paper 1

*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: Sun, 28 Nov 2010 09:52:50 +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/Nov/28/t1290937964br237eo7w2txy7o.htm/, Retrieved Sun, 28 Nov 2010 10:52:46 +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/Nov/28/t1290937964br237eo7w2txy7o.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:

» Textbox « » Textfile « » CSV «

1.579 9.769 2.146 9.321 2.462 9.939 3.695 9.336 4.831 10.195 5.134 9.464 6.250 10.010 5.760 10.213 6.249 9.563 2.917 9.890 1.741 9.305 2.359 9.391 1.511 9.928 2.059 8.686 2.635 9.843 2.867 9.627 4.403 10.074 5.720 9.503 4.502 10.119 5.749 10.000 5.627 9.313 2.846 9.866 1.762 9.172 2.429 9.241 1.169 9.659 2.154 8.904 2.249 9.755 2.687 9.080 4.359 9.435 5.382 8.971 4.459 10.063 6.398 9.793 4.596 9.454 3.024 9.759 1.887 8.820 2.070 9.403 1.351 9.676 2.218 8.642 2.461 9.402 3.028 9.610 4.784 9.294 4.975 9.448 4.607 10.319 6.249 9.548 4.809 9.801 3.157 9.596 1.910 8.923 2.228 9.746 1.594 9.829 2.467 9.125 2.222 9.782 3.607 9.441 4.685 9.162 4.962 9.915 5.770 10.444 5.480 10.209 5.000 9.985 3.228 9.842 1.993 9.429 2.288 10.132 1.580 9.849 2.111 9.172 2.192 10.313 3.601 9.819 4.665 9.955 4.876 10.048 5.813 10.082 5.589 10.541 5.331 10.208 3.075 10.233 2.002 9.439 2.306 9.963 1.507 10.158 1.992 9.225 2.487 10.474 3.490 9.757 4.647 10.490 5.5 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 8 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation huwelijk[t] = -8.06040832663055 + 1.18849990912292geboortes[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) -8.06040832663055 2.919378 -2.761 0.00693 0.003465 geboortes 1.18849990912292 0.296986 4.0019 0.000125 6.3e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.381537512253696 R-squared 0.145570873256739 Adjusted R-squared 0.136481201695641 F-TEST (value) 16.0149761493854 F-TEST (DF numerator) 1 F-TEST (DF denominator) 94 p-value 0.000125324935773441 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.48456585486723 Sum Squared Residuals 207.169963079142 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.579 3.5500472855913 -1.9710472855913 2 2.146 3.0175993263042 -0.871599326304203 3 2.462 3.75209227014217 -1.29009227014217 4 3.695 3.03542682494105 0.659573175058951 5 4.831 4.05634824687764 0.774651753122362 6 5.134 3.18755481330878 1.94644518669122 7 6.25 3.8364757636899 2.4135242363101 8 5.76 4.07774124524185 1.68225875475815 9 6.249 3.30521630431195 2.94378369568805 10 2.917 3.69385577459515 -0.776855774595148 11 1.741 2.99858332775824 -1.25758332775824 12 2.359 3.10079431994281 -0.741794319942809 13 1.511 3.73901877114182 -2.22801877114182 14 2.059 2.26290188401115 -0.203901884011149 15 2.635 3.63799627886637 -1.00299627886637 16 2.867 3.38128029849582 -0.514280298495819 17 4.403 3.91253975787376 0.490460242126235 18 5.72 3.23390630976458 2.48609369023542 19 4.502 3.9660222537843 0.535977746215704 20 5.749 3.82459076459867 1.92440923540133 21 5.627 3.00809132703122 2.61890867296878 22 2.846 3.6653317767762 -0.819331776776196 23 1.762 2.84051283984489 -1.07851283984489 24 2.429 2.92251933357437 -0.493519333574371 25 1.169 3.41931229558775 -2.25031229558775 26 2.154 2.52199486419995 -0.367994864199946 27 2.249 3.53340828686355 -1.28440828686355 28 2.687 2.73117084820558 -0.0441708482055808 29 4.359 3.15308831594422 1.20591168405578 30 5.382 2.60162435811118 2.78037564188882 31 4.459 3.89946625887341 0.559533741126587 32 6.398 3.57857128341022 2.81942871658978 33 4.596 3.17566981421755 1.42033018578245 34 3.024 3.53816228650004 -0.514162286500045 35 1.887 2.42216087183362 -0.535160871833621 36 2.07 3.11505631885229 -1.04505631885229 37 1.351 3.43951679404284 -2.08851679404284 38 2.218 2.21060788800974 0.0073921119902597 39 2.461 3.11386781894316 -0.65286781894316 40 3.028 