*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: Wed, 01 Dec 2010 12:47:37 +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/01/t1291207587kfqinpxzbbjlpca.htm/, Retrieved Wed, 01 Dec 2010 13:46:40 +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/01/t1291207587kfqinpxzbbjlpca.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 «

4 1 27 5 26 49 35 5 4 1 36 4 25 45 34 4 5 1 25 4 17 54 13 2 2 1 27 3 37 36 35 3 3 2 25 3 35 36 28 1 5 2 44 3 15 53 32 3 4 1 50 4 27 46 35 2 4 1 41 4 36 42 36 2 4 1 48 5 25 41 27 2 4 2 43 4 30 45 29 2 5 2 47 2 27 47 27 4 4 2 41 3 33 42 28 1 3 1 44 2 29 45 29 6 4 2 47 5 30 40 28 2 3 2 40 3 25 45 30 2 3 2 46 3 23 40 25 2 4 1 28 3 26 42 15 2 3 1 56 3 24 45 33 1 4 2 49 4 35 47 31 2 2 2 25 4 39 31 37 4 4 2 41 4 23 46 37 4 3 2 26 3 32 34 34 2 4 1 50 5 29 43 32 2 4 1 47 4 26 45 21 5 3 1 52 2 21 42 25 1 3 2 37 5 35 51 32 2 2 2 41 3 23 44 28 1 4 1 45 4 21 47 22 2 5 2 26 4 28 47 25 2 4 1 3 30 41 26 1 2 1 52 4 21 44 34 4 5 1 46 2 29 51 34 1 4 1 58 3 28 46 36 2 3 1 54 5 19 47 36 2 4 1 29 3 26 46 26 2 2 2 50 3 33 38 26 1 3 1 43 2 34 50 34 4 3 2 30 3 33 48 33 1 3 2 47 2 40 36 31 2 5 1 45 3 24 51 33 2 2 48 1 35 35 22 1 4 2 48 3 35 49 29 2 4 2 26 4 32 38 24 2 4 1 46 5 20 47 37 2 2 2 3 35 36 32 2 4 2 50 3 35 47 23 2 3 1 25 4 21 46 29 2 4 1 47 2 33 43 35 2 1 2 47 2 40 53 20 4 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 23 seconds R Server 'George Udny Yule' @ 72.249.76.132 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 Teamwork33[t] = + 49.6085876250846 -0.139372560558695geslacht[t] -0.308848721560136leeftijd[t] -0.397800126959575opleiding[t] -0.197798722000610Neuroticisme[t] -0.498369913461478Extraversie[t] -0.0686083798311122Openheid[t] -0.327316080922906Beroep[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) 49.6085876250846 5.272318 9.4093 0 0 geslacht -0.139372560558695 0.053863 -2.5875 0.010426 0.005213 leeftijd -0.308848721560136 0.048803 -6.3285 0 0 opleiding -0.397800126959575 0.052168 -7.6254 0 0 Neuroticisme -0.197798722000610 0.06838 -2.8927 0.004273 0.002137 Extraversie -0.498369913461478 0.054295 -9.1789 0 0 Openheid -0.0686083798311122 0.056159 -1.2217 0.223369 0.111684 Beroep -0.327316080922906 0.05581 -5.8648 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.771539000887454 R-squared 0.595272429890411 Adjusted R-squared 0.580122199993261 F-TEST (value) 39.291313328676 F-TEST (DF numerator) 7 F-TEST (DF denominator) 187 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.95572041113526 Sum Squared Residuals 11835.8821176488 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4 5.54053271727264 -1.54053271727264 2 4 5.74589718679147 -1.74589718679147 3 5 8.33570181710378 -3.33570181710378 4 2 11.2937880660300 -9.29378806603005 5 3 13.3026012132565 -10.3026012132565 6 5 1.98909573361063 3.01090426638937 7 4 1.11407150950162 2.88592849049838 8 4 4.03839277955215 -0.0383927795521541 9 4 4.7702828756198 -0.7702828756198 10 4 3.45326402631017 0.546735973689828 11 5 2.09271033088406 2.90728966911594 12 4 5.76639963152664 -1.76639963152664 13 3 2.96792251753687 0.0320774824631324 14 4 4.38052696024856 -0.380526960248558 15 3 5.69799554812209 -2.69799554812209 16 3 7.07539212922545 -4.07539212922545 17 4 11.8699894832529 -7.86998948325292 18 3 1.21507822714880 1.78492177285120 19 4 -0.522778499638869 4.52277849963887 20 2 13.0060321043531 -11.0060321043531 21 4 3.75368340947852 0.246316590521480 22 3 13.8449221247115 -10.8449221247115 23 4 2.02160881841858 1.97839118158142 24 4 2.71535538451094 1.28464461548906 25 3 