workshop 7

*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, 21 Dec 2010 20:06:07 +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/21/t1292961886do7n9qb7u7g8hf9.htm/, Retrieved Tue, 21 Dec 2010 21:04:46 +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/21/t1292961886do7n9qb7u7g8hf9.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 «

5 6 5 7 11 2 2 6 2 3 11 1 6 6 6 5 15 1 6 4 4 5 9 2 6 2 6 3 11 1 5 7 3 4 17 1 5 6 5 4 16 1 6 5 3 5 9 1 6 6 5 5 14 1 5 7 4 5 12 1 5 7 1 6 6 2 5 4 6 5 4 1 6 1 6 2 13 1 5 6 6 5 12 1 5 4 4 4 10 1 6 5 6 6 14 2 6 5 5 5 12 1 4 6 3 6 9 1 5 4 5 5 16 2 5 6 4 2 13 2 5 3 5 3 12 1 6 3 6 5 11 1 5 5 3 6 12 2 7 5 4 5 12 2 6 5 5 4 11 1 6 5 4 5 16 2 6 5 5 5 9 1 6 2 6 5 8 2 4 6 7 5 11 1 5 7 2 6 9 2 6 2 4 6 16 2 4 3 6 6 14 1 5 6 5 6 10 2 5 5 5 4 14 1 5 7 5 4 13 2 7 5 6 3 12 1 7 6 6 5 16 2 6 5 1 6 16 1 7 3 4 4 15 1 6 7 2 6 5 2 5 5 3 3 12 2 6 5 4 2 11 1 4 6 5 5 15 1 6 2 4 5 15 2 5 3 3 6 10 2 6 6 4 4 12 1 6 7 6 3 5 1 5 5 4 3 16 1 6 4 5 4 16 1 5 6 4 5 12 2 5 7 5 4 6 2 5 2 6 3 7 2 6 2 6 4 14 2 6 2 4 4 8 2 5 5 4 4 12 1 7 2 6 3 10 2 6 5 4 6 11 2 5 6 2 5 17 1 5 2 6 5 13 1 6 4 5 6 15 1 5 6 6 6 10 1 5 4 6 4 9 2 6 3 5 5 16 1 6 3 5 4 11 2 3 3 5 6 8 2 5 6 5 5 14 2 5 6 3 5 11 2 6 5 4 5 12 1 5 3 1 5 14 2 5 3 5 2 15 1 4 2 2 5 14 2 5 3 6 5 11 2 5 3 5 5 11 2 2 5 2 2 15 1 6 3 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation populariteit[t] = + 13.7438661467663 + 0.336037867571855handgebruik[t] -0.000975929547537062stilheid[t] -0.200868238399153extravert[t] -0.120634791794267blozen[t] -1.43680541322535geslacht[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) 13.7438661467663 1.855041 7.4089 0 0 handgebruik 0.336037867571855 0.229087 1.4669 0.144595 0.072298 stilheid -0.000975929547537062 0.154563 -0.0063 0.994971 0.497485 extravert -0.200868238399153 0.178351 -1.1263 0.261932 0.130966 blozen -0.120634791794267 0.210526 -0.573 0.567528 0.283764 geslacht -1.43680541322535 0.51532 -2.7882 0.006017 0.003008 Multiple Linear Regression - Regression Statistics Multiple R 0.284174894713546 R-squared 0.080755370785455 Adjusted R-squared 0.0488371544932832 F-TEST (value) 2.53007154429432 F-TEST (DF numerator) 5 F-TEST (DF denominator) 144 p-value 0.0315164196338129 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.87291971947705 Sum Squared Residuals 1188.52815089665 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11 10.6958043463340 0.304195653665986 2 11 12.2096400392183 -1.20964003921835 3 15 12.5090489723206 2.49095102767937 4 9 11.4759318949887 -2.47593189498865 5 11 12.7542222740993 -1.75422227409931 6 17 12.8952746821930 4.10472531780704 7 16 12.4945141349422 3.50548586505781 8 9 13.1126296170656 -4.11262961706562 9 14 12.7099172107198 1.29008278928022 10 12 12.5737716519995 -0.573771651999539 11 6 11.6189361621774 -5.61893616217738 12 4 12.1749629638438 -8.17496296384384 13 13 12.8758329954411 0.124167004558888 14 12 12.1730111047488 -0.173011104748771 15 10 12.6973342324364 -2.69733423243642 16 14 10.9525846968485 3.04741530315146 17 12 12.7108931402673 -0.710893140267316 18 9 12.3189431605801 -3.31894316058011 19 16 10.9390257890176 5.06097421098236 20 13 11.4998465437045 1.50015345629548 21 12 12.6180767153791 -0.618076715379068 22 11 12.5119767609632 -1.51197676096324 23 12 11.2191515444741 0.780848455525855 24 12 11.8109938330130 0.189006166987030 25 11 12.8315279320616 -1.83152793206158 26 16 11.4749559654411 4.52504403455888 27 9 12.7108931402673 -3.71089314026732 28 8 11.0761472772854 -3.07614727728542 29 11 11.6361049987778 -0.636104998777763 30 9 11.4180679237782 -2.41806792377822 31 16 11.3572489622895 4.64275103771054 32 14 11.7192662340253 2.28073376597474 33 10 10.8164391381283 -0.816439138128302 34 14 12.4954900644897 1.50450993551027 35 13 11.0567327921693 1.9432672078307 36 12 13.0873323530286 -1.08733235302855 37 16 11.4082814266671 4.59171857333287 38 16 13.3937313020697 2.60626869793034 39 15 13.3703858971277 1.62961410287233 40 5 11.7541057913501 -6.75410579135008 41 12 11.5810559198569 0.418944080143055 42 11 13.2736657540493 -2.27366575404927 43 15 12.0378414755761 2.96215852442393 44 15 11.4778837540837 3.52211624591627 45 10 11.2211034035692 -1.22110340356922 46 12 13.0314202409132 -1.03142024091320 47 5 12.7493426263616 -7.74934262636163 48 16 12.8169930946831 3.18300690531685 49 16 12.8325038616091 3.16749613839088 50 12 11.1379421683217 0.862057831678278 51 6 11.0567327921693 -5.0567327921693 52 7 10.9813789933021 -3.9813789933021 53 14 11.1967820690797 2.80321793092031 54 8 11.598518545878 -3.59851854587799 55 12 12.6963583028889 -0.69635830288888 56 10 11.6534547284458 -1.65345472844581 57 11 11.3543211736468 -0.354321173646848 58 17 12.9764840583454 4.02351594165462 59 13 12.1769148229389 0.823085177061081 60 15 12.5912342780206 2.40876572197941 61 10 12.0523763129545 -2.05237631295450 62 9 10.8587923424128 -1.85879234241276 63 16 12.7128449993624 3.28715500063761 64 11 11.3966743779313 -0.396674377931303 65 8 10.1472911916272 -2.14729119162720 66 14 10.9370739299226 3.06292607007743 67 11 11.3388104067209 -0.338810406720875 68 12 12.9117613786665 -0.911761378666468 69 14 11.7434746721618 2.25652532783821 70 15 12.7387115071733 2.26128849282667 71 14 11.2075444957383 2.79245550426168 72 11 10.7391334801660 0.260866519833972 73 11 10.9400017185652 0.0599982814348192 74 15 12.3312507605602 2.66874923943985 75 7 10.9545365559436 -3.95453655594362 76 12 11.3947225188362 0.605277481163771 77 10 12.6335874823050 -2.63358748230504 78 13 11.555189412046 1.444810587954 79 15 11.0173073765275 3.98269262347254 80 13 12.6953823733413 0.304617626658657 81 15 13.2736657540493 1.72633424595073 82 8 11.2624806783061 -3.26248067830614 83 14 12.3738793431479 1.62612065685208 84 11 11.6334709995558 -0.633470999555812 85 12 10.4264410175362 1.57355898246383 86 16 12.7108931402673 3.28910685973268 87 8 10.0680336745699 -2.06803367456985 88 12 12.3748552726955 -0.374855272695460 89 16 12.2936458965430 3.70635410345696 90 11 11.555189412046 -0.555189412046 91 13 12.5757235110946 0.424276488905387 92 6 13.0333721000083 -7.03337210000827 93 4 11.555189412046 -7.555189412046 94 11 13.0333721000083 -2.03337210000827 95 7 11.2615047487586 -4.2615047487586 96 12 10.8685788395239 1.13142116047615 97 12 12.2027813307499 -0.202781330749864 98 16 13.4515952732801 2.54840472671991 99 15 12.7118690698149 2.28813093018515 100 13 12.7128449993624 0.28715500063761 101 12 12.2542204809012 -0.254220480901194 102 9 12.0388174051236 -3.03881740512361 103 16 13.0353239591033 2.96467604089665 104 11 13.3134978554648 -2.31349785546477 105 14 11.2770155156846 2.72298448431543 106 10 11.3388104067209 -1.33881040672087 107 10 12.5129526905108 -2.51295269051078 108 11 12.6345634118526 -1.63456341185258 109 16 12.6517778612060 3.34822213879396 110 8 12.3120844521116 -4.31208445211162 111 16 12.6180767153791 3.38192328462093 112 12 12.1739870342963 -0.173987034296308 113 11 11.5028872287001 -0.502887228700134 114 16 11.7182903044777 4.28170969552228 115 9 13.1116536875181 -4.11165368751808 116 13 10.8607442015078 2.13925579849217 117 14 12.1865700125797 1.81342998742033 118 10 12.2027813307499 -2.20278133074986 119 12 13.0727975156501 -1.07279751565012 120 11 12.1614040560129 -1.16140405601295 121 10 10.1472911916272 -0.147291191627203 122 12 12.7108931402673 -0.710893140267316 123 13 12.6963583028889 0.30364169711112 124 14 11.8119697625605 2.18803023743949 125 12 12.5119767609632 -0.511976760963238 126 14 12.5129526905108 1.48704730948923 127 13 12.6953823733413 0.304617626658657 128 8 12.3379509599226 -4.33795095992257 129 13 11.6468674254364 1.35313257456360 130 10 12.5747475815471 -2.57474758154708 131 9 10.9921414199607 -1.99214141996073 132 8 11.4749559654411 -3.47495596544111 133 15 11.1379421683217 3.86205783167828 134 15 12.7765917494938 2.22340825050623 135 12 12.979411846988 -0.979411846987993 136 8 9.8073496058652 -1.8073496058652 137 15 12.7367596480783 2.26324035192174 138 9 12.6973342324364 -3.69733423243642 139 14 10.8319499050543 3.16805009494572 140 16 12.9541145829509 3.04588541704908 141 14 13.5431603903175 0.456839609682518 142 14 10.9380498594701 3.06195014052989 143 14 12.616124856284 1.38387514371601 144 14 12.7138209289099 1.28617907109007 145 14 11.1554047943428 2.84459520565723 146 13 11.5182850992731 1.48171490072689 147 12 12.9107854491189 -0.910785449118931 148 13 10.8713753206961 2.12862467930388 149 19 13.2911283800703 5.70887161992968 150 9 11.1418458865119 -2.14184588651187 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.0705137263168394 0.141027452633679 0.92948627368316 10 0.109196336642232 0.218392673284464 0.890803663357768 11 0.122952243682022 0.245904487364043 0.877047756317978 12 0.387868257748136 0.775736515496271 0.612131742251864 13 0.293940891010821 0.587881782021642 0.706059108989179 14 0.227397372255748 0.454794744511497 0.772602627744252 15 0.18968633449798 0.37937266899596 0.81031366550202 16 0.158154676848559 0.316309353697119 0.84184532315144 17 0.122048434441702 0.244096868883404 0.877951565558298 18 0.250302058406765 0.500604116813531 0.749697941593235 19 0.455671351583178 0.911342703166356 0.544328648416822 20 0.709148693847357 0.581702612305286 0.290851306152643 21 0.64245813575641 0.71508372848718 0.35754186424359 22 0.575186337729082 0.849627324541837 0.424813662270918 23 0.581252292498691 0.837495415002619 0.418747707501309 24 0.509460282022067 0.981079435955867 0.490539717977933 25 0.470036577830923 0.940073155661846 0.529963422169077 26 0.54305420924409 0.913891581511821 0.456945790755911 27 0.541909477902455 0.91618104419509 0.458090522097545 28 0.53270411778307 0.934591764433861 0.467295882216930 29 0.502194243886607 0.995611512226785 0.497805756113393 30 0.470640619639616 0.941281239279232 0.529359380360384 31 0.755449928361818 0.489100143276364 0.244550071638182 32 0.788856130685619 0.422287738628762 0.211143869314381 33 0.763367827294141 0.473264345411718 0.236632172705859 34 0.733302552400212 0.533394895199575 0.266697447599788 35 0.692216226106573 0.615567547786855 0.307783773893427 36 0.651136989470399 0.697726021059201 0.348863010529601 37 0.667395517330827 0.665208965338346 0.332604482669173 38 0.76427806091949 0.471443878161019 0.235721939080509 39 0.741876067264013 0.516247865471973 0.258123932735987 40 0.888589947815658 0.222820104368685 0.111410052184342 41 0.860825591075811 0.278348817848377 0.139174408924188 42 0.846092387633954 0.307815224732092 0.153907612366046 43 0.851144442678452 0.297711114643096 0.148855557321548 44 0.860539379379138 0.278921241241724 0.139460620620862 45 0.834031592564668 0.331936814870665 0.165968407435332 46 0.801669045998961 0.396661908002077 0.198330954001039 47 0.945101109832878 