WS 10 MR

*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, 14 Dec 2010 10:17:34 +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/14/t12923218616wijx21vsxo6jl9.htm/, Retrieved Tue, 14 Dec 2010 11:17:41 +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/14/t12923218616wijx21vsxo6jl9.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 24 24 13 13 13 1 25 25 12 12 13 1 17 30 15 10 16 0 18 19 12 9 12 1 18 22 10 10 11 1 16 22 12 12 12 1 20 25 15 13 18 1 16 23 9 12 11 1 18 17 12 12 14 1 17 21 11 6 9 0 23 19 11 5 14 1 30 19 11 12 12 0 23 15 15 11 11 1 18 16 7 14 12 1 15 23 11 14 13 0 12 27 11 12 11 0 21 22 10 12 12 1 15 14 14 11 16 0 20 22 10 11 9 1 31 23 6 7 11 0 27 23 11 9 13 1 34 21 15 11 15 1 21 19 11 11 10 1 31 18 12 12 11 0 19 20 14 12 13 1 16 23 15 11 16 0 20 25 9 11 15 1 21 19 13 8 14 1 22 24 13 9 14 0 17 22 16 12 14 1 24 25 13 10 8 0 25 26 12 10 13 1 26 29 14 12 15 1 25 32 11 8 13 0 17 25 9 12 11 0 32 29 16 11 15 0 33 28 12 12 15 0 13 17 10 7 9 1 32 28 13 11 13 0 25 29 16 11 16 0 29 26 14 12 13 1 22 25 15 9 11 0 18 14 5 15 12 0 17 25 8 11 12 1 20 26 11 11 12 1 15 20 16 11 14 1 20 18 17 11 14 1 33 32 9 15 8 1 29 25 9 11 13 0 23 25 13 12 16 1 26 23 10 12 13 0 18 21 6 9 11 0 20 20 12 12 14 1 11 15 8 12 13 0 28 30 14 13 13 1 26 24 12 11 13 1 22 26 11 9 12 1 17 24 16 9 16 0 12 22 8 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 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 gender[t] = + 1.08151766603924 -0.00662196627596676CoM[t] -0.00894811157503866PersSt[t] + 0.027465763686152Popularity[t] -0.0223949691901545FindFrie[t] -0.0158619397694246`Liked `[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) 1.08151766603924 0.395871 2.732 0.007051 0.003526 CoM -0.00662196627596676 0.007674 -0.8629 0.389552 0.194776 PersSt -0.00894811157503866 0.010337 -0.8657 0.388058 0.194029 Popularity 0.027465763686152 0.016152 1.7005 0.091115 0.045558 FindFrie -0.0223949691901545 0.022279 -1.0052 0.31642 0.15821 `Liked ` -0.0158619397694246 0.021843 -0.7262 0.468862 0.234431 Multiple Linear Regression - Regression Statistics Multiple R 0.210758878378947 R-squared 0.0444193048155517 Adjusted R-squared 0.0125666149760699 F-TEST (value) 1.39452288140808 F-TEST (DF numerator) 5 F-TEST (DF denominator) 150 p-value 0.22948484947114 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.48499470453812 Sum Squared Residuals 35.2829795145027 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1 0.567550909060556 0.432449090939444 2 1 0.546910036713553 0.453089963286447 3 1 0.634746619176585 0.365253380823415 4 0 0.72999931743544 -0.72999931743544 5 1 0.64169042591729 0.35830957408271 6 1 0.649214007691794 0.350785992308206 7 1 0.560712491114565 0.439287508885435 8 1 0.573730544827724 0.426269455172276 9 1 0.648986753476205 0.351013246523795 10 1 0.806030023753915 0.193969976246085 11 0 0.727279719591223 -0.727279719591223 12 1 0.555885050867224 0.444114949132776 13 0 0.786151224803333 -0.786151224803333 14 1 0.507539987779024 0.492460012220976 15 1 0.558770220556837 0.441229779443163 16 0 0.61935749100374 -0.61935749100374 17 0 0.561172648939656 -0.561172648939656 18 1 0.74129960405283 0.258700395947170 19 0 0.637775403714051 -0.637775403714051 20 1 0.503978605580539 0.496021394419461 21 0 0.591281471196008 -0.591281471196008 22 1 0.596173167239768 0.403826832760232 23 1 0.669601596079928 0.330398403920072 24 1 0.601538899621872 0.398461100378128 25 0 0.68631391961685 -0.68631391961685 26 1 0.681610397287668 0.318389602712332 27 0 0.488293666686236 -0.488293666686236 28 1 0.728270271944997 0.271729728055003 29 1 0.654512778603683 0.345487221396317 30 0 0.720731216621586 -0.720731216621586 31 1 0.705097403903103 0.294902596096897 32 0 0.582751863518823 -0.582751863518823 33 1 0.527703271970886 0.472296728029114 34 1 0.546387368762748 0.453612631237252 35 0 0.54921235540168 -0.54921235540168 36 0 0.565297970877544 -0.565297970877544 37 0 0.435366092241853 -0.435366092241853 38 0 0.81844960228163 -0.81844960228163 39 1 0.523572670932976 0.476427329067024 