Workshop 7

*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, 01 Dec 2010 13:16:22 +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/01/t1291209824on7dsyob0441jjo.htm/, Retrieved Wed, 01 Dec 2010 14:23:55 +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/01/t1291209824on7dsyob0441jjo.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 «

2 5 2 3 3 4 4 2 4 2 4 3 4 4 4 4 2 4 2 5 4 2 4 2 2 2 2 4 3 2 2 2 3 2 4 4 5 1 3 2 4 5 3 5 1 2 1 4 4 3 4 3 3 3 4 3 3 3 2 3 2 4 4 2 4 1 3 2 2 4 4 4 4 3 3 3 4 4 2 2 4 2 4 4 3 3 3 2 2 3 4 3 3 2 2 2 4 2 4 4 1 1 3 4 3 4 5 1 1 1 4 4 3 4 2 3 3 4 3 3 2 2 2 2 2 2 3 4 2 2 3 4 4 4 4 2 3 4 4 3 2 4 1 4 2 4 3 5 4 2 4 3 3 4 4 4 4 3 5 2 3 2 4 2 2 2 4 3 3 5 2 3 2 2 4 4 4 2 4 3 3 4 4 4 2 3 2 4 4 3 4 2 2 2 3 4 4 4 3 1 2 4 4 4 4 2 3 2 4 4 1 4 1 2 3 4 5 4 4 4 4 4 4 4 5 2 1 4 1 4 4 2 4 2 5 3 4 4 4 4 2 2 3 4 3 3 5 2 4 2 5 4 2 5 2 4 1 4 3 4 4 2 2 1 2 4 5 3 2 4 2 4 4 4 4 2 4 2 4 3 4 5 2 2 2 5 5 4 4 2 3 1 4 4 3 4 2 2 2 2 3 4 5 2 4 1 4 3 2 4 2 3 2 4 3 2 5 1 1 2 4 4 4 4 2 2 4 2 4 2 4 1 5 2 5 4 4 4 2 2 2 4 4 4 3 1 4 2 4 4 1 4 1 4 1 4 4 4 4 2 2 2 4 4 2 4 2 2 2 4 5 1 2 1 2 1 3 3 4 3 5 4 5 5 3 3 5 2 3 2 4 5 2 4 2 4 2 4 5 4 4 1 2 2 4 4 3 5 1 3 1 4 4 2 3 2 2 3 2 3 2 5 2 2 1 4 4 3 4 1 3 1 4 4 2 5 1 2 2 4 5 1 4 2 3 3 4 4 3 4 1 2 2 3 4 2 5 1 4 2 4 5 3 4 2 2 2 2 4 3 4 1 5 4 4 3 3 5 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation standards[t] = + 2.49017622287169 + 0.053821676528518organization[t] + 0.388162853859228punished[t] + 0.0571215682005039secondrate[t] -0.0518763478238547mistakes[t] -0.0793667772532387competent[t] -0.0329553449719052neat[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) 2.49017622287169 0.553205 4.5014 1.3e-05 7e-06 organization 0.053821676528518 0.093352 0.5765 0.565098 0.282549 punished 0.388162853859228 0.085654 4.5317 1.2e-05 6e-06 secondrate 0.0571215682005039 0.077506 0.737 0.462259 0.23113 mistakes -0.0518763478238547 0.08718 -0.595 0.552697 0.276348 competent -0.0793667772532387 0.096674 -0.821 0.412949 0.206474 neat -0.0329553449719052 0.100045 -0.3294 0.742303 0.371151 Multiple Linear Regression - Regression Statistics Multiple R 0.376181546660612 R-squared 0.14151255604797 Adjusted R-squared 0.107624893786706 F-TEST (value) 4.17593149261663 F-TEST (DF numerator) 6 F-TEST (DF denominator) 152 p-value 0.000647212556059928 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.912678964976017 Sum Squared Residuals 126.613399752674 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2 3.10205748546204 -1.10205748546204 2 2 3.10535737713409 -1.10535737713409 3 4 3.0778669477047 0.922133052295299 4 2 3.20172414306341 -1.20172414306341 5 3 3.04220444218252 -0.0422044421825190 6 4 2.73281563445482 1.26718436554518 7 3 2.76052575905008 0.239474240949922 8 3 3.46935400776472 -0.469354007764716 9 3 3.04629048022892 -0.0462904802289185 10 2 2.87068285740469 -0.870682857404685 11 4 3.90392829390528 0.0960717060947225 12 4 3.04959037190090 0.950409628099096 13 3 3.45669854314088 -0.456698543140882 14 3 3.05507960197223 -0.0550796019722251 15 4 2.57878516364525 1.42121483635475 16 4 2.70340419084957 1.29659580915043 17 3 3.08119115390549 -0.0811911539054871 18 3 3.15999147995018 -0.159991479950184 19 3 2.99111424073308 0.00888575926692188 20 4 3.02931480608163 0.970685193918367 21 2 2.80202621607062 -0.802026216070617 22 5 3.18472415438732 1.81527584561268 23 4 3.91249772048271 0.087502279517288 24 2 3.07594593352884 -1.07594593352884 25 3 3.31266738779243 -0.312667387792432 26 4 3.18472415438732 0.815275845612676 27 4 3.10011215675744 0.899887843242563 28 3 3.12235736581017 -0.122357365810171 29 4 3.37403187421566 0.625968125784343 30 4 3.10011215675744 0.899887843242563 31 1 2.56999604190194 -1.56999604190194 32 4 3.82980673702869 0.170193262971312 33 5 2.71330386586553 2.28669613413447 34 2 3.16247894533459 -1.16247894533459 35 4 3.02406958570498 