Workshop 7 mini-tutorial Concern over mistakes - interactions gender

*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, 24 Nov 2010 15:35:46 +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/24/t1290612879fgdmbsnwt3q54ys.htm/, Retrieved Wed, 24 Nov 2010 16:34:51 +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/24/t1290612879fgdmbsnwt3q54ys.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 «

24 14 0 11 0 12 0 24 0 26 0 25 11 0 7 0 8 0 25 0 23 0 17 6 0 17 0 8 0 30 0 25 0 18 12 12 10 10 8 8 19 19 23 23 18 8 8 12 12 9 9 22 22 19 19 16 10 10 12 12 7 7 22 22 29 29 20 10 10 11 11 4 4 25 25 25 25 16 11 11 11 11 11 11 23 23 21 21 18 16 16 12 12 7 7 17 17 22 22 17 11 11 13 13 7 7 21 21 25 25 23 13 0 14 0 12 0 19 0 24 0 30 12 0 16 0 10 0 19 0 18 0 23 8 8 11 11 10 10 15 15 22 22 18 12 12 10 10 8 8 16 16 15 15 15 11 11 11 11 8 8 23 23 22 22 12 4 4 15 15 4 4 27 27 28 28 21 9 0 9 0 9 0 22 0 20 0 15 8 8 11 11 8 8 14 14 12 12 20 8 8 17 17 7 7 22 22 24 24 31 14 0 17 0 11 0 23 0 20 0 27 15 0 11 0 9 0 23 0 21 0 34 16 16 18 18 11 11 21 21 20 20 21 9 9 14 14 13 13 19 19 21 21 31 14 14 10 10 8 8 18 18 23 23 19 11 11 11 11 8 8 20 20 28 28 16 8 0 15 0 9 0 23 0 24 0 20 9 9 15 15 6 6 25 25 24 24 21 9 9 13 13 9 9 19 19 24 24 22 9 9 16 16 9 9 24 24 23 23 17 9 9 13 13 6 6 22 22 23 23 24 10 10 9 9 6 6 25 25 29 29 25 16 0 18 0 16 0 26 0 24 0 26 11 0 18 0 5 0 29 0 18 0 25 8 8 12 12 7 7 32 32 25 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 12 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Concernovermistakes[t] = -1.39516119998399 + 1.06623615242496DoubtsaboutactionsFemale[t] -0.390338094465476DoubtsaboutactionsMale[t] + 0.444393868835153ParentalexpectationsFemale[t] -0.326763214979501ParentalexpectationsMale[t] + 0.0512374544788622ParentalcritismFemale[t] + 0.189757003967310ParentalcritismMale[t] + 0.435719552669018PersonalstandardsFemale[t] + 0.208567624539643PersonalstandarsMale[t] -0.174498633537879OrganizationFemale[t] + 0.0726673983649172OrganizationMale[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) -1.39516119998399 3.096072 -0.4506 0.652921 0.32646 DoubtsaboutactionsFemale 1.06623615242496 0.241071 4.4229 1.9e-05 9e-06 DoubtsaboutactionsMale -0.390338094465476 0.281008 -1.3891 0.1669 0.08345 ParentalexpectationsFemale 0.444393868835153 0.2182 2.0366 0.04347 0.021735 ParentalexpectationsMale -0.326763214979501 0.268122 -1.2187 0.224893 0.112446 ParentalcritismFemale 0.0512374544788622 0.307068 0.1669 0.867708 0.433854 ParentalcritismMale 0.189757003967310 0.367959 0.5157 0.606832 0.303416 PersonalstandardsFemale 0.435719552669018 0.184997 2.3553 0.019821 0.00991 PersonalstandarsMale 0.208567624539643 0.214222 0.9736 0.331842 0.165921 OrganizationFemale -0.174498633537879 0.205662 -0.8485 0.397546 0.198773 OrganizationMale 0.0726673983649172 0.215099 0.3378 0.735968 0.367984 Multiple Linear Regression - Regression Statistics Multiple R 0.6540902092735 R-squared 0.427834001867451 Adjusted R-squared 0.389174137128765 F-TEST (value) 11.0666192124395 F-TEST (DF numerator) 10 F-TEST (DF denominator) 148 p-value 5.86197757002083e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.47268914595107 Sum Squared Residuals 2960.73233305366 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 24.9556317369705 -0.955631736970475 2 25 20.7336134397219 4.26638656027814 3 17 21.6759718622179 -4.67597186221789 4 18 19.7192156596422 -1.71921565964220 5 18 19.8320656662796 -1.83206566627956 6 16 19.6835605135766 -3.68356051357659 7 20 21.1831329567002 -1.18313295670024 8 16 22.6647428100575 -6.66474281005745 9 18 21.2303316215009 -3.23033162150092 10 17 20.2401269888749 -3.2401269888749 11 23 23.3929766947813 -0.39297669478128 12 30 24.1600451722962 5.83995482770383 13 23 15.1399255248906 7.86007447510942 14 18 18.6010040093999 -0.601004009399916 