minitutorial ws 7 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: Fri, 03 Dec 2010 16:43:09 +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/03/t1291394545usmxjjor8sh2nea.htm/, Retrieved Fri, 03 Dec 2010 17:42:35 +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/03/t1291394545usmxjjor8sh2nea.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 «

1 12 3 7 13 5 2 0 15 3 10 16 6 1 3 12 4 9 12 6 1 3 10 3 10 11 5 1 1 12 2 6 12 3 2 3 15 3 15 18 8 2 1 9 4 10 11 4 1 4 12 2 14 14 4 1 0 11 3 5 9 4 1 3 11 4 15 14 6 1 2 11 3 10 12 6 2 4 15 3 16 11 5 1 3 7 4 13 12 4 1 1 11 2 6 13 6 2 1 11 2 12 11 4 2 2 10 3 10 12 6 1 3 14 2 15 16 6 1 1 10 4 6 9 4 2 1 6 2 8 11 4 1 2 11 1 8 13 2 2 3 15 3 13 15 7 1 4 11 4 15 10 5 2 2 12 2 7 11 4 1 1 14 3 12 13 6 2 2 15 3 15 16 6 1 2 9 4 13 15 7 2 4 13 3 15 14 5 2 2 13 3 13 14 6 1 3 16 4 9 14 4 2 3 13 4 9 8 4 1 3 12 4 15 13 7 1 4 14 3 14 15 7 2 2 11 4 9 13 4 1 2 9 3 9 11 4 2 4 16 2 16 15 6 1 3 12 4 12 15 6 2 4 10 3 10 9 5 1 2 13 2 13 13 6 2 5 16 4 17 16 7 1 3 14 4 13 13 6 1 1 15 4 5 11 3 2 1 5 4 6 12 3 1 1 8 2 9 12 4 1 2 11 2 9 12 6 2 3 16 3 13 14 7 1 9 17 4 20 14 5 2 0 9 5 5 8 4 1 0 9 2 8 13 5 2 2 13 3 14 16 6 1 2 10 3 6 13 6 1 3 6 2 14 11 6 1 1 12 2 9 14 5 2 2 8 3 8 13 4 2 0 14 2 9 13 5 2 5 12 4 16 13 5 2 2 11 3 12 12 4 1 4 16 3 16 16 6 1 3 8 4 11 15 2 1 0 15 1 11 15 8 2 0 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 IHaveManyFriends[t] = + 3.86540692440484 + 0.0137282387328047sum[t] + 0.0117110183160170Popularity[t] + 0.0248224514529821KnowingPeople[t] -0.0377884298430325Liked[t] -0.065730911728197Celebrity[t] -0.265177385683451Gender[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) 3.86540692440484 0.439542 8.7942 0 0 sum 0.0137282387328047 0.071351 0.1924 0.847689 0.423845 Popularity 0.0117110183160170 0.034649 0.338 0.735853 0.367927 KnowingPeople 0.0248224514529821 0.037486 0.6622 0.50889 0.254445 Liked -0.0377884298430325 0.040237 -0.9392 0.349182 0.174591 Celebrity -0.065730911728197 0.070786 -0.9286 0.354617 0.177309 Gender -0.265177385683451 0.149707 -1.7713 0.078566 0.039283 Multiple Linear Regression - Regression Statistics Multiple R 0.203330174641508 R-squared 0.0413431599197461 Adjusted R-squared 0.00247869343000606 F-TEST (value) 1.06377788385852 F-TEST (DF numerator) 6 F-TEST (DF denominator) 148 p-value 0.386976896365664 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.893084065341476 Sum Squared Residuals 118.044673869495 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3 2.84316562513334 0.156834374866657 2 3 3.02511898013376 -0.0251189801337584 3 4 3.15750190930327 0.842498090696731 4 3 3.26242166569545 -0.262421665695448 5 2 2.98759342697985 -0.987593426979854 6 3 2.71819988477117 0.281800115228826 7 4 3.28898508164202 0.711014918357981 8 2 3.35122736907131 -1.35122736907131 9 3 3.2501434819624 -0.250143481962402 10 4 3.21914874001908 0.780851259980919 11 3 2.89170771802398 0.108292281976020 12 3 3.48363970472623 -0.48363970472623 13 4 3.32969844699151 0.670301553008492 14 2 2.74090124363621 -0.740901243636214 15 2 3.09687463549657 -1.09687463549657 16 3 3.14517408539141 -0.145174085391414 17 2 3.17870493528107 -1.17870493528107 18 4 3.01180576814872 0.98819423185128 19 2 3.20420712378800 -1.20420712378800 20 1 3.06719803218777 -2.06719803218777 21 3 3.11282856880596 -0.112828568805956 22 4 3.18458422416876 0.815415775831238 23 2 3.26337902096393 -1.26337902096393 24 3 2.92496900730216 0.0750309926978423 25 3 3.17668771486428 -0.176687714864279 26 4 2.76365683449360 1.23634316550640 27 3 3.05685254142867 -0.0568525414286655 28 3 3.17919763501235 -0.179197635012346 29 4 2.99505356065422 