workshop 7 berekening 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: Tue, 17 Nov 2009 11:48:27 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5.htm/, Retrieved Tue, 17 Nov 2009 19:50:39 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

6.70 2.04 6.40 2.16 6.30 2.75 6.80 2.79 7.30 2.88 7.10 3.36 7.00 2.97 6.80 3.10 6.60 2.49 6.30 2.20 6.10 2.25 6.10 2.09 6.30 2.79 6.30 3.14 6.00 2.93 6.20 2.65 6.40 2.67 6.80 2.26 7.50 2.35 7.50 2.13 7.60 2.18 7.60 2.90 7.40 2.63 7.30 2.67 7.10 1.81 6.90 1.33 6.80 0.88 7.50 1.28 7.60 1.26 7.80 1.26 8.00 1.29 8.10 1.10 8.20 1.37 8.30 1.21 8.20 1.74 8.00 1.76 7.90 1.48 7.60 1.04 7.60 1.62 8.30 1.49 8.40 1.79 8.40 1.80 8.40 1.58 8.40 1.86 8.60 1.74 8.90 1.59 8.80 1.26 8.30 1.13 7.50 1.92 7.20 2.61 7.40 2.26 8.80 2.41 9.30 2.26 9.30 2.03 8.70 2.86 8.20 2.55 8.30 2.27 8.50 2.26 8.60 2.57 8.50 3.07 8.20 2.76 8.10 2.51 7.90 2.87 8.60 3.14 8.70 3.11 8.70 3.16 8.50 2.47 8.40 2.57 8.50 2.89 8.70 2.63 8.70 2.38 8.60 1.69 8.50 1.96 8.30 2.19 8.00 1.87 8.20 1.60 8.10 1.63 8.10 1.22 8.00 1.21 7.90 1.49 7.90 1.64 8.00 1.66 8.00 1.77 7.90 1.82 8.00 1.78 7.70 1.28 7.20 1.29 7.50 1.37 7.30 1.12 7.00 1.51 7.00 2.24 7.00 2.94 7.20 3.09 7 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 8.19716164420583 -0.23130426630238X[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) 8.19716164420583 0.183843 44.5879 0 0 X -0.23130426630238 0.071629 -3.2292 0.001653 0.000826 Multiple Linear Regression - Regression Statistics Multiple R 0.299274017301092 R-squared 0.089564937431534 Adjusted R-squared 0.080975927407303 F-TEST (value) 10.4278534055565 F-TEST (DF numerator) 1 F-TEST (DF denominator) 106 p-value 0.00165294208368527 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.7689867461205 Sum Squared Residuals 62.6821052651534 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.7 7.72530094094887 -1.02530094094887 2 6.4 7.69754442899268 -1.29754442899268 3 6.3 7.56107491187428 -1.26107491187428 4 6.8 7.55182274122218 -0.751822741222184 5 7.3 7.53100535725497 -0.23100535725497 6 7.1 7.41997930942983 -0.319979309429828 7 7 7.51018797328776 -0.510187973287755 8 6.8 7.48011841866845 -0.680118418668446 9 6.6 7.6212140211129 -1.02121402111290 10 6.3 7.68829225834059 -1.38829225834059 11 6.1 7.67672704502547 -1.57672704502547 12 6.1 7.71373572763385 -1.61373572763385 13 6.3 7.55182274122218 -1.25182274122218 14 6.3 7.47086624801635 -1.17086624801635 15 6 7.51944014393985 -1.51944014393985 16 6.2 7.58420533850452 -1.38420533850452 17 6.4 7.57957925317847 -1.17957925317847 18 6.8 7.67441400236244 -0.874414002362446 19 7.5 7.65359661839523 -0.153596618395231 20 7.5 7.70448355698175 -0.204483556981755 21 7.6 7.69291834366664 -0.092918343666636 22 7.6 7.52637927192892 0.0736207280710776 23 7.4 7.58883142383056 -0.188831423830564 24 7.3 7.57957925317847 -0.279579253178470 25 7.1 7.77850092219852 -0.678500922198517 26 6.9 7.88952697002366 -0.989526970023658 27 6.8 7.99361388985973 -1.19361388985973 28 7.5 7.90109218333878 -0.401092183338778 29 7.6 7.90571826866483 -0.305718268664826 30 7.8 7.90571826866483 -0.105718268664826 31 8 7.89877914067575 0.101220859324246 32 8.1 7.9427269512732 0.157273048726793 33 8.2 7.88027479937156 0.319725200628436 34 8.3 7.91728348197994 0.382716518020056 35 8.2 7.79469222083968 0.405307779160316 36 8 7.79006613551364 0.209933864486365 37 7.9 7.8548313300783 0.0451686699216985 38 7.6 7.95660520725135 -0.356605207251349 39 7.6 7.82244873279597 -0.222448732795969 