*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: Tue, 21 Dec 2010 16:54:00 +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/21/t1292950312yqorm89ispoi2e4.htm/, Retrieved Tue, 21 Dec 2010 17:52:03 +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/21/t1292950312yqorm89ispoi2e4.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 «

1,3067 8,7000 113,0000 2579,3900 19,6000 18,9000 3,0000 -2,0000 16,0000 1,2894 8,9000 95,4000 2504,5800 16,0000 16,6000 3,0000 -4,0000 17,0000 1,2770 8,9000 86,2000 2462,3200 17,7000 17,2000 7,0000 -4,0000 23,0000 1,2208 8,1000 111,7000 2467,3800 19,8000 19,2000 4,0000 -7,0000 24,0000 1,2565 8,0000 97,5000 2446,6600 17,0000 17,1000 -4,0000 -9,0000 27,0000 1,3406 8,3000 99,7000 2656,3200 17,4000 17,7000 -6,0000 -13,0000 31,0000 1,3569 8,5000 111,5000 2626,1500 18,9000 18,7000 8,0000 -8,0000 40,0000 1,3686 8,7000 91,8000 2482,6000 15,7000 15,9000 2,0000 -13,0000 47,0000 1,4272 8,6000 86,3000 2539,9100 15,2000 16,0000 -1,0000 -15,0000 43,0000 1,4614 8,3000 88,7000 2502,6600 15,8000 16,8000 -2,0000 -15,0000 60,0000 1,4914 7,9000 95,1000 2466,9200 16,0000 16,0000 0,0000 -15,0000 64,0000 1,4816 7,9000 105,1000 2513,1700 16,1000 16,8000 10,0000 -10,0000 65,0000 1,4562 8,1000 104,5000 2443,2700 16,2000 16,3000 3,0000 -12,0000 65,0000 1,4268 8,3000 89,1000 2293,4100 12,5000 13,6000 6,0000 -11,0000 55,0000 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 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation WER[t] = + 6.82292982030508 + 1.56450104187246WSK[t] -0.00595415777078123INP[t] + 0.000104365867968344BE2[t] + 0.198628141969308Uit[t] -0.248400955218052INV[t] + 0.0354669084592271`CE-AES`[t] -0.0377754001710171`CE-CV`[t] + 0.00229192324071131`CE-WER`[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) 6.82292982030508 0.948497 7.1934 0 0 WSK 1.56450104187246 0.643533 2.4311 0.016699 0.008349 INP -0.00595415777078123 0.010102 -0.5894 0.556818 0.278409 BE2 0.000104365867968344 0.000156 0.6709 0.503705 0.251852 Uit 0.198628141969308 0.108728 1.8268 0.070487 0.035243 INV -0.248400955218052 0.104877 -2.3685 0.019637 0.009819 `CE-AES` 0.0354669084592271 0.011008 3.2219 0.001684 0.000842 `CE-CV` -0.0377754001710171 0.023311 -1.6205 0.108046 0.054023 `CE-WER` 0.00229192324071131 0.00667 0.3436 0.7318 0.3659 Multiple Linear Regression - Regression Statistics Multiple R 0.517622740435239 R-squared 0.267933301415687 Adjusted R-squared 0.213706138557589 F-TEST (value) 4.94094264375992 F-TEST (DF numerator) 8 F-TEST (DF denominator) 108 p-value 3.17793046415993e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.676848102638881 Sum Squared Residuals 49.4773222369521 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.7 7.88059960634872 0.819400393651278 2 8.9 7.88462291400215 1.01537708599786 3 8.9 8.25983729249194 0.640162707508063 4 8.1 7.95014398815123 0.149856011848771 5 8 7.85255776574107 0.147442234258935 6 8.3 8.01266066453041 0.287339335469587 7 8.5 8.34258253605752 0.157417463942484 8 8.7 8.51523401907842 0.184765980921577 9 8.6 8.48147107125976 0.118528928740241 10 8.3 8.4407513073001 -0.140751307300108 11 7.9 8.7643975951517 -0.864397595151697 12 7.9 8.68357688562746 -0.783576885627456 13 8.1 8.61146161258878 -0.511461612588779 14 8.3 8.62298358925588 -0.322983589255877 15 8.1 8.93330965393728 -0.833309653937279 16 7.4 8.45567581740129 -1.05567581740129 17 7.3 8.50787464624604 -1.20787464624604 18 7.7 8.40762955820622 -0.707629558206224 19 8 8.11424777877255 -0.114247778772555 20 8 7.9783045457501 0.0216954542498931 21 7.7 7.81061945026334 -0.110619450263336 22 6.9 7.84624023363816 -0.94624023363816 23 6.6 7.48437436146764 -0.884374361467642 24 6.9 7.4998719545212 -0.599871954521206 25 7.5 7.4566415025416 0.0433584974584010 26 7.9 7.36331378193238 0.536686218067618 27 7.7 7.77446828382875 -0.0744682838287536 28 6.5 7.22206401933694 -0.722064019336944 29 6.1 7.41453707974335 -1.31453707974335 30 6.4 7.2856588895853 -0.885658889585307 31 6.8 7.45992470701426 -0.659924707014256 32 7.1 7.6142212706313 -0.514221270631305 33 7.3 7.19485870520263 0.105141294797368 34 7.2 7.32644429343027 -0.126444293430272 35 7 7.38782488462863 -0.387824884628626 36 7 7.34557152093568 -0.34557152093568 37 7 7.68451423901589 -0.684514239015891 38 7.3 7.65319496004413 -0.353194960044127 39 7.5 7.7979263880165 -0.297926388016500 40 7.2 8.00103915445745 -0.80103915445745 41 7.7 7.979390288288 -0.279390288288002 42 8 8.08052319932402 -0.0805231993240162 43 7.9 7.88448140652153 0.0155185934784696 44 8 7.91345334882553 0.0865466511744678 45 8 7.77665754171948 0.223342458280524 46 7.9 7.9212455508171 -0.0212455508170908 47 7.9 7.9185150495724 -0.018515049572402 48 8 7.86894879901546 0.131051200984542 49 8.1 8.00764660214588 0.0923533978541212 50 8.1 7.68822588223462 0.411774117765384 51 8.2 8.07055121720073 0.12944878279927 52 8 7.84498741640628 0.155012583593718 53 8.3 7.82208741933626 0.477912580663738 54 8.5 7.79399803577272 0.706001964227281 55 8.6 7.38210831022514 1.21789168977486 56 8.7 7.64271449001957 1.05728550998043 57 8.7 7.52047758503253 1.17952241496746 58 8.5 7.26179825849804 1.23820174150196 59 8.4 7.50534149948374 0.894658500516256 60 8.5 7.89844850153489 0.601551498465111 61 8.7 7.80892393764683 0.89107606235317 62 8.7 7.40363011803212 1.29636988196788 63 8.6 8.00840823378085 0.591591766219152 64 7.9 7.4515743223764 0.448425677623603 65 8.1 7.8023520676085 0.297647932391502 66 8.2 7.93273108570312 0.267268914296877 67 8.5 7.98209945399295 0.517900546007047 68 8.6 8.17975519788705 0.420244802112949 69 8.5 8.11672729270084 0.383272707299162 70 8.3 8.05196593541145 0.248034064588551 71 8.2 8.10609065843619 0.0939093415638127 72 8.7 7.97885223229701 0.721147767702986 73 9.3 7.80628438826913 1.49371561173087 74 9.3 7.83843085924388 1.46156914075612 75 8.8 8.30848181389181 0.49151818610819 76 7.4 7.70642040321052 -0.306420403210521 77 7.2 8.23113676620541 -1.03113676620541 78 7.5 8.03720706033422 -0.537207060334223 79 8.3 8.00027767919253 0.299722320807472 80 8.8 8.31845291965533 0.48154708034467 81 8.9 8.31334455314255 0.586655446857452 82 8.6 8.05856674752039 0.541433252479611 83 8.4 8.09552045967789 0.304479540322113 84 8.4 8.03734721750106 0.362652782498944 85 8.4 7.91465577667485 0.485344223325151 86 8.4 7.88739882103328 0.512601178966725 87 8.3 8.14461976731354 0.155380232686461 88 7.6 7.93446594731442 -0.334465947314417 89 7.6 7.96163349868095 -0.361633498680953 90 7.9 7.96195179335233 -0.0619517933523347 91 8 7.5706293368627 0.429370663137305 92 8.2 7.29054333884035 0.90945666115965 93 8.3 7.39158897701423 0.90841102298577 94 8.2 7.2371336375056 0.962866362494395 95 8.1 7.66047621845384 0.439523781546162 96 8 7.66856162310822 