*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: Sun, 26 Dec 2010 20:25:18 +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/26/t1293395012411t5kacvd9ugti.htm/, Retrieved Sun, 26 Dec 2010 21:23:34 +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/26/t1293395012411t5kacvd9ugti.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 «

27 5 26 49 35 18 36 4 25 45 34 10 25 4 17 54 13 23 27 3 37 36 35 14 25 3 35 36 28 20 44 3 15 53 32 15 50 4 27 46 35 18 41 4 36 42 36 19 48 5 25 41 27 19 43 4 30 45 29 14 47 2 27 47 27 15 41 3 33 42 28 14 44 2 29 45 29 16 47 5 30 40 28 13 40 3 25 45 30 13 46 3 23 40 25 14 28 3 26 42 15 23 56 3 24 45 33 17 49 4 35 47 31 14 25 4 39 31 37 21 41 4 23 46 37 15 26 3 32 34 34 19 50 5 29 43 32 20 47 4 26 45 21 18 52 2 21 42 25 13 37 5 35 51 32 20 41 3 23 44 28 12 45 4 21 47 22 17 26 4 28 47 25 13 3 30 41 26 17 52 4 21 44 34 16 46 2 29 51 34 20 58 3 28 46 36 18 54 5 19 47 36 9 29 3 26 46 26 14 50 3 33 38 26 12 43 2 34 50 34 21 30 3 33 48 33 16 47 2 40 36 31 12 45 3 24 51 33 20 48 1 35 35 22 18 48 3 35 49 29 22 26 4 32 38 24 17 46 5 20 47 37 16 3 35 36 32 14 50 3 35 47 23 19 25 4 21 46 29 21 47 2 33 43 35 18 47 2 40 53 20 23 41 3 22 55 28 20 45 2 35 39 26 10 41 4 20 55 36 16 45 5 28 41 26 18 40 3 46 33 33 12 29 4 18 52 25 15 34 5 22 42 29 19 45 5 20 56 32 11 52 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 8 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Extrinsieke_waarden[t] = + 51.6702289080872 -0.395745066818002leeftijd[t] -0.183028527512322opleiding[t] -0.0163919147761365Neuroticisme[t] + 0.0169086989274354Extraversie[t] -0.619153712609344Openheid[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) 51.6702289080872 6.566632 7.8686 0 0 leeftijd -0.395745066818002 0.057026 -6.9397 0 0 opleiding -0.183028527512322 0.059501 -3.0761 0.002409 0.001204 Neuroticisme -0.0163919147761365 0.081075 -0.2022 0.839993 0.419996 Extraversie 0.0169086989274354 0.081675 0.207 0.836213 0.418107 Openheid -0.619153712609344 0.051566 -12.0071 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.772056943864005 R-squared 0.596071924568628 Adjusted R-squared 0.585385996647163 F-TEST (value) 55.7810167679772 F-TEST (DF numerator) 5 F-TEST (DF denominator) 189 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 8.61753412815762 Sum Squared Residuals 14035.4980510427 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 18 18.8019259883771 -0.801925988377056 2 10 15.9911597462033 -5.9911597462033 3 23 33.6298970545535 -10.6298970545535 4 14 18.7678588948077 -4.76785889480773 5 20 23.9262088462614 -3.92620884626141 6 15 14.5457239035711 0.454276096428875 7 18 9.81569996751708 8.18430003248292 8 19 12.5430898275748 6.45691017242522 9 19 15.3256316094306 3.67436839056938 10 14 16.2347532676433 -2.23475326764332 11 15 16.3391306227979 -1.33913062279792 12 14 17.7285238002903 -3.72852380029027 13 16 16.2214571706261 -0.221457170626098 14 13 15.0033546908312 -2.00335469083116 15 13 17.067822856881 -4.06782285688098 16 14 17.7373613539348 -3.73736135393479 17 23 31.0369513362787 -8.03695133627873 18 17 8.89483256474106 8.10516743525894 19 14 12.5738332654908 1.42616673450919 20 21 18.0206857515233 2.97931424847673 21 15 12.204665802765 2.79533419723505 22 19 19.8308998502609 -0.830899850260884 23 20 11.4066226514982 8.59337734850178 24 18 19.6705703603506 -1.67057036035061 25 13 15.6125207079462 -2.61252070794625 26 20 16.5882266228949 3.4117733771051 27 12 17.9262603459065 -5.92626034590651 28 17 19.9586837531128 -2.95868375311282 29 13 25.5056354813939 -12.5056354813939 30 52 34.2340824341964 17.7659175658036 31 46 36.1908416746897 9.80915832531032 32 58 32.9267453343568 25.0732546656432 33 54 34.0681131920053 19.9318868079947 34 29 40.4798713046882 -11.4798713046882 35 50 36.741698108193 13.258301891807 36 43 36.8299411590345 6.17005884096547 