*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: Wed, 22 Dec 2010 18:46:07 +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/22/t1293043547hl7or06sge19bc6.htm/, Retrieved Wed, 22 Dec 2010 19:45:58 +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/2010/Dec/22/t1293043547hl7or06sge19bc6.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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-999 -999 38.6 6.654 5.712 645 3 5 3 6.3 2 4.5 1 6.6 42 3 1 3 -999 -999 14 3.385 44.5 60 1 1 1 -999 -999 -999 0.92 5.7 25 5 2 3 2.1 1.8 69 2547 4603 624 3 5 4 0.1 0.7 27 10.55 0.5 180 4 4 4 15.8 3.9 19 0.023 0.3 35 1 1 1 5.2 1 30.4 160 169 392 4 5 4 10.9 3.6 28 3.3 25.6 63 1 2 1 8.3 1.4 50 52.16 440 230 1 1 1 11 1.5 7 0.425 6.4 112 5 4 4 3.2 0.7 30 465 423 281 5 5 5 7.6 2.7 -999 0.55 2.4 -999 2 1 2 -999 -999 40 187.1 419 365 5 5 5 6.3 2.1 3.5 0.075 1.2 42 1 1 1 8.6 0 50 3 25 28 2 2 2 6.6 4.1 6 0.785 3.5 42 2 2 2 9.5 1.2 10.4 0.2 5 120 2 2 2 4.8 1.3 34 1.41 17.5 -999 1 2 1 12 6.1 7 60 81 -999 1 1 1 -999 0.3 28 529 680 400 5 5 5 3.3 0.5 20 27.66 115 148 5 5 5 11 3.4 3.9 0.12 1 16 3 1 2 -999 -999 39.3 207 406 252 1 4 1 4.7 1.5 41 85 325 310 1 3 1 -999 -999 16.2 36.33 119.5 63 1 1 1 10.4 3.4 9 0.101 4 28 5 1 3 7.4 0.8 7.6 1.04 5.5 68 5 3 4 2.1 0.8 46 521 655 336 5 5 5 2.1 -999 22.4 100 157 100 1 1 1 -999 -999 16.3 35 56 33 3 5 4 7.7 1. 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation SWS[t] = + 101.747169187514 + 0.85898554456651PS[t] + 0.0483563309262616L[t] + 0.00897769792082362WB[t] -0.00229591302762992WBR[t] -0.0465855631325104TG[t] -24.6233778463787P[t] -39.2386116719056S[t] + 11.5295140043652D[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) 101.747169187514 69.11223 1.4722 0.14688 0.07344 PS 0.85898554456651 0.075204 11.4221 0 0 L 0.0483563309262616 0.118259 0.4089 0.684258 0.342129 WB 0.00897769792082362 0.300033 0.0299 0.976241 0.488121 WBR -0.00229591302762992 0.163977 -0.014 0.988881 0.494441 TG -0.0465855631325104 0.104604 -0.4454 0.65788 0.32894 P -24.6233778463787 51.141729 -0.4815 0.632162 0.316081 S -39.2386116719056 33.633703 -1.1666 0.248576 0.124288 D 11.5295140043652 67.319369 0.1713 0.864667 0.432333 Multiple Linear Regression - Regression Statistics Multiple R 0.87368168438707 R-squared 0.763319685633427 Adjusted R-squared 0.727594355163 F-TEST (value) 21.3663435882087 F-TEST (DF numerator) 8 F-TEST (DF denominator) 53 p-value 4.61852778244065e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 215.781932434378 Sum Squared Residuals 2467777.64535107 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -999 -1019.98855021996 20.9885502199617 2 6.3 23.1997715882405 -16.8997715882405 3 -999 -810.9017891256 -188.0982108744 4 -999 -972.862401292858 -26.1375987071421 5 2.1 -134.086488336087 136.186488336087 6 0.1 -113.967656659258 114.067656659258 7 15.8 52.0525305885091 -36.2525305885091 8 5.2 -161.705444917625 166.905444917625 9 10.9 11.6583677803470 -0.758367780347039 10 8.3 41.7784854532164 -33.4784854532164 11 11 -135.807599473810 146.807599473810 12 3.2 -167.750313490837 170.950313490837 13 7.6 36.8705213185434 -29.2705213185434 14 -999 -1032.39350498793 33.3935049879323 15 6.3 49.4291350555717 -43.1291350555717 16 8.6 -1.83482579365001 10.43482579365 17 6.6 -1.06338497633856 7.66338497633856 18 9.5 -6.98404494666681 16.4840449466668 19 4.8 59.448336106581 -54.6483361065811 20 12 101.884670301323 -89.8846703013234 21 -999 -173.749779364453 -825.250220635547 22 3.3 -165.428959208572 168.728959208572 23 11 14.0600049279436 -3.0600049279436 24 -999 -935.340615687664 -63.6593843123363 25 4.7 -40.216033767178 44.916033767178 26 -999 -810.81158510603 -188.18841489397 27 10.4 -23.9767045456582 34.3767045456582 28 7.4 -95.083911500768 102.483911500768 29 2.1 -169.482820352693 171.582820352693 30 2.1 -811.749928402108 813.849928402108 31 -999 -980.887992807227 -18.1120071927734 32 7.7 -53.4024472946121 61.1024472946121 33 17.9 49.9634543470547 -32.0634543470547 34 6.1 40.9700660191365 -34.8700660191365 35 8.2 -22.8584528150715 31.0584528150715 36 8.4 -24.7786764252750 33.1786764252750 37 11.9 -1.00896958411760 12.9089695841176 38 10.8 -0.979621721599862 11.7796217215999 39 13.8 29.2851876082209 -15.4851876082209 40 14.3 22.1848456725160 -7.88484567251605 41 -999 -177.293234109293 -821.706765890707 42 15.2 -7.34458808446734 22.5445880844673 43 10 -113.926646744838 123.926646744838 44 11.9 37.6920520027063 -25.7920520027063 45 6.5 -108.691925502891 115.191925502891 46 7.5 -159.721196195239 167.221196195239 47 -999 -863.368253027925 -135.631746972075 48 10.6 24.7074578556765 -14.1074578556765 