*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 15:27:32 +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/t12912175515o3p7xfwssxh4ta.htm/, Retrieved Wed, 01 Dec 2010 16:32:31 +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/t12912175515o3p7xfwssxh4ta.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 «

0 162556 807 213118 6282154 0 29790 444 81767 4321023 0 87550 412 153198 4111912 1 84738 428 -26007 223193 0 54660 315 126942 1491348 0 42634 168 157214 1629616 1 40949 263 129352 1398893 0 45187 267 234817 1926517 0 37704 228 60448 983660 0 16275 129 47818 1443586 1 25830 104 245546 1073089 1 12679 122 48020 984885 0 18014 393 -1710 1405225 1 43556 190 32648 227132 0 24811 280 95350 929118 1 6575 63 151352 1071292 1 7123 102 288170 638830 0 21950 265 114337 856956 0 37597 234 37884 992426 1 17821 277 122844 444477 0 12988 73 82340 857217 0 22330 67 79801 711969 1 13326 103 165548 702380 1 16189 290 116384 358589 1 7146 83 134028 297978 1 15824 56 63838 585715 0 27664 236 74996 657954 1 11920 73 31080 209458 1 8568 34 32168 786690 1 14416 139 49857 439798 0 3369 26 87161 688779 0 11819 70 106113 574339 0 6984 40 80570 741409 0 4519 42 102129 597793 1 2220 12 301670 644190 1 18562 211 102313 377934 1 10327 74 88577 640273 0 5336 80 112477 697458 0 2365 83 191778 550608 1 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 16 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Orders[t] = -0.0321496042297002 -6.80069463873589Group[t] + 0.00478203964047135Costs[t] + 0.000206178289283141Dividends[t] + 1.78540150556557e-05`Wealth `[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) -0.0321496042297002 5.445825 -0.0059 0.995292 0.497646 Group -6.80069463873589 3.825673 -1.7776 0.076175 0.038088 Costs 0.00478203964047135 0.000233 20.4803 0 0 Dividends 0.000206178289283141 5.6e-05 3.6548 0.00029 0.000145 `Wealth ` 1.78540150556557e-05 7e-06 2.7125 0.006947 0.003474 Multiple Linear Regression - Regression Statistics Multiple R 0.886269902342121 R-squared 0.785474339797513 Adjusted R-squared 0.783460014349603 F-TEST (value) 389.944107895888 F-TEST (DF numerator) 4 F-TEST (DF denominator) 426 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 34.6604478309839 Sum Squared Residuals 511773.670277695 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 807 933.419062945624 -126.419062945624 2 444 236.431001163061 207.568998836939 3 412 523.635661236168 -111.635661236168 4 428 397.010443224226 30.9895567757735 5 315 314.153371187337 0.846628812662884 6 168 265.354630597923 -97.3546305979233 7 263 240.6323277533 22.3676722466998 8 267 298.864106507325 -31.8641065073254 9 228 210.295218680336 17.7047813196641 10 129 113.428385159517 15.5716148404833 11 104 186.472441052786 -82.4724410527862 12 122 81.2834694280367 40.7165305719633 13 393 110.847855911131 282.152144088869 14 190 212.240201273542 -22.2402012735420 15 280 154.862622559133 125.137377440867 16 63 74.9414263297191 -11.9414263297191 17 102 98.0497021768392 3.95029782316082 18 265 143.807532891918 121.192467108082 19 234 205.287841815399 28.7121581846014 20 277 111.651349008465 165.348650991535 21 73 94.3584668097501 -21.3584668097501 22 67 135.915534475740 -68.9155344757398 23 103 103.565322534993 -0.565322534992599 24 290 100.981702921347 189.018297078653 25 83 60.2933784821377 22.7066215178623 26 56 92.4575250874337 -36.4575250874337 27 236 159.467862614777 76.5321373852228 28 73 60.3167557879005 12.6832442120995 29 34 54.8175897103869 -20.8175897103869 30 139 80.2366302963063 58.7633697036937 31 26 46.3467184527456 -20.3467184527456 32 70 88.6192308702534 -18.6192308702534 33 40 63.2145274607636 -23.2145274607636 34 42 53.3076752594239 -11.3076752594239 35 12 