deterministische trend -assignment (jonas poels)

*Unverified author*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 02 Dec 2010 18:51:02 +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/02/t1291315964x56g4tlfs2e0zma.htm/, Retrieved Thu, 02 Dec 2010 19:52:55 +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/02/t1291315964x56g4tlfs2e0zma.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 «

9 24 14 11 12 24 26 9 25 11 7 8 25 23 9 17 6 17 8 30 25 9 18 12 10 8 19 23 9 18 8 12 9 22 19 9 16 10 12 7 22 29 10 20 10 11 4 25 25 10 16 11 11 11 23 21 10 18 16 12 7 17 22 10 17 11 13 7 21 25 10 23 13 14 12 19 24 10 30 12 16 10 19 18 10 23 8 11 10 15 22 10 18 12 10 8 16 15 10 15 11 11 8 23 22 10 12 4 15 4 27 28 10 21 9 9 9 22 20 10 15 8 11 8 14 12 10 20 8 17 7 22 24 10 31 14 17 11 23 20 10 27 15 11 9 23 21 10 34 16 18 11 21 20 10 21 9 14 13 19 21 10 31 14 10 8 18 23 10 19 11 11 8 20 28 10 16 8 15 9 23 24 10 20 9 15 6 25 24 10 21 9 13 9 19 24 10 22 9 16 9 24 23 10 17 9 13 6 22 23 10 24 10 9 6 25 29 10 25 16 18 16 26 24 10 26 11 18 5 29 18 10 25 8 12 7 32 25 10 17 9 17 9 25 21 10 32 16 9 6 29 26 10 33 11 9 6 28 22 10 13 16 12 5 17 22 10 32 12 18 12 28 22 10 25 12 12 7 29 23 10 29 14 18 10 26 30 10 22 9 14 9 25 23 10 18 10 15 8 14 17 10 17 9 16 5 25 23 10 20 10 10 8 26 23 10 15 12 11 8 20 25 10 20 14 14 10 18 24 10 33 14 9 6 32 24 10 29 10 12 8 25 23 10 23 14 17 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Doubtsaboutactions[t] = + 4.09848461408936 + 0.32267807199523month[t] + 0.246895780354797ConcernoverMistakes[t] -0.109244278297377ParentalExpectations[t] + 0.151547174570636ParentalCriticism[t] -0.189756878633375PersonalStandards[t] + 0.113329495895667`Organization `[t] + 0.000994111036614308t + 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) 4.09848461408936 11.14169 0.3679 0.713499 0.35675 month 0.32267807199523 1.115152 0.2894 0.772704 0.386352 ConcernoverMistakes 0.246895780354797 0.040538 6.0905 0 0 ParentalExpectations -0.109244278297377 0.074902 -1.4585 0.146781 0.073391 ParentalCriticism 0.151547174570636 0.094178 1.6091 0.109673 0.054836 PersonalStandards -0.189756878633375 0.057396 -3.3061 0.001182 0.000591 `Organization ` 0.113329495895667 0.058093 1.9508 0.052929 0.026464 t 0.000994111036614308 0.004693 0.2118 0.832533 0.416267 Multiple Linear Regression - Regression Statistics Multiple R 0.490382703106539 R-squared 0.240475195506076 Adjusted R-squared 0.205265436357351 F-TEST (value) 6.82978814170002 F-TEST (DF numerator) 7 F-TEST (DF denominator) 151 p-value 4.7523723134546e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.49660665392697 Sum Squared Residuals 941.189762449296 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14 11.9383609412608 2.06163905873918 2 11 11.4872938812390 -0.487293881238981 3 6 7.69855356508792 -1.69855356508792 4 12 10.5718200777367 1.42817992226325 5 8 9.48328418726645 -1.48328418726645 6 10 9.82068734740888 0.179312652591124 7 10 9.76395678696258 0.236043213037420 8 11 9.76439377225854 1.23560622774146 9 16 10.7956172351207 5.20438276487926 10 11 10.0214322606587 0.978567739341315 11 13 12.4184769097510 0.581523090249037 12 12 12.9461816021611 -0.946181602161125 13 8 12.9774721403172 -4.97747214031722 14 12 10.5670739288329 1.43292607116709 15 11 9.1831447413438 1.81685525865620 16 4 7.32123516068448 -3.32123516068448 17 9 10.9996412635532 -1.99964126355324 18 8 9.76064402319735 -1.76064402319735 19 8 10.0310031133341 -2.03100311333405 20 14 12.7109646443399 1.28903535566007 21 15 12.1900764504960 2.80992354950398 22 16 13.7239096864469 2.27609031355308 23 9 11.7481733683644 -2.74817336836438 24 14 14.31378239371 -0.313782393709995 25 11 11.4299165844033 -0.429916584403258 26 8 9.38220479627382 -1.38220479627382 