Multuple 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: Tue, 17 Nov 2009 12:42:44 -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/17/t1258487199feun5ahojdntqdp.htm/, Retrieved Tue, 17 Nov 2009 20:46:50 +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/17/t1258487199feun5ahojdntqdp.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:

JSSHWWS7P4

Dataseries X:

» Textbox « » Textfile « » CSV «

9,2 7,6 9,2 10 10,9 11,1 9,5 7,8 9,2 9,2 10 10,9 9,6 7,8 9,5 9,2 9,2 10 9,5 7,8 9,6 9,5 9,2 9,2 9,1 7,5 9,5 9,6 9,5 9,2 8,9 7,5 9,1 9,5 9,6 9,5 9 7,1 8,9 9,1 9,5 9,6 10,1 7,5 9 8,9 9,1 9,5 10,3 7,5 10,1 9 8,9 9,1 10,2 7,6 10,3 10,1 9 8,9 9,6 7,7 10,2 10,3 10,1 9 9,2 7,7 9,6 10,2 10,3 10,1 9,3 7,9 9,2 9,6 10,2 10,3 9,4 8,1 9,3 9,2 9,6 10,2 9,4 8,2 9,4 9,3 9,2 9,6 9,2 8,2 9,4 9,4 9,3 9,2 9 8,2 9,2 9,4 9,4 9,3 9 7,9 9 9,2 9,4 9,4 9 7,3 9 9 9,2 9,4 9,8 6,9 9 9 9 9,2 10 6,6 9,8 9 9 9 9,8 6,7 10 9,8 9 9 9,3 6,9 9,8 10 9,8 9 9 7 9,3 9,8 10 9,8 9 7,1 9 9,3 9,8 10 9,1 7,2 9 9 9,3 9,8 9,1 7,1 9,1 9 9 9,3 9,1 6,9 9,1 9,1 9 9 9,2 7 9,1 9,1 9,1 9 8,8 6,8 9,2 9,1 9,1 9,1 8,3 6,4 8,8 9,2 9,1 9,1 8,4 6,7 8,3 8,8 9,2 9,1 8,1 6,6 8,4 8,3 8,8 9,2 7,7 6,4 8,1 8,4 8,3 8,8 7,9 6,3 7,7 8,1 8,4 8,3 7,9 6,2 7,9 7,7 8,1 8,4 8 6,5 7,9 7,9 7,7 8,1 7,9 6,8 8 7,9 7,9 7,7 7,6 6,8 7,9 8 7,9 7,9 7,1 6,4 7,6 7,9 8 7,9 6,8 6,1 7,1 7,6 7,9 8 6,5 5,8 6,8 7,1 7,6 7,9 6,9 6,1 6,5 6,8 7,1 7,6 8,2 7,2 6,9 6 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 0.80797132690177 + 0.0631745437846552X[t] + 1.41002342029138`Yt-1`[t] -0.566106428055065`Yt-2`[t] -0.308555685587945`Yt-3`[t] + 0.332779490804355`Yt-4`[t] + 0.201311751689259M1[t] -0.0408183587718783M2[t] -0.231707208277215M3[t] -0.134527991088984M4[t] -0.0880250719712937M5[t] -0.162099437565197M6[t] + 0.0678126634427705M7[t] + 0.66814134161663M8[t] -0.291632423961118M9[t] -0.225191440884684M10[t] + 0.191229439246727M11[t] -0.00471385316526875t + 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) 0.80797132690177 0.70568 1.145 0.259389 0.129694 X 0.0631745437846552 0.054447 1.1603 0.253171 0.126585 `Yt-1` 1.41002342029138 0.158127 8.917 0 0 `Yt-2` -0.566106428055065 0.280564 -2.0177 0.05072 0.02536 `Yt-3` -0.308555685587945 0.272636 -1.1318 0.264831 0.132415 `Yt-4` 0.332779490804355 0.146386 2.2733 0.028746 0.014373 M1 0.201311751689259 0.135867 1.4817 0.146669 0.073335 M2 -0.0408183587718783 0.149189 -0.2736 0.785873 0.392936 M3 -0.231707208277215 0.162334 -1.4274 0.161645 0.080822 M4 -0.134527991088984 0.145241 -0.9262 0.360166 0.180083 M5 -0.0880250719712937 0.136447 -0.6451 0.522724 0.261362 M6 -0.162099437565197 0.133765 -1.2118 0.233062 0.116531 M7 0.0678126634427705 0.138458 0.4898 0.627112 0.313556 M8 0.66814134161663 0.14402 4.6392 4.1e-05 2e-05 M9 -0.291632423961118 0.186162 -1.5665 0.125511 0.062755 M10 -0.225191440884684 0.203813 -1.1049 0.276154 0.138077 M11 0.191229439246727 0.15326 1.2477 0.219759 0.10988 t -0.00471385316526875 0.003499 -1.347 0.185952 0.092976 Multiple Linear Regression - Regression Statistics Multiple R 0.985384503603201 R-squared 0.970982619941328 Adjusted R-squared 0.958001160441395 F-TEST (value) 74.7976465933113 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.188034632645485 Sum Squared Residuals 1.34356687681666 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.2 9.12644231933897 0.0735576806610257 2 9.5 9.55626262578181 -0.0562626257818134 3 9.6 9.73100995594506 -0.131009955945064 4 9.5 9.52842214093716 -0.0284221409371571 5 9.1 9.26107915324316 -0.161079153243157 6 8.9 8.74387048785545 0.156129512144552 7 9 8.95237032298727 0.0476296770127346 8 10.1 9.9176229183046 0.182377081695388 9 10.3 10.3761497598725 -0.0761497598724539 10 10.2 10.0060704906401 0.193929509359871 11 9.6 9.86373803927828 -0.263738039278278 12 9.2 9.18273764026416 0.0172623597358371 13 9.3 9.26503640298123 0.0349635970187668 14 9.4 9.55012772363526 -0.150127723635256 15 9.4 9.36898875431931 0.0310112456806879 16 9.2 9.24087611065623 -0.0408761106562316 17 9 9.00308287307202 -0.00308287307201619 18 9 8.76983684181062 0.230163158189379 19 9 9.13206278611113 -0.132062786111129 