37 30 31.408840898962 -1.40884089896195 38 47 34.3077680533278 12.6922319466722 39 45 36.0618138574557 8.93818614254427 40 48 33.4292342061729 14.5707657938271 41 48 33.5219929106085 14.4780070893915 42 26 30.1427620121612 -4.14276201216124 43 46 33.4876386588273 12.5123613411727 44 3 35.9796754878379 -32.9796754878379 45 3 -0.0153805393059878 3.01538053930599 46 4 13.6822192009145 -9.68221920091448 47 2 5.7197628956713 -3.7197628956713 48 2 1.37083009095312 0.629169909046876 49 3 0.815673840039175 2.18432615996082 50 2 4.91455172230979 -2.91455172230979 51 4 5.03863398458822 -1.03863398458822 52 5 5.50727174202696 -0.507271742026957 53 3 8.19721570131841 -5.19721570131841 54 4 9.13252796039819 -5.13252796039819 55 5 13.8219406605128 -8.82194066051279 56 5 7.26062196626641 -2.26062196626641 57 3 0.971191390716594 2.02880860928341 58 4 7.66880992076137 -3.66880992076137 59 3 3.45231065607589 -0.452310656075894 60 3 5.9594177569938 -2.9594177569938 61 2 0.475835881277837 1.52416411872216 62 3 17.3782943696117 -14.3782943696117 63 4 12.7437386882196 -8.74373868821962 64 4 13.4585979713591 -9.45859797135909 65 4 2.17154411727049 1.82845588272951 66 4 0.576780993484566 3.42321900651543 67 3 1.49676052720298 1.50323947279702 68 3 -2.6013431852731 5.6013431852731 69 3 9.772780038826 -6.77278003882601 70 2 -3.76579922234027 5.76579922234027 71 3 6.89116000594672 -3.89116000594672 72 3 11.8139072306387 -8.81390723063871 73 3 2.0492648866023 0.9507351133977 74 3 13.3031483914398 -10.3031483914398 75 5 12.7536466972975 -7.75364669729751 76 3 0.0251954168857051 2.97480458311429 77 5 10.3936096618548 -5.39360966185483 78 4 6.2559484230213 -2.2559484230213 79 4 6.98816097820314 -2.98816097820314 80 4 11.4903989747347 -7.4903989747347 81 5 15.9276165330697 -10.9276165330697 82 4 24.8871682212458 -20.8871682212458 83 5 7.16634290676526 -2.16634290676526 84 3 -1.83030746795567 4.83030746795567 85 3 4.87317973850349 -1.87317973850349 86 2 5.51210863818375 -3.51210863818375 87 3 14.6257586715038 -11.6257586715038 88 4 -0.252018397595483 4.25201839759548 89 5 3.85466835716593 1.14533164283407 90 5 10.0378246577564 -5.03782465775643 91 3 12.3389975666148 -9.33899756661483 92 2 2.66691816060563 -0.666918160605627 93 3 -2.51612750444623 5.51612750444623 94 4 2.14233577767784 1.85766422232216 95 1 10.2293245411558 -9.22932454115584 96 4 3.79412033390941 0.205879666090589 97 3 10.6489738847821 -7.64897388478212 98 3 -1.77943567192839 4.77943567192839 99 4 18.4711818380005 -14.4711818380005 100 3 7.67953358786051 -4.67953358786051 101 4 15.3379504035163 -11.3379504035163 102 2 6.20332274669885 -4.20332274669885 103 3 1.01803137412447 1.98196862587553 104 3 4.4643189336306 -1.4643189336306 105 3 14.640862731438 -11.640862731438 106 2 -2.8237849224611 4.8237849224611 107 5 6.39962241430445 -1.39962241430445 108 5 2.03401184765729 2.96598815234271 109 4 12.6031540916678 -8.60315409166782 110 2 3.56166666267819 -1.56166666267819 111 3 -0.275835944929442 3.27583594492944 112 3 18.660940643002 -15.660940643002 113 3 8.6287936007036 -5.6287936007036 114 4 7.94289963663601 -3.94289963663601 115 5 0.974288996520515 4.02571100347948 116 4 4.26201525734727 -0.262015257347274 117 22 15.7466083274186 6.25339167258142 118 16 29.4773687856011 -13.4773687856011 119 36 27.7080807149916 8.29191928500837 120 35 27.6625425294974 7.33745747050262 121 25 32.8032276691393 -7.80322766913934 122 27 27.7377565717583 -0.737756571758339 123 32 39.7176605639378 -7.71766056393782 124 36 28.5156044803947 7.48439551960526 125 51 26.264532296873 24.735467703127 126 30 33.4619034750429 -3.4619034750429 127 20 24.7827617953287 -4.78276179532871 128 29 22.5676857611609 6.43231423883908 129 26 24.8959597334566 1.10404026654335 130 20 25.9520626433011 -5.95206264330109 131 40 26.8246342560579 13.1753657439421 132 29 