WS7 first regression model

*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: Mon, 22 Nov 2010 19:28:14 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/22/t1290454053e7ii8prudsyfhy2.htm/, Retrieved Mon, 22 Nov 2010 20:27:34 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/22/t1290454053e7ii8prudsyfhy2.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 «

41 25 15 9 3 38 25 15 9 4 37 19 14 9 4 36 18 10 14 2 42 18 10 8 4 44 23 9 14 4 40 23 18 15 3 43 25 14 9 4 40 23 11 11 4 45 24 11 14 4 47 32 9 14 4 45 30 17 6 5 45 32 21 10 4 40 24 16 9 4 49 17 14 14 4 48 30 24 8 5 44 25 7 11 4 29 25 9 10 4 42 26 18 16 4 44 23 11 11 5 35 19 13 9 3 32 25 13 11 5 32 25 13 11 5 41 35 18 7 4 29 19 14 13 2 38 20 12 10 4 41 21 12 9 4 38 21 9 9 4 24 23 11 15 3 34 24 8 13 2 38 23 5 16 2 37 19 10 12 3 46 17 11 6 5 48 27 15 4 5 42 27 16 12 4 46 25 12 10 4 43 18 14 14 5 38 22 13 9 4 39 26 10 10 4 34 26 18 14 4 39 23 17 14 4 35 16 12 10 2 41 27 13 9 3 40 25 13 14 3 43 14 11 8 4 37 19 13 9 2 41 20 12 8 4 46 26 12 10 4 26 16 12 9 3 41 18 12 9 3 37 22 9 9 3 39 25 17 9 4 44 29 18 11 5 39 21 7 15 2 36 22 17 8 4 38 22 12 10 2 38 32 12 8 0 38 23 9 14 4 32 31 9 11 4 33 18 13 10 3 46 23 10 12 4 42 24 12 9 4 42 19 10 13 2 43 26 11 14 4 41 14 13 15 2 49 20 6 8 4 45 22 7 7 3 39 24 13 10 4 45 25 11 10 5 31 21 18 13 3 30 21 18 13 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 12 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation StudyForCareer[t] = + 32.7983458450465 + 0.174337617831962PersonalStandards[t] + 0.0374239004797307ParentalExpectations[t] -0.228380669837428Doubts[t] + 1.37570543194539LeaderPreference[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) 32.7983458450465 3.288189 9.9746 0 0 PersonalStandards 0.174337617831962 0.105063 1.6594 0.099315 0.049657 ParentalExpectations 0.0374239004797307 0.131469 0.2847 0.776332 0.388166 Doubts -0.228380669837428 0.156687 -1.4576 0.147233 0.073616 LeaderPreference 1.37570543194539 0.491287 2.8002 0.00584 0.00292 Multiple Linear Regression - Regression Statistics Multiple R 0.365715483024238 R-squared 0.133747814523651 Adjusted R-squared 0.108639055524337 F-TEST (value) 5.32673934730518 F-TEST (DF numerator) 4 F-TEST (DF denominator) 138 p-value 0.000509654484215694 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.01878586322461 Sum Squared Residuals 3475.97319264464 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 41 39.7898350653408 1.2101649346592 2 38 41.1655404972862 -3.16554049728619 3 37 40.0820908898147 -3.08209088981469 4 36 35.8647434569859 0.135256543014120 5 42 39.9864383399012 2.01356166009877 6 44 39.4504185095567 4.54958149044325 7 40 38.1831475120915 1.8168524879085 8 43 41.1281165968065 1.87188340319354 9 40 40.2104083200285 -0.210408320028492 10 45 39.6996039283482 5.30039607165183 11 47 41.0194570700444 5.98054292995559 12 45 44.1729238288631 0.827076171136855 13 45 42.3820665551509 2.61793344484911 14 40 41.028626779934 -1.02862677993396 15 49 38.5915123049636 10.4084876950364 16 48 43.9781297925464 4.0218702074536 17 44 40.4093879537735 3.59061204622651 18 29 40.7126164245704 -11.7126164245704 19 42 39.8534851276954 2.14651487230465 20 44 41.5861137519739 2.41388624802611 21 35 38.6689615573896 -3.66896155738957 22 32 42.0096367885973 -10.0096367885973 23 32 42.0096367885973 -10.0096367885973 24 41 43.4779497167199 -2.47794971671986 25 29 36.4171573465742 -7.41715734657419 26 38 39.9532000368498 -1.95320003684976 27 41 40.3559183245192 0.644081675480845 28 38 40.24364662308 -2.24364662307996 29 24 37.9211802087334 -13.9211802087334 30 34 37.0643020328556 -3.06430203285562 31 38 36.0925507040722 1.90744929592782 32 37 37.8715478464381 -0.871547846438091 33 46 41.6819913941693 4.31800860583074 34 48 44.0318245140827 3.96817548591735 35 42 40.8664976239176 1.13350237608243 36 46 40.8248881260096 5.17511187399043 37 43 40.141555354741 2.85844464525902 38 38 40.5676798428308 -2.56767984283085 39 39 40.9243779428821 -1.92437794288208 40 34 