WS7 - populariteit daginvloed?

*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, 20 Nov 2010 16:26:09 +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/Nov/20/t1290270312ctmcofsih5hcuma.htm/, Retrieved Sat, 20 Nov 2010 17:25:24 +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/Nov/20/t1290270312ctmcofsih5hcuma.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 «

13 13 14 13 3 1 12 12 8 13 5 1 15 10 12 16 6 1 12 9 7 12 6 1 10 10 10 11 5 1 12 12 7 12 3 1 15 13 16 18 8 1 9 12 11 11 4 1 12 12 14 14 4 1 11 6 6 9 4 1 11 5 16 14 6 1 11 12 11 12 6 1 15 11 16 11 5 1 7 14 12 12 4 1 11 14 7 13 6 1 11 12 13 11 4 1 10 12 11 12 6 1 14 11 15 16 6 1 10 11 7 9 4 2 6 7 9 11 4 2 11 9 7 13 2 2 15 11 14 15 7 2 11 11 15 10 5 2 12 12 7 11 4 2 14 12 15 13 6 2 15 11 17 16 6 2 9 11 15 15 7 2 13 8 14 14 5 2 13 9 14 14 6 2 16 12 8 14 4 2 13 10 8 8 4 2 12 10 14 13 7 2 14 12 14 15 7 2 11 8 8 13 4 3 9 12 11 11 4 3 16 11 16 15 6 3 12 12 10 15 6 3 10 7 8 9 5 3 13 11 14 13 6 3 16 11 16 16 7 3 14 12 13 13 6 3 15 9 5 11 3 3 5 15 8 12 3 3 8 11 10 12 4 3 11 11 8 12 6 3 16 11 13 14 7 3 17 11 15 14 5 3 9 15 6 8 4 3 9 11 12 13 5 3 13 12 16 16 6 3 10 12 5 13 6 3 6 9 15 11 6 4 12 12 12 14 5 4 8 12 8 13 4 4 14 13 13 13 5 4 12 11 14 13 5 4 11 9 12 12 4 4 16 9 16 16 6 4 8 11 10 15 2 4 15 11 15 15 8 4 7 12 8 12 3 4 16 12 16 14 6 4 14 9 19 12 6 4 16 11 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Popularity[t] = + 0.305904887836174 + 0.094633084474476FindingFriends[t] + 0.243797535254599KnowingPeople[t] + 0.34915593440117Liked[t] + 0.627017042711136Celebrity[t] -0.00119728221808973Date[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) 0.305904887836174 1.435088 0.2132 0.831491 0.415745 FindingFriends 0.094633084474476 0.096383 0.9818 0.327759 0.163879 KnowingPeople 0.243797535254599 0.061591 3.9583 0.000116 5.8e-05 Liked 0.34915593440117 0.097709 3.5734 0.000474 0.000237 Celebrity 0.627017042711136 0.156594 4.0041 9.8e-05 4.9e-05 Date -0.00119728221808973 0.062964 -0.019 0.984854 0.492427 Multiple Linear Regression - Regression Statistics Multiple R 0.706541857390106 R-squared 0.499201396244261 Adjusted R-squared 0.482508109452403 F-TEST (value) 29.9043203695357 F-TEST (DF numerator) 5 F-TEST (DF denominator) 150 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.11251892472136 Sum Squared Residuals 669.410431095885 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 11.3681814726992 1.63181852730078 2 12 11.0647972621195 0.935202737880534 3 15 13.5252060801035 1.47479391989646 4 12 10.8149615817514 1.1850384182486 5 10 10.6648142948774 -0.664814294877374 6 12 9.21780970704143 2.78219029295857 7 15 16.73664142877 -1.73664142876999 8 9 10.4708609563698 -1.47086095636979 9 12 12.2497213653371 -0.249721365337096 10 11 7.9857629044476 3.01423709555239 11 11 13.3289189299472 -2.32891892994723 12 11 12.0740509761932 -1.07405097619323 13 15 12.2222325908794 2.77776740912056 14 7 11.2530805949745 -4.25308059497451 15 11 11.6372829385250 -0.637282938524961 16 11 10.958456026879 0.0415439731210098 17 10 12.0740509761932 -2.07405097619323 18 14 14.3512317703418 -0.351231770341828 19 10 8.7015285798565 1.29847142014350 20 6 9.50890318127013 -3.50890318127013 21 11 8.65485206308995 2.34514793691005 22 15 14.3840980611791 0.615901938820894 23 11 11.6280818390056 -0.628081839005588 24 12 9.49447353313331 2.50552646686669 25 14 13.3971997693947 0.602800230605293 26 15 14.8376295586329 0.162370441367064 