Workshop 7 Multiple Regression

*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: Fri, 03 Dec 2010 12:39:24 +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/03/t1291379982dtk7hu55vzq0oqq.htm/, Retrieved Fri, 03 Dec 2010 13:39:54 +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/03/t1291379982dtk7hu55vzq0oqq.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:

Determistische Trend

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

2 5 2 2 1 1 1 1 4 1 3 1 4 1 1 7 3 3 1 5 1 1 7 2 2 1 2 2 2 5 2 1 1 1 2 2 5 1 1 1 1 2 1 4 1 3 1 2 2 2 4 3 3 2 1 1 1 6 1 1 1 1 1 2 5 1 1 1 1 2 1 1 1 1 1 3 2 2 5 1 1 1 1 1 1 4 2 1 2 1 1 2 6 3 1 1 1 1 2 7 2 2 1 2 1 2 7 3 3 1 4 2 1 2 2 1 1 1 2 1 6 1 1 1 1 1 1 3 1 1 1 2 1 2 6 1 1 1 3 2 2 6 1 3 1 1 1 1 5 1 1 1 1 2 2 6 3 2 1 1 1 2 4 1 3 2 1 2 2 3 3 1 2 2 1 2 4 1 1 1 1 1 2 5 1 1 2 1 1 2 6 1 1 2 1 2 1 6 3 3 2 1 2 1 4 1 1 1 1 2 2 6 1 3 1 1 1 1 6 1 1 1 1 2 2 5 1 3 1 1 1 2 6 3 1 1 1 1 2 4 1 1 1 1 2 1 6 1 1 1 1 1 2 7 1 3 1 1 2 1 5 2 3 1 1 1 1 6 1 3 1 1 2 2 6 1 1 2 1 1 1 5 2 2 2 4 2 2 7 2 3 1 1 1 2 6 2 2 1 1 1 1 3 1 1 1 4 2 1 4 1 1 1 2 1 2 5 2 3 1 2 1 2 4 2 3 2 1 1 1 3 1 1 1 1 2 2 5 3 1 1 2 1 2 5 1 1 1 1 2 1 4 1 2 1 1 2 1 5 1 1 1 1 1 2 1 2 2 1 1 1 2 2 1 1 2 1 1 2 3 3 3 1 1 2 1 4 2 2 1 2 1 1 3 3 3 1 1 2 1 7 1 1 1 1 2 1 2 1 1 1 1 1 1 4 3 1 1 2 1 1 2 1 1 1 1 2 2 5 1 1 1 2 1 2 6 1 2 1 4 2 2 6 1 2 1 1 2 2 6 1 1 1 1 1 1 6 2 2 1 1 1 2 6 3 3 1 2 1 2 6 1 1 1 3 1 1 6 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 Member[t] = + 1.30465081631826 + 0.0640015744751596Provison[t] + 0.0595463954201537Mother[t] + 0.00440396071597927Father[t] + 0.0496845934749124Illness[t] -0.0440791315888167Tobacco[t] -0.130315178168314Gender[t] -3.27464775207176e-05t + 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) 1.30465081631826 0.2814 4.6363 8e-06 4e-06 Provison 0.0640015744751596 0.028625 2.2358 0.026849 0.013425 Mother 0.0595463954201537 0.054235 1.0979 0.274006 0.137003 Father 0.00440396071597927 0.049819 0.0884 0.929679 0.464839 Illness 0.0496845934749124 0.118093 0.4207 0.674561 0.337281 Tobacco -0.0440791315888167 0.042284 -1.0425 0.298887 0.149444 Gender -0.130315178168314 0.08317 -1.5668 0.119271 0.059636 t -3.27464775207176e-05 0.00088 -0.0372 0.97037 0.485185 Multiple Linear Regression - Regression Statistics Multiple R 0.284283161782614 R-squared 0.0808169160731198 Adjusted R-squared 0.0376338181705147 F-TEST (value) 1.87149417245131 F-TEST (DF numerator) 7 F-TEST (DF denominator) 149 p-value 0.0780617191575274 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.489177752690995 Sum Squared Residuals 35.654936185444 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2 1.62781693820654 0.372183061793461 2 1 1.37640278778327 -0.37640278778327 3 1 1.64338842398272 -0.64338842398272 4 1 1.58132753796720 -0.581327537967204 5 2 1.4929668134122 0.507033186587799 6 2 1.43338767151453 0.566612328485474 7 1 1.33408214040499 -0.334082140404989 8 2 1.67722108799982 0.322778912000182 9 1 1.62760618472544 -0.627606184725438 10 2 1.43325668560444 0.566743314395556 11 1 1.08905937804865 -0.0890593780486509 12 2 1.56350637081772 0.436493629182283 13 1 1.6087030387601 -0.608703038760102 14 2 1.74653524317814 0.253464756821858 15 2 1.71128250488279 0.28871749511721 16 2 1.55672667319545 0.443273326804545 17 1 1.30056913225647 -0.300569132256473 18 1 1.62731146642775 -0.627311466427752 19 1 1.39119486493594 -0.391194864935936 20 2 1.40877253212676 0.591227467873237 21 2 1.63602114842715 0.363978851572851 22 1 1.43286372787420 -0.432863727874195 23 2 1.75064448559643 0.249355514403565 24 2 1.42728917535086 0.572710824649135 25 2 1.55977577038603 0.440224229613969 26 2 1.49904634565727 