27 9 9.53662674775096 -0.536626747750962 28 9 11.5961879912493 -2.59618799124928 29 9 10.4542311586860 -1.45423115868602 30 9 9.47335143639563 -0.473351436395627 31 10 11.7502994625792 -1.75029946257920 32 16 11.7740582368889 4.22594176311113 33 11 9.10568159672916 1.89431840327084 34 8 10.0423757817061 -2.04237578170606 35 9 8.70005677440964 0.299943225590362 36 16 12.6314202583801 3.36857974161985 37 11 12.6157490448223 -1.61574904482227 38 16 9.2868732042673 6.7131267957327 39 12 12.2969260292881 -0.296926029288116 40 12 10.3909520920345 1.60904790796547 41 14 12.5412822855878 1.45871771441223 42 9 10.4958862801234 -1.49588628012339 43 10 10.6558545064659 -0.655854506465923 44 9 8.43871834554534 0.561281654454665 45 10 10.1007501125091 -0.100750112509135 46 12 10.1232213070660 1.87677869293403 47 14 11.6002400954968 2.39975990450321 48 14 12.0943157434829 1.90568425651714 49 10 12.2980569018874 -2.29805690188738 50 14 9.89324877294636 4.10675122705364 51 16 12.2107222858577 3.78927771414227 52 9 10.4575426615236 -1.45754266152363 53 10 11.5954031954859 -1.59540319548588 54 6 9.01244269724991 -3.01244269724991 55 8 11.2759876621775 -3.2759876621775 56 13 12.4340621920867 0.565937807913331 57 10 10.8413004326460 -0.841300432646013 58 8 8.89909901396103 -0.899099013961033 59 7 9.18672055480479 -2.18672055480479 60 15 9.79033033336827 5.20966966663173 61 9 9.91519991506454 -0.915199915064538 62 10 10.2265618496154 -0.226561849615389 63 12 10.2293852797983 1.77061472020166 64 13 10.4887256458230 2.51127435417698 65 10 8.4160868781178 1.58391312188221 66 11 11.8428133001595 -0.842813300159469 67 8 13.6015868713492 -5.6015868713492 68 9 9.13257018067963 -0.132570180679627 69 13 8.6606179865688 4.3393820134312 70 11 10.4327744805518 0.567225519448176 71 8 12.7569392870387 -4.75693928703867 72 9 10.7871768270365 -1.78717682703654 73 9 12.3531004172197 -3.35310041721968 74 15 12.5015992889971 2.49840071100287 75 9 11.1790482555329 -2.17904825553289 76 10 11.5656486191227 -1.56564861912273 77 14 8.94890740412202 5.05109259587798 78 12 10.9673786288825 1.03262137111750 79 12 11.1369017186734 0.86309828132665 80 11 11.5563104403333 -0.556310440333313 81 14 11.4956618273568 2.50433817264318 82 6 11.5822967772443 -5.58229677724427 83 12 11.2726579319011 0.7273420680989 84 8 10.0902354968231 -2.09023549682314 85 14 12.4266650619372 1.57333493806276 86 11 10.8332623687061 0.166737631293865 87 10 9.93884225028811 0.0611577497118848 88 14 10.2675098748811 3.73249012511893 89 12 12.0703882440690 -0.0703882440690459 90 10 11.0097065135456 -1.00970651354563 91 14 13.0120181927287 0.987981807271296 92 5 9.03553738146481 -4.03553738146481 93 11 10.5232989831333 0.476701016866721 94 10 10.2129764002312 -0.212976400231170 95 9 11.4995123315785 -2.49951233157849 96 10 11.4732522121774 -1.47325221217737 97 16 13.7583109166552 2.24168908334482 98 13 12.8298598939219 0.170140106078072 99 9 10.8288966342418 -1.82889663424179 100 10 11.3862219908247 -1.38622199082471 101 10 10.9965922733871 -0.996592273387102 102 7 9.50299179272843 -2.50299179272843 103 9 9.82353919762822 -0.823539197628221 104 8 10.2579389578854 -2.25793895788538 105 14 12.9134524337885 1.08654756621147 106 14 11.6331599260423 2.36684007395766 107 8 11.0958116562564 -3.09581165625638 108 9 11.5479859267524 -2.54798592675245 109 14 11.8396407755936 2.16035922440638 110 14 10.7637637562644 3.23623624373556 111 8 9.89181980729454 -1.89181980729454 112 8 13.7246293482616 -5.72462934826161 113 8 11.0186565149427 -3.01865651494274 114 7 8.59672109006802 -1.59672109006802 115 6 7.55834374557591 -1.55834374557591 116 8 9.46348840514807 -1.46348840514807 117 6 8.3305117942813 -2.3305117942813 118 11 9.92400609449128 1.07599390550872 119 14 11.8864760245055 