*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 22:27:50 +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/t1293402356c82evzo8q421ebi.htm/, Retrieved Sun, 26 Dec 2010 23:25:56 +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/t1293402356c82evzo8q421ebi.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 «

1 27 27 5 5 26 26 49 49 35 35 40 1 36 36 4 4 25 25 45 45 34 34 45 1 25 25 4 4 17 17 54 54 13 13 38 1 27 27 3 3 37 37 36 36 35 35 28 1 50 50 4 4 27 27 46 46 35 35 39 1 41 41 4 4 36 36 42 42 36 36 37 1 48 48 5 5 25 25 41 41 27 27 30 1 44 44 2 2 29 29 45 45 29 29 29 1 28 28 3 3 26 26 42 42 15 15 39 1 56 56 3 3 24 24 45 45 33 33 35 1 50 50 5 5 29 29 43 43 32 32 34 1 47 47 4 4 26 26 45 45 21 21 38 1 52 52 2 2 21 21 42 42 25 25 21 1 45 45 4 4 21 21 47 47 22 22 35 1 3 3 30 30 41 41 26 26 36 1 52 52 4 4 21 21 44 44 34 34 1 46 46 2 2 29 29 51 51 34 34 37 1 58 58 3 3 28 28 46 46 36 36 37 1 54 54 5 5 19 19 47 47 36 36 37 1 29 29 3 3 26 26 46 46 26 26 32 1 43 43 2 2 34 34 50 50 34 34 31 1 45 45 3 3 24 24 51 51 33 33 42 1 46 46 5 5 20 20 47 47 37 37 31 1 25 25 4 4 21 21 46 46 29 29 44 1 47 47 2 2 33 33 43 43 35 35 35 1 41 41 3 3 22 22 55 55 28 28 32 1 29 29 4 4 18 18 52 52 25 25 38 1 45 45 5 5 20 20 56 56 32 32 40 1 54 54 2 2 26 26 46 46 27 27 45 1 28 28 4 4 23 23 51 51 27 27 42 1 37 37 3 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 10 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 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 Intrinsieke_waarden[t] = -24.8966504105951 + 0.456806170205309geslacht[t] + 0.0318319415995288leeftijd[t] + 0.318881581238336leeftijd_man[t] + 0.183119575587883opleiding[t] -0.135353735016668opleiding_man[t] + 0.567135811953931Neuroticisme[t] + 0.09664060341303Neuroticisme_man[t] + 0.114508410025915Extraversie[t] + 0.267732438537323Extraversie_man[t] -0.0326812144046286Openheid[t] + 0.305178309477321Openheid_man[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) -24.8966504105951 4.424988 -5.6264 0 0 geslacht 0.456806170205309 0.066342 6.8856 0 0 leeftijd 0.0318319415995288 0.079683 0.3995 0.690004 0.345002 leeftijd_man 0.318881581238336 0.092332 3.4537 0.000687 0.000344 opleiding 0.183119575587883 0.078929 2.3201 0.021441 0.010721 opleiding_man -0.135353735016668 0.0883 -1.5329 0.12703 0.063515 Neuroticisme 0.567135811953931 0.072647 7.8067 0 0 Neuroticisme_man 0.09664060341303 0.093635 1.0321 0.303387 0.151694 Extraversie 0.114508410025915 0.087561 1.3078 0.192594 0.096297 Extraversie_man 0.267732438537323 0.100938 2.6525 0.008693 0.004346 Openheid -0.0326812144046286 0.073388 -0.4453 0.656613 0.328307 Openheid_man 0.305178309477321 0.068079 4.4827 1.3e-05 6e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.890589690556144 R-squared 0.793149996924889 Adjusted R-squared 0.780716390182669 F-TEST (value) 63.7908221941469 F-TEST (DF numerator) 11 F-TEST (DF denominator) 183 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 8.62015225140121 Sum Squared Residuals 13598.1855452327 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 40 30.7936367857726 9.20636321422736 2 45 31.4370557460495 13.5629442539505 3 38 19.9867243124399 18.0132756875601 4 28 33.0305146423446 -5.03051464234461 5 39 38.3293358401495 0.670664159850525 6 37 39.8904355737311 -2.89043557373109 7 30 32.2569408009133 -2.25694080091332 8 29 35.4398524337144 -6.43985243371439 9 39 22.9231907860715 