MR personal standards

*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: Sun, 12 Dec 2010 15:59:30 +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/12/t12921694744zq2cief76mp0m8.htm/, Retrieved Sun, 12 Dec 2010 16:58:04 +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/12/t12921694744zq2cief76mp0m8.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1 26 9 15 6 25 25 13 1 20 9 15 6 25 24 16 1 21 9 14 13 19 21 19 0 31 14 10 8 18 23 15 1 21 8 10 7 18 17 14 1 18 8 12 9 22 19 13 1 26 11 18 5 29 18 19 1 22 10 12 8 26 27 15 1 22 9 14 9 25 23 14 1 29 15 18 11 23 23 15 0 15 14 9 8 23 29 16 1 16 11 11 11 23 21 16 0 24 14 11 12 24 26 16 1 17 6 17 8 30 25 17 0 19 20 8 7 19 25 15 0 22 9 16 9 24 23 15 1 31 10 21 12 32 26 20 0 28 8 24 20 30 20 18 1 38 11 21 7 29 29 16 0 26 14 14 8 17 24 16 1 25 11 7 8 25 23 19 1 25 16 18 16 26 24 16 0 29 14 18 10 26 30 17 1 28 11 13 6 25 22 17 0 15 11 11 8 23 22 16 1 18 12 13 9 21 13 15 0 21 9 13 9 19 24 14 1 25 7 18 11 35 17 15 0 23 13 14 12 19 24 12 1 23 10 12 8 20 21 14 1 19 9 9 7 21 23 16 0 18 9 12 8 21 24 14 0 18 13 8 9 24 24 7 0 26 16 5 4 23 24 10 0 18 12 10 8 19 23 14 1 18 6 11 8 17 26 16 0 28 14 11 8 24 24 16 0 17 14 12 6 15 21 16 1 29 10 12 8 25 23 14 0 12 4 15 4 27 28 20 1 28 12 16 14 27 22 14 1 20 14 14 10 18 24 11 1 17 9 17 9 25 21 15 1 17 9 13 6 22 23 16 0 20 10 10 8 26 23 14 1 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Personal_standards[t] = + 6.05544463046083 + 1.08554008173790Gender[t] + 0.302404041874856Concern_mistakes[t] -0.370388926577496Doubts_actions[t] + 0.0908521419386202Parental_expectations[t] + 0.226711071855642Parental_criticism[t] + 0.428884960755457Organization[t] + 0.0317689593578859PLC[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) 6.05544463046083 3.016337 2.0075 0.046673 0.023336 Gender 1.08554008173790 0.625966 1.7342 0.085152 0.042576 Concern_mistakes 0.302404041874856 0.057984 5.2153 1e-06 0 Doubts_actions -0.370388926577496 0.119153 -3.1085 0.00229 0.001145 Parental_expectations 0.0908521419386202 0.104748 0.8673 0.387282 0.193641 Parental_criticism 0.226711071855642 0.129779 1.7469 0.082912 0.041456 Organization 0.428884960755457 0.081749 5.2463 1e-06 0 PLC 0.0317689593578859 0.13875 0.229 0.81924 0.40962 Multiple Linear Regression - Regression Statistics Multiple R 0.625922347228241 R-squared 0.391778784759711 Adjusted R-squared 0.360473281034108 F-TEST (value) 12.5146935246180 F-TEST (DF numerator) 7 F-TEST (DF denominator) 136 p-value 2.57371901568604e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.36194190238707 Sum Squared Residuals 1537.16085628353 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25 25.5281585124996 -0.528158512499642 2 25 23.3801561785687 1.61984382143132 3 19 23.9873375773017 -4.98733757730169 4 18 23.3076234384716 -5.30762343847157 5 18 20.7596668651796 -2.75966686517959 6 22 21.3135821292966 0.686417870703412 7 29 22.0216450441640 6.97835495583604 8 26 25.0503269765448 0.949673023455192 9 25 24.0818224564755 0.918177543524528 10 23 24.8249168609581 -1.82491686095814 11 23 20.9833853504259 2.01661464957412 12 23 20.9132540671716 2.08674593282838 13 24 23.506915416333 0.49308458366698 14 30 24.6798911803785 5.32010881962153 15 19 17.9057959422863 1.09420405771365 16 24 23.2097556179727 0.790244382027295 17 32 29.2264367543226 2.77356324567736 18 30 27.4238597175276 2.57614028247235 19 29 30.9988998064258 -1.99889980642575 20 17 22.6196657169651 -5.61966571696511 21 25 23.5444254603085 1.45557453969151 22 26 24.8391210462728 1.16087895372722 23 26 26.9487872779461 -0.948787277946073 24 25 24.0509054143823 0.949094585617727 25 23 19.2740616887474 3.72593831125259 26 21 17.4131067191083 3.58689328089174 27 19 23.0319111516796 -4.03191115167956 28 35 24.0051023415459 10.9948976584541 29 19 22.8626109679091 -3.86261096790906 30 20 22.7476522945290 -2.74765229452904 31 21 22.3304653961623 -1.33046539616229 32 21 21.8071358122607 -0.807135812260728 33 