2.11352397549451 120 11 11.1211527618446 -0.121152761844623 121 11 12.0065792151989 -1.00657921519891 122 11 9.24039168546497 1.75960831453503 123 14 10.4261856396478 3.57381436035225 124 8 10.4438890102675 -2.44388901026750 125 20 11.5552817393326 8.44471826066744 126 11 10.3653475515601 0.634652448439872 127 8 9.21395662788415 -1.21395662788415 128 11 10.7935192952045 0.206480704795504 129 10 10.6938068211101 -0.693806821110146 130 14 13.5123094087277 0.487690591272337 131 11 10.6198101889757 0.380189811024251 132 9 10.6520758892231 -1.65207588922305 133 9 9.79291786424464 -0.792917864244645 134 8 10.2227534824305 -2.22275348243046 135 10 12.1161961158982 -2.11619611589821 136 13 10.7699109692416 2.23008903075835 137 13 10.1651435241787 2.83485647582134 138 12 9.33871465209338 2.66128534790662 139 8 10.4486817159249 -2.44868171592491 140 13 11.0806210853237 1.91937891467632 141 14 12.4752392012792 1.52476079872079 142 12 11.7404095124673 0.259590487532718 143 14 10.9504032607241 3.04959673927592 144 15 11.3812680857315 3.61873191426849 145 13 10.5929297121601 2.40707028783991 146 16 11.934451340224 4.06554865977601 147 9 12.0412791436863 -3.04127914368626 148 9 10.5511480018923 -1.55114800189227 149 9 11.0524282560510 -2.05242825605105 150 8 11.3635933521282 -3.36359335212824 151 7 10.1350992827678 -3.13509928276784 152 16 12.0196638196434 3.98033618035661 153 11 13.4097102544819 -2.40971025448191 154 9 10.0445020094602 -1.04450200946019 155 11 9.91492068599586 1.08507931400414 156 9 9.94122107111782 -0.94122107111782 157 14 12.5708557663133 1.42914423368665 158 13 11.1094471966484 1.89055280335164 159 16 14.526934666488 1.47306533351201 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.0858038157986433 0.171607631597287 0.914196184201357 12 0.0311388328683758 0.0622776657367515 0.968861167131624 13 0.304625697495615 0.609251394991229 0.695374302504385 14 0.492329737864456 0.984659475728911 0.507670262135544 15 0.652614745536411 0.694770508927177 0.347385254463589 16 0.581383240328434 0.837233519343133 0.418616759671566 17 0.488605910463526 0.977211820927051 0.511394089536474 18 0.415433694355204 0.830867388710409 0.584566305644796 19 0.381673344697806 0.763346689395611 0.618326655302194 20 0.569146547006844 0.861706905986313 0.430853452993157 21 0.655379203417038 0.689241593165924 0.344620796582962 22 0.640845941785107 0.718308116429785 0.359154058214893 23 0.608419781860623 0.783160436278754 0.391580218139377 24 0.534840639600807 0.930318720798385 0.465159360399193 25 0.469752142047923 0.939504284095846 0.530247857952077 26 0.403081457747574 0.806162915495148 0.596918542252426 27 0.342195188723639 0.684390377447278 0.657804811276361 28 0.299747187925589 0.599494375851178 0.700252812074411 29 0.244870824682181 0.489741649364361 0.75512917531782 30 0.207910820784677 0.415821641569353 0.792089179215324 31 0.170361431949055 0.340722863898111 0.829638568050945 32 0.283067245674559 0.566134491349119 0.71693275432544 33 0.270316637094823 0.540633274189646 0.729683362905177 34 0.2571493432061 0.5142986864122 0.7428506567939 35 0.216765227922384 0.433530455844767 0.783234772077616 36 0.251986646250178 0.503973292500357 0.748013353749822 37 0.237929554025637 0.475859108051273 0.762070445974363 38 0.650441650958342 0.699116698083317 0.349558349041658 39 0.6017083986221 0.7965832027558 0.3982916013779 40 0.56110031760819 0.87779936478362 0.43889968239181 41 0.51521617013444 0.96956765973112 0.48478382986556 42 0.492396983288677 0.984793966577355 0.507603016711323 43 0.450306919640216 0.900613839280431 0.549693080359784 44 0.397607376291125 0.79521475258225 0.602392623708875 45 0.346319223754877 0.692638447509754 0.653680776245123 46 0.317079734035208 0.634159468070417 