 13 0.03011181688191 0.06022363376382 0.96988818311809 14 0.0317885807008889 0.0635771614017777 0.968211419299111 15 0.180358778775492 0.360717557550983 0.819641221224508 16 0.135777246734515 0.271554493469029 0.864222753265485 17 0.247670156484044 0.495340312968088 0.752329843515956 18 0.259180698709917 0.518361397419835 0.740819301290083 19 0.293110918017502 0.586221836035005 0.706889081982498 20 0.419758407325658 0.839516814651316 0.580241592674342 21 0.416442677972249 0.832885355944497 0.583557322027751 22 0.469374959507336 0.938749919014673 0.530625040492664 23 0.587845067556323 0.824309864887354 0.412154932443677 24 0.525953544849334 0.948092910301332 0.474046455150666 25 0.478938752929972 0.957877505859944 0.521061247070028 26 0.62007093847848 0.759858123043041 0.379929061521521 27 0.695320592082674 0.609358815834652 0.304679407917326 28 0.632306874809046 0.735386250381909 0.367693125190954 29 0.604352262044553 0.791295475910894 0.395647737955447 30 0.610422553523537 0.779154892952925 0.389577446476463 31 0.555795876953899 0.888408246092202 0.444204123046101 32 0.484510904943166 0.969021809886333 0.515489095056833 33 0.511591294700408 0.976817410599184 0.488408705299592 34 0.592592892195949 0.814814215608103 0.407407107804051 35 0.719789803227047 0.560420393545907 0.280210196772953 36 0.769963086944675 0.460073826110650 0.230036913055325 37 0.75498551260716 0.490028974785679 0.245014487392840 38 0.692796527947459 0.614406944105083 0.307203472052541 39 0.667951215945854 0.664097568108292 0.332048784054146 40 0.592015046242473 0.815969907515054 0.407984953757527 41 0.679319460487674 0.641361079024653 0.320680539512327 42 0.722071136055038 0.555857727889923 0.277928863944962 43 0.786815389381868 0.426369221236264 0.213184610618132 44 0.739099045127908 0.521801909744184 0.260900954872092 45 0.676082639945446 0.647834720109109 0.323917360054554 46 0.584189924964324 0.831620150071352 0.415810075035676 47 0.67875764267022 0.642484714659561 0.321242357329780 48 0.70138598943838 0.597228021123241 0.298614010561621 49 0.708155381660479 0.583689236679043 0.291844618339521 50 0.751508481426649 0.496983037146702 0.248491518573351 51 0.831662900062888 0.336674199874223 0.168337099937112 52 0.800381469857227 0.399237060285546 0.199618530142773 53 0.972737234500602 0.0545255309987964 0.0272627654993982 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 1 0.0212765957446809 OK 10% type I error level 5 0.106382978723404 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/10356z1229185269.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/10356z1229185269.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/1nz8s1229185269.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/1nz8s1229185269.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/2yjtq1229185269.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/2yjtq1229185269.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/3fkb41229185269.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/3fkb41229185269.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/4rb111229185269.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/4rb111229185269.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/55y3f1229185269.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/55y3f1229185269.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/6f69j1229185269.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/6f69j1229185269.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/77wc91229185269.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/77wc91229185269.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/89yp11229185269.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/89yp11229185269.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/92xop1229185269.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c/92xop1229185269.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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