gelukte blog minitut7

*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: Thu, 02 Dec 2010 15:54:35 +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/02/t129130517439i12ssse3ow4xe.htm/, Retrieved Thu, 02 Dec 2010 16:52:54 +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/02/t129130517439i12ssse3ow4xe.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 «

2 13 3 13 13 3 1 12 3 7 13 5 0 15 4 10 16 6 3 12 3 9 12 6 3 10 2 10 11 5 1 12 3 6 12 3 3 15 4 15 18 8 1 9 2 10 11 4 4 12 3 14 14 4 0 11 4 5 9 4 3 11 3 15 14 6 2 11 3 10 12 6 4 15 4 16 11 5 3 7 2 13 12 4 1 11 2 6 13 6 1 11 3 12 11 4 2 10 2 10 12 6 3 14 4 15 16 6 1 10 2 6 9 4 1 6 1 8 11 4 2 11 3 8 13 2 3 15 4 13 15 7 4 11 2 15 10 5 2 12 3 7 11 4 1 14 3 12 13 6 2 15 4 15 16 6 2 9 3 13 15 7 4 13 3 15 14 5 2 13 4 13 14 6 3 16 4 9 14 4 3 13 4 9 8 4 3 12 3 15 13 7 4 14 4 14 15 7 2 11 3 9 13 4 2 9 2 9 11 4 4 16 4 16 15 6 3 12 3 12 15 6 4 10 2 10 9 5 2 13 4 13 13 6 5 16 4 17 16 7 3 14 4 13 13 6 1 15 4 5 11 3 1 5 2 6 12 3 1 8 2 9 12 4 2 11 3 9 12 6 3 16 4 13 14 7 9 17 5 20 14 5 0 9 2 5 8 4 0 9 3 8 13 5 2 13 3 14 16 6 2 10 2 6 13 6 3 6 2 14 11 6 1 12 3 9 14 5 2 8 2 8 13 4 0 14 4 9 13 5 5 12 3 16 13 5 2 11 3 12 12 4 4 16 4 16 16 6 3 8 1 11 15 2 0 15 4 11 15 8 0 7 2 6 12 3 4 16 4 16 14 6 1 14 4 15 12 6 1 16 4 11 15 6 4 9 2 9 12 5 2 14 4 12 13 5 4 11 2 15 12 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 Multiple Linear Regression - Estimated Regression Equation Ihavemanyfriends[t] = + 0.0496203608625268 -0.0502689740190555sum[t] + 0.282977054518711Popularity[t] + 0.0269440633049747KnowingPeople[t] -0.0242599319071114Liked[t] -0.0409210098415009Celebrity[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) 0.0496203608625268 0.202278 0.2453 0.806556 0.403278 sum -0.0502689740190555 0.032126 -1.5648 0.119761 0.059881 Popularity 0.282977054518711 0.015892 17.8068 0 0 KnowingPeople 0.0269440633049747 0.016869 1.5973 0.11232 0.05616 Liked -0.0242599319071114 0.019572 -1.2395 0.217109 0.108555 Celebrity -0.0409210098415009 0.031395 -1.3034 0.194445 0.097222 Multiple Linear Regression - Regression Statistics Multiple R 0.895389490189128 R-squared 0.801722339141146 Adjusted R-squared 0.795068726360648 F-TEST (value) 120.494288680425 F-TEST (DF numerator) 5 F-TEST (DF denominator) 149 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.404795214699169 Sum Squared Residuals 24.4150157106586 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3 3.53991480021537 -0.539914800215375 2 3 3.06370032020287 -0.0637003202028701 3 4 3.93003184213015 0.0699681578698532 4 3 3.00038942084032 -0.000389420840319157 5 2 2.52656031685648 -0.526560316856485 6 3 3.14285820848801 -0.142858208488009 7 4 3.78358335310063 0.216416646899371 8 2 2.38504222021739 -0.385042220217386 9 3 3.11816291921492 -0.118162919214916 10 4 2.91506485056321 1.08493514943679 11 3 2.83055688233723 0.169443117662766 12 3 2.79462540364564 0.205374596354361 13 4 4.05284099526083 -0.0528409952608306 14 2 1.77512242114967 0.224877578850334 15 2 2.71285819253768 -0.712858192537684 16 3 3.00488445586476 -0.00488445586475642 17 2 2.51164834912693 -0.511648349126928 18 4 3.63096818207914 0.369031817920857 19 2 2.60876288533042 -0.60876288533042 20 1 1.48222293005130 -0.482222930051305 21 3 2.88016138449458 0.119838615505419 22 4 3.84339603205351 0.156603967946485 23 2 2.91824864578812 -0.918248645788125 24 3 3.10287221983954 -0.102872219839538 25 3 3.72345373592366 -0.723453735923664 