40 8.3 7.85251828741528 0.447481712584723 41 8.4 7.78312700752456 0.616872992475436 42 8.4 7.78081396486154 0.61918603513846 43 8.4 7.83170090344806 0.568299096551937 44 8.4 7.7669357088834 0.633064291116603 45 8.6 7.79469222083968 0.805307779160317 46 8.9 7.82938786078504 1.07061213921496 47 8.8 7.90571826866483 0.894281731335175 48 8.3 7.93578782328413 0.364212176715866 49 7.5 7.75305745290525 -0.253057452905255 50 7.2 7.59345750915661 -0.393457509156612 51 7.4 7.67441400236244 -0.274414002362445 52 8.8 7.63971836241709 1.16028163758291 53 9.3 7.67441400236244 1.62558599763756 54 9.3 7.727613983612 1.57238601638801 55 8.7 7.53563144258102 1.16436855741898 56 8.2 7.60733576513475 0.592664234865244 57 8.3 7.67210095969942 0.627899040300579 58 8.5 7.67441400236244 0.825585997637555 59 8.6 7.60270967980871 0.997290320191292 60 8.5 7.48705754665752 1.01294245334248 61 8.2 7.55876186921126 0.641238130788744 62 8.1 7.61658793578685 0.483412064213149 63 7.9 7.533318399918 0.366681600082007 64 8.6 7.47086624801635 1.12913375198365 65 8.7 7.47780537600542 1.22219462399458 66 8.7 7.4662401626903 1.23375983730970 67 8.5 7.62584010643895 0.874159893561054 68 8.4 7.60270967980871 0.797290320191293 69 8.5 7.52869231459195 0.971307685408054 70 8.7 7.58883142383056 1.11116857616943 71 8.7 7.64665749040616 1.05334250959384 72 8.6 7.8062574341548 0.793742565845198 73 8.5 7.74380528225316 0.75619471774684 74 8.3 7.69060530100361 0.609394698996389 75 8 7.76462266622037 0.235377333779626 76 8.2 7.82707481812202 0.372925181877983 77 8.1 7.82013569013295 0.279864309867055 78 8.1 7.91497043931692 0.185029560683079 79 8 7.91728348197994 0.0827165180200555 80 7.9 7.85251828741528 0.0474817125847223 81 7.9 7.81782264746992 0.0821773525300793 82 8 7.81319656214387 0.186803437856127 83 8 7.78775309285061 0.212246907149388 84 7.9 7.7761878795355 0.123812120464508 85 8 7.78544005018759 0.214559949812412 86 7.7 7.90109218333878 -0.201092183338778 87 7.2 7.89877914067575 -0.698779140675754 88 7.5 7.88027479937156 -0.380274799371564 89 7.3 7.93810086594716 -0.638100865947159 90 7 7.84789220208923 -0.84789220208923 91 7 7.6790400876885 -0.679040087688493 92 7 7.51712710127683 -0.517127101276827 93 7.2 7.48243146133147 -0.28243146133147 94 7.3 7.39684888279959 -0.0968488827995894 95 7.1 7.35521411486516 -0.255214114865161 96 6.8 7.18173591513838 -0.381735915138376 97 6.4 7.23724893905095 -0.837248939050947 98 6.1 6.99206641677042 -0.892066416770424 99 6.5 6.85559689965202 -0.35559689965202 100 7.7 6.83015343035876 0.869846569641242 101 7.9 6.950431648836 0.949568351164005 102 7.5 6.93424035019483 0.565759649805171 103 6.9 7.10540550725859 -0.20540550725859 104 6.6 7.47086624801635 -0.870866248016351 105 6.9 7.58883142383056 -0.688831423830564 106 7.7 7.6605357463843 0.0394642536156976 107 8 7.75074441024223 0.249255589757769 108 8 8.05375299909835 -0.0537529990983487 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.173958538695498 0.347917077390996 0.826041461304502 6 0.0749913074962567 0.149982614992513 0.925008692503743 7 0.0305225682271213 0.0610451364542426 0.969477431772879 8 0.0129721222841324 0.0259442445682647 0.987027877715868 9 0.00499043027402638 0.00998086054805277 0.995009569725974 10 0.00254464321418543 0.00508928642837086 0.997455356785815 11 0.0024417918887141 0.0048835837774282 0.997558208111286 12 0.00151137856220320 0.00302275712440641 0.998488621437797 13 0.0018013630433953 0.0036027260867906 0.998198636956605 14 