5.48564618538407 -2.48564618538407 26 3 0.723517998446159 2.27648200155384 27 2 6.74764702460978 -4.74764702460978 28 4 4.23864647364891 -0.238646473648908 29 5 8.37698342923519 -3.37698342923519 30 4 14.8180591973579 -10.8180591973579 31 1 5.21328217398476 -4.21328217398476 32 1 2.63336413119257 -1.63336413119257 33 1 1.30080629405956 -0.300806294059556 34 1 4.29568543288673 -3.29568543288673 35 1 11.4492260197185 -10.4492260197185 36 2 7.06148343418202 -5.06148343418202 37 1 1.38177084150096 -0.38177084150096 38 2 4.38235803111947 -2.38235803111947 39 2 2.18435628371811 -0.184356283718110 40 1 5.53728230046518 -4.53728230046518 41 48 18.9574978876908 29.0425021123092 42 48 11.2265900216737 36.7734099783263 43 26 17.7058747132766 8.29412528672336 44 46 14.9310029615348 31.0689970384652 45 3 1.99029206300428 1.00970793699572 46 3 10.9230375848442 -7.92303758484422 47 4 3.09697769870321 0.90302230129679 48 2 1.39075373547515 0.609246264524851 49 2 4.43219800826241 -2.43219800826241 50 3 2.35632910200113 0.643670897998866 51 2 6.5941901103881 -4.59419011038809 52 4 -1.24349542802017 5.24349542802017 53 5 7.08161795010098 -2.08161795010098 54 3 8.62435464925371 -5.6243546492537 55 4 7.93807414448493 -3.93807414448493 56 5 4.34926227276939 0.650737727230611 57 5 -2.25235620820234 7.25235620820234 58 3 3.04471462734026 -0.0447146273402611 59 4 8.80647138782632 -4.80647138782632 60 3 2.53769616792165 0.462303832078353 61 3 -0.366947346337223 3.36694734633722 62 2 10.5258592723023 -8.52585927230233 63 3 6.85476884474955 -3.85476884474955 64 4 8.22282882211235 -4.22282882211235 65 4 1.01903260110446 2.98096739889554 66 4 -1.29937044555590 5.2993704455559 67 4 1.24548035955089 2.75451964044911 68 3 1.14744385697765 1.85255614302235 69 3 5.8343252163428 -2.8343252163428 70 3 0.204000393597296 2.79599960640270 71 2 10.5235407011182 -8.52354070111824 72 3 12.1013460772376 -9.1013460772376 73 3 6.89967516579061 -3.89967516579061 74 3 5.81166749650087 -2.81166749650087 75 3 9.30034715547275 -6.30034715547275 76 5 1.66289736310208 3.33710263689792 77 3 12.0793714459706 -9.0793714459706 78 5 -0.838011187951179 5.83801118795118 79 4 0.273092912313519 3.72690708768648 80 4 5.55314304503774 -1.55314304503774 81 4 10.2507998789184 -6.25079987891844 82 5 15.5395409497259 -10.5395409497259 83 4 3.03422765876120 0.965772341238795 84 5 1.09194505520461 3.90805494479539 85 3 3.0599014711538 -0.0599014711538007 86 3 -1.23962805763701 4.23962805763701 87 2 12.8177027760521 -10.8177027760521 88 3 3.09116429662244 -0.0911642966224442 89 4 2.12043148107217 1.87956851892782 90 5 5.56521878961001 -0.565218789610011 91 5 7.90748360989653 -2.90748360989653 92 3 3.13463950510875 -0.134639505108745 93 2 0.564445681945164 1.43555431805484 94 3 0.652659750561406 2.34734024943859 95 4 10.1419236235833 -6.1419236235833 96 1 8.70964586416737 -7.70964586416737 97 4 7.73358717527648 -3.73358717527648 98 3 2.43476895531057 0.565231044689433 99 3 7.3222029494952 -4.32220294949519 100 4 3.01011575485098 0.989884245149022 101 3 12.0729959484639 -9.07299594846386 102 4 6.8762142234261 -2.8762142234261 103 2 7.86510020303653 -5.86510020303653 104 3 1.37593668046909 1.62406331953091 105 3 10.9128298956032 -7.9128298956032 106 3 3.05014031135404 -0.0501403113540434 107 2 7.32350070540654 -5.32350070540654 108 5 0.588350633731435 4.41164936626856 109 5 8.33441345882437 -3.33441345882437 110 4 0.748968793623606 3.25103120637639 111 2 2.27938638218655 -0.279386382186551 112 3 13.1689655495660 -10.1689655495660 113 3 10.5008305852629 -7.50083058526286 