77.4824662456288 -65.4824662456288 36 211 109.773734200934 101.226265799066 37 74 72.2453772357447 1.75462276425525 38 80 61.1275549937128 18.8724450062872 39 83 60.6483976293918 22.3516023706082 40 131 32.7819249965018 98.2180750034982 41 203 66.3011834563275 136.698816543673 42 56 84.8262740777907 -28.8262740777907 43 89 43.1059108262682 45.8940891737318 44 88 57.0292808068801 30.9707191931199 45 39 36.4755040053588 2.5244959946412 46 25 45.3325042027528 -20.3325042027528 47 49 73.3922856762823 -24.3922856762823 48 149 71.7188342328636 77.2811657671364 49 58 50.1166325050986 7.88336749490143 50 41 23.3144550821905 17.6855449178095 51 90 46.8403218310164 43.1596781689836 52 136 56.8525915035582 79.1474084964418 53 97 82.5182523672676 14.4817476327324 54 63 44.9466889625353 18.0533110374647 55 114 58.7055472307458 55.2944527692542 56 77 24.4643941510349 52.5356058489651 57 6 33.1286499751412 -27.1286499751412 58 47 44.9041081139224 2.09589188607761 59 51 25.9188380166281 25.0811619833719 60 85 69.5158177763522 15.4841822236478 61 43 54.8431673408851 -11.8431673408851 62 32 15.5275298885326 16.4724701114674 63 25 41.816087587754 -16.816087587754 64 77 59.6233867123727 17.3766132876273 65 54 24.4684892533561 29.5315107466439 66 251 81.657947375811 169.342052624189 67 15 18.6964552069016 -3.69645520690162 68 44 37.5810350776022 6.4189649223978 69 73 33.7042907497267 39.2957092502733 70 85 47.527588561962 37.4724114380380 71 49 23.7715067591843 25.2284932408157 72 38 32.4536019608739 5.54639803912609 73 35 27.5960774833935 7.40392251660653 74 9 17.9789825398686 -8.97898253986856 75 34 34.1659527348412 -0.165952734841173 76 20 28.8233406698778 -8.8233406698778 77 29 28.2409199312946 0.759080068705391 78 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-1.69385777053537 103 55 36.9722048573065 18.0277951426936 104 8 24.9841230276603 -16.9841230276603 105 28 21.9668967387454 6.03310326125457 106 29 16.3034134421401 12.6965865578599 107 48 29.8066891020832 18.1933108979168 108 16 39.0503521254017 -23.0503521254017 109 47 47.1046607920999 -0.104660792099872 110 20 28.7099645519015 -8.70996455190148 111 22 18.9508591860945 3.04914081390551 112 33 29.1345330022263 3.86546699777367 113 44 40.6741417353235 3.32585826467646 114 13 31.8606858475263 -18.8606858475263 115 6 25.2212329766197 -19.2212329766197 116 35 18.1078415689237 16.8921584310763 117 8 18.5534270303421 -10.5534270303421 118 17 30.0466990555222 -13.0466990555222 119 11 30.3924887203891 -19.3924887203891 120 21 15.4369155990626 5.56308440093743 121 92 73.1984112049216 18.8015887950784 122 12 17.1336259772219 -5.1336259772219 123 112 26.2952823549185 85.7047176450815 124 25 38.6580434459907 -13.6580434459907 125 17 31.9021501333129 -14.9021501333129 126 23 35.2325680963851 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-3.74989712609165 392 6 12.7664011492679 -6.7664011492679 393 8 13.4086848130079 -5.40868481300786 394 6 17.0825035414192 -11.0825035414192 395 9 38.6558981286922 -29.6558981286922 396 8 12.9701866809028 -4.97018668090284 397 26 31.9190570728746 -5.91905707287455 398 239 39.6756540284356 199.324345971564 399 7 14.1549104177616 -7.1549104177616 400 41 18.5819277307980 22.418072269202 401 3 18.1910638441603 -15.1910638441603 402 8 16.0752533298328 -8.0752533298328 403 6 13.5938712163418 -7.59387121634177 404 21 26.5450050875821 -5.54500508758209 405 7 14.1929429373019 -7.19294293730188 406 11 19.2334824959587 -8.23348249595874 407 11 15.8185009938025 -4.81850099380255 408 12 15.4842091041913 -3.48420910419131 409 9 12.6082174225844 -3.6082174225844 410 3 15.2069230048702 -12.2069230048702 411 57 19.3268122793078 37.6731877206922 412 21 53.6110518994485 -32.6110518994485 413 