*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: Sat, 11 Dec 2010 09:59:39 +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/11/t12920615639yf6wmxfgq3mrk4.htm/, Retrieved Sat, 11 Dec 2010 10:59:34 +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/11/t12920615639yf6wmxfgq3mrk4.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 «

13 15 2 9 42 12 12 18 1 9 51 15 15 11 1 9 42 14 12 16 1 8 46 10 10 12 2 14 41 10 12 17 2 14 49 9 15 15 1 15 47 18 9 19 1 11 33 11 11 18 1 8 47 12 11 10 2 14 42 11 11 14 1 9 32 15 15 18 1 6 53 17 7 18 2 14 41 14 11 14 2 8 41 24 11 14 1 11 33 7 10 12 1 16 37 18 14 16 2 11 43 11 6 13 2 13 33 14 11 16 1 7 49 18 15 14 2 9 42 12 11 9 1 15 43 11 12 9 2 16 37 5 14 17 1 10 43 12 15 13 2 14 42 11 9 15 2 12 43 10 13 17 1 6 46 11 13 16 2 4 33 15 16 12 1 12 42 16 13 11 1 14 40 14 12 16 2 13 44 8 14 17 1 9 42 13 11 17 2 14 52 18 9 16 1 14 44 17 16 13 2 10 45 10 12 12 1 14 46 13 10 12 2 8 36 11 13 16 1 8 45 12 16 14 1 10 49 12 14 12 2 9 43 12 15 12 1 9 43 9 5 14 1 11 37 18 8 8 2 15 32 7 11 15 1 9 45 14 16 14 2 9 45 16 17 11 1 10 45 12 9 13 2 8 45 17 9 14 1 8 31 12 13 15 1 14 33 9 10 16 1 10 44 12 6 10 2 11 49 9 12 11 2 9 44 13 8 12 2 12 41 10 14 14 2 13 44 10 12 15 1 14 38 11 11 16 1 15 33 13 16 9 1 11 47 13 8 11 2 9 37 13 15 15 1 8 48 6 7 15 2 7 40 7 16 13 2 10 50 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 11 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation belonging[t] = + 33.7628490052607 + 0.576297541832057popularity[t] + 0.478113214787677hapiness[t] -1.43366386194624gender[t] -0.413115473681592doubsaboutactions[t] + 0.180267632695104parentalexpectations[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) 33.7628490052607 6.588503 5.1245 1e-06 0 popularity 0.576297541832057 0.195775 2.9437 0.003811 0.001906 hapiness 0.478113214787677 0.261714 1.8269 0.069898 0.034949 gender -1.43366386194624 1.23424 -1.1616 0.247428 0.123714 doubsaboutactions -0.413115473681592 0.223849 -1.8455 0.067122 0.033561 parentalexpectations 0.180267632695104 0.172462 1.0453 0.297743 0.148872 Multiple Linear Regression - Regression Statistics Multiple R 0.417432508416781 R-squared 0.174249899083126 Adjusted R-squared 0.144113034086160 F-TEST (value) 5.78195174251427 F-TEST (DF numerator) 5 F-TEST (DF denominator) 137 p-value 7.09848962427984e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.80662163087911 Sum Squared Residuals 6347.22342955535 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 42 44.0042598762073 -2.00425987620725 2 51 46.8367687387696 4.16323126123041 3 42 45.0386012280569 -3.03860122805688 4 46 45.3923196194003 0.607680380599712 5 41 38.4149149725497 2.58508502745032 6 49 41.7778084974571 7.22219150254293 7 47 45.1934317758985 1.80656822410153 8 33 44.0386878499175 -11.0386878499175 9 47 46.1327837725338 0.867216227466201 10 42 38.2152537175015 3.78474628249851 11 32 44.3480183377868 -12.3480183377868 12 53 50.1655430507007 2.83445694929927 13 41 40.27577216656 0.724227833440006 14 41 44.9498786437781 -3.9498786437781 15 33 42.0796463288628 -9.0796463288628 16 37 40.4644889486936 -3.46448894869356 17 43 44.0521720527685 -1.05217205276850 18 33 37.7220240244711 -4.72202402447114 19 49 46.6712786128107 2.32872138718934 20 42 44.6787417450835 -2.67874174508349 