WS 7 - Minitutorial - Depression

*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 17:27:29 +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/t1290533269bh6gjxoxafdux3p.htm/, Retrieved Tue, 23 Nov 2010 18:28:00 +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/t1290533269bh6gjxoxafdux3p.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 «

12 6 15 4 7 2 2 2 2 11 6 15 3 5 4 1 2 2 14 13 14 5 7 7 4 3 4 12 8 10 3 3 3 1 2 3 21 7 10 6 7 7 5 4 4 12 9 12 5 7 2 1 2 3 22 5 18 6 7 7 1 2 3 11 8 12 6 1 2 1 3 4 10 9 14 5 4 1 1 2 3 13 11 18 5 5 2 1 2 4 10 8 9 3 6 6 2 3 3 8 11 11 5 4 1 1 2 2 15 12 11 7 7 1 3 3 3 10 8 17 5 6 1 1 1 3 14 7 8 5 2 2 1 3 3 14 9 16 3 2 2 1 1 2 11 12 21 5 6 2 1 3 3 10 20 24 6 7 1 1 2 2 13 7 21 5 5 7 2 3 4 7 8 14 2 2 1 4 4 5 12 8 7 5 7 2 1 3 3 14 16 18 4 4 4 2 3 3 11 10 18 6 5 2 1 1 1 9 6 13 3 5 1 2 2 4 11 8 11 5 5 1 3 1 3 15 9 13 4 3 5 1 3 4 13 9 13 5 5 2 1 3 3 9 11 18 2 1 1 1 2 3 15 12 14 2 1 3 1 2 1 10 8 12 5 3 1 1 3 4 11 7 9 2 2 2 2 2 4 13 8 12 2 3 5 1 2 2 8 9 8 2 2 2 1 2 2 20 4 5 5 5 6 1 1 1 12 8 10 5 2 4 1 2 3 10 8 11 1 3 1 1 3 4 10 8 11 5 4 3 1 1 1 9 6 12 2 6 6 1 2 3 14 8 12 6 2 7 2 3 3 8 4 15 1 7 4 1 2 2 14 7 12 4 6 1 2 1 4 11 14 16 3 5 5 1 1 3 13 10 14 2 3 3 1 3 3 11 9 17 5 3 2 2 3 2 11 8 10 3 4 2 1 3 3 10 11 17 4 5 2 1 3 2 14 8 12 3 2 2 1 2 1 18 8 13 6 7 1 1 3 3 14 10 13 4 6 2 1 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Depression[t] = + 7.49200704176759 + 0.0408955674052273CriticParents[t] -0.0669085142751476ExpecParents[t] + 0.586756102445572FutureWorrying[t] + 0.202166740732203SleepDepri[t] + 0.35634657786847ChangesLastYear[t] -0.121340486208485FreqSmoking[t] + 0.264713129723179FreqHighAlc[t] + 0.292590037590936FreqBeerOrWine[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) 7.49200704176759 1.445238 5.1839 1e-06 0 CriticParents 0.0408955674052273 0.115754 0.3533 0.724412 0.362206 ExpecParents -0.0669085142751476 0.086583 -0.7728 0.441 0.2205 FutureWorrying 0.586756102445572 0.168257 3.4873 0.000658 0.000329 SleepDepri 0.202166740732203 0.141208 1.4317 0.154525 0.077263 ChangesLastYear 0.35634657786847 0.134922 2.6411 0.00923 0.004615 FreqSmoking -0.121340486208485 0.273487 -0.4437 0.65798 0.32899 FreqHighAlc 0.264713129723179 0.314605 0.8414 0.401593 0.200796 FreqBeerOrWine 0.292590037590936 0.278532 1.0505 0.295364 0.147682 Multiple Linear Regression - Regression Statistics Multiple R 0.453459545887258 R-squared 0.205625559756278 Adjusted R-squared 0.158897651506647 F-TEST (value) 4.40048714908833 F-TEST (DF numerator) 8 F-TEST (DF denominator) 136 p-value 9.38949108088005e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.89966249185119 Sum Squared Residuals 1143.49378906421 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 12 12.0805628449277 -0.0805628449276666 2 11 11.9235069029631 -0.923506902963089 3 14 15.4094415553154 -1.40944155531539 4 12 11.8717505874073 0.128249412592660 5 21 16.1618309539449 4.8381690460551 6 12 13.4046617162138 -1.40466171621376 7 22 15.2081173527299 6.7918826472701 8 11 13.294824974175 -2.29482497417500 9 10 12.3079978875984 -2.30799788759839 10 13 12.9732583214999 0.0267416785001346 11 10 13.7575717009992 -3.75757170099921 12 8 12.2979245276434 -4.29792452764335 13 15 14.4334547170335 0.566545282966519 14 10 12.2059971291089 -2.20599712910895 15 14 12.8443840645661 1.15561593543394 16 14 10.3953785832469 3.6046214167531 17 11 12.9877181789441 -1.98771817894410 18 10 12.9894302733557 -2.98943027335566 19 13 14.5340560419106 -1.53405604191056 20 7 10.8530854073948 -3.85308540739482 21 12 13.9630218499075 -1.96302184990746 22 14 12.9522890770089 1.04771092299107 23 11 12.3766356140442 -1.37663561404422 24 9 11.4521237868814 -2.45212378688137 25 11 12.1626005016107 -1.16260050161066 26 15 