0.500953654342733 27 2 1.61269976712982 0.387300232870182 28 2 1.54635341695914 0.453646583040857 29 1 1.67422138275389 -0.674221382753888 30 1 1.36860018157887 -0.36860018157887 31 2 1.63569368365194 0.364306316348059 32 1 1.49653783757415 -0.496537837574148 33 2 1.57162661622174 0.42837338377826 34 2 1.74588031362773 0.254119686372272 35 2 1.36843644919127 0.631563550808734 36 1 1.62672202983238 -0.626722029832379 37 2 1.56918360109366 0.430816398906337 38 1 1.63100927925429 -0.63100927925429 39 1 1.50511653366346 -0.505116533663461 40 2 1.67627563739721 0.323724362602791 41 1 1.41363909964590 -0.413639099645897 42 2 1.75888144229453 0.241118557705473 43 2 1.69044316062587 0.309556839374133 44 1 1.17190276165197 -0.17190276165197 45 1 1.45434503099556 -0.454345030995557 46 2 1.58666817584531 0.413331824154692 47 2 1.61639757995636 0.383602420043644 48 1 1.30400917050834 -0.304009170508337 49 2 1.63730841040094 0.362691589599059 50 2 1.43194682650362 0.568053173496385 51 1 1.37231646626691 -0.372316466266914 52 1 1.56219651171689 -0.562196511716888 53 2 1.37010782347486 0.629892176525139 54 2 1.41981088881128 0.58018911118872 55 2 1.43168065743796 0.568319342562042 56 1 1.51793517587896 -0.517935175878962 57 1 1.43161516448292 -0.431615164482916 58 1 1.55968800363377 -0.559688003633769 59 1 1.36996256294876 -0.369962562948764 60 1 1.57294662467305 -0.572946624673053 61 1 1.23958189182541 -0.239581891825408 62 2 1.51778991535286 0.482210084647136 63 2 1.36768926272054 0.632310737279465 64 2 1.49989391100946 0.500106088990536 65 2 1.62577238198428 0.374227618015722 66 1 1.68968999164289 -0.68968999164289 67 2 1.70952846971269 0.290471530287314 68 2 1.53751587937408 0.462484120625917 69 1 1.50413413933784 -0.50413413933784 70 1 1.56155585678249 -0.561555856782488 71 1 1.43120793213665 -0.431207932136653 72 2 1.68944229443874 0.310557705561261 73 1 1.69386472701623 -0.693864727016225 74 1 1.62547766368659 -0.625477663686592 75 1 1.68262806507220 -0.682628065072203 76 1 1.69376648758366 -0.693766487583663 77 2 1.6838719391609 0.316128060839099 78 1 1.58567150690367 -0.585671506903672 79 2 1.49285560329366 0.507144396706337 80 2 1.62963392719842 0.37036607280158 81 2 1.69360275519606 0.306397244803941 82 2 1.43525168159990 0.564748318400095 83 1 1.68908208318601 -0.689082083186011 84 2 1.74864695046767 0.251353049532329 85 2 1.68906780857000 0.310932191430003 86 1 1.31157272579451 -0.311572725794508 87 2 1.64932714474212 0.350672855257882 88 2 1.56101763852614 0.438982361473858 89 1 1.49467128835547 -0.494671288355467 90 1 1.36663539292763 -0.366635392927627 91 2 1.61815376529574 0.381846234704261 92 2 1.62483700875219 0.375162991247808 93 2 1.64472670516101 0.355273294838986 94 1 1.55845791210400 -0.558457912103996 95 2 1.51682777815550 0.483172221844497 96 1 1.73844340913121 -0.738443409131208 97 2 1.69307881155573 0.306921188444273 98 1 1.55832692619391 -0.558326926193913 99 1 1.70848058243202 -0.708480582432023 100 2 1.62462625527105 0.375373744728947 101 2 1.28042894771526 0.71957105228474 102 1 1.36624243519738 -0.366242435197378 103 2 1.49411040099212 0.505889599007877 104 2 1.69284958621308 0.307150413786918 105 1 1.60020339750292 -0.600203397502922 106 1 1.74352256724624 -0.743522567246236 107 1 1.62880099064439 -0.628800990644388 108 1 1.28772740085685 -0.287727400856847 109 2 1.75228346758466 0.247716532415341 110 1 1.17837970066771 -0.178379700667712 111 1 1.49626289506801 -0.496262895068006 112 1 1.19818543225999 -0.198185432259987 113 1 1.6440717756106 -0.6440717756106 114 2 1.55588076595545 0.444119234044554 115 1 1.56025198019390 -0.560251980193905 116 2 1.75205424224201 0.247945757757986 