27 9 14.6278955964337 -5.62789559643371 28 13 12.4970087879322 0.502991212067762 29 13 13.2186589151178 -0.218658915117850 30 16 10.7857388715914 5.21426112840858 31 13 8.50153709623545 4.49846290376455 32 12 13.5911531079023 -1.59115310790229 33 14 14.4787311456536 -0.478731145653582 34 11 10.0568533170743 0.943146682925747 35 9 10.4684663919336 -1.46846639193361 36 16 14.2434788067591 1.75652119324092 37 12 12.8753266797060 -0.875326679705963 38 10 9.19261353770623 0.807386462293765 39 13 13.0575718674475 -0.0575718674475427 40 16 15.2196517838714 0.780348216128617 41 14 12.9084074166674 1.09159258333258 42 15 8.09476488427146 6.90523511572854 43 5 9.74311193128328 -4.74311193128328 44 8 10.4791917066057 -2.47919170660571 45 11 11.2456307215188 -0.245630721518783 46 16 13.7899473093052 2.21005269069475 47 17 13.0235082943922 3.97649170560783 48 9 8.48591016588054 0.514089834119459 49 9 11.9429597542272 -2.94295975422721 50 13 14.6872678256347 -1.68726782563472 51 10 10.9580271346306 -0.958027134630632 52 6 12.4125940827328 -6.41259408273276 53 12 12.3855514908848 -0.385551490884766 54 8 10.4341883727541 -2.43418837275407 55 14 12.3748261762127 1.62517382378733 56 12 12.4293575425183 -0.429357542518318 57 11 10.7763233259479 0.223676674052136 58 16 14.4021712899932 1.59782871000679 59 8 10.2714281421689 -2.27142814216886 60 15 15.2525180747087 -0.252518074708661 61 7 9.45801539564176 -2.45801539564176 62 16 13.9877586746143 2.01224132538570 63 14 13.7369401581523 0.263059841847675 64 16 13.7546864540318 2.24531354596821 65 9 9.9405551571314 -0.940555157131408 66 14 12.2801930917382 1.71980690826181 67 11 13.0456492705574 -2.04564927055736 68 13 10.4682519458094 2.53174805419057 69 15 12.9060128522312 2.09398714776876 70 5 5.59978555783491 -0.59978555783491 71 15 12.4281602603002 2.57183973969977 72 13 12.2789958095201 0.721004190479895 73 11 12.0459235889376 -1.04592358893760 74 11 13.9786356182437 -2.97863561824366 75 12 12.4729491480277 -0.47294914802769 76 12 13.3936079227404 -1.39360792274044 77 12 12.268270494848 -0.26827049484801 78 12 11.9004634716222 0.0995365283777986 79 14 10.7821470249371 3.21785297506285 80 6 7.99305006580217 -1.99305006580217 81 7 9.84003762088021 -2.84003762088021 82 14 11.9890584109832 2.01094158901678 83 14 13.8588475709603 0.141152429039728 84 10 11.2529786356608 -1.25297863566079 85 13 8.72542550765988 4.27457449234012 86 12 12.4076924670499 -0.407692467049949 87 9 9.2896929206399 -0.289692920639904 88 12 12.0153499032228 -0.0153499032227770 89 16 15.0063259750179 0.993674024982118 90 10 10.2318023246968 -0.231802324696840 91 14 13.1262576829784 0.873742317021613 92 10 13.5084943543410 -3.50849435434101 93 16 15.3106930216916 0.68930697830841 94 15 13.446942006828 1.55305799317201 95 12 11.3464144379172 0.653585562082827 96 10 9.73348193951555 0.266518060484451 97 8 10.2374723303494 -2.23747233034939 98 8 8.60814451162312 -0.608144511623122 99 11 12.8413755934610 -1.84137559346105 100 13 12.4172204995040 0.582779500496046 101 16 15.4598574724717 0.540142527528288 102 16 14.7177395520358 1.28226044796418 103 14 15.8150515519863 -1.81505155198634 104 11 8.884117990894 2.11588200910600 105 4 6.96583560972476 -2.96583560972476 106 14 14.5695579373496 -0.56955793734961 107 9 10.3457059202035 -1.34570592020351 108 14 15.2489262280544 -1.24892622805439 109 8 10.4510643193501 -2.45106431935008 110 8 10.8995405077844 -2.89954050778435 