13.5686730374838 1.43132696251624 27 13 13.1981328501974 -0.198132850197392 28 9 9.75538643577493 -0.755386435774928 29 15 10.1914291408358 4.80857085916418 30 10 12.7560557753254 -2.75605577532537 31 11 10.9237436646135 0.0762563353865289 32 13 11.5712805745575 1.42871942544252 33 8 10.6086037247257 -2.60860372472569 34 20 13.8397031042181 6.16029689578191 35 12 13.1994426294348 -1.19944262943476 36 10 10.4759398798182 -0.475939879818229 37 10 12.3306278138505 -2.33062781385050 38 9 12.7449262774030 -3.74492627740305 39 14 14.8647940804501 -0.86479408045014 40 8 11.0725370447259 -3.07253704472590 41 14 12.0841375820163 1.91586241798366 42 11 12.567986413666 -1.56798641366600 43 13 11.3638646923948 1.63613530760518 44 11 12.1122347878330 -1.11223478783297 45 11 11.9822838799943 -0.98228387999426 46 10 12.1329437878707 -2.13294378787075 47 14 10.5942401650745 3.40575983492549 48 18 13.7919802888337 4.20801971116633 49 14 12.5897259261661 1.41027407383393 50 11 13.1558663500304 -2.15586635003041 51 12 11.2391194193309 0.760880580669071 52 13 13.1832502296621 -0.183250229662084 53 9 13.9792753460670 -4.97927534606705 54 10 12.3104228182865 -2.31042281828649 55 15 13.6370164586804 1.36298354131957 56 20 14.1001976074003 5.89980239259967 57 12 11.7034382372401 0.296561762759900 58 12 12.1199054413807 -0.119905441380711 59 14 11.2514782680112 2.74852173198882 60 13 14.9218163051899 -1.92181630518988 61 11 14.2759504044091 -3.27595040440913 62 17 13.3148035357248 3.68519646427524 63 12 12.1222701077126 -0.122270107712629 64 13 12.8439185606381 0.156081439361885 65 14 13.8406243973194 0.159375602680603 66 13 10.6938870864331 2.30611291356686 67 15 13.9834842709352 1.0165157290648 68 13 11.8926064789396 1.10739352106036 69 10 13.6050017195018 -3.60500171950182 70 11 11.2954337208940 -0.295433720893954 71 13 12.8333059768669 0.166694023133087 72 17 14.1334685636616 2.86653143633839 73 13 12.9456518202285 0.0543481797715108 74 9 11.8461956050346 -2.84619560503464 75 11 12.3572945704317 -1.35729457043169 76 10 10.0936373227236 -0.0936373227235615 77 9 10.1996736606694 -1.19967366066936 78 12 11.4549756820502 0.545024317949755 79 12 12.4198409594227 -0.419840959422669 80 13 12.2797551037535 0.720244896246507 81 13 12.3625035198552 0.637496480144759 82 22 14.6489865961670 7.35101340383298 83 13 11.9956888503439 1.00431114965606 84 15 13.6658398757758 1.33416012422415 85 13 14.1858042120834 -1.18580421208342 86 15 11.6401309115437 3.35986908845631 87 10 11.6114389131177 -1.61143891311767 88 11 10.7890994809401 0.210900519059895 89 16 14.1353082071647 1.86469179283533 90 11 11.8993022808498 -0.899302280849812 91 11 12.2999647274325 -1.29996472743248 92 10 12.5555985598113 -2.55559855981128 93 10 14.5203082518953 -4.52030825189527 94 16 14.0418752920142 1.9581247079858 95 12 13.3111487926171 -1.31114879261707 96 11 13.6768538243424 -2.67685382434239 97 16 14.3149648204109 1.68503517958910 98 19 14.861638711274 4.13836128872598 99 11 14.8795757571412 -3.87957575714115 100 15 12.521063908045 2.47893609195499 101 24 16.3214780755001 7.67852192449986 102 14 11.5119024489110 2.48809755108905 103 15 13.4467301542144 1.5532698457856 104 11 14.7404744662841 -3.74047446628405 105 15 13.2298164507161 1.77018354928388 106 12 12.7913012044466 -0.791301204446649 107 10 10.6950402700760 -0.695040270075978 108 14 13.6430495931279 0.356950406872131 109 9 12.7424535712216 -3.74245357122163 110 15 10.4692068230364 4.53079317696355 111 15 9.68420656641496 5.31579343358504 112 14 12.8453686162963 1.15463138370367 113 11 14.0815179803778 -3.0815179803778 114 8 14.2146627734475 -6.21466277344749 115 11 14.1080885041867 -3.10808850418670 116 8 11.6187647066161 -3.61876470661612 117 10 11.2876993236633 -1.28769932366329 118 11 