27.7733150480111 1.22668495198891 133 32 25.4188491637894 6.58115083621057 134 33 26.1682680145964 6.83173198540359 135 32 29.1345236799156 2.86547632008437 136 34 26.3332003645612 7.66679963543884 137 24 27.583832387369 -3.583832387369 138 25 25.6900538576242 -0.690053857624205 139 41 26.7597989924682 14.2402010075318 140 39 30.0248790587602 8.97512094123978 141 21 29.2802481334569 -8.28024813345693 142 38 25.867620117534 12.132379882466 143 28 28.6966078704013 -0.696607870401336 144 37 27.0165283217382 9.98347167826177 145 46 12.9600547312141 33.0399452687859 146 39 18.2846457471288 20.7153542528712 147 21 15.08488967255 5.91511032744997 148 31 16.3999202290767 14.6000797709233 149 25 13.7382932191656 11.2617067808344 150 29 8.02997623914485 20.9700237608551 151 31 11.4620350306969 19.5379649693031 152 13 28.0419159658629 -15.0419159658629 153 15 16.2661574009106 -1.26615740091061 154 13 12.5072320380837 0.492767961916336 155 13 20.3538530660089 -7.35385306600887 156 23 21.5919850194141 1.4080149805859 157 18 16.8219860628341 1.17801393716593 158 21 22.4481276054442 -1.44812760544419 159 14 21.1552313637675 -7.15523136376752 160 12 17.5075284190865 -5.5075284190865 161 17 18.8364252752152 -1.83642527521522 162 11 10.8029979415313 0.19700205846867 163 15 25.2145549279422 -10.2145549279422 164 14 11.2965205311292 2.7034794688708 165 19 25.7472534828519 -6.7472534828519 166 12 18.5742659031445 -6.57426590314448 167 14 16.6987196731255 -2.69871967312549 168 18 19.2202746752643 -1.22027467526427 169 25 25.2518562706378 -0.251856270637825 170 22 30.4205522005948 -8.4205522005948 171 15 9.726873472825 5.273126527175 172 18 19.5832303611929 -1.58323036119289 173 18 12.5542987182117 5.44570128178825 174 12 18.9033726306047 -6.90337263060474 175 12 9.60954614863556 2.39045385136445 176 16 18.2248210365886 -2.22482103658862 177 22 13.6849744693746 8.31502553062536 178 15 13.4514324288441 1.54856757115593 179 16 18.9045719679274 -2.90457196792738 180 11 10.1339810442734 0.866018955726642 181 20 11.7977337031301 8.20226629686994 182 14 10.3678540301601 3.63214596983987 183 20 14.4395732452169 5.56042675478307 184 15 7.55036966552363 7.44963033447637 185 12 17.075962674728 -5.07596267472798 186 18 23.4142338804761 -5.4142338804761 187 18 11.5306365375367 6.46936346246327 188 11 18.4728785812341 -7.47287858123412 189 13 21.6327015628821 -8.63270156288208 190 15 20.4684106306467 -5.46841063064672 191 19 21.9170116277901 -2.9170116277901 192 13 16.4592325221835 -3.45923252218351 193 19 7.62164194856319 11.3783580514368 194 18 18.088703787166 -0.0887037871659828 195 18 18.8019259883772 -0.801925988377244 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.0723947308628668 0.144789461725734 0.927605269137133 10 0.0762894511281878 0.152578902256376 0.923710548871812 11 0.0318922481106178 0.0637844962212356 0.968107751889382 12 0.0164236499308531 0.0328472998617062 0.983576350069147 13 0.00638904091082975 0.0127780818216595 0.99361095908917 14 0.00456005823016683 0.00912011646033367 0.995439941769833 15 0.00196687487495836 0.00393374974991672 0.998033125125042 16 0.00069582214442956 0.00139164428885912 0.99930417785557 17 0.000342845487427621 0.000685690974855243 0.999657154512572 18 0.000243954634523016 0.000487909269046031 0.999756045365477 19 0.00011116064328083 0.00022232128656166 0.99988883935672 20 9.03019693515803e-05 0.000180603938703161 0.999909698030648 21 3.19821097182198e-05 6.39642194364396e-05 0.999968017890282 22 1.19745945306387e-05 2.39491890612774e-05 0.99998802540547 23 8.83423140540362e-06 1.76684628108072e-05 0.999991165768595 24 3.00479589071574e-06 6.00959178143148e-06 0.99999699520411 25 1.14563864693173e-06 2.29127729386345e-06 0.999998854361353 26 4.39428532750162e-07 8.78857065500325e-07 0.999999560571467 27 2.88079486265597e-07 5.76158972531194e-07 