Monthly dummie

*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 14:05:11 +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/t1292163450suv9z83z6o5gqwq.htm/, Retrieved Sun, 12 Dec 2010 15:17:33 +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/t1292163450suv9z83z6o5gqwq.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 «

24 14 11 12 24 26 25 11 7 8 25 23 17 6 17 8 30 25 18 12 10 8 19 23 18 8 12 9 22 19 16 10 12 7 22 29 20 10 11 4 25 25 16 11 11 11 23 21 18 16 12 7 17 22 17 11 13 7 21 25 23 13 14 12 19 24 30 12 16 10 19 18 23 8 11 10 15 22 18 12 10 8 16 15 15 11 11 8 23 22 12 4 15 4 27 28 21 9 9 9 22 20 15 8 11 8 14 12 20 8 17 7 22 24 31 14 17 11 23 20 27 15 11 9 23 21 34 16 18 11 21 20 21 9 14 13 19 21 31 14 10 8 18 23 19 11 11 8 20 28 16 8 15 9 23 24 20 9 15 6 25 24 21 9 13 9 19 24 22 9 16 9 24 23 17 9 13 6 22 23 24 10 9 6 25 29 25 16 18 16 26 24 26 11 18 5 29 18 25 8 12 7 32 25 17 9 17 9 25 21 32 16 9 6 29 26 33 11 9 6 28 22 13 16 12 5 17 22 32 12 18 12 28 22 25 12 12 7 29 23 29 14 18 10 26 30 22 9 14 9 25 23 18 10 15 8 14 17 17 9 16 5 25 23 20 10 10 8 26 23 15 12 11 8 20 25 20 14 14 10 18 24 33 14 9 6 32 24 29 10 12 8 25 23 23 14 17 7 25 21 26 16 5 4 23 24 18 9 12 8 21 24 20 10 12 8 20 28 11 6 6 4 15 16 28 8 24 20 30 20 26 13 12 8 24 29 22 10 12 8 26 27 17 8 14 6 24 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 8 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Personal_Standards[t] = + 6.85249471188449 + 0.334156412849869Concern_over_Mistakes[t] -0.370092394686343Doubts_about_actions[t] + 0.1593164710071Parental_Expectations[t] + 0.0649584207606233Parental_Criticism[t] + 0.40340194756281Organization[t] -0.112988112395845M1[t] + 0.30165765044183M2[t] + 0.663745180491903M3[t] + 0.151960848781343M4[t] + 0.37281959143706M5[t] + 1.0266247528337M6[t] + 0.417404861828112M7[t] + 1.66380986838678M8[t] + 1.41001962150359M9[t] + 0.924259509685395M10[t] -0.535671626926254M11[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) 6.85249471188449 2.478774 2.7645 0.006459 0.003229 Concern_over_Mistakes 0.334156412849869 0.059692 5.598 0 0 Doubts_about_actions -0.370092394686343 0.116287 -3.1826 0.001793 0.000897 Parental_Expectations 0.1593164710071 0.106628 1.4941 0.137359 0.068679 Parental_Criticism 0.0649584207606233 0.139434 0.4659 0.642021 0.32101 Organization 0.40340194756281 0.076139 5.2982 0 0 M1 -0.112988112395845 1.367893 -0.0826 0.934286 0.467143 M2 0.30165765044183 1.369878 0.2202 0.826026 0.413013 M3 0.663745180491903 1.362393 0.4872 0.626874 0.313437 M4 0.151960848781343 1.404563 0.1082 0.913997 0.456999 M5 0.37281959143706 1.399561 0.2664 0.790331 0.395165 M6 1.0266247528337 1.401881 0.7323 0.465181 0.23259 M7 0.417404861828112 1.423488 0.2932 0.769777 0.384888 M8 1.66380986838678 1.397453 1.1906 0.235797 0.117898 M9 1.41001962150359 1.382136 1.0202 0.309381 0.15469 M10 0.924259509685395 1.371739 0.6738 0.501542 0.250771 M11 -0.535671626926254 1.422233 -0.3766 0.707002 0.353501 Multiple Linear Regression - Regression Statistics Multiple R 0.622661478001645 R-squared 0.387707316187193 Adjusted R-squared 0.318716591250539 F-TEST (value) 5.61970201854202 F-TEST (DF numerator) 16 F-TEST (DF denominator) 142 p-value 3.06102809766173e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.48067319339608 Sum Squared Residuals 1720.3421948501 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 22.5983998491154 1.40160015088464 2 25 