*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, 18 Dec 2010 11:12:55 +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/18/t1292670752x71m66axk3rabis.htm/, Retrieved Sat, 18 Dec 2010 12:12:43 +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/Dec/18/t1292670752x71m66axk3rabis.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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6654 5712 -999 -999 3.3 38.6 645 3 5 3 1 6.6 6.3 2 8.3 4.5 42 3 1 3 3.385 44.5 -999 -999 12.5 14 60 1 1 1 0.92 5.7 -999 -999 16.5 -999 25 5 2 3 2547 4603 2.1 1.8 3.9 69 624 3 5 4 10.55 179.5 9.1 0.7 9.8 27 180 4 4 4 0.023 0.3 15.8 3.9 19.7 19 35 1 1 1 160 169 5.2 1 6.2 30.4 392 4 5 4 3.3 25.6 10.9 3.6 14.5 28 63 1 2 1 52.16 440 8.3 1.4 9.7 50 230 1 1 1 0.425 6.4 11 1.5 12.5 7 112 5 4 4 465 423 3.2 0.7 3.9 30 281 5 5 5 0.55 2.4 7.6 2.7 10.3 -999 -999 2 1 2 187.1 419 -999 -999 3.1 40 365 5 5 5 0.075 1.2 6.3 2.1 8.4 3.5 42 1 1 1 3 25 8.6 0 8.6 50 28 2 2 2 0.785 3.5 6.6 4.1 10.7 6 42 2 2 2 0.2 5 9.5 1.2 10.7 10.4 120 2 2 2 1.41 17.5 4.8 1.3 6.1 34 -999 1 2 1 60 81 12 6.1 18.1 7 -999 1 1 1 529 680 -999 0.3 -999 28 400 5 5 5 27.66 115 3.3 0.5 3.8 20 148 5 5 5 0.12 1 11 3.4 14.4 3.9 16 3 1 2 207 406 -999 -999 12 39.3 252 1 4 1 85 325 4.7 1.5 6.2 41 310 1 3 1 36.33 119.5 -999 -999 13 16.2 63 1 1 1 0.101 4 10.4 3.4 13.8 9 28 5 1 3 1.04 5.5 7.4 0.8 8.2 7.6 68 5 3 4 521 655 2.1 0.8 2.9 46 336 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 sws[t] = + 11.5047546134694 -0.017006875090139bodyweight[t] + 0.00947466732700596brainweight[t] + 0.88992325198032ps[t] + 0.514276787763356total[t] + 0.0419547242105247lifespan[t] -0.0599250775070244gesttime[t] + 16.8380493297638pindex[t] -0.744570060249773expindex[t] -27.3399375680966dangindex[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) 11.5047546134694 39.841086 0.2888 0.773908 0.386954 bodyweight -0.017006875090139 0.053476 -0.318 0.751735 0.375868 brainweight 0.00947466732700596 0.053318 0.1777 0.859648 0.429824 ps 0.88992325198032 0.045272 19.6573 0 0 total 0.514276787763356 0.069534 7.3961 0 0 lifespan 0.0419547242105247 0.069446 0.6041 0.548383 0.274192 gesttime -0.0599250775070244 0.062245 -0.9627 0.340142 0.170071 pindex 16.8380493297638 31.647525 0.532 0.596958 0.298479 expindex -0.744570060249773 20.792592 -0.0358 0.971571 0.485786 dangindex -27.3399375680966 41.04811 -0.666 0.508326 0.254163 Multiple Linear Regression - Regression Statistics Multiple R 0.961275674554623 R-squared 0.924050922490445 Adjusted R-squared 0.91090588984456 F-TEST (value) 70.2965863519368 F-TEST (DF numerator) 9 F-TEST (DF denominator) 52 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 126.788663342079 Sum Squared Residuals 835918.987907692 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -999 -1007.13664544693 8.13664544692746 2 6.3 -16.9796673864623 23.2796673864623 3 -999 -884.990656654014 -114.009343345986 4 -999 -911.734250438054 -87.2657495619457 5 2.1 -81.6591461872654 83.7591461872654 6 9.1 -35.9506759289773 45.0506759289773 7 15.8 12.5624630058735 3.23753699412646 8 5.2 -53.4822973079091 58.6822973079091 9 10.9 7.76034457506116 3.13965542493884 10 8.3 -1.91058288794458 10.2105828879446 11 11 -15.2442001884180 26.244200188418 12 3.2 -61.5796288215291 64.7796288215291 13 7.6 15.4220201343521 -7.82202013435213 14 -999 -951.57317260671 -47.4268273932907 15 6.3 4.08714252586031 2.21285747413969 16 8.6 -5.95970151069651 14.5597015106965 17 6.6 -4.08202899284149 10.6820289928415 18 9.5 -11.1282006596878 20.6282006596878 19 4.8 65.2411549245888 -60.4411549245888 20 12 74.9011090575539 -62.9011090575539 21 -999 -583.572232744676 -415.427767255324 22 3.3 -49.7389638691691 53.0389638691691 23 11 16.2380382334682 -5.23803823346821 24 -999 -897.963428220233 -101.036571779767 25 4.7 -11.9303906794403 16.6303906794403 26 -999 -884.670684549709 -114.329315450291 27 10.4 21.7892485482383 -11.3892485482383 28 7.4 -14.6910767758762 22.0910767758762 29 2.1 -63.3837791447948 65.4837791447948 30 -999 -888.486729772669 -110.513270227331 31 -999 -1455.21786248575 456.217862485749 32 7.7 -10.4061432735818 18.1061432735818 33 17.9 10.2851089991206 7.61489100087937 34 6.1 5.71097613849235 0.389023861507651 35 8.2 -19.0006772486097 27.2006772486097 36 8.4 -57.0494074834373 65.4494074834373 37 11.9 3.03700034767987 8.86299965232013 38 10.8 2.74362598874067 8.05637401125933 39 13.8 31.5783369458249 -17.7783369458249 40 14.3 21.9280225837138 -7.6280225837138 41 -999 -582.586158997079 -416.413841002921 42 15.2 -8.39095485508576 23.5909548550858 43 10 -35.4947904228951 45.4947904228951 44 11.9 -1.98899291052397 13.8889929105240 