114 3 4.88410224465903 -1.88410224465903 115 4 0.528046547783699 3.4719534522163 116 5 0.74725347854189 4.25274652145811 117 4 8.4615530373401 -4.4615530373401 118 22 28.7331553218017 -6.73315532180165 119 16 28.7063562534502 -12.7063562534502 120 36 28.3289937788209 7.67100622117911 121 35 31.6421659169094 3.35783408309063 122 25 29.6977597761604 -4.69775977616038 123 27 35.7961286537927 -8.79612865379268 124 32 28.920724080713 3.07927591928703 125 36 26.0458888032232 9.95411119677684 126 51 30.0865004817947 20.9134995182053 127 30 27.8595234623834 2.14047653761662 128 20 27.277866023109 -7.27786602310897 129 29 27.2703996928688 1.72960030713118 130 26 26.8725516892917 -0.872551689291674 131 20 27.2764631142216 -7.27646311422158 132 40 27.2570984503173 12.7429015496827 133 29 26.6305781847366 2.36942181526336 134 32 25.9749212545891 6.02507874541087 135 33 29.6265031767956 3.37349682320437 136 32 30.8029015809086 1.19709841909140 137 34 29.0692790867867 4.93072091321325 138 24 25.9200026790982 -1.92000267909818 139 25 27.5735371509640 -2.57353715096395 140 41 30.8526070082941 10.1473929917059 141 39 27.0498192881332 11.9501807118668 142 21 23.9984405957733 -2.99844059577330 143 38 26.1583605405037 11.8416394594963 144 28 25.6089691188871 2.39103088111292 145 37 25.2121961890275 11.7878038109725 146 46 8.46336516478383 37.5366348352162 147 39 7.7239197922127 31.2760802077873 148 21 8.13095912320915 12.8690408767908 149 31 27.4239943197359 3.57600568026408 150 25 19.0462961149689 5.95370388503113 151 29 21.3302045907140 7.66979540928596 152 31 24.36678842447 6.63321157553001 153 3 5.60917080306307 -2.60917080306307 154 2 -3.52386356837696 5.52386356837696 155 2 8.58502339424696 -6.58502339424696 156 5 9.33856342187934 -4.33856342187933 157 4 1.29744107006574 2.70255892993426 158 2 3.07963806270505 -1.07963806270505 159 2 11.3790706800555 -9.37907068005548 160 2 0.64658955929365 1.35341044070635 161 1 1.56356826790288 -0.563568267902879 162 2 1.19659405658357 0.803405943416427 163 2 10.3458673393406 -8.34586733934062 164 2 0.605783745906846 1.39421625409315 165 1 6.49270797027965 -5.49270797027965 166 2 4.31899355966605 -2.31899355966605 167 2 -1.66971537495018 3.66971537495018 168 3 14.8709870052178 -11.8709870052178 169 2 8.7192849866857 -6.7192849866857 170 2 12.7259227019734 -10.7259227019734 171 1 0.315641971749025 0.684358028250975 172 2 7.39731284995304 -5.39731284995304 173 2 -1.83256652753091 3.83256652753091 174 2 1.92067209462259 0.0793279053774057 175 2 -0.646904677578507 2.64690467757851 176 2 1.84768934091230 0.152310659087697 177 1 1.27728608843949 -0.277286088439488 178 2 3.54112084656403 -1.54112084656403 179 2 5.11856335900598 -3.11856335900598 180 4 0.453926863439224 3.54607313656078 181 5 5.37173013726864 -0.371730137268636 182 4 -9.2878401254592 13.2878401254592 183 1 0.787351103925494 0.212648896074506 184 2 -9.31853331394638 11.3185333139464 185 2 0.0747448536181814 1.92525514638182 186 2 4.3314176570819 -2.3314176570819 187 3 2.15130274518603 0.848697254813966 188 2 4.86468470318074 -2.86468470318074 189 2 12.4110232873786 -10.4110232873786 190 2 4.57506500831849 -2.57506500831849 191 3 12.4312069441456 -9.4312069441456 192 2 8.82734113549848 -6.82734113549848 193 3 -6.2816983871182 9.2816983871182 194 2 5.90993265194108 -3.90993265194108 195 3 9.23716042935341 -6.23716042935341 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.00033287520458614 0.00066575040917228 0.999667124795414 12 1.68402933510352e-05 3.36805867020705e-05 0.999983159706649 13 