WS8

*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, 30 Nov 2010 13:01:48 +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/Nov/30/t1291122033odgm44wj735c8q2.htm/, Retrieved Tue, 30 Nov 2010 14:00:43 +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/Nov/30/t1291122033odgm44wj735c8q2.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 «

98,60 627 98,97 696 99,11 825 99,64 677 100,03 656 99,98 785 100,32 412 100,44 352 100,51 839 101,00 729 100,88 696 100,55 641 100,83 695 101,51 638 102,16 762 102,39 635 102,54 721 102,85 854 103,47 418 103,57 367 103,69 824 103,50 687 103,47 601 103,45 676 103,48 740 103,93 691 103,89 683 104,40 594 104,79 729 104,77 731 105,13 386 105,26 331 104,96 707 104,75 715 105,01 657 105,15 653 105,20 642 105,77 643 105,78 718 106,26 654 106,13 632 106,12 731 106,57 392 106,44 344 106,54 792 107,10 852 108,10 649 108,40 629 108,84 685 109,62 617 110,42 715 110,67 715 111,66 629 112,28 916 112,87 531 112,18 357 112,36 917 112,16 828 111,49 708 111,25 858 111,36 775 111,74 785 111,10 1006 111,33 789 111,25 734 111,04 906 110,97 532 111,31 387 111,02 991 111,07 841 111,36 892 111,54 782

