Multiple Regression

*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, 18 Dec 2009 08:13:02 -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/Dec/18/t126114926451welxecwf90ubp.htm/, Retrieved Fri, 18 Dec 2009 16:14:36 +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/Dec/18/t126114926451welxecwf90ubp.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

12.1 0 0 12 0 0 11.8 0 0 12.7 0 0 12.3 0 0 11.9 0 0 12 0 0 12.3 0 0 12.8 0 0 12.4 0 0 12.3 0 0 12.7 0 0 12.7 0 0 12.9 0 0 13 0 0 12.2 0 0 12.3 0 0 12.8 0 0 12.8 0 0 12.8 0 0 12.2 0 0 12.6 0 0 12.8 0 0 12.5 0 0 12.4 0 0 12.3 1 0 11.9 1 0 11.7 1 0 12 1 0 12.1 1 0 11.7 1 0 11.8 1 0 11.8 1 0 11.8 1 0 11.3 1 0 11.3 1 0 11.3 1 0 11.2 0 1 11.4 0 1 12.2 0 1 12.9 0 1 13.1 0 1 13.5 0 1 13.6 0 1 14.4 0 1 14.1 0 1 15.1 0 1 15.8 0 1 15.9 0 1 15.4 0 1 15.5 0 1 14.8 0 1 13.2 0 1 12.7 0 1 12.1 0 1 11.9 0 1 10.6 0 1 10.7 0 1 9.8 0 1 9 0 1 8.3 0 1 9.3 0 1 9 0 1 9.1 0 1 10 0 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Gzhdsidx[t] = + 14.1235749185668 + 1.14362276058632Vr_crisis[t] + 3.74368892508143NA_crisis[t] -0.354213179741224M1[t] -0.811778693901559M2[t] -0.795396053881653M3[t] -0.679013413861747M4[t] -0.579297440508507M5[t] -0.338295840119437M6[t] -0.338579866766198M7[t] -0.178863893412958M8[t] -0.199147920059719M9[t] -0.13943194670648M10[t] -0.09971597335324M11[t] -0.0997159733532392t + 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) 14.1235749185668 0.878121 16.0839 0 0 Vr_crisis 1.14362276058632 0.762734 1.4994 0.140065 0.070032 NA_crisis 3.74368892508143 1.19191 3.1409 0.002828 0.001414 M1 -0.354213179741224 0.944799 -0.3749 0.709314 0.354657 M2 -0.811778693901559 0.965996 -0.8404 0.404711 0.202355 M3 -0.795396053881653 0.960251 -0.8283 0.411425 0.205713 M4 -0.679013413861747 0.955328 -0.7108 0.480533 0.240266 M5 -0.579297440508507 0.95124 -0.609 0.545287 0.272643 M6 -0.338295840119437 0.999318 -0.3385 0.736384 0.368192 M7 -0.338579866766198 0.994807 -0.3403 0.735021 0.36751 M8 -0.178863893412958 0.991101 -0.1805 0.857513 0.428757 M9 -0.199147920059719 0.988209 -0.2015 0.841106 0.420553 M10 -0.13943194670648 0.986138 -0.1414 0.888129 0.444064 M11 -0.09971597335324 0.984894 -0.1012 0.919761 0.45988 t -0.0997159733532392 0.028596 -3.4871 0.001027 0.000514 Multiple Linear Regression - Regression Statistics Multiple R 0.473575913072725 R-squared 0.224274145442665 Adjusted R-squared 0.00707090616661099 F-TEST (value) 1.03255433109644 F-TEST (DF numerator) 14 F-TEST (DF denominator) 50 p-value 0.438381636751226 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.55659732147622 Sum Squared Residuals 121.149761061346 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 12.1 13.6696457654723 -1.56964576547231 2 12 13.1123642779587 -1.11236427795874 3 11.8 13.0290309446254 -1.22903094462541 4 12.7 13.0456976112921 -0.345697611292075 5 12.3 13.0456976112921 -0.745697611292073 6 11.9 13.1869832383279 -1.28698323832790 7 12 13.0869832383279 -1.08698323832790 8 12.3 13.1469832383279 -0.846983238327904 9 12.8 13.0269832383279 -0.226983238327904 10 12.4 12.9869832383279 -0.586983238327904 11 12.3 12.9269832383279 -0.626983238327904 12 12.7 12.9269832383279 -0.226983238327906 13 12.7 12.4730540852334 0.226945914766557 14 12.9 11.9157725977199 0.984227402280131 15 13 11.8324392643865 1.16756073561346 16 12.2 11.8491059310532 0.350894068946797 17 12.3 11.8491059310532 0.450894068946797 18 12.8 11.9903915580890 0.809608441910967 19 12.8 11.8903915580890 0.909608441910967 20 12.8 11.9503915580890 0.849608441910966 21 12.2 11.8303915580890 0.369608441910965 22 12.6 11.7903915580890 0.809608441910965 23 12.8 11.7303915580890 1.06960844191097 24 12.5 11.7303915580890 0.769608441910964 25 12.4 11.2764624049946 1.12353759500543 26 12.3 11.8628036780673 0.437196321932684 27 11.9 11.7794703447340 0.120529655266017 28 11.7 11.7961370114007 -0.096137011400651 29 12 11.7961370114007 0.203862988599348 30 12.1 11.9374226384365 0.162577361563518 31 11.7 11.8374226384365 -0.137422638436483 32 11.8 11.8974226384365 -0.0974226384364817 33 11.8 11.7774226384365 0.0225773615635184 34 11.8 11.7374226384365 0.0625773615635187 35 11.3 11.6774226384365 -0.377422638436482 36 11.3 11.6774226384365 -0.377422638436483 37 11.3 11.2234934853420 0.0765065146579809 38 11.2 13.2662781623236 -2.06627816232356 39 11.4 13.1829448289902 -1.78294482899023 40 12.2 13.1996114956569 -0.999611495656894 41 12.9 13.1996114956569 -0.299611495656894 42 13.1 13.3408971226927 -0.240897122692725 43 13.5 13.2408971226927 