Multiple Linear Regression - Paper

*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, 15 Dec 2010 17:55:34 +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/15/t1292435668pvlb75la6nwa3yz.htm/, Retrieved Wed, 15 Dec 2010 18:54:38 +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/15/t1292435668pvlb75la6nwa3yz.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:

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198563 44164 25943 -7,7 -9 195722 40399 21698 -4,9 -13 202196 36763 20077 -2,4 -8 205816 37903 25673 -3,6 -13 212588 35532 19094 -7 -15 214320 35533 19306 -7 -15 220375 32110 15443 -7,9 -15 204442 33374 15179 -8,8 -10 206903 35462 18288 -14,2 -12 214126 33508 18264 -17,8 -11 226899 36080 16406 -18,2 -11 223532 34560 15678 -22,8 -17 195309 38737 19657 -23,6 -18 186005 38144 18821 -27,6 -19 188906 37594 19493 -29,4 -22 191563 36424 21078 -31,8 -24 189226 36843 19296 -31,4 -24 186413 37246 19985 -27,6 -20 178037 38661 16972 -28,8 -25 166827 40454 16951 -21,9 -22 169362 44928 23126 -13,9 -17 174330 48441 24890 -8 -9 187069 48140 21042 -2,8 -11 186530 45998 20842 -3,3 -13 158114 47369 23904 -1,3 -11 151001 49554 22578 0,5 -9 159612 47510 25452 -1,9 -7 161914 44873 21928 2 -3 164182 45344 25227 1,7 -3 169701 42413 26210 1,9 -6 171297 36912 17436 0,1 -4 166444 43452 21258 2,4 -8 173476 42142 25638 2,3 -1 182516 44382 23516 4,7 -2 202388 43636 23891 5 -2 202300 44167 24617 7,2 -1 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 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation OPENVAC[t] = + 15330.8046235123 -0.0240997112467816NWWZ[t] + 1.31718649571725ONTVANGJOB[t] + 138.518556033151Producentenvertrouwen[t] -238.329374483006consumentenvertrouwen[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) 15330.8046235123 3895.283467 3.9357 0.000156 7.8e-05 NWWZ -0.0240997112467816 0.015172 -1.5884 0.11545 0.057725 ONTVANGJOB 1.31718649571725 0.081207 16.22 0 0 Producentenvertrouwen 138.518556033151 68.049448 2.0356 0.044522 0.022261 consumentenvertrouwen -238.329374483006 92.617326 -2.5733 0.011589 0.005794 Multiple Linear Regression - Regression Statistics Multiple R 0.92485962236526 R-squared 0.85536532108161 Adjusted R-squared 0.849401004425181 F-TEST (value) 143.413800834951 F-TEST (DF numerator) 4 F-TEST (DF denominator) 97 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3295.81532670078 Sum Squared Residuals 1053652670.76843 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 44164 45795.634406502 -1631.63440650202 2 40399 41613.8144666592 -1214.81446665921 3 36763 38477.2831441577 -1714.28314415773 4 37903 46786.4424246534 -8883.44242465337 5 35532 37963.1648832197 -2431.16488321968 6 35533 38200.6677204323 -2667.66772043231 7 32110 32841.7858354475 -731.785835447474 8 33374 31561.7157270282 1812.28427297177 9 35462 35326.1976992218 135.802300778181 10 33508 34383.5168327868 -875.516832786751 11 36080 31572.9512895757 4507.0487104243 12 34560 31487.974137607 3072.025862393 13 38737 37536.7398842403 1200.26011575967 14 38144 36344.0508376112 1799.94916238883 15 37594 37624.9416229956 -30.9416229955946 16 36424 39792.8635004112 -3368.86350041118 17 36843 37557.36561264 -714.365612640035 18 37246 38105.7526109204 -859.752610920367 19 38661 35364.3534859026 3296.64651409741 20 40454 35847.6402457487 4606.35975425133 21 44928 43836.6756646423 1091.32433535773 22 48441 44951.089762345 3489.91023765496 23 48140 40772.5051455907 7367.4948544093 24 45998 40929.4570617587 5068.54293824129 25 47369 45447.8778695338 1921.12213046624 26 49554 43645.3844742047 5908.61552579529 27 47510 46414.3525659045 1095.64743409553 28 44873 41304.0146903041 3568.98530969594 29 45344 45553.1992277576 -209.199227757620 30 42413 47457.6790813323 -5044.67908133234 31 36912 35136.2294789336 1775.77052106636 32 43452 41559.3823410539 1892.61765894613 33 42142 45477.0325458237 -3335.0325458237 34 44382 43034.8753212034 1347.12467879664 35 43636 43091.4663620112 544.533637988771 36 44167 44116.2759812816 50.7240187184041 37 44423 46695.893540059 -2272.89354005897 38 42868 42505.9577881075 362.042211892463 