22.3501737991026 2.64982620089741 3 30 24.289440604982 5.71055939501796 4 19 19.969239125828 -0.969239125827967 5 22 20.4404510197526 1.55954898024736 6 22 23.5898612001837 -1.58986120018371 7 25 22.3494674370374 2.65053256296261 8 23 20.7302555525834 2.26974444741664 9 17 19.5972008934956 -2.59720089349562 10 21 21.9972686559548 -0.997268655954792 11 19 21.8827978343171 -2.88279783431708 12 19 22.8959611609948 -3.89596116099485 13 15 22.741273172611 -7.74127317261104 14 16 16.891720346986 -0.891720346985973 15 23 19.6045611371196 3.39543886288044 16 27 23.4787982160266 3.52120178397344 17 22 20.9982803981574 1.00171960184259 18 14 17.0436984178923 -3.04369841789228 19 22 23.8370243671717 -1.83702436717173 20 23 25.1848214397522 -2.18482143975215 21 23 22.5418994267821 0.458100573217887 22 21 24.8668720012348 -3.86687200123479 23 19 21.5496071654349 -2.5496071654349 24 18 23.4211268547222 -5.42112685472224 25 20 22.5848651810081 -2.58486518100814 26 23 22.195935403893 0.804064596106976 27 25 23.3296809283744 1.67031907162564 28 19 23.0282953297813 -4.02829532978134 29 24 23.6578579507454 0.342142049254587 30 22 21.9680563725895 0.0319436274104594 31 25 25.1109847781952 -0.110984778195152 32 26 24.5374145383417 1.46258546165829 33 29 23.3332883639964 5.66671163600361 34 32 25.6214806718057 6.37851932819433 35 25 20.4310972440142 4.56890275598576 36 29 23.9360710083595 5.0639289916405 37 28 24.394093491994 3.605906508006 38 17 16.6881400166633 0.31185998333674 39 28 26.2901767409732 1.70982325902683 40 29 22.5620085370306 6.43799146296937 41 26 27.3538958629773 -1.35389586297727 42 25 23.9930301701279 1.00696982987215 43 14 19.3510385979061 -5.35103859790606 44 25 23.0182324804033 1.9817675195967 45 26 22.6357955136226 3.36420448637736 46 20 20.7051889143151 -0.705188914315135 47 18 20.3803193595599 -2.38031935955988 48 32 24.2036083154564 7.79639168454357 49 25 24.4388284373862 0.561171562613776 50 25 21.2929861835286 3.70701381647143 51 23 21.0208910910769 1.97910890892308 52 21 21.801551199464 -0.801551199464008 53 20 23.9342381633844 -3.93423816338436 54 15 17.0044493080387 -2.00444930803874 55 30 26.8563426466572 3.14365735334275 56 24 26.5232916809371 -2.52329168093707 57 26 25.2363490715878 0.763650928412178 58 24 21.9916980475719 2.00830195242813 59 22 20.4063567882031 1.59364321179687 60 14 15.0245112216697 -1.02451122166975 61 24 21.6708061937604 2.32919380623961 62 24 22.6130244744736 1.38697552552639 63 24 23.4044970714702 0.595502928529775 64 24 19.7488741574509 4.25112584254914 65 19 18.2214725637827 0.778527436217349 66 31 27.3593378655659 3.64066213443407 67 22 26.6218045032383 -4.62180450323831 68 27 22.596667260238 4.40333273976202 69 19 18.5266848418194 0.473315158180569 70 25 22.7460633732508 2.25393662674918 71 20 23.9538129310552 -3.9538129310552 72 21 21.0374369821878 -0.0374369821877996 73 27 26.8961909213928 0.103809078607211 74 23 24.1537863151167 -1.15378631511665 75 25 25.7380213530364 -0.738021353036382 76 20 21.8920350263386 -1.89203502633858 77 21 18.9339674814873 2.06603251851267 78 22 22.9519985207818 -0.951998520781767 79 23 22.9892512371015 0.0107487628985322 80 25 25.1373752925557 -0.13737529255568 81 25 24.3793692182207 0.620630781779336 82 17 24.3316144685456 -7.3316144685456 83 19 20.3586233541285 -1.35862335412847 84 25 23.4551782966184 1.54482170338161 85 19 21.7349870254826 -2.73498702548261 86 20 22.7858399135692 -2.78583991356919 