ws 7 Model 2: tijd

*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, 23 Nov 2010 11:08:59 +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/Nov/23/t1290510479ovo0541jws8oizd.htm/, Retrieved Tue, 23 Nov 2010 12:07:59 +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/Nov/23/t1290510479ovo0541jws8oizd.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 «

9 13 13 14 13 3 9 12 12 8 13 5 9 15 10 12 16 6 9 12 9 7 12 6 9 10 10 10 11 5 9 12 12 7 12 3 9 15 13 16 18 8 9 9 12 11 11 4 9 12 12 14 14 4 9 11 6 6 9 4 9 11 5 16 14 6 9 11 12 11 12 6 9 15 11 16 11 5 9 7 14 12 12 4 9 11 14 7 13 6 9 11 12 13 11 4 9 10 12 11 12 6 9 14 11 15 16 6 9 10 11 7 9 4 9 6 7 9 11 4 9 11 9 7 13 2 9 15 11 14 15 7 9 11 11 15 10 5 9 12 12 7 11 4 9 14 12 15 13 6 9 15 11 17 16 6 9 9 11 15 15 7 9 13 8 14 14 5 9 13 9 14 14 6 9 16 12 8 14 4 9 13 10 8 8 4 9 12 10 14 13 7 9 14 12 14 15 7 9 11 8 8 13 4 9 9 12 11 11 4 9 16 11 16 15 6 9 12 12 10 15 6 9 10 7 8 9 5 9 13 11 14 13 6 9 16 11 16 16 7 9 14 12 13 13 6 9 15 9 5 11 3 9 5 15 8 12 3 9 8 11 10 12 4 9 11 11 8 12 6 9 16 11 13 14 7 9 17 11 15 14 5 9 9 15 6 8 4 9 9 11 12 13 5 9 13 12 16 16 6 9 10 12 5 13 6 10 6 9 15 11 6 10 12 12 12 14 5 10 8 12 8 13 4 10 14 13 13 13 5 10 12 11 14 13 5 10 11 9 12 12 4 10 16 9 16 16 6 10 8 11 10 15 2 10 15 11 15 15 8 10 7 12 8 12 3 10 16 12 16 14 6 10 14 9 19 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Popularity[t] = + 2.6093942753828 -0.24986593522325Tijd[t] + 0.0962642440224116FindingFriends[t] + 0.243936452579795KnowingPeople[t] + 0.357484705624815Liked[t] + 0.622809523303693Celebrity[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) 2.6093942753828 3.648151 0.7153 0.475557 0.237779 Tijd -0.24986593522325 0.363799 -0.6868 0.493254 0.246627 FindingFriends 0.0962642440224116 0.096159 1.0011 0.318391 0.159196 KnowingPeople 0.243936452579795 0.061481 3.9676 0.000112 5.6e-05 Liked 0.357484705624815 0.097453 3.6683 0.000339 0.000169 Celebrity 0.622809523303693 0.15643 3.9814 0.000106 5.3e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.707651177863378 R-squared 0.500770189531427 Adjusted R-squared 0.484129195849141 F-TEST (value) 30.0925653294667 F-TEST (DF numerator) 5 F-TEST (DF denominator) 150 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.10920750873077 Sum Squared Residuals 667.313447232938 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 11.5428761098157 1.45712389018429 2 12 11.2286121969219 0.771387803078095 3 15 13.7070931593744 1.29290684062560 4 12 10.9612078299537 1.03879217004625 5 10 10.8089872027870 -0.808987202787039 6 12 9.3815719921099 2.61842800789009 7 15 16.9322201596178 -1.93222015961783 8 9 10.6226426201080 -1.62264262010796 9 12 12.4269060947218 -0.426906094721793 10 11 8.11040548182489 2.88959451817511 11 11 13.4865483383319 -2.48654833833189 12 11 12.2257463723402 -1.22574637234017 13 15 12.3688701622882 2.63112983771178 14 7 11.4165922663574 -4.4165922663574 15 11 11.8000137556906 -0.800013755690625 16 11 11.1105155252676 -0.110515525267554 17 10 12.2257463723402 -2.22574637234016 18 14 14.5351667611362 -0.535166761136192 19 10 8.83566315451674 1.16433684548326 20 6 9.65344849483632 -3.65344849483632 21 11 8.8274544423638 2.17254555763621 22 15 14.5565551262353 0.443444873764725 23 11 11.7674490040836 -0.76744900408361 24 12 9.64689680978879 2.35310319021121 25 14 13.5589768882842 0.441023111715841 26 15 15.0230396662958 -0.0230396662957811 27 9 14.8004915788151 -5.80049157881507 28 13 12.6646586419358 0.33534135806416 29 13 13.3837324092619 -0.383732409261944 30 16 10.9632873792430 5.03671262075697 31 13 8.62585065744931 4.37414934255069 32 12 13.7453214709632 -1.74532147096323 33 14 14.6528193702577 -0.652819370257688 34 11 10.2207456975286 0.779254302471436 35 9 10.6226426201080 -1.62264262010796 36 16 14.4216185080912 1.57838149190883 37 12 13.0542640366348 -1.05426403663482 38 10 9.31735215431059 0.682647845689415 39 13 13.2187761916820 -0.218776191681953 40 16 15.4019127370197 0.59808726298032 41 14 13.0711039831246 0.92889601687543 42 15 8.24742164925827 6.75257835074173 43 5 9.91430117675694 -4.91430117675694 44 8 10.6399266291306 -2.63992662913057 45 11 11.3976727705784 -0.39767277057837 46 16 13.9551339680307 2.04486603196933 47 17 13.1973878265829 3.80261217341713 48 9 8.61929897240178 0.38070102759822 49 9 12.1080937632187 -3.10809376321867 50 13 14.8753674577384 -1.87536745773840 51 10 11.1196123624862 -1.11961236248621 52 6 12.3053488097440 -6.30534880974405 53 12 12.3119767776426 -0.311976777642647 54 8 10.3559367383950 -2.35593673839496 55 14 12.2946927686200 1.70530723137996 56 12 12.346100733155 -0.346100733155009 57 11 10.6854051110221 0.314594888977911 58 16 14.3367087904479 1.66329120955209 59 8 10.2168957641744 -2.21689576417438 60 15 15.1734351668955 -0.173435166895513 61 7 9.37564250946645 -2.37564250946645 62 16 13.9105321112655 2.08946788873448 63 14 13.6385793256880 0.361420674311962 64 16 13.6838796677083 2.31612033229167 65 9 9.84459591884701 -0.844595918847013 66 14 12.1984285245976 1.80157147540237 67 11 12.9516262474361 -1.95162624743609 68 13 10.3773251034940 2.62267489650596 69 15 12.8212380479013 2.17876195209868 70 5 5.4672227245147 -0.467222724514699 71 15 12.346100733155 2.65389926684499 72 13 12.1984285245976 0.801571475402373 73 11 11.9717760810404 -0.97177608104044 74 11 13.9175278588459 -2.91752785884590 75 12 12.3847731072767 -0.384773107276702 76 12 13.3091109530609 -1.30911095306091 77 12 12.1811445155750 -0.181144515575018 78 12 11.8133715485080 0.18662845149202 79 14 10.7134214440198 3.28657855598023 80 6 7.90747537537835 -1.90747537537835 81 7 9.75451558019035 -2.75451558019035 82 14 11.8861678781420 2.11383212185797 83 14 13.7974279207534 0.202572079246647 84 10 11.1605424257684 -1.1605424257684 85 13 8.64383315172707 4.35616684827293 86 12 12.3160811337191 -0.316081133719121 87 9 9.1872183774981 -0.187218377498103 88 12 11.9442038105756 0.0557961894243915 89 16 14.9294987143157 1.07050128568428 90 10 10.1461242413275 -0.146124241327528 91 14 13.0656185630140 0.934381436986031 92 10 13.4399432151285 -3.43994321512854 93 16 15.2483110458188 0.751688954181159 94 15 13.3605189175959 1.63948108240412 95 12 11.2568066697908 0.74319333020919 96 10 9.64096732714533 0.35903267285467 97 8 10.1444887545711 -2.14448875457111 98 8 8.51300088965944 -0.513000889659439 99 11 12.7722774123374 -1.77227741233740 100 13 12.3333651427417 0.66663485725827 101 16 15.3959832543762 0.604016745623776 102 16 14.6468898876142 1.35311011238577 103 14 15.7769358743894 -1.77693587438940 104 11 8.80878936930706 2.19121063069294 105 4 6.86958927654913 -2.86958927654913 106 14 14.5037660976662 -0.503766097666178 107 9 10.2724080847858 -1.27240808478583 108 14 15.1734351668955 -1.17343516689551 109 8 10.3859563378308 -2.38595633783085 110 8 10.8335213821123 -2.83352138211233 111 11 12.1194482895978 -1.11944828959782 112 