WS 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, 27 Nov 2009 05:50:39 -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/27/t1259326616y5r6x019dicxi5e.htm/, Retrieved Fri, 27 Nov 2009 13:57:08 +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/27/t1259326616y5r6x019dicxi5e.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:

bhschhwsc

Dataseries X:

» Textbox « » Textfile « » CSV «

3.75 0 3.75 0 3.55 0 3.5 0 3.5 0 3.1 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3.21 0 3.25 0 3.25 0 3.45 0 3.5 0 3.5 0 3.64 0 3.75 0 3.93 0 4 0 4.17 0 4.25 0 4.39 0 4.5 0 4.5 0 4.65 0 4.75 0 4.75 0 4.9 0 5 0 5 0 5 0 5 0 5 0 5 0 5 1 5 1 5 1 5 1 5 1 5 1 5.18 1 5.25 1 5.25 1 4.49 1 3.92 1 3.25 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 Yt[t] = + 2.26908333333333 + 0.0314999999999995Xt[t] + 0.466444444444445M1[t] + 0.433888888888889M2[t] + 0.426333333333335M3[t] + 0.410444444444445M4[t] + 0.377888888888891M5[t] + 0.327000000000002M6[t] + 0.342777777777778M7[t] + 0.351888888888890M8[t] + 0.331000000000001M9[t] + 0.200111111111112M10[t] + 0.0858888888888898M11[t] + 0.0325555555555556t + 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) 2.26908333333333 0.268516 8.4504 0 0 Xt 0.0314999999999995 0.227195 0.1386 0.890209 0.445105 M1 0.466444444444445 0.316836 1.4722 0.146374 0.073187 M2 0.433888888888889 0.31627 1.3719 0.175379 0.08769 M3 0.426333333333335 0.315757 1.3502 0.182199 0.091099 M4 0.410444444444445 0.315297 1.3018 0.198141 0.099071 M5 0.377888888888891 0.31489 1.2001 0.234993 0.117497 M6 0.327000000000002 0.314538 1.0396 0.302831 0.151415 M7 0.342777777777778 0.314239 1.0908 0.279864 0.139932 M8 0.351888888888890 0.313995 1.1207 0.267041 0.133521 M9 0.331000000000001 0.313804 1.0548 0.295892 0.147946 M10 0.200111111111112 0.313668 0.638 0.526004 0.263002 M11 0.0858888888888898 0.313587 0.2739 0.785141 0.39257 t 0.0325555555555556 0.004132 7.8798 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.811842769241558 R-squared 0.659088681969801 Adjusted R-squared 0.582677524480273 F-TEST (value) 8.62555552911409 F-TEST (DF numerator) 13 F-TEST (DF denominator) 58 p-value 2.12817896638740e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.543101049529459 Sum Squared Residuals 17.1076075 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.75 2.76808333333334 0.981916666666659 2 3.75 2.76808333333333 0.981916666666668 3 3.55 2.79308333333333 0.756916666666666 4 3.5 2.80975 0.690250000000001 5 3.5 2.80975 0.690250000000001 6 3.1 2.79141666666666 0.308583333333335 7 3 2.83975 0.160249999999999 8 3 2.88141666666667 0.118583333333332 9 3 2.89308333333333 0.106916666666668 10 3 2.79475 0.205250000000001 11 3 2.71308333333333 0.286916666666667 12 3 2.65975 0.340250000000001 13 3 3.15875 -0.158749999999998 14 3 3.15875 -0.158750000000000 15 3 3.18375 -0.18375 16 3 3.20041666666667 -0.200416666666666 17 3 3.20041666666667 -0.200416666666667 18 3 3.18208333333333 -0.182083333333334 19 3 3.23041666666667 -0.230416666666666 20 3 3.27208333333333 -0.272083333333333 21 3 3.28375 -0.2837500 22 3 3.18541666666667 -0.185416666666667 23 3 3.10375 -0.103750000000000 24 3 3.05041666666667 -0.0504166666666656 25 3 3.54941666666667 -0.549416666666665 26 3 3.54941666666667 -0.549416666666667 27 3 3.57441666666667 -0.574416666666667 28 3 3.59108333333333 -0.591083333333333 29 3 3.59108333333333 -0.591083333333334 30 3 3.57275 -0.57275 31 3 3.62108333333333 -0.621083333333333 32 3 3.66275 -0.66275 33 3 3.67441666666667 -0.674416666666667 34 3 3.57608333333333 -0.576083333333333 35 3 3.49441666666667 -0.494416666666667 36 3.21 3.44108333333333 -0.231083333333332 37 3.25 3.94008333333333 -0.690083333333332 38 3.25 3.94008333333333 -0.690083333333333 39 3.45 3.96508333333333 -0.515083333333334 40 3.5 3.98175 -0.48175 41 3.5 3.98175 -0.48175 42 3.64 3.96341666666667 -0.323416666666667 43 3.75 4.01175 -0.26175 44 3.93 4.05341666666667 -0.123416666666666 45 4 4.06508333333333 -0.0650833333333339 46 4.17 3.96675 0.203249999999999 47 4.25 3.88508333333333 0.364916666666667 48 4.39 3.83175 0.55825 49 4.5 4.33075 0.169250000000001 50 4.5 4.33075 0.16925 51 4.65 4.35575 0.29425 52 4.75 4.37241666666667 0.377583333333333 53 4.75 4.37241666666667 0.377583333333333 54 4.9 4.35408333333333 0.545916666666666 55 5 4.40241666666667 0.597583333333333 56 5 4.44408333333333 0.555916666666666 57 5 4.45575 0.544249999999999 58 5 4.35741666666667 0.642583333333333 59 5 4.27575 0.72425 60 5 4.22241666666667 0.777583333333333 