0.999999711920514 28 9.4298437677921e-08 1.88596875355842e-07 0.999999905701562 29 2.08316000049346e-07 4.16632000098692e-07 0.999999791684 30 8.92803331469963e-08 1.78560666293993e-07 0.999999910719667 31 4.64100000938749e-08 9.28200001877498e-08 0.99999995359 32 5.7954760689335e-08 1.1590952137867e-07 0.99999994204524 33 3.40784191186697e-08 6.81568382373394e-08 0.99999996592158 34 4.81310594233458e-06 9.62621188466916e-06 0.999995186894058 35 2.81668785757114e-06 5.63337571514228e-06 0.999997183312142 36 5.07145750806358e-05 0.000101429150161272 0.99994928542492 37 0.0111861323831179 0.0223722647662358 0.988813867616882 38 0.010252618265036 0.0205052365300721 0.989747381734964 39 0.0124737705319562 0.0249475410639124 0.987526229468044 40 0.018317515607727 0.0366350312154539 0.981682484392273 41 0.0214647843413882 0.0429295686827765 0.978535215658612 42 0.147027082840381 0.294054165680763 0.852972917159619 43 0.189482198184715 0.37896439636943 0.810517801815285 44 0.86094201729296 0.278115965414082 0.139057982707041 45 0.957365261379784 0.0852694772404325 0.0426347386202163 46 0.98596648794297 0.02806702411406 0.01403351205703 47 0.986974010834723 0.0260519783305545 0.0130259891652773 48 0.984170689886283 0.0316586202274344 0.0158293101137172 49 0.97885448055903 0.0422910388819383 0.0211455194409691 50 0.974820712255444 0.0503585754891113 0.0251792877445556 51 0.967907903587872 0.0641841928242555 0.0320920964121277 52 0.96209742334876 0.075805153302482 0.037902576651241 53 0.955571701695147 0.0888565966097064 0.0444282983048532 54 0.9524768812231 0.0950462375537984 0.0475231187768992 55 0.948864418287566 0.102271163424868 0.051135581712434 56 0.93600850645527 0.12798298708946 0.0639914935447301 57 0.92236201080785 0.1552759783843 0.0776379891921502 58 0.909893227208928 0.180213545582145 0.0901067727910724 59 0.892941795020175 0.21411640995965 0.107058204979825 60 0.873387256293887 0.253225487412227 0.126612743706113 61 0.850227947608258 0.299544104783485 0.149772052391742 62 0.878249215617246 0.243501568765508 0.121750784382754 63 0.870568016371885 0.258863967256229 0.129431983628115 64 0.864190542079408 0.271618915841185 0.135809457920592 65 0.841250785369317 0.317498429261366 0.158749214630683 66 0.817254846597496 0.365490306805008 0.182745153402504 67 0.787722756914457 0.424554486171087 0.212277243085543 68 0.764537816541398 0.470924366917203 0.235462183458602 69 0.745426924129488 0.509146151741023 0.254573075870512 70 0.71819589306343 0.56360821387314 0.28180410693657 71 0.689968917270911 0.620062165458178 0.310031082729089 72 0.682994933747915 0.634010132504169 0.317005066252085 73 0.651408861139117 0.697182277721767 0.348591138860883 74 0.650199164730713 0.699601670538574 0.349800835269287 75 0.630077239604601 0.739845520790797 0.369922760395399 76 0.597641446432231 0.804717107135538 0.402358553567769 77 0.580330323911316 0.839339352177367 0.419669676088684 78 0.553100920960466 0.893798158079069 0.446899079039534 79 0.512809196323087 0.974381607353827 0.487190803676913 80 0.489109648617785 0.97821929723557 0.510890351382215 81 0.48412087115578 0.96824174231156 0.51587912884422 82 0.615197139303528 0.769605721392945 0.384802860696472 83 0.574636144405876 0.850727711188249 0.425363855594124 84 0.547339584656173 0.905320830687653 0.452660415343827 85 0.507400920522862 0.985198158954276 0.492599079477138 86 0.47978034936552 0.95956069873104 0.52021965063448 87 0.500619308356743 0.998761383286515 0.499380691643257 88 0.47324527135291 0.94649054270582 0.52675472864709 89 0.432120773000134 0.864241546000268 0.567879226999866 90 0.402473544218776 0.804947088437552 0.597526455781224 91 0.395254936870871 0.790509873741741 0.604745063129129 92 0.358087478500809 0.716174957001618 0.641912521499191 93 0.325331313501545 0.65066262700309 0.674668686498455 94 0.289257218445573 