21 43 38.7576888909785 4.24231110902155 22 37 36.4056013010121 0.59439869898794 23 43 46.5573322358791 -3.55733223587911 24 42 41.9547835291927 0.0452164708072546 25 43 40.0991880224438 2.90081197755616 26 46 47.4532289560783 -1.45322895607832 27 33 47.088753357488 -14.088753357488 28 42 45.2142008290221 -3.21420082902206 29 40 41.8204287759848 -1.82042877598483 30 44 41.5325431236559 2.46745687634411 31 42 47.1507153422558 -5.1507153422558 32 52 42.8239196498810 9.17608035011904 33 44 42.4466075806803 1.55339241931970 34 45 44.0032753330561 0.996724666943933 35 46 41.5419768162453 4.45802318375466 36 36 41.0738754473343 -5.07387544733434 37 45 46.3291524266226 -1.32915242662256 38 49 46.2755876751802 2.72441232481981 39 43 43.1462177736761 -0.146217773676077 40 43 44.6153762793691 -1.61537627936906 41 37 40.6048050375166 -3.6048050375166 42 32 34.3959486579680 -2.39594865796796 43 45 44.6458639198794 0.354136080120617 44 45 45.976109817696 -0.97610981769596 45 45 45.4175455726492 -0.417545572649211 46 45 42.0572969164606 2.94270308353941 47 31 43.067735829719 -12.0677358297190 48 33 42.83154347166 -9.83154347166001 49 44 43.7740288537632 0.225971146236796 50 49 36.2125771639958 12.7874228360042 51 44 41.6957771079194 2.30422289208061 52 41 38.0885508362488 2.91144916375125 53 44 42.0894470431349 1.91055295686515 54 38 42.6157811952182 -4.61578119521817 55 33 42.4650166598824 -9.4650166598824 56 47 43.6521737602553 3.34782623974469 57 37 39.3905869405912 -2.39058694059117 58 48 45.9220284993284 2.07797150067163 59 40 40.4713674091024 -0.471367409102381 60 50 44.5440782311414 5.45592176885862 61 54 48.179740930135 5.82025906986496 62 43 47.5296596868481 -4.52965968684812 63 54 42.5919306727397 11.4080693272602 64 44 44.4871387518542 -0.487138751854244 65 47 41.4846309470944 5.51536905290556 66 33 42.920346022228 -9.92034602222798 67 45 46.7182062462211 -1.71820624622112 68 33 34.3969332011189 -1.39693320111894 69 44 45.5840202417591 -1.58402024175911 70 47 44.9072303150742 2.09276968492577 71 45 40.320391742162 4.679608257838 72 43 41.782476529187 1.21752347081298 73 43 38.1449121622487 4.85508783775133 74 33 44.4236756570102 -11.4236756570102 75 46 43.8551276162629 2.14487238373706 76 47 43.8937556454775 3.10624435452245 77 47 42.603024690897 4.39697530910302 78 0 40.8443202073073 -40.8443202073073 79 43 43.9339183209542 -0.933918320954231 80 46 42.5846458224672 3.41535417753283 81 36 37.6845083783094 -1.68450837830939 82 42 38.7801182696698 3.21988173033017 83 44 43.3244341931839 0.675565806816119 84 47 47.3470839963446 -0.347083996344561 85 41 39.5343753863885 1.46562461361146 86 47 44.4894468096627 2.51055319033731 87 46 45.0783730159141 0.921626984085934 88 47 47.2318141047555 -0.231814104755542 89 46 47.0663239787966 -1.06632397879663 90 46 44.2541932817382 1.74580671826182 91 36 44.9833220743376 -8.98332207433755 92 30 40.2635041789879 -10.2635041789879 93 48 42.3118464376086 5.68815356239138 94 45 40.786087424135 4.21391257586502 95 49 45.565611162557 3.43438883744299 96 55 47.1119280975496 7.88807190245038 97 11 41.2106358874803 -30.2106358874803 98 52 45.8976278737389 6.10237212626114 99 33 35.8460503522182 -2.84605035221816 100 47 48.4024792961153 -1.40247929611528 101 33 39.5825444075711 -6.58254440757113 102 44 44.4244490685566 -0.424449068556609 103 42 40.4258609194790 1.57413908052104 104 55 42.8519735532771 12.1480264467229 105 42 44.2853286544892 -2.28532865448918 106 46 44.9994230957312 1.0005769042688 107 46 46.9155594434609 -0.915559443460863 108 47 45.5699704335528 1.43002956644724 109 33 43.4866317181834 -10.4866317181834 110 53 49.7252325374682 3.27476746253175 111 42 42.2572971430153 -0.257297143015258 112 44 44.0353952489580 -0.0353952489580468 113 55 44.9434007412378 10.0565992587622 114 40 35.4674449791323 4.53255502086767 115 46 45.3200370280216 0.67996297197837 116 53 45.8805423091942 7.11945769080578 117 44 45.870432834188 -1.87043283418797 118 35 40.6678336923207 -5.66783369232071 119 40 39.7329699714004 0.267030028599579 120 44 45.0966228796246 -1.09662287962459 121 46 46.7482594466916 -0.748259446691554 122 45 42.4945197572417 2.50548024275826 123 53 45.1926583643521 7.8073416356479 124 45 43.5121944823426 1.48780551765740 125 48 42.7836312950986 5.21636870490143 126 46 44.4753970014563 1.52460299854366 127 55 41.7597081789892 13.2402918210108 128 47 49.7343574693235 -2.73435746932351 129 43 42.6559438706948 0.344056129305151 130 38 38.0828982613678 -0.0828982613678303 131 40 40.2315434785775 -0.231543478577502 132 47 42.2529378720195 4.74706212798049 133 47 42.1068413684137 4.89315863158632 134 42 40.7197661683714 1.28023383162857 135 53 42.5732128328035 10.4267871671965 136 43 44.0521720527685 -1.05217205276850 137 44 44.8536655636319 -0.853665563631855 138 42 43.2926230380161 -1.29262303801609 139 51 44.5581280393477 6.44187196065227 140 54 42.9524659381300 11.0475340618700 141 41 42.0995565181411 -1.09955651814110 142 51 42.8128256317237 8.18717436827628 143 51 44.4987828731226 6.50121712687743 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.625479598830677 0.749040802338646 0.374520401169323 10 0.48978863462332 0.97957726924664 0.51021136537668 11 0.474338660514287 0.948677321028573 0.525661339485713 12 0.369242547572502 0.738485095145003 0.630757452427498 13 0.28139104572854 0.56278209145708 0.71860895427146 14 0.192075279610892 0.384150559221784 0.807924720389108 15 0.171661427117480 0.343322854234961 0.82833857288252 16 0.125844022450331 0.251688044900662 0.874155977549669 17 0.144721649968746 0.289443299937492 0.855278350031254 18 0.0983061725848849 0.196612345169770 0.901693827415115 19 0.114338625660729 0.228677251321458 0.88566137433927 20 0.100355345061709 0.200710690123417 0.899644654938291 21 0.117473136842153 0.234946273684305 0.882526863157847 22 0.0814774426745284 0.162954885349057 0.918522557325472 23 0.0626062325093802 0.125212465018760 0.93739376749062 24 0.0461221575759448 0.0922443151518895 0.953877842424055 25 0.0364422938838964 0.0728845877677928 0.963557706116104 26 0.0242733352926843 0.0485466705853686 0.975726664707316 27 0.0600328475122006 0.120065695024401 0.9399671524878 28 0.0465530033764704 0.0931060067529408 0.95344699662353 29 0.0322644290169215 0.064528858033843 0.967735570983078 30 0.021885400922732 0.043770801845464 0.978114599077268 31 0.0171853466829018 0.0343706933658037 0.982814653317098 32 0.0192589611838021 0.0385179223676043 0.980741038816198 33 0.0128339538011562 0.0256679076023124 0.987166046198844 34 0.00848461382060335 