16.0768092139285 10 35 37.4672868517959 -2.46728685179594 11 34 37.7404406805468 -3.74044068054683 12 38 32.416218676688 5.58378132331201 13 21 30.6986383675011 -9.69863836750114 14 35 29.4328883463766 5.56711165362338 15 52 14.3270212288606 37.6729787711394 16 46 57.4710793322765 -11.4710793322765 17 58 47.4123727028605 10.5876272971395 18 54 44.4416840621214 9.55831593787863 19 29 33.7032742142993 -4.70327421429932 20 43 36.6305680171423 6.36943198285766 21 45 43.9168837556252 1.08311624437481 22 46 42.856378496054 3.14362150394598 23 25 31.5893165154313 -6.58931651543126 24 47 39.6761498854901 7.32385011450992 25 41 40.4537735899171 0.54622641008292 26 29 34.6584037764682 -5.65840377646823 27 45 37.6994915727006 7.30050842729936 28 54 45.6793730623501 8.32062693764989 29 28 35.2559259776018 -7.25592597760184 30 37 38.8295165851031 -1.82951658510309 31 56 43.1826307006658 12.8173692993342 32 43 27.4742753279008 15.5257246720992 33 34 31.5132884777441 2.48671152225594 34 42 41.1103936194561 0.88960638054387 35 46 38.6725588664301 7.32744113356994 36 25 29.1650247256869 -4.16502472568693 37 25 33.9488329992366 -8.94883299923661 38 25 24.1563714895838 0.843628510416237 39 48 40.4391481007224 7.56085189927763 40 27 30.5982147565159 -3.59821475651589 41 28 38.7505556476681 -10.7505556476681 42 25 37.5476513788366 -12.5476513788366 43 26 22.6539714740947 3.34602852590530 44 51 40.8621659223875 10.1378340776125 45 29 33.7391161628344 -4.73911616283443 46 29 28.3010545598232 0.698945440176777 47 43 35.9544458162869 7.0455541837131 48 44 37.6891127885630 6.31088721143696 49 25 35.5111472850445 -10.5111472850445 50 51 43.7399271142342 7.26007288576583 51 42 35.4238140368366 6.57618596316339 52 25 33.8690116109405 -8.86901161094054 53 51 42.4029901760831 8.59700982391693 54 46 40.9629888454548 5.03701115454525 55 29 38.3042793950757 -9.30427939507572 56 3 31.2085071518208 -28.2085071518208 57 27 39.4058303962283 -12.4058303962283 58 20 20.5458661261612 -0.545866126161166 59 40 27.6160916700083 12.3839083299917 60 33 27.3625498456581 5.63745015434195 61 24 21.3603154623101 2.63968453768987 62 41 28.4689363507312 12.5310636492688 63 28 25.3750432603711 2.62495673962891 64 37 27.9503950164326 9.04960498356739 65 46 26.9315984064556 19.0684015935444 66 39 40.5743895673292 -1.57438956732923 67 25 35.1160904953926 -10.1160904953926 68 1 21.2299621625814 -20.2299621625814 69 1 26.8200584071965 -25.8200584071965 70 1 32.3717235451891 -31.3717235451891 71 47 29.6036991992787 17.3963008007213 72 52 42.1895951672316 9.81040483276836 73 27 39.1255547727848 -12.1255547727848 74 27 25.3250122497682 1.67498775023181 75 25 34.2390648252485 -9.23906482524855 76 28 30.9319082841472 -2.93190828414722 77 25 30.725024561518 -5.725024561518 78 52 28.9330571456895 23.0669428543105 79 44 46.169520769564 -2.16952076956397 80 42 27.7616297302054 14.2383702697946 81 45 38.7665946951805 6.23340530481945 82 45 39.3196743819792 5.68032561802076 83 50 35.2875421178641 14.7124578821359 84 49 40.0373814506392 8.96261854936076 85 52 43.1811388575620 8.81886114243805 86 25 46.7862884096534 -21.7862884096534 87 0 18.5936384590394 -18.5936384590394 88 0 0.406880715063275 -0.406880715063275 89 0 5.51867240334857 -5.51867240334857 90 0 1.35335678549963 -1.35335678549963 91 0 5.25700182434596 -5.25700182434596 92 0 4.68709091742027 -4.68709091742027 93 0 2.45572330527533 -2.45572330527533 94 0 2.39159585217416 -2.39159585217416 95 0 -0.481466077780041 0.481466077780041 96 0 11.9029382805405 -11.9029382805405 97 0 -1.41354231790665 1.41354231790665 98 0 5.15540528016988 -5.15540528016988 99 0 6.01981333863269 -6.01981333863269 100 0 -4.34353351855715 4.34353351855715 101 0 5.94237160123038 -5.94237160123038 102 0 0.149371857934798 -0.149371857934798 103 0 6.80220950107236 -6.80220950107236 104 0 6.29772974432684 -6.29772974432684 105 0 5.65396348052783 -5.65396348052783 106 0 -1.36481505390474 1.36481505390474 107 3 -7.18720697448265 10.1872069744827 108 3 3.1802662966728 -0.180266296672801 109 2 0.875453423923508 1.12454657607649 110 2 -0.0752564717336588 2.07525647173366 111 4 0.0483990312249443 3.95160096877506 112 5 9.1865504788181 -4.18655047881809 113 3 0.568021035686382 2.43197896431362 114 5 4.30011098998379 0.699889010016215 115 3 3.47249956504615 -0.472499565046153 116 4 6.23064736195614 -2.23064736195614 117 3 -3.11322836357031 6.11322836357031 118 3 5.53571664827052 -2.53571664827052 119 3 3.51608485755938 -0.516084857559375 120 4 4.5660333031011 -0.566033303101097 121 4 3.29723526031764 0.702764739682362 122 4 5.53394020983908 -1.53394020983908 123 3 4.66957944265322 -1.66957944265322 124 3 3.14217475433211 -0.142174754332110 125 3 4.87884507651419 -1.87884507651419 126 5 1.68890103889628 3.31109896110372 127 5 5.11168251447321 -0.111682514473212 128 4 8.40772233288463 -4.40772233288463 129 4 9.83630537611625 -5.83630537611625 130 4 9.37159867186852 -5.37159867186852 131 5 6.98526958801426 -1.98526958801426 132 3 3.19554620666392 -0.195546206663925 133 3 2.70817660770559 0.291823392294410 134 2 8.32172030936433 -6.32172030936433 135 5 0.289644700518476 4.71035529948152 136 2 5.42581596480556 -3.42581596480556 137 3 5.99830328195858 -2.99830328195858 138 4 5.52652709164253 -1.52652709164253 139 4 1.36810362232623 2.63189637767377 140 4 3.91707557774060 0.0829244222593952 141 3 8.99189491077936 -5.99189491077936 142 5 7.08647627881006 -2.08647627881006 143 2 6.16977155484524 -4.16977155484524 144 3 3.17941319474193 -0.179413194741927 145 3 -3.70215728769068 6.70215728769068 146 4 6.37798244354825 -2.37798244354825 147 4 5.7640128495289 -1.76401284952890 148 4 3.48376237358109 0.516237626418909 149 2 0.0394722120576991 1.9605277879423 150 3 1.41927209425942 1.58072790574058 151 3 -1.20050846426618 4.20050846426618 152 3 1.30947309383216 1.69052690616784 153 3 6.09248968568503 -3.09248968568503 154 3 0.951566164932712 2.04843383506729 155 3 2.51153001754207 0.48846998245793 156 4 3.03892387062641 0.961076129373592 157 4 6.56092523604318 -2.56092523604318 158 4 4.58334999282383 -0.583349992823831 159 4 5.1397210743365 -1.1397210743365 160 3 8.62129144865626 -5.62129144865626 161 3 0.732005345558761 2.26799465444124 162 5 2.95222847791775 2.04777152208225 163 3 8.24336821575895 -5.24336821575895 164 3 5.17565630770856 -2.17565630770856 165 3 5.00762979573496 -2.00762979573496 166 4 1.85336040298843 2.14663959701157 167 4 -7.0231915172487 11.0231915172487 168 3 4.00677340771636 -1.00677340771636 169 3 4.12475678834978 -1.12475678834978 170 4 5.02088313868326 -1.02088313868326 171 4 3.38896111042517 0.611038889574834 172 4 4.34108699580148 -0.341086995801476 173 3 0.89683596999316 2.10316403000684 174 3 2.94122587471222 0.0587741252877774 175 3 -0.923130948825364 3.92313094882536 176 