MR - Happiness

*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: Sat, 18 Dec 2010 17:31:06 +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/18/t12926934592qyydzseamoeth6.htm/, Retrieved Sat, 18 Dec 2010 18:31:02 +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/18/t12926934592qyydzseamoeth6.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 «

14 11 11 26 9 2 1 1 18 12 8 20 9 1 1 1 11 15 12 21 9 4 1 1 12 10 10 31 14 1 1 2 16 12 7 21 8 5 2 1 18 11 6 18 8 1 1 1 14 5 8 26 11 1 1 1 14 16 16 22 10 1 1 1 15 11 8 22 9 1 1 1 15 15 16 29 15 1 1 1 17 12 7 15 14 2 1 2 19 9 11 16 11 1 1 1 10 11 16 24 14 3 2 2 18 15 16 17 6 1 1 1 14 12 12 19 20 1 1 2 14 16 13 22 9 1 1 2 17 14 19 31 10 1 1 1 14 11 7 28 8 1 1 2 16 10 8 38 11 2 1 1 18 7 12 26 14 4 2 2 14 11 13 25 11 1 1 1 12 10 11 25 16 2 1 1 17 11 8 29 14 1 1 2 9 16 16 28 11 2 4 1 16 14 15 15 11 3 1 2 14 12 11 18 12 1 1 1 11 12 12 21 9 1 2 2 16 11 7 25 7 1 2 1 13 6 9 23 13 1 1 2 17 14 15 23 10 1 1 1 15 9 6 19 9 2 1 1 14 15 14 18 9 1 1 2 16 12 14 18 13 1 1 2 9 12 7 26 16 1 1 2 15 9 15 18 12 1 1 2 17 13 14 18 6 1 1 1 13 15 17 28 14 1 1 2 15 11 14 17 14 1 1 2 16 10 5 29 10 2 2 1 16 13 14 12 4 1 1 2 12 16 8 28 12 1 1 1 11 13 8 20 14 1 1 1 15 14 13 17 9 2 1 1 17 14 14 17 9 1 1 1 13 16 16 20 10 1 1 2 16 9 11 31 14 1 1 1 14 8 10 21 10 1 1 2 11 8 10 19 9 1 1 2 12 12 10 23 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 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Happiness[t] = + 18.9957165366223 -0.027802499147085Popularity[t] + 0.0455463905768971KnowingPeople[t] -0.00637402452264016CMistakes[t] -0.280840510374424DAction[t] + 0.181746271876873Tobacco[t] -0.914459276859251Drugs[t] -0.762811775254925Gender[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) 18.9957165366223 1.559459 12.181 0 0 Popularity -0.027802499147085 0.078309 -0.355 0.723108 0.361554 KnowingPeople 0.0455463905768971 0.066199 0.688 0.492605 0.246302 CMistakes -0.00637402452264016 0.035558 -0.1793 0.857999 0.429 DAction -0.280840510374424 0.073563 -3.8177 0.000203 0.000102 Tobacco 0.181746271876873 0.201833 0.9005 0.369447 0.184723 Drugs -0.914459276859251 0.368378 -2.4824 0.014259 0.007129 Gender -0.762811775254925 0.401175 -1.9014 0.059343 0.029672 Multiple Linear Regression - Regression Statistics Multiple R 0.430048346921455 R-squared 0.184941580689876 Adjusted R-squared 0.143296259995199 F-TEST (value) 4.44087301057839 F-TEST (DF numerator) 7 F-TEST (DF denominator) 137 p-value 0.000177261813259388 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.19879831012477 Sum Squared Residuals 662.355819179235 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14 15.1838316030313 -1.18383160303134 2 18 14.8758878074126 3.12411219258742 3 11 15.5135306633869 -4.5135306633869 4 12 12.7854569899845 -0.785456989984462 5 16 14.9173337133357 1.08266628666429 6 18 15.1061860848256 2.89381391517442 7 14 14.4705801335575 -0.47058013355749 8 14 14.8354603760197 -0.835460376019715 9 15 14.8909422575144 0.109057742485613 10 15 13.4144421516362 1.5855578483638 11 17 12.8769434841987 4.12305651580128 12 19 14.5597495539262 4.44025044607376 13 10 12.5245842728517 -2.52458427285174 14 18 16.0184950392777 1.9815049607223 15 14 11.2123900048692 2.78760999513078 16 14 14.2168499394085 -0.216849939408522 17 17 14.9703383253408 2.02966167465919 18 14 14.3251804549211 -0.325180454921148 19 16 14.4368256154273 1.56317438457275 20 18 12.6226069299641 5.37739307003592 21 14 14.5378711160821 -0.537871116082105 22 12 13.2521245540802 -1.25212455408015 23 17 12.6793097587289 4.32069024127114 24 9 11.9547441598086 -2.95474415980857 25 16 14.2099774135199 1.79002258648013 26 14 14.1827534970653 -0.182753497065283 27 11 13.3744282930834 -2.37442829308335 28 16 14.4734955372592 1.52650446274083 29 13 13.1829533025515 -0.182953302551449 30 17 14.8391449592143 2.16085504078565 31 15 15.0563228200996 -0.0563228200995568 32 14 14.3156949272231 -0.315694927223065 33 16 