0.578514436891145 0.710742781554427 95 0.300153715957942 0.600307431915883 0.699846284042058 96 0.285867711901971 0.571735423803942 0.714132288098029 97 0.296854832418104 0.593709664836207 0.703145167581896 98 0.27040909206173 0.54081818412346 0.72959090793827 99 0.306694294572367 0.613388589144735 0.693305705427633 100 0.294467332261794 0.588934664523589 0.705532667738206 101 0.317527974828147 0.635055949656294 0.682472025171853 102 0.300769702299692 0.601539404599384 0.699230297700308 103 0.28737465138832 0.574749302776639 0.71262534861168 104 0.264048971956385 0.52809794391277 0.735951028043615 105 0.316767377098638 0.633534754197276 0.683232622901362 106 0.296357114379426 0.592714228758853 0.703642885620574 107 0.286184040489085 0.57236808097817 0.713815959510915 108 0.252985913751947 0.505971827503893 0.747014086248053 109 0.297786232900732 0.595572465801464 0.702213767099268 110 0.289118512026595 0.57823702405319 0.710881487973405 111 0.276958965641601 0.553917931283202 0.723041034358399 112 0.474597035461039 0.949194070922078 0.525402964538961 113 0.572540977698774 0.854918044602452 0.427459022301226 114 0.645384748680498 0.709230502639004 0.354615251319502 115 0.729039271146088 0.541921457707824 0.270960728853912 116 0.914257016287726 0.171485967424548 0.085742983712274 117 0.949926123466218 0.100147753067565 0.0500738765337825 118 0.973291705320636 0.0534165893587288 0.0267082946793644 119 0.990509832463255 0.0189803350734902 0.0094901675367451 120 0.993544791311742 0.0129104173765167 0.00645520868825833 121 0.99279326731267 0.014413465374658 0.00720673268732901 122 0.992111148746544 0.0157777025069123 0.00788885125345614 123 0.99112125829681 0.017757483406382 0.008878741703191 124 0.992902770726053 0.0141944585478936 0.0070972292739468 125 0.999879150642847 0.000241698714305661 0.00012084935715283 126 0.999813214120672 0.000373571758655841 0.00018678587932792 127 0.99981383272145 0.000372334557100211 0.000186167278550105 128 0.999841049626683 0.000317900746634294 0.000158950373317147 129 0.999763317785016 0.000473364429967246 0.000236682214983623 130 0.999958793746341 8.24125073173378e-05 4.12062536586689e-05 131 0.999980165704682 3.96685906359102e-05 1.98342953179551e-05 132 0.9999678764983 6.42470033992808e-05 3.21235016996404e-05 133 0.999954055999806 9.18880003874685e-05 4.59440001937342e-05 134 0.999927717768847 0.000144564462305916 7.22822311529578e-05 135 0.99991994828715 0.000160103425701462 8.00517128507312e-05 136 0.99992265295135 0.000154694097297919 7.73470486489596e-05 137 0.999911296139834 0.000177407720332245 8.87038601661227e-05 138 0.999883729628087 0.000232540743826768 0.000116270371913384 139 0.99990308727116 0.000193825457680616 9.69127288403079e-05 140 0.999972331594112 5.53368117752725e-05 2.76684058876362e-05 141 0.99999024542561 1.95091487794429e-05 9.75457438972146e-06 142 0.999994807592565 1.03848148699478e-05 5.19240743497392e-06 143 0.99999206438651 1.5871226979395e-05 7.93561348969751e-06 144 0.999989905769648 2.01884607037678e-05 1.00942303518839e-05 145 0.999999983605012 3.27899768223162e-08 1.63949884111581e-08 146 0.999999999248946 1.50210778805624e-09 7.51053894028118e-10 147 0.999999998477509 3.04498298034612e-09 1.52249149017306e-09 148 0.9999999992382 1.52360107378778e-09 7.6180053689389e-10 149 0.999999999283877 1.43224624477926e-09 7.16123122389632e-10 150 0.99999999912155 1.75689946393858e-09 8.78449731969291e-10 151 0.99999999930507 1.38985904424466e-09 6.94929522122331e-10 152 0.999999999741757 5.16485347143555e-10 2.58242673571777e-10 153 0.999999999317002 1.36599522993272e-09 6.8299761496636e-10 154 0.999999998298027 3.40394630649229e-09 1.70197315324615e-09 155 0.999999996294928 7.41014420560039e-09 3.70507210280019e-09 156 0.999999998071426 3.8571481297483e-09 1.92857406487415e-09 157 0.999999996368407 