40 0 0.595789795039887 -0.595789795039887 41 0 0.566405587406951 -0.566405587406951 42 1 0.748082013709222 0.251917986290778 43 0 0.448109714366642 -0.448109714366642 44 0 0.528279621136258 -0.528279621136258 45 1 0.581862901791775 0.418137098208225 46 1 0.774266341513752 0.225733658486248 47 1 0.786518496970147 0.213481503029853 48 1 0.361025025698751 0.638974974301249 49 1 0.460419849741384 0.539580150258616 50 0 0.540033913643364 -0.540033913643364 51 1 0.503252766215359 0.496747233784641 52 0 0.563170451937875 -0.563170451937875 53 0 0.608898486199155 -0.608898486199155 54 1 0.619235625582866 0.380764374417134 55 0 0.514840138192609 -0.514840138192609 56 1 0.571631151202779 0.428368848797221 57 1 0.61340890762015 0.386591092379849 58 1 0.738296021503123 0.261703978496877 59 0 0.540647967932934 -0.540647967932934 60 1 0.791249273784374 0.208750726215626 61 0 0.48736699983499 -0.48736699983499 62 0 0.676347128367642 -0.676347128367642 63 1 0.733568320656584 0.266431679343416 64 1 0.702746056616272 0.297253943383728 65 1 0.700577756584718 0.299422243415282 66 0 0.521665029531758 -0.521665029531758 67 1 0.522418749866141 0.477581250133859 68 0 0.612073314674852 -0.612073314674852 69 1 0.788947457637709 0.211052542362291 70 0 0.46986510206541 -0.46986510206541 71 0 0.676576956009456 -0.676576956009456 72 1 0.64990004435566 0.35009995564434 73 1 0.47497124791822 0.52502875208178 74 0 0.58177468713684 -0.58177468713684 75 0 0.585078008817895 -0.585078008817895 76 1 0.60489452714068 0.395105472859320 77 1 0.655633048904579 0.344366951095421 78 1 0.689822113303154 0.310177886696846 79 0 0.64360764512231 -0.64360764512231 80 0 0.393973435307590 -0.393973435307590 81 0 0.438687090604151 -0.438687090604151 82 1 0.870011975528285 0.129988024471715 83 0 0.656827248521837 -0.656827248521837 84 1 0.585740218870816 0.414259781129184 85 1 0.657170342171295 0.342829657828705 86 0 0.611605430271883 -0.611605430271883 87 1 0.552033287682353 0.447966712317647 88 1 0.711276889035221 0.288723110964779 89 1 0.609682059131488 0.390317940868512 90 1 0.641552108556938 0.358447891443062 91 1 0.64161364029208 0.35838635970792 92 1 0.635667762582288 0.364332237417712 93 1 0.740369861233897 0.259630138766103 94 1 0.720807499705334 0.279192500294666 95 1 0.660691189629087 0.339308810370913 96 1 0.421333810199027 0.578666189800973 97 1 0.545082476564071 0.454917523435929 98 0 0.471542413283351 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0.78452499468138 0.215475005318620 123 1 0.495169789756424 0.504830210243576 124 1 0.647079112074987 0.352920887925013 125 0 0.734331618794769 -0.734331618794769 126 1 0.599950374624117 0.400049625375883 127 1 0.702251269634671 0.297748730365329 128 1 0.588967979668425 0.411032020331575 129 0 0.675308420250516 -0.675308420250516 130 0 0.497597420127003 -0.497597420127003 131 1 0.638001282552826 0.361998717447174 132 0 0.791832997621212 -0.791832997621212 133 0 0.515264643390786 -0.515264643390786 134 1 0.642491429179673 0.357508570820327 135 0 0.720466257281799 -0.720466257281799 136 0 0.531339958862547 -0.531339958862547 137 1 0.708417026888586 0.291582973111414 138 1 0.507589368284139 0.492410631715861 139 0 0.796214679485656 -0.796214679485656 140 1 0.622548268877454 0.377451731122546 141 0 0.554091397673951 -0.554091397673951 142 1 0.489611582950292 0.510388417049708 143 0 0.624135921039856 -0.624135921039856 144 1 0.655150810139247 0.344849189860753 145 1 0.591294124967497 0.408705875032503 146 0 0.42519945189715 -0.42519945189715 147 0 0.381453244543720 -0.381453244543720 148 0 0.461971999911925 -0.461971999911925 149 1 0.82965972730202 0.170340272697980 150 1 0.689655668622405 0.310344331377595 151 0 0.620932489394654 -0.620932489394654 152 1 0.598269484897025 0.401730515102975 153 1 0.380891646726953 0.619108353273047 154 1 0.688462191205449 0.311537808794551 155 1 0.682156416000308 0.317843583999692 156 1 0.660107465792249 0.339892534207751 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.269344119896837 0.538688239793673 