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: Sat, 18 Dec 2010 23:03:08 +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/19/t1292713281vhw0cz1y7ptnz94.htm/, Retrieved Sun, 19 Dec 2010 00:01:33 +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/19/t1292713281vhw0cz1y7ptnz94.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 11 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Happiness[t] = + 18.9957165366223 -0.0278024991470845Popularity[t] + 0.0455463905768971KnowingPeople[t] -0.00637402452264036CMistakes[t] -0.280840510374423DAction[t] + 0.181746271876873Tobacco[t] -0.914459276859248Drugs[t] -0.762811775254921Gender[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 -0.0278024991470845 0.078309 -0.355 0.723108 0.361554 KnowingPeople 0.0455463905768971 0.066199 0.688 0.492605 0.246302 CMistakes -0.00637402452264036 0.035558 -0.1793 0.857999 0.429 DAction -0.280840510374423 0.073563 -3.8177 0.000203 0.000102 Tobacco 0.181746271876873 0.201833 0.9005 0.369447 0.184723 Drugs -0.914459276859248 0.368378 -2.4824 0.014259 0.007129 Gender -0.762811775254921 0.401175 -1.9014 0.059343 0.029672 Multiple Linear Regression - Regression Statistics Multiple R 0.430048346921455 R-squared 0.184941580689876 Adjusted R-squared 0.143296259995198 F-TEST (value) 4.44087301057838 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 662.355819179236 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14 15.1838316030315 -1.18383160303153 2 18 14.8758878074126 3.12411219258743 3 11 15.5135306633869 -4.51353066338689 4 12 12.7854569899845 -0.785456989984458 5 16 14.9173337133357 1.08266628666430 6 18 15.1061860848256 2.89381391517442 7 14 14.4705801335575 -0.470580133557483 8 14 14.8354603760197 -0.835460376019714 9 15 14.8909422575144 0.109057742485617 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 1.98150496072231 15 14 11.2123900048692 2.78760999513077 16 14 14.2168499394085 -0.216849939408523 17 17 14.9703383253408 2.02966167465919 18 14 14.3251804549211 -0.325180454921146 19 16 14.4368256154272 1.56317438457275 20 18 12.6226069299641 5.37739307003592 21 14 14.5378711160821 -0.537871116082101 22 12 13.2521245540801 -1.25212455408015 23 17 12.6793097587289 4.32069024127114 24 9 11.9547441598086 -2.95474415980858 25 16 14.2099774135199 1.79002258648013 26 14 14.1827534970653 -0.182753497065282 27 11 13.3744282930834 -2.37442829308336 28 16 14.4734955372592 1.52650446274084 29 13 13.1829533025514 -0.182953302551449 30 17 14.8391449592143 2.16085504078566 31 15 15.0563228200996 -0.0563228200995524 32 14 14.3156949272231 -0.315694927223066 33 16 13.2757403831666 2.72425961683337 34 9 12.0634019218240 -3.06340192182396 35 15 13.6855347815592 1.31446521844080 36 17 15.9766332318954 1.02336676810457 37 13 12.9843913018552 0.0156086981447623 38 15 13.0290763964619 1.97092360353807 39 16 13.7239338979155 2.27606610208450 40 16 15.8137466245252 0.186253375474806 41 12 13.8711640835198 -1.87116408351985 42 11 13.4438827563934 -2.44388275639338 43 15 15.2488831074477 -0.248883107447689 44 17 15.1126832261477 1.88731677385229 45 13 14.0853966498101 -1.08539664981007 46 16 13.6216176549634 2.37838234503664 47 14 14.0281642750027 -0.0281642750027268 48 11 14.3217528344224 -3.32175283442243 49 12 13.5436559631263 -1.54365596312634 50 12 15.0266304790701 -3.02663047907007 51 15 15.1942063280719 -0.194206328071934 52 16 15.3512119659202 0.648788034079823 