3.07037544078863e-06 6.14075088157727e-06 0.99999692962456 14 1.75905338270447e-07 3.51810676540894e-07 0.999999824094662 15 7.81530113846575e-08 1.56306022769315e-07 0.999999921846989 16 6.20055851749139e-09 1.24011170349828e-08 0.999999993799441 17 6.37318481244552e-10 1.27463696248910e-09 0.999999999362682 18 8.30729340803286e-11 1.66145868160657e-10 0.999999999916927 19 1.25424226072459e-11 2.50848452144919e-11 0.999999999987458 20 9.65306075663017e-13 1.93061215132603e-12 0.999999999999035 21 6.51834263203616e-14 1.30366852640723e-13 0.999999999999935 22 8.81323916358107e-15 1.76264783271621e-14 0.99999999999999 23 6.00199157128413e-16 1.20039831425683e-15 1 24 3.83528145289316e-17 7.67056290578633e-17 1 25 2.57106945427485e-18 5.14213890854971e-18 1 26 9.59638355367987e-18 1.91927671073597e-17 1 27 1.83285164602989e-17 3.66570329205979e-17 1 28 1.80028824634064e-18 3.60057649268128e-18 1 29 5.18210214275806e-19 1.03642042855161e-18 1 30 9.48919697945464e-20 1.89783939589093e-19 1 31 1.07345389169835e-20 2.14690778339670e-20 1 32 1.40360915249209e-21 2.80721830498417e-21 1 33 1.28116512082332e-22 2.56233024164665e-22 1 34 1.28383369198434e-23 2.56766738396867e-23 1 35 2.79389759195507e-24 5.58779518391014e-24 1 36 8.2392892877798e-25 1.64785785755596e-24 1 37 1.82766597670785e-25 3.6553319534157e-25 1 38 3.18555873555979e-26 6.37111747111957e-26 1 39 3.75587313646106e-27 7.51174627292212e-27 1 40 7.35866422008247e-27 1.47173284401649e-26 1 41 0.000495616384556795 0.00099123276911359 0.999504383615443 42 0.00317990618054873 0.00635981236109746 0.996820093819451 43 0.0104320345527272 0.0208640691054544 0.989567965447273 44 0.108055815291530 0.216111630583061 0.89194418470847 45 0.147994976707494 0.295989953414988 0.852005023292506 46 0.230367689582788 0.460735379165576 0.769632310417212 47 0.194096249443078 0.388192498886156 0.805903750556922 48 0.170253522691918 0.340507045383837 0.829746477308082 49 0.158347993859045 0.31669598771809 0.841652006140955 50 0.137607960595654 0.275215921191309 0.862392039404346 51 0.114460916602913 0.228921833205826 0.885539083397087 52 0.117859284560198 0.235718569120396 0.882140715439802 53 0.0964696978235196 0.192939395647039 0.90353030217648 54 0.0956562496336237 0.191312499267247 0.904343750366376 55 0.0945145705838663 0.189029141167733 0.905485429416134 56 0.0796857763207415 0.159371552641483 0.920314223679259 57 0.06842521923307 0.13685043846614 0.93157478076693 58 0.0682260376364578 0.136452075272916 0.931773962363542 59 0.0762357766628257 0.152471553325651 0.923764223337174 60 0.0656846104832113 0.131369220966423 0.934315389516789 61 0.0604496555981367 0.120899311196273 0.939550344401863 62 0.0880364910330842 0.176072982066168 0.911963508966916 63 0.090715668590879 0.181431337181758 0.909284331409121 64 0.0830595432798348 0.166119086559670 0.916940456720165 65 0.0705455095903715 0.141091019180743 0.929454490409628 66 0.0582216686340089 0.116443337268018 0.94177833136599 67 0.0481445417094064 0.0962890834188129 0.951855458290594 68 0.0455114598621629 0.0910229197243259 0.954488540137837 69 0.0380903553077889 0.0761807106155778 0.96190964469221 70 0.0362778853512967 0.0725557707025935 0.963722114648703 71 0.0331172913800711 0.0662345827601422 0.966882708619929 72 0.026934155026753 0.053868310053506 0.973065844973247 73 0.0289406019235625 0.057881203847125 0.971059398076437 74 0.0326067044158062 0.0652134088316125 0.967393295584194 75 0.0270021102717822 0.0540042205435645 0.972997889728218 76 0.0231241133286393 0.0462482266572786 0.97687588667136 77 