0.730655880103163 10 0.401481700012637 0.802963400025275 0.598518299987363 11 0.281572464102914 0.563144928205828 0.718427535897086 12 0.203088488774319 0.406176977548637 0.796911511225681 13 0.194537410042627 0.389074820085254 0.805462589957373 14 0.127564434969883 0.255128869939766 0.872435565030117 15 0.090331877557498 0.180663755114996 0.909668122442502 16 0.451102636534222 0.902205273068444 0.548897363465778 17 0.651302532980379 0.697394934039242 0.348697467019621 18 0.616431233382205 0.76713753323559 0.383568766617795 19 0.628283592742434 0.743432814515133 0.371716407257567 20 0.575673037712668 0.848653924574663 0.424326962287332 21 0.645735144465154 0.708529711069692 0.354264855534846 22 0.60132596687015 0.7973480662597 0.39867403312985 23 0.593171957901073 0.813656084197854 0.406828042098927 24 0.547952242377893 0.904095515244215 0.452047757622107 25 0.602989648630081 0.794020702739838 0.397010351369919 26 0.559982708003252 0.880034583993497 0.440017291996748 27 0.675919854866595 0.64816029026681 0.324080145133405 28 0.653686739787263 0.692626520425473 0.346313260212737 29 0.62162874186615 0.7567425162677 0.37837125813385 30 0.669360632092061 0.661278735815878 0.330639367907939 31 0.659307177705908 0.681385644588184 0.340692822294092 32 0.704396451741916 0.591207096516169 0.295603548258084 33 0.671049561022812 0.657900877954376 0.328950438977188 34 0.643903490875072 0.712193018249856 0.356096509124928 35 0.672195626996752 0.655608746006496 0.327804373003248 36 0.729106754104582 0.541786491790837 0.270893245895418 37 0.755749376987444 0.488501246025112 0.244250623012556 38 0.787536435625111 0.424927128749778 0.212463564374889 39 0.773725008128987 0.452549983742025 0.226274991871012 40 0.789861865893306 0.420276268213389 0.210138134106694 41 0.803043587018917 0.393912825962166 0.196956412981083 42 0.797714746095872 0.404570507808257 0.202285253904128 43 0.809265768389623 0.381468463220753 0.190734231610377 44 0.810726660964244 0.378546678071511 0.189273339035756 45 0.801951710410965 0.39609657917807 0.198048289589035 46 0.777512205810999 0.444975588378002 0.222487794189001 47 0.746198280371163 0.507603439257674 0.253801719628837 48 0.75245301453801 0.495093970923979 0.247546985461989 49 0.752059553143548 0.495880893712903 0.247940446856452 50 0.762645347475986 0.474709305048027 0.237354652524014 51 0.757890511526414 0.484218976947171 0.242109488473586 52 0.762610357958958 0.474779284082083 0.237389642041042 53 0.777747231563432 0.444505536873136 0.222252768436568 54 0.774894341203244 0.450211317593513 0.225105658796756 55 0.787260166868525 0.42547966626295 0.212739833131475 56 0.776750849958469 0.446498300083063 0.223249150041531 57 0.763934855153971 0.472130289692057 0.236065144846029 58 0.742012948887443 0.515974102225114 0.257987051112557 59 0.745418667056703 0.509162665886595 0.254581332943297 60 0.716797185384698 0.566405629230603 0.283202814615302 61 0.715082697217686 0.569834605564629 0.284917302782314 62 0.74918562337009 0.501628753259821 0.250814376629911 63 0.724018197932567 0.551963604134865 0.275981802067433 64 0.699633092823895 0.600733814352211 0.300366907176105 65 0.680843433138998 0.638313133722003 0.319156566861002 66 0.687760342789626 0.624479314420749 0.312239657210374 67 0.685335473024336 0.629329053951329 0.314664526975664 68 0.709209620372103 0.581580759255794 0.290790379627897 69 0.67837619172075 0.6432476165585 0.32162380827925 70 0.680177779820418 0.639644440359165 0.319822220179582 71 0.716859854363099 0.566280291273802 0.283140145636901 72 0.695823386907608 0.608353226184784 0.304176613092392 73 0.702100871248071 0.595798257503858 0.297899128751929 74 0.717325917471952 0.565348165056097 0.282674082528048 75 0.73633457276144 0.52733085447712 0.26366542723856 76 0.723483345614607 0.553033308770786 0.276516654385393 77 0.7011632313265 0.597673537346999 0.298836768673500 78 0.674905436689288 0.650189126621424 0.325094563310712 79 0.708835971048264 0.582328057903473 0.291164028951736 80 0.693674301811922 0.612651396376156 