15 22.1684582022969 -7.1684582022969 414 32 39.3101315790935 -7.31013157909347 415 11 21.6764771078722 -10.6764771078722 416 2 16.5818881157542 -14.5818881157542 417 23 23.2746844396856 -0.274684439685605 418 20 23.4234430130124 -3.42344301301238 419 24 18.4245275275497 5.57547247245027 420 1 14.3211035147670 -13.3211035147670 421 1 16.9218426442436 -15.9218426442436 422 74 26.0539117593638 47.9460882406362 423 68 50.6097255898916 17.3902744101084 424 20 35.1332585849377 -15.1332585849377 425 20 20.2383964438426 -0.238396443842626 426 82 42.0661474663178 39.9338525336822 427 21 23.6209125213084 -2.62091252130842 428 244 201.418083352231 42.5819166477692 429 32 58.3304757067437 -26.3304757067437 430 86 88.6457179255465 -2.64571792554648 431 69 85.0903133361254 -16.0903133361254 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.999888309807523 0.000223380384953446 0.000111690192476723 9 0.99971601625962 0.000567967480758204 0.000283983740379102 10 0.999944326048282 0.000111347903435968 5.56739517179838e-05 11 0.999975308984952 4.93820300957664e-05 2.46910150478832e-05 12 0.999990220578247 1.95588435054679e-05 9.77942175273394e-06 13 0.99999999999711 5.78190163958698e-12 2.89095081979349e-12 14 0.999999999999592 8.1515054134217e-13 4.07575270671085e-13 15 0.99999999999999 2.07925590510214e-14 1.03962795255107e-14 16 0.999999999999977 4.55076031171992e-14 2.27538015585996e-14 17 1 2.72883676661331e-16 1.36441838330666e-16 18 1 1.56459828818006e-17 7.8229914409003e-18 19 1 1.17937912835475e-17 5.89689564177376e-18 20 1 4.41671723776943e-23 2.20835861888471e-23 21 1 2.32205286608012e-25 1.16102643304006e-25 22 1 1.64623762560501e-29 8.23118812802506e-30 23 1 2.90613081756171e-29 1.45306540878086e-29 24 1 7.55189160013179e-37 3.77594580006589e-37 25 1 1.74824284464163e-36 8.74121422320815e-37 26 1 3.75246167757155e-40 1.87623083878577e-40 27 1 6.00091362593121e-40 3.00045681296560e-40 28 1 1.11537102762859e-40 5.57685513814295e-41 29 1 3.01228311228821e-43 1.50614155614411e-43 30 1 8.32341482140184e-43 4.16170741070092e-43 31 1 4.510538552963e-44 2.2552692764815e-44 32 1 1.50150933276047e-44 7.50754666380233e-45 33 1 1.47394299065837e-45 7.36971495329184e-46 34 1 1.35770577437513e-45 6.78852887187564e-46 35 1 1.17669491618826e-47 5.88347458094131e-48 36 1 6.20782543196744e-49 3.10391271598372e-49 37 1 4.66393540262158e-49 2.33196770131079e-49 38 1 1.67848802165089e-48 8.39244010825443e-49 39 1 4.15895118742841e-48 2.07947559371420e-48 40 1 3.69983186044036e-50 1.84991593022018e-50 41 1 1.58565078340206e-55 7.92825391701031e-56 42 1 4.66161122567031e-57 2.33080561283515e-57 43 1 9.77030351106103e-57 4.88515175553052e-57 44 1 3.53501125101307e-56 1.76750562550654e-56 45 1 2.30255512733417e-56 1.15127756366709e-56 46 1 1.42493942495448e-56 7.12469712477238e-57 47 1 6.00080540327774e-57 3.00040270163887e-57 48 1 3.94220075585415e-58 1.97110037792708e-58 49 1 8.89829176426223e-58 4.44914588213111e-58 50 1 1.36212903444475e-57 6.81064517222373e-58 51 1 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3.01719504637518e-67 1.50859752318759e-67 94 1 3.36927763944726e-68 1.68463881972363e-68 95 1 1.76191315990128e-68 8.80956579950642e-69 96 1 3.40672422377208e-68 1.70336211188604e-68 97 1 2.96454461960163e-68 1.48227230980081e-68 98 1 6.30670709368747e-68 3.15335354684374e-68 99 1 1.89439781460534e-68 9.47198907302669e-69 100 1 2.30385950380709e-68 1.15192975190355e-68 101 1 6.12390128580849e-68 3.06195064290424e-68 102 1 1.53110184512056e-67 7.65550922560281e-68 103 1 5.34072877927289e-67 2.67036438963644e-67 104 1 2.95659581229616e-67 1.47829790614808e-67 105 1 7.34898756948569e-67 3.67449378474284e-67 106 1 1.82155911058267e-66 9.10779555291335e-67 