26 4 3.96421421061691 0.0357857893830908 27 3 2.19580267896031 0.804197321039694 28 3 3.3871630271971 -0.387163027197100 29 4 3.39289183878376 0.607108161216239 30 4 4.16561979478394 -0.165619794783941 31 4 3.46224822267048 0.537751777329523 32 3 3.09687285892156 -0.0968728589215549 33 4 3.53709406682072 0.462905933179277 34 3 2.82526342811655 0.174736571883446 35 2 2.30782918289336 -0.307829182893356 36 4 4.19785731230960 -0.197857312309595 37 3 3.00844181503391 -0.008441815033909 38 2 2.52481120665165 -0.524811206651652 39 4 3.41715177069087 0.582848229309127 40 4 4.1093514598469 -0.109351459846902 41 4 3.64985985119053 0.350140148809472 42 4 3.98910524064628 0.0108947593537228 43 2 1.16201882685703 0.837981173142966 44 2 2.05086117048659 -0.050861170486589 45 3 2.76768134034066 0.232318659659336 46 4 4.15063301847934 -0.150633018479337 47 5 4.40244669170154 0.597553308298461 48 2 2.3733706734329 -0.373370673432902 49 3 2.29198219397077 0.708017806029232 50 3 3.37131603827451 -0.371316038274513 51 2 2.37961216399992 -0.379612163999918 52 2 1.46150734216004 0.53849265783996 53 3 3.09332851490571 -0.093328514905708 54 2 1.94938820125545 0.0506117987445524 55 4 3.73381152986930 0.266188470130704 56 3 3.10512099387142 -0.105120993871420 57 3 2.93035554993859 0.0696444500614104 58 4 4.17359738040248 -0.173597380402484 59 1 2.01327357302010 -1.01327357302010 60 4 3.89939381765923 0.100606182340769 61 2 1.77824190991351 0.221758090086489 62 4 4.22211724421671 -0.222117244216706 63 4 3.8285458577457 0.171454142254301 64 4 4.21394391784189 -0.213943917841888 65 2 2.14211029310663 -0.142110293106632 66 4 3.71410577174611 0.285894228253891 67 2 2.8288077721324 -0.828807772132401 68 4 3.37093730662869 0.629062693371308 69 4 3.90951199499516 0.0904880050048423 70 1 1.29554632272428 -0.295546322724285 71 4 3.92710809412258 0.0728919058774222 72 4 3.35753483249426 0.642465167505738 73 3 2.76732079154973 0.23267920845027 74 3 2.68056404929187 0.319435950708131 75 3 3.03363676813405 -0.0336367681340510 76 3 3.11084980545808 -0.110849805458081 77 3 3.14908668390172 -0.149086683901719 78 3 3.09788268868963 -0.0978826886896326 79 4 3.64993465976557 0.350065340234425 80 1 1.48323275981938 -0.483232759819382 81 3 1.71399598935793 1.28600401064207 82 4 3.62746996166948 0.372530038330522 83 3 3.55375514475511 -0.553755144755113 84 3 2.55256935896843 0.447430641031571 85 4 3.35224137827358 0.647758621726417 86 3 3.10454652146932 -0.104546521469324 87 2 2.30514505149549 -0.305145051495492 88 3 3.04574367228452 -0.045743672284516 89 4 4.10877698744481 -0.108776987444805 90 3 2.54322139479087 0.456778605209126 91 4 3.54651683957333 0.453483160426675 92 3 2.46943176930146 0.530568230698538 93 4 4.13267637056098 -0.132676370560983 94 4 3.98672503231919 0.0132749676808122 95 3 3.06825449398679 -0.0682544939867946 96 2 2.61051199553525 -0.610511995535253 97 1 1.81003890237160 -0.810038902371603 98 2 2.03614494351331 -0.0361449435133078 99 2 2.81665474437575 -0.816654744375754 100 3 3.31299467006187 -0.312994670061868 101 4 4.15962043386596 -0.159620433865957 102 4 4.12999223916312 -0.129992239163119 103 3 3.68298626630303 -0.682986266303031 104 3 2.75840818473816 0.241591815261844 105 1 0.891269608670017 0.108730391329983 106 3 3.44134990917624 -0.441349909176243 107 3 2.33079354481650 0.669206455183498 108 4 3.54644203099828 0.453557969001722 109 2 2.04326231651387 -0.0432623165138671 110 2 1.97808137476525 