0.682920265964792 47 0.295981527330942 0.591963054661884 0.704018472669058 48 0.268117089910056 0.536234179820112 0.731882910089944 49 0.282592035349496 0.565184070698993 0.717407964650504 50 0.336775896500894 0.673551793001789 0.663224103499106 51 0.370792769777342 0.741585539554683 0.629207230222658 52 0.361104707823912 0.722209415647825 0.638895292176088 53 0.347412812569508 0.694825625139015 0.652587187430492 54 0.37523922403124 0.75047844806248 0.62476077596876 55 0.394336161800895 0.78867232360179 0.605663838199105 56 0.349041081226948 0.698082162453896 0.650958918773052 57 0.308474979890596 0.616949959781192 0.691525020109404 58 0.269839198752162 0.539678397504324 0.730160801247838 59 0.253607093667186 0.507214187334373 0.746392906332814 60 0.402506486830677 0.805012973661354 0.597493513169323 61 0.358464436891628 0.716928873783257 0.641535563108372 62 0.315873446267091 0.631746892534183 0.684126553732909 63 0.293859771920228 0.587719543840457 0.706140228079772 64 0.302982330926661 0.605964661853323 0.697017669073339 65 0.277194667858765 0.554389335717531 0.722805332141235 66 0.245183598557638 0.490367197115275 0.754816401442362 67 0.428811311698767 0.857622623397535 0.571188688301233 68 0.382745278825644 0.765490557651289 0.617254721174356 69 0.479903355215207 0.959806710430414 0.520096644784793 70 0.437478221857139 0.874956443714277 0.562521778142861 71 0.550596682282998 0.898806635434003 0.449403317717002 72 0.522113926781177 0.955772146437646 0.477886073218823 73 0.542069975804689 0.915860048390621 0.457930024195311 74 0.553643363353635 0.89271327329273 0.446356636646365 75 0.532762122010308 0.934475755979383 0.467237877989692 76 0.497792059069485 0.99558411813897 0.502207940930515 77 0.663497945339603 0.673004109320795 0.336502054660397 78 0.632510322134065 0.73497935573187 0.367489677865935 79 0.596079587356567 0.807840825286866 0.403920412643433 80 0.550056557404442 0.899886885191116 0.449943442595558 81 0.559925938025181 0.880148123949637 0.440074061974819 82 0.711607074825698 0.576785850348605 0.288392925174302 83 0.677932237775239 0.644135524449522 0.322067762224761 84 0.653435467736096 0.693129064527809 0.346564532263904 85 0.630926994169835 0.73814601166033 0.369073005830165 86 0.589880483893530 0.820239032212941 0.410119516106470 87 0.546337990976118 0.907324018047765 0.453662009023882 88 0.632733252950353 0.734533494099294 0.367266747049647 89 0.587448140875334 0.825103718249331 0.412551859124666 90 0.543968537860284 0.912062924279433 0.456031462139716 91 0.511257639908836 0.977484720182327 0.488742360091164 92 0.554485158738186 0.891029682523628 0.445514841261814 93 0.517222957388731 0.965554085222537 0.482777042611269 94 0.472653710734203 0.945307421468407 0.527346289265797 95 0.454513496408202 0.909026992816403 0.545486503591798 96 0.412435755054992 0.824871510109984 0.587564244945008 97 0.413720410321425 0.82744082064285 0.586279589678575 98 0.369891718757121 0.739783437514241 0.63010828124288 99 0.335607354931774 0.671214709863549 0.664392645068226 100 0.296577998195953 0.593155996391906 0.703422001804047 101 0.256453648091754 0.512907296183509 0.743546351908245 102 0.237556104187566 0.475112208375132 0.762443895812434 103 0.200942998485039 0.401885996970078 0.799057001514961 104 0.181595477622871 0.363190955245742 0.818404522377129 105 0.158707550647705 0.31741510129541 0.841292449352295 106 0.167046775198965 0.334093550397929 0.832953224801035 107 0.166380396706854 0.332760793413708 0.833619603293146 108 0.158847598483307 0.317695196966613 0.841152401516693 109 0.156399077164869 0.312798154329737 0.843600922835131 110 0.199630460600614 0.399260921201228 0.800369539399386 111 0.171521914328051 0.343043828656101 0.82847808567195 112 0.345764320114814 0.691528640229627 