117 1 1.63287748658516 -0.63287748658516 118 2 1.49603366972536 0.503966330274639 119 2 1.62390163552011 0.376098364479894 120 1 1.49173393095419 -0.491733930954189 121 1 1.72871438908318 -0.728714389083178 122 2 1.68790740724076 0.312092592759243 123 2 1.36555475916944 0.634445240830557 124 1 1.55978754699637 -0.559787546996369 125 2 1.46928450812322 0.530715491876779 126 2 1.67655403400267 0.323445965997333 127 2 1.42942534773452 0.57057465226548 128 2 1.61689092728845 0.383109072711555 129 1 1.47475898409923 -0.474758984099234 130 2 1.61930717845125 0.380692821548753 131 2 1.62796385684486 0.372036143155137 132 2 1.64350081087673 0.356499189123267 133 2 1.49312800666650 0.506871993333498 134 1 1.63898013886669 -0.638980138866686 135 1 1.75138084083009 -0.751380840830094 136 2 1.66324104951397 0.336758950486033 137 2 1.57692103811792 0.423078961882078 138 2 1.70720346980871 0.292796530191285 139 2 1.56375152888856 0.436248471111435 140 1 1.48854603894690 -0.488546038946904 141 2 1.38488776546041 0.615112234539587 142 2 1.69160522006730 0.308394779932705 143 2 1.74791640946407 0.252083590535935 144 1 1.70160036805336 -0.701600368053356 145 1 1.36923829737997 -0.369238297379966 146 2 1.55675509527292 0.443244904727081 147 2 1.51264321720658 0.487356782793419 148 2 1.68705599882522 0.312944001174781 149 1 1.57894254628372 -0.578942546283721 150 2 1.69139446658616 0.308605533413844 151 2 1.54904159015807 0.450958409841931 152 2 1.62732739915596 0.372672600844045 153 1 1.49247307711609 -0.492473077116087 154 1 1.3645396183663 -0.364539618366301 155 2 1.80591956432288 0.194080435677119 156 1 1.49919326429555 -0.499193264295553 157 2 1.85553866484275 0.144461335157248 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.67982530765519 0.640349384689621 0.320174692344810 12 0.691855261741494 0.616289476517011 0.308144738258506 13 0.682212540612385 0.635574918775229 0.317787459387615 14 0.572613111367561 0.854773777264877 0.427386888632439 15 0.570178314132812 0.859643371734376 0.429821685867188 16 0.54517196585765 0.9096560682847 0.45482803414235 17 0.714441184029244 0.571117631941512 0.285558815970756 18 0.730873667965302 0.538252664069396 0.269126332034698 19 0.657056433730041 0.685887132539918 0.342943566269959 20 0.721161054285058 0.557677891429884 0.278838945714942 21 0.664594463118519 0.670811073762962 0.335405536881481 22 0.707361794436215 0.585276411127569 0.292638205563785 23 0.638953603578301 0.722092792843398 0.361046396421699 24 0.605138502998064 0.789722994003873 0.394861497001936 25 0.592949455709706 0.814101088580588 0.407050544290294 26 0.581339129025235 0.83732174194953 0.418660870974765 27 0.52940192973465 0.9411961405307 0.47059807026535 28 0.475263489867534 0.950526979735069 0.524736510132465 29 0.699526468336759 0.600947063326482 0.300473531663241 30 0.699986842157624 0.600026315684752 0.300013157842376 31 0.659601996640957 0.680796006718085 0.340398003359043 32 0.680633043872982 0.638733912254037 0.319366956127018 33 0.64842677214371 0.70314645571258 0.35157322785629 34 0.597565384684599 0.804869230630803 0.402434615315401 35 0.604499786453763 0.791000427092474 0.395500213546237 36 0.661231590077965 0.67753681984407 0.338768409922035 37 0.627910356868258 0.744179286263483 0.372089643131741 38 0.679420160479528 0.641159679040944 0.320579839520472 39 0.686492809735902 0.627014380528196 0.313507190264098 40 0.651860478652178 0.696279042695644 0.348139521347822 41 0.618710574975745 0.76257885004851 0.381289425024255 42 0.579954271067465 0.84009145786507 0.420045728932535 43 0.547433959075272 0.905132081849457 0.452566040924728 44 0.496514681102756 0.993029362205513 0.503485318897244 