0.0206482436675468 0.0412964873350936 0.979351756332453 78 0.0210914910283709 0.0421829820567418 0.97890850897163 79 0.0178793107674851 0.0357586215349701 0.982120689232515 80 0.0164781036859608 0.0329562073719217 0.98352189631404 81 0.0145107063670632 0.0290214127341263 0.985489293632937 82 0.0170035929767594 0.0340071859535189 0.98299640702324 83 0.0130490453616224 0.0260980907232447 0.986950954638378 84 0.0118898346676151 0.0237796693352303 0.988110165332385 85 0.00918911699749905 0.0183782339949981 0.9908108830025 86 0.0101696299615358 0.0203392599230715 0.989830370038464 87 0.0095236321974334 0.0190472643948668 0.990476367802567 88 0.00808521933309408 0.0161704386661882 0.991914780666906 89 0.00651240632070169 0.0130248126414034 0.993487593679298 90 0.00483525293394835 0.0096705058678967 0.995164747066052 91 0.00362960560109183 0.00725921120218366 0.996370394398908 92 0.00269768460111892 0.00539536920223783 0.997302315398881 93 0.00212443517343127 0.00424887034686253 0.997875564826569 94 0.00166058666228039 0.00332117332456077 0.99833941333772 95 0.00144651863673261 0.00289303727346522 0.998553481363267 96 0.00141535224172600 0.00283070448345201 0.998584647758274 97 0.00110740759343828 0.00221481518687657 0.998892592406562 98 0.00100209189681138 0.00200418379362277 0.998997908103189 99 0.00133019066234686 0.00266038132469372 0.998669809337653 100 0.00095664093920176 0.00191328187840352 0.999043359060798 101 0.000773751067222732 0.00154750213444546 0.999226248932777 102 0.000534033515846563 0.00106806703169313 0.999465966484153 103 0.00094747976683932 0.00189495953367864 0.99905252023316 104 0.000707379345372503 0.00141475869074501 0.999292620654628 105 0.000606026374006649 0.00121205274801330 0.999393973625993 106 0.000557426505532916 0.00111485301106583 0.999442573494467 107 0.000489429201126042 0.000978858402252084 0.999510570798874 108 0.000363701110243876 0.000727402220487753 0.999636298889756 109 0.000289012670613916 0.000578025341227832 0.999710987329386 110 0.000218957291383775 0.00043791458276755 0.999781042708616 111 0.000168013360465878 0.000336026720931756 0.999831986639534 112 0.000180902060568207 0.000361804121136415 0.999819097939432 113 0.000153578359685278 0.000307156719370556 0.999846421640315 114 0.000123549539256226 0.000247099078512452 0.999876450460744 115 9.01596979440711e-05 0.000180319395888142 0.999909840302056 116 6.66064684600807e-05 0.000133212936920161 0.99993339353154 117 0.000103987029327127 0.000207974058654253 0.999896012970673 118 0.000308162474005278 0.000616324948010556 0.999691837525995 119 0.00076774905454671 0.00153549810909342 0.999232250945453 120 0.00406605784107092 0.00813211568214185 0.99593394215893 121 0.00839055923455759 0.0167811184691152 0.991609440765442 122 0.00866062707945106 0.0173212541589021 0.99133937292055 123 0.00673147562645574 0.0134629512529115 0.993268524373544 124 0.00730720534994802 0.0146144106998960 0.992692794650052 125 0.0104289665423140 0.0208579330846280 0.989571033457686 126 0.287860525331193 0.575721050662387 0.712139474668807 127 0.267738017772090 0.535476035544181 0.73226198222791 128 0.470093015764438 0.940186031528877 0.529906984235562 129 0.454690246629006 0.909380493258013 0.545309753370994 130 0.463705514889523 0.927411029779046 0.536294485110477 131 0.549705710394492 0.900588579211016 0.450294289605508 132 0.678161597293281 0.643676805413438 0.321838402706719 133 0.649275817986575 0.70144836402685 0.350724182013425 134 0.614059414360325 0.77188117127935 0.385940585639675 135 0.633866790508844 0.732266418982311 