WS7.2

*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: Sat, 21 Nov 2009 05:05:26 -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/21/t1258805326exq5u60xxrtbwaf.htm/, Retrieved Sat, 21 Nov 2009 13:08:58 +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/21/t1258805326exq5u60xxrtbwaf.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 «

269285 8.2 269829 8 270911 7.5 266844 6.8 271244 6.5 269907 6.6 271296 7.6 270157 8 271322 8.1 267179 7.7 264101 7.5 265518 7.6 269419 7.8 268714 7.8 272482 7.8 268351 7.5 268175 7.5 270674 7.1 272764 7.5 272599 7.5 270333 7.6 270846 7.7 270491 7.7 269160 7.9 274027 8.1 273784 8.2 276663 8.2 274525 8.2 271344 7.9 271115 7.3 270798 6.9 273911 6.6 273985 6.7 271917 6.9 273338 7 270601 7.1 273547 7.2 275363 7.1 281229 6.9 277793 7 279913 6.8 282500 6.4 280041 6.7 282166 6.6 290304 6.4 283519 6.3 287816 6.2 285226 6.5 287595 6.8 289741 6.8 289148 6.4 288301 6.1 290155 5.8 289648 6.1 288225 7.2 289351 7.3 294735 6.9 305333 6.1 309030 5.8 310215 6.2 321935 7.1 325734 7.7 320846 7.9 323023 7.7 319753 7.4 321753 7.5 320757 8 324479 8.1