13.4296441481942 -2.42964414819416 119 13 13.9924275035589 -0.992427503558935 120 11 13.6864081234133 -2.6864081234133 121 20 12.6859973663376 7.31400263366237 122 10 12.0897220832703 -2.08972208327026 123 12 10.7450502790942 1.25494972090576 124 14 12.4057624221899 1.59423757781014 125 23 13.7439933852377 9.25600661476226 126 14 12.6418698839366 1.35813011606345 127 16 15.0988549553836 0.901145044616358 128 11 13.2935633971562 -2.29356339715624 129 12 13.9791757181162 -1.97917571811624 130 10 13.5188769807189 -3.51887698071890 131 14 13.7236415136882 0.276358486311758 132 12 9.64600343102413 2.35399656897587 133 12 13.4775159069585 -1.47751590695849 134 11 10.0239195735866 0.97608042641338 135 12 12.2535525196977 -0.253552519697721 136 13 15.6147192027332 -2.61471920273317 137 17 16.2518729175104 0.748127082489616 138 11 12.1626005016107 -1.16260050161066 139 12 13.5154478216585 -1.51544782165847 140 19 14.9582927796672 4.04170722033279 141 15 13.6658398757758 1.33416012422415 142 14 13.3077033417826 0.692296658217409 143 11 13.4296441481942 -2.42964414819416 144 9 10.4591886130326 -1.45918861303263 145 18 11.6539904487848 6.34600955121516 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.192625916249130 0.385251832498259 0.80737408375087 13 0.299178553490283 0.598357106980566 0.700821446509717 14 0.455826015851385 0.91165203170277 0.544173984148615 15 0.400852228842388 0.801704457684777 0.599147771157612 16 0.553425921034396 0.893148157931209 0.446574078965604 17 0.469288556542059 0.938577113084117 0.530711443457941 18 0.383439008529484 0.766878017058968 0.616560991470516 19 0.4899877617262 0.9799755234524 0.5100122382738 20 0.419335969986465 0.83867193997293 0.580664030013535 21 0.352317848399479 0.704635696798958 0.647682151600521 22 0.398227228468739 0.796454456937478 0.601772771531261 23 0.444379302368579 0.888758604737159 0.555620697631421 24 0.372311506143255 0.74462301228651 0.627688493856745 25 0.315646327630738 0.631292655261475 0.684353672369262 26 0.274395939183484 0.548791878366967 0.725604060816516 27 0.224567112903166 0.449134225806332 0.775432887096834 28 0.189364914144113 0.378729828288226 0.810635085855887 29 0.247970962540508 0.495941925081016 0.752029037459492 30 0.207940257680449 0.415880515360899 0.79205974231955 31 0.170976250671609 0.341952501343218 0.829023749328391 32 0.134191744151734 0.268383488303468 0.865808255848266 33 0.129331584603051 0.258663169206103 0.870668415396949 34 0.146633033695004 0.293266067390009 0.853366966304996 35 0.149075587878985 0.298151175757969 0.850924412121015 36 0.154133783813512 0.308267567627025 0.845866216186488 37 0.194418374656501 0.388836749313002 0.805581625343499 38 0.246768149425659 0.493536298851319 0.753231850574341 39 0.270391572681662 0.540783145363325 0.729608427318338 40 0.263992609285095 0.527985218570191 0.736007390714905 41 0.275657559902065 0.551315119804131 0.724342440097935 42 0.244713460056936 0.489426920113873 0.755286539943063 43 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0.791403969875491 61 0.259587648450418 0.519175296900837 0.740412351549582 62 0.297408185294235 0.59481637058847 0.702591814705765 63 0.255341430577098 0.510682861154195 0.744658569422902 64 0.216811313463058 0.433622626926116 0.783188686536942 65 0.18277089627203 0.36554179254406 0.81722910372797 66 0.170406673849962 0.340813347699924 0.829593326150038 67 0.146233525047382 0.292467050094764 0.853766474952618 68 0.122440494408183 0.244880988816366 0.877559505591817 69 0.131407143055225 0.262814286110451 0.868592856944775 70 0.106506882616367 0.213013765232734 0.893493117383633 71 0.0852151160175108 0.170430232035022 0.914784883982489 72 0.091578288854766 0.183156577709532 0.908421711145234 73 0.0726656465614145 0.145331293122829 