p_Stress_MR2

*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 20:39:07 +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/t12914087917cf6oqho6mftj1m.htm/, Retrieved Fri, 03 Dec 2010 21:40:02 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/03/t12914087917cf6oqho6mftj1m.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 «

10 53 7 6 15 11 12 2 4 25 25 3.4 6 86 4 6 15 12 11 4 3 25 24 4 13 66 6 5 14 15 14 7 5 19 21 3.2 12 67 5 4 10 10 12 3 3 18 23 3.2 8 76 4 4 10 12 21 7 6 18 17 2.6 6 78 3 6 12 11 12 2 5 22 19 3.2 10 53 5 7 18 5 22 7 6 29 18 3.8 10 80 6 5 12 16 11 2 6 26 27 3.6 9 74 5 4 14 11 10 1 5 25 23 3.6 9 76 6 6 18 15 13 2 5 23 23 4 7 79 7 1 9 12 10 6 3 23 29 3.4 5 54 6 4 11 9 8 1 5 23 21 2.6 14 67 7 6 11 11 15 1 7 24 26 4.4 6 87 6 6 17 15 10 1 5 30 25 4 10 58 4 5 8 12 14 2 5 19 25 3.8 10 75 6 3 16 16 14 2 3 24 23 3.6 7 88 4 7 21 14 11 2 5 32 26 3.8 10 64 5 2 24 11 10 1 6 30 20 3.6 8 57 3 5 21 10 13 7 5 29 29 3.8 6 66 3 5 14 7 7 1 2 17 24 3.6 10 54 4 3 7 11 12 2 5 25 23 4 12 56 5 5 18 10 14 4 4 26 24 2.8 7 86 3 5 18 11 11 2 6 26 30 5 15 80 7 6 13 16 9 1 3 25 22 4.4 8 76 7 4 11 14 11 1 5 23 22 3.2 10 69 4 4 13 12 15 5 4 21 13 3.4 13 67 4 4 13 12 13 2 5 19 24 3.2 8 80 5 2 18 11 9 1 2 35 17 5 11 54 6 3 14 6 15 3 2 19 24 3.6 7 71 5 6 12 14 10 1 5 20 21 4.8 9 84 4 6 9 9 11 2 2 21 23 3.8 10 74 6 5 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 13 seconds R Server 'George Udny Yule' @ 72.249.76.132 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation PStress[t] = + 5.25986734334252 -0.0356695703760594BelInSprt[t] + 0.162920650667659KunnenRekRel[t] -0.149394398367188ExtraCurAct[t] -0.0485862018705257Verwouders[t] + 0.0628810522000706Populariteit[t] + 0.399104485306294Depressie[t] -0.180064637594153Slaapgebrek[t] + 0.218592098994588ToekZorgen[t] -0.0278347970425497PersStand[t] + 0.0512352154322369MateGeorgZijn[t] + 0.274485494053298`Eetgewoonten `[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) 5.25986734334252 2.332939 2.2546 0.025831 0.012915 BelInSprt -0.0356695703760594 0.017485 -2.04 0.043377 0.021689 KunnenRekRel 0.162920650667659 0.111263 1.4643 0.145531 0.072766 ExtraCurAct -0.149394398367188 0.128863 -1.1593 0.248449 0.124225 Verwouders -0.0485862018705257 0.051476 -0.9439 0.346987 0.173494 Populariteit 0.0628810522000706 0.058194 1.0805 0.2819 0.14095 Depressie 0.399104485306294 0.064174 6.2191 0 0 Slaapgebrek -0.180064637594153 0.095025 -1.8949 0.060324 0.030162 ToekZorgen 0.218592098994588 0.118693 1.8417 0.067804 0.033902 PersStand -0.0278347970425497 0.045309 -0.6143 0.54007 0.270035 MateGeorgZijn 0.0512352154322369 0.049032 1.0449 0.297989 0.148994 `Eetgewoonten ` 0.274485494053298 0.336225 0.8164 0.415779 0.20789 Multiple Linear Regression - Regression Statistics Multiple R 0.627502539554768 R-squared 0.393759437147683 Adjusted R-squared 0.342462158752487 F-TEST (value) 7.67602979078433 F-TEST (DF numerator) 11 F-TEST (DF denominator) 130 p-value 4.1419778717966e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.93510610595773 Sum Squared Residuals 486.802633370934 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 10.3981109080137 -0.398110908013729 2 6 7.9307644073114 -1.9307644073114 3 13 10.2446393548474 2.75536064515261 4 12 9.69055368833448 2.30944631166553 5 8 12.3877245340208 -4.38772453402081 6 6 8.94023575027133 -2.94023575027133 7 10 12.5675446540060 -2.56754465400602 8 10 10.0492825674870 -0.0492825674870383 9 9 9.2234580613084 -0.223458061308392 10 9 10.2561427858389 -1.25614278583888 11 7 9.0956231693256 -2.09562316932559 12 5 9.00027131926342 -4.00027131926342 13 14 12.4797916272559 1.52020837274413 14 6 8.80274174681465 -2.80274174681465 15 10 11.5769840172752 -1.57698401727518 16 10 10.7243402999109 -0.724340299910876 17 7 8.19431898730253 -1.19431898730253 18 10 9.31879301058324 0.681206989416764 19 8 9.319515426293 -1.31951542629299 20 6 7.20287855468407 -1.20287855468407 21 10 10.9913651602625 -0.991365160262543 22 12 10.1003340226233 1.89966597737672 23 7 9.27856605897553 -2.27856605897553 24 15 8.73154910303879 6.26845089696121 25 8 10.1061066504412 -2.10610665044122 26 10 9.95111422878907 0.0488857712109341 27 13 10.5473902757347 2.45260972426531 28 8 7.85752427466995 0.142475725330050 29 11 11.1326201089110 -0.132620108911009 30 7 9.6835796832557 -2.68357968325569 31 9 8.25172165280749 0.748278347192514 32 10 9.56953393831114 0.430466061688862 33 8 8.00479404745797 -0.0047940474579675 34 15 13.3532068798352 1.64679312016478 35 9 10.0967881638929 -1.09678816389294 36 7 8.32709910760245 -1.32709910760245 37 11 9.47665861837684 1.52334138162316 38 9 7.90674553643083 1.09325446356917 39 8 9.75241055995031 -1.75241055995031 40 8 7.94686347625122 0.0531365237487764 41 12 8.90319009623835 3.09680990376165 42 13 10.2481422166660 2.75185778333403 43 9 8.96473341285834 0.0352665871416619 44 11 9.36958836483915 1.63041163516085 45 8 8.21249800844928 -0.212498008449276 46 10 10.7058538796630 -0.70585387966297 47 13 12.4032130358754 0.596786964124592 48 12 11.2466332735131 0.75336672648686 49 12 9.60728612433318 2.39271387566682 50 9 8.70624332551038 0.293756674489619 51 8 10.2410357542843 -2.2410357542843 52 9 8.55762631800724 0.442373681992759 53 12 8.42497649328264 3.57502350671736 54 12 11.7291685446556 0.270831455344415 55 16 14.2656209990965 1.73437900090345 56 11 8.98955211908735 2.01044788091265 57 13 10.3114039347229 2.68859606527706 58 10 10.6043806251253 -0.604380625125327 59 9 10.4930309173501 -1.49303091735006 60 14 9.53372243554468 4.46627756445532 61 13 12.0238673740869 0.976132625913069 62 12 10.3686770457662 1.63132295423380 63 9 10.9704701946977 -1.97047019469774 64 9 10.5620657528630 -1.56206575286303 65 10 11.2215090481749 -1.22150904817487 66 8 10.5261813542289 -2.52618135422892 67 9 10.3583406162232 -1.35834061622320 68 9 8.96856824902976 0.0314317509702444 69 11 8.79366133164637 2.20633866835363 70 7 9.66716176259284 -2.66716176259284 71 11 11.5494906558691 -0.549490655869072 72 9 9.05759852497023 -0.0575985249702288 73 11 8.92020106403112 2.07979893596888 74 9 9.5046003349402 -0.504600334940199 75 8 10.2485637172436 -2.24856371724359 76 9 8.13216001404625 0.867839985953749 77 8 9.17537537308887 -1.17537537308887 78 9 9.85183779669558 -0.851837796695581 79 10 10.2374896572663 -0.237489657266274 80 9 9.80237244222467 -0.802372442224675 81 17 13.9385372585523 3.06146274144766 82 7 9.51395394474114 -2.51395394474114 83 11 11.2241087785954 -0.224108778595412 84 9 9.98558539165466 -0.985585391654661 85 10 9.7293345616771 0.270665438322909 86 11 8.53972306578796 2.46027693421204 87 8 8.21679797336594 -0.216797973365939 88 12 12.3408963627430 -0.340896362742957 89 10 10.1569392294030 -0.156939229403048 90 7 8.9166794745976 -1.91667947459759 91 9 8.92492753825314 0.0750724617468596 92 7 8.31127672596746 -1.31127672596746 93 12 10.6119121268344 1.38808787316558 94 8 9.39329723101702 -1.39329723101702 95 13 10.5624668971608 2.43753310283921 96 9 10.8127248916724 -1.81272489167239 97 15 12.7299136650588 2.27008633494117 98 8 9.54154819418306 -1.54154819418306 99 14 11.5706115353743 2.42938846462566 100 14 13.6941418024811 0.305858197518861 101 9 10.270549884361 -1.27054988436099 102 13 11.4607229887744 1.53927701122564 103 11 9.12743591038398 1.87256408961602 104 10 11.9494520075191 -1.94945200751907 105 6 10.0784477328994 -4.07844773289936 106 8 8.44642226215375 -0.446422262153754 107 10 11.4946303402216 -1.49463034022156 108 10 7.87956511202029 2.12043488797971 109 10 8.72543291550262 1.27456708449738 110 12 12.1598856141247 -0.159885614124670 111 10 9.60003572512358 0.399964274876418 112 9 9.06045856008726 -0.0604585600872634 113 9 7.59764563027118 1.40235436972881 114 11 9.45421742633968 1.54578257366032 115 7 8.09598356364117 -1.09598356364117 116 7 8.90290304289432 -1.90290304289432 117 5 7.9683552701553 -2.96835527015529 118 9 8.97439709909747 0.0256029009025346 119 11 9.95527117373667 1.04472882626333 120 15 12.2735813768076 2.72641862319243 121 9 8.11680001335884 0.883199986641165 122 9 9.40529665852555 -0.405296658525544 123 8 9.49762161910805 -1.49762161910805 124 13 15.2398428922856 -2.23984289228564 125 10 10.3238172510627 -0.323817251062680 126 13 11.3107833727572 1.68921662724279 127 9 7.53561601652311 1.46438398347689 128 11 9.63582873557402 1.36417126442598 129 8 10.7003556913825 -2.70035569138245 130 10 9.2178972564349 0.782102743565107 131 9 8.9625165628174 0.0374834371826047 132 8 7.8349603794144 0.165039620585609 133 8 7.85545901328822 0.144540986711776 134 13 10.4362022385799 2.5637977614201 135 11 10.8163593529652 0.183640647034757 136 8 10.1061066504412 -2.10610665044122 137 12 10.3053422149668 1.69465778503318 138 15 11.8405420551234 3.15945794487656 139 11 11.2241087785954 -0.224108778595412 140 10 10.303389349951 -0.303389349951004 141 5 7.9683552701553 -2.96835527015529 142 11 7.24424973489903 3.75575026510097 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.92971477875061 0.140570442498779 0.0702852212493896 16 0.923624798454755 0.152750403090490 0.0763752015452451 17 0.88744075326573 0.22511849346854 0.11255924673427 18 0.825654898638606 0.348690202722788 0.174345101361394 19 0.805854802863317 0.388290394273366 0.194145197136683 20 0.782211086954685 0.435577826090631 0.217788913045315 21 0.711368765438526 0.577262469122947 0.288631234561474 22 0.819711513007594 0.360576973984811 0.180288486992406 23 0.791747046348054 0.416505907303891 0.208252953651946 24 0.950686378744025 0.0986272425119492 0.0493136212559746 25 0.940626542089682 0.118746915820637 0.0593734579103184 26 0.91412370022215 0.171752599555701 0.0858762997778506 27 0.957999265381667 0.0840014692366666 0.0420007346183333 28 0.93881165812473 0.122376683750540 0.06118834187527 29 0.915350857187883 0.169298285624233 0.0846491428121165 30 0.96999459341824 0.060010813163519 0.0300054065817595 31 0.969139862586762 0.0617202748264755 0.0308601374132378 32 0.95940901482896 0.0811819703420789 0.0405909851710394 33 0.944003640017449 0.111992719965103 0.0559963599825513 34 0.950066733818739 0.0998665323625226 0.0499332661812613 35 0.934246455000651 0.131507089998698 0.0657535449993488 36 0.924098206319373 0.151803587361254 0.0759017936806268 37 0.912040013390085 0.175919973219830 0.0879599866099148 38 0.889759671682952 0.220480656634097 0.110240328317048 39 0.871424571332092 0.257150857335816 0.128575428667908 40 0.854422864713495 0.29115427057301 0.145577135286505 41 0.873082047305222 0.253835905389556 0.126917952694778 42 0.879002337100278 0.241995325799445 0.120997662899722 43 0.854394839519212 0.291210320961576 0.145605160480788 44 0.832946797033082 0.334106405933837 0.167053202966918 45 0.80403281194084 0.39193437611832 0.19596718805916 46 0.764423985969424 0.471152028061151 0.235576014030576 47 0.77178978473309 0.456420430533822 0.228210215266911 48 0.732922320648086 0.534155358703828 0.267077679351914 49 0.762022037173552 0.475955925652895 0.237977962826448 50 0.727421680829643 0.545156638340713 0.272578319170357 51 0.732025870033391 0.535948259933218 0.267974129966609 52 0.691088379811213 0.617823240377574 0.308911620188787 53 0.783824333450139 0.432351333099722 0.216175666549861 54 0.745537443643084 0.508925112713832 0.254462556356916 55 0.77738578569718 0.445228428605640 0.222614214302820 56 0.813046770074056 0.373906459851887 0.186953229925944 57 0.841678435578654 0.316643128842693 0.158321564421347 58 0.80877101799277 0.382457964014461 0.191228982007231 59 0.7963047125591 0.407390574881799 0.203695287440900 60 0.953771384347292 0.0924572313054154 0.0462286156527077 61 0.943846394200954 0.112307211598091 0.0561536057990457 62 0.939181944150273 0.121636111699454 0.060818055849727 63 