53 15 15.1113719669578 -0.111371966957814 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 -1.39045125492264 60 15 13.7445136357822 1.25548636421785 61 10 13.9796685196258 -3.97966851962584 62 11 14.9184285830303 -3.91842858303025 63 12 13.3881385221454 -1.38813852214537 64 15 13.6621012802075 1.33789871979248 65 15 14.5594202219167 0.440579778083251 66 14 13.2319552149993 0.768044785000675 67 16 12.7063639487742 3.29363605122582 68 15 15.6057619488741 -0.605761948874102 69 15 15.4100005604537 -0.410000560453668 70 13 14.0790226252874 -1.07902262528743 71 17 14.7571156439112 2.2428843560888 72 13 13.3163197666959 -0.316319766695935 73 15 13.8151599257999 1.18484007420015 74 13 14.3141936282855 -1.31419362828546 75 15 13.1379829527698 1.86201704723019 76 16 14.2129161618388 1.78708383816118 77 15 14.7202496789135 0.279750321086534 78 16 13.8524758540173 2.14752414598272 79 15 14.5040776326839 0.495922367316139 80 14 14.8077247998208 -0.807724799820837 81 15 12.7867586762960 2.21324132370396 82 7 13.2415770178752 -6.24157701787518 83 17 15.4846834406241 1.51531655937586 84 13 16.2435482236231 -3.24354822362315 85 15 14.5419387395482 0.458061260451782 86 14 14.0916607159115 -0.0916607159114877 87 13 12.4824989068191 0.517501093180875 88 16 14.9707320947092 1.02926790529078 89 12 14.5054558148220 -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 -1.72671725651346 97 15 14.2022880032944 0.797711996705624 98 9 13.9461349596996 -4.94613495969958 99 16 14.848208425065 1.15179157493501 100 10 12.6384430932132 -2.63844309321322 101 10 11.9097875817659 -1.9097875817659 102 15 14.6911359205765 0.308864079423518 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 14.5066278380580 1.49337216194197 108 14 13.0726846832598 0.927315316740201 109 14 14.1847437244906 -0.184743724490588 110 14 14.5128154083329 -0.512815408332917 111 14 14.0170569178191 -0.0170569178190783 112 12 13.6018834798003 -1.60188347980030 113 14 14.6330315207020 -0.633031520702012 114 15 15.0048416954307 -0.0048416954307306 115 15 13.9909487181334 1.00905128186662 116 13 12.1582021180912 0.841797881908786 117 17 14.2047418933306 2.79525810666936 118 17 14.5929638975792 2.40703610242076 119 19 15.5996371750345 3.40036282496553 120 15 14.5346364961072 0.465363503892837 121 13 13.8401108452435 -0.840110845243483 122 9 11.9320485170236 -2.93204851702361 123 15 15.1418406191236 -0.141840619123571 124 15 14.5914630993864 0.408536900613567 125 16 15.266293596663 0.733706403337001 126 11 13.3044149073808 -2.30441490738084 127 14 13.6626050079972 0.337394992002850 128 11 13.5778324867961 -2.57783248679606 129 15 12.4537809612442 2.54621903875583 130 13 12.8035839216858 0.196416078314241 131 16 12.9074247518848 3.09257524811517 132 14 15.6099354605564 -1.60993546055643 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 -0.0329331774602981 142 13 16.2435482236231 -3.24354822362315 143 14 14.7118651812982 -0.711865181298224 144 19 15.5996371750345 3.40036282496553 145 13 15.3188635876083 -2.31886358760825 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.249709661191154 0.499419322382307 0.750290338808846 12 0.210505369739085 0.421010739478171 0.789494630260915 13 0.232084565003127 0.464169130006253 0.767915434996874 14 0.397914059144243 0.795828118288486 0.602085940855757 15 0.340552533668906 0.681105067337812 0.659447466331094 16 0.241130777796968 0.482261555593936 0.758869222203032 