3.36107580004073 -0.333075800040728 41 4.784 2.98550982875789 1.79849017124211 42 4.975 3.16853881476282 1.80646118523718 43 4.607 4.20372223560888 0.403277764391119 44 6.249 3.28738880567511 2.96161119432489 45 4.809 3.58807928268321 1.22092071731679 46 3.157 3.34443680131301 -0.187436801313008 47 1.91 2.54457636247328 -0.634576362473282 48 2.228 3.52271178768145 -1.29471178768145 49 1.594 3.62135728013865 -2.02735728013865 50 2.467 2.78465334411611 -0.317653344116112 51 2.222 3.56549778440987 -1.34349778440987 52 3.607 3.16021931539896 0.446780684601044 53 4.685 2.82862784075366 1.85637215924634 54 4.962 3.72356827232322 1.23843172767678 55 5.77 4.35228472424925 1.41771527575075 56 5.48 4.07298724560536 1.40701275439464 57 5 3.80676326596182 1.19323673403818 58 3.228 3.63680777895725 -0.408807778957247 59 1.993 3.14595731648948 -1.15295731648948 60 2.288 3.98147275260289 -1.69347275260289 61 1.58 3.64512727832111 -2.06512727832111 62 2.111 2.84051283984489 -0.72951283984489 63 2.192 4.19659123615414 -2.00459123615414 64 3.601 3.60947228104742 -0.0084722810474205 65 4.665 3.77110826868814 0.893891731311863 66 4.876 3.88163876023657 0.994361239763432 67 5.813 3.92204775714675 1.89095224285325 68 5.589 4.46756921543417 1.12143078456583 69 5.331 4.07179874569624 1.25920125430376 70 3.075 4.10151124342431 -1.02651124342431 71 2.002 3.15784231558071 -1.15584231558071 72 2.306 3.78061626796112 -1.47461626796112 73 1.507 4.01237375024009 -2.50537375024009 74 1.992 2.9035033350284 -0.911503335028404 75 2.487 4.38793972152293 -1.90093972152293 76 3.49 3.5357852866818 -0.0457852866817977 77 4.647 4.4069557200689 0.2400442799311 78 5.594 4.15855923906221 1.43544076093779 79 5.611 4.35228472424925 1.25871527575075 80 5.788 4.58523070643734 1.20276929356266 81 6.204 4.6505982014391 1.5534017985609 82 3.013 4.75875169316928 -1.74575169316928 83 1.931 3.62492277986602 -1.69392277986602 84 2.549 3.52390028759057 -0.974900287590569 85 1.504 4.31306422724819 -2.80906422724819 86 2.09 3.24341430903756 -1.15341430903756 87 2.702 4.30236772806608 -1.60036772806608 88 2.939 3.46922929177092 -0.530229291770915 89 4.5 4.46638071552504 0.0336192844749552 90 6.208 3.95770275442044 2.25029724557956 91 6.415 4.91206818144614 1.50293181855386 92 5.657 5.23058615709108 0.426413842908916 93 5.964 4.28097472970187 1.68302527029813 94 3.163 4.81579968880718 -1.65279968880718 95 1.997 3.68910177495865 -1.69210177495865 96 2.422 4.08130674496922 -1.65930674496922 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.542004549354245 0.915990901291509 0.457995450645755 6 0.684574676083912 0.630850647832175 0.315425323916088 7 0.803498257240918 0.393003485518164 0.196501742759082 8 0.751927203520611 0.496145592958778 0.248072796479389 9 0.861151979262762 0.277696041474477 0.138848020737238 10 0.842025600350674 0.315948799298651 0.157974399649326 11 0.828132091467973 0.343735817064054 0.171867908532027 12 0.776935732085422 0.446128535829156 0.223064267914578 13 0.863095757708223 0.273808484583554 0.136904242291777 14 0.810831459433524 0.378337081132952 0.189168540566476 15 0.779305610889601 0.441388778220797 0.220694389110399 16 0.720177916956563 0.559644166086874 0.279822083043437 17 0.652448344425302 0.695103311149396 0.347551655574698 18 0.755483957061639 0.489032085876722 0.244516042938361 19 0.694887548184813 0.610224903630373 0.305112451815187 20 0.707014937691085 0.58597012461783 0.292985062308915 21 0.798803636587615 0.40239272682477 0.201196363412385 22 0.7717734567374 0.4564530865252 0.2282265432626 23 0.749101084358892 0.501797831282217 0.250898915641108 24 0.698239183306252 0.603521633387497 0.301760816693748 25 0.772606869294472 0.454786261411056 0.227393130705528 26 0.720224035872286 0.559551928255429 0.279775964127714 27 0.708218597679171 0.583562804641658 