Multiple Regression - Totale Productie (C)

*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: Thu, 11 Dec 2008 17:16:36 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/12/t122904105098axlrm0oj6w79y.htm/, Retrieved Fri, 12 Dec 2008 00:17:30 +0000

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/2008/Dec/12/t122904105098axlrm0oj6w79y.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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User-defined keywords:

Dataseries X:

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101.5 1 100.7 1 110.6 1 96.8 1 100.0 1 104.8 1 86.8 1 92.0 1 100.2 1 106.6 1 102.1 1 93.7 1 97.6 1 96.9 1 105.6 1 102.8 1 101.7 1 104.2 1 92.7 1 91.9 1 106.5 1 112.3 1 102.8 1 96.5 1 101.0 0 98.9 0 105.1 0 103.0 0 99.0 0 104.3 0 94.6 0 90.4 0 108.9 0 111.4 0 100.8 0 102.5 0 98.2 0 98.7 0 113.3 0 104.6 0 99.3 0 111.8 0 97.3 0 97.7 0 115.6 0 111.9 0 107.0 0 107.1 0 100.6 0 99.2 0 108.4 0 103.0 0 99.8 0 115.0 0 90.8 0 95.9 0 114.4 0 108.2 0 112.6 0 109.1 0 105.0 0 105.0 0 118.5 0 103.7 0 112.5 0 116.6 0 96.6 0 101.9 0 116.5 0 119.3 0 115.4 0 108.5 0 111.5 0 108.8 0 121.8 0 109.6 0 112.2 0 119.6 0 104.1 0 105.3 0 115.0 0 124.1 0 116.8 0 107.5 0 115.6 0 116.2 0 116.3 0 119.0 0 111.9 0 118.6 0 106.9 0 103.2 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 91.7816770186336 + 2.91902173913043X[t] + 1.56184329710144M1[t] + 0.508896221532095M2[t] + 9.68094914596273M3[t] + 2.31550207039337M4[t] + 1.32505499482402M5[t] + 8.40960791925466M6[t] -7.4558391563147M7[t] -6.62128623188405M8[t] + 8.14098408385093M9[t] + 10.2987512939959M10[t] + 4.88508993271221M11[t] + 0.227947075569358t + 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) 91.7816770186336 1.729404 53.0713 0 0 X 2.91902173913043 1.188504 2.456 0.016271 0.008135 M1 1.56184329710144 1.677807 0.9309 0.354786 0.177393 M2 0.508896221532095 1.676385 0.3036 0.762266 0.381133 M3 9.68094914596273 1.675195 5.779 0 0 M4 2.31550207039337 1.674235 1.383 0.170605 0.085302 M5 1.32505499482402 1.673506 0.7918 0.430888 0.215444 M6 8.40960791925466 1.673008 5.0266 3e-06 2e-06 M7 -7.4558391563147 1.672743 -4.4573 2.7e-05 1.4e-05 M8 -6.62128623188405 1.672709 -3.9584 0.000165 8.3e-05 M9 8.14098408385093 1.72833 4.7103 1.1e-05 5e-06 M10 10.2987512939959 1.727769 5.9607 0 0 M11 4.88508993271221 1.727432 2.8279 0.005951 0.002975 t 0.227947075569358 0.019694 11.5742 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.929251546713724 R-squared 0.86350843706985 Adjusted R-squared 0.840759843248157 F-TEST (value) 37.958761048626 F-TEST (DF numerator) 13 F-TEST (DF denominator) 78 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.23151893143709 Sum Squared Residuals 814.531739130434 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.5 96.4904891304348 5.00951086956518 2 100.7 95.6654891304348 5.03451086956522 3 110.6 105.065489130435 5.53451086956522 4 96.8 97.9279891304348 -1.12798913043478 5 100 97.1654891304348 2.83451086956522 6 104.8 104.477989130435 0.322010869565221 7 86.8 88.8404891304348 -2.04048913043478 8 92 89.9029891304348 2.09701086956521 9 100.2 104.893206521739 -4.69320652173913 10 106.6 107.278920807453 -0.678920807453423 11 102.1 102.093206521739 0.00679347826086579 12 93.7 97.4360636645963 -3.73606366459626 13 97.6 99.225854037267 -1.62585403726708 14 96.9 98.400854037267 -1.50085403726708 15 105.6 107.800854037267 -2.20085403726708 16 102.8 