15 15 21.8399281995460 -6.83992819954597 16 12 18.5813478732644 -6.58134787326438 17 21 18.7575035696276 2.24249643037235 18 15 15.0319617825192 -0.0319617825191914 19 20 19.4290738428007 0.570926157199318 20 31 28.1820297440604 2.81797025593958 21 27 26.3049291409789 0.695070859021141 22 34 25.6809045576001 8.31909544239991 23 21 19.5706788637631 1.42932113623686 24 31 20.4267245983525 10.5732754016475 25 19 19.2960792568822 -0.296079256882225 26 16 20.0953556487311 -4.09535564873111 27 20 21.5615776662287 -1.56157766622867 28 21 18.1835766706039 2.81642332939608 29 22 21.8597357533871 0.140264246612860 30 17 19.4952860620643 -2.49528606206434 31 24 21.0225356251894 2.97746437481057 32 25 31.6242473139953 -6.62424731399534 33 26 28.0836050118374 -2.08360501183738 34 25 25.1819611104361 -0.181961110436063 35 17 22.8253160547974 -5.82531605479738 36 32 27.9605663872999 4.03943361270013 37 33 24.3441138609856 8.65588613901437 38 13 20.7483427046086 -7.74834270460857 39 32 27.524654554323 4.47534544567698 40 25 26.1563542809939 -1.15635428099394 41 29 26.2912375175486 2.70876248245137 42 22 22.2687616228845 -0.268761622884495 43 18 16.3451243379960 1.65487566200404 44 17 21.5400450968111 -4.54004509681111 45 20 21.4362784464713 -1.43627844647134 46 15 20.2774710203606 -5.27747102036060 47 20 21.2774048954945 -1.27740489549451 48 33 28.7452942733528 4.2547057266472 49 29 21.8893466314726 7.11065336852737 50 23 25.7228174277025 -2.72281742770246 51 26 23.9251189073850 2.07488109261505 52 18 19.1135259127194 -1.11352591271941 53 20 18.8382557004381 1.16174429956186 54 11 11.6173838989214 -0.617383898921388 55 28 28.9470387379516 -0.947038737951617 56 26 23.2408235003185 2.75917649968147 57 22 21.6270716499901 0.372928350009873 58 17 20.3274242475508 -3.32742424755080 59 12 14.0840671864101 -2.08406718641008 60 14 19.5911845552550 -5.59118455525504 61 17 21.1855506676186 -4.18555066761861 62 21 21.3579272392682 -0.35792723926819 63 19 22.6610235267971 -3.66102352679714 64 18 23.5204515192069 -5.5204515192069 65 10 18.6769230831868 -8.67692308318678 66 29 22.8615156952045 6.1384843047955 67 31 18.9642843781129 12.0357156218871 68 19 22.4879407202966 -3.48794072029661 69 9 19.7094623303634 -10.7094623303634 70 20 22.8230088484444 -2.82300884844441 71 28 17.7560086894510 10.2439913105490 72 19 17.6958132073873 1.30418679261273 73 30 22.5554727447564 7.44452725524362 74 29 29.1691638647069 -0.169163864706898 75 26 21.7048199095196 4.29518009048036 76 23 20.0597461352033 2.94025386479671 77 13 22.0268267043072 -9.02682670430719 78 21 22.261763029964 -1.26176302996401 79 19 21.6853769574391 -2.68537695743908 80 28 22.8817749447823 5.11822505521774 81 23 25.3642907765806 -2.36429077658059 82 18 14.1873899058047 3.81261009419526 83 21 22.0263941226815 -1.02639412268147 84 20 21.9672880959095 -1.96728809590953 85 23 19.7054421518195 3.29455784818049 86 21 20.0505630084062 0.949436991593838 87 21 22.1702458275280 -1.17024582752803 88 15 22.9195424195024 -7.9195424195024 89 28 27.1270949862954 0.872905013704601 90 19 17.2767745089170 1.72322549108303 91 26 20.1511288013935 5.8488711986065 92 10 14.1688477941492 -4.1688477941492 93 16 17.9829323926034 -1.9829323926034 94 22 20.7119391332540 1.28806086674598 95 19 19.1723904702372 -0.172390470237221 96 31 28.6955741681138 2.30442583188620 97 31 27.1349132604197 3.86508673958027 98 29 23.7923736069948 5.20762639300516 99 19 18.0708334229614 0.929166577038564 100 22 18.4833492687734 3.51665073122662 101 23 22.2183729090744 0.781627090925593 102 15 16.3228432163017 -1.32284321630167 103 20 23.0792351412715 -3.07923514127152 104 18 19.4761712035652 -1.47617120356515 105 23 21.7039311124801 1.29606888751994 106 25 19.9917345810447 5.00826541895532 107 21 16.7413292031872 4.2586707968128 108 24 19.3410522456067 