0.00350468496070329 0.00700936992140657 0.996495315039297 15 0.0098575672911791 0.0197151345823582 0.99014243270882 16 0.00916582512972444 0.0183316502594489 0.990834174870276 17 0.00650159805270711 0.0130031961054142 0.993498401947293 18 0.00676208433825451 0.0135241686765090 0.993237915661745 19 0.0346682017660394 0.0693364035320789 0.96533179823396 20 0.0802066961053498 0.160413392210700 0.91979330389465 21 0.136213068328418 0.272426136656835 0.863786931671582 22 0.186545734342992 0.373091468685985 0.813454265657008 23 0.194163944246571 0.388327888493143 0.805836055753429 24 0.185042360729269 0.370084721458539 0.81495763927073 25 0.168810397222794 0.337620794445588 0.831189602777206 26 0.157279821958286 0.314559643916571 0.842720178041715 27 0.161348128729680 0.322696257459361 0.83865187127032 28 0.170546479828528 0.341092959657056 0.829453520171472 29 0.175516556297864 0.351033112595728 0.824483443702136 30 0.188373212034584 0.376746424069168 0.811626787965416 31 0.213494773488633 0.426989546977266 0.786505226511367 32 0.226223565310899 0.452447130621798 0.773776434689101 33 0.25569252352688 0.51138504705376 0.74430747647312 34 0.274747886353549 0.549495772707098 0.725252113646451 35 0.309535337953206 0.619070675906412 0.690464662046794 36 0.305604554019976 0.611209108039952 0.694395445980024 37 0.274173854766594 0.548347709533188 0.725826145233406 38 0.244863326132868 0.489726652265737 0.755136673867131 39 0.214379305959134 0.428758611918269 0.785620694040866 40 0.222194568815796 0.444389137631591 0.777805431184204 41 0.265651703095328 0.531303406190655 0.734348296904672 42 0.30331906537413 0.60663813074826 0.69668093462587 43 0.312747660068185 0.625495320136371 0.687252339931815 44 0.343376100308725 0.686752200617451 0.656623899691275 45 0.393037424757472 0.786074849514944 0.606962575242528 46 0.485763953347577 0.971527906695155 0.514236046652423 47 0.505595557640991 0.988808884718017 0.494404442359009 48 0.454748676249809 0.909497352499619 0.545251323750191 49 0.414136029553497 0.828272059106995 0.585863970446503 50 0.388640497711726 0.777280995423451 0.611359502288274 51 0.355061000598861 0.710122001197723 0.644938999401139 52 0.520665537811266 0.958668924377468 0.479334462188734 53 0.778543335853324 0.442913328293351 0.221456664146676 54 0.90733160485352 0.185336790292960 0.0926683951464799 55 0.951862211182744 0.096275577634513 0.0481377888172565 56 0.950247063980332 0.099505872039335 0.0497529360196675 57 0.947157127415851 0.105685745168298 0.0528428725841489 58 0.951391314424137 0.097217371151725 0.0486086855758625 59 0.963891642690464 0.0722167146190717 0.0361083573095358 60 0.97527478662677 0.0494504267464588 0.0247252133732294 61 0.973641819673195 0.0527163606536107 0.0263581803268053 62 0.968107864349304 0.0637842713013921 0.0318921356506961 63 0.960006204846994 0.0799875903060123 0.0399937951530061 64 0.974297307788222 0.0514053844235551 0.0257026922117775 65 0.986059905678784 0.0278801886424313 0.0139400943212156 66 0.993155258993591 0.0136894820128172 0.00684474100640858 67 0.994204220257868 0.0115915594842643 0.00579577974213215 68 0.994675447441758 0.0106491051164841 0.00532455255824205 69 0.996392094594465 0.00721581081107028 0.00360790540553514 70 0.998337045405397 0.0033259091892064 0.0016629545946032 71 0.999268491638083 0.00146301672383397 0.000731508361916986 72 0.999494682246213 0.00101063550757494 0.000505317753787469 73 0.999656605996087 0.000686788007826346 0.000343394003913173 74 0.999701880779954 