87 26 22.5915206417489 3.40847935825114 88 23 20.4876803122754 2.51231968772456 89 27 24.4739094000259 2.5260905999741 90 17 21.3651148582308 -4.36511485823084 91 17 23.2084174838922 -6.20841748389221 92 19 21.3533357694662 -2.35333576946622 93 17 20.5748857342479 -3.57488573424785 94 22 22.27480789833 -0.274807898330005 95 21 22.6288813498089 -1.62888134980886 96 32 28.1240171402766 3.87598285972337 97 21 24.0769821528683 -3.07698215286835 98 21 24.1521256301243 -3.15212563012434 99 18 21.3723276805754 -3.37232768057538 100 18 20.9103911893702 -2.91039118937019 101 23 22.7207600235368 0.279239976463159 102 19 21.0738079701298 -2.07380797012977 103 20 20.753224596279 -0.75322459627898 104 21 23.4845549731727 -2.48455497317272 105 20 24.8675401713321 -4.86754017133209 106 17 19.0776735155312 -2.07767351553116 107 18 19.2790753612779 -1.27907536127787 108 19 20.3131446122449 -1.31314461224493 109 22 21.4878539874189 0.512146012581074 110 15 18.4265069306332 -3.42650693063323 111 14 18.9306777030369 -4.93067770303689 112 18 26.4103072897199 -8.41030728971993 113 24 21.498289634365 2.501710365635 114 35 24.0824552382229 10.9175447617771 115 29 19.3240721116116 9.67592788838835 116 21 22.9796209967813 -1.97962099678128 117 25 21.2666064485026 3.73339355149745 118 20 18.8746326657889 1.12536733421109 119 22 22.1454594740557 -0.1454594740557 120 13 16.2431973967479 -3.24319739674791 121 26 22.6089180570591 3.39108194294092 122 17 16.5822635590892 0.417736440910772 123 25 20.2959976756243 4.70400232437565 124 20 20.4867606355997 -0.486760635599708 125 19 17.9867276561936 1.01327234380639 126 21 22.9975235473161 -1.99752354731611 127 22 20.8561517435908 1.14384825640918 128 24 23.889015679515 0.110984320484983 129 21 23.8799128354004 -2.87991283540036 130 26 25.9798195562401 0.0201804437599126 131 24 19.6441396336953 4.35586036630469 132 16 19.7710716822945 -3.7710716822945 133 23 21.6550579872736 1.3449420127264 134 18 20.5118425264845 -2.5118425264845 135 16 22.542310427475 -6.54231042747502 136 26 23.59207763797 2.40792236202999 137 19 18.7985157316019 0.201484268398134 138 21 17.3527913880341 3.64720861196585 139 21 22.1678896151079 -1.16788961510787 140 22 19.8638308674614 2.13616913253863 141 23 20.7720144850642 2.22798551493581 142 29 25.2745119899244 3.72548801007562 143 21 18.3120666627572 2.68793333724276 144 21 19.4400476037968 1.55995239620316 145 23 21.2433556202828 1.7566443797172 146 27 22.7470884671403 4.2529115328597 147 25 25.5018999362049 -0.501899936204934 148 21 20.6319813431448 0.368018656855218 149 10 16.9816341139897 -6.9816341139897 150 20 23.2178751428864 -3.21787514288639 151 26 22.5743308822111 3.4256691177889 152 24 24.7015834687921 -0.701583468792133 153 29 32.3884529959283 -3.38845299592828 154 19 19.2583682415068 -0.258368241506766 155 24 21.0277628416921 2.97223715830786 156 19 20.1346277246302 -1.13462772463022 157 24 22.8683879223467 1.13161207765331 158 22 21.6085664331955 0.391433566804476 159 17 24.0879970083019 -7.08799700830191 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.757430088246457 0.485139823507086 0.242569911753543 21 0.701305383097988 0.597389233804025 0.298694616902012 22 0.579490272149178 0.841019455701645 0.420509727850822 23 0.453100827960766 0.906201655921531 0.546899172039234 24 0.398295763542315 0.796591527084629 0.601704236457685 25 0.295997842478438 0.591995684956876 0.704002157521562 26 0.226468938381629 