*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: Tue, 28 Dec 2010 11:47:43 +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/28/t1293536724g4w3es2qehr5590.htm/, Retrieved Tue, 28 Dec 2010 12:45:27 +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/28/t1293536724g4w3es2qehr5590.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 40 26 49 35 36 4 45 25 45 34 25 4 38 17 54 13 27 3 28 37 36 35 25 3 35 36 28 44 3 38 15 53 32 50 4 39 27 46 35 41 4 37 36 42 36 48 5 30 25 41 27 43 4 30 30 45 29 47 2 30 27 47 27 41 3 26 33 42 28 44 2 29 29 45 29 47 5 31 30 40 28 40 3 27 25 45 30 46 3 25 23 40 25 28 3 39 26 42 15 56 3 35 24 45 33 49 4 27 35 47 31 25 4 40 39 31 37 41 4 34 23 46 37 26 3 32 32 34 34 50 5 34 29 43 32 47 4 38 26 45 21 52 2 21 21 42 25 37 5 33 35 51 32 41 3 27 23 44 28 45 4 35 21 47 22 26 4 33 28 47 25 3 36 30 41 26 52 4 21 44 34 46 2 37 29 51 34 58 3 37 28 46 36 54 5 37 19 47 36 29 3 32 26 46 26 50 3 25 33 38 26 43 2 31 34 50 34 30 3 33 33 48 33 47 2 18 40 36 31 45 3 42 24 51 33 48 1 26 35 35 22 48 3 26 35 49 29 26 4 32 32 38 24 46 5 31 20 47 37 3 29 35 36 32 50 3 35 35 47 23 25 4 44 21 46 29 47 2 35 33 43 35 47 2 30 40 53 20 41 3 32 22 55 28 45 2 24 35 39 26 41 4 34 20 55 36 45 5 27 28 41 26 40 3 31 46 33 33 29 4 38 18 52 25 34 5 41 22 42 29 45 5 40 20 56 32 52 3 2 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 7 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 Intrinsieke_waarden[t] = + 92.3771983580845 -0.304005715886726leeftijd[t] -0.516637029897194opleiding[t] -0.541055956742284Neuroticisme[t] -0.47626425329013Extraversie[t] -0.332630416187557`Openheid `[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) 92.3771983580845 7.2609 12.7226 0 0 leeftijd -0.304005715886726 0.081221 -3.7429 0.000241 0.000121 opleiding -0.516637029897194 0.087621 -5.8963 0 0 Neuroticisme -0.541055956742284 0.073745 -7.3369 0 0 Extraversie -0.47626425329013 0.091552 -5.2021 1e-06 0 `Openheid ` -0.332630416187557 0.082601 -4.027 8.2e-05 4.1e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.647112133930724 R-squared 0.418754113880375 Adjusted R-squared 0.403377238586205 F-TEST (value) 27.2327183429225 F-TEST (DF numerator) 5 F-TEST (DF denominator) 189 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.8843861455204 Sum Squared Residuals 26694.1018365531 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 40 32.5393910265764 7.46060897342359 2 45 33.0987199995837 11.9012800004163 3 38 43.4700909886035 -5.47009098860349 4 28 33.8124848549776 -5.81248485497759 5 35 35.7779925241264 -0.777992524126385 6 15 11.2850314523279 3.71496854767208 7 27 15.8166613895576 11.1833386104424 8 36 16.2094821097181 19.7905178902819 9 25 26.012521920403 -1.01252192040295 10 30 21.8692536379901 8.13074636200994 11 27 24.3434641599845 2.65653584001546 12 33 27.3371308455452 5.66286915445479 13 29 22.9939020996607 6.0060979003393 14 30 26.5585678425206 3.44143215747942 15 25 23.5795366064658 1.42046339353421 16 23 35.6867592077983 -12.6867592077983 17 26 22.8207197554027 3.17928024459731 18 24 17.0197563588552 6.98024364114482 19 35 28.7023934637431 6.29760653625693 20 39 22.4633352042144 16.5366647957856 21 23 22.4367742752767 0.563225724723339 22 32 23.7123883032342 8.2876116967658 23 29 19.1520189561287 9.84798104387127 24 26 19.8831193441958 6.11688065580424 25 21 33.9815273841012 -12.9815273841012 26 35 17.335990829213 17.664009170787 27 23 25.4057514859759 -2.40575148597589 28 21 28.5230450879891 -7.52304508798912 29 28 35.7780259602269 -7.77802596022694 30 41 25.7701639781102 15.2298360218898 31 34 25.1126210933218 8.88737890667822 32 34 12.0951804227501 21.9048195772499 33 36 16.1940666085117 19.8059333914883 34 36 34.5555605276701 1.44443947232986 35 26 23.9103953680852 2.08960463191481 36 26 28.1833250494164 -2.18332504941643 37 34 27.5718773267478 6.42812267325217 38 33 25.1769263339136 7.8230736660864 39 31 21.8712483531659 9.12875164683409 40 33 23.6372316542499 9.36276834575006 41 22 27.6068327512708 -5.6068327512708 42 29 29.8050986306249 -0.805098630624858 43 24 25.4353701352059 -1.43537013520592 44 37 34.9382478529768 2.06175214702324 45 50 34.9661263275063 15.0338736724937 46 25 36.1011883120994 -11.1011883120994 47 47 34.682297047448 12.317702952552 48 47 32.5474003688628 14.4525996311372 49 41 41.7506616865069 -0.750661686506878 50 45 37.036528588181 7.96347141181904 51 41 42.0785958985905 -1.07859589859045 52 45 32.1798846322191 12.8201153677809 53 40 34.7920423625532 5.20795763744681 54 29 39.046374803845 -10.046374803845 55 34 34.1029920598119 -0.102992059811904 56 45 36.2180261847556 8.78197381524437 57 52 36.9749587045486 15.0250412954514 58 41 38.7858518395974 2.21414816040256 59 48 45.6205940475501 2.37940595244995 60 45 38.7243566396743 6.27564336032571 61 54 32.2480674278596 21.7519325721404 62 25 38.6519987606336 -13.6519987606336 63 26 35.4307443692423 -9.43074436924228 64 28 33.5926395228081 -5.59263952280808 65 50 40.4463909927611 9.5536090072389 66 48 34.1661942258873 13.8338057741127 67 51 34.1065071973854 16.8934928026146 68 53 36.7269005856528 16.2730994143472 69 37 34.4676237315608 2.53237626843921 70 56 49.8684484073243 6.13155159267575 71 43 41.1615281952559 1.83847180474405 72 34 34.5386225249966 -0.538622524996606 73 42 44.7650788261328 -2.76507882613283 74 32 35.6497987584591 -3.64979875845913 75 31 31.2451694013108 -0.245169401310842 76 46 38.5745563275581 7.42544367244187 77 30 44.703843978225 -14.703843978225 78 47 27.4658060966868 19.5341939033132 79 33 25.051008746873 7.94899125312705 80 25 31.1538757106491 -6.15387571064914 81 25 39.5294990328182 -14.5294990328182 82 21 36.1234666806205 -15.1234666806205 83 36 33.2657898805252 2.73421011947483 84 50 43.1827162920628 6.81728370793717 85 48 39.358808879254 8.64119112074601 86 48 30.2534881778978 17.7465118221022 87 25 40.5219061335348 -15.5219061335348 88 48 41.6484818875072 6.35151811249281 89 49 34.695092975994 14.304907024006 90 27 30.1836863881547 -3.18368638815467 91 28 30.0967677433173 -2.09676774331727 92 43 30.3163324681325 12.6836675318675 93 48 40.1913783095229 7.8086216904771 94 48 31.3640727050642 16.6359272949358 95 25 45.9924981336161 -20.9924981336161 96 49 45.4532715018327 3.5467284981673 97 26 37.0553285366894 -11.0553285366894 98 51 42.0116404235593 8.98835957644072 99 25 27.9960979307587 -2.99609793075874 100 29 37.5064670741246 -8.50646707412458 101 29 42.2527611638624 -13.2527611638624 102 43 34.1791762394126 8.82082376058744 103 46 46.1626339579283 -0.162633957928265 104 44 30.7479034617849 13.2520965382151 105 25 36.3060603054146 -11.3060603054146 106 51 38.5673791613834 12.4326208386166 107 42 37.5445385294616 4.4554614705384 108 53 32.1664057170225 