0.000596238440091527 0.000298119220045764 75 0.99955339250518 0.000893214989638366 0.000446607494819183 76 0.999463255244162 0.00107348951167546 0.000536744755837729 77 0.999288527296104 0.00142294540779285 0.000711472703896423 78 0.998998731910946 0.00200253617810876 0.00100126808905438 79 0.99848761696171 0.00302476607658110 0.00151238303829055 80 0.997679368841317 0.00464126231736545 0.00232063115868273 81 0.996610981947727 0.00677803610454609 0.00338901805227305 82 0.99572328864161 0.00855342271678187 0.00427671135839093 83 0.994966840177136 0.0100663196457271 0.00503315982286356 84 0.993636295985729 0.0127274080285413 0.00636370401427064 85 0.993383866190169 0.0132322676196616 0.0066161338098308 86 0.990155584196286 0.0196888316074277 0.00984441580371384 87 0.985019130835986 0.0299617383280278 0.0149808691640139 88 0.97637897901529 0.047242041969420 0.023621020984710 89 0.963982206977695 0.0720355860446097 0.0360177930223048 90 0.95376547131709 0.0924690573658181 0.0462345286829090 91 0.938176761115546 0.123646477768907 0.0618232388844537 92 0.91404925613587 0.171901487728261 0.0859507438641305 93 0.873986329793627 0.252027340412745 0.126013670206372 94 0.820047392630186 0.359905214739628 0.179952607369814 95 0.752667982400279 0.494664035199442 0.247332017599721 96 0.684523704101643 0.630952591796713 0.315476295898357 97 0.697659617188729 0.604680765622543 0.302340382811271 98 0.796224620280132 0.407550759439735 0.203775379719868 99 0.81547686167772 0.369046276644559 0.184523138322280 100 0.759599407802273 0.480801184395455 0.240400592197727 101 0.792512838322796 0.414974323354409 0.207487161677204 102 0.851458114016506 0.297083771966988 0.148541885983494 103 0.820319291693959 0.359361416612083 0.179680708306041 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.202020202020202 NOK 5% type I error level 36 0.363636363636364 NOK 10% type I error level 48 0.484848484848485 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/10cin61258483701.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/10cin61258483701.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/1kfiu1258483701.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/1kfiu1258483701.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/2m0kj1258483701.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/2m0kj1258483701.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/3yopu1258483701.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/3yopu1258483701.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/4fg601258483701.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/4fg601258483701.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/5i1v61258483701.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/5i1v61258483701.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/6w4iq1258483701.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/6w4iq1258483701.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/7uj651258483701.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/7uj651258483701.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/8a4eo1258483701.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/8a4eo1258483701.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/9n5ma1258483701.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258483827wlitpc2g7t810k5/9n5ma1258483701.ps (open in new window)

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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