0.654235679885186 113 0.405465460602408 0.810930921204816 0.594534539397592 114 0.374330180067252 0.748660360134504 0.625669819932748 115 0.367888738057408 0.735777476114817 0.632111261942592 116 0.327246972285949 0.654493944571898 0.672753027714051 117 0.329008673321287 0.658017346642574 0.670991326678713 118 0.287877595467371 0.575755190934742 0.712122404532629 119 0.260283096540282 0.520566193080563 0.739716903459718 120 0.217012660512035 0.434025321024071 0.782987339487965 121 0.203100683036968 0.406201366073935 0.796899316963032 122 0.181240976574765 0.36248195314953 0.818759023425235 123 0.185090443596284 0.370180887192569 0.814909556403716 124 0.200209067941855 0.400418135883709 0.799790932058145 125 0.723947485280984 0.552105029438032 0.276052514719016 126 0.680929692253162 0.638140615493677 0.319070307746838 127 0.625275071556268 0.749449856887465 0.374724928443732 128 0.562348010854704 0.875303978290593 0.437651989145296 129 0.497716602182154 0.995433204364307 0.502283397817846 130 0.43431998970816 0.86863997941632 0.56568001029184 131 0.379134114736901 0.758268229473801 0.6208658852631 132 0.326860236335412 0.653720472670824 0.673139763664588 133 0.269520940495751 0.539041880991502 0.730479059504249 134 0.238643713635136 0.477287427270273 0.761356286364864 135 0.238829350733863 0.477658701467726 0.761170649266137 136 0.197887620191866 0.395775240383731 0.802112379808134 137 0.203971828744407 0.407943657488815 0.796028171255593 138 0.208335899438686 0.416671798877371 0.791664100561314 139 0.232037630659643 0.464075261319286 0.767962369340357 140 0.177179864214275 0.35435972842855 0.822820135785725 141 0.129925000857304 0.259850001714607 0.870074999142696 142 0.094955212459136 0.189910424918272 0.905044787540864 143 0.0966968206972833 0.193393641394567 0.903303179302717 144 0.0869216071763365 0.173843214352673 0.913078392823663 145 0.0959863740849664 0.191972748169933 0.904013625915034 146 0.305867826794157 0.611735653588314 0.694132173205843 147 0.202998205543034 0.405996411086068 0.797001794456966 148 0.122980635953368 0.245961271906737 0.877019364046631 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 1 0.0072463768115942 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/10lnvs1291315851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/10lnvs1291315851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/1w4yh1291315851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/1w4yh1291315851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/2w4yh1291315851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/2w4yh1291315851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/3pegk1291315851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/3pegk1291315851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/4pegk1291315851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/4pegk1291315851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/5pegk1291315851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/5pegk1291315851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/605x51291315851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/605x51291315851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/7sww71291315851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/7sww71291315851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/8sww71291315851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/8sww71291315851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/9sww71291315851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291315964x56g4tlfs2e0zma/9sww71291315851.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = 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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