4.65894775439326 109 25 24.5186724221499 0.481327577850142 110 17 18.4507979278914 -1.45079792789142 111 13 14.1957793317179 -1.19577933171794 112 28 17.9392667723412 10.0607332276588 113 21 18.6651814314984 2.33481856850155 114 25 28.9233362224049 -3.92333622240485 115 9 17.7434171205448 -8.74341712054482 116 16 17.8322751332771 -1.83227513327707 117 19 21.2694460686835 -2.26944606868348 118 17 19.4953101488352 -2.49531014883523 119 25 23.7311897997386 1.26881020026137 120 20 14.9966677486644 5.00333225133562 121 29 22.3382892819647 6.66171071803534 122 14 18.4618287427413 -4.46182874274126 123 22 26.5375657702661 -4.53756577026611 124 15 16.5037366023520 -1.50373660235205 125 19 27.7595806428129 -8.7595806428129 126 20 21.1553065330896 -1.15530653308961 127 15 17.181479479492 -2.18147947949202 128 20 22.0768904360628 -2.07689043606279 129 18 20.0620172664905 -2.06201726649045 130 33 25.4949901995315 7.50500980046847 131 22 23.8497850004963 -1.84978500049630 132 16 16.197583732195 -0.197583732194997 133 17 19.3578237675966 -2.35782376759664 134 16 15.2897277629553 0.710272237044736 135 21 17.9678706574515 3.03212934254853 136 26 29.3558991345934 -3.35589913459335 137 18 20.4538798139395 -2.45387981393953 138 18 22.6199887858023 -4.61998878580227 139 17 18.6786223132061 -1.67862231320610 140 22 24.5828587305535 -2.58285873055353 141 30 24.688005405543 5.31199459445701 142 30 27.3748522959997 2.62514770400032 143 24 28.2221548425602 -4.2221548425602 144 21 21.8577776158595 -0.857777615859544 145 21 24.6204916139374 -3.62049161393738 146 29 27.1109158109398 1.88908418906023 147 31 23.0289785251433 7.9710214748567 148 20 19.1622257411094 0.837774258890564 149 16 16.0835409788700 -0.083540978870048 150 22 19.0930351105193 2.90696488948072 151 20 21.3101919577802 -1.31019195778019 152 28 26.2481964654844 1.7518035345156 153 38 25.9281446966976 12.0718553033024 154 22 20.9145909606628 1.08540903933719 155 20 24.9515260240323 -4.95152602403229 156 17 18.8997093899867 -1.89970938998675 157 28 24.3082470802872 3.69175291971283 158 22 23.4999486705712 -1.49994867057124 159 31 28.8451597429609 2.15484025703915 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 14 0.136188910364565 0.272377820729131 0.863811089635435 15 0.072535377796889 0.145070755593778 0.927464622203111 16 0.0941979791975617 0.188395958395123 0.905802020802438 17 0.0640779308611252 0.128155861722250 0.935922069138875 18 0.0551065247807902 0.110213049561580 0.94489347521921 19 0.0917937929395126 0.183587585879025 0.908206207060487 20 0.0583579982131267 0.116715996426253 0.941642001786873 21 0.066236228606797 0.132472457213594 0.933763771393203 22 0.293418485887096 0.586836971774192 0.706581514112904 23 0.226255350465230 0.452510700930459 0.77374464953477 24 0.629131679412837 0.741736641174327 0.370868320587164 25 0.551562725368469 0.896874549263063 0.448437274631531 26 0.481779974826631 0.963559949653263 0.518220025173369 27 0.420501154995057 0.841002309990114 0.579498845004943 28 0.354604376747289 0.709208753494579 0.645395623252711 29 0.293389555010102 0.586779110020204 0.706610444989898 30 0.237522803264176 0.475045606528351 0.762477196735824 31 0.363998908888021 0.727997817776043 0.636001091111979 32 0.442370619866143 0.884741239732286 0.557629380133857 33 0.424379318896276 0.848758637792552 0.575620681103724 34 0.511773594377129 0.976452811245742 0.488226405622871 35 0.51021882455659 0.97956235088682 0.48978117544341 36 0.539734683091027 0.920530633817947 0.460265316908973 37 0.70607040649355 0.587859187012901 0.293929593506450 38 0.808458651696205 0.383082696607589 0.191541348303795 39 0.801823725707052 0.396352548585897 0.198176274292949 40 0.762820698351423 0.474358603297155 0.237179301648577 41 0.734200256128316 0.531599487743369 0.265799743871684 42 0.685151270184539 0.629697459630922 