45 0.47455409530917 0.94910819061834 0.52544590469083 46 0.468653287753509 0.937306575507017 0.531346712246491 47 0.443746952755121 0.887493905510242 0.556253047244879 48 0.410349869054726 0.820699738109453 0.589650130945274 49 0.39046304450924 0.78092608901848 0.60953695549076 50 0.403859991373833 0.807719982747666 0.596140008626167 51 0.386048969383153 0.772097938766306 0.613951030616847 52 0.399903124799702 0.799806249599404 0.600096875200298 53 0.424437834476018 0.848875668952035 0.575562165523982 54 0.451360151385384 0.902720302770767 0.548639848614616 55 0.453004843798106 0.906009687596211 0.546995156201894 56 0.459310509933242 0.918621019866483 0.540689490066758 57 0.466743535625579 0.933487071251159 0.53325646437442 58 0.466516239416457 0.933032478832914 0.533483760583543 59 0.440006420487865 0.88001284097573 0.559993579512135 60 0.437557827870450 0.875115655740899 0.562442172129550 61 0.395174390106445 0.79034878021289 0.604825609893555 62 0.42081854866655 0.8416370973331 0.57918145133345 63 0.488074964856136 0.976149929712271 0.511925035143864 64 0.496379736625234 0.992759473250468 0.503620263374766 65 0.481227004497757 0.962454008995515 0.518772995502243 66 0.522062566770266 0.955874866459469 0.477937433229734 67 0.496089157809247 0.992178315618495 0.503910842190753 68 0.501244635807474 0.997510728385052 0.498755364192526 69 0.49905218362282 0.99810436724564 0.50094781637718 70 0.502266502760986 0.995466994478028 0.497733497239014 71 0.478296787592764 0.956593575185528 0.521703212407236 72 0.457790460050803 0.915580920101606 0.542209539949197 73 0.48872737454782 0.97745474909564 0.51127262545218 74 0.50158476440001 0.99683047119998 0.49841523559999 75 0.521465453054906 0.957069093890188 0.478534546945094 76 0.548926151943295 0.902147696113411 0.451073848056705 77 0.545885343727991 0.908229312544018 0.454114656272009 78 0.549102683968762 0.901794632062476 0.450897316031238 79 0.570364899869858 0.859270200260284 0.429635100130142 80 0.56354937973978 0.87290124052044 0.43645062026022 81 0.545517184212283 0.908965631575433 0.454482815787717 82 0.580733956550715 0.83853208689857 0.419266043449285 83 0.603262975256490 0.793474049487019 0.396737024743510 84 0.577462159181818 0.845075681636363 0.422537840818182 85 0.559656668913352 0.880686662173295 0.440343331086648 86 0.521598579038624 0.956802841922752 0.478401420961376 87 0.51064708207809 0.97870583584382 0.48935291792191 88 0.516967246527583 0.966065506944835 0.483032753472417 89 0.505234837769139 0.989530324461722 0.494765162230861 90 0.475278596697704 0.950557193395408 0.524721403302296 91 0.463809403627254 0.927618807254508 0.536190596372746 92 0.460521231685268 0.921042463370537 0.539478768314732 93 0.451756771521707 0.903513543043414 0.548243228478293 94 0.451770361467037 0.903540722934075 0.548229638532963 95 0.461988632082628 0.923977264165256 0.538011367917372 96 0.483788945420964 0.967577890841927 0.516211054579036 97 0.470424732355787 0.940849464711574 0.529575267644213 98 0.479152161856416 0.958304323712831 0.520847838143584 99 0.508214389645377 0.983571220709247 0.491785610354623 100 0.49675068328839 0.99350136657678 0.50324931671161 101 0.642158056831007 0.715683886337986 0.357841943168993 102 0.618117659681928 0.763764680636143 0.381882340318072 103 0.623639631413669 0.752720737172662 0.376360368586331 104 0.605827093578087 0.788345812843826 0.394172906421913 105 0.5978686002026 0.804262799594799 0.402131399797400 106 0.644496237514239 0.711007524971522 0.355503762485761 107 0.670258329553832 0.659483340892336 0.329741670446168 108 0.626248082843601 0.747503834312797 0.373751917156399 109 0.585576591345766 0.828846817308468 