0.452937876763258 0.773531061618371 27 0.16875529749765 0.337510594995301 0.83124470250235 28 0.215091006110741 0.430182012221482 0.784908993889259 29 0.153246074352864 0.306492148705728 0.846753925647136 30 0.1207661965199 0.2415323930398 0.8792338034801 31 0.0890305257935367 0.178061051587073 0.910969474206463 32 0.0598377976279819 0.119675595255964 0.940162202372018 33 0.16270136528274 0.32540273056548 0.83729863471726 34 0.258165920890453 0.516331841780907 0.741834079109546 35 0.333645706474624 0.667291412949249 0.666354293525376 36 0.473623156311012 0.947246312622025 0.526376843688988 37 0.45265403926011 0.905308078520219 0.54734596073989 38 0.393627562432702 0.787255124865404 0.606372437567298 39 0.341324685574297 0.682649371148594 0.658675314425703 40 0.478786711196823 0.957573422393646 0.521213288803177 41 0.441375449563054 0.882750899126108 0.558624550436946 42 0.410868484588936 0.821736969177872 0.589131515411064 43 0.382445635036374 0.764891270072748 0.617554364963626 44 0.384559773906192 0.769119547812385 0.615440226093808 45 0.346320312132223 0.692640624264447 0.653679687867777 46 0.292232898062271 0.584465796124543 0.707767101937729 47 0.264305760740406 0.528611521480813 0.735694239259594 48 0.406058592009267 0.812117184018534 0.593941407990733 49 0.350937438920874 0.701874877841749 0.649062561079126 50 0.352636129441421 0.705272258882841 0.647363870558579 51 0.373962270196831 0.747924540393662 0.626037729803169 52 0.325267931889747 0.650535863779495 0.674732068110253 53 0.374967105249708 0.749934210499415 0.625032894750292 54 0.344189363711345 0.68837872742269 0.655810636288655 55 0.483387715533512 0.966775431067024 0.516612284466488 56 0.515353030655374 0.969293938689252 0.484646969344626 57 0.479538063411828 0.959076126823656 0.520461936588172 58 0.437027765465332 0.874055530930664 0.562972234534668 59 0.390905544835725 0.78181108967145 0.609094455164275 60 0.353160651356885 0.706321302713771 0.646839348643115 61 0.340891297374978 0.681782594749957 0.659108702625022 62 0.309817424042619 0.619634848085239 0.69018257595738 63 0.282445980731749 0.564891961463497 0.717554019268251 64 0.321488148466317 0.642976296932634 0.678511851533683 65 0.285368676766168 0.570737353532336 0.714631323233832 66 0.287060464600539 0.574120929201077 0.712939535399461 67 0.337942708461556 0.675885416923111 0.662057291538445 68 0.351191990462686 0.702383980925372 0.648808009537314 69 0.307810044093833 0.615620088187667 0.692189955906167 70 0.282188724983379 0.564377449966758 0.717811275016621 71 0.307826574755285 0.615653149510569 0.692173425244715 72 0.267457927345407 0.534915854690814 0.732542072654593 73 0.227260046688535 0.45452009337707 0.772739953311465 74 0.197169720948464 0.394339441896929 0.802830279051536 75 0.196215200310022 0.392430400620043 0.803784799689979 76 0.176078179659131 0.352156359318261 0.823921820340869 77 0.16730410771109 0.334608215422179 0.83269589228891 78 0.138376594004867 0.276753188009734 0.861623405995133 79 0.113189954357792 0.226379908715584 0.886810045642208 80 0.0939762060425665 0.187952412085133 0.906023793957434 81 0.0768987251293899 0.15379745025878 0.92310127487061 82 0.173304959345508 0.346609918691015 0.826695040654492 83 0.144972482426725 0.289944964853449 0.855027517573275 84 0.12275053660968 0.245501073219359 0.87724946339032 85 0.113143230046173 0.226286460092347 0.886856769953827 86 0.108020529073349 0.216041058146699 