0.366133209491156 136 0.685859131530483 0.628281736939035 0.314140868469517 137 0.652990260678853 0.694019478642294 0.347009739321147 138 0.721265397092075 0.55746920581585 0.278734602907925 139 0.739364267278729 0.521271465442543 0.260635732721271 140 0.764437487655662 0.471125024688676 0.235562512344338 141 0.855628538169026 0.288742923661948 0.144371461830974 142 0.932707021435948 0.134585957128105 0.0672929785640523 143 0.96134466024695 0.0773106795060985 0.0386553397530492 144 0.955714252987129 0.0885714940257421 0.0442857470128711 145 0.983025860741988 0.0339482785160233 0.0169741392580117 146 0.999999700603649 5.98792702817675e-07 2.99396351408838e-07 147 0.999999905004232 1.89991536010069e-07 9.49957680050347e-08 148 0.999999798657258 4.02685483950036e-07 2.01342741975018e-07 149 0.99999989295785 2.14084299996821e-07 1.07042149998410e-07 150 0.99999984197132 3.16057360029883e-07 1.58028680014941e-07 151 0.999999832011587 3.35976826948974e-07 1.67988413474487e-07 152 1 1.06444353769508e-25 5.32221768847539e-26 153 1 3.85032867366682e-25 1.92516433683341e-25 154 1 3.06513006702655e-24 1.53256503351327e-24 155 1 1.27714000607843e-23 6.38570003039214e-24 156 1 5.15910326060828e-24 2.57955163030414e-24 157 1 1.99373826439351e-24 9.96869132196754e-25 158 1 1.71868711764767e-23 8.59343558823833e-24 159 1 1.26125181945533e-22 6.30625909727666e-23 160 1 1.24053009161772e-21 6.20265045808858e-22 161 1 7.36099897388477e-21 3.68049948694239e-21 162 1 4.71692695730218e-20 2.35846347865109e-20 163 1 3.64605909355228e-19 1.82302954677614e-19 164 1 1.92702546181660e-18 9.63512730908301e-19 165 1 3.20242167371673e-18 1.60121083685836e-18 166 1 2.73093426699121e-17 1.36546713349560e-17 167 1 2.60498225001989e-16 1.30249112500995e-16 168 0.999999999999999 2.02742854939359e-15 1.01371427469680e-15 169 0.99999999999999 1.82470912534778e-14 9.12354562673888e-15 170 0.999999999999932 1.36885398848659e-13 6.84426994243295e-14 171 0.99999999999991 1.80747820577943e-13 9.03739102889716e-14 172 0.999999999999153 1.69322386992273e-12 8.46611934961367e-13 173 0.99999999999496 1.00797412815165e-11 5.03987064075823e-12 174 0.99999999995214 9.57194822140053e-11 4.78597411070027e-11 175 0.99999999957932 8.41359193607225e-10 4.20679596803612e-10 176 0.999999996287266 7.42546718335215e-09 3.71273359167607e-09 177 0.99999999474894 1.05021203862190e-08 5.25106019310952e-09 178 0.999999984504122 3.09917565628446e-08 1.54958782814223e-08 179 0.999999947105484 1.05789032311418e-07 5.2894516155709e-08 180 0.999999407110877 1.18577824551565e-06 5.92889122757827e-07 181 0.999996321776106 7.35644778822883e-06 3.67822389411442e-06 182 0.999981819427568 3.63611448630276e-05 1.81805724315138e-05 183 0.99993001691448 0.000139966171039948 6.99830855199738e-05 184 0.999636291786149 0.00072741642770249 0.000363708213851245 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 102 0.586206896551724 NOK 5% type I error level 123 0.706896551724138 NOK 10% type I error level 134 0.770114942528736 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

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Software written by Ed van Stee & Patrick Wessa

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

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

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

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

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

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

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