49 7.4 49.4500082748432 -42.0500082748432 50 8.4 -47.7132297017045 56.1132297017045 51 5.7 -12.3094586953449 18.0094586953449 52 4.9 -22.2957438554390 27.1957438554390 53 -999 -1024.34644921044 25.3464492104357 54 3.2 -165.324047451923 168.524047451923 55 -999 -864.639046277187 -134.360953722813 56 8.1 60.293687659716 -52.193687659716 57 11 35.7210768395585 -24.7210768395585 58 4.9 14.6917342828975 -9.79173428289748 59 13.2 -27.3443197157807 40.5443197157807 60 9.7 -76.5466925864284 86.2466925864284 61 12.8 66.1288512629375 -53.3288512629375 62 -999 -859.10291098299 -139.897089017009 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 9.96973862224977e-07 1.99394772444995e-06 0.999999003026138 13 8.49260436245877e-08 1.69852087249175e-07 0.999999915073956 14 2.46545252722155e-09 4.9309050544431e-09 0.999999997534547 15 4.77975522074018e-11 9.55951044148035e-11 0.999999999952202 16 7.21835601031414e-13 1.44367120206283e-12 0.999999999999278 17 1.79564723558843e-14 3.59129447117687e-14 0.999999999999982 18 3.20455910313872e-16 6.40911820627745e-16 1 19 9.98058767162516e-18 1.99611753432503e-17 1 20 1.31542306454434e-19 2.63084612908868e-19 1 21 0.705842943502995 0.588314112994009 0.294157056497005 22 0.637633725615691 0.724732548768618 0.362366274384309 23 0.54895564434901 0.90208871130198 0.45104435565099 24 0.462912500910653 0.925825001821306 0.537087499089347 25 0.379949463749086 0.759898927498172 0.620050536250914 26 0.333257744374903 0.666515488749805 0.666742255625097 27 0.258305163028266 0.516610326056531 0.741694836971734 28 0.202685680245681 0.405371360491362 0.797314319754319 29 0.228803130144019 0.457606260288039 0.77119686985598 30 0.999576128750102 0.000847742499796067 0.000423871249898033 31 0.99909151284502 0.00181697430996150 0.000908487154980748 32 0.998094974771878 0.00381005045624387 0.00190502522812194 33 0.996209025993802 0.00758194801239593 0.00379097400619796 34 0.9998839372825 0.000232125435000408 0.000116062717500204 35 0.99973095016094 0.00053809967811941 0.000269049839059705 36 0.99995348929557 9.30214088604778e-05 4.65107044302389e-05 37 0.999881195113487 0.000237609773025733 0.000118804886512867 38 0.999755074258572 0.000489851482855775 0.000244925741427888 39 0.999362298760348 0.00127540247930338 0.000637701239651692 40 0.99841836536591 0.00316326926818024 0.00158163463409012 41 1 3.93149082272239e-22 1.96574541136119e-22 42 1 5.67553500437773e-21 2.83776750218887e-21 43 1 3.07398365230395e-19 1.53699182615197e-19 44 1 2.01560109383107e-17 1.00780054691553e-17 45 1 9.70110436599107e-16 4.85055218299554e-16 46 0.999999999999963 7.46333488779698e-14 3.73166744389849e-14 47 0.999999999996005 7.99054504135233e-12 3.99527252067617e-12 48 0.999999999686606 6.26788520370011e-10 3.13394260185005e-10 49 0.999999968497706 6.30045888298112e-08 3.15022944149056e-08 50 0.999996975078445 6.04984311026282e-06 3.02492155513141e-06 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 30 0.769230769230769 NOK 5% type I error level 30 0.769230769230769 NOK 10% type I error level 30 0.769230769230769 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/10yh181293043559.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/10yh181293043559.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/19y4e1293043559.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/19y4e1293043559.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/228lz1293043559.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/228lz1293043559.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/328lz1293043559.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/328lz1293043559.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/428lz1293043559.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/428lz1293043559.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/5dzl21293043559.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/5dzl21293043559.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/6dzl21293043559.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/6dzl21293043559.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/7oqkn1293043559.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/7oqkn1293043559.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/8oqkn1293043559.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/8oqkn1293043559.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/9yh181293043559.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293043547hl7or06sge19bc6/9yh181293043559.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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