0.314848729815461 43 0.64554515477703 0.708909690445939 0.354454845222969 44 0.621338009539417 0.757323980921165 0.378661990460583 45 0.571327760866353 0.857344478267295 0.428672239133647 46 0.603199264078346 0.793601471843307 0.396800735921654 47 0.559277792535784 0.881444414928432 0.440722207464216 48 0.535131662307378 0.929736675385245 0.464868337692622 49 0.60471760366537 0.790564792669259 0.395282396334629 50 0.567456877444068 0.865086245111865 0.432543122555932 51 0.575651937760853 0.848696124478293 0.424348062239147 52 0.525616273226835 0.94876745354633 0.474383726773165 53 0.473281407473505 0.94656281494701 0.526718592526495 54 0.422306154152561 0.844612308305123 0.577693845847439 55 0.388955240224147 0.777910480448293 0.611044759775853 56 0.35535580993921 0.71071161987842 0.64464419006079 57 0.308294190599876 0.616588381199752 0.691705809400124 58 0.282689166410376 0.565378332820751 0.717310833589625 59 0.254194344681965 0.50838868936393 0.745805655318035 60 0.277863808893585 0.55572761778717 0.722136191106415 61 0.278376070281072 0.556752140562144 0.721623929718928 62 0.240738593915282 0.481477187830564 0.759261406084718 63 0.226116862797559 0.452233725595117 0.773883137202442 64 0.276418619131157 0.552837238262313 0.723581380868843 65 0.395130854615923 0.790261709231845 0.604869145384077 66 0.432263437367072 0.864526874734145 0.567736562632928 67 0.729578271029146 0.540843457941708 0.270421728970854 68 0.707252635957242 0.585494728085515 0.292747364042758 69 0.866477997060144 0.267044005879713 0.133522002939856 70 0.850930056363087 0.298139887273827 0.149069943636913 71 0.94255888951024 0.114882220979520 0.0574411104897602 72 0.929414695566743 0.141170608866515 0.0705853044332574 73 0.954999991016652 0.090000017966697 0.0450000089833485 74 0.942420766790874 0.115158466418252 0.0575792332091261 75 0.943552051177067 0.112895897645866 0.0564479488229332 76 0.937623627434597 0.124752745130806 0.062376372565403 77 0.976048448457155 0.0479031030856904 0.0239515515428452 78 0.969431381710656 0.0611372365786886 0.0305686182893443 79 0.964136113428501 0.0717277731429975 0.0358638865714987 80 0.966305112680776 0.0673897746384484 0.0336948873192242 81 0.959868850801987 0.080262298396026 0.040131149198013 82 0.958114424248614 0.0837711515027715 0.0418855757513858 83 0.946297700596989 0.107404598806023 0.0537022994030114 84 0.934990550165795 0.13001889966841 0.065009449834205 85 0.926805962897658 0.146388074204683 0.0731940371023417 86 0.912996710336182 0.174006579327635 0.0870032896638177 87 0.896618370538067 0.206763258923866 0.103381629461933 88 0.944617257172584 0.110765485654831 0.0553827428274157 89 0.931193735997972 0.137612528004056 0.0688062640020281 90 0.915650822025923 0.168698355948153 0.0843491779740767 91 0.923858738235336 0.152282523529328 0.076141261764664 92 0.922069219801066 0.155861560397868 0.0779307801989338 93 0.904055283737818 0.191889432524365 0.0959447162621824 94 0.883439607624637 0.233120784750726 0.116560392375363 95 0.856906783228661 0.286186433542677 0.143093216771339 96 0.830819853447907 0.338360293104186 0.169180146552093 97 0.840774167048842 0.318451665902315 0.159225832951158 98 0.850967826233296 0.298064347533409 0.149032173766704 99 0.823453189121504 0.353093621756993 0.176546810878496 100 0.808645169425626 0.382709661148747 0.191354830574374 101 0.772539916440882 0.454920167118237 0.227460083559118 102 0.732876720703347 0.534246558593306 0.267123279296653 103 0.7389591179219 0.522081764156201 0.261040882078100 104 0.699572121481993 0.600855757036014 0.300427878518007 105 0.655520149221231 0.688959701557538 0.344479850778769 106 0.655167280718719 0.689665438562562 0.344832719281281 107 0.654141191372305 0.691717617255391 0.345858808627696 108 0.685388760952075 0.629222478095851 0.314611239047925 109 