3 1.95104122601555 1.04895877398445 177 5 -2.08135080141399 7.08135080141399 178 3 3.24164880550645 -0.241648805506448 179 4 5.86718464795906 -1.86718464795906 180 4 -2.15390527880547 6.15390527880547 181 2 6.42419318666336 -4.42419318666336 182 3 1.15478836862340 1.84521163137660 183 3 4.29512973614624 -1.29512973614624 184 4 9.9061909167957 -5.90619091679569 185 4 1.76366364749270 2.23633635250730 186 3 2.08418869399221 0.915811306007786 187 3 8.44244547326483 -5.44244547326483 188 4 3.61893271088321 0.381067289116789 189 4 3.06010849349226 0.93989150650774 190 3 -1.39357192611549 4.39357192611549 191 3 2.91486164413092 0.0851383558690758 192 4 -3.13049546656044 7.13049546656044 193 5 5.50321367004779 -0.503213670047788 194 5 15.7379695514650 -10.7379695514650 195 4 26.73659702224 -22.73659702224 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.726022849578412 0.547954300843175 0.273977150421588 16 0.586502175978991 0.826995648042018 0.413497824021009 17 0.448358595927143 0.896717191854285 0.551641404072857 18 0.327928661988363 0.655857323976726 0.672071338011637 19 0.222783742566517 0.445567485133034 0.777216257433483 20 0.145357047331419 0.290714094662838 0.854642952668581 21 0.0899249221024942 0.179849844204988 0.910075077897506 22 0.0539465153843473 0.107893030768695 0.946053484615653 23 0.0307197550569960 0.0614395101139921 0.969280244943004 24 0.0178572184182221 0.0357144368364442 0.982142781581778 25 0.0096165976841875 0.019233195368375 0.990383402315812 26 0.00503755122718785 0.0100751024543757 0.994962448772812 27 0.00262301337036026 0.00524602674072053 0.99737698662964 28 0.00148128598756459 0.00296257197512917 0.998518714012435 29 0.000701911124957644 0.00140382224991529 0.999298088875042 30 0.000345433427168485 0.00069086685433697 0.999654566572831 31 0.0002178939428257 0.0004357878856514 0.999782106057174 32 0.000111196766943028 0.000222393533886055 0.999888803233057 33 5.38781460423764e-05 0.000107756292084753 0.999946121853958 34 3.35881149093968e-05 6.71762298187937e-05 0.99996641188509 35 1.77708100972846e-05 3.55416201945692e-05 0.999982229189903 36 8.92005580104038e-06 1.78401116020808e-05 0.9999910799442 37 4.30002820651524e-06 8.60005641303049e-06 0.999995699971793 38 1.87643309057237e-06 3.75286618114475e-06 0.99999812356691 39 9.75207805957436e-07 1.95041561191487e-06 0.999999024792194 40 3.90226247271059e-07 7.80452494542119e-07 0.999999609773753 41 2.51238558385365e-07 5.02477116770731e-07 0.999999748761442 42 1.04002302771598e-07 2.08004605543197e-07 0.999999895997697 43 5.96951212467888e-08 1.19390242493578e-07 0.999999940304879 44 3.58909148641954e-08 7.17818297283908e-08 0.999999964109085 45 1.478678391191e-08 2.957356782382e-08 0.999999985213216 46 5.60867652294127e-09 1.12173530458825e-08 0.999999994391323 47 3.1258452237175e-09 6.251690447435e-09 0.999999996874155 48 1.77995168098964e-09 3.55990336197928e-09 0.999999998220048 49 6.9145046767396e-10 1.38290093534792e-09 0.99999999930855 50 5.30693739602464e-10 1.06138747920493e-09 0.999999999469306 51 4.95843526817165e-10 9.9168705363433e-10 0.999999999504157 52 2.10804087657194e-10 4.21608175314388e-10 0.999999999789196 53 7.87832038312808e-10 1.57566407662562e-09 0.999999999212168 54 6.4853906362962e-09 1.29707812725924e-08 0.99999999351461 55 9.69230749034174e-09 1.93846149806835e-08 0.999999990307692 56 5.1143821791711e-05 