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 CPI[t] = + 102.109995585164 + 0.00584361109028173Faillissementen[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) 102.109995585164 2.22663 45.8585 0 0 Faillissementen 0.00584361109028173 0.003189 1.8322 0.071176 0.035588 Multiple Linear Regression - Regression Statistics Multiple R 0.2139203175466 R-squared 0.0457619022592383 Adjusted R-squared 0.0321299294343703 F-TEST (value) 3.35695374742513 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 0.0711755913075947 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.2066072515111 Sum Squared Residuals 1238.68811979260 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.6 105.773939738771 -7.17393973877095 2 98.97 106.177148904000 -7.20714890400045 3 99.11 106.930974734647 -7.82097473464679 4 99.64 106.066120293285 -6.4261202932851 5 100.03 105.943404460389 -5.91340446038918 6 99.98 106.697230291036 -6.71723029103552 7 100.32 104.517563354360 -4.19756335436044 8 100.44 104.166946688944 -3.72694668894354 9 100.51 107.012785289911 -6.50278528991073 10 101 106.369988069980 -5.36998806997974 11 100.88 106.177148904000 -5.29714890400045 12 100.55 105.855750294035 -5.30575029403496 13 100.83 106.171305292910 -5.34130529291017 14 101.51 105.838219460764 -4.3282194607641 15 102.16 106.562827235959 -4.40282723595905 16 102.39 105.820688627493 -3.43068862749326 17 102.54 106.323239181257 -3.78323918125748 18 102.85 107.100439456265 -4.25043945626497 19 103.47 104.552625020902 -1.08262502090213 20 103.57 104.254600855298 -0.684600855297766 21 103.69 106.925131123557 -3.23513112355651 22 103.5 106.124556404188 -2.62455640418791 23 103.47 105.622005850424 -2.15200585042369 24 103.45 106.060276682195 -2.61027668219481 25 103.48 106.434267791973 -2.95426779197284 26 103.93 106.147930848549 -2.21793084854903 27 103.89 106.101181959827 -2.21118195982679 28 104.4 105.581100572792 -1.18110057279171 29 104.79 106.369988069980 -1.57998806997974 30 104.77 106.381675292160 -1.61167529216031 31 105.13 104.365629466013 0.764370533986883 32 105.26 104.044230856048 1.21576914395239 33 104.96 106.241428625994 -1.28142862599355 34 104.75 106.288177514716 -1.5381775147158 35 105.01 105.949248071479 -0.939248071479456 36 105.15 105.925873627118 -0.775873627118328 37 105.2 105.861593905125 -0.661593905125232 38 105.77 105.867437516216 -0.0974375162155204 39 105.78 106.305708347987 -0.525708347986645 40 106.26 105.931717238209 0.32828276179139 41 106.13 105.803157794222 0.326842205777578 42 106.12 106.381675292160 -0.261675292160304 43 106.57 104.400691132555 2.16930886744519 44 106.44 104.120197800221 2.31980219977872 45 106.54 106.738135568667 -0.198135568667488 46 107.1 107.088752234084 0.0112477659155968 47 108.1 105.902499182757 2.19750081724279 48 108.4 105.785626960952 2.61437303904843 49 108.84 106.112869182007 2.72713081799265 50 109.62 105.715503627868 3.90449637213181 51 110.42 106.288177514716 4.1318224852842 52 110.67 106.288177514716 4.3818224852842 53 111.66 105.785626960952 5.87437303904842 54 112.28 107.462743343862 4.81725665613757 55 112.87 105.212953074104 7.65704692589604 56 112.18 104.196164744395 7.98383525560506 57 112.36 107.468586954953 4.89141304504729 58 112.16 106.948505567918 5.21149443208236 59 111.49 106.247272237084 5.24272776291617 60 111.25 107.123813900626 4.12618609937391 61 111.36 106.638794180133 4.7212058198673 62 111.74 106.697230291036 5.04276970896447 63 111.1 107.988668341988 3.11133165801221 64 111.33 106.720604735397 4.60939526460335 65 111.25 106.399206125431 4.85079387456885 66 111.04 107.404307232960 3.63569276704040 67 110.97 105.218796685194 5.75120331480575 68 111.31 104.371473077103 6.9385269228966 69 111.02 107.901014175634 3.11898582436644 70 111.07 107.024472512091 4.04552748790869 71 111.36 107.322496677696 4.03750332230433 72 111.54 106.679699457765 4.86030054223533 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00643062129253263 0.0128612425850653 0.993569378707467 6 0.00159963217997478 0.00319926435994956 0.998400367820025 7 0.000424917605126498 0.000849835210252996 0.999575082394873 8 7.19326592917486e-05 0.000143865318583497 0.999928067340708 9 6.76801527059401e-05 0.000135360305411880 0.999932319847294 10 6.1251738648522e-05 0.000122503477297044 0.999938748261352 11 3.27571337805318e-05 6.55142675610637e-05 0.99996724286622 12 1.14391896602778e-05 2.28783793205555e-05 0.99998856081034 13 5.60762694100396e-06 1.12152538820079e-05 0.99999439237306 14 6.08796943080518e-06 1.21759388616104e-05 0.99999391203057 15 1.71110105254843e-05 3.42220210509687e-05 0.999982888989475 16 3.50598219088603e-05 7.01196438177206e-05 0.999964940178091 17 6.57989771804992e-05 0.000131597954360998 0.99993420102282 18 0.000127102461328258 0.000254204922656516 0.999872897538672 19 0.000335630539051721 0.000671261078103441 0.999664369460948 20 0.000398906644123061 0.000797813288246122 0.999601093355877 21 0.00109987865438494 0.00219975730876987 0.998900121345615 22 0.00162829160591533 0.00325658321183066 0.998371708394085 23 0.00197638366598431 0.00395276733196862 0.998023616334016 24 0.00261694874633757 0.00523389749267514 0.997383051253662 25 0.00386460632013555 0.0077292126402711 0.996135393679864 26 0.00594845250558333 0.0118969050111667 0.994051547494417 27 0.0088094289224485 0.017618857844897 0.991190571077551 28 0.0127995647809938 0.0255991295619876 0.987200435219006 29 0.0231471851786114 0.0462943703572229 0.976852814821389 30 0.0387253999307816 0.0774507998615632 0.961274600069218 31 0.0453697935673204 0.0907395871346408 0.95463020643268 32 0.0456880548493623 0.0913761096987246 0.954311945150638 33 0.0712250472671665 0.142450094534333 0.928774952732834 34 0.109433881531157 0.218867763062315 0.890566118468843 35 0.155322585218564 0.310645170437127 0.844677414781436 36 0.218383207209663 0.436766414419327 0.781616792790337 37 0.301667836475998 0.603335672951997 0.698332163524002 38 0.402374613159346 0.804749226318691 0.597625386840654 39 0.545285205485311 0.909429589029377 0.454714794514688 40 0.665242067959318 0.669515864081365 0.334757932040682 41 0.777867533992098 0.444264932015803 0.222132466007902 42 0.894971252849956 0.210057494300088 0.105028747150044 43 0.932102616282265 0.135794767435470 0.0678973837177351 44 0.971324185445222 0.0573516291095551 0.0286758145547775 45 0.996334517755363 0.00733096448927454 0.00366548224463727 46 0.999811455366968 0.000377089266064803 0.000188544633032401 47 0.999985110546918 2.97789061647867e-05 1.48894530823933e-05 48 0.999999427711169 1.14457766239746e-06 5.72288831198732e-07 49 0.999999989886336 2.02273286426521e-08 1.01136643213260e-08 50 0.99999999961079 7.78420848778272e-10 3.89210424389136e-10 51 0.999999999881673 2.36654807852616e-10 1.18327403926308e-10 52 0.999999999931169 1.37662419028644e-10 6.88312095143218e-11 53 0.999999999847127 3.05745467685714e-10 1.52872733842857e-10 54 0.999999999895579 2.08842987132549e-10 1.04421493566274e-10 55 0.999999999987335 2.53302067431205e-11 1.26651033715603e-11 56 0.999999999983515 3.29694328016840e-11 1.64847164008420e-11 57 0.999999999997333 5.33486478909265e-12 2.66743239454632e-12 58 0.999999999999808 3.84789615230864e-13 1.92394807615432e-13 59 0.999999999998304 3.39243523319892e-12 1.69621761659946e-12 60 0.999999999975602 4.87961113212484e-11 2.43980556606242e-11 61 0.999999999681459 6.37082684147208e-10 3.18541342073604e-10 62 0.999999999641743 7.1651321117081e-10 3.58256605585405e-10 63 0.999999993216299 1.35674020977489e-08 6.78370104887444e-09 64 0.99999989304974 2.13900517744144e-07 1.06950258872072e-07 65 0.999998080202973 3.83959405469148e-06 1.91979702734574e-06 66 0.99997082986388 5.83402722397129e-05 2.91701361198564e-05 67 0.999754662594524 0.000490674810951721 0.000245337405475860 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 43 0.682539682539683 NOK 5% type I error level 48 0.761904761904762 NOK 10% type I error level 52 0.825396825396825 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/105s5h1291122100.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/105s5h1291122100.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/1r0pq1291122100.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/1r0pq1291122100.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/2r0pq1291122100.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/2r0pq1291122100.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/3r0pq1291122100.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/3r0pq1291122100.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/42r6t1291122100.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/42r6t1291122100.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/52r6t1291122100.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/52r6t1291122100.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/62r6t1291122100.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/62r6t1291122100.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/7ujoe1291122100.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/7ujoe1291122100.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/85s5h1291122100.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/85s5h1291122100.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/95s5h1291122100.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291122033odgm44wj735c8q2/95s5h1291122100.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