*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, 18 Nov 2009 10:37:10 -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/18/t1258566016g30clivhee8slgj.htm/, Retrieved Wed, 18 Nov 2009 18:40:28 +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/18/t1258566016g30clivhee8slgj.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:

ws7

Dataseries X:

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8031 4871 6820 7291 7862 4649 8031 6820 7357 4922 7862 8031 7213 4879 7357 7862 7079 4853 7213 7357 7012 4545 7079 7213 7319 4733 7012 7079 8148 5191 7319 7012 7599 4983 8148 7319 6908 4593 7599 8148 7878 4656 6908 7599 7407 4513 7878 6908 7911 4857 7407 7878 7323 4681 7911 7407 7179 4897 7323 7911 6758 4547 7179 7323 6934 4692 6758 7179 6696 4390 6934 6758 7688 5341 6696 6934 8296 5415 7688 6696 7697 4890 8296 7688 7907 5120 7697 8296 7592 4422 7907 7697 7710 4797 7592 7907 9011 5689 7710 7592 8225 5171 9011 7710 7733 4265 8225 9011 8062 5215 7733 8225 7859 4874 8062 7733 8221 4590 7859 8062 8330 4994 8221 7859 8868 4988 8330 8221 9053 5110 8868 8330 8811 5141 9053 8868 8120 4395 8811 9053 7953 4523 8120 8811 8878 5306 7953 8120 8601 5365 8878 7953 8361 5496 8601 8878 9116 5647 8361 8601 9310 5443 9116 8361 9891 5546 9310 9116 10147 5912 9891 9310 10317 5665 10147 9891 10682 5963 10317 10147 10276 5861 10682 10317 10614 5366 10276 10682 9413 5619 10614 10276 11 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 707.247503352974 + 0.466569037099199X[t] + 0.294685419695422Y1[t] + 0.259943907693913Y2[t] + 795.753299651672M1[t] + 187.164289089145M2[t] + 0.215252841099507M3[t] + 76.4850064780137M4[t] + 150.436085547817M5[t] + 385.019127983302M6[t] + 437.49798746352M7[t] + 902.749795565776M8[t] + 506.825073622954M9[t] -91.7651885083793M10[t] + 534.126565021445M11[t] + 12.7222622255647t + 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) 707.247503352974 648.651455 1.0903 0.280407 0.140204 X 0.466569037099199 0.120211 3.8812 0.000285 0.000142 Y1 0.294685419695422 0.118524 2.4863 0.016033 0.008016 Y2 0.259943907693913 0.115304 2.2544 0.028248 0.014124 M1 795.753299651672 252.48783 3.1517 0.002649 0.001324 M2 187.164289089145 235.147308 0.7959 0.429549 0.214775 M3 0.215252841099507 233.951578 9e-04 0.999269 0.499635 M4 76.4850064780137 231.90745 0.3298 0.742821 0.37141 M5 150.436085547817 219.206858 0.6863 0.495476 0.247738 M6 385.019127983302 217.246622 1.7723 0.081992 0.040996 M7 437.49798746352 227.393554 1.924 0.059636 0.029818 M8 902.749795565776 227.585267 3.9666 0.000216 0.000108 M9 506.825073622954 228.544122 2.2176 0.030809 0.015404 M10 -91.7651885083793 221.047664 -0.4151 0.679686 0.339843 M11 534.126565021445 229.067195 2.3317 0.023474 0.011737 t 12.7222622255647 7.147717 1.7799 0.080718 0.040359 Multiple Linear Regression - Regression Statistics Multiple R 0.98068862930769 R-squared 0.961750187653395 Adjusted R-squared 0.951125239779338 F-TEST (value) 90.5181087995464 F-TEST (DF numerator) 15 F-TEST (DF denominator) 54 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 346.635162288257 Sum Squared Residuals 6488420.52966873 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8031 7693.38643825949 337.613561740513 2 7862 7228.37182641385 633.628173586149 3 7357 7446.50863580825 -89.5086358082493 4 7213 7322.691525729 -109.691525729004 5 7079 7223.52769823822 -144.527698238224 6 7012 7250.20997052561 -238.209970525612 7 7319 7348.54966445547 -29.5496644554662 8 8148 8113.26453580572 34.7354641942780 9 7599 7957.11270896137 -358.112708961367 10 6908 7242.99398865238 -334.993988652379 11 7878 7564.66502341152 313.334976588477 12 7407 7082.76496519852 324.235034801477 13 7911 8165.08903362444 -254.089033624437 14 7323 7513.19400576067 -190.194005760673 15 7179 7397.48284644844 -218.482846448444 16 6758 7127.89398116604 -369.893981166042 17 6934 7120.7253484411 -186.725348441098 18 6696 7169.55505262545 -473.555052625446 19 7688 7654.07832647918 33.9216735208152 20 8296 8397.03979185905 -101.039791859053 21 7697 8205.9216792719 -508.921679271895 22 7907 7708.89388737928 198.106112620716 23 7592 7928.02025266682 -336.020252666817 24 7710 7543.3416521948 166.6583478052 25 9011 8720.887343765 290.112656235 26 8225 8297.39694634228 -72.3969463422772 27 7733 7807.0229087371 -74.0229087371023 28 8062 7989.95437190626 72.0456280937434 29 7859 7886.58677204519 -27.5867720451868 30 8221 8027.08687560319 193.913124396809 31 8330 8334.68939696493 -4.68939696492713 32 8868 8936.08445840215 -68.0844584021506 33 9053 8796.67806294577 256.321937054231 34 8811 8419.64032817305 391.359671826946 35 8120 8686.96959360952 -566.969593609522 36 7953 7958.75207689088 -5.75207689087666 37 8878 8903.71748951116 -25.7174895111573 38 8601 8564.55169499643 36.4483050035718 39 8361 8610.26571819518 -249.265718195181 40 9116 8626.98069550152 489.019304498476 41 9310 8778.57490725216 531.425092747840 