107 1 2.51733652772472e-66 1.25866826386236e-66 108 1 1.50842528554383e-66 7.54212642771914e-67 109 1 3.41931084067853e-66 1.70965542033927e-66 110 1 7.7714642922715e-66 3.88573214613575e-66 111 1 1.9111452546244e-65 9.555726273122e-66 112 1 3.38820357338547e-65 1.69410178669274e-65 113 1 9.94575570380726e-65 4.97287785190363e-65 114 1 1.36420954726542e-64 6.82104773632712e-65 115 1 1.97293581486179e-64 9.86467907430895e-65 116 1 2.83600957948645e-64 1.41800478974322e-64 117 1 4.51985517392014e-64 2.25992758696007e-64 118 1 5.2739805550467e-64 2.63699027752335e-64 119 1 7.09196947896969e-64 3.54598473948484e-64 120 1 1.66704477380931e-63 8.33522386904657e-64 121 1 5.21296528960951e-63 2.60648264480475e-63 122 1 1.19927464581004e-62 5.99637322905019e-63 123 1 2.77575080926888e-66 1.38787540463444e-66 124 1 3.04467832485493e-66 1.52233916242746e-66 125 1 6.0397047923502e-66 3.0198523961751e-66 126 1 1.34102452592876e-65 6.70512262964378e-66 127 1 2.53854873721029e-65 1.26927436860515e-65 128 1 6.14278438857855e-65 3.07139219428928e-65 129 1 1.17354179772885e-64 5.86770898864426e-65 130 1 2.32776463529215e-64 1.16388231764607e-64 131 1 5.62902344614125e-64 2.81451172307062e-64 132 1 1.22683198308674e-63 6.1341599154337e-64 133 1 2.53504191809921e-63 1.26752095904961e-63 134 1 5.53368223652243e-63 2.76684111826121e-63 135 1 1.21145322961169e-62 6.05726614805846e-63 136 1 2.80757263966508e-62 1.40378631983254e-62 137 1 7.26361463115215e-62 3.63180731557608e-62 138 1 1.63424677311149e-61 8.17123386555744e-62 139 1 3.55910162924980e-61 1.77955081462490e-61 140 1 7.90925947359622e-61 3.95462973679811e-61 141 1 2.03095157298186e-60 1.01547578649093e-60 142 1 4.58536084956122e-60 2.29268042478061e-60 143 1 1.19379430703505e-59 5.96897153517523e-60 144 1 3.01118548339194e-59 1.50559274169597e-59 145 1 8.23030552633285e-59 4.11515276316642e-59 146 1 2.12668378481782e-58 1.06334189240891e-58 147 1 4.92513523488224e-58 2.46256761744112e-58 148 1 1.30947365840714e-57 6.5473682920357e-58 149 1 3.10201777920519e-57 1.55100888960259e-57 150 1 7.24412990574268e-57 3.62206495287134e-57 151 1 1.90638020590761e-56 9.53190102953804e-57 152 1 1.4254165139141e-56 7.1270825695705e-57 153 1 3.74359434930584e-56 1.87179717465292e-56 154 1 9.87317107073e-56 4.936585535365e-56 155 1 2.46103562943228e-55 1.23051781471614e-55 156 1 5.86814526235789e-55 2.93407263117894e-55 157 1 1.40314145181337e-54 7.01570725906683e-55 158 1 3.47731159292084e-54 1.73865579646042e-54 159 1 8.8644180248764e-54 4.4322090124382e-54 160 1 2.13154393517142e-53 1.06577196758571e-53 161 1 5.13438995823876e-53 2.56719497911938e-53 162 1 1.23859222586419e-52 6.19296112932093e-53 163 1 2.99166225407030e-52 1.49583112703515e-52 164 1 7.92889043551724e-52 3.96444521775862e-52 165 1 1.71914507648965e-51 8.59572538244823e-52 166 1 4.23062458727136e-51 2.11531229363568e-51 167 1 1.02197051489434e-50 5.10985257447169e-51 168 1 1.87181925354451e-50 9.35909626772253e-51 169 1 2.36364068414696e-50 1.18182034207348e-50 170 1 5.7211234654816e-50 2.8605617327408e-50 171 1 1.38497629527574e-49 6.92488147637868e-50 172 1 3.53501707185836e-49 1.76750853592918e-49 173 1 8.55154064353082e-49 4.27577032176541e-49 174 1 2.28418531250795e-48 1.14209265625398e-48 175 1 5.5154257448786e-48 2.7577128724393e-48 176 1 1.45756561149133e-47 7.28782805745664e-48 177 1 6.93000093471658e-55 3.46500046735829e-55 178 1 1.3860137128853e-54 6.9300685644265e-55 179 1 3.60684101548812e-54 1.80342050774406e-54 180 1 9.38160539129885e-54 4.69080269564942e-54 181 1 2.7792003247515e-53 1.38960016237575e-53 182 1 6.4259042972637e-53 3.21295214863185e-53 183 1 1.56260687511043e-52 7.81303437555215e-53 184 1 4.25257091833519e-52 2.12628545916759e-52 