*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, 14 Dec 2010 19:16:09 +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/14/t1292354084al2hfb5gurbljc2.htm/, Retrieved Tue, 14 Dec 2010 20:14:54 +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/14/t1292354084al2hfb5gurbljc2.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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645 3 1.194 42 3 2.116 60 1 2.526 25 3 2.803 624 4 1.361 180 4 2.282 35 1 2.981 392 4 1.825 63 1 2.674 230 1 2.272 112 4 2.526 281 5 1.361 0 2 2.332 365 5 1.131 42 1 2.128 28 2 2.152 42 2 2.370 120 2 2.370 0 1 1.808 0 1 2.896 400 5 0 148 5 1.335 16 2 2.667 252 1 2.485 310 1 1.825 63 1 2.565 28 3 2.625 68 4 2.104 336 5 1.065 100 1 2.380 33 4 0 21.5 4 2.208 50 1 2.991 267 1 2.079 30 1 2.361 45 3 2.416 19 3 2.580 30 3 2.549 12 1 2.965 120 1 2.856 440 5 0 140 2 2.833 170 4 2.389 17 2 2.617 115 4 2.128 31 5 2.128 63 2 2.526 21 3 2.580 52 1 2.282 164 2 2.262 225 2 1.887 225 3 1.686 150 5 0.956 151 5 1.335 90 2 2.398 0 2 2.332 60 2 2.588 200 3 1.686 46 2 2.760 210 4 2.332 14 1 2.965 38 1 0

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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation slaap[t] = + 2.888164675015 -0.00194428057832854`aantal-dagen-dat-baby-in-buik-is`[t] -0.204810229975063`danger-high-voltage`[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) 2.888164675015 0.154129 18.7387 0 0 `aantal-dagen-dat-baby-in-buik-is` -0.00194428057832854 0.000556 -3.4955 0.000905 0.000453 `danger-high-voltage` -0.204810229975063 0.056433 -3.6293 0.000595 0.000298 Multiple Linear Regression - Regression Statistics Multiple R 0.648596505305391 R-squared 0.420677426694366 Adjusted R-squared 0.401039373361971 F-TEST (value) 21.4215441609189 F-TEST (DF numerator) 2 F-TEST (DF denominator) 59 p-value 1.01426936405247e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.580105600010601 Sum Squared Residuals 19.8548279226559 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.194 1.01967301206789 0.174326987932111 2 2.116 2.19207420080001 -0.076074200800009 3 2.526 2.56669761034022 -0.0406976103402205 4 2.803 2.22512697063159 0.577873029368406 5 1.361 0.855692674237737 0.505307325762263 6 2.282 1.71895325101561 0.563046748984392 7 2.981 2.61530462479843 0.365695375201565 8 1.825 1.30676576840996 0.518234231590042 9 2.674 2.56086476860524 0.113135231394764 10 2.272 2.23616991202437 0.0358300879756299 11 2.526 1.85116433034195 0.674835669658051 12 1.361 1.31777068262936 0.0432293173706377 13 2.332 2.47854421506487 -0.146544215064871 14 1.131 1.15445111404977 -0.0234511140497651 15 2.128 2.60169466075014 -0.473694660750135 16 2.152 2.42410435887167 -0.272104358871672 17 2.37 2.39688443077507 -0.0268844307750720 18 2.37 2.24523054566545 0.124769454334554 19 1.808 2.68335444503993 -0.875354445039934 20 2.896 2.68335444503993 0.212645554960066 21 0 1.08640129380827 -1.08640129380827 22 1.335 1.57635999954706 -0.241359999547058 23 2.667 2.44743572581161 0.219564274188386 24 2.485 2.19339573930114 0.291604260698858 25 1.825 2.08062746575809 -0.255627465758087 26 2.565 2.56086476860524 0.00413523139476413 27 2.625 2.21929412889661 0.405705871103391 28 2.104 1.93671267578840 0.167287324211596 29 1.065 1.21083525082129 -0.145835250821293 30 2.38 2.48892638720708 -0.10892638720708 31 0 2.00476249602990 -2.00476249602990 32 2.208 2.02712172268068 0.180878277319319 33 2.991 2.58614041612351 0.404859583876493 34 2.079 2.16423153062621 -0.0852315306262139 35 2.361 2.62502602769008 -0.264026027690077 36 2.416 2.18624135906502 0.229758640934976 37 2.58 2.23679265410157 0.343207345898435 38 2.549 2.21540556773995 0.333594432260048 39 2.965 2.66002307809999 0.304976921900009 40 2.856 2.45004077564051 0.405959224359491 41 0 1.00863007067512 -1.00863007067512 42 2.833 2.20634493409888 0.626655065901125 43 2.389 1.73839605679889 0.650603943201107 44 2.617 2.44549144523329 0.171508554766714 45 2.128 1.84533148860696 0.282668511393037 46 2.128 1.80384082721150 0.324159172788503 47 2.526 2.35605453863017 0.169945461369827 48 2.58 2.23290409294491 0.347095907055092 49 2.282 2.58225185496685 -0.300251854966850 50 2.262 2.15968220021899 0.102317799781010 51 1.887 2.04108108494095 -0.154081084940950 52 1.686 1.83627085496589 -0.150270854965887 53 0.956 1.5724714383904 -0.616471438390401 54 1.335 1.57052715781207 -0.235527157812072 55 2.398 2.30355896301530 0.0944410369846979 56 2.332 2.47854421506487 -0.146544215064871 57 2.588 2.36188738036516 0.226112619634842 58 1.686 1.8848778694241 -0.1988778694241 59 2.76 2.38910730846176 0.370892691538242 60 2.332 1.66062483366575 0.671375166334248 61 2.965 2.65613451694333 0.308865483056666 62 0 2.60947178306345 -2.60947178306345 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.119406534340869 