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] = + 317487.905437682 -5289.50501950168X[t] + 4994.36570923069M1[t] + 6573.16604386424M2[t] + 7132.07362427232M3[t] + 3824.18911972193M4[t] + 2881.13794850487M5[t] + 2923.21219557962M6[t] + 5193.80628833877M7[t] + 6833.78978898882M8[t] + 414.960401560134M9[t] -1019.94060234020M10[t] -352.491104290366M11[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) 317487.905437682 28089.187605 11.3029 0 0 X -5289.50501950168 3793.843694 -1.3942 0.168854 0.084427 M1 4994.36570923069 11596.336277 0.4307 0.668381 0.33419 M2 6573.16604386424 11638.185385 0.5648 0.574511 0.287256 M3 7132.07362427232 11551.60165 0.6174 0.539512 0.269756 M4 3824.18911972193 11471.859556 0.3334 0.740135 0.370067 M5 2881.13794850487 11460.143441 0.2514 0.802438 0.401219 M6 2923.21219557962 11488.68054 0.2544 0.800102 0.400051 M7 5193.80628833877 11497.759898 0.4517 0.653245 0.326622 M8 6833.78978898882 11509.159816 0.5938 0.555101 0.27755 M9 414.960401560134 11969.722155 0.0347 0.97247 0.486235 M10 -1019.94060234020 11974.531075 -0.0852 0.932431 0.466215 M11 -352.491104290366 11994.947476 -0.0294 0.976663 0.488331 Multiple Linear Regression - Regression Statistics Multiple R 0.213187085790247 R-squared 0.0454487335477382 Adjusted R-squared -0.162816997314573 F-TEST (value) 0.218224733178908 F-TEST (DF numerator) 12 F-TEST (DF denominator) 55 p-value 0.996878762606105 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 18919.7074278135 Sum Squared Residuals 19687543103.4735 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 269285 279108.329986999 -9823.32998699944 2 269829 281745.031325533 -11916.0313255327 3 270911 284948.691415692 -14037.6914156916 4 266844 285343.460424792 -18499.4604247924 5 271244 285987.260759426 -14743.2607594258 6 269907 285500.384504550 -15593.3845045504 7 271296 282481.473577808 -11185.4735778079 8 270157 282005.655070657 -11848.6550706572 9 271322 275057.875181278 -3735.87518127840 10 267179 275738.776185179 -8559.77618517873 11 264101 277464.126687129 -13363.1266871289 12 265518 277287.667289469 -11769.6672894691 13 269419 281224.131994799 -11805.1319947995 14 268714 282802.932329433 -14088.932329433 15 272482 283361.839909841 -10879.8399098411 16 268351 281640.806911141 -13289.8069111412 17 268175 280697.755739924 -12522.7557399241 18 270674 282855.631994800 -12181.6319947996 19 272764 283010.424079758 -10246.4240797580 20 272599 284650.407580408 -12051.4075804081 21 270333 277702.627691029 -7369.62769102923 22 270846 275738.776185179 -4892.77618517873 23 270491 276406.225683229 -5915.22568322856 24 269160 275700.815783619 -6540.81578361859 25 274027 279637.280488949 -5610.28048894894 26 273784 280687.130321632 -6903.13032163233 27 276663 281246.037902040 -4583.03790204042 28 274525 277938.15339749 -3413.15339749002 29 271344 278581.953732123 -7237.95373212347 30 271115 281797.730990899 -10682.7309908992 31 270798 286184.127091459 -15386.1270914590 32 273911 289410.962097960 -15499.9620979596 33 273985 282463.182208581 -8478.18220858074 34 271917 279970.38020078 -8053.38020078007 35 273338 280108.87919688 -6770.87919687974 36 270601 279932.41979922 -9331.41979921994 37 273547 284397.835006500 -10850.8350065005 38 275363 286505.585843084 -11142.5858430842 39 281229 288122.394427393 -6893.39442739259 40 277793 284285.559420892 -6492.55942089202 41 279913 284400.409253575 -4487.40925357531 42 282500 286558.285508451 -4058.28550845072 43 280041 287242.028095359 -7201.02809535937 44 282166 289410.962097960 -7244.96209795959 45 290304 284050.033714431 6253.96628556876 46 283519 283144.083212481 374.916787518922 47 287816 284340.483212481 3475.51678751892 48 285226 283106.122810921 2119.87718907906 49 287595 286513.637014301 1081.36298569888 50 289741 288092.437348935 1648.56265106533 51 289148 290767.146937143 -1619.14693714342 52 288301 289046.113938444 -745.113938443531 53 290155 289689.914273077 465.085726923015 54 289648 288145.137014301 1502.86298569877 55 288225 284597.275585609 3627.72441439147 56 289351 285708.308584308 3642.69141569158 57 294735 281405.28120468 13329.7187953196 58 305333 284201.984216381 21131.0157836186 59 309030 286456.285220282 22573.7147797182 60 310215 284692.974316771 25522.0256832286 61 321935 284926.785508451 37008.2144915494 62 325734 283331.882831383 42402.1171686168 63 320846 282832.889407891 38013.1105921091 64 323023 280582.905907241 42440.0940927591 65 319753 281226.706241874 38526.2937581257 66 321753 280739.829986999 41013.1700130011 67 320757 280365.671570007 40391.3284299928 68 324479 281476.704568707 43002.2954312929 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 4.04726383938087e-05 8.09452767876174e-05 0.999959527361606 17 3.9635164909055e-05 7.927032981811e-05 0.99996036483509 18 2.7899084026308e-06 5.5798168052616e-06 0.999997210091597 19 2.1997906532819e-07 4.3995813065638e-07 0.999999780020935 20 2.5628955899716e-08 5.1257911799432e-08 0.999999974371044 21 2.06248990383508e-09 4.12497980767016e-09 0.99999999793751 22 8.60435482866107e-10 1.72087096573221e-09 0.999999999139564 23 2.56441790436846e-09 5.12883580873693e-09 0.999999997435582 24 5.96786655598481e-10 1.19357331119696e-09 0.999999999403213 25 3.21063236347167e-10 6.42126472694335e-10 0.999999999678937 26 1.37950811913018e-10 2.75901623826036e-10 0.99999999986205 27 5.37675037074263e-11 1.07535007414853e-10 0.999999999946233 28 2.35299604439305e-11 4.7059920887861e-11 0.99999999997647 29 7.35448261911208e-12 1.47089652382242e-11 0.999999999992645 30 2.07098427221814e-12 4.14196854443628e-12 0.999999999997929 31 3.27682319596922e-13 6.55364639193843e-13 0.999999999999672 32 2.24942649977273e-13 4.49885299954545e-13 0.999999999999775 33 1.22913332100359e-13 2.45826664200718e-13 0.999999999999877 34 7.51178128900476e-14 1.50235625780095e-13 0.999999999999925 35 4.03046746371523e-13 8.06093492743047e-13 0.999999999999597 36 9.98760480636597e-13 1.99752096127319e-12 0.999999999999001 37 1.93846373570703e-12 3.87692747141405e-12 0.999999999998062 38 3.78648413945245e-12 7.57296827890491e-12 0.999999999996213 39 1.08072496182894e-11 2.16144992365788e-11 0.999999999989193 40 1.35726153600103e-10 2.71452307200205e-10 0.999999999864274 41 4.67434826407799e-09 9.34869652815598e-09 0.999999995325652 42 3.31522028514376e-08 6.63044057028753e-08 0.999999966847797 43 2.34034184644552e-08 4.68068369289105e-08 0.999999976596581 44 1.81622521119419e-08 3.63245042238839e-08 0.999999981837748 45 1.31141761759197e-07 2.62283523518394e-07 0.999999868858238 46 6.70895848320283e-07 1.34179169664057e-06 0.999999329104152 47 2.3674338603258e-05 4.7348677206516e-05 0.999976325661397 48 0.00165179409963221 0.00330358819926441 0.998348205900368 49 0.0443011645309187 0.0886023290618375 0.955698835469081 50 0.102251011352816 0.204502022705633 0.897748988647184 51 0.062598301759448 0.125196603518896 0.937401698240552 52 0.0364844893224037 0.0729689786448073 0.963515510677596 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 33 0.891891891891892 NOK 5% type I error level 33 0.891891891891892 NOK 10% type I error level 35 0.945945945945946 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/10elfx1258805121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/10elfx1258805121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/1isa31258805121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/1isa31258805121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/2gmu11258805121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/2gmu11258805121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/3bxoy1258805121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/3bxoy1258805121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/4fw2z1258805121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/4fw2z1258805121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/5ar0r1258805121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/5ar0r1258805121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/60v7u1258805121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/60v7u1258805121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/761i21258805121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/761i21258805121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/8nph51258805121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/8nph51258805121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/94fza1258805121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805326exq5u60xxrtbwaf/94fza1258805121.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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