13.2757403831666 2.72425961683337 34 9 12.063401921824 -3.06340192182395 35 15 13.6855347815592 1.3144652184408 36 17 15.9766332318954 1.02336676810457 37 13 12.9843913018552 0.0156086981447637 38 15 13.0290763964619 1.97092360353807 39 16 13.7239338979155 2.2760661020845 40 16 15.8137466245252 0.186253375474805 41 12 13.8711640835198 -1.87116408351985 42 11 13.4438827563934 -2.44388275639338 43 15 15.2488831074477 -0.248883107447692 44 17 15.1126832261477 1.88731677385229 45 13 14.0853966498101 -1.08539664981007 46 16 13.6216176549634 2.37838234503663 47 14 14.0281642750027 -0.0281642750027282 48 11 14.3217528344224 -3.32175283442243 49 12 13.5436559631263 -1.54365596312634 50 12 15.0266304790701 -3.02663047907007 51 15 15.1942063280719 -0.194206328071937 52 16 15.3512119659202 0.648788034079818 53 15 15.1113719669578 -0.111371966957816 54 12 13.0078972731827 -1.00789727318273 55 12 13.9167541087356 -1.91675410873562 56 8 12.6239617231306 -4.62396172313061 57 13 14.3862312588195 -1.38623125881954 58 11 14.8003556334316 -3.80035563343157 59 14 15.3904512549226 -1.39045125492265 60 15 13.7445136357822 1.25548636421785 61 10 13.9796685196258 -3.97966851962584 62 11 14.9184285830303 -3.91842858303026 63 12 13.3881385221454 -1.38813852214537 64 15 13.6621012802075 1.33789871979248 65 15 14.5594202219167 0.440579778083251 66 14 13.2319552149993 0.76804478500068 67 16 12.7063639487742 3.29363605122582 68 15 15.6057619488741 -0.605761948874105 69 15 15.4100005604537 -0.410000560453672 70 13 14.0790226252874 -1.07902262528743 71 17 14.7571156439112 2.2428843560888 72 13 13.3163197666959 -0.316319766695937 73 15 13.8151599257999 1.18484007420015 74 13 14.3141936282855 -1.31419362828546 75 15 13.1379829527698 1.86201704723019 76 16 14.2129161618388 1.78708383816117 77 15 14.7202496789135 0.279750321086532 78 16 13.8524758540173 2.14752414598272 79 15 14.5040776326839 0.495922367316138 80 14 14.8077247998208 -0.807724799820839 81 15 12.786758676296 2.21324132370395 82 7 13.2415770178752 -6.24157701787518 83 17 15.4846834406241 1.51531655937586 84 13 16.2435482236232 -3.24354822362315 85 15 14.5419387395482 0.45806126045178 86 14 14.0916607159115 -0.0916607159114886 87 13 12.4824989068191 0.517501093180879 88 16 14.9707320947092 1.02926790529078 89 12 14.505455814822 -2.50545581482197 90 14 15.4631880430515 -1.46318804305149 91 17 14.3894658787945 2.61053412120552 92 15 14.8432663472504 0.156733652749603 93 17 12.9146170369047 4.08538296309534 94 12 13.0957942989674 -1.09579429896735 95 16 14.1334323683441 1.86656763165585 96 11 12.7267172565135 -1.72671725651345 97 15 14.2022880032944 0.79771199670562 98 9 13.9461349596996 -4.94613495969958 99 16 14.848208425065 1.151791574935 100 10 12.6384430932132 -2.63844309321322 101 10 11.9097875817659 -1.9097875817659 102 15 14.6911359205765 0.308864079423516 103 11 14.1189463288907 -3.11894632889067 104 13 15.0133882751135 -2.01338827511348 105 14 12.5461231480931 1.45387685190692 106 18 15.2049641496919 2.79503585030809 107 16 14.506627838058 1.49337216194197 108 14 13.0726846832598 0.927315316740199 109 14 14.1847437244906 -0.18474372449059 110 14 14.5128154083329 -0.512815408332923 111 14 14.0170569178191 -0.0170569178190805 112 12 13.6018834798003 -1.6018834798003 113 14 14.633031520702 -0.633031520702015 114 15 15.0048416954307 -0.00484169543073433 115 15 13.9909487181334 1.00905128186662 116 13 12.1582021180912 0.84179788190879 117 17 14.2047418933306 2.79525810666936 118 17 14.5929638975792 2.40703610242076 119 19 15.5996371750345 3.40036282496553 120 15 14.5346364961072 0.465363503892836 121 13 13.8401108452435 -0.84011084524348 122 9 11.9320485170236 -2.9320485170236 123 15 15.1418406191236 -0.14184061912357 124 15 14.5914630993864 0.408536900613566 125 16 15.266293596663 0.733706403336993 126 11 13.3044149073808 -2.30441490738084 127 14 13.6626050079972 0.33739499200285 128 11 13.5778324867961 -2.57783248679606 129 15 12.4537809612442 