0.89197947092665 87 0.134450256987584 0.268900513975169 0.865549743012416 88 0.133884299338039 0.267768598676078 0.866115700661961 89 0.122142753286297 0.244285506572594 0.877857246713703 90 0.137022923668244 0.274045847336487 0.862977076331756 91 0.222345261571378 0.444690523142757 0.777654738428622 92 0.203627402308833 0.407254804617666 0.796372597691167 93 0.201465833918451 0.402931667836902 0.798534166081549 94 0.167081662161434 0.334163324322867 0.832918337838567 95 0.144713615508955 0.28942723101791 0.855286384491045 96 0.174180445582542 0.348360891165085 0.825819554417458 97 0.1793810974998 0.358762194999601 0.8206189025002 98 0.175243195000591 0.350486390001182 0.824756804999409 99 0.169689067597422 0.339378135194843 0.830310932402578 100 0.153577874386716 0.307155748773432 0.846422125613284 101 0.128265494474379 0.256530988948757 0.871734505525621 102 0.11602038614173 0.23204077228346 0.88397961385827 103 0.110888781055962 0.221777562111924 0.889111218944038 104 0.095386211133143 0.190772422266286 0.904613788866857 105 0.119463157948182 0.238926315896364 0.880536842051818 106 0.11066435195929 0.221328703918579 0.88933564804071 107 0.101138461989135 0.202276923978271 0.898861538010865 108 0.079792772079411 0.159585544158822 0.92020722792059 109 0.066001410853454 0.132002821706908 0.933998589146546 110 0.06661986955296 0.13323973910592 0.93338013044704 111 0.066402665950305 0.13280533190061 0.933597334049695 112 0.189661786981588 0.379323573963176 0.810338213018412 113 0.184156110987378 0.368312221974755 0.815843889012623 114 0.709300461453205 0.581399077093591 0.290699538546795 115 0.920716115651404 0.158567768697191 0.0792838843485957 116 0.896781829260608 0.206436341478784 0.103218170739392 117 0.932452219219888 0.135095561560223 0.0675477807801116 118 0.909532939441305 0.180934121117389 0.0904670605586947 119 0.92501658644059 0.149966827118819 0.0749834135594094 120 0.94092821529941 0.118143569401181 0.0590717847005905 121 0.921645000133646 0.156709999732707 0.0783549998663537 122 0.893829439338044 0.212341121323912 0.106170560661956 123 0.973182285040208 0.053635429919585 0.0268177149597925 124 0.958949522819624 0.0821009543607516 0.0410504771803758 125 0.942029402498017 0.115941195003967 0.0579705975019833 126 0.919290955379192 0.161418089241616 0.080709044620808 127 0.891152243929228 0.217695512141544 0.108847756070772 128 0.856561792979406 0.286876414041188 0.143438207020594 129 0.833100649568395 0.333798700863209 0.166899350431605 130 0.79565975605079 0.408680487898418 0.204340243949209 131 0.736863007331624 0.526273985336752 0.263136992668376 132 0.666299524712448 0.667400950575104 0.333700475287552 133 0.598642025444179 0.802715949111642 0.401357974555821 134 0.519200493673635 0.96159901265273 0.480799506326365 135 0.450774785013379 0.901549570026758 0.549225214986621 136 0.384688754745018 0.769377509490037 0.615311245254982 137 0.46953662127103 0.93907324254206 0.53046337872897 138 0.657909620584821 0.684180758830358 0.342090379415179 139 0.52986783693551 0.94026432612898 0.47013216306449 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 2 0.0166666666666667 OK

Charts produced by software:

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Parameters (Session):

par1 = 5 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Include Monthly 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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