paper - multiple regression - persoonlijke redenen

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sun, 19 Dec 2010 10:24:31 +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/19/t12927541777y6nr6qas3too6i.htm/, Retrieved Sun, 19 Dec 2010 11:23:09 +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/19/t12927541777y6nr6qas3too6i.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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46 11 52 26 23 44 8 39 25 15 42 10 42 28 25 41 12 35 30 18 48 12 32 28 21 49 10 49 40 19 51 8 33 28 15 47 10 47 27 22 49 11 46 25 19 46 7 40 27 20 51 10 33 32 26 54 9 39 28 26 52 9 37 21 21 52 11 56 40 18 45 12 36 29 19 52 5 24 27 19 56 10 56 31 18 54 11 32 33 19 50 12 41 28 24 35 9 24 26 28 48 3 42 25 20 37 10 47 37 27 47 7 25 13 18 31 9 33 32 19 45 9 43 32 24 47 10 45 38 21 44 9 44 30 22 30 19 46 33 25 40 14 31 22 19 44 5 31 29 15 43 13 42 33 34 51 7 28 31 23 48 8 38 23 19 55 11 59 42 26 48 11 43 35 15 53 12 29 31 15 49 9 38 31 17 44 13 39 38 30 45 12 50 34 19 40 11 44 33 28 44 18 29 23 23 41 8 29 18 23 46 14 36 33 21 47 10 43 26 18 48 13 28 29 19 43 13 39 23 24 46 8 35 18 15 53 10 43 36 20 33 8 28 21 24 47 9 49 31 9 43 10 33 31 20 45 9 39 29 20 49 9 36 24 10 45 9 24 35 44 37 10 47 37 20 42 8 34 29 20 43 11 33 31 11 44 11 43 34 21 39 10 41 38 21 37 23 40 27 19 53 9 39 33 17 48 12 54 36 16 47 9 43 27 14 49 9 45 33 19 47 8 29 24 21 56 9 45 31 16 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 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Persoonlijke_redenen[t] = + 7.80479634304143 + 0.0803501248472529`Carrièremogelijkheden`[t] + 0.401757129542155Geen_Motivatie[t] + 0.346563713966344Leermogelijkheden[t] + 0.0272609904231642Ouders[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) 7.80479634304143 4.467205 1.7471 0.082792 0.041396 `Carrièremogelijkheden` 0.0803501248472529 0.077406 1.038 0.301033 0.150516 Geen_Motivatie 0.401757129542155 0.151553 2.6509 0.008945 0.004473 Leermogelijkheden 0.346563713966344 0.05562 6.2309 0 0 Ouders 0.0272609904231642 0.077773 0.3505 0.72647 0.363235 Multiple Linear Regression - Regression Statistics Multiple R 0.540252823616273 R-squared 0.291873113425356 Adjusted R-squared 0.271784407423239 F-TEST (value) 14.5292142457852 F-TEST (DF numerator) 4 F-TEST (DF denominator) 141 p-value 5.84552628524193e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.96128070174827 Sum Squared Residuals 3470.61717441711 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 26 34.5685464169613 -8.56854641696128 2 25 28.4791585736927 -3.47915857369266 3 28 30.4342736292131 -2.43427362921313 4 30 28.5406648327236 1.45933516727636 5 28 28.1452075360249 -0.145207536024866 6 40 33.2591045583693 6.74089544163067 7 28 26.9622271638254 1.03777283617463 8 27 32.4870598520116 -5.48705985201163 9 25 32.6211705460125 -7.62117054601245 10 27 28.7209703599272 -1.72097035992717 11 32 28.0656123175645 3.93438768243552 12 28 29.9842878463621 -1.98428784636215 13 21 28.9941552166191 -7.99415521661913 14 40 36.3005970697945 3.69940293020552 15 29 29.2358900365022 -0.235890036502157 16 27 22.8272764360417 4.17272356395829 17 31 36.2202404396413 -5.22024043964134 18 33 28.1710291747199 4.8289708252801 19 28 31.5067641826860 -3.50676418268596 20 26 23.3137017456155 2.68629825438449 21 25 27.9677695193857 -2.96776951938575 22 37 31.8198635556549 5.18013644434508 23 13 23.5483427944329 -10.5483427944329 24 32 25.8660257581151 6.13397424188488 25 32 30.5928695977559 1.40713040224408 26 38 31.7666714336558 6.23332856634423 27 30 30.8045612060287 -0.80456120602868 28 33 34.4721411527909 -1.47214115279087 29 22 27.9048351015185 -5.90483510151848 30 29 24.5013774733354 4.49862252666456 31 33 31.9652440564953 1.03475594350467 32 31 25.0457393878368 5.9542606121632 33 23 28.563039320808 -5.56303932080798 34 42 37.7994265096206 4.20057349037941 35 35 31.3920853175735 3.60791468242649 36 31 27.3437010758231 3.65629892417688 37 31 28.9906245943511 2.00937540564894 38 38 30.8968590777509 7.1031409222491 39 34 34.087782032031 -0.0877820320309693 40 33 31.450240908263 