2.54621903875583 130 13 12.8035839216858 0.196416078314244 131 16 12.9074247518848 3.09257524811517 132 14 15.6099354605564 -1.60993546055644 133 15 13.9899869945072 1.01001300549285 134 16 14.5181167515597 1.48188324844031 135 16 14.987346998376 1.01265300162401 136 11 12.4350752898802 -1.43507528988019 137 13 13.4421651686227 -0.442165168622658 138 16 14.2099774135199 1.79002258648013 139 12 14.0979678174859 -2.09796781748592 140 9 13.4928538066929 -4.49285380669292 141 13 13.0329331774603 -0.0329331774602952 142 13 16.2435482236232 -3.24354822362315 143 14 14.7118651812982 -0.71186518129823 144 19 15.5996371750345 3.40036282496553 145 13 15.3188635876083 -2.31886358760826 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.249709661191154 0.499419322382309 0.750290338808846 12 0.210505369739087 0.421010739478173 0.789494630260913 13 0.232084565003127 0.464169130006255 0.767915434996873 14 0.397914059144242 0.795828118288485 0.602085940855758 15 0.340552533668904 0.681105067337807 0.659447466331096 16 0.241130777796967 0.482261555593933 0.758869222203033 17 0.568505313276329 0.862989373447342 0.431494686723671 18 0.47714341881963 0.95428683763926 0.52285658118037 19 0.531619741790979 0.936760516418042 0.468380258209021 20 0.88027259336957 0.23945481326086 0.11972740663043 21 0.858823975931402 0.282352048137196 0.141176024068598 22 0.856592912536113 0.286814174927773 0.143407087463887 23 0.890057538345726 0.219884923308547 0.109942461654274 24 0.926990130132666 0.146019739734667 0.0730098698673336 25 0.904225972200996 0.191548055598008 0.0957740277990038 26 0.88695483814733 0.226090323705339 0.11304516185267 27 0.912335915099383 0.175328169801233 0.0876640849006165 28 0.894054031345209 0.211891937309583 0.105945968654791 29 0.892513269675928 0.214973460648143 0.107486730324072 30 0.884013865591507 0.231972268816987 0.115986134408493 31 0.863950834479203 0.272098331041595 0.136049165520797 32 0.831934419258155 0.33613116148369 0.168065580741845 33 0.819505870036874 0.360988259926252 0.180494129963126 34 0.887880474159393 0.224239051681213 0.112119525840607 35 0.862869931012943 0.274260137974114 0.137130068987057 36 0.829648384141502 0.340703231716996 0.170351615858498 37 0.789432847657596 0.421134304684809 0.210567152342404 38 0.761592806043712 0.476814387912577 0.238407193956288 39 0.762796330242138 0.474407339515723 0.237203669757862 40 0.719698687983326 0.560602624033348 0.280301312016674 41 0.699385533842549 0.601228932314902 0.300614466157451 42 0.73334366435983 0.533312671280339 0.26665633564017 43 0.689093927844304 0.621812144311392 0.310906072155696 44 0.664426471970888 0.671147056058224 0.335573528029112 45 0.625452665790739 0.749094668418522 0.374547334209261 46 0.614710385753558 0.770579228492884 0.385289614246442 47 0.585188996637593 0.829622006724814 0.414811003362407 48 0.711466801770123 0.577066396459754 0.288533198229877 49 0.702481729660043 0.595036540679915 0.297518270339957 50 0.747279683643738 0.505440632712523 0.252720316356262 51 0.703633685554569 0.592732628890863 0.296366314445431 52 0.659346974042482 0.681306051915037 0.340653025957518 53 0.618954285730317 0.762091428539365 0.381045714269683 54 0.585477770042774 0.829044459914451 0.414522229957226 55 0.566566648664829 0.866866702670343 0.433433351335171 56 0.757576537306772 0.484846925386455 0.242423462693228 57 0.731184884741196 0.537630230517608 0.268815115258804 58 0.804527097753856 0.390945804492289 0.195472902246144 59 0.790832164584861 0.418335670830277 0.209167835415139 60 0.765137431214466 0.469725137571068 0.234862568785534 61 0.84769611896354 0.304607762072919 0.152303881036459 62 0.900836695940028 0.198326608119944 0.099163304059972 63 0.886533701742371 0.226932596515257 0.113466298257629 64 0.870893498086672 0.258213003826657 0.129106501913328 65 0.844575143171901 0.310849713656198 0.155424856828099 66 0.816317632789097 0.367364734421806 0.183682367210903 67 0.852869097930114 0.294261804139772 