185 1 1.01340771192041e-51 5.06703855960204e-52 186 1 2.61182445779544e-51 1.30591222889772e-51 187 1 7.03292052605794e-51 3.51646026302897e-51 188 1 1.94016366081090e-50 9.70081830405449e-51 189 1 5.18022488575958e-50 2.59011244287979e-50 190 1 1.32127798232274e-49 6.60638991161368e-50 191 1 3.60428302692010e-49 1.80214151346005e-49 192 1 9.16614311966651e-49 4.58307155983326e-49 193 1 2.51026086205653e-48 1.25513043102826e-48 194 1 6.3619961018282e-48 3.1809980509141e-48 195 1 1.68872191024995e-47 8.44360955124974e-48 196 1 3.50293420970676e-47 1.75146710485338e-47 197 1 5.71399481010168e-47 2.85699740505084e-47 198 1 1.19042109725089e-46 5.95210548625446e-47 199 1 2.48927990007506e-46 1.24463995003753e-46 200 1 6.49762684713923e-46 3.24881342356962e-46 201 1 1.35991006728431e-45 6.79955033642153e-46 202 1 2.85048557985556e-45 1.42524278992778e-45 203 1 5.97912449903476e-45 2.98956224951738e-45 204 1 1.42853117918701e-44 7.14265589593506e-45 205 1 2.17529250920727e-44 1.08764625460364e-44 206 1 5.24489386548647e-44 2.62244693274324e-44 207 1 1.04581369599861e-43 5.22906847999307e-44 208 1 2.17903651485755e-43 1.08951825742877e-43 209 1 3.54756198937897e-43 1.77378099468948e-43 210 1 7.92622444301071e-43 3.96311222150536e-43 211 1 1.9495593998531e-42 9.7477969992655e-43 212 1 4.78546064423128e-42 2.39273032211564e-42 213 1 1.17052500498978e-41 5.85262502494888e-42 214 1 3.06580057138026e-41 1.53290028569013e-41 215 1 7.68105114046843e-41 3.84052557023421e-41 216 1 1.92806448701013e-40 9.64032243505064e-41 217 1 4.95602079625776e-40 2.47801039812888e-40 218 1 1.26765100361920e-39 6.33825501809602e-40 219 1 3.15377639065437e-39 1.57688819532719e-39 220 1 5.46676026165903e-39 2.73338013082952e-39 221 1 1.35236657368170e-38 6.76183286840852e-39 222 1 3.24338721762045e-38 1.62169360881022e-38 223 1 7.88549092739843e-38 3.94274546369921e-38 224 1 1.61977114736324e-37 8.0988557368162e-38 225 1 3.91145149262973e-37 1.95572574631487e-37 226 1 7.25236446818805e-37 3.62618223409403e-37 227 1 1.78431010107838e-36 8.9215505053919e-37 228 1 4.22046022001801e-36 2.11023011000900e-36 229 1 9.67463941520718e-36 4.83731970760359e-36 230 1 2.27528938686944e-35 1.13764469343472e-35 231 1 5.60367224496219e-35 2.80183612248109e-35 232 1 1.37341733429100e-34 6.86708667145499e-35 233 1 3.1993844624611e-34 1.59969223123055e-34 234 1 7.5800078960422e-34 3.7900039480211e-34 235 1 1.75498996034112e-33 8.77494980170559e-34 236 1 4.04930655610652e-33 2.02465327805326e-33 237 1 9.31695207583964e-33 4.65847603791982e-33 238 1 2.23208962016908e-32 1.11604481008454e-32 239 1 5.10230218536462e-32 2.55115109268231e-32 240 1 1.16253647902117e-31 5.81268239510584e-32 241 1 2.64007337946451e-31 1.32003668973225e-31 242 1 6.14463724049072e-31 3.07231862024536e-31 243 1 1.38588720064987e-30 6.92943600324935e-31 244 1 3.23167408320765e-30 1.61583704160383e-30 245 1 3.68909144633198e-30 1.84454572316599e-30 246 1 7.13242852426575e-30 3.56621426213288e-30 247 1 1.37272302073205e-29 6.86361510366024e-30 248 1 2.96330730591261e-29 1.48165365295631e-29 249 1 5.65945620293755e-29 2.82972810146878e-29 250 1 1.07469188331680e-28 5.37345941658402e-29 251 1 2.26303841617482e-28 1.13151920808741e-28 252 1 4.25971297573008e-28 2.12985648786504e-28 253 1 9.07078154623757e-28 4.53539077311879e-28 254 1 1.23682235576465e-27 6.18411177882324e-28 255 1 1.32744131318422e-27 6.6372065659211e-28 256 1 2.91579286589329e-27 1.45789643294664e-27 257 1 6.43815997608775e-27 3.21907998804387e-27 258 1 5.38771183060979e-27 2.69385591530489e-27 259 1 1.00064975658013e-26 5.00324878290065e-27 260 1 2.18867823858995e-26 1.09433911929498e-26 261 1 4.02263487968829e-26 2.01131743984414e-26 262 1 