111 11 12.1926934752815 -1.19269347528154 112 12 13.6396980631175 -1.63969806311749 113 11 11.4408330762675 -0.44083307626747 114 14 13.6019301565974 0.3980698434026 115 15 14.3343055984551 0.665694401544895 116 16 13.4019386729764 2.59806132702365 117 16 13.4965717574508 2.50342824254917 118 11 12.7320984147256 -1.73209841472558 119 14 13.7403692927054 0.259630707294573 120 14 10.9619976482425 3.03800235175745 121 12 11.4057866671182 0.594213332881809 122 14 12.5642048319721 1.43579516802792 123 8 10.2306050424788 -2.23060504247875 124 13 13.8350023771799 -0.835002377179903 125 16 13.7403692927054 2.25963070729457 126 12 10.9191744327029 1.08082556729709 127 16 15.4489177116754 0.551082288324562 128 12 13.3912133583043 -1.39121335830426 129 11 11.5158322358234 -0.515832235823396 130 4 6.42206570808217 -2.42206570808217 131 16 15.4489177116754 0.551082288324562 132 15 12.5651876680660 2.43481233193401 133 10 11.4780643293033 -1.47806432930331 134 13 13.2089681705627 -0.208968170562678 135 15 13.2527742221962 1.74722577780377 136 12 10.6743940613544 1.32560593864560 137 14 13.6457362082310 0.354263791769049 138 7 10.6722139430424 -3.6722139430424 139 19 14.0883279448885 4.91167205511149 140 12 12.6790912635727 -0.679091263572652 141 12 12.2851464414440 -0.28514644144402 142 13 13.4953744752327 -0.495374475232739 143 15 12.9396522586128 2.06034774138719 144 8 8.28363660463377 -0.283636604633769 145 12 10.9169943143909 1.08300568560910 146 10 10.8028762727601 -0.80287627276006 147 8 11.4055722209940 -3.40557222099401 148 10 14.3722271983119 -4.37222719831194 149 15 13.8883364612675 1.11166353873254 150 16 14.6097721423289 1.39022785767111 151 13 13.1886128641229 -0.188612864122853 152 16 15.0621063575646 0.937893642435368 153 9 10.2222240669521 -1.22222406695212 154 14 13.1604854768672 0.839514523132779 155 14 12.6952458286474 1.30475417135261 156 12 10.1540969208414 1.84590307915861 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.109732326342069 0.219464652684137 0.890267673657931 10 0.0478334606174779 0.0956669212349558 0.952166539382522 11 0.0725632970548877 0.145126594109775 0.927436702945112 12 0.0379086060481914 0.0758172120963829 0.962091393951809 13 0.440216848849322 0.880433697698644 0.559783151150678 14 0.760291219104802 0.479417561790395 0.239708780895198 15 0.679090383786212 0.641819232427576 0.320909616213788 16 0.58962820657995 0.820743586840099 0.410371793420050 17 0.529743023336689 0.94051395332662 0.47025697666331 18 0.442218125560302 0.884436251120604 0.557781874439698 19 0.360695671345517 0.721391342691033 0.639304328654483 20 0.547648792862149 0.904702414275702 0.452351207137851 21 0.508989005839123 0.982021988321755 0.491010994160877 22 0.551326979645805 0.89734604070839 0.448673020354195 23 0.484808020958034 0.969616041916068 0.515191979041966 24 0.472439346095565 0.94487869219113 0.527560653904435 25 0.428515489407216 0.857030978814433 0.571484510592784 26 0.365029840767791 0.730059681535582 0.634970159232209 27 0.633228084209867 0.733543831580266 0.366771915790133 28 0.576387895918473 0.847224208163054 0.423612104081527 29 0.515143066332431 0.969713867335138 0.484856933667569 30 0.67575325628607 0.648493487427862 0.324246743713931 31 0.785614438622079 0.428771122755842 0.214385561377921 32 0.747004056509213 0.505991886981574 0.252995943490787 33 0.699155669188381 0.601688661623239 0.300844330811619 34 0.66800657021906 0.66398685956188 0.33199342978094 