42 9891 9327.36344446424 563.636555535757 43 10147 9784.970180684 362.029819316010 44 10317 10374.1685766605 -57.1685766605008 45 10682 10246.6458517167 435.354148283332 46 10276 9764.93845252358 511.061547476424 47 10614 10147.8380408268 466.161959173201 48 9413 9738.54214975034 -325.542149750339 49 11068 10795.1206422572 272.879357742764 50 9772 10063.5640826306 -291.564082630640 51 10350 10201.2436778774 148.756322122634 52 10541 10234.253423745 306.746576255003 53 10049 10218.1239872525 -169.123987252504 54 10714 10427.9479123932 286.052087606756 55 10759 10701.6597157779 57.3402842220646 56 11684 11350.3605503842 333.639449615771 57 11462 11376.0135126369 85.9864873630511 58 10485 10841.0661003073 -356.06610030728 59 11056 10932.5070894853 123.492910514661 60 10184 10343.5991559655 -159.599155965460 61 11082 11702.7990525827 -620.799052582683 62 10554 10669.9214438561 -115.921443856131 63 11315 10832.4762129337 482.523787066343 64 10847 11235.2260019522 -388.226001952177 65 11104 11107.4612867708 -3.46128677082668 66 11026 11357.8367443883 -331.836744388264 67 11073 11492.0527156385 -419.052715638495 68 12073 12215.0820868883 -142.082086888344 69 12328 12238.6281844674 89.3718155326487 70 11172 11581.4672429644 -409.467242964426 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0874764190938288 0.174952838187658 0.912523580906171 20 0.0290072282524568 0.0580144565049137 0.970992771747543 21 0.0617330996682827 0.123466199336565 0.938266900331717 22 0.146203420713269 0.292406841426537 0.853796579286731 23 0.096062768767564 0.192125537535128 0.903937231232436 24 0.0561680213940211 0.112336042788042 0.943831978605979 25 0.0292667253502862 0.0585334507005725 0.970733274649714 26 0.0146573907777165 0.029314781555433 0.985342609222283 27 0.189530968583204 0.379061937166408 0.810469031416796 28 0.185388480573317 0.370776961146634 0.814611519426683 29 0.170684793787243 0.341369587574486 0.829315206212757 30 0.221420809522135 0.442841619044271 0.778579190477865 31 0.165341083100847 0.330682166201693 0.834658916899153 32 0.129219474288945 0.25843894857789 0.870780525711055 33 0.178386648182715 0.35677329636543 0.821613351817285 34 0.143057830975217 0.286115661950435 0.856942169024783 35 0.405434247054796 0.810868494109592 0.594565752945204 36 0.342128013800418 0.684256027600836 0.657871986199582 37 0.267159828608103 0.534319657216205 0.732840171391897 38 0.228148747319811 0.456297494639622 0.771851252680189 39 0.593226360962795 0.81354727807441 0.406773639037205 40 0.616626617937766 0.766746764124468 0.383373382062234 41 0.658042850822447 0.683914298355106 0.341957149177553 42 0.592533192962954 0.814933614074091 0.407466807037046 43 0.509706619098529 0.980586761802942 0.490293380901471 44 0.655237517175295 0.68952496564941 0.344762482824705 45 0.811630129385387 0.376739741229227 0.188369870614613 46 0.726577717611627 0.546844564776746 0.273422282388373 47 0.65132090312198 0.697358193756041 0.348679096878021 48 0.685803617081301 0.628392765837397 0.314196382918699 49 0.732960713638195 0.534078572723611 0.267039286361805 50 0.66093738131166 0.67812523737668 0.33906261868834 51 0.524036283969995 0.95192743206001 0.475963716030005 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 1 0.0303030303030303 OK 10% type I error level 3 0.090909090909091 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/106fb51258565825.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/106fb51258565825.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/10xa41258565825.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/10xa41258565825.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/2sexw1258565825.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/2sexw1258565825.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/31vp31258565825.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/31vp31258565825.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/4ocqn1258565825.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/4ocqn1258565825.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/53zlh1258565825.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/53zlh1258565825.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/6zn4q1258565825.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/6zn4q1258565825.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/7c9we1258565825.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/7c9we1258565825.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/8xacr1258565825.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/8xacr1258565825.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/9mefi1258565825.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258566016g30clivhee8slgj/9mefi1258565825.ps (open in new window)

Parameters (Session):

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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