MRLM 4

*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: Fri, 17 Dec 2010 19:28:52 +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/17/t1292614211hu9coxl6kuvkngv.htm/, Retrieved Fri, 17 Dec 2010 20:30:22 +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/17/t1292614211hu9coxl6kuvkngv.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 216234,00 627 2 213586,00 696 3 209465,00 825 4 204045,00 677 5 200237,00 656 6 203666,00 785 7 241476,00 412 8 260307,00 352 9 243324,00 839 10 244460,00 729 11 233575,00 696 12 237217,00 641 1 235243,00 695 2 230354,00 638 3 227184,00 762 4 221678,00 635 5 217142,00 721 6 219452,00 854 7 256446,00 418 8 265845,00 367 9 248624,00 824 10 241114,00 687 11 229245,00 601 12 231805,00 676 1 219277,00 740 2 219313,00 691 3 212610,00 683 4 214771,00 594 5 211142,00 729 6 211457,00 731 7 240048,00 386 8 240636,00 331 9 230580,00 707 10 208795,00 715 11 197922,00 657 12 194596,00 653 1 194581,00 642 2 185686,00 643 3 178106,00 718 4 172608,00 654 5 167302,00 632 6 168053,00 731 7 202300,00 392 8 202388,00 344 9 182516,00 792 10 173476,00 852 11 166444,00 649 12 171297,00 629 1 169701,00 685 2 164182,00 617 3 161914,00 715 4 159612,00 715 5 151001,00 629 6 158114,00 916 7 186530,00 531 8 187069,00 357 9 174330,00 917 10 169362,00 828 11 166827,00 708 12 1780 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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation werklozen[t] = + 243874.214772576 + 1987.33178070898month[t] -34.9130537947866faillissementen[t] -768.694615234996t + 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) 243874.214772576 12484.709297 19.5338 0 0 month 1987.33178070898 746.816471 2.6611 0.00971 0.004855 faillissementen -34.9130537947866 16.831543 -2.0743 0.041843 0.020922 t -768.694615234996 127.636776 -6.0225 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.659881280806462 R-squared 0.435443304758776 Adjusted R-squared 0.410536391733428 F-TEST (value) 17.4828291372608 F-TEST (DF numerator) 3 F-TEST (DF denominator) 68 p-value 1.61545887777947e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 21531.3661705891 Sum Squared Residuals 31524781583.6951 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 216234 223202.367208719 -6968.36720871884 2 213586 222012.003662353 -8426.00366235274 3 209465 218726.856888299 -9261.85688829924 4 204045 225112.626015402 -21067.6260154017 5 200237 227064.437310566 -26827.4373105662 6 203666 223779.290536513 -20113.2905365127 7 241476 238020.496767442 3455.50323255793 8 260307 241333.917160603 18973.0828393968 9 243324 225549.897128016 17774.1028719838 10 244460 230608.970210917 13851.0297890833 11 233575 232979.738151619 595.261848381402 12 237217 236118.593275806 1098.40672419415 13 235243 211603.944167854 23639.0558321464 14 230354 214812.625399630 15541.3746003696 15 227184 211702.043894551 15481.9561054491 16 221678 217354.638891963 4323.36110803725 17 217142 215570.753431085 1571.24656891491 18 219452 212145.954441852 7306.04555814755 19 256446 228586.683061853 27859.3169381466 20 265845 231585.885970861 34259.1140291385 21 248624 216849.257552118 31774.742447882 22 241114 222850.983087478 18263.0169125223 23 229245 227072.142879303 2172.85712069663 24 231805 225672.301010168 6132.69898983164 25 219277 200808.521364268 18468.4786357317 26 219313 203737.898165687 15575.1018343132 27 212610 205235.839761519 7374.16023848095 28 214771 209561.738714729 5209.26128527096 29 211142 206067.113617907 5074.88638209317 30 211457 207215.924675791 4241.07532420876 31 240048 220479.565400467 19568.4345995334 32 240636 223618.420524654 17017.5794753462 33 230580 211709.749463288 18870.2505367119 34 208795 212649.082198404 -3854.08219840376 35 197922 215892.676483975 -17970.6764839754 36 194596 217250.965864628 -22654.9658646285 37 194581 195005.665253337 -424.66525333738 38 185686 196189.389365017 -10503.3893650166 39 178106 194789.547495882 -16683.5474958816 40 172608 198242.620104222 -25634.6201042219 41 167302 200229.344453181 -32927.3444531812 42 168053 197991.589292971 -29938.5892929713 43 202300 211045.751694878 -8745.75169487792 44 202388 213940.215442502 -11552.2154425017 45 182516 199517.804507911 -17001.8045079113 46 173476 198641.658445698 -25165.6584456980 47 166444 206947.645531514 -40503.6455315137 48 171297 208864.543772883 -37567.5437728834 49 169701 184280.068557342 -14579.0685573416 50 164182 187872.793380861 -23690.7933808611 51 161914 185669.951274446 -23755.9512744460 52 159612 186888.58843992 -27276.5884399199 53 151001 191109.748231746 -40108.7482317456 54 158114 182308.338958116 -24194.3389581158 55 186530 196968.501834583 -10438.5018345826 56 187069 204262.010360349 -17193.0103603495 57 174330 185929.337400743 -11599.3374007430 58 169362 190255.236353953 -20893.2363539530 59 166827 195663.439974801 -28836.4399748013 60 178037 191645.119071057 -13608.1190710573 61 186413 171913.558332991 14499.4416670091 62 189226 172783.064960517 16442.9350394830 63 191563 166285.917237343 25277.0827626569 64 188906 175080.687076286 13825.3129237142 65 186005 178219.542200473 7785.45779952697 66 195309 173433.134113244 21875.8658867563 67 223532 187709.253397968 35822.7466020321 68 226899 193990.283363686 32908.7166363141 69 214126 174121.436037109 40004.5639628912 70 206903 180577.031271801 26325.9687281992 71 204442 