7.26318532274455e-09 3.63159266137227e-09 158 0.999999993453617 1.30927665955757e-08 6.54638329778787e-09 159 0.999999988729325 2.25413489443101e-08 1.1270674472155e-08 160 0.999999982793302 3.44133956909037e-08 1.72066978454518e-08 161 0.999999958262614 8.34747718957871e-08 4.17373859478935e-08 162 0.999999918348773 1.63302454774757e-07 8.16512273873784e-08 163 0.999999829699923 3.40600154753477e-07 1.70300077376738e-07 164 0.999999678449957 6.43100086565324e-07 3.21550043282662e-07 165 0.99999916956323 1.66087353855275e-06 8.30436769276376e-07 166 0.999998669022422 2.66195515550043e-06 1.33097757775022e-06 167 0.999997422387743 5.15522451391295e-06 2.57761225695647e-06 168 0.99999340089502 1.3198209960926e-05 6.59910498046298e-06 169 0.9999973992823 5.20143540045805e-06 2.60071770022902e-06 170 0.999996907296178 6.18540764446703e-06 3.09270382223351e-06 171 0.999991774552317 1.64508953661313e-05 8.22544768306563e-06 172 0.999987117165562 2.57656688756474e-05 1.28828344378237e-05 173 0.99996789038431 6.42192313814328e-05 3.21096156907164e-05 174 0.999919631068325 0.000160737863350349 8.03689316751746e-05 175 0.999876801265958 0.000246397468083848 0.000123198734041924 176 0.999671939838245 0.000656120323510655 0.000328060161755328 177 0.999680331779331 0.000639336441337592 0.000319668220668796 178 0.999145428653433 0.00170914269313476 0.000854571346567379 179 0.997803214238383 0.00439357152323311 0.00219678576161656 180 0.99796402526612 0.00407194946775897 0.00203597473387949 181 0.995503555916877 0.0089928881662452 0.0044964440831226 182 0.988648932021545 0.0227021359569103 0.0113510679784551 183 0.98558846881045 0.0288230623791004 0.0144115311895502 184 0.96601892306712 0.06796215386576 0.03398107693288 185 0.980532103885766 0.0389357922284673 0.0194678961142336 186 0.93936530478758 0.12126939042484 0.06063469521242 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 80 0.449438202247191 NOK 5% type I error level 100 0.561797752808989 NOK 10% type I error level 109 0.612359550561798 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/10kljd1293395106.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/10kljd1293395106.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/1d24j1293395106.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/1d24j1293395106.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/2d24j1293395106.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/2d24j1293395106.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/3ou341293395106.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/3ou341293395106.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/4ou341293395106.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/4ou341293395106.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/5ou341293395106.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/5ou341293395106.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/6z32p1293395106.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/6z32p1293395106.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/7rc2s1293395106.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/7rc2s1293395106.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/8rc2s1293395106.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/8rc2s1293395106.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/9rc2s1293395106.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293395012411t5kacvd9ugti/9rc2s1293395106.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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') }

Copyright

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

Software written by Ed van Stee & Patrick Wessa

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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.

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