0.259102877307275 44 13.6 13.3008971226927 0.299102877307274 45 14.4 13.1808971226927 1.21910287730728 46 14.1 13.1408971226927 0.959102877307275 47 15.1 13.0808971226927 2.01910287730727 48 15.8 13.0808971226927 2.71910287730727 49 15.9 12.6269679695983 3.27303203040174 50 15.4 12.0696864820847 3.33031351791531 51 15.5 11.9863531487514 3.51364685124864 52 14.8 12.0030198154180 2.79698018458198 53 13.2 12.0030198154180 1.19698018458198 54 12.7 12.1443054424539 0.555694557546145 55 12.1 12.0443054424539 0.0556945575461452 56 11.9 12.1043054424539 -0.204305442453855 57 10.6 11.9843054424539 -1.38430544245385 58 10.7 11.9443054424539 -1.24430544245385 59 9.8 11.8843054424539 -2.08430544245385 60 9 11.8843054424539 -2.88430544245386 61 8.3 11.4303762893594 -3.13037628935939 62 9.3 10.8730948018458 -1.57309480184582 63 9 10.7897614685125 -1.78976146851249 64 9.1 10.8064281351792 -1.70642813517915 65 10 10.8064281351792 -0.806428135179155 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.0525114110838033 0.105022822167607 0.947488588916197 19 0.0151861755346312 0.0303723510692624 0.984813824465369 20 0.00376422202248959 0.00752844404497918 0.99623577797751 21 0.00291113803924841 0.00582227607849683 0.997088861960752 22 0.000776476688011299 0.00155295337602260 0.999223523311989 23 0.000186390806718766 0.000372781613437532 0.999813609193281 24 6.18628731656522e-05 0.000123725746331304 0.999938137126834 25 1.94693720460980e-05 3.89387440921959e-05 0.999980530627954 26 4.11468038336778e-06 8.22936076673556e-06 0.999995885319617 27 9.14167094156606e-07 1.82833418831321e-06 0.999999085832906 28 2.20229721397927e-07 4.40459442795853e-07 0.999999779770279 29 4.17067864395790e-08 8.34135728791579e-08 0.999999958293214 30 7.55283373741456e-09 1.51056674748291e-08 0.999999992447166 31 1.48242957741428e-09 2.96485915482856e-09 0.99999999851757 32 2.80690555070802e-10 5.61381110141605e-10 0.99999999971931 33 4.62643356937396e-11 9.25286713874791e-11 0.999999999953736 34 7.15441217555591e-12 1.43088243511118e-11 0.999999999992846 35 3.17719617874822e-12 6.35439235749644e-12 0.999999999996823 36 1.23674037443182e-12 2.47348074886365e-12 0.999999999998763 37 3.53246745482788e-13 7.06493490965576e-13 0.999999999999647 38 2.97632862910995e-13 5.95265725821989e-13 0.999999999999702 39 7.76944712071632e-13 1.55388942414326e-12 0.999999999999223 40 1.70443753469765e-11 3.40887506939529e-11 0.999999999982956 41 3.87611781565107e-09 7.75223563130213e-09 0.999999996123882 42 1.43117096916203e-07 2.86234193832406e-07 0.999999856882903 43 6.05289451548266e-06 1.21057890309653e-05 0.999993947105485 44 0.000268634524988981 0.000537269049977961 0.99973136547501 45 0.00150038037376636 0.00300076074753272 0.998499619626234 46 0.00850590214025706 0.0170118042805141 0.991494097859743 47 0.0134729816392295 0.0269459632784589 0.98652701836077 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 26 0.866666666666667 NOK 5% type I error level 29 0.966666666666667 NOK 10% type I error level 29 0.966666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/10n4aw1261149177.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/10n4aw1261149177.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/1kf771261149177.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/1kf771261149177.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/2pglf1261149177.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/2pglf1261149177.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/3tg4z1261149177.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/3tg4z1261149177.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/4nz4h1261149177.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/4nz4h1261149177.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/5pbj81261149177.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/5pbj81261149177.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/6lty51261149177.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/6lty51261149177.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/79cmj1261149177.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/79cmj1261149177.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/88cco1261149177.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/88cco1261149177.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/98jhs1261149177.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t126114926451welxecwf90ubp/98jhs1261149177.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