0.975930414295017 36 3 3.13168862423322 -0.13168862423322 37 2 3.29588709428222 -1.29588709428222 38 4 3.25360049088726 0.746399509112735 39 5 3.10341204842942 1.89658795157058 40 4 3.19018906992985 0.809810930070154 41 4 2.98449014286031 1.01550985713969 42 4 3.15198850458129 0.848011495418709 43 3 3.23467948803532 -0.234679488035315 44 4 3.29588709428222 0.704112905717782 45 2 3.13306750172934 -1.13306750172934 46 2 2.65152784302572 -0.651527843025719 47 4 3.0979714474157 0.9020285525843 48 2 2.74682566204598 -0.746825662045977 49 4 3.04299058855693 0.957009411443068 50 4 2.71524919457019 1.28475080542981 51 1 2.82094721892257 -1.82094721892257 52 4 3.04299058855693 0.957009411443068 53 2 3.01003524358503 -1.01003524358503 54 1 2.71138285168967 -1.71138285168967 55 4 4.06586013425421 -0.0658601342542102 56 3 3.12097848831405 -0.120978488314049 57 2 3.12427837998603 -1.12427837998603 58 4 2.65482773469770 1.34517226530230 59 3 2.81764732725058 0.182352672749419 60 2 3.12898146368294 -1.12898146368294 61 2 3.14868861290931 -1.14868861290931 62 3 2.76382565072206 0.236174349277937 63 2 2.67569406625432 -0.675694066254317 64 1 3.04823580893358 -2.04823580893358 65 3 2.73419451195094 0.265805488049057 66 2 2.78993720265532 -0.789937202655325 67 3 3.20172414306341 -0.20172414306341 68 3 2.75539508862341 0.244604911376588 69 3 2.70340419084957 0.296595809150427 70 2 3.10011215675744 -1.10011215675744 71 3 2.60100605816919 0.398993941830814 72 2 2.65482773469770 -0.654827734697704 73 4 3.54209668714518 0.457903312854817 74 4 3.67858503259893 0.321414967401074 75 4 3.26293174931031 0.737068250689687 76 2 3.07594593352884 -1.07594593352884 77 3 2.71194930289821 0.288050697101792 78 4 3.63217360031759 0.367826399682408 79 3 3.06385692011355 -0.0638569201135457 80 4 3.09486693638079 0.905133063619212 81 2 3.07434736086684 -1.07434736086684 82 3 3.11494712156299 -0.114947121562987 83 3 2.79656130052810 0.203438699471904 84 4 3.73994951902215 0.260050480977849 85 2 2.8431924279753 -0.843192427975301 86 4 2.84843764835195 1.15156235164805 87 2 2.71194930289821 -0.711949302898208 88 2 2.67044884587767 -0.670448845877668 89 4 3.87643786447589 0.123562135524106 90 3 3.12782228135269 -0.127822281352693 91 4 3.04299058855693 0.957009411443068 92 2 2.81572631307472 -0.815726313074717 93 2 2.65812762636969 -0.65812762636969 94 3 2.60100605816919 0.398993941830814 95 3 3.49022033932133 -0.490220339321328 96 5 3.86138320450447 1.13861679549553 97 2 3.92831421229975 -1.92831421229975 98 3 3.52647557596522 -0.526475575965219 99 4 3.28655583585917 0.71344416414083 100 3 3.20718905860593 -0.207189058605931 101 4 3.60059713284181 0.399402867158191 102 3 2.8190262047467 0.180973795253297 103 3 3.44518778453612 -0.445187784536117 104 2 3.07594593352884 -1.07594593352884 105 3 3.27971204282053 -0.279712042820527 106 2 3.30386575512231 -1.30386575512231 107 3 3.18826805575398 -0.188268055753982 108 2 3.80174985639076 -1.80174985639076 109 4 3.57310670341242 0.426893296587575 110 2 2.8432167425041 -0.8432167425041 111 4 2.62325126722192 1.37674873277808 112 4 2.99635946110973 1.00364053889027 113 1 2.69632819171824 -1.69632819171824 114 5 3.58906205970824 1.41093794029176 115 2 3.05680523551102 -1.05680523551102 116 3 3.23135528183453 -0.23135528183453 117 4 3.05837949364421 0.94162050635579 118 1 2.62460583018924 -1.62460583018924 119 5 3.5676417878699 1.43235821213010 120 3 2.87752665044333 0.122473349556671 121 3 2.79678099569397 0.203219004306032 122 3 3.10149103425356 -0.101491034253559 123 3 3.52453024726056 -0.524530247260556 124 2 3.01744548783221 -1.01744548783221 125 2 2.60295138687385 -0.60295138687385 126 4 3.18277882568266 0.817221174317339 127 4 2.93326934114771 1.06673065885229 128 3 3.17947893401068 -0.179478934010675 129 3 2.79131608015145 0.208683919848553 130 3 3.04823580893358 -0.0482358089335819 