17 0.568505313276329 0.862989373447342 0.431494686723671 18 0.477143418819631 0.954286837639261 0.522856581180369 19 0.531619741790981 0.936760516418037 0.468380258209019 20 0.88027259336957 0.239454813260859 0.119727406630430 21 0.858823975931402 0.282352048137196 0.141176024068598 22 0.856592912536119 0.286814174927763 0.143407087463881 23 0.890057538345728 0.219884923308545 0.109942461654272 24 0.926990130132666 0.146019739734668 0.0730098698673339 25 0.904225972200997 0.191548055598006 0.0957740277990032 26 0.88695483814733 0.226090323705339 0.113045161852670 27 0.912335915099384 0.175328169801232 0.0876640849006158 28 0.89405403134521 0.211891937309582 0.105945968654791 29 0.89251326967593 0.214973460648142 0.107486730324071 30 0.884013865591507 0.231972268816985 0.115986134408493 31 0.863950834479202 0.272098331041596 0.136049165520798 32 0.831934419258156 0.336131161483688 0.168065580741844 33 0.819505870036878 0.360988259926245 0.180494129963122 34 0.887880474159393 0.224239051681215 0.112119525840607 35 0.862869931012943 0.274260137974115 0.137130068987058 36 0.829648384141502 0.340703231716996 0.170351615858498 37 0.789432847657595 0.421134304684810 0.210567152342405 38 0.76159280604371 0.476814387912581 0.238407193956290 39 0.762796330242139 0.474407339515722 0.237203669757861 40 0.719698687983326 0.560602624033348 0.280301312016674 41 0.699385533842548 0.601228932314905 0.300614466157452 42 0.733343664359833 0.533312671280334 0.266656335640167 43 0.689093927844303 0.621812144311394 0.310906072155697 44 0.664426471970889 0.671147056058222 0.335573528029111 45 0.625452665790736 0.749094668418528 0.374547334209264 46 0.614710385753559 0.770579228492882 0.385289614246441 47 0.585188996637591 0.829622006724818 0.414811003362409 48 0.711466801770124 0.577066396459752 0.288533198229876 49 0.702481729660041 0.595036540679918 0.297518270339959 50 0.747279683643738 0.505440632712523 0.252720316356262 51 0.703633685554569 0.592732628890863 0.296366314445431 52 0.659346974042481 0.681306051915038 0.340653025957519 53 0.61895428573032 0.76209142853936 0.38104571426968 54 0.585477770042772 0.829044459914456 0.414522229957228 55 0.566566648664832 0.866866702670337 0.433433351335168 56 0.757576537306774 0.484846925386451 0.242423462693226 57 0.7311848847412 0.5376302305176 0.2688151152588 58 0.804527097753853 0.390945804492294 0.195472902246147 59 0.790832164584861 0.418335670830278 0.209167835415139 60 0.765137431214466 0.469725137571068 0.234862568785534 61 0.847696118963544 0.304607762072912 0.152303881036456 62 0.900836695940028 0.198326608119944 0.099163304059972 63 0.886533701742372 0.226932596515257 0.113466298257628 64 0.870893498086672 0.258213003826657 0.129106501913328 65 0.8445751431719 0.310849713656198 0.155424856828099 66 0.816317632789096 0.367364734421808 0.183682367210904 67 0.852869097930114 0.294261804139772 0.147130902069886 68 0.82512274933879 0.349754501322421 0.174877250661210 69 0.793345144693776 0.413309710612449 0.206654855306224 70 0.768760874639912 0.462478250720177 0.231239125360088 71 0.7685206828493 0.462958634301399 0.231479317150700 72 0.73024736763171 0.53950526473658 0.26975263236829 73 0.700643417395328 0.598713165209344 0.299356582604672 74 0.68363445304018 0.63273109391964 0.31636554695982 75 0.669973007941782 0.660053984116435 0.330026992058217 76 0.651346960201457 0.697306079597087 0.348653039798543 77 0.604321870285023 