0.000102287643583422 0.999948856178208 57 0.000532148231934575 0.00106429646386915 0.999467851768065 58 0.000364022912430179 0.000728045824860358 0.99963597708757 59 0.00496257844778121 0.00992515689556242 0.995037421552219 60 0.00373324290612421 0.00746648581224843 0.996266757093876 61 0.00433231627909076 0.00866463255818152 0.99566768372091 62 0.0120815299858236 0.0241630599716472 0.987918470014176 63 0.0186645380480343 0.0373290760960687 0.981335461951966 64 0.0151888475958370 0.0303776951916739 0.984811152404163 65 0.0347595746126749 0.0695191492253498 0.965240425387325 66 0.644627432917542 0.710745134164916 0.355372567082458 67 0.827959975949198 0.344080048101603 0.172040024050802 68 0.99549880632455 0.00900238735090175 0.00450119367545087 69 0.999999507969513 9.8406097467456e-07 4.9203048733728e-07 70 1 1.75266381181876e-15 8.7633190590938e-16 71 1 2.41501192897529e-19 1.20750596448765e-19 72 1 1.31156119108310e-22 6.55780595541548e-23 73 1 4.26047181738403e-26 2.13023590869201e-26 74 1 1.16521809713759e-25 5.82609048568796e-26 75 1 8.98331190888704e-26 4.49165595444352e-26 76 1 1.19475099814744e-25 5.97375499073722e-26 77 1 2.20036477666766e-27 1.10018238833383e-27 78 1 5.21148960204894e-35 2.60574480102447e-35 79 1 5.01729086429533e-36 2.50864543214767e-36 80 1 3.32773286230554e-36 1.66386643115277e-36 81 1 2.60227300593062e-37 1.30113650296531e-37 82 1 2.61395175304279e-37 1.30697587652140e-37 83 1 3.63126769916008e-41 1.81563384958004e-41 84 1 2.84601866934800e-45 1.42300933467400e-45 85 1 2.49046336878845e-78 1.24523168439423e-78 86 1 2.19569407871288e-78 1.09784703935644e-78 87 1 1.72976671580459e-83 8.64883357902294e-84 88 1 1.41434594042342e-82 7.0717297021171e-83 89 1 1.51755672465563e-81 7.58778362327814e-82 90 1 1.69986073335995e-80 8.49930366679975e-81 91 1 2.15905800105628e-79 1.07952900052814e-79 92 1 1.25468568967617e-78 6.27342844838083e-79 93 1 1.73454075378966e-77 8.67270376894828e-78 94 1 2.10406774039786e-76 1.05203387019893e-76 95 1 1.61649458817140e-75 8.08247294085699e-76 96 1 1.97646728004629e-74 9.88233640023144e-75 97 1 2.36542352007081e-73 1.18271176003541e-73 98 1 3.18223477861644e-72 1.59111738930822e-72 99 1 1.79087920402013e-71 8.95439602010063e-72 100 1 1.94125578219306e-70 9.70627891096532e-71 101 1 3.83878111263253e-70 1.91939055631627e-70 102 1 5.71341880132168e-70 2.85670940066084e-70 103 1 6.86751301840809e-69 3.43375650920405e-69 104 1 8.6752745267291e-68 4.33763726336455e-68 105 1 1.10627778300253e-66 5.53138891501267e-67 106 1 1.37616289168879e-65 6.88081445844395e-66 107 1 8.93188057097138e-65 4.46594028548569e-65 108 1 4.99181380072563e-64 2.49590690036282e-64 109 1 1.48075370128362e-63 7.40376850641812e-64 110 1 4.50330024823416e-63 2.25165012411708e-63 111 1 5.12525248701876e-62 2.56262624350938e-62 112 1 7.4459860524187e-62 3.72299302620935e-62 113 1 8.1772074277939e-61 4.08860371389695e-61 114 1 3.35826688998412e-60 1.67913344499206e-60 115 1 3.56328773196142e-59 1.78164386598071e-59 116 1 3.43126549698685e-58 1.71563274849342e-58 117 1 3.60393788572785e-57 1.80196894286393e-57 118 1 3.83105609837442e-56 1.91552804918721e-56 119 1 3.80001257458069e-55 1.90000628729035e-55 120 1 4.34957321334726e-54 2.17478660667363e-54 121 1 4.37287574767176e-53 2.18643787383588e-53 122 1 3.84728910153567e-52 1.92364455076784e-52 123 1 4.11387141142132e-51 2.05693570571066e-51 124 1 4.26033009014125e-50 