0.0169692276412067 0.991515386179397 35 0.00730537334364158 0.0146107466872832 0.992694626656358 36 0.00491177241641232 0.00982354483282465 0.995088227583588 37 0.0032527911652132 0.0065055823304264 0.996747208834787 38 0.00236295507723269 0.00472591015446538 0.997637044922767 39 0.00152424228584383 0.00304848457168765 0.998475757714156 40 0.000946264288637114 0.00189252857727423 0.999053735711363 41 0.000595651408963467 0.00119130281792693 0.999404348591037 42 0.000347862533781218 0.000695725067562437 0.999652137466219 43 0.000243585395748413 0.000487170791496826 0.999756414604252 44 0.000139895356023047 0.000279790712046094 0.999860104643977 45 7.86207430430635e-05 0.000157241486086127 0.999921379256957 46 9.93624592464402e-05 0.000198724918492880 0.999900637540754 47 0.000177055510202218 0.000354111020404435 0.999822944489798 48 0.000566704699634688 0.00113340939926938 0.999433295300365 49 0.000397170271791237 0.000794340543582473 0.999602829728209 50 0.0044244395442087 0.0088488790884174 0.995575560455791 51 0.00321309209460319 0.00642618418920638 0.996786907905397 52 0.00230252549001735 0.00460505098003470 0.997697474509983 53 0.00152203883988141 0.00304407767976283 0.998477961160119 54 0.00120395913717537 0.00240791827435075 0.998796040862825 55 0.00204356865281831 0.00408713730563662 0.997956431347182 56 0.00163860073497579 0.00327720146995158 0.998361399265024 57 0.00110806384769146 0.00221612769538293 0.998891936152309 58 0.000917739650250278 0.00183547930050056 0.99908226034975 59 0.000599281338231905 0.00119856267646381 0.999400718661768 60 0.000497624690026418 0.000995249380052836 0.999502375309974 61 0.000533329253905136 0.00106665850781027 0.999466670746095 62 0.000400558965733979 0.000801117931467958 0.999599441034266 63 0.00188341861353910 0.00376683722707821 0.998116581386461 64 0.00124204751161695 0.0024840950232339 0.998757952488383 65 0.00107889834091087 0.00215779668182175 0.99892110165909 66 0.00186592272656233 0.00373184545312467 0.998134077273438 67 0.00126569957793323 0.00253139915586647 0.998734300422067 68 0.000855590040042557 0.00171118008008511 0.999144409959957 69 0.00056457342228636 0.00112914684457272 0.999435426577714 70 0.000398911826995469 0.000797823653990937 0.999601088173005 71 0.00030133700853065 0.0006026740170613 0.99969866299147 72 0.000190551569050373 0.000381103138100746 0.99980944843095 73 0.000155192316221530 0.000310384632443060 0.999844807683778 74 0.000339927224344043 0.000679854448688086 0.999660072775656 75 0.000237952979395825 0.000475905958791649 0.999762047020604 76 0.000164171763506551 0.000328343527013102 0.999835828236493 77 0.000147613137363427 0.000295226274726853 0.999852386862637 78 0.895765879239366 0.208468241521267 0.104234120760634 79 0.871536685851211 0.256926628297578 0.128463314148789 80 0.849033315550944 0.301933368898112 0.150966684449056 81 0.832311290034144 0.335377419931711 0.167688709965856 82 0.804666708916257 0.390666582167485 0.195333291083743 83 0.768370056234955 0.463259887530089 0.231629943765045 84 0.730195616494612 0.539608767010776 0.269804383505388 85 0.687106366796466 0.625787266407068 0.312893633203534 86 0.660107309729117 0.679785380541767 0.339892690270883 87 0.638865525486242 0.722268949027517 0.361134474513758 88 0.591136008230444 