20.8335942829775 109 25 34.6731417780003 -9.6731417780003 110 49 32.594629812848 16.405370187152 111 51 36.4986504576048 14.5013495423952 112 20 44.0841045910609 -24.0841045910609 113 44 40.8381302354604 3.16186976453962 114 38 33.4086729349642 4.5913270650358 115 46 32.3989968925344 13.6010031074656 116 42 35.1068360738483 6.8931639261517 117 29 19.3784644370256 9.62153556297436 118 4 21.2967246445253 -17.2967246445253 119 2 14.666621903141 -12.666621903141 120 3 16.4396784459303 -13.4396784459303 121 3 19.4273755534065 -16.4273755534065 122 13 50.2730748361739 -37.2730748361739 123 22 29.9084777145267 -7.90847771452665 124 29 25.231695778222 3.76830422177804 125 30 25.4872194130074 4.51278058699261 126 24 27.6734778966619 -3.67347789666189 127 20 23.0984776134085 -3.09847761340853 128 29 22.0107919430443 6.98920805695573 129 26 23.208771279855 2.79122872014495 130 20 21.500645022831 -1.50064502283098 131 40 18.8976352858518 21.1023647141482 132 29 18.100695514524 10.899304485476 133 32 17.4317527485975 14.5682472514025 134 33 32.8095482731763 0.190451726823726 135 32 21.1134735543895 10.8865264456105 136 34 21.037311972095 12.962688027905 137 24 24.016547310793 -0.016547310792958 138 25 23.6611404462044 1.33885955379561 139 41 34.5008553337865 6.49914466621346 140 39 24.6773955478687 14.3226044521313 141 21 23.9763709875974 -2.97637098759737 142 38 26.4398026775625 11.5601973224375 143 28 28.7780775037463 -0.778077503746348 144 37 38.7140546792898 -1.71405467928981 145 46 28.9112648957223 17.0887351042777 146 39 14.7908535486701 24.2091464513299 147 21 38.780305885236 -17.780305885236 148 31 33.753746160025 -2.75374616002501 149 25 28.1176105717317 -3.11761057173165 150 29 13.7709962940994 15.2290037059006 151 31 42.0360213842561 -11.0360213842561 152 40 42.4240390559347 -2.42403905593473 153 49 34.5702391824497 14.4297608175503 154 38 42.0895052201835 -4.08950522018346 155 32 38.6754747923305 -6.67547479233046 156 46 41.868658812673 4.13134118732701 157 32 32.5133332386866 -0.513333238686616 158 41 47.3196832689716 -6.31968326897162 159 43 40.0651727064665 2.93482729353352 160 44 31.0776470938772 12.9223529061228 161 5 16.7399385020929 -11.7399385020929 162 3 26.7186080374193 -23.7186080374193 163 1 21.9011043646141 -20.9011043646141 164 2 21.3240454814979 -19.3240454814979 165 5 9.57740478933114 -4.57740478933114 166 4 19.5031923206486 -15.5031923206486 167 4 27.321978216279 -23.321978216279 168 4 9.37828119328644 -5.37828119328644 169 3 19.9414576990592 -16.9414576990592 170 4 13.4385706627882 -9.43857066278816 171 3 22.4876931676216 -19.4876931676216 172 3 17.7183904207536 -14.7183904207536 173 4 15.8052158379184 -11.8052158379184 174 4 12.9331713735988 -8.93317137359878 175 2 18.2126673382932 -16.2126673382932 176 3 10.8562759109007 -7.85627591090074 177 5 13.9188822022708 -8.91888220227084 178 2 12.0159889606893 -10.0159889606893 179 5 18.5411390670181 -13.5411390670181 180 3 14.396718147705 -11.396718147705 181 2 5.36960708630196 -3.36960708630196 182 3 6.38005898918652 -3.38005898918652 183 3 1.97359655525689 1.02640344474311 184 4 10.1361983489485 -6.13619834894854 185 4 18.9183453140679 -14.9183453140679 186 3 17.6287945328651 -14.6287945328651 187 3 25.3155455445228 -22.3155455445228 188 4 27.4724203861792 -23.4724203861792 189 4 18.09587655163 -14.09587655163 190 3 