1.54975909173704 41 23 29.2491806528361 -6.24918065283609 42 18 24.9905589828728 -6.99055898287277 43 33 30.1742764012800 2.82572359871995 44 26 30.9917610344536 -4.99176103445359 45 29 27.1061878288553 1.89381217114468 46 23 30.6529430103647 -7.65294301036466 47 18 27.2536039675218 -9.25360396752179 48 36 31.5283837643834 4.47161623561656 49 21 24.0284552605516 -3.02845526055157 50 31 32.424037274901 -1.42403727490102 51 31 27.2592453762475 3.74075462375253 52 29 29.0975707801979 -0.0975707801978863 53 24 28.1066702334562 -4.10667023345622 54 35 24.5533788408587 10.4466211591413 55 37 31.6290366226928 5.37096337730723 56 29 26.7219447062823 2.27805529371775 57 31 27.4156535919812 3.58434640801885 58 34 31.2342507607235 2.76574923927652 59 38 29.7376155790124 8.26238442098762 60 27 34.3986723185532 -7.39867231855322 61 33 29.6585888077064 3.34141119229358 62 36 35.6333042911686 0.366695708831389 63 27 30.4809599432188 -3.48095994321878 64 33 31.4710925729618 1.52890742703820 65 24 25.4181377511100 -1.41813775110996 66 31 31.9517604756231 -0.951760475623074 67 31 32.324920250589 -1.32492025058899 68 23 28.5630458261139 -5.56304582611387 69 38 34.7794701082355 3.22052989176447 70 30 27.95591643706 2.04408356293999 71 39 35.6362469503646 3.36375304963542 72 28 23.6553019908915 4.34469800910845 73 39 30.2979686770973 8.70203132290269 74 19 26.1909503744663 -7.19095037446631 75 32 28.9626986745043 3.03730132549570 76 32 28.5378826115365 3.46211738846346 77 35 29.9325545920088 5.0674454079912 78 42 34.539852580116 7.46014741988399 79 25 29.6467357253807 -4.64673572538069 80 11 28.5728762812253 -17.5728762812253 81 31 30.7296660306747 0.270333969325325 82 30 23.1424996060801 6.85750039391993 83 30 33.7621729703385 -3.76217297033851 84 31 29.8774446480904 1.12255535190962 85 28 32.0035197512 -4.00351975119998 86 34 28.945946079228 5.05405392077203 87 32 28.2408709044203 3.75912909557968 88 30 30.1595594438298 -0.159559443829771 89 27 27.9495970882990 -0.949597088298955 90 36 36.5982802116402 -0.598280211640183 91 32 27.3677013748680 4.63229862513197 92 27 25.6885664078384 1.31143359216161 93 35 32.8658480250914 2.13415197490855 94 34 28.8041027059615 5.19589729403848 95 32 30.0387393661629 1.96126063383712 96 28 27.8525073787872 0.147492621212848 97 29 32.0566153909300 -3.05661539092996 98 18 29.9821835652104 -11.9821835652104 99 34 31.6863213088085 2.31367869119148 100 35 28.6167933846557 6.38320661534434 101 34 29.2316814741987 4.76831852580129 102 26 26.8792108159779 -0.879210815977853 103 30 28.3569956265668 1.64300437343320 104 35 33.8998077813015 1.10019221869848 105 17 26.1622565376209 -9.16225653762091 106 34 28.336034468987 5.66396553101298 107 30 30.277678954247 -0.277678954247020 108 31 26.8918234922764 4.10817650772359 109 25 25.7150594812629 -0.715059481262907 110 16 25.2211074675736 -9.22110746757359 111 35 32.6820053703139 2.31799462968605 112 28 25.0672820031828 2.93271799681721 113 42 32.2494436174688 9.75055638253123 114 30 31.5039884668048 -1.50398846680475 115 37 34.1331319818835 2.86686801811646 116 26 26.8818770389781 -0.881877038978112 117 28 27.8727840910257 0.127215908974335 118 33 31.6813578401182 1.31864215988179 119 29 28.2716755277233 0.728324472276659 120 21 24.9451190560569 -3.94511905605693 121 38 33.5253181474884 4.47468185251158 122 18 30.3566927149412 -12.3566927149412 123 38 32.4974847755011 5.50251522449893 124 30 28.0089025839090 1.99109741609095 125 35 33.4169391152193 1.58306088478065 126 34 33.4771090100973 0.522890989902743 127 21 25.1562227012576 -4.15622270125759 128 30 31.343198240147 -1.34319824014698 129 32 24.9430082695993 7.05699173040068 130 23 30.8276201394543 -7.82762013945429 131 31 36.7373543743313 -5.73735437433132 132 26 26.9084796052836 -0.908479605283575 133 29 31.1071113342955 -2.10711133429553 134 28 32.6598878025076 -4.65988780250764 135 29 28.773969517388 0.226030482612012 136 36 33.4156092563722 2.58439074362783 137 36 30.3608882666329 5.63911173336714 138 31 32.4080460913174 -1.40804609131745 139 30 29.8983158287069 0.101684171293118 140 29 30.6480630133317 -1.64806301333173 141 35 32.3968579384153 2.60314206158469 142 26 30.1407990496709 -4.14079904967088 143 25 26.6437953448559 -1.64379534485589 144 25 29.173053918624 -4.17305391862399 145 20 27.6639581755974 -7.66395817559738 146 27 30.5012283324315 -3.50122833243147 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.778904434512768 0.442191130974464 0.221095565487232 9 0.815486197952955 0.369027604094089 0.184513802047045 10 0.712546404008694 0.574907191982612 0.287453595991306 11 0.638285821390546 0.723428357218909 0.361714178609454 12 0.546518662062971 0.906962675874058 0.453481337937029 13 0.66180822147311 0.676383557053779 0.338191778526889 14 0.70460925178234 0.59078149643532 0.29539074821766 15 0.616893253045359 0.766213493909282 0.383106746954641 16 0.574951809269166 0.850096381461668 0.425048190730834 17 0.532589570094839 0.934820859810322 0.467410429905161 18 0.475745069972837 0.951490139945674 0.524254930027163 19 0.411978420405358 0.823956840810715 0.588021579594642 20 0.376835483096960 0.753670966193921 0.62316451690304 21 0.307324065378455 0.61464813075691 0.692675934621545 22 0.419895962967869 0.839791925935738 0.580104037032131 23 0.675599396805923 0.648801206388155 0.324400603194077 24 0.677415266072443 0.645169467855114 0.322584733927557 25 0.624488966770941 0.751022066458118 0.375511033229059 26 0.678982081210764 0.642035837578473 0.321017918789236 27 0.618036744661834 0.763926510676331 0.381963255338166 28 0.587594155160494 0.824811689679012 0.412405844839506 29 0.606799880744797 0.786400238510405 0.393200119255203 30 0.588191130586328 0.823617738827344 0.411808869413672 31 0.529535899515633 0.940928200968734 0.470464100484367 32 0.545881009356048 0.908237981287903 0.454118990643952 33 0.549984768467958 0.900030463064083 0.450015231532042 34 0.558130230896364 0.883739538207272 0.441869769103636 35 0.556245022086359 0.887509955827281 0.443754977913641 36 0.542839987725206 0.914320024549589 0.457160012274794 37 0.496614810560682 0.993229621121364 0.503385189439318 38 0.532541963531025 0.93491607293795 0.467458036468975 39 0.476964868455405 0.95392973691081 0.523035131544595 40 0.423346777739021 0.846693555478042 0.576653222260979 41 0.449222674687588 0.898445349375176 0.550777325312412 42 0.516479524902634 0.967040950194733 0.483520475097366 43 0.484957352312477 0.969914704624954 0.515042647687523 44 0.475545904433933 0.951091808867867 0.524454095566067 45 0.43289955864277 0.86579911728554 0.56710044135723 46 0.498530300411884 0.997060600823769 0.501469699588116 47 0.598603598217163 0.802792803565673 0.401396401782837 48 0.591764011717808 0.816471976564384 0.408235988282192 49 0.556598892809376 0.886802214381248 0.443401107190624 50 0.509030228138654 0.981939543722691 0.490969771861346 51 0.492195709405078 0.984391418810155 0.507804290594922 52 0.441327208067762 0.882654416135525 0.558672791932238 53 0.413340475204151 0.826680950408303 0.586659524795849 54 0.51429859423526 0.97140281152948 0.48570140576474 55 0.538067580542142 0.923864838915716 0.461932419457858 56 0.499179468832809 0.998358937665618 0.500820531167191 57 0.500376536778833 0.999246926442334 0.499623463221167 58 0.467268465277807 0.934536930555614 0.532731534722193 59 0.554004979264936 0.891990041470129 0.445995020735064 60 0.596425142075416 0.807149715849168 0.403574857924584 61 0.574591761256734 0.850816477486532 0.425408238743266 62 0.530413899390676 0.93917220121865 0.469586100609325 63 0.50333805430039 0.99332389139922 0.49666194569961 64 0.458954443506997 0.917908887013994 0.541045556493003 65 0.415742332269027 0.831484664538054 0.584257667730973 66 0.369888664713722 0.739777329427444 0.630111335286278 67 0.327872065861289 0.655744131722579 0.672127934138711 68 0.340574255849619 0.681148511699238 0.659425744150381 69 0.319264734844988 0.638529469689976 0.680735265155012 70 0.28313023454785 0.5662604690957 0.71686976545215 71 0.264961147757985 0.52992229551597 0.735038852242015 72 0.253913191454833 0.507826382909665 0.746086808545167 73 0.341380827314387 0.682761654628774 0.658619172685613 74 0.399619146237643 0.799238292475285 