12 13.5468635002750 -1.54686350027496 113 11 11.3576193324226 -0.357619332422551 114 14 13.5362074591509 0.463792540849051 115 15 14.2725652354997 0.727434764500338 116 16 13.3263949620835 2.67360503791648 117 16 13.4226592061059 2.57734079389407 118 11 12.6740099018768 -1.67400990187679 119 14 13.6665956586857 0.333404341314276 120 14 10.8779335990669 3.12206640093309 121 12 11.3316825487141 0.668317451285861 122 14 12.4764889326898 1.52351106731022 123 8 10.1461242413275 -2.14612424132753 124 13 13.7628599027081 -0.762859902708136 125 16 13.6665956586857 2.33340434131428 126 12 10.8483580621639 1.15164193783612 127 16 15.3832476639629 0.616752336037055 128 12 13.3091109530609 -1.30911095306091 129 11 11.4390468963934 -0.439046896393411 130 4 6.26564450652239 -2.26564450652239 131 16 15.3832476639629 0.616752336037055 132 15 12.4810373512991 2.51896264870089 133 10 11.4283908552694 -1.42839085526940 134 13 13.1445987980138 -0.144598798013771 135 15 13.1787227535261 1.82127724647387 136 12 10.5998731909748 1.40012680902525 137 14 13.5703314146633 0.429668585336687 138 7 10.5953247723654 -3.59532477236542 139 19 14.0240803643105 4.97591963568946 140 12 12.6073211947574 -0.607321194757413 141 12 12.2111641150109 -0.211164115010906 142 13 13.4226592061059 -0.422659206105929 143 15 12.8920095707482 2.10799042925183 144 8 8.14888821613602 -0.148888216136022 145 12 10.8438096435545 1.15619035644545 146 10 10.7220742187056 -0.722074218705579 147 8 11.3362309673235 -3.33623096732347 148 10 14.3128730963778 -4.31287309637777 149 15 13.8142678672431 1.18573213275689 150 16 14.5378900531785 1.46210994682146 151 13 13.1038468746028 -0.103846874602806 152 16 15.0043745932390 0.995625406760954 153 9 10.1461242413275 -1.14612424132753 154 14 13.0997425185263 0.900257481473668 155 14 12.6114255508339 1.38857444916611 156 12 10.1033475111294 1.89665248887064 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.109906662350947 0.219813324701894 0.890093337649053 10 0.047806090317179 0.095612180634358 0.952193909682821 11 0.0727296562774194 0.145459312554839 0.92727034372258 12 0.0380783547992044 0.0761567095984087 0.961921645200796 13 0.440280494707312 0.880560989414624 0.559719505292688 14 0.762425475487704 0.475149049024592 0.237574524512296 15 0.682033878402258 0.635932243195484 0.317966121597742 16 0.59292815677398 0.81414368645204 0.40707184322602 17 0.534717353757111 0.930565292485778 0.465282646242889 18 0.44771630995205 0.8954326199041 0.55228369004795 19 0.370408181143548 0.740816362287096 0.629591818856452 20 0.665175271208369 0.669649457583262 0.334824728791631 21 0.600554256360074 0.798891487279853 0.399445743639926 22 0.566806898538364 0.866386202923272 0.433193101461636 23 0.496029330634726 0.992058661269452 0.503970669365274 24 0.476882391069905 0.95376478213981 0.523117608930095 25 0.438890147624153 0.877780295248306 0.561109852375847 26 0.380807745583849 0.761615491167698 0.619192254416151 27 0.648471629683341 0.703056740633318 0.351528370316659 28 0.590758099117408 0.818483801765184 0.409241900882592 29 0.530622184936296 0.938755630127408 0.469377815063704 30 0.688564414527427 0.622871170945147 0.311435585472573 31 0.805635768966311 0.388728462067378 0.194364231033689 32 0.769789044527452 0.460421910945095 0.230210955472548 33 0.729291048358663 0.541417903282673 0.270708951641337 34 0.684742495850205 0.63051500829959 0.315257504149795 35 0.678574997945677 0.642850004108646 