24.7491279382184 -21.7491279382184 191 3 27.7905759433758 -24.7905759433758 192 4 7.41100623324648 -3.41100623324648 193 5 23.3665736085699 -18.3665736085699 194 5 16.8557878146264 -11.8557878146264 195 4 11.915572111654 -7.91557211165402 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.0672137114263673 0.134427422852735 0.932786288573633 10 0.0327532101519906 0.0655064203039811 0.96724678984801 11 0.0248013173887826 0.0496026347775652 0.975198682611217 12 0.0107972137146223 0.0215944274292445 0.989202786285378 13 0.00369694004882416 0.00739388009764833 0.996303059951176 14 0.0012507393690378 0.0025014787380756 0.998749260630962 15 0.00044899963429115 0.0008979992685823 0.99955100036571 16 0.000135726555132932 0.000271453110265864 0.999864273444867 17 0.000260436710623747 0.000520873421247494 0.999739563289376 18 0.00015119480434473 0.000302389608689459 0.999848805195655 19 0.00109684366787493 0.00219368733574985 0.998903156332125 20 0.000623879879651347 0.00124775975930269 0.999376120120349 21 0.000301296482449305 0.000602592964898609 0.99969870351755 22 0.000135442734513243 0.000270885469026485 0.999864557265487 23 5.63708273697442e-05 0.000112741654739488 0.99994362917263 24 2.28557000142778e-05 4.57114000285557e-05 0.999977144299986 25 1.52932330628172e-05 3.05864661256344e-05 0.999984706766937 26 3.64338020357563e-05 7.28676040715126e-05 0.999963566197964 27 1.79571001928842e-05 3.59142003857683e-05 0.999982042899807 28 9.01006869843972e-06 1.80201373968794e-05 0.999990989931302 29 4.79425746253434e-06 9.58851492506868e-06 0.999995205742537 30 2.48574171451135e-06 4.9714834290227e-06 0.999997514258286 31 1.35879113787931e-06 2.71758227575862e-06 0.999998641208862 32 7.44895022277805e-07 1.48979004455561e-06 0.999999255104978 33 4.17058319103472e-07 8.34116638206943e-07 0.999999582941681 34 1.77808876021986e-07 3.55617752043973e-07 0.999999822191124 35 1.50370896498666e-07 3.00741792997332e-07 0.999999849629103 36 1.95733983287409e-07 3.91467966574818e-07 0.999999804266017 37 1.19836717999224e-07 2.39673435998448e-07 0.999999880163282 38 5.14557242066258e-08 1.02911448413252e-07 0.999999948544276 39 2.71737775985396e-08 5.43475551970791e-08 0.999999972826222 40 1.45296568553963e-08 2.90593137107926e-08 0.999999985470343 41 2.04367517711704e-08 4.08735035423407e-08 0.999999979563248 42 2.81220837939698e-08 5.62441675879396e-08 0.999999971877916 43 2.10222559048526e-08 4.20445118097051e-08 0.999999978977744 44 1.11290202599758e-08 2.22580405199516e-08 0.99999998887098 45 8.30664316587167e-09 1.66132863317433e-08 0.999999991693357 46 6.914092914075e-07 1.382818582815e-06 0.999999308590709 47 4.25345995243362e-07 8.50691990486723e-07 0.999999574654005 48 2.70133547472604e-07 5.40267094945209e-07 0.999999729866453 49 1.33386644511617e-07 2.66773289023234e-07 0.999999866613355 50 6.56522326325853e-08 1.31304465265171e-07 0.999999934347767 51 3.00367398771681e-08 6.00734797543361e-08 0.99999996996326 52 1.58173523240309e-08 3.16347046480617e-08 0.999999984182648 53 7.78054766117728e-09 1.55610953223546e-08 0.999999992219452 54 1.58450885611884e-08 3.16901771223767e-08 0.999999984154911 55 1.77703602262092e-08 3.55407204524185e-08 0.99999998222964 56 1.04752913865117e-08 2.09505827730234e-08 0.999999989524709 57 1.42172503032749e-08 2.84345006065498e-08 0.99999998578275 58 6.70933142702049e-09 