0.306325698188078 81 0.68920541016596 0.62158917966808 0.31079458983404 82 0.649444748363507 0.701110503272987 0.350555251636493 83 0.68333980789951 0.63332038420098 0.31666019210049 84 0.666411546777012 0.667176906445976 0.333588453222988 85 0.645111026222406 0.709777947555188 0.354888973777594 86 0.669089627571891 0.661820744856217 0.330910372428109 87 0.654541603053221 0.690916793893559 0.345458396946779 88 0.622049024460372 0.755901951079256 0.377950975539628 89 0.605585817595009 0.788828364809983 0.394414182404991 90 0.581389415246039 0.837221169507922 0.418610584753961 91 0.566524231193313 0.866951537613373 0.433475768806687 92 0.538081879942404 0.923836240115193 0.461918120057596 93 0.50376840511788 0.99246318976424 0.49623159488212 94 0.469708649280165 0.93941729856033 0.530291350719835 95 0.440654214892685 0.88130842978537 0.559345785107315 96 0.456619855375023 0.913239710750046 0.543380144624977 97 0.455078995243063 0.910157990486127 0.544921004756937 98 0.451308533820853 0.902617067641707 0.548691466179147 99 0.495510805561754 0.991021611123509 0.504489194438246 100 0.472710320391663 0.945420640783327 0.527289679608337 101 0.507791931569947 0.984416136860106 0.492208068430053 102 0.469753865308793 0.939507730617585 0.530246134691207 103 0.48256013924644 0.96512027849288 0.51743986075356 104 0.452452607516601 0.904905215033203 0.547547392483399 105 0.466497730222333 0.932995460444665 0.533502269777667 106 0.457851163916454 0.915702327832908 0.542148836083546 107 0.503519808293217 0.992960383413567 0.496480191706783 108 0.537379380847981 0.925241238304038 0.462620619152019 109 0.522592445375967 0.954815109248066 0.477407554624033 110 0.480594290381874 0.961188580763748 0.519405709618126 111 0.435298155452941 0.870596310905883 0.564701844547059 112 0.413694112745110 0.827388225490221 0.58630588725489 113 0.394369287518901 0.788738575037803 0.605630712481099 114 0.407216729021689 0.814433458043377 0.592783270978311 115 0.364946858071138 0.729893716142276 0.635053141928862 116 0.32418406419937 0.64836812839874 0.67581593580063 117 0.292334669782695 0.584669339565389 0.707665330217305 118 0.271740693511328 0.543481387022656 0.728259306488672 119 0.287877075719686 0.575754151439371 0.712122924280314 120 0.261334513495097 0.522669026990193 0.738665486504903 121 0.257877632972559 0.515755265945119 0.74212236702744 122 0.223836126021864 0.447672252043728 0.776163873978136 123 0.213583382651105 0.42716676530221 0.786416617348895 124 0.191630681590525 0.383261363181049 0.808369318409475 125 0.224373184595841 0.448746369191682 0.775626815404159 126 0.209778169862793 0.419556339725587 0.790221830137207 127 0.182660207389046 0.365320414778093 0.817339792610954 128 0.172583419981115 0.345166839962231 0.827416580018885 129 0.225930994267703 0.451861988535406 0.774069005732297 130 0.287793333240505 0.575586666481009 0.712206666759495 131 0.289854296202797 0.579708592405594 0.710145703797203 132 0.379058504607866 0.758117009215733 0.620941495392134 133 0.413665063568939 0.827330127137878 0.586334936431061 134 0.425739916863469 0.851479833726937 0.574260083136531 135 0.452495047376641 0.904990094753281 0.547504952623359 136 0.449292880425979 0.898585760851957 0.550707119574021 137 0.384102826969723 0.768205653939445 0.615897173030277 138 0.365876895906559 0.731753791813118 0.634123104093441 139 0.482696970911781 0.965393941823561 0.517303029088219 140 0.418888884105943 0.837777768211885 0.581111115894057 141 0.58972219554018 0.820555608919639 0.410277804459820 142 0.566321121042214 0.867357757915572 0.433678878957786 143 0.717828909477004 0.564342181045991 0.282171090522996 144 0.606667980130846 0.786664039738308 0.393332019869154 145 0.623847631812556 0.752304736374887 0.376152368187444 146 0.542877340933563 0.914245318132873 0.457122659066437 147 0.51340933567571 0.97318132864858 0.48659066432429 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 = 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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