35 0.674127760395117 0.651744479209767 0.325872239604883 36 0.681879440344056 0.636241119311889 0.318120559655944 37 0.648895360446243 0.702209279107515 0.351104639553757 38 0.59964638768087 0.80070722463826 0.40035361231913 39 0.545677464766908 0.908645070466185 0.454322535233092 40 0.516899303518623 0.966201392962754 0.483100696481377 41 0.47695980062112 0.95391960124224 0.52304019937888 42 0.756466814005642 0.487066371988716 0.243533185994358 43 0.951208463021776 0.0975830739564483 0.0487915369782241 44 0.96239092051831 0.0752181589633814 0.0376090794816907 45 0.951166801615597 0.0976663967688057 0.0488331983844028 46 0.956997103691428 0.0860057926171443 0.0430028963085722 47 0.97938888625303 0.0412222274939395 0.0206111137469698 48 0.97275725223996 0.0544854955200808 0.0272427477600404 49 0.980406011357663 0.039187977284674 0.019593988642337 50 0.977271607676881 0.0454567846462377 0.0227283923231189 51 0.972524692924262 0.0549506141514769 0.0274753070757384 52 0.997283162359902 0.00543367528019548 0.00271683764009774 53 0.996084008803852 0.00783198239229663 0.00391599119614831 54 0.996682988511785 0.00663402297642927 0.00331701148821464 55 0.996706869426715 0.00658626114657042 0.00329313057328521 56 0.995342840543343 0.00931431891331428 0.00465715945665714 57 0.993506829882361 0.0129863402352772 0.00649317011763862 58 0.992949994914736 0.0141000101705283 0.00705000508526415 59 0.994502483801805 0.0109950323963897 0.00549751619819487 60 0.992773569170312 0.0144528616593765 0.00722643082968827 61 0.993514173201735 0.0129716535965305 0.00648582679826523 62 0.99432346964115 0.0113530607177012 0.00567653035885061 63 0.992452697737018 0.0150946045259632 0.0075473022629816 64 0.993170112478232 0.0136597750435366 0.0068298875217683 65 0.99132118824474 0.0173576235105211 0.00867881175526056 66 0.990725501566732 0.0185489968665366 0.00927449843326828 67 0.99033430604444 0.0193313879111183 0.00966569395555914 68 0.992138135789397 0.0157237284212055 0.00786186421060273 69 0.992450282163557 0.0150994356728853 0.00754971783644266 70 0.989832111473835 0.0203357770523309 0.0101678885261655 71 0.991554868697349 0.0168902626053025 0.00844513130265125 72 0.988873365472085 0.02225326905583 0.011126634527915 73 0.986018843274534 0.0279623134509329 0.0139811567254665 74 0.98977068868429 0.0204586226314218 0.0102293113157109 75 0.9861640044799 0.0276719910402 0.0138359955201 76 0.983576708689035 0.0328465826219308 0.0164232913109654 77 0.978257061542919 0.0434858769141629 0.0217429384570814 78 0.971388475531427 0.0572230489371462 0.0286115244685731 79 0.982499485833596 0.0350010283328089 0.0175005141664045 80 0.98155601880475 0.0368879623905008 0.0184439811952504 81 0.984382562756068 0.0312348744878645 0.0156174372439322 82 0.985625207805358 0.0287495843892847 0.0143747921946423 83 0.980746255353035 0.0385074892939293 0.0192537446469647 84 0.976074547456143 0.047850905087715 0.0239254525438575 85 0.994130243519745 0.0117395129605102 0.00586975648025509 86 0.991856810864198 0.0162863782716042 0.0081431891358021 87 0.9889140053842 0.0221719892315976 0.0110859946157988 88 0.985491406105674 0.0290171877886515 0.0145085938943257 89 0.98187731264427 0.0362453747114609 0.0181226873557305 90 0.976056160626933 0.0478876787461345 0.0239438393730673 91 0.97138847642604 0.0572230471479186 0.0286115235739593 92 0.983093098792092 0.0338138024158166 0.0169069012079083 93 0.978149555768749 0.043700888462503 