0.0219186252347451 111 3 2.86423958699695 0.135760413003053 112 3 3.11657861704474 -0.116578617044742 113 3 2.73618310325176 0.263816896748235 114 4 3.64798980880447 0.352010191195533 115 4 3.79219203684143 0.207807963158571 116 4 4.21849809162581 -0.218498091625812 117 4 4.26152876046308 -0.26152876046308 118 2 2.64369754940724 -0.643697549407243 119 3 3.60589416116023 -0.605894161160229 120 4 3.72539858688477 0.274601413115228 121 3 3.13880369853113 -0.138803698531134 122 4 3.76892891954909 0.231071080450911 123 2 1.90367340102032 0.0963265989796834 124 4 3.3462420173556 0.6537579826444 125 4 4.22211724421671 -0.222117244216706 126 3 3.00887717239989 -0.00887717239989009 127 4 4.08813620812859 -0.0881362081285879 128 3 3.18082453760032 -0.180824537600323 129 3 2.78702654967292 0.212973450327083 130 1 1.05552512846779 -0.0555251284677942 131 4 4.04510553929132 -0.0451055392913202 132 4 3.87894877909929 0.121051220900711 133 2 2.46588742528562 -0.465887425285616 134 3 3.22144413741038 -0.221444137410377 135 4 3.93190188451621 0.0680981154837921 136 3 3.13156539334935 -0.131565393349346 137 4 3.65254398258839 0.347456017411609 138 2 1.86269325241933 0.137306747580671 139 5 5.06649423398891 -0.0664942339889137 140 3 3.10418597267839 -0.104185972678390 141 4 3.26628570987422 0.733714290125781 142 3 3.36594777547879 -0.365947775478786 143 4 4.03881792511812 -0.0388179251181231 144 2 2.10312111907293 -0.103121119072934 145 3 3.13518454594024 -0.135184545940240 146 3 2.37599301140902 0.624006988590976 147 2 1.90753217022092 0.092467829779084 148 NA NA 0.0375348995879236 149 4 4.12848274556799 -0.128482745567992 150 4 4.29901772352534 -0.299017723525342 151 3 3.12905721797009 -0.129057217970089 152 4 4.33021907241441 -0.330219072414406 153 2 1.57077677148044 0.429223228519564 154 4 3.65441402497445 0.345585975025548 155 4 4.21971069702110 -0.219710697021105 156 3 NA NA Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.0763749284487003 0.152749856897401 0.9236250715513 10 0.789713070269334 0.420573859461331 0.210286929730666 11 0.773561564484629 0.452876871030743 0.226438435515371 12 0.671250629349592 0.657498741300815 0.328749370650408 13 0.634216858277358 0.731566283445284 0.365783141722642 14 0.681095170509505 0.63780965898099 0.318904829490495 15 0.851903168816455 0.296193662367089 0.148096831183545 16 0.803045120986422 0.393909758027157 0.196954879013578 17 0.830575960252561 0.338848079494877 0.169424039747439 18 0.8279082456424 0.344183508715199 0.172091754357600 19 0.847965735160502 0.304068529678995 0.152034264839498 20 0.815465792583215 0.36906841483357 0.184534207416785 21 0.80012958859045 0.399740822819101 0.199870411409551 22 0.746681961471968 0.506636077056064 0.253318038528032 23 0.848045648319572 0.303908703360856 0.151954351680428 24 0.803885839275634 0.392228321448732 0.196114160724366 25 0.8798740169682 0.240251966063599 0.120125983031799 26 0.842937157865523 0.314125684268953 0.157062842134477 27 0.921914273702845 0.156171452594310 0.0780857262971552 28 0.90918772149562 0.18162455700876 0.09081227850438 29 0.931392376398512 0.137215247202976 0.0686076236014878 30 0.910003493657766 0.179993012684469 0.0899965063422344 31 0.955124210110826 0.0897515797783478 0.0448757898891739 32 0.939825551384247 0.120348897231505 0.0601744486157527 33 0.939675582170525 0.120648835658951 0.0603244178294754 34 0.92581244327636 0.148375113447280 0.0741875567236399 35 0.910122278888943 