0.791356259429954 0.395678129714977 78 0.599323263224419 0.801353473551162 0.400676736775581 79 0.553772009859634 0.892455980280731 0.446227990140366 80 0.511862673995357 0.976274652009286 0.488137326004643 81 0.515683130595052 0.968633738809895 0.484316869404948 82 0.796746423186167 0.406507153627666 0.203253576813833 83 0.780097659731766 0.439804680536467 0.219902340268234 84 0.821151426767422 0.357697146465156 0.178848573232578 85 0.78807643637456 0.423847127250879 0.211923563625440 86 0.749881385044836 0.500237229910327 0.250118614955164 87 0.711629742625002 0.576740514749996 0.288370257374998 88 0.684300624151771 0.631398751696458 0.315699375848229 89 0.689970767268842 0.620058465462317 0.310029232731158 90 0.669444138148268 0.661111723703464 0.330555861851732 91 0.699432204288688 0.601135591422623 0.300567795711311 92 0.652544215170268 0.694911569659463 0.347455784829732 93 0.823934421691819 0.352131156616363 0.176065578308181 94 0.794113045495833 0.411773909008334 0.205886954504167 95 0.795607016650218 0.408785966699564 0.204392983349782 96 0.77741771291142 0.445164574177159 0.222582287088580 97 0.752558876375206 0.494882247249588 0.247441123624794 98 0.899540348946936 0.200919302106128 0.100459651053064 99 0.87958935637192 0.240821287256159 0.120410643628079 100 0.879153213269572 0.241693573460855 0.120846786730428 101 0.86333663730399 0.27332672539202 0.13666336269601 102 0.832178179559715 0.33564364088057 0.167821820440285 103 0.862684777454958 0.274630445090083 0.137315222545042 104 0.886633956006366 0.226732087987268 0.113366043993634 105 0.891715929862091 0.216568140275818 0.108284070137909 106 0.908586051024546 0.182827897950908 0.091413948975454 107 0.884974583211856 0.230050833576289 0.115025416788144 108 0.898964617627392 0.202070764745216 0.101035382372608 109 0.873400859807019 0.253198280385962 0.126599140192981 110 0.90266108084191 0.194677838316179 0.0973389191580897 111 0.882956250587048 0.234087498825904 0.117043749412952 112 0.851205655375482 0.297588689249037 0.148794344624518 113 0.81040189003717 0.37919621992566 0.18959810996283 114 0.765976169976212 0.468047660047577 0.234023830023788 115 0.724421485402817 0.551157029194367 0.275578514597183 116 0.665575438178859 0.668849123642282 0.334424561821141 117 0.713252741151518 0.573494517696964 0.286747258848482 118 0.69002896580762 0.619942068384759 0.309971034192379 119 0.703773818298343 0.592452363403315 0.296226181701657 120 0.802212943325253 0.395574113349495 0.197787056674748 121 0.74984300517293 0.50031398965414 0.25015699482707 122 0.855684742578497 0.288630514843005 0.144315257421503 123 0.82163581444932 0.356728371101359 0.178364185550680 124 0.78268797215557 0.434624055688859 0.217312027844430 125 0.71195205458444 0.576095890831121 0.288047945415560 126 0.664905699247686 0.670188601504629 0.335094300752314 127 0.576830495071415 0.84633900985717 0.423169504928585 128 0.532828728802137 0.934342542395725 0.467171271197863 129 0.437633828212867 0.875267656425734 0.562366171787133 130 0.345298966383008 0.690597932766017 0.654701033616992 131 0.424558044333715 0.84911608866743 0.575441955666285 132 0.56960838636571 0.860783227268579 0.430391613634290 133 0.433754457427058 0.867508914854117 0.566245542572942 134 0.646973282891342 0.706053434217315 0.353026717108658 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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