7.32973820224045e-26 3.66486910112022e-26 263 1 1.66354920173391e-25 8.31774600866955e-26 264 1 3.60083171309428e-25 1.80041585654714e-25 265 1 6.46404059637563e-25 3.23202029818781e-25 266 1 1.38699696151425e-24 6.93498480757123e-25 267 1 2.45826073868199e-24 1.22913036934099e-24 268 1 4.99960502897566e-24 2.49980251448783e-24 269 1 8.73824972429923e-24 4.36912486214961e-24 270 1 1.54717754260539e-23 7.73588771302693e-24 271 1 2.66716858355167e-23 1.33358429177583e-23 272 1 4.53641982454968e-23 2.26820991227484e-23 273 1 7.60189499349131e-23 3.80094749674566e-23 274 1 1.34157743930609e-22 6.70788719653047e-23 275 1 2.81141938062635e-22 1.40570969031318e-22 276 1 5.92394606775556e-22 2.96197303387778e-22 277 1 1.01969947277590e-21 5.09849736387952e-22 278 1 2.11623175691044e-21 1.05811587845522e-21 279 1 4.54402411306804e-21 2.27201205653402e-21 280 1 9.60620442647603e-21 4.80310221323802e-21 281 1 1.56440725717145e-20 7.82203628585726e-21 282 1 3.19648069780796e-20 1.59824034890398e-20 283 1 3.76935688850645e-20 1.88467844425323e-20 284 1 7.68496375196715e-20 3.84248187598358e-20 285 1 1.58174722791266e-19 7.9087361395633e-20 286 1 3.19632304333127e-19 1.59816152166563e-19 287 1 5.40795239723794e-19 2.70397619861897e-19 288 1 1.10364610264605e-18 5.51823051323027e-19 289 1 1.71609808446892e-18 8.5804904223446e-19 290 1 3.01298985590835e-18 1.50649492795418e-18 291 1 4.5782972234488e-18 2.2891486117244e-18 292 1 9.07155615203689e-18 4.53577807601844e-18 293 1 1.84675337994577e-17 9.23376689972887e-18 294 1 3.63138659415602e-17 1.81569329707801e-17 295 1 6.9926252712991e-17 3.49631263564955e-17 296 1 1.28555651021448e-16 6.42778255107242e-17 297 1 2.49590250703985e-16 1.24795125351993e-16 298 1 3.61577327983424e-16 1.80788663991712e-16 299 1 6.97563625762685e-16 3.48781812881343e-16 300 1 1.34152335117960e-15 6.70761675589802e-16 301 0.999999999999999 2.13556713338507e-15 1.06778356669253e-15 302 0.999999999999998 4.09451971615187e-15 2.04725985807594e-15 303 0.999999999999996 7.8269087103169e-15 3.91345435515845e-15 304 0.999999999999995 1.06820389137918e-14 5.34101945689588e-15 305 0.99999999999999 1.96999771460090e-14 9.84998857300452e-15 306 0.999999999999982 3.58061658100968e-14 1.79030829050484e-14 307 0.999999999999976 4.70911524109245e-14 2.35455762054622e-14 308 0.999999999999956 8.78571201024044e-14 4.39285600512022e-14 309 0.999999999999933 1.34567981962178e-13 6.72839909810891e-14 310 0.999999999999916 1.67245715426112e-13 8.36228577130561e-14 311 0.999999999999902 1.96938825636663e-13 9.84694128183315e-14 312 0.99999999999982 3.61230018422443e-13 1.80615009211222e-13 313 0.9999999999998 3.98896347769816e-13 1.99448173884908e-13 314 0.99999999999969 6.20274219022196e-13 3.10137109511098e-13 315 0.999999999999426 1.14863541138594e-12 5.74317705692968e-13 316 0.999999999998947 2.10631601478571e-12 1.05315800739286e-12 317 0.999999999998075 3.8497816805135e-12 1.92489084025675e-12 318 0.999999999996969 6.06299637941013e-12 3.03149818970507e-12 319 0.999999999994437 1.11264945584829e-11 5.56324727924147e-12 320 0.999999999989987 2.00264612417094e-11 1.00132306208547e-11 321 0.999999999982075 3.58498498730842e-11 1.79249249365421e-11 322 0.999999999968088 6.38246157173717e-11 3.19123078586859e-11 323 0.999999999943499 1.13002253555009e-10 5.65011267775045e-11 324 0.99999999994438 1.11240303163787e-10 5.56201515818935e-11 325 0.999999999901814 1.96372056466813e-10 9.81860282334067e-11 326 0.999999999826692 3.46616275635464e-10 1.73308137817732e-10 327 0.999999999846118 3.07764713783473e-10 1.53882356891737e-10 328 0.999999999881982 2.36036949061477e-10 1.18018474530739e-10 329 0.99999999979186 4.16279910962553e-10 2.08139955481277e-10 330 