0.331438376891778 97 7.8 7.69701132344973 0.102988676550268 98 7.6 7.47622939149247 0.123770608507532 99 7.5 7.81715899667062 -0.317158996670620 100 6.8 7.59729117674517 -0.797291176745172 101 6.9 7.7431244159272 -0.843124415927207 102 7.1 7.60313875251465 -0.503138752514646 103 7.3 7.51999076607887 -0.219990766078876 104 7.4 7.60795709130201 -0.207957091302014 105 7.6 7.57673213117608 0.0232678688239221 106 7.6 7.50301282335388 0.0969871766461182 107 7.5 7.42877664734613 0.071223352653871 108 7.5 7.29270871401231 0.207291285987688 109 6.8 7.2299040051492 -0.429904005149202 110 6.4 7.21007040820369 -0.810070408203688 111 6.2 7.56795017473225 -1.36795017473225 112 6 7.1606083107077 -1.16060831070770 113 6.3 7.19685796603196 -0.896857966031963 114 6.3 7.23793300348144 -0.93793300348144 115 6.1 7.13101721908886 -1.03101721908886 116 6.1 7.20162217845749 -1.10162217845749 117 6.3 7.19783143409797 -0.897831434097968 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.108352426853603 0.216704853707206 0.891647573146397 13 0.0622158987064055 0.124431797412811 0.937784101293595 14 0.0233463816404635 0.0466927632809269 0.976653618359537 15 0.00827783743187112 0.0165556748637422 0.991722162568129 16 0.00362509837910222 0.00725019675820443 0.996374901620898 17 0.00187478863289506 0.00374957726579012 0.998125211367105 18 0.00827450987331766 0.0165490197466353 0.991725490126682 19 0.0176653013545646 0.0353306027091292 0.982334698645435 20 0.0152692449664801 0.0305384899329602 0.98473075503352 21 0.0113868868345613 0.0227737736691226 0.988613113165439 22 0.0581038573677066 0.116207714735413 0.941896142632293 23 0.167793039065673 0.335586078131347 0.832206960934327 24 0.219428796499579 0.438857592999159 0.780571203500421 25 0.247285823178448 0.494571646356895 0.752714176821552 26 0.201214383462347 0.402428766924693 0.798785616537653 27 0.163330081576006 0.326660163152012 0.836669918423994 28 0.258425275733887 0.516850551467775 0.741574724266113 29 0.455588196809412 0.911176393618823 0.544411803190588 30 0.5454270131316 0.9091459737368 0.4545729868684 31 0.605442314724712 0.789115370550575 0.394557685275288 32 0.553746359065659 0.892507281868683 0.446253640934341 33 0.51180094585102 0.97639810829796 0.48819905414898 34 0.476498618796125 0.95299723759225 0.523501381203875 35 0.434642635147145 0.86928527029429 0.565357364852855 36 0.421433295565466 0.842866591130932 0.578566704434534 37 0.414881647956122 0.829763295912245 0.585118352043878 38 0.361026057380541 0.722052114761082 0.638973942619459 39 0.337080626785269 0.674161253570537 0.662919373214731 40 0.345352067041688 0.690704134083375 0.654647932958312 41 0.32302553821092 0.64605107642184 0.67697446178908 42 0.290897255177697 0.581794510355394 0.709102744822303 43 0.246259733486681 0.492519466973363 0.753740266513319 44 0.225034300425773 0.450068600851547 0.774965699574227 45 0.182975235545974 0.365950471091948 0.817024764454026 46 0.155408637457508 0.310817274915015 0.844591362542492 47 0.199320308837236 0.398640617674471 0.800679691162764 48 0.161730785146611 0.323461570293222 0.838269214853389 49 0.128770549265527 0.257541098531054 0.871229450734473 50 0.108047429142791 0.216094858285581 0.89195257085721 51 0.0870442412928027 0.174088482585605 0.912955758707197 52 0.0701409978354222 0.140281995670844 0.929859002164578 53 0.0674297779662575 