131 4 3.55748559223246 0.442514407767539 132 3 3.12235736581017 -0.122357365810171 133 4 2.72348437603177 1.27651562396823 134 4 2.8431924279753 1.15680757202470 135 2 3.20172414306341 -1.20172414306341 136 4 3.10011215675744 0.899887843242563 137 2 2.63396140314109 -0.633961403141091 138 4 3.08998027564879 0.910019724351206 139 3 3.54015135844052 -0.54015135844052 140 3 3.69275194036640 -0.692751940366404 141 2 2.9787930212251 -0.9787930212251 142 2 3.88714800039506 -1.88714800039506 143 5 4.12243956577497 0.877560434225028 144 2 2.70670408252156 -0.706704082521559 145 4 3.40149798911624 0.598502010883758 146 3 3.10535737713409 -0.105357377134086 147 3 3.05700061614809 -0.0570006161480889 148 3 3.12895714915414 -0.128957149154143 149 3 2.62987536509469 0.370124634905308 150 4 3.9335594326764 0.0664405673236026 151 4 3.23465517350652 0.765344826493484 152 4 3.10011215675744 0.899887843242563 153 4 3.31358609981437 0.686413900185632 154 4 2.69246184883772 1.30753815116228 155 5 3.9335837472052 1.06641625279480 156 3 3.15531271078208 -0.155312710782077 157 3 3.0207453795042 -0.020745379504198 158 4 3.87643786447589 0.123562135524106 159 4 3.91055239177805 0.0894476082219515 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.557292840048168 0.885414319903664 0.442707159951832 11 0.428577993909615 0.85715598781923 0.571422006090385 12 0.293484322228889 0.586968644457779 0.706515677771111 13 0.297504193536540 0.595008387073079 0.70249580646346 14 0.268085050266299 0.536170100532599 0.7319149497337 15 0.325623178972869 0.651246357945739 0.67437682102713 16 0.251032501687683 0.502065003375366 0.748967498312317 17 0.182642131617135 0.36528426323427 0.817357868382865 18 0.170586148351033 0.341172296702066 0.829413851648967 19 0.137433267355014 0.274866534710027 0.862566732644986 20 0.182569190205202 0.365138380410405 0.817430809794798 21 0.149663060060346 0.299326120120693 0.850336939939654 22 0.576810046779266 0.846379906441468 0.423189953220734 23 0.546629107143079 0.906741785713842 0.453370892856921 24 0.574844799726146 0.850310400547708 0.425155200273854 25 0.517068327163657 0.965863345672687 0.482931672836343 26 0.495746398650624 0.991492797301247 0.504253601349376 27 0.472956853472589 0.945913706945178 0.527043146527411 28 0.406511138230604 0.813022276461208 0.593488861769396 29 0.35807148018037 0.71614296036074 0.64192851981963 30 0.333560786014326 0.667121572028653 0.666439213985674 31 0.620662218776042 0.758675562447916 0.379337781223958 32 0.560534843348105 0.878930313303791 0.439465156651896 33 0.71067937036125 0.578641259277502 0.289320629638751 34 0.770876992956183 0.458246014087634 0.229123007043817 35 0.76554496653225 0.468910066935500 0.234455033467750 36 0.726645250602488 0.546709498795024 0.273354749397512 37 0.747358117478136 0.505283765043728 0.252641882521864 38 0.742012056403521 0.515975887192957 0.257987943596479 39 0.817999456040336 0.364001087919328 0.182000543959664 40 0.80597018934018 0.388059621319641 0.194029810659820 41 0.792324258773187 0.415351482453625 0.207675741226813 42 0.769936468285233 0.460127063429533 0.230063531714767 43 0.729567102296558 0.540865795406885 0.270432897703442 44 0.712165696348897 0.575668607302207 0.287834303651103 45 0.749615148606466 0.500769702787068 0.250384851393534 46 0.730837147045808 0.538325705908384 0.269162852954192 47 0.745948826771773 0.508102346456455 0.254051173228227 48 0.753026845393788 0.493946309212425 0.246973154606212 49 0.740678740792205 0.518642518415590 0.259321259207795 50 0.75562320259184 0.48875359481632 0.24437679740816 51 0.86902872098857 0.26194255802286 0.13097127901143 52 0.862329484521148 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0.0775408243853397 70 0.92953620627298 0.140927587454039 0.0704637937270194 71 0.916512411740356 0.166975176519287 0.0834875882596436 72 0.907577725360862 0.184844549278275 