0.99999999963506 7.29879764992307e-10 3.64939882496154e-10 331 0.9999999993639 1.27220110057724e-09 6.36100550288622e-10 332 0.999999998897835 2.20433041821226e-09 1.10216520910613e-09 333 0.999999998101713 3.79657299218454e-09 1.89828649609227e-09 334 0.999999996700603 6.59879376057275e-09 3.29939688028638e-09 335 0.999999994352646 1.12947072249066e-08 5.64735361245332e-09 336 0.999999990332979 1.93340428912296e-08 9.66702144561482e-09 337 0.999999984928948 3.01421036912511e-08 1.50710518456255e-08 338 0.999999974778691 5.04426179208371e-08 2.52213089604185e-08 339 0.999999957870216 8.42595683545327e-08 4.21297841772664e-08 340 0.999999930862456 1.38275088397928e-07 6.91375441989638e-08 341 0.999999886266262 2.27467475579009e-07 1.13733737789504e-07 342 0.999999814278268 3.71443464079250e-07 1.85721732039625e-07 343 0.999999698707266 6.0258546754522e-07 3.0129273377261e-07 344 0.999999514442181 9.71115637654163e-07 4.85557818827082e-07 345 0.999999227302643 1.54539471332355e-06 7.72697356661776e-07 346 0.999998770493961 2.45901207761162e-06 1.22950603880581e-06 347 0.99999803572282 3.9285543604052e-06 1.9642771802026e-06 348 0.99999691692962 6.16614075901415e-06 3.08307037950708e-06 349 0.999995194343063 9.61131387358003e-06 4.80565693679001e-06 350 0.999992493731316 1.5012537368196e-05 7.506268684098e-06 351 0.99998835877853 2.32824429385460e-05 1.16412214692730e-05 352 0.99998206010104 3.58797979201954e-05 1.79398989600977e-05 353 0.999972696672329 5.46066553426401e-05 2.73033276713201e-05 354 0.999959012913263 8.1974173474486e-05 4.0987086737243e-05 355 0.999938775036049 0.000122449927901892 6.12249639509459e-05 356 0.99990922053198 0.000181558936039223 9.07794680196114e-05 357 0.999866528145468 0.000266943709063556 0.000133471854531778 358 0.99980506871957 0.000389862560858613 0.000194931280429306 359 0.999717473921773 0.000565052156453104 0.000282526078226552 360 0.999593660922552 0.000812678154895252 0.000406339077447626 361 0.99941429816638 0.00117140366723955 0.000585701833619773 362 0.999170726314566 0.00165854737086881 0.000829273685434405 363 0.998835140743062 0.00232971851387581 0.00116485925693790 364 0.998522214866517 0.00295557026696562 0.00147778513348281 365 0.997941382950692 0.00411723409861662 0.00205861704930831 366 0.997167887812454 0.00566422437509274 0.00283211218754637 367 0.99613577580163 0.0077284483967382 0.0038642241983691 368 0.994771249040633 0.0104575019187334 0.00522875095936671 369 0.99298408275229 0.0140318344954185 0.00701591724770924 370 0.99057699510069 0.0188460097986181 0.00942300489930904 371 0.987569306514357 0.0248613869712865 0.0124306934856433 372 0.98359844627306 0.0328031074538820 0.0164015537269410 373 0.979317546345408 0.0413649073091835 0.0206824536545918 374 0.973447391685 0.0531052166299985 0.0265526083149992 375 0.966147558576902 0.0677048828461963 0.0338524414230982 376 0.957223036473675 0.08555392705265 0.042776963526325 377 0.946730092916337 0.106539814167327 0.0532699070836634 378 0.933313046428963 0.133373907142075 0.0666869535710375 379 0.91795236081946 0.164095278361079 0.0820476391805397 380 0.89988625198729 0.200227496025418 0.100113748012709 381 0.878956947976826 0.242086104046348 0.121043052023174 382 0.85381224192835 0.292375516143298 0.146187758071649 383 0.836877616158946 0.326244767682108 0.163122383841054 384 0.807081701464683 0.385836597070635 0.192918298535317 385 0.773262776290625 0.45347444741875 0.226737223709375 386 0.737257011058951 0.525485977882097 0.262742988941049 387 0.696595623973404 0.606808752053192 0.303404376026596 388 0.652941248230263 0.694117503539474 0.347058751769737 389 0.606828951913791 0.786342096172417 0.393171048086209 390 0.558485187115941 0.883029625768118 