0.179755442222115 0.0898777211110573 36 0.891902670430846 0.216194659138308 0.108097329569154 37 0.863524165216835 0.27295166956633 0.136475834783165 38 0.860812425501872 0.278375148996256 0.139187574498128 39 0.884521206254842 0.230957587490316 0.115478793745158 40 0.858462371487166 0.283075257025669 0.141537628512834 41 0.848499669821338 0.303000660357325 0.151500330178662 42 0.815494672574335 0.36901065485133 0.184505327425665 43 0.910313564579187 0.179372870841626 0.089686435420813 44 0.887780004099082 0.224439991801837 0.112219995900918 45 0.86822918519644 0.263541629607119 0.131770814803560 46 0.842971451061006 0.314057097877989 0.157028548938994 47 0.885598825348103 0.228802349303794 0.114401174651897 48 0.87516670589592 0.249666588208160 0.124833294104080 49 0.91398597078595 0.172028058428098 0.0860140292140492 50 0.915388270401751 0.169223459196498 0.084611729598249 51 0.917484135712949 0.165031728574103 0.0825158642870515 52 0.93261429037956 0.134771419240879 0.0673857096204395 53 0.916270029297047 0.167459941405905 0.0837299707029527 54 0.895952673377351 0.208094653245298 0.104047326622649 55 0.882816675350097 0.234366649299805 0.117183324649903 56 0.858549216596162 0.282901566807677 0.141450783403838 57 0.83017606095505 0.3396478780899 0.16982393904495 58 0.805686491052923 0.388627017894154 0.194313508947077 59 0.925656367704867 0.148687264590265 0.0743436322951327 60 0.910044201991837 0.179911596016326 0.0899557980081628 61 0.89708585227742 0.205828295445161 0.102914147722581 62 0.881511298356415 0.236977403287170 0.118488701643585 63 0.860945574072868 0.278108851854264 0.139054425927132 64 0.842349003721778 0.315301992556444 0.157650996278222 65 0.81530528983106 0.369389420337879 0.184694710168940 66 0.798038226457291 0.403923547085418 0.201961773542709 67 0.88133163439869 0.237336731202619 0.118668365601309 68 0.908424402132488 0.183151195735024 0.0915755978675119 69 0.888340809244507 0.223318381510985 0.111659190755493 70 0.88067448510066 0.238651029798681 0.119325514899341 71 0.857851362442107 0.284297275115785 0.142148637557893 72 0.891857536305409 0.216284927389182 0.108142463694591 73 0.875968734277982 0.248062531444035 0.124031265722018 74 0.86772173860457 0.264556522790861 0.132278261395431 75 0.84084805243443 0.31830389513114 0.15915194756557 76 0.812552221847998 0.374895556304005 0.187447778152002 77 0.784449868480701 0.431100263038598 0.215550131519299 78 0.750874065734973 0.498251868530054 0.249125934265027 79 0.735944331218349 0.528111337563302 0.264055668781651 80 0.757474274172372 0.485051451655256 0.242525725827628 81 0.963958300244643 0.072083399510713 0.0360416997553565 82 0.961520212558959 0.0769595748820821 0.0384797874410411 83 0.970376601027835 0.0592467979443306 0.0296233989721653 84 0.972711635262402 0.0545767294751955 0.0272883647375977 85 0.980235020329632 0.0395299593407357 0.0197649796703678 86 0.974144766852205 0.0517104662955894 0.0258552331477947 87 0.97152230557127 0.05695538885746 0.02847769442873 88 0.962931897682224 0.0741362046355526 0.0370681023177763 89 0.953403980355778 0.0931920392884437 0.0465960196442218 90 0.955995927673701 0.088008144652598 0.044004072326299 91 0.961551917517628 0.0768961649647442 0.0384480824823721 92 0.972712850406384 0.0545742991872327 0.0272871495936164 93 0.964863989937818 0.0702720201243633 0.0351360100621817 94 0.95411218627097 0.09177562745806 0.04588781372903 95 0.941451392658435 0.117097214683129 0.0585486073415647 