0.0218504442312515 94 0.975447144052553 0.0491057118948942 0.0245528559474471 95 0.969263366009464 0.0614732679810728 0.0307366339905364 96 0.961362010690354 0.0772759786192916 0.0386379893096458 97 0.957766317042826 0.0844673659143478 0.0422336829571739 98 0.945951249835362 0.108097500329275 0.0540487501646376 99 0.940975331168636 0.118049337662727 0.0590246688313636 100 0.927576095201148 0.144847809597704 0.0724239047988522 101 0.910046230250982 0.179907539498037 0.0899537697490184 102 0.89586644876648 0.208267102467041 0.104133551233521 103 0.901750648992908 0.196498702014184 0.0982493510070922 104 0.93592171543225 0.128156569135500 0.0640782845677502 105 0.933126584117226 0.133746831765548 0.066873415882774 106 0.915366425464635 0.169267149070730 0.0846335745353651 107 0.896991737865532 0.206016524268935 0.103008262134468 108 0.891440600454891 0.217118799090218 0.108559399545109 109 0.887263373489942 0.225473253020116 0.112736626510058 110 0.90504062255482 0.189918754890359 0.0949593774451794 111 0.893555306290796 0.212889387418408 0.106444693709204 112 0.921601018936764 0.156797962126472 0.0783989810632361 113 0.899993722750785 0.200012554498430 0.100006277249215 114 0.87465357556155 0.2506928488769 0.12534642443845 115 0.845373780576912 0.309252438846176 0.154626219423088 116 0.847493292799829 0.305013414400342 0.152506707200171 117 0.857816436644454 0.284367126711093 0.142183563355546 118 0.846274096123627 0.307451807752747 0.153725903876373 119 0.80970547163423 0.380589056731539 0.190294528365770 120 0.866610074544322 0.266779850911356 0.133389925455678 121 0.841727180934622 0.316545638130755 0.158272819065378 122 0.820500446297295 0.35899910740541 0.179499553702705 123 0.807159276986429 0.385681446027142 0.192840723013571 124 0.766077986582995 0.467844026834011 0.233922013417005 125 0.770079067549306 0.459841864901387 0.229920932450694 126 0.756640301322839 0.486719397354322 0.243359698677161 127 0.704220769419521 0.591558461160958 0.295779230580479 128 0.672219235858587 0.655561528282827 0.327780764141413 129 0.678450557814505 0.64309888437099 0.321549442185495 130 0.67083467524328 0.658330649513439 0.329165324756719 131 0.611218910135329 0.777562179729341 0.388781089864671 132 0.596622454026247 0.806755091947505 0.403377545973753 133 0.569621223431652 0.860757553136696 0.430378776568348 134 0.494839198306356 0.989678396612712 0.505160801693644 135 0.466044420676628 0.932088841353255 0.533955579323372 136 0.462599010992722 0.925198021985445 0.537400989007278 137 0.386926968913845 0.77385393782769 0.613073031086155 138 0.447548600580040 0.895097201160081 0.55245139941996 139 0.802035821194748 0.395928357610503 0.197964178805251 140 0.827418680766961 0.345162638466078 0.172581319233039 141 0.790552960739502 0.418894078520995 0.209447039260498 142 0.711298383720851 0.577403232558297 0.288701616279149 143 0.657294189701565 0.68541162059687 0.342705810298435 144 0.545068287359323 0.909863425281354 0.454931712640677 145 0.536845720468542 0.926308559062917 0.463154279531458 146 0.669325694184239 0.661348611631522 0.330674305815761 147 0.571604863782527 0.856790272434947 0.428395136217473 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.0359712230215827 NOK 5% type I error level 44 0.316546762589928 NOK 10% type I error level 57 0.410071942446043 NOK

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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