24 19.9664998945467 4.0335001054533 34 23 19.9637605427927 3.03623945720735 35 19 20.0853797878955 -1.08537978789554 36 17 24.8342983720192 -7.83429837201917 37 24 22.9519173748990 1.04808262510104 38 15 17.9762480302365 -2.97624803023651 39 25 25.4198464672891 -0.419846467289087 40 27 23.1165219314615 3.88347806853850 41 27 25.6714546103921 1.32854538960788 42 18 22.1853588943757 -4.18535889437572 43 25 22.0163577107640 2.98364228923597 44 22 21.8623548083114 0.137645191688583 45 26 21.4309657248002 4.56903427519975 46 23 24.4543758064382 -1.45437580643825 47 16 21.9150740505523 -5.91507405055234 48 27 20.8501991950921 6.14980080490794 49 25 24.4472185571175 0.552781442882529 50 14 15.8739961454201 -1.87399614542012 51 19 21.3914097840986 -2.39140978409855 52 20 25.2661621646739 -5.2661621646739 53 16 21.0647508466983 -5.06475084669831 54 18 20.6279428593715 -2.62794285937146 55 22 22.1020407413569 -0.102040741356915 56 21 19.2940825344445 1.70591746555548 57 22 23.0734203791412 -1.07342037914117 58 22 23.8987305343088 -1.89873053430883 59 32 25.5820670025999 6.41793299740007 60 23 20.3984227208104 2.6015772791896 61 31 26.1087294209528 4.89127057904723 62 18 21.8054063105134 -3.80540631051343 63 23 23.0881694317347 -0.088169431734685 64 26 25.8512867893350 0.148713210664950 65 24 22.2238657217127 1.77613427828726 66 19 17.2706264248124 1.72937357518757 67 14 16.1204986229292 -2.12049862292919 68 20 21.9132784977955 -1.91327849779554 69 22 21.2150000975043 0.784999902495717 70 24 22.0449269282509 1.95507307174909 71 25 22.6218215012809 2.37817849871912 72 21 24.9664048238158 -3.96640482381575 73 28 23.9869011368326 4.01309886316737 74 24 21.8403117299078 2.1596882700922 75 20 20.1903276444363 -0.190327644436281 76 21 23.9493784943219 -2.94937849432193 77 23 23.5371687827720 -0.537168782771954 78 13 15.7149475787263 -2.71494757872626 79 24 23.5553928635235 0.444607136476478 80 21 23.8520101633807 -2.85201016338069 81 21 23.9829661870797 -2.98296618707973 82 17 19.2472413739475 -2.24724137394748 83 14 19.3163708168531 -5.31637081685314 84 29 21.2653528619880 7.73464713801195 85 25 23.1582768621260 1.84172313787405 86 16 17.7716091429477 -1.77160914294767 87 25 21.4618772666986 3.53812273330137 88 25 21.9082001622716 3.09179983772836 89 21 22.206057948523 -1.20605794852298 90 23 23.2210621532137 -0.22106215321368 91 22 26.0814630599885 -4.0814630599885 92 19 19.9006245138135 -0.900624513813497 93 24 23.7509403131750 0.249059686825032 94 26 23.1308197173747 2.8691802826253 95 25 24.8753173719389 0.124682628061089 96 20 23.0252186614217 -3.02521866142169 97 22 22.8871322320673 -0.887132232067326 98 14 18.675339626853 -4.67533962685301 99 20 24.9379417722663 -4.93794177226632 100 32 23.8288111566847 8.17118884331528 101 21 20.5979867085993 0.402013291400737 102 22 21.8990354927191 0.100964507280927 103 28 26.6728908367733 1.32710916322673 104 25 25.4557822942169 -0.455782294216869 105 17 16.1327210204083 0.867278979591746 106 21 23.0677355498766 -2.06773554987658 107 23 21.7185921718228 1.28140782817722 108 27 23.0821235829297 3.91787641707033 109 22 22.4889450032043 -0.488945003204296 110 19 19.2103892415976 -0.210389241597643 111 20 23.455805491358 -3.45580549135801 112 17 18.4948405905726 -1.49484059057259 113 24 22.9650766950745 1.03492330492554 114 21 21.4326119615744 -0.432611961574358 115 21 22.1653696875348 -1.16536968753478 116 24 26.0383152451805 -2.03831524518054 117 19 17.8494131917813 1.15058680821873 118 22 21.3626110017254 0.637388998274601 119 26 23.5481197986673 2.45188020133268 120 17 17.2209531967291 -0.220953196729138 121 17 19.1435131919336 -2.14351319193355 122 19 21.5832486502609 -2.58324865026095 123 15 17.0675154742249 -2.06751547422494 124 17 20.2257849734361 -3.22578497343608 125 27 27.9103307750372 -0.910330775037198 126 19 20.8044944192226 -1.80449441922263 127 21 21.728310751151 -0.728310751150981 128 25 20.8724532462038 4.12754675379621 129 19 