0.817727983539111 0.408863991769556 89 0.549962971564927 0.900074056870145 0.450037028435073 90 0.518301177254985 0.96339764549003 0.481698822745015 91 0.587563984971085 0.82487203005783 0.412436015028915 92 0.662472876937075 0.675054246125851 0.337527123062925 93 0.643619335086792 0.712761329826415 0.356380664913208 94 0.621559295856643 0.756881408286714 0.378440704143357 95 0.579326705380457 0.841346589239085 0.420673294619543 96 0.584887752033363 0.830224495933274 0.415112247966637 97 0.998529304112251 0.00294139177549745 0.00147069588774873 98 0.998021377107474 0.00395724578505237 0.00197862289252619 99 0.998517000651073 0.00296599869785316 0.00148299934892658 100 0.998283791478668 0.00343241704266308 0.00171620852133154 101 0.99885410165978 0.00229179668043973 0.00114589834021987 102 0.998129210647506 0.00374157870498806 0.00187078935249403 103 0.99726721218381 0.00546557563237986 0.00273278781618993 104 0.998413010877918 0.00317397824416377 0.00158698912208188 105 0.998393982653714 0.00321203469257201 0.00160601734628601 106 0.997364296080438 0.00527140783912368 0.00263570391956184 107 0.99616526112611 0.00766947774778048 0.00383473887389024 108 0.994058855097942 0.0118822898041155 0.00594114490205773 109 0.997546025614996 0.00490794877000809 0.00245397438500404 110 0.996153821946647 0.00769235610670508 0.00384617805335254 111 0.994132555303736 0.0117348893925270 0.00586744469626352 112 0.992105286486273 0.0157894270274544 0.0078947135137272 113 0.993234058281636 0.0135318834367274 0.00676594171836368 114 0.99076594005831 0.0184681198833785 0.00923405994168925 115 0.985298988740902 0.0294020225181955 0.0147010112590977 116 0.980537996974147 0.0389240060517069 0.0194620030258534 117 0.985886636101466 0.0282267277970688 0.0141133638985344 118 0.988752618193024 0.0224947636139524 0.0112473818069762 119 0.994915974257321 0.0101680514853573 0.00508402574267863 120 0.990966249035018 0.0180675019299635 0.00903375096498175 121 0.989187878645253 0.0216242427094938 0.0108121213547469 122 0.993758473418765 0.0124830531624707 0.00624152658123534 123 0.99378960246765 0.0124207950646997 0.00621039753234983 124 0.991870673756406 0.0162586524871871 0.00812932624359357 125 0.985117712902062 0.0297645741958761 0.0148822870979380 126 0.972965053553615 0.0540698928927708 0.0270349464463854 127 0.957188567054805 0.0856228658903897 0.0428114329451948 128 0.961471489415025 0.0770570211699497 0.0385285105849748 129 0.983176279830487 0.0336474403390265 0.0168237201695132 130 0.971796168726139 0.0564076625477226 0.0282038312738613 131 0.95039050098662 0.0992189980267622 0.0496094990133811 132 0.906213123271643 0.187573753456715 0.0937868767283573 133 0.846983848070659 0.306032303858682 0.153016151929341 134 0.728984590377958 0.542030819244083 0.271015409622042 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 55 0.436507936507937 NOK 5% type I error level 79 0.626984126984127 NOK 10% type I error level 88 0.698412698412698 NOK

Charts produced by software:

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Parameters (Session):

par1 = 5 ; par2 = equal ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = no ;

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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