0.109797780334244 0.0548988901671219 48 0.950701192585392 0.0985976148292154 0.0492988074146077 49 0.953635440165398 0.0927291196692047 0.0463645598346023 50 0.940577732054244 0.118844535891513 0.0594222679457564 51 0.966739149320707 0.0665217013585867 0.0332608506792934 52 0.976934812511357 0.0461303749772851 0.0230651874886426 53 0.97517728239125 0.0496454352174996 0.0248227176087498 54 0.979028159031268 0.0419436819374635 0.0209718409687318 55 0.972509222537568 0.0549815549248634 0.0274907774624317 56 0.967154062445693 0.0656918751086139 0.0328459375543069 57 0.95726902097442 0.085461958051159 0.0427309790255795 58 0.967592206981258 0.0648155860374846 0.0324077930187423 59 0.958568254969513 0.0828634900609738 0.0414317450304869 60 0.954317794029245 0.0913644119415094 0.0456822059707547 61 0.948675838878017 0.102648322243966 0.0513241611219831 62 0.94105536126136 0.117889277477281 0.0589446387386405 63 0.94434315687292 0.111313686254159 0.0556568431270794 64 0.929797489633644 0.140405020732712 0.0702025103663559 65 0.920801906136081 0.158396187727837 0.0791980938639185 66 0.921967386561514 0.156065226876972 0.078032613438486 67 0.903723881113924 0.192552237772153 0.0962761188860764 68 0.884077551863005 0.231844896273991 0.115922448136995 69 0.875229651168431 0.249540697663138 0.124770348831569 70 0.865564431666886 0.268871136666228 0.134435568333114 71 0.866671271266654 0.266657457466693 0.133328728733346 72 0.839051216863974 0.321897566272052 0.160948783136026 73 0.807968880061465 0.384062239877071 0.192031119938535 74 0.80149576537271 0.397008469254581 0.198504234627291 75 0.829809206491471 0.340381587017057 0.170190793508529 76 0.799360369836422 0.401279260327155 0.200639630163578 77 0.794034529664901 0.411930940670198 0.205965470335099 78 0.766440347481864 0.467119305036273 0.233559652518136 79 0.795114497088591 0.409771005822818 0.204885502911409 80 0.75933058319005 0.481338833619901 0.240669416809950 81 0.73141445049698 0.537171099006038 0.268585549503019 82 0.741468874312217 0.517062251375566 0.258531125687783 83 0.712215718461419 0.575568563077163 0.287784281538581 84 0.671188255870058 0.657623488259883 0.328811744129942 85 0.644770779427235 0.710458441145529 0.355229220572765 86 0.654868320876502 0.690263358246995 0.345131679123498 87 0.631937532591915 0.73612493481617 0.368062467408085 88 0.585334527550578 0.829330944898845 0.414665472449422 89 0.608605569852376 0.782788860295249 0.391394430147624 90 0.561670198746826 0.876659602506348 0.438329801253174 91 0.514428542037523 0.971142915924953 0.485571457962477 92 0.738293792233788 0.523412415532423 0.261706207766212 93 0.92557339649721 0.148853207005581 0.0744266035027906 94 0.91855375451825 0.162892490963501 0.0814462454817507 95 0.949712179135656 0.100575641728687 0.0502878208643437 96 0.93628564527533 0.127428709449339 0.0637143547246695 97 0.91837235505479 0.163255289890422 0.081627644945211 98 0.91351092540441 0.172978149191180 0.0864890745955902 99 0.90578876817484 0.188422463650318 0.0942112318251592 100 0.881975734891841 0.236048530216317 0.118024265108159 101 0.853813850928584 0.292372298142831 0.146186149071416 102 0.85478432606811 0.290431347863779 0.145215673931890 103 0.860669681698059 0.278660636603882 0.139330318301941 104 0.850258964308271 0.299482071383458 0.149741035691729 105 0.839852953666904 0.320294092666192 0.160147046333096 106 0.828264316539248 0.343471366921504 0.171735683460752 107 0.815536100174407 0.368927799651187 0.184463899825593 108 0.78724278051079 0.42551443897842 0.21275721948921 109 0.820061032371469 