61 5 4.75291666666667 0.247083333333334 62 5 4.75291666666667 0.247083333333333 63 5 4.77791666666667 0.222083333333333 64 5 4.79458333333333 0.205416666666667 65 5 4.79458333333333 0.205416666666666 66 5 4.77625 0.223749999999999 67 5.18 4.82458333333333 0.355416666666666 68 5.25 4.86625 0.38375 69 5.25 4.87791666666667 0.372083333333333 70 4.49 4.77958333333333 -0.289583333333333 71 3.92 4.69791666666667 -0.777916666666667 72 3.25 4.64458333333333 -1.39458333333333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0183464105815522 0.0366928211631044 0.981653589418448 18 0.0538916651401183 0.107783330280237 0.946108334859882 19 0.0711117666584304 0.142223533316861 0.92888823334157 20 0.0642970021960806 0.128594004392161 0.935702997803919 21 0.0507583176731013 0.101516635346203 0.949241682326899 22 0.0396457839863998 0.0792915679727996 0.9603542160136 23 0.033523396310532 0.067046792621064 0.966476603689468 24 0.0341889678363749 0.0683779356727497 0.965811032163625 25 0.0181383352221632 0.0362766704443263 0.981861664777837 26 0.00927939234617044 0.0185587846923409 0.99072060765383 27 0.00470123306300318 0.00940246612600635 0.995298766936997 28 0.00231215356433267 0.00462430712866534 0.997687846435667 29 0.00108528531860556 0.00217057063721112 0.998914714681394 30 0.000761690507238535 0.00152338101447707 0.999238309492761 31 0.000562176238375916 0.00112435247675183 0.999437823761624 32 0.000375811228208713 0.000751622456417426 0.999624188771791 33 0.000233430199229907 0.000466860398459814 0.99976656980077 34 0.000131112315036765 0.00026222463007353 0.999868887684963 35 7.19339846871662e-05 0.000143867969374332 0.999928066015313 36 0.000126343612511606 0.000252687225023212 0.999873656387488 37 9.01578437089638e-05 0.000180315687417928 0.999909842156291 38 6.3743267410514e-05 0.000127486534821028 0.99993625673259 39 9.90635803595545e-05 0.000198127160719109 0.99990093641964 40 0.000165269185497201 0.000330538370994402 0.999834730814503 41 0.000237844487969219 0.000475688975938439 0.99976215551203 42 0.00081066869037153 0.00162133738074306 0.999189331309629 43 0.00351088784158653 0.00702177568317305 0.996489112158413 44 0.0148133394014294 0.0296266788028588 0.98518666059857 45 0.0514436858360653 0.102887371672131 0.948556314163935 46 0.0994014784728197 0.198802956945639 0.90059852152718 47 0.139371179891806 0.278742359783612 0.860628820108194 48 0.156990561446664 0.313981122893328 0.843009438553336 49 0.174184139721131 0.348368279442261 0.82581586027887 50 0.186039799684137 0.372079599368275 0.813960200315863 51 0.187859060478916 0.375718120957832 0.812140939521084 52 0.179084645094056 0.358169290188113 0.820915354905944 53 0.165147134476745 0.33029426895349 0.834852865523255 54 0.143755847181456 0.287511694362913 0.856244152818544 55 0.133550290380869 0.267100580761739 0.86644970961913 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.435897435897436 NOK 5% type I error level 21 0.538461538461538 NOK 10% type I error level 24 0.615384615384615 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/10o4bx1259326235.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/10o4bx1259326235.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/13f9x1259326235.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/13f9x1259326235.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/23cuf1259326235.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/23cuf1259326235.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/3ow801259326235.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/3ow801259326235.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/47ur61259326235.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/47ur61259326235.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/5zfww1259326235.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/5zfww1259326235.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/689wx1259326235.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/689wx1259326235.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/7oqfn1259326235.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/7oqfn1259326235.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/8idd61259326235.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/8idd61259326235.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/9b5lr1259326235.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259326616y5r6x019dicxi5e/9b5lr1259326235.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