39 43908 42662.3830293242 1245.6169706758 40 42013 46694.7916205058 -4681.79162050578 41 38846 40878.2487076894 -2032.2487076894 42 35087 42234.7420759862 -7147.74207598616 43 33026 34802.4158819743 -1776.41588197429 44 34646 35173.2136862997 -527.213686299735 45 37135 37860.6962398456 -725.696239845623 46 37985 37846.1273021295 138.872697870462 47 43121 35957.5114601682 7163.48853983182 48 43722 35117.0848234402 8604.9151765598 49 43630 39162.0112952085 4467.98870479149 50 42234 41497.8917245258 736.108275474193 51 39351 33880.1986440828 5470.80135591722 52 39327 40758.7463981821 -1431.74639818209 53 35704 36187.9330116257 -483.933011625743 54 30466 31965.6397188304 -1499.6397188304 55 28155 28935.4141891399 -780.414189139894 56 29257 30788.6235758032 -1531.62357580315 57 29998 30703.9810441188 -705.981044118816 58 32529 33318.4629076466 -789.462907646628 59 34787 29727.8547587736 5059.14524122644 60 33855 27527.9006540394 6327.09934596063 61 34556 35705.6122547508 -1149.61225475076 62 31348 31284.7172950443 63.2827049556657 63 30805 30905.4428387911 -100.442838791059 64 28353 31344.5720480388 -2991.57204803882 65 24514 27231.0463571610 -2717.04635716104 66 21106 25898.9179292679 -4792.91792926789 67 21346 22895.2790393013 -1549.27903930126 68 23335 25574.9397873113 -2239.93978731130 69 24379 26396.4882549267 -2017.48825492674 70 26290 27334.4953048007 -1044.49530480067 71 30084 25124.4447898648 4959.55521013518 72 29429 25431.1351347044 3997.86486529561 73 30632 32491.8869788746 -1859.88697887465 74 27349 27059.062333314 289.937666686021 75 27264 27858.2518935449 -594.251893544895 76 27474 30690.9973020239 -3216.99730202385 77 24482 26181.0535969657 -1699.05359696573 78 21453 25956.8440632864 -4503.84406328642 79 18788 22043.6069029253 -3255.60690292527 80 19282 22743.5180583278 -3461.51805832778 81 19713 25622.2881523186 -5909.28815231857 82 21917 24849.2847204037 -2932.28472040374 83 23812 22923.7683216576 888.231678342392 84 23785 22801.813827183 983.186172817004 85 24696 25218.5979234613 -522.597923461317 86 24562 24255.3386828485 306.66131715145 87 23580 24227.4332020364 -647.433202036437 88 24939 27692.5676993234 -2753.56769932343 89 23899 27957.2684589690 -4058.26845896896 90 21454 25307.5826567359 -3853.58265673588 91 19761 21846.3129309042 -2085.31293090419 92 19815 21742.9949124308 -1927.99491243084 93 20780 25220.807155982 -4440.80715598202 94 23462 23774.7741293678 -312.774129367844 95 25005 20480.7622674789 4524.23773252114 96 24725 19667.5323410615 5057.46765893849 97 26198 23113.6119159629 3084.38808403709 98 27543 24098.3373599152 3444.6626400848 99 26471 24655.191036396 1815.80896360401 100 26558 24822.220646742 1735.779353258 101 25317 24351.9843872297 965.01561277028 102 22896 23403.1370894171 -507.137089417064 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.156007819074939 0.312015638149878 0.843992180925061 9 0.120786281313153 0.241572562626305 0.879213718686847 10 0.0678252154171858 0.135650430834372 0.932174784582814 11 0.140885469847399 0.281770939694799 0.8591145301526 12 0.0878214798928583 0.175642959785717 0.912178520107142 13 0.0473849794215333 0.0947699588430666 0.952615020578467 14 0.0261268003540299 0.0522536007080598 0.97387319964597 15 0.0141240857316479 0.0282481714632958 0.985875914268352 16 0.0131770529399734 0.0263541058799468 0.986822947060027 17 0.00648398606548718 0.0129679721309744 0.993516013934513 18 0.00339190856034605 0.0067838171206921 0.996608091439654 19 0.00397999441288618 0.00795998882577236 0.996020005587114 20 0.00373334639122325 0.0074666927824465 0.996266653608777 21 0.00352687632265799 0.00705375264531599 0.996473123677342 22 0.00573898389338175 0.0114779677867635 0.994261016106618 23 0.0702114454262502 0.140422890852500 0.92978855457375 24 0.107225957827647 0.214451915655294 0.892774042172353 25 0.0961663255359292 0.192332651071858 0.903833674464071 26 0.101396442662206 0.202792885324411 0.898603557337794 27 0.097379609058999 0.194759218117998 0.902620390941001 28 0.118120994250651 0.236241988501301 0.88187900574935 29 0.119097257789862 