45 6.5 -34.7887856429552 41.2887856429552 46 7.5 -40.6370470154457 48.1370470154457 47 -999 -896.49504954742 -102.50495045258 48 10.6 -12.6912255891155 23.2912255891155 49 7.4 5.13457602389283 2.26542397610717 50 8.4 -12.7584739871665 21.1584739871665 51 5.7 -19.8786803754774 25.5786803754774 52 4.9 -31.3616647723908 36.2616647723908 53 -999 -940.021076900148 -58.978923099852 54 3.2 -49.7347381393811 52.9347381393811 55 -999 -899.130254308336 -99.869745691664 56 8.1 73.8697876937131 -65.7697876937131 57 11 -4.75426761064344 15.7542676106434 58 4.9 -29.1112542937503 34.0112542937503 59 13.2 13.6511213015473 -0.451121301547339 60 9.7 -38.0045846525441 47.7045846525441 61 12.8 32.2111490176230 -19.4111490176230 62 -999 -1370.50099475960 371.500994759604 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 5.85530466046474e-06 1.17106093209295e-05 0.99999414469534 14 2.49212117623842e-07 4.98424235247683e-07 0.999999750787882 15 3.98543394847371e-09 7.97086789694742e-09 0.999999996014566 16 1.21712211831184e-10 2.43424423662368e-10 0.999999999878288 17 9.92726712757985e-12 1.98545342551597e-11 0.999999999990073 18 2.48247394380322e-13 4.96494788760645e-13 0.999999999999752 19 6.35539135521118e-15 1.27107827104224e-14 0.999999999999994 20 2.34335570564125e-16 4.68671141128249e-16 1 21 3.33350154203865e-16 6.66700308407729e-16 1 22 8.35528973323593e-18 1.67105794664719e-17 1 23 1.97642588343573e-19 3.95285176687147e-19 1 24 5.04058730927046e-21 1.00811746185409e-20 1 25 1.40080590755669e-22 2.80161181511339e-22 1 26 3.83930412639129e-24 7.67860825278258e-24 1 27 8.45628647743007e-26 1.69125729548601e-25 1 28 2.12207622127697e-27 4.24415244255393e-27 1 29 3.61629807051675e-27 7.23259614103349e-27 1 30 9.35930372294388e-29 1.87186074458878e-28 1 31 0.902620666720271 0.194758666559458 0.097379333279729 32 0.865002988309182 0.269994023381636 0.134997011690818 33 0.811984244875292 0.376031510249415 0.188015755124708 34 0.914670732841112 0.170658534317775 0.0853292671588876 35 0.88875952533311 0.222480949333782 0.111240474666891 36 0.987863550418147 0.0242728991637053 0.0121364495818526 37 0.978915348696273 0.0421693026074549 0.0210846513037275 38 0.964350020467864 0.0712999590642716 0.0356499795321358 39 0.94789343657785 0.104213126844302 0.0521065634221509 40 0.94085475307039 0.118290493859219 0.0591452469296094 41 1 5.27468078619429e-17 2.63734039309714e-17 42 1 2.40005524123124e-16 1.20002762061562e-16 43 0.999999999999997 6.4527984397826e-15 3.2263992198913e-15 44 0.999999999999896 2.08111843611861e-13 1.04055921805931e-13 45 0.999999999996525 6.95001013431229e-12 3.47500506715615e-12 46 0.99999999980438 3.91240936915283e-10 1.95620468457641e-10 47 0.999999989581404 2.08371916402400e-08 1.04185958201200e-08 48 0.999999626662796 7.46674409060586e-07 3.73337204530293e-07 49 0.99998053327302 3.89334539618300e-05 1.94667269809150e-05 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.72972972972973 NOK 5% type I error level 29 0.783783783783784 NOK 10% type I error level 30 0.810810810810811 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/10tpeb1292670767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/10tpeb1292670767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/1nozi1292670767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/1nozi1292670767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/2nozi1292670767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/2nozi1292670767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/3xxh31292670767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/3xxh31292670767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/4xxh31292670767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/4xxh31292670767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/5xxh31292670767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/5xxh31292670767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/68oy51292670767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/68oy51292670767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/78oy51292670767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/78oy51292670767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/8jff81292670767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/8jff81292670767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/9jff81292670767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292670752x71m66axk3rabis/9jff81292670767.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = 3 ; 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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