0.637640122438182 0.724719755123636 0.362359877561818 110 0.598151657072604 0.803696685854793 0.401848342927396 111 0.546997579950251 0.906004840099499 0.453002420049749 112 0.860827225543269 0.278345548913463 0.139172774456732 113 0.879163551263294 0.241672897473411 0.120836448736705 114 0.9025284663066 0.194943067386798 0.0974715336933991 115 0.947569137015113 0.104861725969774 0.0524308629848868 116 0.940673179125673 0.118653641748654 0.0593268208743271 117 0.960195198408814 0.079609603182372 0.039804801591186 118 0.95608901472285 0.087821970554298 0.043910985277149 119 0.944720475851003 0.110559048297994 0.0552795241489971 120 0.956836010460145 0.0863279790797101 0.0431639895398551 121 0.950384670349506 0.099230659300988 0.049615329650494 122 0.949344547164177 0.101310905671647 0.0506554528358233 123 0.956512461057247 0.086975077885506 0.043487538942753 124 0.939252822009671 0.121494355980658 0.0607471779903289 125 0.938653358636966 0.122693282726068 0.061346641363034 126 0.920281481428365 0.159437037143270 0.0797185185716349 127 0.898150850601471 0.203698298797058 0.101849149398529 128 0.869961774323246 0.260076451353508 0.130038225676754 129 0.827957124327661 0.344085751344678 0.172042875672339 130 0.899890922476868 0.200218155046264 0.100109077523132 131 0.866199261249623 0.267601477500753 0.133800738750377 132 0.856123230208268 0.287753539583464 0.143876769791732 133 0.86753004672445 0.264939906551102 0.132469953275551 134 0.816935963952132 0.366128072095735 0.183064036047868 135 0.791923818910293 0.416152362179413 0.208076181089707 136 0.81112037593408 0.377759248131841 0.188879624065920 137 0.758985942763162 0.482028114473676 0.241014057236838 138 0.899810344826434 0.200379310347131 0.100189655173566 139 0.885331304361785 0.229337391276430 0.114668695638215 140 0.834515392909607 0.330969214180785 0.165484607090393 141 0.803130011767431 0.393739976465138 0.196869988232569 142 0.714779052261661 0.570441895476677 0.285220947738338 143 0.773087212734041 0.453825574531918 0.226912787265959 144 0.66189548639924 0.67620902720152 0.33810451360076 145 0.505973044459840 0.988053911080319 0.494026955540160 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 1 0.00757575757575758 OK 10% type I error level 12 0.090909090909091 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/10xn8g1290612933.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/10xn8g1290612933.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/18mbm1290612933.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/18mbm1290612933.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/28mbm1290612933.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/28mbm1290612933.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/31dtp1290612933.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/31dtp1290612933.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/41dtp1290612933.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/41dtp1290612933.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/51dtp1290612933.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/51dtp1290612933.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/6b4aa1290612933.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/6b4aa1290612933.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/7mw9d1290612933.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/7mw9d1290612933.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/8mw9d1290612933.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/8mw9d1290612933.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/9xn8g1290612933.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290612879fgdmbsnwt3q54ys/9xn8g1290612933.ps (open in new window)

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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