1.00494643934578 30 4 3.45182847044781 0.548171529552189 31 4 3.20291727644993 0.797082723550066 32 3 2.87449085499227 0.125509145007726 33 4 3.22573604586781 0.77426395413219 34 3 3.01271348323839 -0.0127134832383897 35 2 3.27846609194192 -1.27846609194192 36 4 2.85342658844967 1.14657341155033 37 3 3.35172676411432 -0.351726764114318 38 2 2.95180867917193 -0.951808679171928 39 4 3.21349744055648 0.786502559443522 40 4 3.2424253219042 0.7575746780958 41 4 3.03569246031796 0.964307539682044 42 4 3.17079368445119 0.829206315548814 43 2 3.21466318202999 -1.21466318202999 44 2 2.86688526657100 -0.866885266570997 45 3 3.16232801696500 -0.162328016965005 46 4 3.29645006562167 0.703549934378332 47 5 3.2645098751734 1.7354901248266 48 2 2.81912678290554 -0.819126782905536 49 3 3.12844322677926 -0.128443226779263 50 3 3.00809584973645 -0.00809584973645293 51 2 3.24913648651511 -1.24913648651511 52 2 2.85502209819634 -0.855022098196342 53 3 2.90060315378333 0.0993968462166744 54 2 2.90250432593860 -0.902504325938604 55 4 3.12148064314147 0.878519356858532 56 3 3.33799183006979 -0.337991830069789 57 3 3.24067766209889 -0.240677662098888 58 4 3.30986109632886 0.690138903671143 59 1 2.69109065228993 -1.69109065228993 60 4 3.18048748235042 0.819512517649584 61 2 3.05107713610150 -1.05107713610150 62 4 3.03722479150414 0.962775208495864 63 4 3.11316911847860 0.886830881521404 64 4 3.20182800481622 0.798171995183779 65 2 3.26960554344661 -1.26960554344661 66 4 3.30845383843795 0.691546161562049 67 2 2.89266507329246 -0.89266507329246 68 4 3.02750964472255 0.972490355277447 69 4 3.48994939236043 0.510050607639567 70 1 3.13179124663654 -2.13179124663654 71 4 3.31017345404919 0.689826545950815 72 4 3.24896298757412 0.751037012425881 73 3 2.82951185208635 0.170488147913645 74 3 2.96755413832152 0.0324458616784802 75 3 3.24382573672515 -0.243825736725148 76 3 3.32252262684340 -0.322522626843396 77 3 3.25991174554738 -0.259911745547381 78 3 3.22308067097283 -0.223080670972829 79 4 3.26120159288545 0.738798407114549 80 1 3.15330726080800 -2.15330726080800 81 3 3.09213774643536 -0.0921377464353552 82 4 3.16761072256089 0.83238927743911 83 3 3.21090499711961 -0.210904997119611 84 3 3.31676075989791 -0.316760759897914 85 4 2.92824118779298 1.07175881220702 86 3 3.02567946162844 -0.0256794616284401 87 2 2.85578435853910 -0.855784358539096 88 3 3.02025798520799 -0.0202579852079889 89 4 3.29036414758061 0.709635852419388 90 3 2.7756870535999 0.224312946400099 91 4 3.12000438895407 0.879995611045933 92 3 3.17494675037069 -0.174946750370691 93 4 3.30378124312618 0.696218756873818 94 4 3.24805527248445 0.75194472751555 95 3 2.92250066972985 0.077499330270149 96 2 2.92130067146565 -0.92130067146565 97 1 3.32005428927109 -2.32005428927109 98 2 2.89371824312904 -0.893718243129044 99 2 2.91589201499877 -0.915892014998768 100 3 3.19976920182367 -0.199769201823673 101 4 3.18791272876074 0.812087271239258 102 4 3.07567159812939 0.924328401870607 103 3 2.92699976610063 0.0730002338993726 104 3 3.28929622482925 -0.289296224829254 105 1 2.68277592032929 -1.68277592032929 106 3 3.35624051405572 -0.356240514055724 107 3 3.06020909021472 -0.0602090902147237 108 4 3.20481723407212 0.795182765927881 109 2 3.10129789250089 -1.10129789250089 110 2 3.15813336846974 -1.15813336846974 111 3 3.10099344462538 -0.100993444625378 112 3 2.96752793913388 0.0324720608661216 113 3 3.08974708184656 -0.0897470818465619 114 4 3.05021768750994 0.94978231249006 115 4 2.98770399446273 1.01229600553727 116 4 3.11240685813584 0.887593141864158 117 4 2.95608648142934 1.04391351857066 118 2 3.04138333820227 -1.04138333820227 119 3 3.05830259642842 -0.0583025964284245 120 4 3.06046527159213 0.939534728407873 121 3 3.39558956638139 -0.395589566381394 122 4 3.05404365418372 0.945956345816282 123 2 2.99112162970047 -0.991121629700468 124 4 3.0510771361015 0.948922863898498 125 4 2.71718160219863 1.28281839780137 126 3 3.04584269700373 -0.0458426970037253 127 4 3.12817366616779 0.871826333832212 128 3 3.14703915574956 -0.147039155749563 129 3 3.35710850296545 -0.357108502965445 130 1 3.18631721901407 -2.18631721901407 131 4 3.20449143731425 0.795508562685747 132 4 3.03728659638393 0.962713403616073 133 2 3.24596706300650 -1.24596706300650 134 3 3.1777972201914 -0.177797220191398 135 4 2.93371898831459 1.06628101168541 136 3 3.22945934351415 -0.22945934351415 137 4 3.05315059266468 0.94684940733532 138 2 3.21167531506542 -1.21167531506542 139 5 3.3103189087961 1.6896810912039 140 3 3.32300863126295 -0.323008631262951 141 4 2.88919779787591 1.11080220212409 142 3 2.91622586935968 0.0837741306403156 143 4 3.59559652183133 0.404403478168671 144 2 2.99709212995336 -0.997092129953358 145 3 2.94472270809768 0.0552772919023161 146 3 3.02371050770976 -0.0237105077097607 147 2 2.94302259636237 -0.943022596362367 148 NA NA 1.34684323230002 149 4 3.14016094040700 0.859839059592996 150 4 4.11157360873192 -0.111573608731922 151 3 2.16300105870724 0.836998941292765 152 4 5.07865286912638 -1.07865286912638 153 2 1.11696019788834 0.883039802111663 154 4 3.1155879750197 0.884412024980298 155 4 4.67049564861749 -0.670495648617487 156 3 NA NA Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.19438666977734 0.38877333955468 0.80561333022266 11 0.100310983263191 0.200621966526382 0.899689016736809 12 0.117689840670092 0.235379681340183 0.882310159329908 13 0.0674101520785916 0.134820304157183 0.932589847921408 14 0.122431516636203 0.244863033272406 0.877568483363797 15 0.115088904700659 0.230177809401319 0.88491109529934 16 0.126277392160798 0.252554784321596 0.873722607839202 17 0.168136669146557 0.336273338293114 0.831863330853443 18 0.235341986563416 0.470683973126833 0.764658013436584 19 0.378931100424672 0.757862200849344 0.621068899575328 20 0.350740583061022 0.701481166122043 0.649259416938978 21 0.278119257278167 0.556238514556334 0.721880742721833 22 0.236225385962252 0.472450771924503 0.763774614037748 23 0.217529081980677 0.435058163961354 0.782470918019323 24 0.165265398739801 0.330530797479602 0.834734601260199 25 0.127336430931609 0.254672861863218 0.87266356906839 26 0.111343297477091 0.222686594954182 0.888656702522909 27 0.0852698350392569 0.170539670078514 0.914730164960743 28 0.0610144173328423 0.122028834665685 0.938985582667158 29 0.242577581011113 0.485155162022227 0.757422418988887 30 0.208610387890928 0.417220775781856 0.791389612109072 31 0.172415156318289 0.344830312636578 0.827584843681711 32 0.143027116362098 0.286054232724195 0.856972883637902 33 0.207962972511321 0.415925945022642 0.792037027488679 34 0.165538041348113 0.331076082696226 0.834461958651887 35 0.196979708369477 0.393959416738954 0.803020291630523 36 0.219409972388827 0.438819944777654 0.780590027611173 37 0.206420006412563 0.412840012825127 0.793579993587437 38 0.239346653276238 0.478693306552476 0.760653346723762 39 0.225467038807730 0.450934077615459 0.77453296119227 40 0.211809344132132 0.423618688264263 0.788190655867868 41 0.277742589875089 0.555485179750178 0.722257410124911 42 0.310763578385449 0.621527156770897 0.689236421614551 43 0.327677616977739 0.655355233955477 0.672322383022261 44 0.368646169850586 0.737292339701171 0.631353830149414 45 0.323997896443365 0.64799579288673 0.676002103556635 46 0.290724250775288 0.581448501550576 