0.238813068681739 0.880593465659131 7 0.0899117498651155 0.179823499730231 0.910088250134884 8 0.0400325494119201 0.0800650988238403 0.95996745058808 9 0.0152709439691955 0.0305418879383910 0.984729056030805 10 0.00550050569426642 0.0110010113885328 0.994499494305734 11 0.00253428711599112 0.00506857423198224 0.99746571288401 12 0.00643231704257774 0.0128646340851555 0.993567682957422 13 0.00523183597804667 0.0104636719560933 0.994768164021953 14 0.00561619752008504 0.0112323950401701 0.994383802479915 15 0.00866221581068628 0.0173244316213726 0.991337784189314 16 0.00664763055756503 0.0132952611151301 0.993352369442435 17 0.00323649685258929 0.00647299370517858 0.99676350314741 18 0.00149862865720682 0.00299725731441365 0.998501371342793 19 0.0075200360608555 0.015040072121711 0.992479963939145 20 0.00523867526585456 0.0104773505317091 0.994761324734145 21 0.0848809162930255 0.169761832586051 0.915119083706974 22 0.0637599544611239 0.127519908922248 0.936240045538876 23 0.0445051691343771 0.0890103382687542 0.955494830865623 24 0.0318842251844771 0.0637684503689543 0.968115774815523 25 0.0226122560210625 0.045224512042125 0.977387743978938 26 0.0136726275197918 0.0273452550395837 0.986327372480208 27 0.0104179505443756 0.0208359010887511 0.989582049455624 28 0.00617852010014867 0.0123570402002973 0.993821479899851 29 0.00383828795614049 0.00767657591228099 0.99616171204386 30 0.00214711893380799 0.00429423786761598 0.997852881066192 31 0.263335953209698 0.526671906419397 0.736664046790302 32 0.214934369329073 0.429868738658145 0.785065630670927 33 0.186830877082073 0.373661754164145 0.813169122917927 34 0.147294754974501 0.294589509949003 0.852705245025499 35 0.114295415519837 0.228590831039674 0.885704584480163 36 0.0857337052714964 0.171467410542993 0.914266294728504 37 0.0661490540398806 0.132298108079761 0.93385094596012 38 0.0494810401368622 0.0989620802737244 0.950518959863138 39 0.0361135338083238 0.0722270676166475 0.963886466191676 40 0.0315055327046801 0.0630110654093602 0.96849446729532 41 0.0602582473039364 0.120516494607873 0.939741752696064 42 0.0662556634571288 0.132511326914258 0.933744336542871 43 0.0665967637324185 0.133193527464837 0.933403236267581 44 0.047113129559459 0.094226259118918 0.95288687044054 45 0.0323039294796008 0.0646078589592016 0.9676960705204 46 0.0215127696665323 0.0430255393330646 0.978487230333468 47 0.0141512029446865 0.0283024058893729 0.985848797055314 48 0.0108126013432680 0.0216252026865360 0.989187398656732 49 0.00639294351323836 0.0127858870264767 0.993607056486762 50 0.00353362905828807 0.00706725811657614 0.996466370941712 51 0.00173026837289619 0.00346053674579238 0.998269731627104 52 0.000788105149310448 0.00157621029862090 0.99921189485069 53 0.000908310286529943 0.00181662057305989 0.99909168971347 54 0.00488128014855718 0.00976256029711437 0.995118719851443 55 0.00286753003116067 0.00573506006232133 0.99713246996884 56 0.00431649110942338 0.00863298221884676 0.995683508890577 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.235294117647059 NOK 5% type I error level 29 0.568627450980392 NOK 10% type I error level 37 0.725490196078431 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/10gpbb1292354159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/10gpbb1292354159.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/1rodz1292354159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/1rodz1292354159.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/21fvk1292354159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/21fvk1292354159.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/31fvk1292354159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/31fvk1292354159.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/4c6u51292354159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/4c6u51292354159.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/5c6u51292354159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/5c6u51292354159.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/6c6u51292354159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/6c6u51292354159.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/75yb81292354159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/75yb81292354159.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/85yb81292354159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/85yb81292354159.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/9gpbb1292354159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354084al2hfb5gurbljc2/9gpbb1292354159.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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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