96 0.96183449344205 0.0763310131159009 0.0381655065579505 97 0.984312812341865 0.0313743753162702 0.0156871876581351 98 0.979342614449367 0.0413147711012653 0.0206573855506326 99 0.992589362119093 0.0148212757618141 0.00741063788090707 100 0.991903017676194 0.0161939646476113 0.00809698232380563 101 0.988899297352491 0.0222014052950179 0.0111007026475090 102 0.984899801919942 0.0302003961601151 0.0151001980800575 103 0.991648954829349 0.0167020903413029 0.00835104517065145 104 0.989750883984274 0.020498232031451 0.0102491160157255 105 0.985708342069768 0.0285833158604648 0.0142916579302324 106 0.986631541418253 0.026736917163494 0.013368458581747 107 0.993866510223154 0.0122669795536914 0.00613348977684568 108 0.994705443515149 0.0105891129697023 0.00529455648485116 109 0.992173072798675 0.0156538544026504 0.00782692720132518 110 0.988672001971102 0.0226559960577966 0.0113279980288983 111 0.984504421430063 0.0309911571398732 0.0154955785699366 112 0.979170155006539 0.0416596899869225 0.0208298449934613 113 0.975163696818487 0.0496726063630258 0.0248363031815129 114 0.974562604155848 0.0508747916883043 0.0254373958441522 115 0.969821273392215 0.0603574532155691 0.0301787266077846 116 0.962539117322857 0.0749217653542856 0.0374608826771428 117 0.95735229074297 0.0852954185140595 0.0426477092570298 118 0.97283609428868 0.0543278114226409 0.0271639057113204 119 0.986312300722303 0.0273753985553933 0.0136876992776967 120 0.98365626694285 0.0326874661142997 0.0163437330571498 121 0.97724605071211 0.0455078985757805 0.0227539492878903 122 0.970315779960979 0.0593684400780423 0.0296842200390212 123 0.958059820746089 0.0838803585078225 0.0419401792539112 124 0.981241726597376 0.0375165468052485 0.0187582734026242 125 0.975752161024939 0.0484956779501219 0.0242478389750609 126 0.963951071365013 0.0720978572699748 0.0360489286349874 127 0.94913075939428 0.101738481211438 0.0508692406057192 128 0.938177663914962 0.123644672170077 0.0618223360850384 129 0.923105483373853 0.153789033252294 0.0768945166261469 130 0.905011637956625 0.18997672408675 0.094988362043375 131 0.868804878913017 0.262390242173965 0.131195121086983 132 0.828327647201064 0.343344705597873 0.171672352798936 133 0.834751347894708 0.330497304210584 0.165248652105292 134 0.799149769344368 0.401700461311265 0.200850230655632 135 0.736814273240063 0.526371453519874 0.263185726759937 136 0.672395696115575 0.655208607768851 0.327604303884425 137 0.645922343167185 0.70815531366563 0.354077656832815 138 0.564176351308381 0.871647297383238 0.435823648691619 139 0.472545068849117 0.945090137698234 0.527454931150883 140 0.384484334567115 0.76896866913423 0.615515665432885 141 0.909975348645885 0.180049302708231 0.0900246513541153 142 0.876851552632096 0.246296894735808 0.123148447367904 143 0.870657074919597 0.258685850160805 0.129342925080403 144 0.786667940667247 0.426664118665506 0.213332059332753 145 0.69051518749708 0.61896962500584 0.30948481250292 146 0.538535189692523 0.922929620614954 0.461464810307477 147 0.429948419982576 0.859896839965152 0.570051580017424 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 23 0.165467625899281 NOK 10% type I error level 46 0.330935251798561 NOK

Charts produced by software:

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Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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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