*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: Mon, 06 Dec 2010 18:34:18 +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/06/t1291660379oatrj6whl89f7oj.htm/, Retrieved Mon, 06 Dec 2010 19:33:09 +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/06/t1291660379oatrj6whl89f7oj.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:

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2454.62 11527.72 10364.91 10383 -0.4 0 2.3 3.19 2407.6 2472.81 2408.64 2440.25 2448.05 11383.89 10152.09 10431 3 -4 2.4 3.35 2454.62 2407.6 2472.81 2408.64 2497.84 10989.34 10032.8 10574 0.4 -2 2.2 3.24 2448.05 2454.62 2407.6 2472.81 2645.64 11079.42 10204.59 10653 1.2 -2 2 3.23 2497.84 2448.05 2454.62 2407.6 2756.76 11028.93 10001.6 10805 0.6 -6 2.9 3.31 2645.64 2497.84 2448.05 2454.62 2849.27 10973 10411.75 10872 -1.3 -7 2.6 3.25 2756.76 2645.64 2497.84 2448.05 2921.44 11068.05 10673.38 10625 -3.2 -6 2.3 3.2 2849.27 2756.76 2645.64 2497.84 2981.85 11394.84 10539.51 10407 -1.8 -6 2.3 3.1 2921.44 2849.27 2756.76 2645.64 3080.58 11545.71 10723.78 10463 -3.6 -3 2.6 2.93 2981.85 2921.44 2849.27 2756.76 3106.22 11809.38 10682.06 10556 -4.2 -2 3.1 2.92 3080.58 2981.85 2921.44 2849.27 3119.31 11395.64 10283.19 10646 -6.9 -5 2.8 2.9 3106.22 3080.58 2981.85 2921.44 3061.26 11082.38 10377.18 10702 -8 -11 2.5 2.87 3119.31 3106.22 3080.58 2981.85 3097.31 11402.75 10486.64 11353 -7.5 -11 2.9 2.76 3061.26 3119 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation BEL_20[t] = -203.213533723905 + 0.0883416305203353Nikkei[t] + 0.136821877484492DJ_Indust[t] -0.00815216372016688Goudprijs[t] -7.96149871283661Conjunct_Seizoenzuiver[t] + 9.82043023620331Cons_vertrouw[t] -8.46487453756446Alg_consumptie_index_BE[t] -242.695105335560Gem_rente_kasbon_5j[t] + 0.448559634759112Y1[t] -0.0614225929551998Y2[t] + 0.0748163256792463Y3[t] + 0.0777147845854144Y4[t] + 3.31975176170273t + 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) -203.213533723905 364.963374 -0.5568 0.579961 0.28998 Nikkei 0.0883416305203353 0.012873 6.8626 0 0 DJ_Indust 0.136821877484492 0.0279 4.9041 9e-06 4e-06 Goudprijs -0.00815216372016688 0.01353 -0.6025 0.549348 0.274674 Conjunct_Seizoenzuiver -7.96149871283661 4.120919 -1.932 0.058617 0.029308 Cons_vertrouw 9.82043023620331 4.577992 2.1451 0.036457 0.018228 Alg_consumptie_index_BE -8.46487453756446 11.338721 -0.7465 0.458576 0.229288 Gem_rente_kasbon_5j -242.695105335560 40.289138 -6.0238 0 0 Y1 0.448559634759112 0.095333 4.7052 1.8e-05 9e-06 Y2 -0.0614225929551998 0.111788 -0.5495 0.584958 0.292479 Y3 0.0748163256792463 0.110224 0.6788 0.500185 0.250093 Y4 0.0777147845854144 0.076327 1.0182 0.31313 0.156565 t 3.31975176170273 3.587279 0.9254 0.358863 0.179432 Multiple Linear Regression - Regression Statistics Multiple R 0.995516590819115 R-squared 0.991053282596114 Adjusted R-squared 0.989065123173029 F-TEST (value) 498.477773506692 F-TEST (DF numerator) 12 F-TEST (DF denominator) 54 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 88.5147247296587 Sum Squared Residuals 423082.250674232 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2454.62 2659.41920362837 -204.799203628373 2 2448.05 2541.93556256269 -93.8855625626862 3 2497.84 2555.91613445387 -58.0761344538721 4 2645.64 2608.99244986361 36.6475501363918 5 2756.76 2583.70190270218 173.058097297817 6 2849.27 2704.03979724598 145.230202754016 7 2921.44 2842.78645399472 78.6535460052832 8 2981.85 2918.04978651937 63.8002134806256 9 3080.58 3080.18552436485 0.394475635151281 10 3106.22 3166.28843998531 -60.0684399853104 11 3119.31 3092.74315548307 26.5668445169261 12 3061.26 3056.82589415939 4.434105840607 13 3097.31 3100.19504336697 -2.88504336697123 14 3161.69 3187.7893931429 -26.0993931429015 15 3257.16 3243.55605369406 13.6039463059389 16 3277.01 3281.93014502248 -4.92014502247929 17 3295.32 3347.25156043464 -51.9315604346404 18 3363.99 3450.2886889283 -86.2986889282974 19 3494.17 3615.31060874214 -121.140608742139 20 3667.03 3734.99754659932 -67.9675465993223 21 3813.06 3838.11036554343 -25.050365543431 22 3917.96 3894.86521947672 23.0947805232830 23 3895.51 3972.03584861159 -76.5258486115918 24 3801.06 3847.75211774549 -46.6921177454914 25 3570.12 3714.74121197296 -144.621211972964 26 3701.61 3603.46725960944 98.1427403905601 27 3862.27 3761.76937591638 100.500624083617 28 3970.1 3894.18212616223 75.917873837766 29 4138.52 4065.57244798251 72.9475520174938 30 4199.75 4152.29035403403 47.4596459659651 31 4290.89 4180.74605303013 110.143946969868 32 4443.91 4343.89356144805 100.016438551948 33 4502.64 4434.40355447463 68.2364455253744 34 4356.98 4354.98570935479 1.99429064520637 35 4591.27 4421.81018660151 169.459813398486 36 4696.96 4663.55804512789 33.4019548721148 37 4621.4 4636.3311982691 -14.9311982691006 38 4562.84 4576.68871303656 -13.8487130365623 39 4202.52 4415.51234255912 -212.992342559122 40 4296.49 4294.35415271497 2.13584728502725 41 4435.23 4471.02998433135 -35.7999843313469 42 4105.18 4229.14070327235 -123.960703272347 