0.600380853762357 75 0.370579010414127 0.741158020828255 0.629420989585872 76 0.347864232062444 0.695728464124887 0.652135767937556 77 0.349430509577277 0.698861019154555 0.650569490422723 78 0.409831391261444 0.819662782522888 0.590168608738556 79 0.414350708155617 0.828701416311235 0.585649291844383 80 0.89152465905965 0.216950681880701 0.108475340940351 81 0.872135360280294 0.255729279439413 0.127864639719706 82 0.917983820656237 0.164032358687525 0.0820161793437626 83 0.908362317729337 0.183275364541326 0.0916376822706628 84 0.890986951550373 0.218026096899254 0.109013048449627 85 0.882721656586876 0.234556686826249 0.117278343413124 86 0.881315587102416 0.237368825795167 0.118684412897584 87 0.86841104838722 0.263177903225558 0.131588951612779 88 0.839559116875456 0.320881766249088 0.160440883124544 89 0.808352196951251 0.383295606097498 0.191647803048749 90 0.772809983984985 0.45438003203003 0.227190016015015 91 0.807461160023239 0.385077679953522 0.192538839976761 92 0.776305655660629 0.447388688678742 0.223694344339371 93 0.760079881497999 0.479840237004002 0.239920118502001 94 0.775381014978958 0.449237970042085 0.224618985021042 95 0.740990129696984 0.518019740606032 0.259009870303016 96 0.699733661360939 0.600532677278122 0.300266338639061 97 0.668113790073184 0.663772419853632 0.331886209926816 98 0.882534854106083 0.234930291787834 0.117465145893917 99 0.863748407559847 0.272503184880306 0.136251592440153 100 0.86949401330037 0.261011973399259 0.130505986699630 101 0.86757146769101 0.264857064617982 0.132428532308991 102 0.854554285231502 0.290891429536995 0.145445714768497 103 0.839745163613292 0.320509672773415 0.160254836386708 104 0.804485264939401 0.391029470121198 0.195514735060599 105 0.883654278804799 0.232691442390403 0.116345721195202 106 0.899621998243895 0.200756003512211 0.100378001756105 107 0.873620154686978 0.252759690626044 0.126379845313022 108 0.914853697543266 0.170292604913468 0.0851463024567339 109 0.890054170971964 0.219891658056073 0.109945829028036 110 0.917734611069709 0.164530777860583 0.0822653889302913 111 0.905524872449473 0.188950255101055 0.0944751275505274 112 0.881702281327566 0.236595437344868 0.118297718672434 113 0.945960204639657 0.108079590720685 0.0540397953603426 114 0.926793949247665 0.146412101504670 0.0732060507523352 115 0.910731254559288 0.178537490881425 0.0892687454407124 116 0.89087630164056 0.218247396718879 0.109123698359440 117 0.868955405730246 0.262089188539509 0.131044594269754 118 0.834092986749619 0.331814026500763 0.165907013250381 119 0.793269200475399 0.413461599049203 0.206730799524601 120 0.769871582543021 0.460256834913958 0.230128417456979 121 0.800949815818845 0.398100368362311 0.199050184181155 122 0.961929655869262 0.0761406882614753 0.0380703441307377 123 0.970089662587913 0.0598206748241744 0.0299103374120872 124 0.95406499818409 0.091870003631819 0.0459350018159095 125 0.93212019325426 0.135759613491481 0.0678798067457403 126 0.914158515064586 0.171682969870827 0.0858414849354136 127 0.913414503082466 0.173170993835069 0.0865854969175343 128 0.879255645763407 0.241488708473186 0.120744354236593 129 0.887803686637628 0.224392626724744 0.112196313362372 130 0.914124655364858 0.171750689270283 0.0858753446351417 131 0.93933732820087 0.12132534359826 0.06066267179913 132 0.903133873506081 0.193732252987839 0.0968661264939193 133 0.852435054651004 0.295129890697992 0.147564945348996 134 0.792392800204572 0.415214399590855 0.207607199795427 135 0.700022740612337 0.599954518775326 0.299977259387663 136 0.598102670192813 0.803794659614374 0.401897329807187 137 0.912763934318 0.174472131363999 0.0872360656819995 138 0.828180823485135 0.34363835302973 0.171819176514865 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 3 0.0229007633587786 OK

Charts produced by software:

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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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Software written by Ed van Stee & Patrick Wessa

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