40.3102464673702 -6.31024646737021 41 39 39.7498097133946 -0.749809713394591 42 35 36.5044387016311 -1.50443870163113 43 41 40.0636625000453 0.936337499954736 44 40 38.5730839151942 1.4269160848058 45 43 39.3265117690531 3.67348823094688 46 37 37.2932561254442 -0.293256125444175 47 41 40.4099613765246 0.590038623475379 48 46 40.9992257438415 5.00077425615846 49 26 38.1085248034139 -12.1085248034140 50 41 38.4572000390779 2.54279996092212 51 37 39.0422788089665 -2.04227880896653 52 39 41.2403882982457 -2.24038829824566 53 44 42.8941067623238 1.10589323767623 54 39 36.0471039392051 2.95289606079485 55 36 40.9457561145872 -4.9457561145872 56 38 37.5504644086229 0.449535591377098 57 38 36.9991910627266 1.00080893727341 58 38 39.4504185095567 -1.45041850955675 59 32 41.5302614617247 -9.53026146172473 60 33 38.2662432697202 -5.26624326972018 61 46 39.9446037497113 6.05539625028867 62 42 40.8789311780150 1.12106882198496 63 42 36.2674617446553 5.73253825534473 64 43 40.0482791640121 2.95172083598791 65 41 35.0512840172598 5.9487159827402 66 49 40.1854179736462 8.81458202635376 67 45 39.4241923476819 5.57580765231807 68 39 40.6879744086573 -1.68797440865734 69 45 42.1631696574752 2.83683034252476 70 31 38.2912336161024 -7.29123361610243 71 30 38.2912336161024 -8.29123361610243 72 45 41.0072486082288 3.99275139177116 73 48 42.3707455783587 5.62925442164133 74 28 38.1679892683402 -10.1679892683402 75 35 37.5670835601486 -2.56708356014864 76 38 40.5884845917848 -2.58848459178484 77 39 40.3847459378605 -1.38474593786045 78 40 40.2023854556412 -0.202385455641188 79 38 38.8321014366166 -0.832101436616619 80 42 38.3547604402697 3.64523955973032 81 36 35.3084923004385 0.691507699561477 82 49 43.6617711067136 5.33822889328639 83 41 40.5590835556924 0.440916444307584 84 18 35.9728295609968 -17.9728295609968 85 36 38.0833093647498 -2.08330936474978 86 42 38.7227795171131 3.27722048288688 87 41 42.5786698297113 -1.57866982971132 88 43 39.936580885324 3.06341911467597 89 46 39.6695404995143 6.3304595004857 90 37 40.7212127117088 -3.72121271170881 91 38 39.0882989965847 -1.08829899658469 92 43 40.6134749381671 2.38652506183290 93 41 42.8070504995487 -1.80705049954875 94 35 34.5939492548338 0.406050745166188 95 42 39.429962091072 2.57003790892803 96 36 39.6873954665326 -3.68739546653261 97 35 41.083905647432 -6.083905647432 98 33 36.2680351674064 -3.2680351674064 99 36 38.1249188626578 -2.12491886265777 100 48 39.3639356695329 8.63606433046715 101 41 39.9532000368498 1.04679996315024 102 47 38.2873963491434 8.71260365085661 103 41 39.0384415420075 1.96155845799251 104 31 42.1467755982314 -11.1467755982314 105 36 41.115683042709 -5.11568304270899 106 46 41.2403882982457 4.75961170175434 107 39 38.0599031643293 0.94009683567069 108 44 40.6965706957958 3.30342930420422 109 43 37.0144445782784 5.98555542172159 110 32 41.0654772576626 -9.06547725766257 111 40 40.4694258414509 -0.469425841450911 112 40 39.1091037455387 0.890896254461311 113 46 37.1035351715790 8.89646482842096 114 45 39.2085935624112 5.79140643758881 115 39 40.6837888112291 -1.68378881122908 116 44 41.6469438528741 2.35305614712591 117 35 39.7414385185381 -4.74143851853807 118 38 37.7138293801319 0.286170619868135 119 38 36.4503956496257 1.54960435037434 120 36 37.3399387058038 -1.33993870580377 121 42 38.0544817514085 3.94551824859152 122 39 39.7704912241613 -0.770491224161283 123 41 42.8899211648955 -1.88992116489551 124 41 39.3595249798227 1.64047502017732 125 47 38.7105710552976 8.28942894470244 126 39 38.6105078156739 0.38949218432607 127 40 39.4717966812619 0.528203318738132 128 44 40.2109817427796 3.78901825722038 129 42 39.5008493868851 2.49915061311492 130 35 38.4988095369859 -3.49880953698587 131 46 42.5534543910472 3.44654560895285 132 43 37.6265480250749 5.37345197492507 133 40 40.0396828768737 -0.0396828768736622 134 44 39.9654084986653 4.03459150133467 135 37 40.4595937388199 -3.45959373881991 136 46 40.6671696597033 5.33283034029665 137 39 37.1685508699075 