180015.102693741 24426.8973062594 72 220375 185074.175776641 35300.8242233588 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.162896978287816 0.325793956575631 0.837103021712184 8 0.135308885971723 0.270617771943447 0.864691114028277 9 0.256452867989277 0.512905735978555 0.743547132010723 10 0.160303491329654 0.320606982659308 0.839696508670346 11 0.112893300552428 0.225786601104856 0.887106699447572 12 0.0774407964128913 0.154881592825783 0.922559203587109 13 0.0437772821429183 0.0875545642858366 0.956222717857082 14 0.0270292168139974 0.0540584336279949 0.972970783186003 15 0.0146605532320928 0.0293211064641856 0.985339446767907 16 0.0143342456080928 0.0286684912161857 0.985665754391907 17 0.0121622380457011 0.0243244760914022 0.987837761954299 18 0.00665862043125093 0.0133172408625019 0.99334137956875 19 0.00398964266035496 0.00797928532070992 0.996010357339645 20 0.00294959049680818 0.00589918099361636 0.997050409503192 21 0.00282638251186230 0.00565276502372461 0.997173617488138 22 0.00212681776019525 0.0042536355203905 0.997873182239805 23 0.00492756848734629 0.00985513697469259 0.995072431512654 24 0.00523835973212856 0.0104767194642571 0.994761640267871 25 0.00394981665759584 0.00789963331519167 0.996050183342404 26 0.00336627806531531 0.00673255613063062 0.996633721934685 27 0.00367743992894116 0.00735487985788231 0.996322560071059 28 0.00462829202219319 0.00925658404438638 0.995371707977807 29 0.00481418708084084 0.00962837416168168 0.99518581291916 30 0.00532633771414841 0.0106526754282968 0.994673662285852 31 0.00754578639901771 0.0150915727980354 0.992454213600982 32 0.0146913825510092 0.0293827651020184 0.98530861744899 33 0.0417194608533298 0.0834389217066595 0.95828053914667 34 0.0998253798975122 0.199650759795024 0.900174620102488 35 0.246652925159334 0.493305850318669 0.753347074840666 36 0.437998268664243 0.875996537328487 0.562001731335757 37 0.548613773192487 0.902772453615025 0.451386226807513 38 0.644024270784262 0.711951458431476 0.355975729215738 39 0.718915674015249 0.562168651969502 0.281084325984751 40 0.777207521426267 0.445584957147466 0.222792478573733 41 0.82120117380563 0.357597652388739 0.178798826194370 42 0.82916455987519 0.34167088024962 0.17083544012481 43 0.89656803016574 0.206863939668519 0.103431969834259 44 0.961439383726297 0.0771212325474054 0.0385606162737027 45 0.98809343286481 0.0238131342703811 0.0119065671351905 46 0.996691135303033 0.0066177293939346 0.0033088646969673 47 0.997790985998404 0.00441802800319185 0.00220901400159593 48 0.999248004046353 0.00150399190729464 0.000751995953647321 49 0.999456734752528 0.00108653049494482 0.00054326524747241 50 0.999071153857482 0.00185769228503624 0.000928846142518119 51 0.998379059193304 0.00324188161339234 0.00162094080669617 52 0.996760029357111 0.00647994128577765 0.00323997064288883 53 0.998169951759996 0.00366009648000827 0.00183004824000414 54 0.996115278239491 0.00776944352101756 0.00388472176050878 55 0.995630460259918 0.00873907948016352 0.00436953974008176 56 0.991877684338965 0.0162446313220697 0.00812231566103484 57 0.99225193784279 0.0154961243144201 0.00774806215721003 58 0.984285058659305 0.0314298826813906 0.0157149413406953 59 0.980477203724058 0.0390455925518843 0.0195227962759421 60 0.96139284214015 0.0772143157197011 0.0386071578598506 61 0.941772001627165 0.116455996745671 0.0582279983728353 62 0.90708250452056 0.185834990958882 0.092917495479441 63 0.90911472622775 0.181770547544499 0.0908852737722493 64 0.831710602294855 0.336578795410290 0.168289397705145 65 0.86674697136338 0.266506057273241 0.133253028636620 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.338983050847458 NOK 5% type I error level 33 0.559322033898305 NOK 10% type I error level 38 0.64406779661017 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/1039yh1292614124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/1039yh1292614124.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/1w8jo1292614124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/1w8jo1292614124.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/27z081292614124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/27z081292614124.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/37z081292614124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/37z081292614124.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/47z081292614124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/47z081292614124.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/5i9ic1292614124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/5i9ic1292614124.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/6i9ic1292614124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/6i9ic1292614124.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/7bizf1292614124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/7bizf1292614124.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/8bizf1292614124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/8bizf1292614124.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/939yh1292614124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292614211hu9coxl6kuvkngv/939yh1292614124.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal 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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