23.5903406449817 -4.59034064498166 130 22 22.3806732140214 -0.380673214021431 131 20 24.0059751536554 -4.00597515365536 132 15 24.3751919882858 -9.3751919882858 133 20 22.1738054993449 -2.17380549934492 134 29 25.1514904273491 3.8485095726509 135 19 18.8110305100301 0.188969489969935 136 29 23.5480923050921 5.45190769490787 137 24 21.5909587606737 2.40904123932625 138 23 19.2740616887474 3.72593831125259 139 22 23.1606745143394 -1.16067451433936 140 23 22.241182759058 0.758817240941995 141 22 19.7672512302356 2.23274876976438 142 29 21.2653528619880 7.73464713801195 143 26 23.5481197986673 2.45188020133268 144 21 22.1325135819139 -1.13251358191387 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.407790321635213 0.815580643270426 0.592209678364787 12 0.294979593416552 0.589959186833104 0.705020406583448 13 0.417792154326291 0.835584308652582 0.582207845673709 14 0.312743074847016 0.625486149694032 0.687256925152984 15 0.233843499028182 0.467686998056365 0.766156500971818 16 0.180378079116759 0.360756158233518 0.819621920883241 17 0.151824877982531 0.303649755965062 0.84817512201747 18 0.120171181539924 0.240342363079848 0.879828818460076 19 0.0857323922617428 0.171464784523486 0.914267607738257 20 0.257846315802488 0.515692631604976 0.742153684197512 21 0.299519419571712 0.599038839143424 0.700480580428288 22 0.233714322226062 0.467428644452124 0.766285677773938 23 0.182607506298966 0.365215012597933 0.817392493701034 24 0.140459127461442 0.280918254922883 0.859540872538558 25 0.112667570096892 0.225335140193785 0.887332429903108 26 0.0887034582818459 0.177406916563692 0.911296541718154 27 0.0893436102711374 0.178687220542275 0.910656389728863 28 0.602600493304796 0.794799013390407 0.397399506695204 29 0.563381129672741 0.873237740654519 0.436618870327259 30 0.532853826378948 0.934292347242104 0.467146173621052 31 0.481547429547711 0.963094859095422 0.518452570452289 32 0.418746796545828 0.837493593091657 0.581253203454172 33 0.64227188995315 0.7154562200937 0.35772811004685 34 0.73395168524346 0.532096629513081 0.266048314756540 35 0.69309856973512 0.613802860529759 0.306901430264880 36 0.843144889535111 0.313710220929777 0.156855110464889 37 0.819762773499612 0.360474453000777 0.180237226500388 38 0.858783753449523 0.282432493100954 0.141216246550477 39 0.824671212562638 0.350657574874724 0.175328787437362 40 0.831298310505368 0.337403378989265 0.168701689494632 41 0.799369055281098 0.401261889437804 0.200630944718902 42 0.839153972702075 0.32169205459585 0.160846027297925 43 0.820172896018357 0.359654207963286 0.179827103981643 44 0.786194708258304 0.427610583483393 0.213805291741696 45 0.816042965095236 0.367914069809527 0.183957034904764 46 0.789291294547685 0.42141741090463 0.210708705452315 47 0.865950772621343 0.268098454757314 0.134049227378657 48 0.906514996803174 0.186970006393651 0.0934850031968255 49 0.886791303498899 0.226417393002203 0.113208696501101 50 0.891708131897757 0.216583736204486 0.108291868102243 51 0.882001371300719 0.235997257398562 0.117998628699281 52 0.901895479694886 0.196209040610228 0.098104520305114 53 0.932528745002496 0.134942509995008 0.0674712549975039 54 0.926625452792553 0.146749094414894 0.0733745472074471 55 0.908792704161523 0.182414591676955 0.0912072958384773 56 0.893839293850933 0.212321412298134 0.106160706149067 57 0.876232586220957 0.247534827558086 0.123767413779043 58 0.857060660004051 0.285878679991897 0.142939339995949 59 0.928029737423412 0.143940525153177 0.0719702625765884 60 0.91939778940985 0.161204421180301 0.0806022105901504 61 0.94355401914095 0.112891961718102 0.0564459808590508 62 0.947511863188112 0.104976273623776 0.0524881368118879 63 0.932726321343343 0.134547357313314 0.0672736786566569 64 0.915444914524453 0.169110170951094 0.084555085475547 65 0.900668528940778 0.198662942118445 0.0993314710592224 66 0.881185103461232 0.237629793077535 0.118814896538768 67 0.870164730484025 0.25967053903195 0.129835269515975 