100.663354037267 2.13664596273292 17 101.7 99.900854037267 1.79914596273292 18 104.2 107.213354037267 -3.01335403726707 19 92.7 91.575854037267 1.12414596273292 20 91.9 92.638354037267 -0.738354037267077 21 106.5 107.628571428571 -1.12857142857143 22 112.3 110.014285714286 2.28571428571429 23 102.8 104.828571428571 -2.02857142857143 24 96.5 100.171428571429 -3.67142857142857 25 101 99.042197204969 1.95780279503106 26 98.9 98.217197204969 0.682802795031059 27 105.1 107.617197204969 -2.51719720496895 28 103 100.479697204969 2.52030279503106 29 99 99.717197204969 -0.717197204968944 30 104.3 107.029697204969 -2.72969720496895 31 94.6 91.392197204969 3.20780279503105 32 90.4 92.454697204969 -2.05469720496894 33 108.9 107.444914596273 1.45508540372671 34 111.4 109.830628881988 1.56937111801243 35 100.8 104.644914596273 -3.84491459627329 36 102.5 99.9877717391304 2.51222826086956 37 98.2 101.777562111801 -3.57756211180123 38 98.7 100.952562111801 -2.25256211180124 39 113.3 110.352562111801 2.94743788819875 40 104.6 103.215062111801 1.38493788819875 41 99.3 102.452562111801 -3.15256211180125 42 111.8 109.765062111801 2.03493788819876 43 97.3 94.1275621118012 3.17243788819876 44 97.7 95.1900621118012 2.50993788819876 45 115.6 110.180279503106 5.4197204968944 46 111.9 112.565993788820 -0.66599378881987 47 107 107.380279503106 -0.380279503105589 48 107.1 102.723136645963 4.37686335403726 49 100.6 104.512927018634 -3.91292701863354 50 99.2 103.687927018634 -4.48792701863354 51 108.4 113.087927018634 -4.68792701863353 52 103 105.950427018634 -2.95042701863354 53 99.8 105.187927018634 -5.38792701863354 54 115 112.500427018634 2.49957298136646 55 90.8 96.8629270186335 -6.06292701863354 56 95.9 97.9254270186335 -2.02542701863354 57 114.4 112.915644409938 1.48435559006212 58 108.2 115.301358695652 -7.10135869565217 59 112.6 110.115644409938 2.48435559006211 60 109.1 105.458501552795 3.64149844720496 61 105 107.248291925466 -2.24829192546583 62 105 106.423291925466 -1.42329192546584 63 118.5 115.823291925466 2.67670807453416 64 103.7 108.685791925466 -4.98579192546583 65 112.5 107.923291925466 4.57670807453416 66 116.6 115.235791925466 1.36420807453416 67 96.6 99.5982919254658 -2.99829192546584 68 101.9 100.660791925466 1.23920807453416 69 116.5 115.651009316770 0.848990683229813 70 119.3 118.036723602484 1.26327639751553 71 115.4 112.851009316770 2.54899068322982 72 108.5 108.193866459627 0.306133540372671 73 111.5 109.983656832298 1.51634316770187 74 108.8 109.158656832298 -0.358656832298142 75 121.8 118.558656832298 3.24134316770186 76 109.6 111.421156832298 -1.82115683229814 77 112.2 110.658656832298 1.54134316770186 78 119.6 117.971156832298 1.62884316770186 79 104.1 102.333656832298 1.76634316770186 80 105.3 103.396156832298 1.90384316770186 81 115 118.386374223602 -3.38637422360249 82 124.1 120.772088509317 3.32791149068322 83 116.8 115.586374223602 1.21362577639752 84 107.5 110.929231366460 -3.42923136645963 85 115.6 112.719021739130 2.88097826086957 86 116.2 111.894021739130 4.30597826086957 87 116.3 121.294021739130 -4.99402173913044 88 119 114.156521739130 4.84347826086957 89 111.9 113.394021739130 -1.49402173913043 90 118.6 120.706521739130 -2.10652173913044 91 106.9 105.069021739130 1.83097826086957 92 103.2 106.131521739130 -2.93152173913044 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.756439337826052 0.487121324347895 0.243560662173948 