0.321425002054323 36 0.696046725136891 0.607906549726218 0.303953274863109 37 0.657461496552484 0.685077006895031 0.342538503447516 38 0.60697750960248 0.78604498079504 0.39302249039752 39 0.556730688369831 0.886538623260339 0.443269311630169 40 0.538605447997595 0.92278910400481 0.461394552002405 41 0.508311632950459 0.983376734099081 0.491688367049541 42 0.810236636428485 0.37952672714303 0.189763363571515 43 0.95197289275812 0.0960542144837615 0.0480271072418808 44 0.961813346734773 0.0763733065304534 0.0381866532652267 45 0.95028616899627 0.0994276620074618 0.0497138310037309 46 0.956556216379778 0.0868875672404444 0.0434437836202222 47 0.982990647295339 0.0340187054093223 0.0170093527046611 48 0.979507027889909 0.0409859442201828 0.0204929721100914 49 0.981964075268303 0.036071849463395 0.0180359247316975 50 0.977181049424331 0.045637901151338 0.022818950575669 51 0.971950631846768 0.0560987363064637 0.0280493681532319 52 0.989213770246685 0.0215724595066294 0.0107862297533147 53 0.992021211472385 0.0159575770552291 0.00797878852761455 54 0.990517691569532 0.0189646168609360 0.00948230843046798 55 0.994617128490356 0.0107657430192882 0.0053828715096441 56 0.993122561310692 0.0137548773786151 0.00687743868930756 57 0.990831232614518 0.0183375347709633 0.00916876738548164 58 0.991280963771185 0.0174380724576304 0.00871903622881522 59 0.992312265296982 0.0153754694060361 0.00768773470301807 60 0.990855175793665 0.0182896484126692 0.00914482420633459 61 0.990917912065896 0.0181641758682076 0.00908208793410379 62 0.993065239536639 0.0138695209267229 0.00693476046336143 63 0.991149317536692 0.0177013649266160 0.00885068246330802 64 0.992460090806872 0.0150798183862564 0.00753990919312822 65 0.990093690376082 0.0198126192478351 0.00990630962391757 66 0.989750698963102 0.020498602073796 0.010249301036898 67 0.989314157253514 0.0213716854929723 0.0106858427464862 68 0.99153770394825 0.0169245921035005 0.00846229605175024 69 0.991956718723516 0.0160865625529681 0.00804328127648406 70 0.989120355113185 0.0217592897736303 0.0108796448868151 71 0.990924766516761 0.0181504669664779 0.00907523348323894 72 0.988072944694745 0.0238541106105107 0.0119270553052554 73 0.984973684941786 0.0300526301164285 0.0150263150582142 74 0.98894285797129 0.0221142840574187 0.0110571420287093 75 0.985114615101298 0.0297707697974029 0.0148853848987015 76 0.982568122634924 0.0348637547301524 0.0174318773650762 77 0.977090785248985 0.045818429502031 0.0229092147510155 78 0.969879978832463 0.0602400423350736 0.0301200211675368 79 0.981141511125037 0.0377169777499269 0.0188584888749634 80 0.980182037275351 0.039635925449298 0.019817962724649 81 0.98334582602042 0.0333083479591612 0.0166541739795806 82 0.984404236259004 0.0311915274819918 0.0155957637409959 83 0.979174889751477 0.0416502204970468 0.0208251102485234 84 0.974369422253804 0.0512611554923918 0.0256305777461959 85 0.993012434727943 0.0139751305441137 0.00698756527205687 86 0.990512454251632 0.0189750914967356 0.00948754574836781 87 0.987079350340338 0.0258412993193240 0.0129206496596620 88 0.983003809966486 0.0339923800670284 0.0169961900335142 89 0.978768324192718 0.0424633516145631 0.0212316758072815 90 0.971950909906926 0.0560981801861482 0.0280490900930741 91 0.966062113904202 0.0678757721915955 0.0339378860957977 92 0.980640650034898 0.0387186999302035 0.0193593499651018 93 0.975059675709882 0.0498806485802355 0.0249403242901177 94 0.971401751311768 