0.927334353438586 74 0.0729803783849186 0.145960756769837 0.927019621615081 75 0.0602030234768755 0.120406046953751 0.939796976523125 76 0.0482657480817911 0.0965314961635822 0.95173425191821 77 0.0419288928221853 0.0838577856443705 0.958071107177815 78 0.0323428614940421 0.0646857229880843 0.967657138505958 79 0.0245612606367160 0.0491225212734321 0.975438739363284 80 0.0187288099665433 0.0374576199330867 0.981271190033457 81 0.0145848119417764 0.0291696238835528 0.985415188058224 82 0.0772304576769332 0.154460915353866 0.922769542323067 83 0.0617937855269883 0.123587571053977 0.938206214473012 84 0.0495823656109909 0.0991647312219817 0.95041763438901 85 0.0395100603219933 0.0790201206439866 0.960489939678007 86 0.04045340023924 0.08090680047848 0.95954659976076 87 0.0338060264302043 0.0676120528604086 0.966193973569796 88 0.0252906708935400 0.0505813417870801 0.97470932910646 89 0.0218443626254198 0.0436887252508397 0.97815563737458 90 0.0171102176003264 0.0342204352006528 0.982889782399674 91 0.0140076239375292 0.0280152478750584 0.98599237606247 92 0.0135403407510975 0.0270806815021949 0.986459659248903 93 0.0194307227263764 0.0388614454527527 0.980569277273624 94 0.0156493628519552 0.0312987257039105 0.984350637148045 95 0.0122548017796604 0.0245096035593208 0.98774519822034 96 0.0127884572841935 0.0255769145683871 0.987211542715806 97 0.0113122322928564 0.0226244645857128 0.988687767707144 98 0.0178957000494474 0.0357914000988949 0.982104299950552 99 0.020217840215409 0.040435680430818 0.97978215978459 100 0.0166235193367434 0.0332470386734867 0.983376480663257 101 0.0826466351110462 0.165293270222092 0.917353364888954 102 0.0756485408084938 0.151297081616988 0.924351459191506 103 0.062039208968117 0.124078417936234 0.937960791031883 104 0.0622281130500313 0.124456226100063 0.937771886949969 105 0.0508474192411014 0.101694838482203 0.949152580758899 106 0.039148867812884 0.078297735625768 0.960851132187116 107 0.0333396281963746 0.0666792563927491 0.966660371803626 108 0.0240155174551972 0.0480310349103945 0.975984482544803 109 0.0223247641607205 0.0446495283214409 0.97767523583928 110 0.0323414694254969 0.0646829388509938 0.967658530574503 111 0.0432307964816776 0.0864615929633552 0.956769203518322 112 0.0323370129829684 0.0646740259659367 0.967662987017032 113 0.0316426459996855 0.063285291999371 0.968357354000315 114 0.0582824833704015 0.116564966740803 0.941717516629599 115 0.0600374629431044 0.120074925886209 0.939962537056896 116 0.0950809153277885 0.190161830655577 0.904919084672211 117 0.0732660571948747 0.146532114389749 0.926733942805125 118 0.0608945966588741 0.121789193317748 0.939105403341126 119 0.0442052588804292 0.0884105177608583 0.95579474111957 120 0.0532328954116544 0.106465790823309 0.946767104588346 121 0.217780961705628 0.435561923411257 0.782219038294372 122 0.244419201412835 0.488838402825671 0.755580798587165 123 0.189846551377590 0.379693102755179 0.81015344862241 124 0.166034303938112 0.332068607876225 0.833965696061888 125 0.581131900378031 0.837736199243938 0.418868099621969 126 0.865921123766255 0.26815775246749 0.134078876233745 127 0.8580350864207 0.283929827158601 0.141964913579301 128 0.795145551723492 0.409708896553017 0.204854448276508 129 0.732768894656602 0.534462210686795 0.267231105343398 130 0.71097626261815 0.578047474763699 0.289023737381849 131 0.706498922170355 0.587002155659291 0.293501077829645 132 0.613284437896728 0.773431124206545 0.386715562103272 133 0.779367601863615 0.441264796272769 0.220632398136385 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 17 0.139344262295082 NOK 10% type I error level 32 0.262295081967213 NOK

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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