0.147130902069886 68 0.82512274933879 0.349754501322421 0.17487725066121 69 0.793345144693776 0.413309710612447 0.206654855306224 70 0.768760874639912 0.462478250720175 0.231239125360088 71 0.7685206828493 0.4629586343014 0.2314793171507 72 0.73024736763171 0.539505264736581 0.26975263236829 73 0.700643417395328 0.598713165209345 0.299356582604672 74 0.683634453040181 0.632731093919637 0.316365546959819 75 0.669973007941785 0.660053984116431 0.330026992058215 76 0.651346960201458 0.697306079597084 0.348653039798542 77 0.604321870285025 0.79135625942995 0.395678129714975 78 0.599323263224418 0.801353473551163 0.400676736775582 79 0.553772009859633 0.892455980280734 0.446227990140367 80 0.511862673995356 0.976274652009289 0.488137326004644 81 0.515683130595054 0.96863373880989 0.484316869404946 82 0.796746423186168 0.406507153627663 0.203253576813832 83 0.780097659731765 0.43980468053647 0.219902340268235 84 0.821151426767421 0.357697146465158 0.178848573232579 85 0.78807643637456 0.423847127250878 0.211923563625439 86 0.749881385044835 0.500237229910329 0.250118614955165 87 0.711629742625002 0.576740514749995 0.288370257374998 88 0.684300624151772 0.631398751696456 0.315699375848228 89 0.689970767268842 0.620058465462316 0.310029232731158 90 0.669444138148268 0.661111723703464 0.330555861851732 91 0.699432204288688 0.601135591422624 0.300567795711312 92 0.652544215170264 0.694911569659472 0.347455784829736 93 0.823934421691819 0.352131156616363 0.176065578308181 94 0.794113045495834 0.411773909008333 0.205886954504166 95 0.795607016650217 0.408785966699565 0.204392983349783 96 0.77741771291142 0.44516457417716 0.22258228708858 97 0.752558876375209 0.494882247249583 0.247441123624791 98 0.899540348946936 0.200919302106128 0.100459651053064 99 0.879589356371921 0.240821287256157 0.120410643628079 100 0.879153213269571 0.241693573460857 0.120846786730429 101 0.86333663730399 0.273326725392021 0.13666336269601 102 0.832178179559715 0.33564364088057 0.167821820440285 103 0.86268477745496 0.27463044509008 0.13731522254504 104 0.886633956006367 0.226732087987267 0.113366043993633 105 0.89171592986209 0.21656814027582 0.10828407013791 106 0.908586051024545 0.182827897950909 0.0914139489754545 107 0.884974583211857 0.230050833576287 0.115025416788143 108 0.898964617627392 0.202070764745216 0.101035382372608 109 0.87340085980702 0.25319828038596 0.12659914019298 110 0.90266108084191 0.19467783831618 0.0973389191580902 111 0.882956250587048 0.234087498825904 0.117043749412952 112 0.851205655375483 0.297588689249034 0.148794344624517 113 0.81040189003717 0.379196219925661 0.18959810996283 114 0.765976169976212 0.468047660047577 0.234023830023788 115 0.724421485402816 0.551157029194368 0.275578514597184 116 0.665575438178858 0.668849123642285 0.334424561821142 117 0.713252741151518 0.573494517696965 0.286747258848482 118 0.69002896580762 0.61994206838476 0.30997103419238 119 0.703773818298344 0.592452363403313 0.296226181701656 120 0.802212943325252 0.395574113349495 0.197787056674748 121 0.749843005172931 0.500313989654138 0.250156994827069 122 0.855684742578498 0.288630514843003 0.144315257421502 123 0.821635814449319 0.356728371101363 0.178364185550681 124 0.78268797215557 0.434624055688859 0.217312027844429 125 0.711952054584441 0.576095890831118 0.288047945415559 126 0.664905699247685 0.67018860150463 0.335094300752315 127 0.576830495071415 0.84633900985717 0.423169504928585 128 0.532828728802137 0.934342542395726 0.467171271197863 129 0.437633828212866 0.875267656425731 0.562366171787134 130 0.345298966383009 0.690597932766018 0.654701033616991 131 0.424558044333715 0.84911608866743 0.575441955666285 132 0.569608386365709 0.860783227268581 0.430391613634291 133 0.433754457427057 0.867508914854114 0.566245542572943 134 0.646973282891341 0.706053434217318 0.353026717108659 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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