1.83144913009249 138 40 38.241027831056 1.75897216894399 139 37 38.942788992094 -1.94278899209403 140 29 39.5006242946032 -10.5006242946032 141 33 37.8703120309455 -4.87031203094553 142 35 39.9490144394215 -4.94901443942151 143 42 35.6867936644768 6.31320633552321 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.20569609932454 0.41139219864908 0.79430390067546 9 0.121640829292750 0.243281658585499 0.87835917070725 10 0.0667930002726221 0.133586000545244 0.933206999727378 11 0.0315447589391750 0.0630895178783501 0.968455241060825 12 0.0140102291448438 0.0280204582896875 0.985989770855156 13 0.00666216294255826 0.0133243258851165 0.993337837057442 14 0.00274139446716000 0.00548278893431999 0.99725860553284 15 0.0282107566452222 0.0564215132904445 0.971789243354778 16 0.0164977695155477 0.0329955390310953 0.983502230484452 17 0.00929135592084342 0.0185827118416868 0.990708644079157 18 0.255029770747557 0.510059541495113 0.744970229252443 19 0.278828170611487 0.557656341222975 0.721171829388513 20 0.217753951803760 0.435507903607521 0.78224604819624 21 0.170123790053772 0.340247580107545 0.829876209946228 22 0.52561464929379 0.94877070141242 0.47438535070621 23 0.729309336976603 0.541381326046793 0.270690663023397 24 0.673896907161858 0.652206185676284 0.326103092838142 25 0.761537961007974 0.476924077984052 0.238462038992026 26 0.708695561715563 0.582608876568874 0.291304438284437 27 0.660889420097674 0.678221159804651 0.339110579902326 28 0.601799677375223 0.796400645249553 0.398200322624777 29 0.895904988991119 0.208190022017762 0.104095011008881 30 0.869155530786472 0.261688938427056 0.130844469213528 31 0.850674639716314 0.298650720567372 0.149325360283686 32 0.813825565929754 0.372348868140492 0.186174434070246 33 0.814062555500532 0.371874888998937 0.185937444499468 34 0.805657362013399 0.388685275973202 0.194342637986601 35 0.764467626767877 0.471064746464246 0.235532373232123 36 0.768894389486124 0.462211221027752 0.231105610513876 37 0.728718561739882 0.542562876520237 0.271281438260118 38 0.689363355375915 0.621273289248169 0.310636644624085 39 0.64259651631462 0.714806967370761 0.357403483685380 40 0.679662891142391 0.640674217715218 0.320337108857609 41 0.630371266955897 0.739257466088207 0.369628733044103 42 0.580634608629585 0.83873078274083 0.419365391370415 43 0.536729761436859 0.926540477126281 0.463270238563141 44 0.491473320181855 0.98294664036371 0.508526679818145 45 0.466138507571625 0.93227701514325 0.533861492428375 46 0.416530548675527 0.833061097351055 0.583469451324473 47 0.365828802528282 0.731657605056563 0.634171197471718 48 0.367590480334007 0.735180960668014 0.632409519665993 49 0.579874753862139 0.840250492275722 0.420125246137861 50 0.55199407413055 0.8960118517389 0.44800592586945 51 0.505639385431425 0.98872122913715 0.494360614568575 52 0.462437073860794 0.924874147721588 0.537562926139206 53 0.413451318607395 0.82690263721479 0.586548681392605 54 0.392043890261153 0.784087780522307 0.607956109738847 55 0.38287293675074 0.76574587350148 0.61712706324926 56 0.342576496534445 0.685152993068891 0.657423503465555 57 0.311299065281302 0.622598130562603 0.688700934718699 58 0.271965930946886 0.543931861893773 0.728034069053114 59 0.397711514000532 0.795423028001064 0.602288485999468 60 0.395841210041014 0.791682420082028 0.604158789958986 61 0.41843261307624 0.83686522615248 0.58156738692376 62 0.373279229301257 0.746558458602514 0.626720770698743 63 0.391408543366494 0.782817086732989 0.608591456633505 64 0.358589442388606 0.717178884777212 0.641410557611394 65 0.377184234458326 0.754368468916651 0.622815765541674 66 0.476321001923766 0.952642003847532 0.523678998076234 67 0.483164945521744 0.966329891043489 0.516835054478256 68 0.440045115752154 0.880090231504308 0.559954884247846 69 0.405740365092623 0.811480730185246 0.594259634907377 70 0.455607419728473 0.911214839456946 0.544392580271527 71 0.539727499138264 0.920545001723472 0.460272500861736 72 0.521890081334246 