Model 1

*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, 28 Dec 2010 19:46:23 +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/28/t12935655435hc0lyzimw1ykf0.htm/, Retrieved Tue, 28 Dec 2010 20:45:54 +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/28/t12935655435hc0lyzimw1ykf0.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 «

4,24 0 4,15 0 3,93 0 3,7 0 3,7 0 3,65 0 3,55 0 3,43 0 3,47 0 3,58 0 3,67 0 3,72 0 3,8 0 3,76 0 3,63 0 3,48 0 3,41 0 3,43 0 3,5 0 3,62 0 3,58 0 3,52 0 3,45 0 3,36 0 3,27 0 3,21 0 3,19 0 3,16 0 3,12 0 3,06 0 3,01 0 2,98 0 2,97 0 3,02 0 3,07 0 3,18 0 3,29 1 3,43 1 3,61 1 3,74 1 3,87 1 3,88 1 4,09 1 4,19 1 4,2 1 4,29 1 4,37 1 4,47 1 4,61 1 4,65 1 4,69 1 4,82 1 4,86 1 4,87 1 5,01 1 5,03 1 5,13 1 5,18 1 5,21 1 5,26 1 5,25 1 5,2 1 5,16 1 5,19 1 5,39 1 5,58 1 5,76 1 5,89 1 5,98 1 6,02 1 5,62 1 4,87 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Rente[t] = + 3.46027777777778 + 1.33583333333333dummy[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) 3.46027777777778 0.093 37.2074 0 0 dummy 1.33583333333333 0.131521 10.1568 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.771848119056216 R-squared 0.595749518890619 Adjusted R-squared 0.589974512017628 F-TEST (value) 103.159967077589 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 2.10942374678780e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.557998371448953 Sum Squared Residuals 21.7953527777778 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.24 3.46027777777775 0.779722222222249 2 4.15 3.46027777777778 0.689722222222225 3 3.93 3.46027777777778 0.469722222222222 4 3.7 3.46027777777778 0.239722222222222 5 3.7 3.46027777777778 0.239722222222222 6 3.65 3.46027777777778 0.189722222222221 7 3.55 3.46027777777778 0.0897222222222212 8 3.43 3.46027777777778 -0.0302777777777785 9 3.47 3.46027777777778 0.00972222222222156 10 3.58 3.46027777777778 0.119722222222221 11 3.67 3.46027777777778 0.209722222222221 12 3.72 3.46027777777778 0.259722222222222 13 3.8 3.46027777777778 0.339722222222221 14 3.76 3.46027777777778 0.299722222222221 15 3.63 3.46027777777778 0.169722222222221 16 3.48 3.46027777777778 0.0197222222222213 17 3.41 3.46027777777778 -0.0502777777777785 18 3.43 3.46027777777778 -0.0302777777777785 19 3.5 3.46027777777778 0.0397222222222214 20 3.62 3.46027777777778 0.159722222222221 21 3.58 3.46027777777778 0.119722222222221 22 3.52 3.46027777777778 0.0597222222222214 23 3.45 3.46027777777778 -0.0102777777777785 24 3.36 3.46027777777778 -0.100277777777779 25 3.27 3.46027777777778 -0.190277777777779 26 3.21 3.46027777777778 -0.250277777777779 27 3.19 3.46027777777778 -0.270277777777779 28 3.16 3.46027777777778 -0.300277777777778 29 3.12 3.46027777777778 -0.340277777777779 30 3.06 3.46027777777778 -0.400277777777779 31 3.01 3.46027777777778 -0.450277777777779 32 2.98 3.46027777777778 -0.480277777777779 33 2.97 3.46027777777778 -0.490277777777778 34 3.02 3.46027777777778 -0.440277777777779 35 3.07 3.46027777777778 -0.390277777777779 36 3.18 3.46027777777778 -0.280277777777778 37 3.29 4.79611111111111 -1.50611111111111 38 3.43 4.79611111111111 -1.36611111111111 39 3.61 4.79611111111111 -1.18611111111111 40 3.74 4.79611111111111 -1.05611111111111 41 3.87 4.79611111111111 -0.926111111111111 42 3.88 4.79611111111111 -0.916111111111111 43 4.09 4.79611111111111 -0.706111111111111 44 4.19 4.79611111111111 -0.60611111111111 45 4.2 4.79611111111111 -0.596111111111111 46 4.29 4.79611111111111 -0.506111111111111 47 4.37 4.79611111111111 -0.426111111111111 48 4.47 4.79611111111111 -0.326111111111111 49 4.61 4.79611111111111 -0.186111111111111 50 4.65 4.79611111111111 -0.146111111111111 51 4.69 4.79611111111111 -0.106111111111111 52 4.82 4.79611111111111 0.0238888888888892 53 4.86 4.79611111111111 0.0638888888888893 54 4.87 4.79611111111111 0.073888888888889 55 5.01 4.79611111111111 0.213888888888889 56 5.03 4.79611111111111 0.233888888888889 57 5.13 4.79611111111111 0.333888888888889 58 5.18 4.79611111111111 0.383888888888889 59 5.21 4.79611111111111 0.413888888888889 60 5.26 4.79611111111111 0.463888888888889 61 5.25 4.79611111111111 0.453888888888889 62 5.2 4.79611111111111 0.403888888888889 63 5.16 4.79611111111111 0.363888888888889 64 5.19 4.79611111111111 0.393888888888889 65 5.39 4.79611111111111 0.593888888888889 66 5.58 4.79611111111111 0.783888888888889 67 5.76 4.79611111111111 0.963888888888889 68 5.89 4.79611111111111 1.09388888888889 69 5.98 4.79611111111111 1.18388888888889 70 6.02 4.79611111111111 1.22388888888889 71 5.62 4.79611111111111 0.823888888888889 72 4.87 4.79611111111111 0.073888888888889 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.151476406393511 0.302952812787022 0.84852359360649 6 0.0948099252613786 0.189619850522757 0.905190074738621 7 0.0698073607478735 0.139614721495747 0.930192639252126 8 0.063285000171339 0.126570000342678 0.93671499982866 9 0.0445798339583122 0.0891596679166243 0.955420166041688 10 0.0240205620161544 0.0480411240323087 0.975979437983846 11 0.0113111316810147 0.0226222633620294 0.988688868318985 12 