0.0924222746391377 73 0.892498793067421 0.215002413865157 0.107501206932579 74 0.87419102866988 0.251617942660240 0.125808971330120 75 0.865367659230335 0.269264681539329 0.134632340769665 76 0.874148817193528 0.251702365612944 0.125851182806472 77 0.851298527977268 0.297402944045463 0.148701472022732 78 0.827113949877847 0.345772100244306 0.172886050122153 79 0.795398730585842 0.409202538828315 0.204601269414158 80 0.79367606729175 0.412647865416499 0.206323932708249 81 0.804731781135991 0.390536437728017 0.195268218864009 82 0.771493073656722 0.457013852686557 0.228506926343278 83 0.737934279321576 0.524131441356847 0.262065720678424 84 0.705153400350395 0.589693199299209 0.294846599649605 85 0.699655059105463 0.600689881789074 0.300344940894537 86 0.719240247571872 0.561519504856256 0.280759752428128 87 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0.504346608577102 105 0.462239713174687 0.924479426349375 0.537760286825313 106 0.517074664820175 0.96585067035965 0.482925335179825 107 0.480168136647306 0.960336273294612 0.519831863352694 108 0.620936977463203 0.758126045073595 0.379063022536797 109 0.577437416563446 0.845125166873108 0.422562583436554 110 0.629130096853137 0.741739806293725 0.370869903146863 111 0.692160329174684 0.615679341650632 0.307839670825316 112 0.741266958245193 0.517466083509615 0.258733041754807 113 0.814844502742372 0.370310994515256 0.185155497257628 114 0.8502055564086 0.299588887182799 0.149794443591400 115 0.869567121948956 0.260865756102089 0.130432878051044 116 0.849244100541206 0.301511798917589 0.150755899458794 117 0.864669349615623 0.270661300768754 0.135330650384377 118 0.919071846452506 0.161856307094989 0.0809281535474943 119 0.938772480755107 0.122455038489786 0.0612275192448929 120 0.933363772704679 0.133272454590642 0.066636227295321 121 0.912020166141083 0.175959667717834 0.0879798338589172 122 0.89442248039711 0.211155039205781 0.105577519602891 123 0.877495660913396 0.245008678173207 0.122504339086604 124 0.875143337704454 0.249713324591091 0.124856662295546 125 0.856148402708545 0.287703194582910 0.143851597291455 126 0.844607142659802 0.310785714680396 0.155392857340198 127 0.835749860022727 0.328500279954547 0.164250139977273 128 0.80054599879934 0.398908002401321 0.199454001200660 129 0.751699405268847 0.496601189462305 0.248300594731153 130 0.695533188042167 0.608933623915666 0.304466811957833 131 0.653792371388599 0.692415257222802 0.346207628611401 132 0.609579755737935 0.780840488524129 0.390420244262065 133 0.582801553440062 0.834396893119877 0.417198446559938 134 0.59596902032953 0.80806195934094 0.40403097967047 135 0.787494618377667 0.425010763244666 0.212505381622333 136 0.782980391057417 0.434039217885165 0.217019608942583 137 0.7772347611762 0.4455304776476 0.2227652388238 138 0.756201029962479 0.487597940075042 0.243798970037521 139 0.725770002051751 0.548459995896498 0.274229997948249 140 0.735731289912163 0.528537420175673 0.264268710087837 141 0.71484654277179 0.570306914456419 0.285153457228210 142 0.943321599484692 0.113356801030616 0.0566784005153082 143 0.909160140361217 0.181679719277567 0.0908398596387835 144 0.95638129007044 0.0872374198591184 0.0436187099295592 145 0.94009078371553 0.119818432568939 0.0599092162844693 146 0.889131280269129 0.221737439461742 0.110868719730871 147 0.816815294781307 0.366369410437387 0.183184705218693 148 0.70675967188751 0.586480656224981 0.293240328112490 149 0.555570642768593 0.888858714462815 0.444429357231407 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 4 0.0285714285714286 OK

Charts produced by software:

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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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Software written by Ed van Stee & Patrick Wessa

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