20 9.8 9.69756303256258 0.102436967437426 21 10 9.7755858887564 0.224414111243603 22 9.8 9.67275001466025 0.127249985339748 23 9.3 9.45502143224368 -0.155021432243681 24 9 8.87811762520137 0.121882374798632 25 9 9.0693462013224 -0.0693462013223991 26 9.1 9.08637356512408 0.0136264348759192 27 9.1 8.95163271037835 0.148367289621646 28 9.1 8.87501867559757 0.224981324402428 29 9.2 8.89226962736966 0.307730372630335 30 8.8 8.97512679096313 -0.175126790963134 31 8.3 8.55443521036992 -0.254435210369914 32 8.4 8.65957769103144 -0.25957769103144 33 8.1 8.26952839728224 -0.169528397282241 34 7.7 7.86016899601578 -0.160168996015784 35 7.9 7.67413581494246 0.225864185057544 36 7.9 8.10616697818912 -0.206166978189116 37 8 8.23208438123136 -0.232084381231361 38 7.9 7.95037218933016 -0.0503721893301582 39 7.6 7.62371239998578 -0.0237123999857807 40 7.1 7.29365599465418 -0.193655994654177 41 6.8 6.84544643338126 -0.0454464333812602 42 6.5 6.66704079602276 -0.167040796022757 43 6.9 6.71246030488262 0.187539695117377 44 8.2 8.03758538486161 0.162414615138386 45 8.7 8.6787359540889 0.0212640459110920 46 8.3 8.46101049868384 -0.161010498683835 47 7.9 7.70710471353558 0.192895286464416 48 7.5 7.43297775634535 0.0670222436546477 49 7.8 7.60709069512603 0.192909304873968 50 8.3 8.0568638961287 0.243136103871308 51 8.4 8.42465617937149 -0.0246561793714894 52 8.2 8.16202707815486 0.0379729218451374 53 7.7 7.7981219129339 -0.098121912933902 54 7.2 7.24412508334804 -0.0441250833480401 55 7.3 7.14867137564907 0.151328624350930 56 8.1 8.28765097323976 -0.187650973239759 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.05508249326653 0.11016498653306 0.94491750673347 22 0.0491675824545699 0.0983351649091397 0.95083241754543 23 0.0257834265174718 0.0515668530349436 0.974216573482528 24 0.0151030274168766 0.0302060548337531 0.984896972583123 25 0.00974422248765228 0.0194884449753046 0.990255777512348 26 0.00364348251490151 0.00728696502980302 0.996356517485099 27 0.00132478563944305 0.00264957127888611 0.998675214360557 28 0.00310759093076395 0.00621518186152791 0.996892409069236 29 0.163744455252367 0.327488910504733 0.836255544747633 30 0.162011889963743 0.324023779927486 0.837988110036257 31 0.128800205659218 0.257600411318437 0.871199794340782 32 0.663627257077455 0.672745485845091 0.336372742922545 33 0.785562295018356 0.428875409963287 0.214437704981644 34 0.994752422859302 0.0104951542813958 0.00524757714069789 35 0.982351599806551 0.0352968003868976 0.0176484001934488 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.2 NOK 5% type I error level 7 0.466666666666667 NOK 10% type I error level 9 0.6 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/10pxwf1258486958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/10pxwf1258486958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/1j9n01258486958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/1j9n01258486958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/2fjhh1258486958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/2fjhh1258486958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/33y1e1258486958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/33y1e1258486958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/4fyy41258486958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/4fyy41258486958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/5wyl61258486958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/5wyl61258486958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/6va7g1258486958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/6va7g1258486958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/7tgai1258486958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/7tgai1258486958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/8tmy41258486958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/8tmy41258486958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/9ers81258486958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258487199feun5ahojdntqdp/9ers81258486958.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