*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, 24 Nov 2009 02:36:47 -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/24/t1259055660pl9rnsyu5l5hegi.htm/, Retrieved Tue, 24 Nov 2009 10:41:12 +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/24/t1259055660pl9rnsyu5l5hegi.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 «

103,52 0 103,5 0 103,52 0 103,53 0 103,53 0 103,53 0 103,52 0 103,54 0 103,59 0 103,59 0 103,59 0 103,59 0 103,63 0 103,74 0 103,7 0 103,72 0 103,81 0 103,8 0 104,22 0 106,91 1 107,06 1 107,17 1 107,25 1 107,28 1 107,24 1 107,23 1 107,34 1 107,34 1 107,3 1 107,24 1 107,3 1 107,32 1 107,28 1 107,33 1 107,33 1 107,33 1 107,28 1 107,28 1 107,29 1 107,29 1 107,23 1 107,24 1 107,24 1 107,2 1 107,23 1 107,2 1 107,21 1 107,24 1 107,21 1 113,89 1 114,05 1 114,05 1 114,05 1 114,05 1 115,12 1 115,68 1 116,05 1 116,18 1 116,35 1 116,44 1 117 1 117,61 1 118,17 1 118,33 1 118,33 1 118,42 1 118,5 1 118,67 1 119,09 1 119,14 1 119,23 1 119,33 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 103.640526315790 + 7.77683217477653X[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) 103.640526315790 0.983599 105.3687 0 0 X 7.77683217477653 1.146427 6.7835 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.629791177928378 R-squared 0.396636927796414 Adjusted R-squared 0.388017455336363 F-TEST (value) 46.0163808904445 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 3.09137426768302e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.28740968238986 Sum Squared Residuals 1286.73172492552 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 103.52 103.640526315789 -0.120526315788941 2 103.5 103.640526315789 -0.140526315789487 3 103.52 103.640526315790 -0.120526315789508 4 103.53 103.640526315790 -0.110526315789503 5 103.53 103.640526315790 -0.110526315789503 6 103.53 103.640526315790 -0.110526315789503 7 103.52 103.640526315790 -0.120526315789508 8 103.54 103.640526315790 -0.100526315789498 9 103.59 103.640526315790 -0.0505263157895004 10 103.59 103.640526315790 -0.0505263157895004 11 103.59 103.640526315790 -0.0505263157895004 12 103.59 103.640526315790 -0.0505263157895004 13 103.63 103.640526315790 -0.0105263157895084 14 103.74 103.640526315790 0.099473684210491 15 103.7 103.640526315790 0.059473684210499 16 103.72 103.640526315790 0.079473684210495 17 103.81 103.640526315790 0.169473684210498 18 103.8 103.640526315790 0.159473684210493 19 104.22 103.640526315790 0.579473684210495 20 106.91 111.417358490566 -4.50735849056604 21 107.06 111.417358490566 -4.35735849056604 22 107.17 111.417358490566 -4.24735849056604 23 107.25 111.417358490566 -4.16735849056604 24 107.28 111.417358490566 -4.13735849056604 25 107.24 111.417358490566 -4.17735849056604 26 107.23 111.417358490566 -4.18735849056603 27 107.34 111.417358490566 -4.07735849056603 28 107.34 111.417358490566 -4.07735849056603 29 107.3 111.417358490566 -4.11735849056604 30 107.24 111.417358490566 -4.17735849056604 31 107.3 111.417358490566 -4.11735849056604 32 107.32 111.417358490566 -4.09735849056604 33 107.28 111.417358490566 -4.13735849056604 34 107.33 111.417358490566 -4.08735849056604 35 107.33 111.417358490566 -4.08735849056604 36 107.33 111.417358490566 -4.08735849056604 37 107.28 111.417358490566 -4.13735849056604 38 107.28 111.417358490566 -4.13735849056604 39 107.29 111.417358490566 -4.12735849056603 40 107.29 111.417358490566 -4.12735849056603 41 107.23 111.417358490566 -4.18735849056603 42 107.24 111.417358490566 -4.17735849056604 43 107.24 111.417358490566 -4.17735849056604 44 107.2 111.417358490566 -4.21735849056603 45 107.23 111.417358490566 -4.18735849056603 46 107.2 111.417358490566 -4.21735849056603 47 107.21 111.417358490566 -4.20735849056604 48 107.24 111.417358490566 -4.17735849056604 49 107.21 111.417358490566 -4.20735849056604 50 113.89 111.417358490566 2.47264150943396 51 114.05 111.417358490566 2.63264150943396 52 114.05 111.417358490566 2.63264150943396 53 114.05 111.417358490566 2.63264150943396 54 114.05 111.417358490566 2.63264150943396 55 115.12 111.417358490566 3.70264150943397 56 115.68 111.417358490566 4.26264150943397 57 116.05 111.417358490566 4.63264150943396 58 116.18 111.417358490566 4.76264150943397 59 116.35 111.417358490566 4.93264150943396 60 116.44 111.417358490566 5.02264150943396 61 117 111.417358490566 5.58264150943396 62 117.61 111.417358490566 6.19264150943396 63 118.17 111.417358490566 6.75264150943396 64 118.33 111.417358490566 6.91264150943396 65 118.33 111.417358490566 6.91264150943396 66 118.42 111.417358490566 7.00264150943396 67 118.5 111.417358490566 7.08264150943396 68 118.67 111.417358490566 7.25264150943396 69 119.09 111.417358490566 7.67264150943397 70 119.14 111.417358490566 7.72264150943396 71 119.23 111.417358490566 7.81264150943397 72 119.33 111.417358490566 7.91264150943396 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 4.48941822190818e-08 8.97883644381637e-08 