0.441514812884059 391 0.508675892851861 0.982648214296278 0.491324107148139 392 0.458353563617443 0.916707127234886 0.541646436382557 393 0.408175076453953 0.816350152907907 0.591824923546047 394 0.362495926474402 0.724991852948804 0.637504073525598 395 0.492287564967505 0.98457512993501 0.507712435032495 396 0.439394599259895 0.878789198519791 0.560605400740104 397 0.388740998063952 0.777481996127903 0.611259001936048 398 0.999858073653366 0.000283852693268105 0.000141926346634053 399 0.999734787104395 0.000530425791210471 0.000265212895605236 400 0.999721723554778 0.000556552890443471 0.000278276445221736 401 0.999537677222665 0.00092464555467001 0.000462322777335005 402 0.999159470722121 0.00168105855575744 0.000840529277878718 403 0.998492489247196 0.00301502150560892 0.00150751075280446 404 0.997412154037939 0.00517569192412216 0.00258784596206108 405 0.995541573529211 0.0089168529415773 0.00445842647078865 406 0.992587488481393 0.0148250230372131 0.00741251151860657 407 0.987703550935362 0.0245928981292757 0.0122964490646378 408 0.980037769398158 0.0399244612036836 0.0199622306018418 409 0.968383026197283 0.0632339476054341 0.0316169738027170 410 0.953364088671947 0.0932718226561069 0.0466359113280534 411 0.971553280476168 0.0568934390476646 0.0284467195238323 412 0.984255941784705 0.0314881164305894 0.0157440582152947 413 0.976887708468894 0.0462245830622125 0.0231122915311062 414 0.976823926528839 0.0463521469423222 0.0231760734711611 415 0.9607863417021 0.0784273165958008 0.0392136582979004 416 0.938876645800157 0.122246708399686 0.0611233541998432 417 0.906309138491787 0.187381723016427 0.0936908615082134 418 0.925672093464244 0.148655813071512 0.0743279065357559 419 0.875982238786569 0.248035522426863 0.124017761213431 420 0.808896169360934 0.382207661278132 0.191103830639066 421 0.743680393859164 0.512639212281673 0.256319606140836 422 0.940794074549555 0.118411850900891 0.0592059254504453 423 0.853615903679206 0.292768192641588 0.146384096320794 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 368 0.884615384615385 NOK 5% type I error level 380 0.913461538461538 NOK 10% type I error level 387 0.930288461538462 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/10kaoe1291217232.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/10kaoe1291217232.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/1dr9k1291217232.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/1dr9k1291217232.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/260qn1291217232.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/260qn1291217232.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/360qn1291217232.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/360qn1291217232.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/460qn1291217232.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/460qn1291217232.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/5hr8p1291217232.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/5hr8p1291217232.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/6hr8p1291217232.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/6hr8p1291217232.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/7ri7s1291217232.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/7ri7s1291217232.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/8ri7s1291217232.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/8ri7s1291217232.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/9kaoe1291217232.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912175515o3p7xfwssxh4ta/9kaoe1291217232.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') }

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