0.238194515579723 0.880902742210138 30 0.179139421533579 0.358278843067159 0.82086057846642 31 0.260400184890893 0.520800369781786 0.739599815109107 32 0.24502678504725 0.4900535700945 0.75497321495275 33 0.241488556552581 0.482977113105161 0.75851144344742 34 0.210668376245925 0.42133675249185 0.789331623754075 35 0.194054459091402 0.388108918182804 0.805945540908598 36 0.169045088501060 0.338090177002121 0.83095491149894 37 0.152057319551366 0.304114639102731 0.847942680448635 38 0.129834514541326 0.259669029082652 0.870165485458674 39 0.106080195086369 0.212160390172738 0.893919804913631 40 0.107261643803038 0.214523287606076 0.892738356196962 41 0.0973697056004665 0.194739411200933 0.902630294399534 42 0.238197327135519 0.476394654271038 0.761802672864481 43 0.270020161507318 0.540040323014636 0.729979838492682 44 0.237121106557419 0.474242213114839 0.76287889344258 45 0.205734251291714 0.411468502583428 0.794265748708286 46 0.199115254648485 0.398230509296969 0.800884745351515 47 0.487940800751118 0.975881601502235 0.512059199248882 48 0.784410706878657 0.431178586242686 0.215589293121343 49 0.824505495805922 0.350989008388155 0.175494504194078 50 0.800085118243386 0.399829763513229 0.199914881756614 51 0.885454714501039 0.229090570997922 0.114545285498961 52 0.861189583407727 0.277620833184545 0.138810416592273 53 0.83394543469374 0.332109130612519 0.166054565306259 54 0.82716830168459 0.345663396630818 0.172831698315409 55 0.81703048434408 0.365939031311839 0.182969515655920 56 0.806079768887679 0.387840462224642 0.193920231112321 57 0.771929079706037 0.456141840587926 0.228070920293963 58 0.73658643332021 0.52682713335958 0.26341356667979 59 0.81341230893927 0.373175382121461 0.186587691060731 60 0.938061184240802 0.123877631518396 0.061938815759198 61 0.939731681037576 0.120536637924847 0.0602683189624237 62 0.952889058734305 0.0942218825313903 0.0471109412656951 63 0.951677604178293 0.096644791643415 0.0483223958217075 64 0.94416643192151 0.111667136156978 0.055833568078489 65 0.943130528518465 0.113738942963070 0.0568694714815349 66 0.967377417009054 0.0652451659818917 0.0326225829909458 67 0.963264512300039 0.0734709753999225 0.0367354876999613 68 0.956381227788869 0.0872375444222618 0.0436187722111309 69 0.945567030522759 0.108865938954482 0.0544329694772411 70 0.927567481702464 0.144865036595071 0.0724325182975357 71 0.952825337691984 0.0943493246160328 0.0471746623080164 72 0.980262425422828 0.0394751491543435 0.0197375745771717 73 0.975252345133218 0.0494953097335634 0.0247476548667817 74 0.980001082143918 0.0399978357121649 0.0199989178560825 75 0.98362936676069 0.0327412664786184 0.0163706332393092 76 0.98559747176054 0.0288050564789218 0.0144025282394609 77 0.981144489294775 0.0377110214104502 0.0188555107052251 78 0.974574450981833 0.0508510980363347 0.0254255490181674 79 0.972502756933833 0.0549944861323342 0.0274972430661671 80 0.974404824726537 0.0511903505469262 0.0255951752734631 81 0.976068573023096 0.0478628539538083 0.0239314269769041 82 0.967032711378456 0.065934577243087 0.0329672886215435 83 0.955312040135714 0.0893759197285721 0.0446879598642861 84 0.954486248524586 0.0910275029508276 0.0455137514754138 85 0.936709537410235 0.12658092517953 0.063290462589765 86 0.910609949554672 0.178780100890657 0.0893900504453283 87 0.888148159381322 0.223703681237357 0.111851840618678 88 0.959790734941048 0.0804185301179039 0.0402092650589519 89 0.977596449080854 0.0448071018382921 0.0224035509191461 90 0.989541346093836 0.0209173078123269 0.0104586539061635 91 0.98912200043931 0.0217559991213787 0.0108779995606893 92 0.985698777549726 0.0286024449005483 0.0143012224502742 93 0.98853679916037 0.0229264016792589 0.0114632008396294 94 0.983962978689277 0.0320740426214460 0.0160370213107230 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.0459770114942529 NOK 5% type I error level 21 0.241379310344828 NOK 10% type I error level 36 0.413793103448276 NOK

Charts produced by software:

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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