0.709275749224712 47 0.429558762196919 0.859117524393837 0.570441237803081 48 0.41118757141649 0.82237514283298 0.58881242858351 49 0.363697023186660 0.727394046373321 0.63630297681334 50 0.322052394515229 0.644104789030457 0.677947605484772 51 0.388421259290439 0.776842518580879 0.61157874070956 52 0.373002169874617 0.746004339749234 0.626997830125383 53 0.329342515675676 0.658685031351352 0.670657484324324 54 0.317427418217653 0.634854836435306 0.682572581782347 55 0.313012738469981 0.626025476939963 0.686987261530019 56 0.273500753494923 0.547001506989845 0.726499246505077 57 0.234385076570592 0.468770153141183 0.765614923429408 58 0.251219632660511 0.502439265321022 0.748780367339489 59 0.339418366075157 0.678836732150315 0.660581633924843 60 0.346920370032165 0.693840740064331 0.653079629967835 61 0.35629847403542 0.71259694807084 0.64370152596458 62 0.399791239702411 0.799582479404822 0.600208760297589 63 0.416321510598703 0.832643021197405 0.583678489401297 64 0.400052412196166 0.800104824392332 0.599947587803834 65 0.440857765979384 0.881715531958768 0.559142234020616 66 0.416568675566461 0.833137351132921 0.583431324433539 67 0.416866358996175 0.83373271799235 0.583133641003825 68 0.420552735791621 0.841105471583243 0.579447264208379 69 0.388304051022434 0.776608102044869 0.611695948977566 70 0.615691776692418 0.768616446615163 0.384308223307582 71 0.597335862127659 0.805328275744682 0.402664137872341 72 0.586237080961244 0.827525838077511 0.413762919038756 73 0.540891639136081 0.918216721727837 0.459108360863919 74 0.493675701884291 0.987351403768582 0.506324298115709 75 0.449819641400081 0.899639282800161 0.550180358599919 76 0.4086063046056 0.8172126092112 0.5913936953944 77 0.366427469685794 0.732854939371589 0.633572530314206 78 0.325079604977779 0.650159209955557 0.674920395022221 79 0.314170910369535 0.628341820739071 0.685829089630465 80 0.53476459718757 0.93047080562486 0.46523540281243 81 0.488892599268215 0.97778519853643 0.511107400731785 82 0.487853187488527 0.975706374977054 0.512146812511473 83 0.442840454560461 0.885680909120921 0.55715954543954 84 0.400663427682790 0.801326855365579 0.59933657231721 85 0.415501816484622 0.831003632969245 0.584498183515378 86 0.369397829962347 0.738795659924693 0.630602170037653 87 0.368399264336653 0.736798528673306 0.631600735663347 88 0.324017154903698 0.648034309807395 0.675982845096302 89 0.312498570783434 0.624997141566869 0.687501429216566 90 0.274925415905993 0.549850831811987 0.725074584094007 91 0.279121485333738 0.558242970667476 0.720878514666262 92 0.240759000899899 0.481518001799798 0.7592409991001 93 0.227905576487228 0.455811152974456 0.772094423512772 94 0.222437467105323 0.444874934210647 0.777562532894677 95 0.192391814468449 0.384783628936897 0.807608185531551 96 0.196036184120967 0.392072368241934 0.803963815879033 97 0.427245642051016 0.854491284102032 0.572754357948984 98 0.442858104930995 0.885716209861991 0.557141895069005 99 0.45895005873614 0.91790011747228 0.54104994126386 100 0.41070979868449 0.82141959736898 0.58929020131551 101 0.418113373870701 0.836226747741402 0.581886626129299 102 0.424180182100955 0.84836036420191 0.575819817899045 103 0.383800464425538 0.767600928851077 0.616199535574462 104 0.342026638647447 0.684053277294893 0.657973361352553 105 0.539421627283261 0.921156745433478 0.460578372716739 106 0.490248311513997 0.980496623027994 0.509751688486003 107 0.438629601185585 0.87725920237117 0.561370398814415 108 0.433038783496616 0.866077566993232 0.566961216503384 109 0.470539267850806 0.941078535701612 0.529460732149194 110 0.524093087959794 0.95181382408041 0.475906912040205 