1.3418662854041e-08 0.999999993290669 59 7.09423144472389e-09 1.41884628894478e-08 0.999999992905769 60 3.60374708149876e-09 7.20749416299753e-09 0.999999996396253 61 6.28648990382731e-09 1.25729798076546e-08 0.99999999371351 62 5.49367142736086e-08 1.09873428547217e-07 0.999999945063286 63 2.53612134218436e-07 5.07224268436872e-07 0.999999746387866 64 5.89716557741003e-07 1.17943311548201e-06 0.999999410283442 65 6.35911449346476e-07 1.27182289869295e-06 0.99999936408855 66 5.43201449942562e-07 1.08640289988512e-06 0.99999945679855 67 6.87915598153579e-07 1.37583119630716e-06 0.999999312084402 68 1.15881651738289e-06 2.31763303476577e-06 0.999998841183483 69 7.74171759185466e-07 1.54834351837093e-06 0.99999922582824 70 1.88674086463135e-06 3.77348172926271e-06 0.999998113259135 71 1.06535167015015e-06 2.1307033403003e-06 0.99999893464833 72 8.07317299838075e-07 1.61463459967615e-06 0.9999991926827 73 4.51221627025701e-07 9.02443254051402e-07 0.999999548778373 74 4.82529523692141e-07 9.65059047384282e-07 0.999999517470476 75 4.58540313524617e-07 9.17080627049233e-07 0.999999541459687 76 3.02354568271632e-07 6.04709136543264e-07 0.999999697645432 77 3.65491759163354e-07 7.30983518326707e-07 0.99999963450824 78 3.95007724882972e-07 7.90015449765944e-07 0.999999604992275 79 4.15731402250013e-07 8.31462804500027e-07 0.999999584268598 80 9.25968580811202e-07 1.8519371616224e-06 0.99999907403142 81 2.04690410281715e-06 4.0938082056343e-06 0.999997953095897 82 6.58064580461795e-06 1.31612916092359e-05 0.999993419354195 83 4.43950285612772e-06 8.87900571225544e-06 0.999995560497144 84 4.15252537449052e-06 8.30505074898105e-06 0.999995847474626 85 3.63129821268454e-06 7.26259642536908e-06 0.999996368701787 86 4.56078210356307e-06 9.12156420712615e-06 0.999995439217896 87 8.40716417992426e-06 1.68143283598485e-05 0.99999159283582 88 7.15150734065786e-06 1.43030146813157e-05 0.99999284849266 89 8.92039471454802e-06 1.7840789429096e-05 0.999991079605285 90 1.02366186803802e-05 2.04732373607604e-05 0.99998976338132 91 1.03604120976993e-05 2.07208241953985e-05 0.999989639587902 92 8.9622499205936e-06 1.79244998411872e-05 0.99999103775008 93 8.25009810997574e-06 1.65001962199515e-05 0.99999174990189 94 1.22786964859153e-05 2.45573929718306e-05 0.999987721303514 95 2.81423319013909e-05 5.62846638027818e-05 0.999971857668099 96 2.37131179505214e-05 4.74262359010429e-05 0.99997628688205 97 2.9932988455323e-05 5.98659769106461e-05 0.999970067011545 98 3.2653863801272e-05 6.5307727602544e-05 0.9999673461362 99 4.15028715719197e-05 8.30057431438393e-05 0.999958497128428 100 3.91137076518404e-05 7.82274153036807e-05 0.999960886292348 101 4.09953813490006e-05 8.19907626980013e-05 0.99995900461865 102 3.36639360758372e-05 6.73278721516743e-05 0.999966336063924 103 2.40067102914424e-05 4.80134205828848e-05 0.999975993289709 104 2.61081031032069e-05 5.22162062064138e-05 0.999973891896897 105 3.33895978984065e-05 6.6779195796813e-05 0.999966610402102 106 4.35316166928938e-05 8.70632333857876e-05 0.999956468383307 107 3.04681687699929e-05 6.09363375399857e-05 0.99996953183123 108 0.000104327635339449 0.000208655270678899 0.99989567236466 109 0.000107619540403436 0.000215239080806872 0.999892380459597 110 0.000203512599168283 0.000407025198336567 0.999796487400832 111 0.000369349653513339 0.000738699307026679 