68 0.853166782169898 0.293666435660205 0.146833217830102 69 0.82437012693007 0.35125974613986 0.17562987306993 70 0.805629773698222 0.388740452603556 0.194370226301778 71 0.78868819234752 0.422623615304961 0.211311807652480 72 0.801465631961183 0.397068736077634 0.198534368038817 73 0.818581929467528 0.362836141064944 0.181418070532472 74 0.798456804599906 0.403086390800188 0.201543195400094 75 0.761498939084454 0.477002121831091 0.238501060915546 76 0.753022059954844 0.493955880090312 0.246977940045156 77 0.712300362596795 0.575399274806411 0.287699637403205 78 0.70091139563752 0.598177208724962 0.299088604362481 79 0.65644516244883 0.68710967510234 0.34355483755117 80 0.641585475847718 0.716829048304563 0.358414524152282 81 0.636095346056664 0.727809307886672 0.363904653943336 82 0.632890277621233 0.734219444757534 0.367109722378767 83 0.686062376649718 0.627875246700564 0.313937623350282 84 0.83769392390746 0.324612152185081 0.162306076092541 85 0.812513143296102 0.374973713407796 0.187486856703898 86 0.79661047639345 0.406779047213099 0.203389523606550 87 0.799502388168362 0.400995223663277 0.200497611831638 88 0.816464348235225 0.36707130352955 0.183535651764775 89 0.78339704810654 0.433205903786921 0.216602951893460 90 0.746605675799604 0.506788648400792 0.253394324200396 91 0.758321952872928 0.483356094254144 0.241678047127072 92 0.719801442525557 0.560397114948885 0.280198557474443 93 0.673945459705156 0.652109080589689 0.326054540294845 94 0.680211936059367 0.639576127881267 0.319788063940633 95 0.635989434518303 0.728021130963394 0.364010565481697 96 0.622614348812623 0.754771302374755 0.377385651187377 97 0.583688652688822 0.832622694622356 0.416311347311178 98 0.608739683393346 0.782520633213309 0.391260316606654 99 0.66180808467333 0.676383830653339 0.338191915326670 100 0.850353134968208 0.299293730063585 0.149646865031792 101 0.818146007339377 0.363707985321246 0.181853992660623 102 0.781838113767196 0.436323772465607 0.218161886232803 103 0.751481463813953 0.497037072372094 0.248518536186047 104 0.720712564533897 0.558574870932206 0.279287435466103 105 0.671101588471283 0.657796823057434 0.328898411528717 106 0.634438938384555 0.73112212323089 0.365561061615445 107 0.613740374375351 0.772519251249297 0.386259625624649 108 0.640538574810669 0.718922850378662 0.359461425189331 109 0.586797182138511 0.826405635722978 0.413202817861489 110 0.530111762437989 0.939776475124023 0.469888237562011 111 0.513751963155068 0.972496073689864 0.486248036844932 112 0.456716991694877 0.913433983389753 0.543283008305123 113 0.395358976063606 0.790717952127212 0.604641023936394 114 0.335341204770515 0.67068240954103 0.664658795229485 115 0.278887165775193 0.557774331550386 0.721112834224807 116 0.240928052818031 0.481856105636062 0.759071947181969 117 0.196069406138827 0.392138812277655 0.803930593861173 118 0.155777643743741 0.311555287487481 0.84422235625626 119 0.136830345126733 0.273660690253466 0.863169654873267 120 0.110100709649669 0.220201419299338 0.889899290350331 121 0.0851784656427788 0.170356931285558 0.914821534357221 122 0.085111019561369 0.170222039122738 0.914888980438631 123 0.127794206222588 0.255588412445177 0.872205793777412 124 0.142735721962214 0.285471443924429 0.857264278037786 125 0.161018428813273 0.322036857626546 0.838981571186727 126 0.160241071253657 0.320482142507313 0.839758928746343 127 0.133904565299101 0.267809130598202 0.866095434700899 128 0.0993811334141412 0.198762266828282 0.900618866585859 129 0.0717761353710451 0.143552270742090 0.928223864628955 130 0.0821705604727717 0.164341120945543 0.917829439527228 131 0.0705851616740477 0.141170323348095 0.929414838325952 132 0.798922578997165 0.40215484200567 0.201077421002835 133 0.74055293875381 0.51889412249238 0.25944706124619 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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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