0.414423408654234 110 0.533843734115418 0.932312531769164 0.466156265884582 111 0.51644016306375 0.9671196738725 0.48355983693625 112 0.463447166962441 0.926894333924882 0.536552833037559 113 0.507023008226357 0.985953983547287 0.492976991773643 114 0.514769611173835 0.97046077765233 0.485230388826165 115 0.511072832937848 0.977854334124305 0.488927167062153 116 0.463632632012731 0.927265264025462 0.536367367987269 117 0.568753057957114 0.862493884085773 0.431246942042886 118 0.580766952401619 0.838466095196763 0.419233047598381 119 0.571798247535298 0.856403504929404 0.428201752464702 120 0.563712108109974 0.872575783780053 0.436287891890026 121 0.58394552087313 0.83210895825374 0.41605447912687 122 0.536266955415861 0.927466089168278 0.463733044584139 123 0.551883661250912 0.896232677498176 0.448116338749088 124 0.573614214543265 0.85277157091347 0.426385785456735 125 0.551712392055219 0.896575215889561 0.448287607944781 126 0.502590074544107 0.994819850911787 0.497409925455893 127 0.480179314443606 0.960358628887212 0.519820685556394 128 0.425042887170208 0.850085774340415 0.574957112829792 129 0.439532594877236 0.879065189754472 0.560467405122764 130 0.397160077908624 0.794320155817248 0.602839922091376 131 0.359855609383133 0.719711218766267 0.640144390616867 132 0.307188238265935 0.61437647653187 0.692811761734065 133 0.322992306708315 0.64598461341663 0.677007693291685 134 0.38236371889931 0.76472743779862 0.61763628110069 135 0.586182958335772 0.827634083328455 0.413817041664228 136 0.509416422802484 0.981167154395031 0.490583577197516 137 0.445404822177674 0.890809644355348 0.554595177822326 138 0.368057438631944 0.736114877263888 0.631942561368056 139 0.312395351824833 0.624790703649666 0.687604648175167 140 0.51988370257557 0.96023259484886 0.48011629742443 141 0.532991182305024 0.934017635389952 0.467008817694976 142 0.424833420472175 0.84966684094435 0.575166579527825 143 0.326372646590854 0.652745293181707 0.673627353409146 144 0.702863406558344 0.594273186883311 0.297136593441656 145 0.659691001809013 0.680617996381975 0.340308998190988 146 0.500898862223595 0.99820227555281 0.499101137776405 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:

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/10zedd1291379952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/10zedd1291379952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/1bdg21291379952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/1bdg21291379952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/2bdg21291379952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/2bdg21291379952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/33nxn1291379952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/33nxn1291379952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/43nxn1291379952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/43nxn1291379952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/53nxn1291379952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/53nxn1291379952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/6weep1291379952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/6weep1291379952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/7p5es1291379952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/7p5es1291379952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/8p5es1291379952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/8p5es1291379952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/9p5es1291379952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t1291379982dtk7hu55vzq0oqq/9p5es1291379952.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by