0.0571964973764641 0.0285982486882320 95 0.963836515751585 0.0723269684968307 0.0361634842484154 96 0.954094242784984 0.0918115144300316 0.0459057572150158 97 0.950129405708244 0.099741188583512 0.049870594291756 98 0.936448127685328 0.127103744629344 0.063551872314672 99 0.932977888590807 0.134044222818386 0.067022111409193 100 0.917336546230434 0.165326907539131 0.0826634537695656 101 0.8982931633317 0.203413673336601 0.101706836668301 102 0.881389562990497 0.237220874019006 0.118610437009503 103 0.89257092689047 0.214858146219059 0.107429073109529 104 0.92385056418278 0.152298871634438 0.0761494358172192 105 0.922381277673484 0.155237444653032 0.0776187223265161 106 0.903370468583187 0.193259062833627 0.0966295314168133 107 0.884854868241492 0.230290263517016 0.115145131758508 108 0.880169397777762 0.239661204444476 0.119830602222238 109 0.879634867598516 0.240730264802967 0.120365132401484 110 0.903225554060957 0.193548891878086 0.096774445939043 111 0.893394915728447 0.213210168543106 0.106605084271553 112 0.92333019312296 0.153339613754081 0.0766698068770406 113 0.902142952545248 0.195714094909503 0.0978570474547515 114 0.876592354961236 0.246815290077529 0.123407645038764 115 0.846976202918196 0.306047594163607 0.153023797081804 116 0.846647747340669 0.306704505318662 0.153352252659331 117 0.854962391426605 0.29007521714679 0.145037608573395 118 0.844658968811386 0.310682062377229 0.155341031188614 119 0.807719138755045 0.38456172248991 0.192280861244955 120 0.86142115213684 0.277157695726320 0.138578847863160 121 0.834280057868296 0.331439884263408 0.165719942131704 122 0.809772713482967 0.380454573034066 0.190227286517033 123 0.799699963807939 0.400600072384123 0.200300036192061 124 0.758667569670462 0.482664860659075 0.241332430329538 125 0.758507882661758 0.482984234676484 0.241492117338242 126 0.739929519960295 0.52014096007941 0.260070480039705 127 0.686939981741838 0.626120036516324 0.313060018258162 128 0.658397591648663 0.683204816702673 0.341602408351337 129 0.67558695308933 0.64882609382134 0.32441304691067 130 0.667330144009447 0.665339711981106 0.332669855990553 131 0.606684239999214 0.786631520001573 0.393315760000786 132 0.59403583599494 0.811928328010119 0.405964164005059 133 0.564406161296073 0.871187677407855 0.435593838703927 134 0.489944398133652 0.979888796267304 0.510055601866348 135 0.463421676888927 0.926843353777854 0.536578323111073 136 0.456962237171405 0.91392447434281 0.543037762828595 137 0.383352154124569 0.766704308249138 0.616647845875431 138 0.446176669766455 0.89235333953291 0.553823330233545 139 0.794397111075275 0.41120577784945 0.205602888924725 140 0.824534882837644 0.350930234324713 0.175465117162356 141 0.779023815647074 0.441952368705852 0.220976184352926 142 0.694285994334067 0.611428011331865 0.305714005665933 143 0.655336380992217 0.689327238015566 0.344663619007783 144 0.540614026189965 0.91877194762007 0.459385973810035 145 0.518640475046831 0.962719049906337 0.481359524953169 146 0.633246698154205 0.73350660369159 0.366753301845795 147 0.570596811126392 0.858806377747216 0.429403188873608 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 42 0.302158273381295 NOK 10% type I error level 57 0.410071942446043 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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Software written by Ed van Stee & Patrick Wessa

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