0.999999955105818 6 1.48936788031150e-10 2.97873576062301e-10 0.999999999851063 7 3.68370850672314e-13 7.36741701344627e-13 0.999999999999632 8 2.46720343835644e-15 4.93440687671288e-15 0.999999999999998 9 1.65570516334699e-15 3.31141032669399e-15 0.999999999999998 10 6.14831937223353e-17 1.22966387444671e-16 1 11 1.32168788705674e-18 2.64337577411347e-18 1 12 2.21773586180352e-20 4.43547172360704e-20 1 13 1.51734534586769e-21 3.03469069173538e-21 1 14 4.79506582668233e-21 9.59013165336467e-21 1 15 3.79285067676758e-22 7.58570135353516e-22 1 16 3.12960649708841e-23 6.25921299417682e-23 1 17 1.24035444243874e-23 2.48070888487748e-23 1 18 1.67278439035596e-24 3.34556878071192e-24 1 19 3.93552234610264e-22 7.87104469220529e-22 1 20 1.51178590861411e-23 3.02357181722822e-23 1 21 6.93285959542567e-25 1.38657191908513e-24 1 22 3.90331210525806e-26 7.80662421051611e-26 1 23 2.56502675287941e-27 5.13005350575881e-27 1 24 1.58403090376922e-28 3.16806180753845e-28 1 25 7.339093868117e-30 1.4678187736234e-29 1 26 3.19237540411187e-31 6.38475080822375e-31 1 27 2.12301415109164e-32 4.24602830218327e-32 1 28 1.29252148591915e-33 2.58504297183829e-33 1 29 6.3750245527402e-35 1.27500491054804e-34 1 30 2.78331940240981e-36 5.56663880481962e-36 1 31 1.39837264457149e-37 2.79674528914298e-37 1 32 7.62541544071102e-39 1.52508308814220e-38 1 33 3.81328475781301e-40 7.62656951562602e-40 1 34 2.29119597759008e-41 4.58239195518016e-41 1 35 1.43550210780219e-42 2.87100421560438e-42 1 36 9.52907316021083e-44 1.90581463204217e-43 1 37 6.1181707651741e-45 1.22363415303482e-44 1 38 4.37553446014350e-46 8.75106892028701e-46 1 39 3.61984423218559e-47 7.23968846437118e-47 1 40 3.53470727561178e-48 7.06941455122356e-48 1 41 4.32625188387039e-49 8.65250376774077e-49 1 42 6.88201314497856e-50 1.37640262899571e-49 1 43 1.57411786130581e-50 3.14823572261162e-50 1 44 6.51121154588855e-51 1.30224230917771e-50 1 45 4.95791005788827e-51 9.91582011577654e-51 1 46 1.16105013115708e-50 2.32210026231415e-50 1 47 1.41301114269706e-49 2.82602228539413e-49 1 48 3.6725639591611e-47 7.3451279183222e-47 1 49 3.84085774702536e-41 7.68171549405073e-41 1 50 3.24351135942703e-05 6.48702271885406e-05 0.999967564886406 51 0.0301719775185354 0.0603439550370709 0.969828022481465 52 0.280419067277532 0.560838134555063 0.719580932722468 53 0.670384262374515 0.65923147525097 0.329615737625485 54 0.922859034375704 0.154281931248593 0.0771409656242964 55 0.983097646777899 0.0338047064442021 0.0169023532221010 56 0.995663363850163 0.00867327229967389 0.00433663614983694 57 0.998611993818176 0.00277601236364849 0.00138800618182425 58 0.999551098232475 0.000897803535049933 0.000448901767524967 59 0.99986647423669 0.000267051526620119 0.000133525763310059 60 0.99997935439231 4.12912153780045e-05 2.06456076890022e-05 61 0.99999611817532 7.7636493584692e-06 3.8818246792346e-06 62 0.999997985028813 4.02994237350239e-06 2.01497118675119e-06 63 0.999995235425308 9.52914938448087e-06 4.76457469224044e-06 64 0.999983362338363 3.32753232733442e-05 1.66376616366721e-05 65 0.999945671904782 0.000108656190436553 5.43280952182767e-05 66 0.999805912848085 0.000388174303830706 0.000194087151915353 67 0.999390369102224 0.00121926179555277 0.000609630897776385 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 58 0.92063492063492 NOK 5% type I error level 59 0.936507936507937 NOK 10% type I error level 60 0.952380952380952 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/10blz81259055402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/10blz81259055402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/1zkqd1259055402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/1zkqd1259055402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/2t4sx1259055402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/2t4sx1259055402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/36jhy1259055402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/36jhy1259055402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/477am1259055402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/477am1259055402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/5ij691259055402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/5ij691259055402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/6vfd21259055402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/6vfd21259055402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/7vswx1259055402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/7vswx1259055402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/8kj0k1259055402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/8kj0k1259055402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/988jg1259055402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t1259055660pl9rnsyu5l5hegi/988jg1259055402.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') }

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