0.359877935257063 0.179938967628531 110 0.899407595936181 0.201184808127638 0.100592404063819 111 0.912559849425703 0.174880301148593 0.0874401505742965 112 0.888471555683161 0.223056888633677 0.111528444316839 113 0.857928824118323 0.284142351763354 0.142071175881677 114 0.898402481871502 0.203195036256996 0.101597518128498 115 0.9440948369482 0.111810326103599 0.0559051630517994 116 0.934223725145728 0.131552549708544 0.065776274854272 117 0.914315141252407 0.171369717495185 0.0856848587475927 118 0.892378174142852 0.215243651714297 0.107621825857148 119 0.873408518061051 0.253182963877898 0.126591481938949 120 0.837745136739123 0.324509726521754 0.162254863260877 121 0.797042304701199 0.405915390597602 0.202957695298801 122 0.767887642719232 0.464224714561536 0.232112357280768 123 0.712065338834029 0.575869322331943 0.287934661165971 124 0.656911175931975 0.68617764813605 0.343088824068025 125 0.606772208731098 0.786455582537804 0.393227791268902 126 0.541304248348867 0.917391503302267 0.458695751651133 127 0.469169644783022 0.938339289566044 0.530830355216978 128 0.682758101500745 0.63448379699851 0.317241898499255 129 0.725498436571231 0.549003126857537 0.274501563428769 130 0.72144125207727 0.55711749584546 0.27855874792273 131 0.650623132611241 0.698753734777518 0.349376867388759 132 0.86127867563159 0.277442648736819 0.138721324368409 133 0.85713781749422 0.285724365011560 0.142862182505780 134 0.866744743497585 0.26651051300483 0.133255256502415 135 0.808310113488068 0.383379773023864 0.191689886511932 136 0.792226042201866 0.415547915596268 0.207773957798134 137 0.72529361737199 0.549412765256021 0.274706382628010 138 0.790033541144007 0.419932917711985 0.209966458855993 139 0.73081908402237 0.538361831955261 0.269180915977631 140 0.595776421950923 0.808447156098153 0.404223578049077 141 0.46277076357373 0.92554152714746 0.53722923642627 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 3 0.0225563909774436 OK 10% type I error level 12 0.0902255639097744 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/10hjy31292961952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/10hjy31292961952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/1tija1292961952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/1tija1292961952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/2l9iv1292961952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/2l9iv1292961952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/3l9iv1292961952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/3l9iv1292961952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/4l9iv1292961952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/4l9iv1292961952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/5w0hx1292961952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/5w0hx1292961952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/6w0hx1292961952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/6w0hx1292961952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/77azi1292961952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/77azi1292961952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/8hjy31292961952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/8hjy31292961952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/9hjy31292961952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961886do7n9qb7u7g8hf9/9hjy31292961952.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 5 ; 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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by