0.0050217303278652 0.0100434606557304 0.994978269672135 13 0.00223512891716673 0.00447025783433346 0.997764871082833 14 0.000935469434208496 0.00187093886841699 0.999064530565791 15 0.000400442073226219 0.000800884146452437 0.999599557926774 16 0.000238845222529865 0.000477690445059731 0.99976115477747 17 0.000173710525245029 0.000347421050490059 0.999826289474755 18 0.000108181903620846 0.000216363807241693 0.99989181809638 19 5.2381688033346e-05 0.000104763376066692 0.999947618311967 20 2.08767563803064e-05 4.17535127606127e-05 0.99997912324362 21 8.43437871486584e-06 1.68687574297317e-05 0.999991565621285 22 3.68149888313095e-06 7.3629977662619e-06 0.999996318501117 23 1.88849858977337e-06 3.77699717954674e-06 0.99999811150141 24 1.31152628326649e-06 2.62305256653298e-06 0.999998688473717 25 1.29868468940956e-06 2.59736937881912e-06 0.99999870131531 26 1.54079466668992e-06 3.08158933337984e-06 0.999998459205333 27 1.72853343001125e-06 3.4570668600225e-06 0.99999827146657 28 1.94627858076838e-06 3.89255716153675e-06 0.99999805372142 29 2.29968432243382e-06 4.59936864486764e-06 0.999997700315678 30 3.09295221401991e-06 6.18590442803981e-06 0.999996907047786 31 4.41709637518244e-06 8.83419275036488e-06 0.999995582903625 32 6.06867414200189e-06 1.21373482840038e-05 0.999993931325858 33 7.48789816416574e-06 1.49757963283315e-05 0.999992512101836 34 6.93318973752320e-06 1.38663794750464e-05 0.999993066810262 35 5.18983623420795e-06 1.03796724684159e-05 0.999994810163766 36 2.89174394687952e-06 5.78348789375903e-06 0.999997108256053 37 6.2297774238386e-06 1.24595548476772e-05 0.999993770222576 38 1.42037461013191e-05 2.84074922026383e-05 0.999985796253899 39 3.23383583514384e-05 6.46767167028768e-05 0.999967661641649 40 7.66629215368358e-05 0.000153325843073672 0.999923337078463 41 0.000186487108984287 0.000372974217968575 0.999813512891016 42 0.000515177270202374 0.00103035454040475 0.999484822729798 43 0.00134760472286791 0.00269520944573583 0.998652395277132 44 0.00337363325708251 0.00674726651416503 0.996626366742917 45 0.00847773547950086 0.0169554709590017 0.9915222645205 46 0.0203268806626711 0.0406537613253423 0.97967311933733 47 0.0454885943345327 0.0909771886690654 0.954511405665467 48 0.090960000802519 0.181920001605038 0.909039999197481 49 0.155311474519630 0.310622949039259 0.84468852548037 50 0.240060113813244 0.480120227626488 0.759939886186756 51 0.342579737729159 0.685159475458318 0.657420262270841 52 0.434562949468246 0.869125898936492 0.565437050531754 53 0.519166601025711 0.961666797948577 0.480833398974289 54 0.601573199720843 0.796853600558314 0.398426800279157 55 0.649904015947377 0.700191968105247 0.350095984052623 56 0.687253717687832 0.625492564624337 0.312746282312169 57 0.70169194190572 0.59661611618856 0.29830805809428 58 0.701736254393592 0.596527491212816 0.298263745606408 59 0.690507644913285 0.61898471017343 0.309492355086715 60 0.665745833944626 0.668508332110749 0.334254166055374 61 0.634577317778608 0.730845364442784 0.365422682221392 62 0.610129946983112 0.779740106033776 0.389870053016888 63 0.603959127362142 0.792081745275715 0.396040872637858 64 0.601877547246621 0.796244905506758 0.398122452753379 65 0.537915968360336 0.924168063279327 0.462084031639664 66 0.431276191397175 0.86255238279435 0.568723808602825 67 0.316085219220906 0.632170438441812 0.683914780779094 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 32 0.507936507936508 NOK 5% type I error level 37 0.587301587301587 NOK 10% type I error level 39 0.619047619047619 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/105lft1293565576.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/105lft1293565576.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/1gj0h1293565576.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/1gj0h1293565576.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/2gj0h1293565576.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/2gj0h1293565576.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/3rbzk1293565576.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/3rbzk1293565576.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/4rbzk1293565576.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/4rbzk1293565576.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/5rbzk1293565576.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/5rbzk1293565576.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/622y51293565576.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/622y51293565576.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/7vbgq1293565576.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/7vbgq1293565576.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/8vbgq1293565576.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/8vbgq1293565576.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/9vbgq1293565576.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935655435hc0lyzimw1ykf0/9vbgq1293565576.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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