0.999630650346487 112 0.00091422528196823 0.00182845056393646 0.999085774718032 113 0.000756930473615374 0.00151386094723075 0.999243069526385 114 0.000672990340933313 0.00134598068186663 0.999327009659067 115 0.00119928859854599 0.00239857719709199 0.998800711401454 116 0.00138297855831109 0.00276595711662219 0.998617021441689 117 0.00153087011378846 0.00306174022757691 0.998469129886212 118 0.00491498711911829 0.00982997423823658 0.995085012880882 119 0.0109472720131267 0.0218945440262534 0.989052727986873 120 0.0180181541903633 0.0360363083807265 0.981981845809637 121 0.0313083866520335 0.0626167733040671 0.968691613347966 122 0.158656219496077 0.317312438992154 0.841343780503923 123 0.149270437262102 0.298540874524204 0.850729562737898 124 0.129027211785226 0.258054423570452 0.870972788214774 125 0.110650327182755 0.22130065436551 0.889349672817245 126 0.0944041597353675 0.188808319470735 0.905595840264632 127 0.0800492380655093 0.160098476131019 0.91995076193449 128 0.0678866998610088 0.135773399722018 0.932113300138991 129 0.0547435964885508 0.109487192977102 0.94525640351145 130 0.0448569360290805 0.089713872058161 0.95514306397092 131 0.0766250117581846 0.153250023516369 0.923374988241815 132 0.0750720824569008 0.150144164913802 0.9249279175431 133 0.0906318100870802 0.18126362017416 0.90936818991292 134 0.0747140294642378 0.149428058928476 0.925285970535762 135 0.076433160524441 0.152866321048882 0.923566839475559 136 0.0969189333804107 0.193837866760821 0.90308106661959 137 0.0821569892032106 0.164313978406421 0.91784301079679 138 0.0679123685869531 0.135824737173906 0.932087631413047 139 0.0723393834445029 0.144678766889006 0.927660616555497 140 0.141619896156443 0.283239792312887 0.858380103843557 141 0.139016355487818 0.278032710975636 0.860983644512182 142 0.485795104184852 0.971590208369704 0.514204895815148 143 0.543435330454046 0.913129339091909 0.456564669545954 144 0.618026516752202 0.763946966495596 0.381973483247798 145 0.732548251151517 0.534903497696967 0.267451748848483 146 0.827232277808064 0.345535444383871 0.172767722191936 147 0.833914225375298 0.332171549249405 0.166085774624702 148 0.805381870937217 0.389236258125566 0.194618129062783 149 0.803104147926977 0.393791704146045 0.196895852073023 150 0.967295597885298 0.0654088042294047 0.0327044021147023 151 0.962455966107606 0.0750880677847889 0.0375440338923945 152 0.952589124564715 0.0948217508705697 0.0474108754352848 153 0.973931846705307 0.0521363065893854 0.0260681532946927 154 0.96705835296491 0.0658832940701822 0.0329416470350911 155 0.963711804377513 0.0725763912449741 0.036288195622487 156 0.983088305959569 0.0338233880808625 0.0169116940404313 157 0.978242386575687 0.0435152268486264 0.0217576134243132 158 0.991127044907146 0.017745910185707 0.00887295509285352 159 0.998889263497788 0.00222147300442455 0.00111073650221227 160 1 3.92917506869403e-23 1.96458753434701e-23 161 1 7.5713629963432e-23 3.7856814981716e-23 162 1 3.53480096721305e-22 1.76740048360652e-22 163 1 1.90048674271785e-22 9.50243371358923e-23 164 1 4.7979147068331e-22 2.39895735341655e-22 165 1 1.97369744228535e-21 9.86848721142673e-22 166 1 1.33122940162832e-20 6.65614700814161e-21 167 1 9.93309456159492e-20 4.96654728079746e-20 168 1 1.00550669335647e-18 5.02753346678233e-19 169 1 5.64378661186278e-18 2.82189330593139e-18 