0.941285506587688 0.117428986824625 0.0587144934123124 64 0.936285253010511 0.127429493978978 0.0637147469894889 65 0.93571422727802 0.128571545443961 0.0642857727219803 66 0.94209539421283 0.115809211574340 0.0579046057871702 67 0.933127162716067 0.133745674567865 0.0668728372839326 68 0.915058717105969 0.169882565788062 0.084941282894031 69 0.924177990344299 0.151644019311402 0.0758220096557012 70 0.9395011518774 0.120997696245201 0.0604988481226003 71 0.92471227841932 0.150575443161359 0.0752877215806795 72 0.906016149272972 0.187967701454056 0.0939838507270279 73 0.91051301018374 0.178973979632520 0.0894869898162599 74 0.889212897563023 0.221574204873954 0.110787102436977 75 0.891500121658463 0.216999756683075 0.108499878341537 76 0.880042434691725 0.239915130616549 0.119957565308275 77 0.867877485426901 0.264245029146197 0.132122514573099 78 0.848360614402493 0.303278771195015 0.151639385597507 79 0.815539745383673 0.368920509232653 0.184460254616326 80 0.786815608789514 0.426368782420973 0.213184391210486 81 0.838014802724251 0.323970394551497 0.161985197275749 82 0.855350176111901 0.289299647776197 0.144649823888099 83 0.825778599329818 0.348442801340363 0.174221400670182 84 0.813985518602451 0.372028962795098 0.186014481397549 85 0.77784952258766 0.444300954824680 0.222150477412340 86 0.865177603598935 0.26964479280213 0.134822396401065 87 0.841552262239566 0.316895475520867 0.158447737760434 88 0.806750836096694 0.386498327806612 0.193249163903306 89 0.768935134385705 0.46212973122859 0.231064865614295 90 0.788325260626642 0.423349478746716 0.211674739373358 91 0.750726914622084 0.498546170755831 0.249273085377916 92 0.752691803530692 0.494616392938616 0.247308196469308 93 0.752250969449042 0.495498061101915 0.247749030550958 94 0.716810723345391 0.566378553309217 0.283189276654609 95 0.741292276678179 0.517415446643643 0.258707723321821 96 0.715634653964131 0.568730692071737 0.284365346035869 97 0.716478402692004 0.567043194615991 0.283521597307996 98 0.680193239497066 0.639613521005869 0.319806760502934 99 0.662147068680894 0.675705862638212 0.337852931319106 100 0.666044310614649 0.667911378770703 0.333955689385351 101 0.675564244760069 0.648871510479862 0.324435755239931 102 0.733438618965027 0.533122762069946 0.266561381034973 103 0.727681143648352 0.544637712703296 0.272318856351648 104 0.721566305894113 0.556867388211775 0.278433694105887 105 0.835339838456405 0.329320323087189 0.164660161543595 106 0.796005275212615 0.407989449574770 0.203994724787385 107 0.81729746048752 0.36540507902496 0.18270253951248 108 0.811034916883064 0.377930166233872 0.188965083116936 109 0.777866037311534 0.444267925376933 0.222133962688466 110 0.830557305485285 0.338885389029431 0.169442694514715 111 0.787046669284647 0.425906661430706 0.212953330715353 112 0.744379480365427 0.511241039269147 0.255620519634573 113 0.690895046774984 0.618209906450032 0.309104953225016 114 0.658928542405352 0.682142915189295 0.341071457594648 115 0.620022676924429 0.759954646151142 0.379977323075571 116 0.556828405892557 0.886343188214885 0.443171594107443 117 0.587078001500914 0.825843996998171 0.412921998499086 118 0.50554997580218 0.98890004839564 0.49445002419782 119 0.420152201892729 0.840304403785457 0.579847798107271 120 0.409249032550156 0.818498065100312 0.590750967449844 121 0.331363599999721 0.662727199999441 0.66863640000028 122 0.245640176315916 0.491280352631831 0.754359823684084 123 0.173455647644881 0.346911295289763 0.826544352355119 124 0.283385922777439 0.566771845554878 0.716614077222561 125 0.304775863222750 0.609551726445499 0.69522413677725 126 0.232158480209327 0.464316960418654 0.767841519790673 127 0.212077514085522 0.424155028171045 0.787922485914478 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 7 0.0619469026548673 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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