0.134859555932515 0.932570222033743 54 0.0753988888900655 0.150797777780131 0.924601111109934 55 0.0758726592807692 0.151745318561538 0.92412734071923 56 0.0733816835721637 0.146763367144327 0.926618316427836 57 0.0607220186098884 0.121444037219777 0.939277981390112 58 0.05037641176535 0.1007528235307 0.94962358823465 59 0.0386617361733185 0.077323472346637 0.961338263826681 60 0.0597362591914077 0.119472518382815 0.940263740808592 61 0.117062065462182 0.234124130924364 0.882937934537818 62 0.187492565439027 0.374985130878054 0.812507434560973 63 0.163103203416153 0.326206406832306 0.836896796583847 64 0.131790930128072 0.263581860256144 0.868209069871928 65 0.106722509822576 0.213445019645151 0.893277490177424 66 0.0908555794307968 0.181711158861594 0.909144420569203 67 0.0730393405903758 0.146078681180752 0.926960659409624 68 0.055251426666618 0.110502853333236 0.944748573333382 69 0.0423030578755635 0.084606115751127 0.957696942124436 70 0.0362647404412902 0.0725294808825803 0.96373525955871 71 0.0292238753605711 0.0584477507211422 0.970776124639429 72 0.028003602349033 0.056007204698066 0.971996397650967 73 0.198487116908809 0.396974233817619 0.80151288309119 74 0.427931056043818 0.855862112087636 0.572068943956182 75 0.620600818843375 0.758798362313251 0.379399181156625 76 0.744530775122502 0.510938449754996 0.255469224877498 77 0.925959163670856 0.148081672658288 0.0740408363291442 78 0.957565951081307 0.0848680978373863 0.0424340489186932 79 0.941108776693459 0.117782446613083 0.0588912233065413 80 0.933860416918355 0.132279166163290 0.0661395830816448 81 0.962776346914523 0.0744473061709544 0.0372236530854772 82 0.956322970642216 0.0873540587155678 0.0436770293577839 83 0.94771836948944 0.104563261021118 0.0522816305105588 84 0.969183265857902 0.0616334682841951 0.0308167341420976 85 0.974553507727058 0.050892984545884 0.025446492272942 86 0.98044865819719 0.0391026836056222 0.0195513418028111 87 0.981904708040803 0.0361905839183935 0.0180952919591967 88 0.973555561245855 0.0528888775082902 0.0264444387541451 89 0.968300097496396 0.0633998050072086 0.0316999025036043 90 0.979501376303428 0.0409972473931442 0.0204986236965721 91 0.993846035460967 0.0123079290780664 0.00615396453903322 92 0.991134523212515 0.0177309535749699 0.00886547678748494 93 0.984080075147293 0.0318398497054142 0.0159199248527071 94 0.981781746279472 0.0364365074410555 0.0182182537205277 95 0.9733415435781 0.0533169128437983 0.0266584564218991 96 0.967455435863747 0.0650891282725063 0.0325445641362532 97 0.963781963785179 0.0724360724296426 0.0362180362148213 98 0.948600667490543 0.102798665018915 0.0513993325094575 99 0.957884135366984 0.0842317292660325 0.0421158646330163 100 0.97051359250154 0.0589728149969181 0.0294864074984590 101 0.997409723361614 0.00518055327677172 0.00259027663838586 102 0.998368068703683 0.0032638625926346 0.0016319312963173 103 0.99503830952703 0.00992338094594218 0.00496169047297109 104 0.989346720860075 0.0213065582798506 0.0106532791399253 105 0.960008235402595 0.079983529194811 0.0399917645974055 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.0531914893617021 NOK 5% type I error level 19 0.202127659574468 NOK 10% type I error level 37 0.393617021276596 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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