18 0.616231250093695 0.76753749981261 0.383768749906305 19 0.689032051013069 0.621935897973862 0.310967948986931 20 0.567005734235567 0.865988531528865 0.432994265764433 21 0.607973814657839 0.784052370684322 0.392026185342161 22 0.615457381277643 0.769085237444714 0.384542618722357 23 0.514817664004175 0.97036467199165 0.485182335995825 24 0.421891871464874 0.843783742929749 0.578108128535126 25 0.338418415607594 0.676836831215188 0.661581584392406 26 0.262145062541537 0.524290125083073 0.737854937458464 27 0.232628211815031 0.465256423630062 0.767371788184969 28 0.217481509774281 0.434963019548562 0.782518490225719 29 0.171442095350176 0.342884190700352 0.828557904649824 30 0.128640530746259 0.257281061492519 0.87135946925374 31 0.152775925883555 0.305551851767111 0.847224074116445 32 0.120147038201369 0.240294076402738 0.879852961798631 33 0.135347677121896 0.270695354243792 0.864652322878104 34 0.103349465576902 0.206698931153804 0.896650534423098 35 0.0960274111822957 0.192054822364591 0.903972588817704 36 0.140466226058946 0.280932452117893 0.859533773941054 37 0.137113911844108 0.274227823688216 0.862886088155892 38 0.106116646171221 0.212233292342441 0.89388335382878 39 0.124161899636858 0.248323799273716 0.875838100363142 40 0.102542004744863 0.205084009489726 0.897457995255137 41 0.0887995628271278 0.177599125654256 0.911200437172872 42 0.103256850619545 0.206513701239089 0.896743149380456 43 0.114743769990127 0.229487539980253 0.885256230009873 44 0.117284560752992 0.234569121505983 0.882715439247008 45 0.239618727817376 0.479237455634753 0.760381272182624 46 0.196387655875229 0.392775311750459 0.80361234412477 47 0.156894205596083 0.313788411192166 0.843105794403917 48 0.22791876271832 0.45583752543664 0.77208123728168 49 0.218148421674590 0.436296843349181 0.78185157832541 50 0.218805060851451 0.437610121702901 0.78119493914855 51 0.220103810861098 0.440207621722196 0.779896189138902 52 0.180576530577181 0.361153061154361 0.81942346942282 53 0.213446330366757 0.426892660733514 0.786553669633243 54 0.227538260905338 0.455076521810676 0.772461739094662 55 0.288628086058786 0.577256172117572 0.711371913941214 56 0.236379920036174 0.472759840072347 0.763620079963827 57 0.214669902145014 0.429339804290028 0.785330097854986 58 0.423986867325442 0.847973734650883 0.576013132674558 59 0.416594082734470 0.833188165468939 0.58340591726553 60 0.472070614645845 0.94414122929169 0.527929385354155 61 0.465352461943784 0.930704923887569 0.534647538056215 62 0.443261411700519 0.886522823401039 0.556738588299481 63 0.430927848781262 0.861855697562525 0.569072151218738 64 0.579554784968822 0.840890430062356 0.420445215031178 65 0.615686707368863 0.768626585262274 0.384313292631137 66 0.539514593137283 0.920970813725435 0.460485406862717 67 0.620914172303243 0.758171655393514 0.379085827696757 68 0.53126629502713 0.93746740994574 0.46873370497287 69 0.470970861914307 0.941941723828615 0.529029138085693 70 0.414960433791495 0.82992086758299 0.585039566208505 71 0.325553303943093 0.651106607886187 0.674446696056907 72 0.249492294470830 0.498984588941661 0.75050770552917 73 0.181358922050193 0.362717844100386 0.818641077949807 74 0.205915515977773 0.411831031955546 0.794084484022227 75 0.267065684905786 0.534131369811571 0.732934315094214 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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