0.291781402320829 28 0.649432980450205 0.70113403909959 0.350567019549795 29 0.626172961678065 0.74765407664387 0.373827038321935 30 0.755397207486384 0.489205585027232 0.244602792513616 31 0.708366051639289 0.583267896721423 0.291633948360711 32 0.81425079887743 0.371498402245141 0.185749201122571 33 0.805658940555676 0.388682118888647 0.194341059444324 34 0.76926604454586 0.461467910908279 0.23073395545414 35 0.727720990647411 0.544558018705177 0.272279009352589 36 0.703408341050207 0.593183317899587 0.296591658949793 37 0.754653798540467 0.490692402919066 0.245346201459533 38 0.705080140465173 0.589839719069654 0.294919859534827 39 0.66171645640686 0.676567087186281 0.33828354359314 40 0.608573507239829 0.782852985520341 0.391426492760171 41 0.63573247962777 0.728535040744461 0.364267520372231 42 0.664464574154464 0.671070851691071 0.335535425845536 43 0.611442825414786 0.777114349170429 0.388557174585214 44 0.773600107139246 0.452799785721509 0.226399892860754 45 0.762806056322487 0.474387887355025 0.237193943677513 46 0.71783133822626 0.56433732354748 0.28216866177374 47 0.67379007422437 0.65241985155126 0.32620992577563 48 0.659134067869188 0.681731864261624 0.340865932130812 49 0.700594906875655 0.598810186248691 0.299405093124345 50 0.650343784593892 0.699312430812215 0.349656215406108 51 0.635160099663993 0.729679800672013 0.364839900336007 52 0.590488425121721 0.819023149756558 0.409511574878279 53 0.666511087744794 0.666977824510412 0.333488912255206 54 0.664403836194965 0.67119232761007 0.335596163805035 55 0.656614834030614 0.686770331938772 0.343385165969386 56 0.657942966023528 0.684114067952945 0.342057033976472 57 0.65553502873384 0.68892994253232 0.34446497126616 58 0.603795510363307 0.792408979273385 0.396204489636693 59 0.564129926800543 0.871740146398913 0.435870073199457 60 0.575519788265539 0.848960423468923 0.424480211734461 61 0.60772469796204 0.78455060407592 0.39227530203796 62 0.554321822126268 0.891356355747465 0.445678177873732 63 0.599150666050508 0.801698667898983 0.400849333949492 64 0.541562378679735 0.91687524264053 0.458437621320265 65 0.518892862281405 0.96221427543719 0.481107137718595 66 0.502103308549699 0.995793382900601 0.497896691450301 67 0.581024555561687 0.837950888876626 0.418975444438313 68 0.556421242397937 0.887157515204126 0.443578757602063 69 0.566767416992003 0.866465166015995 0.433232583007997 70 0.520474458042764 0.959051083914473 0.479525541957236 71 0.465375788677438 0.930751577354877 0.534624211322562 72 0.433377707752863 0.866755415505725 0.566622292247137 73 0.518818618793365 0.96236276241327 0.481181381206635 74 0.45510625133232 0.91021250266464 0.54489374866768 75 0.490766445337976 0.981532890675953 0.509233554662024 76 0.428777569754422 0.857555139508843 0.571222430245578 77 0.358747423905755 0.71749484781151 0.641252576094245 78 0.376265097339806 0.752530194679611 0.623734902660194 79 0.370492684493662 0.740985368987323 0.629507315506338 80 0.352352397528443 0.704704795056887 0.647647602471557 81 0.380625711059027 0.761251422118053 0.619374288940973 82 0.38988952861852 0.779779057237039 0.61011047138148 83 0.341706764603457 0.683413529206913 0.658293235396543 84 0.265725713463112 0.531451426926225 0.734274286536888 85 0.420360810018369 0.840721620036738 0.579639189981631 86 0.332148220972014 0.664296441944028 0.667851779027986 87 0.327922175015247 0.655844350030493 0.672077824984753 88 0.232234426931447 0.464468853862893 0.767765573068553 89 0.149599373547874 0.299198747095748 0.850400626452126 90 0.311661485705067 0.623322971410135 0.688338514294933 91 0.276972414497288 0.553944828994576 0.723027585502712 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 = 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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