0.956219837331508 0.478109918665754 73 0.541138864164847 0.917722271670305 0.458861135835152 74 0.679312005831201 0.641375988337598 0.320687994168799 75 0.645487115982475 0.70902576803505 0.354512884017525 76 0.612842503973591 0.774314992052817 0.387157496026409 77 0.568015199186862 0.863969601626276 0.431984800813138 78 0.519647641360737 0.960704717278525 0.480352358639263 79 0.472466225739499 0.944932451478999 0.5275337742605 80 0.457677151253960 0.915354302507921 0.54232284874604 81 0.413450601522614 0.826901203045229 0.586549398477386 82 0.458661791211851 0.917323582423702 0.541338208788149 83 0.414041658624579 0.828083317249158 0.585958341375421 84 0.930635691846233 0.138728616307533 0.0693643081537666 85 0.917335345062322 0.165329309875356 0.082664654937678 86 0.905401764285274 0.189196471429452 0.094598235714726 87 0.883129043462344 0.233741913075312 0.116870956537656 88 0.868569734383122 0.262860531233756 0.131430265616878 89 0.884077803622432 0.231844392755137 0.115922196377568 90 0.86776292255654 0.264474154886920 0.132237077443460 91 0.8407516617468 0.3184966765064 0.1592483382532 92 0.813904180960628 0.372191638078743 0.186095819039372 93 0.779055215458174 0.441889569083652 0.220944784541826 94 0.743405520337671 0.513188959324657 0.256594479662329 95 0.707398879989082 0.585202240021835 0.292601120010918 96 0.683341483023322 0.633317033953356 0.316658516976678 97 0.700470876382504 0.599058247234992 0.299529123617496 98 0.749065865712429 0.501868268575142 0.250934134287571 99 0.73891213522459 0.522175729550819 0.261087864775410 100 0.815781267916886 0.368437464166229 0.184218732083114 101 0.779092881564418 0.441814236871164 0.220907118435582 102 0.798635349294715 0.40272930141057 0.201364650705285 103 0.759389836035727 0.481220327928547 0.240610163964273 104 0.858074406410861 0.283851187178278 0.141925593589139 105 0.866354437301629 0.267291125396743 0.133645562698371 106 0.86132432240618 0.277351355187641 0.138675677593821 107 0.826664525282621 0.346670949434757 0.173335474717379 108 0.80412091294143 0.391758174117141 0.195879087058571 109 0.809099423949612 0.381801152100776 0.190900576050388 110 0.877813362222292 0.244373275555416 0.122186637777708 111 0.843361091697746 0.313277816604508 0.156638908302254 112 0.802783162349905 0.394433675300190 0.197216837650095 113 0.87027131815881 0.25945736368238 0.12972868184119 114 0.874397180394518 0.251205639210964 0.125602819605482 115 0.83956674356422 0.320866512871559 0.160433256435779 116 0.81407857242034 0.371842855159319 0.185921427579660 117 0.817689589976894 0.364620820046212 0.182310410023106 118 0.767501088111567 0.464997823776866 0.232498911888433 119 0.709916842589217 0.580166314821566 0.290083157410783 120 0.658858473597584 0.682283052804832 0.341141526402416 121 0.610528433904082 0.778943132191836 0.389471566095918 122 0.538384177099891 0.923231645800219 0.461615822900109 123 0.464775958233355 0.92955191646671 0.535224041766645 124 0.387960876193185 0.77592175238637 0.612039123806815 125 0.508857154275379 0.982285691449243 0.491142845724621 126 0.426067426507824 0.852134853015648 0.573932573492176 127 0.342122922162054 0.684245844324109 0.657877077837945 128 0.332515011331952 0.665030022663904 0.667484988668048 129 0.263159256719035 0.526318513438069 0.736840743280965 130 0.197952883415036 0.395905766830071 0.802047116584964 131 0.327804315889824 0.655608631779648 0.672195684110176 132 0.249597574529497 0.499195149058994 0.750402425470503 133 0.223368800209415 0.446737600418831 0.776631199790584 134 0.194484117829710 0.388968235659420 0.80551588217029 135 0.119511060150797 0.239022120301595 0.880488939849203 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0078125 OK 5% type I error level 5 0.0390625 OK 10% type I error level 7 0.0546875 OK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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Software written by Ed van Stee & Patrick Wessa

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