111 0.474485583888622 0.948971167777244 0.525514416111378 112 0.434358512646001 0.868717025292002 0.565641487353999 113 0.386663590401783 0.773327180803567 0.613336409598217 114 0.39678224302364 0.79356448604728 0.60321775697636 115 0.398444683315495 0.79688936663099 0.601555316684505 116 0.389751040205413 0.779502080410825 0.610248959794587 117 0.386561569326856 0.773123138653712 0.613438430673144 118 0.412707843167974 0.825415686335949 0.587292156832026 119 0.36862433809713 0.73724867619426 0.63137566190287 120 0.347295647677709 0.694591295355418 0.652704352322291 121 0.296928828024128 0.593857656048257 0.703071171975872 122 0.285065988015971 0.570131976031942 0.714934011984029 123 0.327491426305593 0.654982852611186 0.672508573694407 124 0.317117892600731 0.634235785201462 0.682882107399269 125 0.305266757304878 0.610533514609755 0.694733242695122 126 0.251952545083617 0.503905090167235 0.748047454916383 127 0.225524159888903 0.451048319777805 0.774475840111097 128 0.179974903551005 0.35994980710201 0.820025096448995 129 0.177872224842616 0.355744449685233 0.822127775157384 130 0.340991198992459 0.681982397984918 0.659008801007541 131 0.353037784149528 0.706075568299056 0.646962215850472 132 0.383268853505721 0.766537707011443 0.616731146494279 133 0.376483645654738 0.752967291309476 0.623516354345262 134 0.305589417764359 0.611178835528718 0.694410582235641 135 0.29460018198806 0.58920036397612 0.70539981801194 136 0.233957266920166 0.467914533840333 0.766042733079834 137 0.183765777188006 0.367531554376013 0.816234222811994 138 0.227009952935514 0.454019905871028 0.772990047064486 139 0.401976659416357 0.803953318832715 0.598023340583643 140 0.337483403085208 0.674966806170416 0.662516596914792 141 0.28929066408121 0.57858132816242 0.71070933591879 142 0.213715362445509 0.427430724891017 0.786284637554491 143 0.165177565540786 0.330355131081571 0.834822434459214 144 0.207593634490401 0.415187268980803 0.792406365509599 145 0.128836089560995 0.25767217912199 0.871163910439005 146 0.113712065006030 0.227424130012059 0.88628793499397 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/10coxd1291394578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/10coxd1291394578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/1nn011291394578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/1nn011291394578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/2nn011291394578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/2nn011291394578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/3ffz41291394578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/3ffz41291394578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/4ffz41291394578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/4ffz41291394578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/5ffz41291394578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/5ffz41291394578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/686hp1291394578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/686hp1291394578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/786hp1291394578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/786hp1291394578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/8jfgs1291394578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/8jfgs1291394578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/9jfgs1291394578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291394545usmxjjor8sh2nea/9jfgs1291394578.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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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