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: Mon, 27 Dec 2010 13:12:45 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u.htm/, Retrieved Mon, 27 Dec 2010 14:27:21 +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/2010/Dec/27/t1293456429cqzn2kg8n14ry4u.htm/}, year = {2010}, } @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 = {2010}, 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 «

921365 0 987921 0 1132614 0 1332224 0 1418133 0 1411549 0 1695920 0 1636173 0 1539653 0 1395314 0 1127575 0 1036076 0 989236 0 1008380 0 1207763 0 1368839 0 1469798 0 1498721 0 1761769 0 1653214 0 1599104 0 1421179 0 1163995 0 1037735 0 1015407 0 1039210 0 1258049 0 1469445 0 1552346 0 1549144 0 1785895 0 1662335 0 1629440 0 1467430 0 1202209 0 1076982 0 1039367 0 1063449 0 1335135 0 1491602 0 1591972 0 1641248 0 1898849 0 1798580 0 1762444 0 1622044 0 1368955 0 1262973 0 1195650 0 1269530 0 1479279 0 1607819 0 1712466 0 1721766 0 1949843 1 1821326 1 1757802 1 1590367 1 1260647 1 1149235 1 1016367 1 1027885 1 1262159 1 1520854 1 1544144 1 1564709 1 1821776 1 1741365 1 1623386 1 1498658 1 1241822 1 1136029 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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation passagiers[t] = + 1103434.48290598 + 39211.5512820513dummy[t] -80404.4081196572M1[t] -43907.2414529915M2[t] + 169196.758547008M3[t] + 355160.758547009M4[t] + 438173.425213676M5[t] + 454553.091880342M6[t] + 702503.666666667M7[t] + 602327.166666667M8[t] + 535466.5M9[t] + 382660.333333333M10[t] + 111028.833333333M11[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 1103434.48290598 39892.29548 27.6603 0 0 dummy 39211.5512820513 26432.202411 1.4835 0.143271 0.071635 M1 -80404.4081196572 55199.091154 -1.4566 0.150521 0.075261 M2 -43907.2414529915 55199.091154 -0.7954 0.42955 0.214775 M3 169196.758547008 55199.091154 3.0652 0.003278 0.001639 M4 355160.758547009 55199.091154 6.4342 0 0 M5 438173.425213676 55199.091154 7.9381 0 0 M6 454553.091880342 55199.091154 8.2348 0 0 M7 702503.666666667 55023.017049 12.7675 0 0 M8 602327.166666667 55023.017049 10.9468 0 0 M9 535466.5 55023.017049 9.7317 0 0 M10 382660.333333333 55023.017049 6.9546 0 0 M11 111028.833333333 55023.017049 2.0179 0.048161 0.02408 Multiple Linear Regression - Regression Statistics Multiple R 0.947095602577047 R-squared 0.89699008042078 Adjusted R-squared 0.876038910336871 F-TEST (value) 42.8133644483026 F-TEST (DF numerator) 12 F-TEST (DF denominator) 59 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 95302.661114966 Sum Squared Residuals 535873235720.048 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 921365 1023030.07478632 -101665.074786320 2 987921 1059527.24145299 -71606.2414529916 3 1132614 1272631.24145299 -140017.241452991 4 1332224 1458595.24145299 -126371.241452991 5 1418133 1541607.90811966 -123474.908119658 6 1411549 1557987.57478632 -146438.574786325 7 1695920 1805938.14957265 -110018.14957265 8 1636173 1705761.64957265 -69588.6495726496 9 1539653 1638900.98290598 -99247.9829059835 10 1395314 1486094.81623932 -90780.8162393164 11 1127575 1214463.31623932 -86888.316239316 12 1036076 1103434.48290598 -67358.482905983 13 989236 1023030.07478633 -33794.0747863257 14 1008380 1059527.24145299 -51147.2414529914 15 1207763 1272631.24145299 -64868.2414529914 16 1368839 1458595.24145299 -89756.2414529915 17 1469798 1541607.90811966 -71809.9081196583 18 1498721 1557987.57478632 -59266.5747863248 19 1761769 1805938.14957265 -44169.1495726494 20 1653214 1705761.64957265 -52547.6495726496 21 1599104 1638900.98290598 -39796.9829059829 22 1421179 1486094.81623932 -64915.8162393163 23 1163995 1214463.31623932 -50468.3162393163 24 1037735 1103434.48290598 -65699.482905983 25 1015407 1023030.07478633 -7623.07478632566 26 1039210 1059527.24145299 -20317.2414529914 27 1258049 1272631.24145299 -14582.2414529915 28 1469445 1458595.24145299 10849.7585470085 29 1552346 1541607.90811966 10738.0918803417 30 1549144 1557987.57478632 -8843.57478632481 31 1785895 1805938.14957265 -20043.1495726494 32 1662335 1705761.64957265 -43426.6495726496 33 1629440 1638900.98290598 -9460.98290598286 34 1467430 1486094.81623932 -18664.8162393163 35 1202209 1214463.31623932 -12254.3162393163 36 1076982 1103434.48290598 -26452.4829059830 37 1039367 1023030.07478633 16336.9252136743 38 1063449 1059527.24145299 3921.75854700859 39 1335135 1272631.24145299 62503.7585470085 40 1491602 1458595.24145299 33006.7585470085 41 1591972 1541607.90811966 50364.0918803418 42 1641248 1557987.57478632 83260.4252136752 43 1898849 1805938.14957265 92910.8504273505 44 1798580 1705761.64957265 92818.3504273504 45 1762444 1638900.98290598 123543.017094017 46 1622044 1486094.81623932 135949.183760684 47 1368955 1214463.31623932 154491.683760684 48 1262973 1103434.48290598 159538.517094017 49 1195650 1023030.07478633 172619.925213674 50 1269530 1059527.24145299 210002.758547009 51 1479279 1272631.24145299 206647.758547009 52 1607819 1458595.24145299 