170 1 5.21918766637356e-17 2.60959383318678e-17 171 1 4.04668000188201e-16 2.023340000941e-16 172 0.999999999999998 3.62051334790331e-15 1.81025667395166e-15 173 0.999999999999987 2.63125022056261e-14 1.3156251102813e-14 174 0.99999999999988 2.37978124191467e-13 1.18989062095734e-13 175 0.999999999999568 8.64322946759029e-13 4.32161473379515e-13 176 0.999999999996126 7.74716567708688e-12 3.87358283854344e-12 177 0.999999999984305 3.13895638437187e-11 1.56947819218593e-11 178 0.999999999950161 9.96775817252916e-11 4.98387908626458e-11 179 0.999999999913023 1.7395449689015e-10 8.6977248445075e-11 180 0.99999999893923 2.12154161172487e-09 1.06077080586243e-09 181 0.999999995364944 9.27011244588052e-09 4.63505622294026e-09 182 0.999999958925543 8.21489142068034e-08 4.10744571034017e-08 183 0.999999754405653 4.9118869423603e-07 2.45594347118015e-07 184 0.999996029533232 7.94093353627038e-06 3.97046676813519e-06 185 0.999938820727423 0.000122358545154941 6.11792725774705e-05 186 0.999506244001031 0.000987511997937489 0.000493755998968744 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 134 0.752808988764045 NOK 5% type I error level 141 0.792134831460674 NOK 10% type I error level 150 0.842696629213483 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/10szt91293536851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/10szt91293536851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/1lgwx1293536851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/1lgwx1293536851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/2w7di1293536851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/2w7di1293536851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/3w7di1293536851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/3w7di1293536851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/4w7di1293536851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/4w7di1293536851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/5ozv31293536851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/5ozv31293536851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/6ozv31293536851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/6ozv31293536851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/7z8u61293536851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/7z8u61293536851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/8szt91293536851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/8szt91293536851.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/9szt91293536851.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536724g4w3es2qehr5590/9szt91293536851.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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

Disclaimer

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

Privacy Policy

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

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

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

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

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

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

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

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

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

FreeStatistics.org is powered by