2.13016504507062e-50 125 1 3.76094943254647e-49 1.88047471627323e-49 126 1 1.25928982296388e-48 6.2964491148194e-49 127 1 3.62134832210156e-48 1.81067416105078e-48 128 1 3.04148487575771e-47 1.52074243787885e-47 129 1 2.53149867144649e-46 1.26574933572325e-46 130 1 2.35081473931622e-45 1.17540736965811e-45 131 1 3.69215345068567e-45 1.84607672534284e-45 132 1 4.1192216577937e-44 2.05961082889685e-44 133 1 4.3637845752335e-43 2.18189228761675e-43 134 1 1.50277493017161e-42 7.51387465085804e-43 135 1 3.47862137457059e-42 1.73931068728530e-42 136 1 8.64212623953184e-42 4.32106311976592e-42 137 1 8.67672746054363e-41 4.33836373027181e-41 138 1 8.85315146480307e-40 4.42657573240153e-40 139 1 8.59778997447567e-39 4.29889498723784e-39 140 1 8.21588729971402e-38 4.10794364985701e-38 141 1 8.15168636954464e-37 4.07584318477232e-37 142 1 1.67428932473831e-36 8.37144662369157e-37 143 1 2.09138467186452e-36 1.04569233593226e-36 144 1 2.23460731429790e-35 1.11730365714895e-35 145 1 2.12190401115087e-34 1.06095200557543e-34 146 1 2.36483068396898e-33 1.18241534198449e-33 147 1 2.10185601813744e-32 1.05092800906872e-32 148 1 2.03719627982232e-31 1.01859813991116e-31 149 1 4.50919079680292e-31 2.25459539840146e-31 150 1 4.84096202912991e-30 2.42048101456496e-30 151 1 3.80080197658476e-29 1.90040098829238e-29 152 1 3.63353321065011e-28 1.81676660532506e-28 153 1 3.78864658892408e-27 1.89432329446204e-27 154 1 2.26706698664797e-26 1.13353349332399e-26 155 1 2.62724353429186e-25 1.31362176714593e-25 156 1 2.40116182938443e-24 1.20058091469221e-24 157 1 1.96452098714571e-23 9.82260493572855e-24 158 1 1.72628350504968e-22 8.63141752524842e-23 159 1 1.42553112085323e-21 7.12765560426616e-22 160 1 1.56696689193834e-20 7.8348344596917e-21 161 1 1.73786362259842e-19 8.6893181129921e-20 162 1 2.59062256866739e-19 1.29531128433370e-19 163 1 2.90734916838291e-18 1.45367458419145e-18 164 1 2.04232115687496e-17 1.02116057843748e-17 165 1 1.75300264226723e-16 8.76501321133617e-17 166 1 2.07332466429810e-15 1.03666233214905e-15 167 0.99999999999999 2.13026496974066e-14 1.06513248487033e-14 168 0.999999999999946 1.08856315509955e-13 5.44281577549774e-14 169 0.99999999999941 1.17858597814816e-12 5.89292989074081e-13 170 0.999999999995166 9.66847469413014e-12 4.83423734706507e-12 171 0.999999999952786 9.44283310520995e-11 4.72141655260497e-11 172 0.999999999539163 9.2167412350288e-10 4.6083706175144e-10 173 0.999999996318078 7.36384364301155e-09 3.68192182150578e-09 174 0.99999996726378 6.5472439509585e-08 3.27362197547925e-08 175 0.999999692874193 6.14251614105645e-07 3.07125807052823e-07 176 0.999998000172054 3.99965589189037e-06 1.99982794594518e-06 177 0.999988232915036 2.35341699279647e-05 1.17670849639824e-05 178 0.999898334707426 0.000203330585147695 0.000101665292573848 179 0.999172147823619 0.00165570435276219 0.000827852176381096 180 0.994123735970031 0.0117525280599371 0.00587626402996855 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 147 0.885542168674699 NOK 5% type I error level 154 0.927710843373494 NOK 10% type I error level 156 0.939759036144578 NOK

Charts produced by software:

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Parameters (Session):

par1 = 12 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 12 ; 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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