149223.758547009 53 1712466 1541607.90811966 170858.091880342 54 1721766 1557987.57478632 163778.425213675 55 1949843 1845149.7008547 104693.299145299 56 1821326 1744973.2008547 76352.7991452992 57 1757802 1678112.53418803 79689.465811966 58 1590367 1525306.36752137 65060.6324786325 59 1260647 1253674.86752137 6972.13247863245 60 1149235 1142646.03418803 6588.96581196577 61 1016367 1062241.62606838 -45874.6260683769 62 1027885 1098738.79273504 -70853.7927350427 63 1262159 1311842.79273504 -49683.7927350426 64 1520854 1497806.79273504 23047.2072649574 65 1544144 1580819.45940171 -36675.4594017095 66 1564709 1597199.12606838 -32490.1260683761 67 1821776 1845149.7008547 -23373.7008547008 68 1741365 1744973.2008547 -3608.20085470078 69 1623386 1678112.53418803 -54726.5341880341 70 1498658 1525306.36752137 -26648.3675213675 71 1241822 1253674.86752137 -11852.8675213675 72 1136029 1142646.03418803 -6617.03418803423 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.151578992053053 0.303157984106107 0.848421007946947 17 0.0919875358194997 0.183975071638999 0.9080124641805 18 0.0946873721350929 0.189374744270186 0.905312627864907 19 0.0709885610660065 0.141977122132013 0.929011438933994 20 0.0375818579525553 0.0751637159051106 0.962418142047445 21 0.0271193487673325 0.0542386975346649 0.972880651232668 22 0.015875710794204 0.031751421588408 0.984124289205796 23 0.00969066161052813 0.0193813232210563 0.990309338389472 24 0.00537819118157408 0.0107563823631482 0.994621808818426 25 0.00474733667349122 0.00949467334698243 0.99525266332651 26 0.00316129019830049 0.00632258039660097 0.9968387098017 27 0.00563984430562428 0.0112796886112486 0.994360155694376 28 0.0153026101597093 0.0306052203194186 0.98469738984029 29 0.0245602879796676 0.0491205759593351 0.975439712020332 30 0.0319079255651135 0.0638158511302271 0.968092074434886 31 0.0325149505571447 0.0650299011142895 0.967485049442855 32 0.0336870266332182 0.0673740532664364 0.966312973366782 33 0.0366649863152602 0.0733299726305203 0.96333501368474 34 0.048131989306732 0.096263978613464 0.951868010693268 35 0.061429505744196 0.122859011488392 0.938570494255804 36 0.095023676931629 0.190047353863258 0.904976323068371 37 0.114381009206341 0.228762018412683 0.885618990793658 38 0.153329980041251 0.306659960082503 0.846670019958749 39 0.266960788628744 0.533921577257487 0.733039211371256 40 0.395259402791487 0.790518805582973 0.604740597208513 41 0.486748919622896 0.97349783924579 0.513251080377104 42 0.592843847473193 0.814312305053613 0.407156152526807 43 0.73789744909713 0.524205101805741 0.262102550902870 44 0.855674143219 0.288651713562002 0.144325856781001 45 0.899469821754189 0.201060356491622 0.100530178245811 46 0.936009699531245 0.127980600937509 0.0639903004687546 47 0.94517269755022 0.109654604899559 0.0548273024497793 48 0.951145798445916 0.0977084031081681 0.0488542015540841 49 0.942199237151057 0.115601525697886 0.0578007628489428 50 0.951112237130416 0.097775525739167 0.0488877628695835 51 0.951007286239593 0.097985427520814 0.048992713760407 52 0.935575807572686 0.128848384854628 0.064424192427314 53 0.896551868622379 0.206896262755242 0.103448131377621 54 0.83163152984362 0.33673694031276 0.16836847015638 55 0.834757839621251 0.330484320757498 0.165242160378749 56 0.754804519844492 0.490390960311015 0.245195480155508 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.048780487804878 NOK 5% type I error level 8 0.195121951219512 NOK 10% type I error level 18 0.439024390243902 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/10hm7i1293455557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/10hm7i1293455557.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/1ibqt1293455556.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/1ibqt1293455556.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/2lus91293455557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/2lus91293455557.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/3lus91293455557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/3lus91293455557.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/4lus91293455557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/4lus91293455557.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/5lus91293455557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/5lus91293455557.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/6elrc1293455557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/6elrc1293455557.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/77v8f1293455557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/77v8f1293455557.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/87v8f1293455557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/87v8f1293455557.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/97v8f1293455557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293456429cqzn2kg8n14ry4u/97v8f1293455557.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by