*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: Wed, 18 Nov 2009 08:02:28 -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/18/t1258556589w0g39c5ypnbpjyn.htm/, Retrieved Wed, 18 Nov 2009 16:03:21 +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/2009/Nov/18/t1258556589w0g39c5ypnbpjyn.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

7.2 97.78 7.5 8.3 8.8 8.9 7.4 97.69 7.2 7.5 8.3 8.8 8.8 96.67 7.4 7.2 7.5 8.3 9.3 98.29 8.8 7.4 7.2 7.5 9.3 98.2 9.3 8.8 7.4 7.2 8.7 98.71 9.3 9.3 8.8 7.4 8.2 98.54 8.7 9.3 9.3 8.8 8.3 98.2 8.2 8.7 9.3 9.3 8.5 96.92 8.3 8.2 8.7 9.3 8.6 99.06 8.5 8.3 8.2 8.7 8.5 99.65 8.6 8.5 8.3 8.2 8.2 99.82 8.5 8.6 8.5 8.3 8.1 99.99 8.2 8.5 8.6 8.5 7.9 100.33 8.1 8.2 8.5 8.6 8.6 99.31 7.9 8.1 8.2 8.5 8.7 101.1 8.6 7.9 8.1 8.2 8.7 101.1 8.7 8.6 7.9 8.1 8.5 100.93 8.7 8.7 8.6 7.9 8.4 100.85 8.5 8.7 8.7 8.6 8.5 100.93 8.4 8.5 8.7 8.7 8.7 99.6 8.5 8.4 8.5 8.7 8.7 101.88 8.7 8.5 8.4 8.5 8.6 101.81 8.7 8.7 8.5 8.4 8.5 102.38 8.6 8.7 8.7 8.5 8.3 102.74 8.5 8.6 8.7 8.7 8 102.82 8.3 8.5 8.6 8.7 8.2 101.72 8 8.3 8.5 8.6 8.1 103.47 8.2 8 8.3 8.5 8.1 102.98 8.1 8.2 8 8.3 8 102.68 8.1 8.1 8.2 8 7.9 102.9 8 8.1 8.1 8.2 7.9 103.03 7.9 8 8.1 8.1 8 101.29 7.9 7.9 8 8.1 8 103.69 8 7.9 7.9 8 7.9 103.68 8 8 7.9 7.9 8 104.2 7.9 8 8 7.9 7.7 104.08 8 7.9 8 8 7.2 104.16 7.7 8 7.9 8 7.5 103.05 7.2 7.7 8 7.9 7. 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] = -4.2624270514545 + 0.0544200722822084X[t] + 1.46532978957155Y1[t] -0.781224197342366Y2[t] -0.145033370420391Y3[t] + 0.348490784025746Y4[t] -0.148355004747847M1[t] -0.124115508443071M2[t] + 0.67376339796129M3[t] -0.401217733032853M4[t] + 0.0053311824447823M5[t] + 0.140524604956648M6[t] + 0.0365083464145630M7[t] + 0.196594506474901M8[t] + 0.134986853801317M9[t] -0.0889181651253318M10[t] -0.0096689769740506M11[t] -0.0176296130558109t + 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) -4.2624270514545 3.264481 -1.3057 0.199505 0.099753 X 0.0544200722822084 0.030272 1.7977 0.08017 0.040085 Y1 1.46532978957155 0.136492 10.7356 0 0 Y2 -0.781224197342366 0.261591 -2.9864 0.00492 0.00246 Y3 -0.145033370420391 0.262515 -0.5525 0.583857 0.291928 Y4 0.348490784025746 0.143398 2.4302 0.019921 0.00996 M1 -0.148355004747847 0.102582 -1.4462 0.156315 0.078158 M2 -0.124115508443071 0.105851 -1.1725 0.248277 0.124139 M3 0.67376339796129 0.111311 6.053 0 0 M4 -0.401217733032853 0.139823 -2.8695 0.00668 0.00334 M5 0.0053311824447823 0.153972 0.0346 0.972561 0.48628 M6 0.140524604956648 0.125257 1.1219 0.268949 0.134475 M7 0.0365083464145630 0.1003 0.364 0.717882 0.358941 M8 0.196594506474901 0.103155 1.9058 0.064259 0.032129 M9 0.134986853801317 0.128021 1.0544 0.298352 0.149176 M10 -0.0889181651253318 0.112482 -0.7905 0.434137 0.217069 M11 -0.0096689769740506 0.106662 -0.0907 0.928246 0.464123 t -0.0176296130558109 0.006378 -2.764 0.008758 0.004379 Multiple Linear Regression - Regression Statistics Multiple R 0.986303646694806 R-squared 0.972794883483472 Adjusted R-squared 0.96062417346292 F-TEST (value) 79.9291809467797 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.147598926463559 Sum Squared Residuals 0.827846837541415 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.2 7.22386990047083 -0.0238699004708295 2 7.4 7.44863000502446 -0.048630005024465 3 8.8 8.64258534608562 0.157414653914379 4 9.3 9.29806936897006 0.00193063102993888 5 9.3 9.18748797410115 0.112512025898855 6 8.7 8.80884535996655 -0.108845359966547 7 8.2 8.2141206147636 -0.0141206147635935 8 8.3 8.24838935282469 0.051610647175313 9 8.5 8.72365949445464 -0.223659494454642 10 8.6 8.67694957013093 -0.0769495701309264 11 8.5 8.57221639830667 -0.072216398306669 12 8.2 8.35469418013999 -0.154694180139990 13 8.1 7.89167927725019 0.208320722749811 14 7.9 8.05397868076527 -0.153978680765273 15 8.6 8.57243689492944 0.0275631050705577 16 8.7 8.66916987426751 0.030830125732488 17 8.7 8.65192281318834 0.0480771868116619 18 8.5 8.51089127452276 -0.0108912745227593 19 8.4 8.32126605100396 0.0787339489960404 20 8.5 8.51263714270496 -0.0126371427049567 21 8.7 8.6146832536157 0.0853167463843062 22 8.7 8.65697510485363 0.0430248951463683 23 8.6 8.50918801997626 0.0908119800237387 24 8.5 8.3915562504567 0.108443749543299 25 8.3 8.24645045625687 0.0535495437431318 26 8 8.05697374415038 -0.0569737441503771 27 8.2 8.47366111922497 -0.273661119224971 28 8.1 7.9978763144674 0.102123685532596 29 8.1 8.03116381736629 0.0688361826337123 30 8 8.07697011558011 -0.0769701155801146 31 7.9 7.90496517477434 -0.0049651747743374 32 7.9 7.95123669355006 -0.0512366935500586 33 8 7.8699342588259 0.130065741174103 34 8 7.88519503791736 0.114804962082644 35 7.9 7.8332989141532 0.0667010858468063 36 8 7.69260039965899 0.307399600341011 37 7.7 7.77958985027543 -0.0795898502754307 38 7.2 7.28733531974331 -0.0873353197433102 39 7.5 7.45952828183093 0.0404717181690688 40 7.3 7.36310397922667 -0.0631039792266722 41 7 7.18167550007751 -0.181675500077507 42 7 6.82479564440986 0.175204355590137 43 7 7.12004900636047 -0.120049006360466 44 7.2 7.2569970351532 -0.0569970351532001 45 7.3 7.29172299310377 0.00827700689623347 46 7.1 7.18088028709809 -0.0808802870980856 47 6.8 6.88529666756388 -0.085296667563876 48 6.4 6.66114916974432 -0.261149169744320 49 6.1 6.25841051574668 -0.158410515746683 50 6.5 6.15308225031657 0.346917749683426 51 7.7 7.65178835792903 0.0482116420709653 52 7.9 7.97178046306835 -0.071780463068351 53 7.5 7.54774989526672 -0.0477498952667225 54 6.9 6.87849760552072 0.0215023944792845 55 6.6 6.53959915309764 0.0604008469023566 56 6.9 6.8307397757671 0.0692602242329024 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.108430814775104 0.216861629550209 0.891569185224896 22 0.065623032439734 0.131246064879468 0.934376967560266 23 0.0243472325451167 0.0486944650902333 0.975652767454883 24 0.0373026020705165 0.074605204141033 0.962697397929484 25 0.0163048471546606 0.0326096943093213 0.98369515284534 26 0.0146404853785898 0.0292809707571795 0.98535951462141 27 0.305188465798217 0.610376931596435 0.694811534201783 28 0.206229475982799 0.412458951965599 0.7937705240172 29 0.135059804563043 0.270119609126086 0.864940195436957 30 0.159969789922414 0.319939579844828 0.840030210077586 31 0.117582081318026 0.235164162636053 0.882417918681974 32 0.100394684213045 0.20078936842609 0.899605315786955 33 0.074822366564232 0.149644733128464 0.925177633435768 34 0.048200445245442 0.096400890490884 0.951799554754558 35 0.0216877117024728 0.0433754234049457 0.978312288297527 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 4 0.266666666666667 NOK 10% type I error level 6 0.4 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/10ys7g1258556542.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/10ys7g1258556542.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/1mfqd1258556542.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/1mfqd1258556542.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/203y81258556542.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/203y81258556542.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/33rq21258556542.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/33rq21258556542.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/4lvz01258556542.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/4lvz01258556542.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/5ft8a1258556542.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/5ft8a1258556542.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/64pws1258556542.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/64pws1258556542.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/7kx1v1258556542.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/7kx1v1258556542.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/8h6hl1258556542.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/8h6hl1258556542.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/9uerj1258556542.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556589w0g39c5ypnbpjyn/9uerj1258556542.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