43 4116.68 4147.34408845802 -30.664088458021 44 3844.49 3843.16521232370 1.32478767630371 45 3720.98 3740.23749408303 -19.2574940830285 46 3674.4 3636.9857864662 37.4142135338013 47 3857.62 3762.81994038574 94.800059614259 48 3801.06 3789.29676478551 11.7632352144877 49 3504.37 3508.77366761102 -4.40366761102274 50 3032.6 3086.16492879333 -53.5649287933256 51 3047.03 2952.45109207508 94.5789079249208 52 2962.34 2975.40150003758 -13.0615000375790 53 2197.82 2319.6498329147 -121.829832914701 54 2014.45 1946.76607366469 67.6839263353147 55 1862.83 1948.58907253136 -85.7590725313615 56 1905.41 1856.68702295079 48.7229770492113 57 1810.99 1692.54657881721 118.443421182787 58 1670.07 1668.85437627914 1.21562372086194 59 1864.44 1800.73897813541 63.7010218645875 60 2052.02 2007.22681279366 44.7931872063417 61 2029.6 2106.75674890070 -77.1567489006961 62 2070.83 2095.46278865392 -24.6327886539187 63 2293.41 2327.02320810613 -33.6132081061270 64 2443.27 2457.91506716159 -14.6450671615913 65 2513.17 2475.49937611897 37.6706238810282 66 2466.92 2534.16777929508 -67.247779295082 67 2502.66 2552.28193248797 -49.621932487967 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0447137874999940 0.0894275749999879 0.955286212500006 17 0.061757068406472 0.123514136812944 0.938242931593528 18 0.040667329611643 0.081334659223286 0.959332670388357 19 0.0281458271947754 0.0562916543895507 0.971854172805225 20 0.0153592209311108 0.0307184418622217 0.98464077906889 21 0.00786178427128056 0.0157235685425611 0.99213821572872 22 0.0033243110952363 0.0066486221904726 0.996675688904764 23 0.00564916844256068 0.0112983368851214 0.99435083155744 24 0.00579593022632133 0.0115918604526427 0.994204069773679 25 0.0335891409288954 0.0671782818577908 0.966410859071105 26 0.486502654773996 0.973005309547992 0.513497345226004 27 0.448537037423198 0.897074074846397 0.551462962576802 28 0.532422533525277 0.935154932949447 0.467577466474723 29 0.496174563549343 0.992349127098685 0.503825436450657 30 0.616651276336791 0.766697447326418 0.383348723663209 31 0.63681864730105 0.726362705397899 0.363181352698950 32 0.656854213157489 0.686291573685022 0.343145786842511 33 0.613672464474116 0.772655071051767 0.386327535525884 34 0.614032041834531 0.771935916330937 0.385967958165469 35 0.803667495903999 0.392665008192002 0.196332504096001 36 0.771227857851337 0.457544284297326 0.228772142148663 37 0.774400498427645 0.451199003144709 0.225599501572355 38 0.84900093228862 0.301998135422759 0.150999067711380 39 0.968676335591503 0.0626473288169948 0.0313236644084974 40 0.95263339082761 0.0947332183447816 0.0473666091723908 41 0.93853644056616 0.122927118867679 0.0614635594338396 42 0.953527123744608 0.0929457525107846 0.0464728762553923 43 0.97067560856031 0.0586487828793783 0.0293243914396891 44 0.969737457042438 0.0605250859151244 0.0302625429575622 45 0.970485602580258 0.0590287948394842 0.0295143974197421 46 0.941883756738354 0.116232486523293 0.0581162432616464 47 0.89530855205419 0.209382895891621 0.104691447945811 48 0.824230435664718 0.351539128670563 0.175769564335282 49 0.713621893797073 0.572756212405853 0.286378106202927 50 0.885103114975875 0.22979377004825 0.114896885024125 51 0.811012178222632 0.377975643554735 0.188987821777368 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0277777777777778 NOK 5% type I error level 5 0.138888888888889 NOK 10% type I error level 15 0.416666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/10edf1291660450.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/10edf1291660450.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/10kc3k1291660450.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/10kc3k1291660450.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/20edf1291660450.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/20edf1291660450.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/3o25c1291660450.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/3o25c1291660450.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/4o25c1291660450.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/4o25c1291660450.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/5o25c1291660450.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/5o25c1291660450.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/6hb4f1291660450.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/6hb4f1291660450.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/7r33h1291660450.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/7r33h1291660450.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/8r33h1291660450.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/8r33h1291660450.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/9r33h1291660450.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660379oatrj6whl89f7oj/9r33h1291660450.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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') }

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